From 8b04212bb6e346b4a4eb2b874d50ee0c518ce3b3 Mon Sep 17 00:00:00 2001 From: WieraB Date: Tue, 21 Oct 2025 16:32:30 +0100 Subject: [PATCH 01/16] Added specklegen to src/pyvale. --- .../specklegen/runfiles/main_specklegen.py | 143 +++++++++++++++ src/pyvale/specklegen/scripts/base/case1.sh | 23 +++ src/pyvale/specklegen/scripts/base/case2.sh | 23 +++ .../scripts/reduce_overlap/case1.sh | 23 +++ .../scripts/reduce_overlap/case2.sh | 23 +++ .../specklegen/utils/speckle_generation.py | 105 +++++++++++ .../utils/speckle_pattern_diagnostics.py | 167 ++++++++++++++++++ 7 files changed, 507 insertions(+) create mode 100644 src/pyvale/specklegen/runfiles/main_specklegen.py create mode 100644 src/pyvale/specklegen/scripts/base/case1.sh create mode 100644 src/pyvale/specklegen/scripts/base/case2.sh create mode 100644 src/pyvale/specklegen/scripts/reduce_overlap/case1.sh create mode 100644 src/pyvale/specklegen/scripts/reduce_overlap/case2.sh create mode 100644 src/pyvale/specklegen/utils/speckle_generation.py create mode 100644 src/pyvale/specklegen/utils/speckle_pattern_diagnostics.py diff --git a/src/pyvale/specklegen/runfiles/main_specklegen.py b/src/pyvale/specklegen/runfiles/main_specklegen.py new file mode 100644 index 00000000..2af2a6db --- /dev/null +++ b/src/pyvale/specklegen/runfiles/main_specklegen.py @@ -0,0 +1,143 @@ +import numpy as np +import argparse +import json +import sys +import os +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +from utils.speckle_pattern_diagnostics import speckle_pattern_diagnostics +from utils.speckle_generation import generate_speckles + +def main() -> None: + + parser = argparse.ArgumentParser() + + # Load parameter definitions from the JSON file + with open('./scripts/configs/config_input_params_spec.json') as f: + parameters_input = json.load(f) + + # Merge all dictionaries + parameters = {**parameters_input} + + # Automatically add arguments from the JSON dictionary + for param, param_args in parameters.items(): + # Convert the "type" and "required" from string to actual Python type + # Check if "type" and "required" are present in the JSON file + if "type" in param_args.keys(): + param_args["type"] = eval(param_args["type"]) + if "required" in param_args.keys(): + param_args["required"] = eval(param_args["required"]) + parser.add_argument(param, **param_args) + + # Parse arguments + args = parser.parse_args() + + print('Args in simulation:') + print(args) + print('') + print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') + print('') + + print('Start') + + # Extract parameteres and revert to default values if not provided by user + speckle_size = args.speckle_size if args.speckle_size is not None else 5.0 + screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 + screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 + bit_depth = args.bit_depth if args.bit_depth is not None else 8 + theme = args.theme if args.theme is not None else 'white_on_black' + random_seed = args.random_seed if args.random_seed is not None else 10 + sigma = args.sigma if args.sigma is not None else 1.0 + reduce_overlap = args.reduce_overlap + attempts = args.attempts if args.attempts is not None else 100 + + assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." + assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." + assert reduce_overlap in ['True', 'False'], "reduce_overlap should be either 'True' or 'False' (a string)." + + reduce_overlap_bool = True if reduce_overlap == "True" else False + if reduce_overlap_bool: + print("Reducing overlap between speckles") + else: + print("Not reducing overlap between speckles") + + np.random.seed(random_seed) + + subfolder = f"/{speckle_size}_{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{random_seed}_{sigma}_{reduce_overlap}" + print(subfolder) + save_path = args.output_path + subfolder + if not os.path.exists(save_path): + os.makedirs(save_path) + + # Calculate parameteres aiming for approximately 50/50 black/white ratio + speckle_area = np.pi * (speckle_size / 2) ** 2 + total_area = screen_size_width * screen_size_height + total_speckles = int((0.5 * total_area) / speckle_area) + print(f"Total number of speckles = {total_speckles}") + dynamic_range: int = 2**bit_depth - 1 + background_colour = 0 if theme == 'white_on_black' else dynamic_range + foreground_colour = dynamic_range if theme == 'white_on_black' else 0 + + # Generate speckle pattern + image, results = generate_speckles(screen_size_width, screen_size_height, + speckle_size, foreground_colour, + total_speckles, reduce_overlap_bool, + bit_depth, background_colour, + sigma, attempts) + + # save the speckle placement results + np.savetxt(f"{save_path}/speckle_placement_results.csv", results, delimiter=",", + header="speckle_number, attempts, overlap(1/0/2), cent_x, cent_y", comments='', fmt=['%d', '%d', '%d', '%.3f', '%.3f']) + + # Diagnostics + print("") + print('Starting speckle pattern diagnostics...') + results = speckle_pattern_diagnostics(image, dynamic_range, save_path) + + # save the diagnostics results + with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: + json.dump(results, f, indent=4) + + ratio = results.get("black_white_ratio", None) + mean_gradient = results.get("mean_intensity_gradient", None) + std_dev = results.get("std_dev_irradiance", None) + avg = results.get("avg_irradiance", None) + contrast = results.get("contrast", None) + entropy = results.get("shannon_entropy", None) + peak_to_mean = results.get("peak_to_mean_ratio", None) + skew = results.get("skewness", None) + kurt = results.get("kurtosis", None) + avg_speckle_size_fwhm = results.get("avg_speckle_size_fwhm", None) + avg_speckle_size_e2 = results.get("avg_speckle_size_e2", None) + H_fit_stats = results.get("H_fit_stats", None) + V_fit_stats = results.get("V_fit_stats", None) + + print("") + print("Speckle statistics:") + + print(f"Black/White ratio: {np.round(ratio, 3)}") + print(f"Mean intensity gradient: {np.round(mean_gradient, 3)}") + print(f"Standard deviation of irradiance values: {np.round(std_dev, 3)}") + print(f"Average irradiance value: {np.round(avg, 3)}") + print(f"Contrast (std/mean): {np.round(contrast, 3)}") + print(f"Skewness: {np.round(skew, 3)}") + print(f"Kurtosis: {np.round(kurt, 3)}") + print(f"Shannon entropy: {entropy}") + print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") + print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") + print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") + print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") + + error = np.abs(avg_speckle_size_fwhm - speckle_size) * 100 / speckle_size + print(f"Percentage error between requested speckle size and measured speckle size from FWHM: {np.round(error, 3)} %") + error = np.abs(avg_speckle_size_e2 - speckle_size) * 100 / speckle_size + print(f"Percentage error between requested speckle size and measured speckle size from 1/e^2: {np.round(error, 3)} %") + np.save(f"{save_path}/image.npy", image) + print("") + print('End :)') + print("") + print("") + print("") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/pyvale/specklegen/scripts/base/case1.sh b/src/pyvale/specklegen/scripts/base/case1.sh new file mode 100644 index 00000000..f492c788 --- /dev/null +++ b/src/pyvale/specklegen/scripts/base/case1.sh @@ -0,0 +1,23 @@ +SIMULATION="base" +CASE="case1" + +> logs/$SIMULATION/$CASE.log + +for SPECKLE_SIZE in $(seq 10 5 25) +do +for SIGMA_COEF in 1 3 5 +do + python -u runfiles/main_specklegen.py \ + --output_path ./output/$SIMULATION/$CASE/ \ + --speckle_size $SPECKLE_SIZE \ + --screen_size_width 1000 \ + --screen_size_height 800 \ + --bit_depth 8 \ + --theme "white_on_black" \ + --random_seed 10 \ + --sigma $(($SPECKLE_SIZE/$SIGMA_COEF)) \ + --reduce_overlap "False" \ + >> logs/$SIMULATION/$CASE.log 2>&1 # Append to the log file + +done +done \ No newline at end of file diff --git a/src/pyvale/specklegen/scripts/base/case2.sh b/src/pyvale/specklegen/scripts/base/case2.sh new file mode 100644 index 00000000..db5554e3 --- /dev/null +++ b/src/pyvale/specklegen/scripts/base/case2.sh @@ -0,0 +1,23 @@ +SIMULATION="base" +CASE="case2" + +> logs/$SIMULATION/$CASE.log + +for SPECKLE_SIZE in $(seq 10 5 25) +do +for SIGMA_COEF in 1 3 5 +do + python -u runfiles/main_specklegen.py \ + --output_path ./output/$SIMULATION/$CASE/ \ + --speckle_size $SPECKLE_SIZE \ + --screen_size_width 1000 \ + --screen_size_height 800 \ + --bit_depth 8 \ + --theme "black_on_white" \ + --random_seed 10 \ + --sigma $(($SPECKLE_SIZE/$SIGMA_COEF)) \ + --reduce_overlap "False" \ + >> logs/$SIMULATION/$CASE.log 2>&1 # Append to the log file + +done +done \ No newline at end of file diff --git a/src/pyvale/specklegen/scripts/reduce_overlap/case1.sh b/src/pyvale/specklegen/scripts/reduce_overlap/case1.sh new file mode 100644 index 00000000..2c008d80 --- /dev/null +++ b/src/pyvale/specklegen/scripts/reduce_overlap/case1.sh @@ -0,0 +1,23 @@ +SIMULATION="reduce_overlap" +CASE="case1" + +> logs/$SIMULATION/$CASE.log + +for SPECKLE_SIZE in $(seq 10 5 25) +do +for SIGMA_COEF in 1 3 5 +do + python -u runfiles/main_specklegen.py \ + --output_path ./output/$SIMULATION/$CASE/ \ + --speckle_size $SPECKLE_SIZE \ + --screen_size_width 1000 \ + --screen_size_height 800 \ + --bit_depth 8 \ + --theme "white_on_black" \ + --random_seed 10 \ + --sigma $(($SPECKLE_SIZE/$SIGMA_COEF)) \ + --reduce_overlap "True" \ + >> logs/$SIMULATION/$CASE.log 2>&1 # Append to the log file + +done +done \ No newline at end of file diff --git a/src/pyvale/specklegen/scripts/reduce_overlap/case2.sh b/src/pyvale/specklegen/scripts/reduce_overlap/case2.sh new file mode 100644 index 00000000..4139dadc --- /dev/null +++ b/src/pyvale/specklegen/scripts/reduce_overlap/case2.sh @@ -0,0 +1,23 @@ +SIMULATION="reduce_overlap" +CASE="case2" + +> logs/$SIMULATION/$CASE.log + +for SPECKLE_SIZE in $(seq 10 5 25) +do +for SIGMA_COEF in 1 3 5 +do + python -u runfiles/main_specklegen.py \ + --output_path ./output/$SIMULATION/$CASE/ \ + --speckle_size $SPECKLE_SIZE \ + --screen_size_width 1000 \ + --screen_size_height 800 \ + --bit_depth 8 \ + --theme "black_on_white" \ + --random_seed 10 \ + --sigma $(($SPECKLE_SIZE/$SIGMA_COEF)) \ + --reduce_overlap "True" \ + >> logs/$SIMULATION/$CASE.log 2>&1 # Append to the log file + +done +done \ No newline at end of file diff --git a/src/pyvale/specklegen/utils/speckle_generation.py b/src/pyvale/specklegen/utils/speckle_generation.py new file mode 100644 index 00000000..ddf40d2d --- /dev/null +++ b/src/pyvale/specklegen/utils/speckle_generation.py @@ -0,0 +1,105 @@ +import numpy as np +from scipy import ndimage + +def pixelsInDisk(cent_x: int, cent_y: int, + screen_size_width: int, screen_size_height: int, + speckle_size: float, foreground_colour: int, image: np.ndarray) -> None: + + box_max_x = int(np.ceil(cent_x + speckle_size / 2)) + box_min_x = int(np.floor(cent_x - speckle_size / 2)) + + box_max_y = int(np.ceil(cent_y + speckle_size / 2)) + box_min_y = int(np.floor(cent_y - speckle_size / 2)) + + for yy in range(box_min_y, box_max_y): + for xx in range(box_min_x, box_max_x): + if yy < 0 or yy >= screen_size_height or xx < 0 or xx >= screen_size_width: + continue + distance = (xx + 0.5 - cent_x)**2 + (yy + 0.5 - cent_y)**2 + if distance <= (speckle_size / 2)**2: + image[yy, xx] = foreground_colour + +def generate_speckles(screen_size_width: int, screen_size_height: int, + speckle_size: float, foreground_colour: int, + total_speckles: int, reduce_overlap: bool, + bit_depth: int, background_colour: int, + sigma: float, attempts_tot: int = 100) -> np.ndarray: + + image: np.ndarray = np.ones((screen_size_height,screen_size_width), + dtype=np.uint16 if bit_depth == 16 else np.uint8) * background_colour + + results = np.zeros([total_speckles, 5]) # speckle number, attempts, with/without/not checked overlap (1/0/2), cent_x, cent_y + + if reduce_overlap: + for i in range(int(total_speckles)): + # reduce overlap by checking if the new speckle overlaps with existing ones + over_lap = True + attempts = 0 + # print(f"Placing speckle {i+1}/{total_speckles}") + while over_lap and attempts < attempts_tot: + # print(f"Attempt {attempts+1} to place speckle {i+1}") + over_lap = False + cent_x = np.random.uniform(0, screen_size_width) + cent_y = np.random.uniform(0, screen_size_height) + + # check colour + if image[int(cent_y), int(cent_x)] == foreground_colour: + over_lap = True + attempts += 1 + # print(f"Overlap detected at centre ({cent_x}, {cent_y})") + continue + + box_max_x = int(np.ceil(cent_x + speckle_size / 2)) + box_min_x = int(np.floor(cent_x - speckle_size / 2)) + + box_max_y = int(np.ceil(cent_y + speckle_size / 2)) + box_min_y = int(np.floor(cent_y - speckle_size / 2)) + + for yy in range(box_min_y, box_max_y): + for xx in range(box_min_x, box_max_x): + if yy < 0 or yy >= screen_size_height or xx < 0 or xx >= screen_size_width: + continue + distance = (xx + 0.5 - cent_x)**2 + (yy + 0.5 - cent_y)**2 + if distance <= (speckle_size / 2)**2: + if image[yy, xx] == foreground_colour: + over_lap = True + break + if over_lap: + # print(f"Overlap detected at ({xx}, {yy})") + break + results[i, 0] = i + 1 + results[i, 1] = attempts + 1 + results[i, 2] = 0 if over_lap else 1 + results[i, 3] = cent_x + results[i, 4] = cent_y + attempts += 1 + # if attempts == attempts_tot: + # print("Could not place speckle without overlap, placing anyway.") + + for yy in range(box_min_y, box_max_y): + for xx in range(box_min_x, box_max_x): + if yy < 0 or yy >= screen_size_height or xx < 0 or xx >= screen_size_width: + continue + distance = (xx + 0.5 - cent_x)**2 + (yy + 0.5 - cent_y)**2 + if distance <= (speckle_size / 2)**2: + image[yy, xx] = foreground_colour + # print(f"({xx}, {yy}) distance: {distance}") + else: + for i in range(int(total_speckles)): + cent_x = np.random.uniform(0, screen_size_width) + cent_y = np.random.uniform(0, screen_size_height) + pixelsInDisk(cent_x, cent_y, + screen_size_width, screen_size_height, + speckle_size, foreground_colour, image) + results[i, 0] = i + 1 + results[i, 1] = 1 + results[i, 2] = 2 + results[i, 3] = cent_x + results[i, 4] = cent_y + + + # Apply Gaussian blur + image_blur = np.copy(image) + image_blur = ndimage.gaussian_filter(image_blur, sigma=sigma) + + return image_blur, results \ No newline at end of file diff --git a/src/pyvale/specklegen/utils/speckle_pattern_diagnostics.py b/src/pyvale/specklegen/utils/speckle_pattern_diagnostics.py new file mode 100644 index 00000000..57fec9f0 --- /dev/null +++ b/src/pyvale/specklegen/utils/speckle_pattern_diagnostics.py @@ -0,0 +1,167 @@ +import numpy as np +import matplotlib.pyplot as plt +import matplotlib +import seaborn as sns +import scipy.fftpack +from scipy.signal import correlate +from scipy.optimize import curve_fit +from scipy.signal import fftconvolve +from scipy.stats import skew, kurtosis +from skimage.measure import shannon_entropy + +def speckle_pattern_diagnostics(image: np.ndarray, dynamic_range: int, + save_path: str) -> dict: + + HFWHM, HeSquared, H_fit_stats, VFWHM, VeSquared, V_fit_stats, popt_H, popt_V, h_profile, v_profile = speckle_size(image) + avg_speckle_size_fwhm = np.mean([HFWHM, VFWHM]) + avg_speckle_size_e2 = np.mean([HeSquared, VeSquared]) + + # Calculate black/white ratio + black_pixels = np.sum(image < (dynamic_range // 2)) + white_pixels = np.sum(image >= (dynamic_range // 2)) + ratio = black_pixels / white_pixels + + # Calculate mean intensity gradient (simple finite difference) + grad_y, grad_x = np.gradient(image) + mean_gradient = np.mean(np.sqrt(grad_x ** 2 + grad_y ** 2)) + std_dev = np.std(image) + avg = np.mean(image) + contrast = std_dev / avg + skewness = skew(image.flatten()) + kurt = kurtosis(image.flatten()) + entropy = shannon_entropy(image) + peak_to_mean = np.max(image) / np.mean(image) + + results = { + "black_white_ratio": ratio, + "mean_intensity_gradient": mean_gradient, + "std_dev_irradiance": std_dev, + "avg_irradiance": avg, + "contrast": contrast, + "skewness": skewness, + "kurtosis": kurt, + "shannon_entropy": entropy, + "peak_to_mean_ratio": peak_to_mean, + "avg_speckle_size_fwhm": avg_speckle_size_fwhm, + "avg_speckle_size_e2": avg_speckle_size_e2, + "H_fit_stats": H_fit_stats, + "V_fit_stats": V_fit_stats + } + + # Set Seaborn theme + sns.set_theme(style="darkgrid") + matplotlib.rcParams['font.family'] = 'Sans-serif' + plt.rc('text', usetex=True) + fontsize1= 17 + + fig, axes = plt.subplots(1, 1, figsize=(7.0, 5.0)) + ax = axes + ax.imshow(image, cmap='gray', vmin=0, vmax=dynamic_range) + cbar = plt.colorbar(ax.images[0], ax=ax, fraction=0.046, pad=0.04) + cbar.ax.tick_params(labelsize=fontsize1-2) + cbar.set_label('Irradiance', fontsize=fontsize1-2) + ax.tick_params(axis='both', which='major', labelsize=fontsize1-2) + ax.set_xlabel("Position [pixel]", fontsize=fontsize1-2) + ax.set_ylabel("Position [pixel]", fontsize=fontsize1-2) + ax.set_title("Speckle pattern", fontsize=fontsize1) + # plt.savefig(f"{save_path}/speckle_pattern.tiff", dpi=300, format='tiff', bbox_inches='tight') + plt.savefig(f"{save_path}/speckle_pattern.jpg", dpi=300, format='jpg', bbox_inches='tight') + + f_image = scipy.fftpack.fft2(image) + f_image_shifted = scipy.fftpack.fftshift(f_image) + magnitude_spectrum = np.abs(f_image_shifted) + magnitude_spectrum_log = np.log1p(magnitude_spectrum) + fig, axes = plt.subplots(1, 1, figsize=(7.0, 5.0)) + ax = axes + ax.imshow(magnitude_spectrum_log, cmap='gray') + cbar = plt.colorbar(ax.images[0], ax=ax, fraction=0.046, pad=0.04) + cbar.ax.tick_params(labelsize=fontsize1-2) + ax.tick_params(axis='both', which='major', labelsize=fontsize1-2) + ax.set_title("Spatial frequency (log scale)", fontsize=fontsize1) + ax.set_xlabel("Frequency [1/pixel]", fontsize=fontsize1-2) + ax.set_ylabel("Frequency [1/pixel]", fontsize=fontsize1-2) + # plt.savefig(f"{save_path}/PSD_spectrum.tiff", dpi=300, format='tiff', bbox_inches='tight') + plt.savefig(f"{save_path}/frequency_spectrum.jpg", dpi=300, format='jpg', bbox_inches='tight') + + fig, axes = plt.subplots(1, 1, figsize=(7.0, 5.0)) + ax = axes + ax.hist(image.ravel(), density=True, bins=int(dynamic_range/10), color='blue', alpha=0.7, log=True) + ax.set_title("Histogram of irradiance values", fontsize=fontsize1) + ax.set_xlabel("Pixel value", fontsize=fontsize1-2) + ax.set_ylabel("Density (log scale)", fontsize=fontsize1-2) + ax.tick_params(axis='both', which='major', labelsize=fontsize1-2) + # plt.savefig(f"{save_path}/pixel_value_histogram.tiff", dpi=300, format='tiff', bbox_inches='tight') + plt.savefig(f"{save_path}/pixel_value_histogram.jpg", dpi=300, format='jpg', bbox_inches='tight') + + plt.figure(figsize=(7.0, 5.0)) + fontsize1= 17 + x_H = np.arange(1, h_profile.size + 1) + x_V = np.arange(1, v_profile.size + 1) + plt.subplot(2, 1, 1) + plt.plot(x_H, h_profile, 'b-', label='Horisontal autocov.', + linewidth=2) + plt.plot(x_H, gaussian(x_H, *popt_H), 'r--', label='Gaussian interpol.', linewidth=2) + plt.title('Horisontal autocovariance', fontsize=fontsize1) + plt.xlabel('Lag [pixels]', fontsize=fontsize1-2) + plt.ylabel('Autocov. ' + r"[pixel$^2$]", fontsize=fontsize1-2) + plt.legend(fontsize=fontsize1-2) + plt.tick_params(axis='both', which='major', labelsize=fontsize1-2) + plt.subplot(2, 1, 2) + plt.plot(x_V, v_profile, 'b-', label='Vertical autocov.', + linewidth=2) + plt.plot(x_V, gaussian(x_V, *popt_V), 'r--', label='Gaussian interpol.', linewidth=2) + plt.title('Vertical autocovariance', fontsize=fontsize1) + plt.xlabel('Lag [pixels]', fontsize=fontsize1- 2) + plt.ylabel('Autocov. ' + r"[pixel$^2$]", fontsize=fontsize1-2) + plt.legend(fontsize=fontsize1-2) + plt.tick_params(axis='both', which='major', labelsize=fontsize1-2) + plt.tight_layout() + plt.savefig(f"{save_path}/autocovariance.jpg", dpi=300, format='jpg', bbox_inches='tight') + + return results + +def speckle_size(image: np.ndarray) -> tuple: + # image = (image - np.mean(image)) / np.std(image) + image = (image - np.mean(image)) + + # Autocorrelation (FFT analysis) + f_image_norm = scipy.fftpack.fft2(image) + power_spectrum = np.abs(f_image_norm)**2 + autocorr = scipy.fftpack.ifft2(power_spectrum).real + autocorr = scipy.fftpack.fftshift(autocorr) + autocorr /= np.max(autocorr) + centre_y, centre_x = np.array(autocorr.shape) // 2 + h_profile = autocorr[centre_y, :] + v_profile = autocorr[:, centre_x] + + HFWHM, HeSquared, H_fit_stats, VFWHM, VeSquared, V_fit_stats, popt_H, popt_V = fit_gaussian(h_profile, v_profile) + + return HFWHM, HeSquared, H_fit_stats, VFWHM, VeSquared, V_fit_stats, popt_H, popt_V, h_profile, v_profile + +def gaussian(x: np.ndarray, a1: float, b1: float, c1: float) -> np.ndarray: + return a1 * np.exp(-((x - b1)/c1) ** 2) + +def fit_gaussian(H: np.ndarray, V: np.ndarray) -> tuple: + + range_H = np.arange(1, H.size + 1) + range_V = np.arange(1, V.size + 1) + + low_H = np.where(H > 0.2)[0] + low_V = np.where(V > 0.2)[0] + + popt_H, _ = curve_fit(gaussian, range_H[low_H], H[low_H], p0=[1, np.argmax(H), 1]) + popt_V, _ = curve_fit(gaussian, range_V[low_V], V[low_V], p0=[1, np.argmax(V), 1]) + + # Extract FWHM and 1/e^2 widths + HFWHM = 2 * popt_H[2] * np.sqrt(-np.log(0.5 / popt_H[0])) # FWHM for horisontal + VFWHM = 2 * popt_V[2] * np.sqrt(-np.log(0.5 / popt_V[0])) # FWHM for vertical + HeSquared = popt_H[2] * np.sqrt(-np.log(0.1353353 / popt_H[0])) # 1/e^2 for horisontal + VeSquared = popt_V[2] * np.sqrt(-np.log(0.1353353 / popt_V[0])) # 1/e^2 for vertical + + # R-squared goodness of fit + H_fit_stats = {'R_squared': 1 - np.sum((H[low_H] - gaussian(range_H[low_H], *popt_H)) ** 2) / np.sum((H[low_H] - np.mean(H[low_H])) ** 2)} + V_fit_stats = {'R_squared': 1 - np.sum((V[low_V] - gaussian(range_V[low_V], *popt_V)) ** 2) / np.sum((V[low_V] - np.mean(V[low_V])) ** 2)} + + return HFWHM, HeSquared, H_fit_stats, VFWHM, VeSquared, V_fit_stats, popt_H, popt_V + + From 85c314d2e7c3a3d2aeaae80caf664f4d38459155 Mon Sep 17 00:00:00 2001 From: WieraB Date: Wed, 22 Oct 2025 15:42:08 +0100 Subject: [PATCH 02/16] Added Perlin and fractal noise with examples. --- .../runfiles/main_specklegen_perlin_noise.py | 128 +++++++++++++++ .../specklegen/scripts/perlin_noise/case1.sh | 21 +++ .../specklegen/scripts/perlin_noise/case2.sh | 25 +++ .../specklegen/utils/speckle_generation.py | 152 +++++++++++++----- 4 files changed, 286 insertions(+), 40 deletions(-) create mode 100644 src/pyvale/specklegen/runfiles/main_specklegen_perlin_noise.py create mode 100644 src/pyvale/specklegen/scripts/perlin_noise/case1.sh create mode 100644 src/pyvale/specklegen/scripts/perlin_noise/case2.sh diff --git a/src/pyvale/specklegen/runfiles/main_specklegen_perlin_noise.py b/src/pyvale/specklegen/runfiles/main_specklegen_perlin_noise.py new file mode 100644 index 00000000..41e70bd7 --- /dev/null +++ b/src/pyvale/specklegen/runfiles/main_specklegen_perlin_noise.py @@ -0,0 +1,128 @@ +import numpy as np +import argparse +import json +import sys +import os +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +from utils.speckle_pattern_diagnostics import speckle_pattern_diagnostics +from utils.speckle_generation import generate_speckles_perlin_noise + +def main() -> None: + + parser = argparse.ArgumentParser() + + # Load parameter definitions from the JSON file + with open('./scripts/configs/config_input_params_spec_perlin.json') as f: + parameters_input = json.load(f) + + # Merge all dictionaries + parameters = {**parameters_input} + + # Automatically add arguments from the JSON dictionary + for param, param_args in parameters.items(): + # Convert the "type" and "required" from string to actual Python type + # Check if "type" and "required" are present in the JSON file + if "type" in param_args.keys(): + param_args["type"] = eval(param_args["type"]) + if "required" in param_args.keys(): + param_args["required"] = eval(param_args["required"]) + parser.add_argument(param, **param_args) + + # Parse arguments + args = parser.parse_args() + + print('Args in simulation:') + print(args) + print('') + print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') + print('') + + print('Start') + + # Extract parameteres and revert to default values if not provided by user + screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 + screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 + bit_depth = args.bit_depth if args.bit_depth is not None else 8 + theme = args.theme if args.theme is not None else 'white_on_black' + octaves = args.octaves if args.octaves is not None else "Not defined" + + type_gen = args.type_gen + res_width = args.res_width + res_height = args.res_height + + assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." + assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." + + if type_gen == "perlin": + subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{type_gen}_{res_width}_{res_height}" + elif type_gen == "fractal": + subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{type_gen}_{res_width}_{res_height}_{octaves}" + print(subfolder) + save_path = args.output_path + subfolder + if not os.path.exists(save_path): + os.makedirs(save_path) + + dynamic_range: int = 2**bit_depth - 1 + background_colour = 0 if theme == 'white_on_black' else dynamic_range + foreground_colour = dynamic_range if theme == 'white_on_black' else 0 + + # Generate speckle pattern + if type_gen == "perlin": + image = generate_speckles_perlin_noise(screen_size_width, screen_size_height, + res_height, res_width, + foreground_colour, bit_depth, background_colour, type_gen) + elif type_gen == "fractal": + image = generate_speckles_perlin_noise(screen_size_width, screen_size_height, + res_height, res_width, + foreground_colour, bit_depth, background_colour, type_gen, + octaves=octaves) + + # Diagnostics + print("") + print('Starting speckle pattern diagnostics...') + results = speckle_pattern_diagnostics(image, dynamic_range, save_path) + + # save the diagnostics results + with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: + json.dump(results, f, indent=4) + + ratio = results.get("black_white_ratio", None) + mean_gradient = results.get("mean_intensity_gradient", None) + std_dev = results.get("std_dev_irradiance", None) + avg = results.get("avg_irradiance", None) + contrast = results.get("contrast", None) + entropy = results.get("shannon_entropy", None) + peak_to_mean = results.get("peak_to_mean_ratio", None) + skew = results.get("skewness", None) + kurt = results.get("kurtosis", None) + avg_speckle_size_fwhm = results.get("avg_speckle_size_fwhm", None) + avg_speckle_size_e2 = results.get("avg_speckle_size_e2", None) + H_fit_stats = results.get("H_fit_stats", None) + V_fit_stats = results.get("V_fit_stats", None) + + print("") + print("Speckle statistics:") + + print(f"Black/White ratio: {np.round(ratio, 3)}") + print(f"Mean intensity gradient: {np.round(mean_gradient, 3)}") + print(f"Standard deviation of irradiance values: {np.round(std_dev, 3)}") + print(f"Average irradiance value: {np.round(avg, 3)}") + print(f"Contrast (std/mean): {np.round(contrast, 3)}") + print(f"Skewness: {np.round(skew, 3)}") + print(f"Kurtosis: {np.round(kurt, 3)}") + print(f"Shannon entropy: {entropy}") + print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") + print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") + print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") + print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") + + np.save(f"{save_path}/image.npy", image) + print("") + print('End :)') + print("") + print("") + print("") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/pyvale/specklegen/scripts/perlin_noise/case1.sh b/src/pyvale/specklegen/scripts/perlin_noise/case1.sh new file mode 100644 index 00000000..2fb09c43 --- /dev/null +++ b/src/pyvale/specklegen/scripts/perlin_noise/case1.sh @@ -0,0 +1,21 @@ +SIMULATION="perlin_noise" +CASE="case1" + +> logs/$SIMULATION/$CASE.log + +for RES_WIDTH in 10 25 100 +do +for RES_HEIGHT in 10 25 100 +do + python -u runfiles/main_specklegen_perlin_noise.py \ + --output_path ./output/$SIMULATION/$CASE/ \ + --screen_size_width 1000 \ + --screen_size_height 800 \ + --res_width $RES_WIDTH \ + --res_height $RES_HEIGHT \ + --bit_depth 8 \ + --theme "white_on_black" \ + --type_gen "perlin" \ + >> logs/$SIMULATION/$CASE.log 2>&1 # Append to the log file +done +done \ No newline at end of file diff --git a/src/pyvale/specklegen/scripts/perlin_noise/case2.sh b/src/pyvale/specklegen/scripts/perlin_noise/case2.sh new file mode 100644 index 00000000..d33f1f5a --- /dev/null +++ b/src/pyvale/specklegen/scripts/perlin_noise/case2.sh @@ -0,0 +1,25 @@ +SIMULATION="perlin_noise" +CASE="case2" + +> logs/$SIMULATION/$CASE.log + +for RES_WIDTH in 10 25 100 +do +for RES_HEIGHT in 10 25 100 +do +for OCTAVES in 1 2 3 +do + python -u runfiles/main_specklegen_perlin_noise.py \ + --output_path ./output/$SIMULATION/$CASE/ \ + --screen_size_width 1000 \ + --screen_size_height 800 \ + --res_width $RES_WIDTH \ + --res_height $RES_HEIGHT \ + --bit_depth 8 \ + --theme "white_on_black" \ + --type_gen "fractal" \ + --octaves $OCTAVES \ + >> logs/$SIMULATION/$CASE.log 2>&1 # Append to the log file +done +done +done \ No newline at end of file diff --git a/src/pyvale/specklegen/utils/speckle_generation.py b/src/pyvale/specklegen/utils/speckle_generation.py index ddf40d2d..26ee2eff 100644 --- a/src/pyvale/specklegen/utils/speckle_generation.py +++ b/src/pyvale/specklegen/utils/speckle_generation.py @@ -1,23 +1,56 @@ import numpy as np from scipy import ndimage +from perlin_numpy import ( + generate_perlin_noise_2d, generate_fractal_noise_2d +) def pixelsInDisk(cent_x: int, cent_y: int, screen_size_width: int, screen_size_height: int, - speckle_size: float, foreground_colour: int, image: np.ndarray) -> None: + speckle_size: float, foreground_colour: int, image: np.ndarray, + check_overlap: bool) -> int: + + """ Set pixels in a disk shape on the image array, disk centered at (cent_x, cent_y) with given speckle_size """ + "Inputs:" + " cent_x, cent_y: center coordinates of the disk" + " screen_size_width, screen_size_height: dimensions of the image" + " speckle_size: diameter of the disk" + " foreground_colour: colour value to set for the disk pixels" + " image: 2D numpy array representing the image" + " check_overlap: if True, check for overlap with existing foreground pixels and does not modify the image" + " Output: modifies the image array" box_max_x = int(np.ceil(cent_x + speckle_size / 2)) box_min_x = int(np.floor(cent_x - speckle_size / 2)) - + box_max_y = int(np.ceil(cent_y + speckle_size / 2)) box_min_y = int(np.floor(cent_y - speckle_size / 2)) - - for yy in range(box_min_y, box_max_y): - for xx in range(box_min_x, box_max_x): - if yy < 0 or yy >= screen_size_height or xx < 0 or xx >= screen_size_width: - continue - distance = (xx + 0.5 - cent_x)**2 + (yy + 0.5 - cent_y)**2 - if distance <= (speckle_size / 2)**2: - image[yy, xx] = foreground_colour + + if check_overlap: + over_lap = False + + for yy in range(box_min_y, box_max_y): + for xx in range(box_min_x, box_max_x): + if yy < 0 or yy >= screen_size_height or xx < 0 or xx >= screen_size_width: + continue + distance = (xx + 0.5 - cent_x)**2 + (yy + 0.5 - cent_y)**2 + if distance <= (speckle_size / 2)**2: + if image[yy, xx] == foreground_colour: + over_lap = True + break + if over_lap: + # print(f"Overlap detected at ({xx}, {yy})") + break + return 0 if over_lap else 1 + + else: + for yy in range(box_min_y, box_max_y): + for xx in range(box_min_x, box_max_x): + if yy < 0 or yy >= screen_size_height or xx < 0 or xx >= screen_size_width: + continue + distance = (xx + 0.5 - cent_x)**2 + (yy + 0.5 - cent_y)**2 + if distance <= (speckle_size / 2)**2: + image[yy, xx] = foreground_colour + def generate_speckles(screen_size_width: int, screen_size_height: int, speckle_size: float, foreground_colour: int, @@ -25,6 +58,20 @@ def generate_speckles(screen_size_width: int, screen_size_height: int, bit_depth: int, background_colour: int, sigma: float, attempts_tot: int = 100) -> np.ndarray: + """ Generate a speckle pattern image with given parameters """ + """ The speckles are disks of given speckle_size, placed randomly on the image, based on the uniform probability distribution """ + """ Inputs: """ + """ screen_size_width, screen_size_height: dimensions of the image """ + """ speckle_size: diameter of each speckle disk """ + """ foreground_colour: colour value for the speckles """ + """ total_speckles: total number of speckles to place """ + """ reduce_overlap: if True, attempts to reduce overlap between speckles """ + """ bit_depth: bit depth of the image (8 or 16) """ + """ background_colour: colour value for the background """ + """ sigma: standard deviation for Gaussian blur applied after speckle placement """ + """ attempts_tot: maximum number of attempts to place each speckle without overlap """ + """ Output: speckle pattern image as a 2D numpy array and speckle generation stats """ + image: np.ndarray = np.ones((screen_size_height,screen_size_width), dtype=np.uint16 if bit_depth == 16 else np.uint8) * background_colour @@ -33,6 +80,7 @@ def generate_speckles(screen_size_width: int, screen_size_height: int, if reduce_overlap: for i in range(int(total_speckles)): # reduce overlap by checking if the new speckle overlaps with existing ones + check_overlap = True over_lap = True attempts = 0 # print(f"Placing speckle {i+1}/{total_speckles}") @@ -48,25 +96,11 @@ def generate_speckles(screen_size_width: int, screen_size_height: int, attempts += 1 # print(f"Overlap detected at centre ({cent_x}, {cent_y})") continue - - box_max_x = int(np.ceil(cent_x + speckle_size / 2)) - box_min_x = int(np.floor(cent_x - speckle_size / 2)) - - box_max_y = int(np.ceil(cent_y + speckle_size / 2)) - box_min_y = int(np.floor(cent_y - speckle_size / 2)) - - for yy in range(box_min_y, box_max_y): - for xx in range(box_min_x, box_max_x): - if yy < 0 or yy >= screen_size_height or xx < 0 or xx >= screen_size_width: - continue - distance = (xx + 0.5 - cent_x)**2 + (yy + 0.5 - cent_y)**2 - if distance <= (speckle_size / 2)**2: - if image[yy, xx] == foreground_colour: - over_lap = True - break - if over_lap: - # print(f"Overlap detected at ({xx}, {yy})") - break + over_lap_int = pixelsInDisk(cent_x, cent_y, + screen_size_width, screen_size_height, + speckle_size, foreground_colour, image, check_overlap) + over_lap = True if over_lap_int == 0 else False + results[i, 0] = i + 1 results[i, 1] = attempts + 1 results[i, 2] = 0 if over_lap else 1 @@ -75,22 +109,18 @@ def generate_speckles(screen_size_width: int, screen_size_height: int, attempts += 1 # if attempts == attempts_tot: # print("Could not place speckle without overlap, placing anyway.") - - for yy in range(box_min_y, box_max_y): - for xx in range(box_min_x, box_max_x): - if yy < 0 or yy >= screen_size_height or xx < 0 or xx >= screen_size_width: - continue - distance = (xx + 0.5 - cent_x)**2 + (yy + 0.5 - cent_y)**2 - if distance <= (speckle_size / 2)**2: - image[yy, xx] = foreground_colour - # print(f"({xx}, {yy}) distance: {distance}") + # Place the speckle finally + pixelsInDisk(cent_x, cent_y, + screen_size_width, screen_size_height, + speckle_size, foreground_colour, image, check_overlap=False) else: + check_overlap = False for i in range(int(total_speckles)): cent_x = np.random.uniform(0, screen_size_width) cent_y = np.random.uniform(0, screen_size_height) pixelsInDisk(cent_x, cent_y, screen_size_width, screen_size_height, - speckle_size, foreground_colour, image) + speckle_size, foreground_colour, image, check_overlap) results[i, 0] = i + 1 results[i, 1] = 1 results[i, 2] = 2 @@ -102,4 +132,46 @@ def generate_speckles(screen_size_width: int, screen_size_height: int, image_blur = np.copy(image) image_blur = ndimage.gaussian_filter(image_blur, sigma=sigma) - return image_blur, results \ No newline at end of file + return image_blur, results + + +def generate_speckles_perlin_noise(screen_size_width: int, screen_size_height: int, + res_width: int, res_height: int, + foreground_colour: int, + bit_depth: int, background_colour: int, type_gen: str, + **kwargs) -> np.ndarray: + """Generate a speckle pattern image using Perlin or fractal noise. """ + """ Inputs: """ + """ screen_size_width, screen_size_height: dimensions of the image """ + """ res_width, res_height: number of periods of noise to generate along 2 axes """ + """ foreground_colour: colour value for the speckles """ + """ bit_depth: bit depth of the image (8 or 16) """ + """ background_colour: colour value for the background """ + """ type_gen: type of noise to generate ('perlin' or 'fractal')""" + """ kwargs: Additional keyword arguments for fractal noise generation (e.g., octaves)""" + """ Output: speckle pattern image as a 2D numpy array """ + + if type_gen == "perlin": + # screen size must be a multiple of res + assert screen_size_width % res_width == 0, "The screen size must be a multiple of res." + assert screen_size_height % res_height == 0, "The screen size must be a multiple of res." + image = generate_perlin_noise_2d(shape = (screen_size_height, screen_size_width), + res = (res_height, res_width)) + + if type_gen == "fractal": + # screen size must be a multiple of lacunarity^(octaves-1)*res + assert screen_size_width % res_width == 0, "The screen size must be a multiple of lacunarity^(octaves-1)*res." + assert screen_size_height % res_height == 0, "The screen size must be a multiple of lacunarity^(octaves-1)*res." + octaves = kwargs["octaves"] + image = generate_fractal_noise_2d(shape = (screen_size_height, screen_size_width), + res = (res_height, res_width), + octaves = octaves) + + # scale to background and foreground colours + min_val = np.min(image) + max_val = np.max(image) + image = (image - min_val) / (max_val - min_val) # Normalise to [0, 1] + image = image * (foreground_colour - background_colour) + background_colour + image = image.astype(np.uint16 if bit_depth == 16 else np.uint8) + + return image \ No newline at end of file From 8c9bd525e093cda96c2fe0bc16b62916ed1db019 Mon Sep 17 00:00:00 2001 From: WieraB Date: Thu, 23 Oct 2025 11:07:58 +0100 Subject: [PATCH 03/16] Added Simplex noise with examples. --- .../runfiles/main_specklegen_simplex_noise.py | 122 ++++++++++++++++++ .../specklegen/scripts/simplex_noise/case1.sh | 17 +++ .../specklegen/utils/speckle_generation.py | 53 +++++++- 3 files changed, 189 insertions(+), 3 deletions(-) create mode 100644 src/pyvale/specklegen/runfiles/main_specklegen_simplex_noise.py create mode 100644 src/pyvale/specklegen/scripts/simplex_noise/case1.sh diff --git a/src/pyvale/specklegen/runfiles/main_specklegen_simplex_noise.py b/src/pyvale/specklegen/runfiles/main_specklegen_simplex_noise.py new file mode 100644 index 00000000..7a0a6820 --- /dev/null +++ b/src/pyvale/specklegen/runfiles/main_specklegen_simplex_noise.py @@ -0,0 +1,122 @@ +import numpy as np +import argparse +import json +import sys +import os +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +from utils.speckle_pattern_diagnostics import speckle_pattern_diagnostics +from utils.speckle_generation import generate_speckles_simplex_noise + +def main() -> None: + + parser = argparse.ArgumentParser() + + # Load parameter definitions from the JSON file + with open('./scripts/configs/config_input_params_spec_simplex.json') as f: + parameters_input = json.load(f) + + # Merge all dictionaries + parameters = {**parameters_input} + + # Automatically add arguments from the JSON dictionary + for param, param_args in parameters.items(): + # Convert the "type" and "required" from string to actual Python type + # Check if "type" and "required" are present in the JSON file + if "type" in param_args.keys(): + param_args["type"] = eval(param_args["type"]) + if "required" in param_args.keys(): + param_args["required"] = eval(param_args["required"]) + parser.add_argument(param, **param_args) + + # Parse arguments + args = parser.parse_args() + + print('Args in simulation:') + print(args) + print('') + print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') + print('') + + print('Start') + + # Extract parameteres and revert to default values if not provided by user + speckle_size = args.speckle_size if args.speckle_size is not None else 5.0 + screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 + screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 + bit_depth = args.bit_depth if args.bit_depth is not None else 8 + theme = args.theme if args.theme is not None else 'white_on_black' + seed = args.seed if args.seed is not None else 1234 + + assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." + assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." + + subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{speckle_size}_{seed}" + print(subfolder) + save_path = args.output_path + subfolder + if not os.path.exists(save_path): + os.makedirs(save_path) + + dynamic_range: int = 2**bit_depth - 1 + background_colour = 0 if theme == 'white_on_black' else dynamic_range + foreground_colour = dynamic_range if theme == 'white_on_black' else 0 + + # Generate speckle pattern + image = generate_speckles_simplex_noise(screen_size_width, screen_size_height, + foreground_colour, + bit_depth, background_colour, + feature_size=speckle_size, + seed=seed) + + # Diagnostics + print("") + print('Starting speckle pattern diagnostics...') + results = speckle_pattern_diagnostics(image, dynamic_range, save_path) + + # save the diagnostics results + with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: + json.dump(results, f, indent=4) + + ratio = results.get("black_white_ratio", None) + mean_gradient = results.get("mean_intensity_gradient", None) + std_dev = results.get("std_dev_irradiance", None) + avg = results.get("avg_irradiance", None) + contrast = results.get("contrast", None) + entropy = results.get("shannon_entropy", None) + peak_to_mean = results.get("peak_to_mean_ratio", None) + skew = results.get("skewness", None) + kurt = results.get("kurtosis", None) + avg_speckle_size_fwhm = results.get("avg_speckle_size_fwhm", None) + avg_speckle_size_e2 = results.get("avg_speckle_size_e2", None) + H_fit_stats = results.get("H_fit_stats", None) + V_fit_stats = results.get("V_fit_stats", None) + + print("") + print("Speckle statistics:") + + print(f"Black/White ratio: {np.round(ratio, 3)}") + print(f"Mean intensity gradient: {np.round(mean_gradient, 3)}") + print(f"Standard deviation of irradiance values: {np.round(std_dev, 3)}") + print(f"Average irradiance value: {np.round(avg, 3)}") + print(f"Contrast (std/mean): {np.round(contrast, 3)}") + print(f"Skewness: {np.round(skew, 3)}") + print(f"Kurtosis: {np.round(kurt, 3)}") + print(f"Shannon entropy: {entropy}") + print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") + print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") + print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") + print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") + + error = np.abs(avg_speckle_size_fwhm - speckle_size) * 100 / speckle_size + print(f"Percentage error between requested speckle size and measured speckle size from FWHM: {np.round(error, 3)} %") + error = np.abs(avg_speckle_size_e2 - speckle_size) * 100 / speckle_size + print(f"Percentage error between requested speckle size and measured speckle size from 1/e^2: {np.round(error, 3)} %") + np.save(f"{save_path}/image.npy", image) + print("") + print('End :)') + print("") + print("") + print("") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/pyvale/specklegen/scripts/simplex_noise/case1.sh b/src/pyvale/specklegen/scripts/simplex_noise/case1.sh new file mode 100644 index 00000000..68923c51 --- /dev/null +++ b/src/pyvale/specklegen/scripts/simplex_noise/case1.sh @@ -0,0 +1,17 @@ +SIMULATION="simplex_noise" +CASE="case1" + +> logs/$SIMULATION/$CASE.log + +for SPECKLE_SIZE in 10.0 15.0 25.0 30.0 40.0 +do + python -u runfiles/main_specklegen_simplex_noise.py \ + --output_path ./output/$SIMULATION/$CASE/ \ + --screen_size_width 1000 \ + --screen_size_height 800 \ + --speckle_size $SPECKLE_SIZE \ + --bit_depth 8 \ + --theme "white_on_black" \ + --seed 1234 \ + >> logs/$SIMULATION/$CASE.log 2>&1 # Append to the log file +done \ No newline at end of file diff --git a/src/pyvale/specklegen/utils/speckle_generation.py b/src/pyvale/specklegen/utils/speckle_generation.py index 26ee2eff..cff44f7c 100644 --- a/src/pyvale/specklegen/utils/speckle_generation.py +++ b/src/pyvale/specklegen/utils/speckle_generation.py @@ -1,8 +1,8 @@ import numpy as np from scipy import ndimage from perlin_numpy import ( - generate_perlin_noise_2d, generate_fractal_noise_2d -) + generate_perlin_noise_2d, generate_fractal_noise_2d) +import opensimplex as simplex def pixelsInDisk(cent_x: int, cent_y: int, screen_size_width: int, screen_size_height: int, @@ -140,6 +140,10 @@ def generate_speckles_perlin_noise(screen_size_width: int, screen_size_height: i foreground_colour: int, bit_depth: int, background_colour: int, type_gen: str, **kwargs) -> np.ndarray: + + """This function uses noise implementation from perlin-numpy package + (https://github.com/pvigier/perlin-numpy/tree/master)""" + """Generate a speckle pattern image using Perlin or fractal noise. """ """ Inputs: """ """ screen_size_width, screen_size_height: dimensions of the image """ @@ -174,4 +178,47 @@ def generate_speckles_perlin_noise(screen_size_width: int, screen_size_height: i image = image * (foreground_colour - background_colour) + background_colour image = image.astype(np.uint16 if bit_depth == 16 else np.uint8) - return image \ No newline at end of file + return image + +def generate_speckles_simplex_noise(screen_size_width: int, screen_size_height: int, + foreground_colour: int, + bit_depth: int, background_colour: int, + feature_size: float, + seed: int) -> np.ndarray: + + """This function uses noise implementation from opensimplex package + (https://pypi.org/project/opensimplex/) + (https://code.larus.se/lmas/opensimplex)""" + + """Generate a speckle pattern image using OpenSimplex noise (patent-free). """ + """ Inputs: """ + """ screen_size_width, screen_size_height: dimensions of the image """ + """ foreground_colour: colour value for the speckles """ + """ bit_depth: bit depth of the image (8 or 16) """ + """ background_colour: colour value for the background """ + """ feature_size: controls the size of features in the noise pattern (speckle size) """ + """ seed: seed for the noise generation """ + """ Output: speckle pattern image as a 2D numpy array """ + + simplex.seed(seed) + + # image: np.ndarray = np.zeros((screen_size_height,screen_size_width)) + + # for y in range(0, screen_size_height): + # for x in range(0, screen_size_width): + # value = simplex.noise2(x/feature_size, y/feature_size) + # image[y, x] = value + + ix, iy = np.arange(screen_size_width), np.arange(screen_size_height) + image = simplex.noise2array(ix/feature_size, iy/feature_size) + + # scale to background and foreground colours + min_val = np.min(image) + max_val = np.max(image) + print(f"Min and max values before scaling: {min_val}, {max_val}") + image = (image - min_val) / (max_val - min_val) # Normalise to [0, 1] + image = image * (foreground_colour - background_colour) + background_colour + image = image.astype(np.uint16 if bit_depth == 16 else np.uint8) + + return image + From 8cef9fe8b3be5457576a1593902d8ccb17d5ff6d Mon Sep 17 00:00:00 2001 From: WieraB Date: Thu, 23 Oct 2025 15:45:33 +0100 Subject: [PATCH 04/16] Modified Simplex noise and corresponding examples, updated pyproject.toml --- .../specklegen/runfiles/main_specklegen_simplex_noise.py | 9 ++++++--- src/pyvale/specklegen/scripts/simplex_noise/case1.sh | 8 ++++++-- src/pyvale/specklegen/utils/speckle_generation.py | 7 ++++--- 3 files changed, 16 insertions(+), 8 deletions(-) diff --git a/src/pyvale/specklegen/runfiles/main_specklegen_simplex_noise.py b/src/pyvale/specklegen/runfiles/main_specklegen_simplex_noise.py index 7a0a6820..c187ea82 100644 --- a/src/pyvale/specklegen/runfiles/main_specklegen_simplex_noise.py +++ b/src/pyvale/specklegen/runfiles/main_specklegen_simplex_noise.py @@ -40,7 +40,8 @@ def main() -> None: print('Start') # Extract parameteres and revert to default values if not provided by user - speckle_size = args.speckle_size if args.speckle_size is not None else 5.0 + speckle_size_width = args.speckle_size_width if args.speckle_size_width is not None else 5.0 + speckle_size_height = args.speckle_size_height if args.speckle_size_height is not None else 5.0 screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 bit_depth = args.bit_depth if args.bit_depth is not None else 8 @@ -50,7 +51,7 @@ def main() -> None: assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." - subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{speckle_size}_{seed}" + subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{speckle_size_width}_{speckle_size_height}_{seed}" print(subfolder) save_path = args.output_path + subfolder if not os.path.exists(save_path): @@ -64,7 +65,8 @@ def main() -> None: image = generate_speckles_simplex_noise(screen_size_width, screen_size_height, foreground_colour, bit_depth, background_colour, - feature_size=speckle_size, + feature_size_width=speckle_size_width, + feature_size_height=speckle_size_height, seed=seed) # Diagnostics @@ -106,6 +108,7 @@ def main() -> None: print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") + speckle_size = (speckle_size_width + speckle_size_height) / 2 error = np.abs(avg_speckle_size_fwhm - speckle_size) * 100 / speckle_size print(f"Percentage error between requested speckle size and measured speckle size from FWHM: {np.round(error, 3)} %") error = np.abs(avg_speckle_size_e2 - speckle_size) * 100 / speckle_size diff --git a/src/pyvale/specklegen/scripts/simplex_noise/case1.sh b/src/pyvale/specklegen/scripts/simplex_noise/case1.sh index 68923c51..7dd29235 100644 --- a/src/pyvale/specklegen/scripts/simplex_noise/case1.sh +++ b/src/pyvale/specklegen/scripts/simplex_noise/case1.sh @@ -3,15 +3,19 @@ CASE="case1" > logs/$SIMULATION/$CASE.log -for SPECKLE_SIZE in 10.0 15.0 25.0 30.0 40.0 +for SPECKLE_SIZE_WIDTH in 10 15 20 30 40 +do +for SPECKLE_SIZE_HEIGHT in 10 15 20 30 40 do python -u runfiles/main_specklegen_simplex_noise.py \ --output_path ./output/$SIMULATION/$CASE/ \ --screen_size_width 1000 \ --screen_size_height 800 \ - --speckle_size $SPECKLE_SIZE \ + --speckle_size_width $SPECKLE_SIZE_WIDTH \ + --speckle_size_height $SPECKLE_SIZE_HEIGHT \ --bit_depth 8 \ --theme "white_on_black" \ --seed 1234 \ >> logs/$SIMULATION/$CASE.log 2>&1 # Append to the log file +done done \ No newline at end of file diff --git a/src/pyvale/specklegen/utils/speckle_generation.py b/src/pyvale/specklegen/utils/speckle_generation.py index cff44f7c..feec34e2 100644 --- a/src/pyvale/specklegen/utils/speckle_generation.py +++ b/src/pyvale/specklegen/utils/speckle_generation.py @@ -183,7 +183,7 @@ def generate_speckles_perlin_noise(screen_size_width: int, screen_size_height: i def generate_speckles_simplex_noise(screen_size_width: int, screen_size_height: int, foreground_colour: int, bit_depth: int, background_colour: int, - feature_size: float, + feature_size_width: float, feature_size_height: float, seed: int) -> np.ndarray: """This function uses noise implementation from opensimplex package @@ -196,7 +196,8 @@ def generate_speckles_simplex_noise(screen_size_width: int, screen_size_height: """ foreground_colour: colour value for the speckles """ """ bit_depth: bit depth of the image (8 or 16) """ """ background_colour: colour value for the background """ - """ feature_size: controls the size of features in the noise pattern (speckle size) """ + """ feature_size_width: controls the size of features in the noise pattern (speckle size width-wise) """ + """ feature_size_height: controls the size of features in the noise pattern (speckle size height-wise) """ """ seed: seed for the noise generation """ """ Output: speckle pattern image as a 2D numpy array """ @@ -210,7 +211,7 @@ def generate_speckles_simplex_noise(screen_size_width: int, screen_size_height: # image[y, x] = value ix, iy = np.arange(screen_size_width), np.arange(screen_size_height) - image = simplex.noise2array(ix/feature_size, iy/feature_size) + image = simplex.noise2array(ix/feature_size_width, iy/feature_size_height) # scale to background and foreground colours min_val = np.min(image) From 0a2a9ea8efaa7f69593676aacdd1a7436e2357ad Mon Sep 17 00:00:00 2001 From: WieraB Date: Thu, 23 Oct 2025 16:04:18 +0100 Subject: [PATCH 05/16] Removed print from Simplex function, added log directory creation if it doesn't exist. --- src/pyvale/specklegen/runfiles/main_specklegen.py | 2 +- .../specklegen/runfiles/main_specklegen_perlin_noise.py | 2 +- .../specklegen/runfiles/main_specklegen_simplex_noise.py | 2 +- src/pyvale/specklegen/scripts/base/case1.sh | 4 ++++ src/pyvale/specklegen/scripts/base/case2.sh | 4 ++++ src/pyvale/specklegen/scripts/perlin_noise/case1.sh | 4 ++++ src/pyvale/specklegen/scripts/perlin_noise/case2.sh | 4 ++++ src/pyvale/specklegen/scripts/reduce_overlap/case1.sh | 4 ++++ src/pyvale/specklegen/scripts/reduce_overlap/case2.sh | 4 ++++ src/pyvale/specklegen/scripts/simplex_noise/case1.sh | 4 ++++ src/pyvale/specklegen/utils/speckle_generation.py | 2 +- 11 files changed, 32 insertions(+), 4 deletions(-) diff --git a/src/pyvale/specklegen/runfiles/main_specklegen.py b/src/pyvale/specklegen/runfiles/main_specklegen.py index 2af2a6db..32c70e89 100644 --- a/src/pyvale/specklegen/runfiles/main_specklegen.py +++ b/src/pyvale/specklegen/runfiles/main_specklegen.py @@ -121,7 +121,7 @@ def main() -> None: print(f"Contrast (std/mean): {np.round(contrast, 3)}") print(f"Skewness: {np.round(skew, 3)}") print(f"Kurtosis: {np.round(kurt, 3)}") - print(f"Shannon entropy: {entropy}") + print(f"Shannon entropy: {np.round(entropy, 3)}") print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") diff --git a/src/pyvale/specklegen/runfiles/main_specklegen_perlin_noise.py b/src/pyvale/specklegen/runfiles/main_specklegen_perlin_noise.py index 41e70bd7..1f6dff6b 100644 --- a/src/pyvale/specklegen/runfiles/main_specklegen_perlin_noise.py +++ b/src/pyvale/specklegen/runfiles/main_specklegen_perlin_noise.py @@ -110,7 +110,7 @@ def main() -> None: print(f"Contrast (std/mean): {np.round(contrast, 3)}") print(f"Skewness: {np.round(skew, 3)}") print(f"Kurtosis: {np.round(kurt, 3)}") - print(f"Shannon entropy: {entropy}") + print(f"Shannon entropy: {np.round(entropy, 3)}") print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") diff --git a/src/pyvale/specklegen/runfiles/main_specklegen_simplex_noise.py b/src/pyvale/specklegen/runfiles/main_specklegen_simplex_noise.py index c187ea82..50e0f4cb 100644 --- a/src/pyvale/specklegen/runfiles/main_specklegen_simplex_noise.py +++ b/src/pyvale/specklegen/runfiles/main_specklegen_simplex_noise.py @@ -102,7 +102,7 @@ def main() -> None: print(f"Contrast (std/mean): {np.round(contrast, 3)}") print(f"Skewness: {np.round(skew, 3)}") print(f"Kurtosis: {np.round(kurt, 3)}") - print(f"Shannon entropy: {entropy}") + print(f"Shannon entropy: {np.round(entropy, 3)}") print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") diff --git a/src/pyvale/specklegen/scripts/base/case1.sh b/src/pyvale/specklegen/scripts/base/case1.sh index f492c788..0cb70955 100644 --- a/src/pyvale/specklegen/scripts/base/case1.sh +++ b/src/pyvale/specklegen/scripts/base/case1.sh @@ -1,6 +1,10 @@ SIMULATION="base" CASE="case1" +if [ ! -d logs/$SIMULATION ]; then + mkdir -p logs/$SIMULATION +fi + > logs/$SIMULATION/$CASE.log for SPECKLE_SIZE in $(seq 10 5 25) diff --git a/src/pyvale/specklegen/scripts/base/case2.sh b/src/pyvale/specklegen/scripts/base/case2.sh index db5554e3..5f0904fb 100644 --- a/src/pyvale/specklegen/scripts/base/case2.sh +++ b/src/pyvale/specklegen/scripts/base/case2.sh @@ -1,6 +1,10 @@ SIMULATION="base" CASE="case2" +if [ ! -d logs/$SIMULATION ]; then + mkdir -p logs/$SIMULATION +fi + > logs/$SIMULATION/$CASE.log for SPECKLE_SIZE in $(seq 10 5 25) diff --git a/src/pyvale/specklegen/scripts/perlin_noise/case1.sh b/src/pyvale/specklegen/scripts/perlin_noise/case1.sh index 2fb09c43..48aa5e3c 100644 --- a/src/pyvale/specklegen/scripts/perlin_noise/case1.sh +++ b/src/pyvale/specklegen/scripts/perlin_noise/case1.sh @@ -1,6 +1,10 @@ SIMULATION="perlin_noise" CASE="case1" +if [ ! -d logs/$SIMULATION ]; then + mkdir -p logs/$SIMULATION +fi + > logs/$SIMULATION/$CASE.log for RES_WIDTH in 10 25 100 diff --git a/src/pyvale/specklegen/scripts/perlin_noise/case2.sh b/src/pyvale/specklegen/scripts/perlin_noise/case2.sh index d33f1f5a..225ed28e 100644 --- a/src/pyvale/specklegen/scripts/perlin_noise/case2.sh +++ b/src/pyvale/specklegen/scripts/perlin_noise/case2.sh @@ -1,6 +1,10 @@ SIMULATION="perlin_noise" CASE="case2" +if [ ! -d logs/$SIMULATION ]; then + mkdir -p logs/$SIMULATION +fi + > logs/$SIMULATION/$CASE.log for RES_WIDTH in 10 25 100 diff --git a/src/pyvale/specklegen/scripts/reduce_overlap/case1.sh b/src/pyvale/specklegen/scripts/reduce_overlap/case1.sh index 2c008d80..f273b507 100644 --- a/src/pyvale/specklegen/scripts/reduce_overlap/case1.sh +++ b/src/pyvale/specklegen/scripts/reduce_overlap/case1.sh @@ -1,6 +1,10 @@ SIMULATION="reduce_overlap" CASE="case1" +if [ ! -d logs/$SIMULATION ]; then + mkdir -p logs/$SIMULATION +fi + > logs/$SIMULATION/$CASE.log for SPECKLE_SIZE in $(seq 10 5 25) diff --git a/src/pyvale/specklegen/scripts/reduce_overlap/case2.sh b/src/pyvale/specklegen/scripts/reduce_overlap/case2.sh index 4139dadc..e1b8b2ee 100644 --- a/src/pyvale/specklegen/scripts/reduce_overlap/case2.sh +++ b/src/pyvale/specklegen/scripts/reduce_overlap/case2.sh @@ -1,6 +1,10 @@ SIMULATION="reduce_overlap" CASE="case2" +if [ ! -d logs/$SIMULATION ]; then + mkdir -p logs/$SIMULATION +fi + > logs/$SIMULATION/$CASE.log for SPECKLE_SIZE in $(seq 10 5 25) diff --git a/src/pyvale/specklegen/scripts/simplex_noise/case1.sh b/src/pyvale/specklegen/scripts/simplex_noise/case1.sh index 7dd29235..88f50229 100644 --- a/src/pyvale/specklegen/scripts/simplex_noise/case1.sh +++ b/src/pyvale/specklegen/scripts/simplex_noise/case1.sh @@ -1,6 +1,10 @@ SIMULATION="simplex_noise" CASE="case1" +if [ ! -d logs/$SIMULATION ]; then + mkdir -p logs/$SIMULATION +fi + > logs/$SIMULATION/$CASE.log for SPECKLE_SIZE_WIDTH in 10 15 20 30 40 diff --git a/src/pyvale/specklegen/utils/speckle_generation.py b/src/pyvale/specklegen/utils/speckle_generation.py index feec34e2..a2029a3a 100644 --- a/src/pyvale/specklegen/utils/speckle_generation.py +++ b/src/pyvale/specklegen/utils/speckle_generation.py @@ -216,7 +216,7 @@ def generate_speckles_simplex_noise(screen_size_width: int, screen_size_height: # scale to background and foreground colours min_val = np.min(image) max_val = np.max(image) - print(f"Min and max values before scaling: {min_val}, {max_val}") + # print(f"Min and max values before scaling: {min_val}, {max_val}") image = (image - min_val) / (max_val - min_val) # Normalise to [0, 1] image = image * (foreground_colour - background_colour) + background_colour image = image.astype(np.uint16 if bit_depth == 16 else np.uint8) From 4e35282834623c4f028b46e80861068be5a24210 Mon Sep 17 00:00:00 2001 From: WieraB Date: Mon, 27 Oct 2025 10:28:54 +0000 Subject: [PATCH 06/16] Re-arranged specklegen examples for doxyfile, amended docyfiles to include speckle gen, and added specklegen tests. --- docs/apidoc.sh | 2 +- docs/source/api_py.rst | 2 + docs/source/conf.py | 2 + docs/source/examples/examples.rst | 7 + pyproject.toml | 6 +- .../specklegen/ex1a_random_disks_overlap.py | 146 ++++++++++++++++++ .../ex1b_random_disks_reduce_overlap.py | 146 ++++++++++++++++++ .../specklegen/ex2a_perlin_noise_isotropic.py | 130 ++++++++++++++++ .../ex2b_perlin_noise_anisotropic.py | 130 ++++++++++++++++ .../ex3a_fractal_noise_isotropic.py | 132 ++++++++++++++++ .../ex3b_fractal_noise_anisotropic.py | 131 ++++++++++++++++ .../ex4a_simplex_noise_isotropic.py | 122 +++++++++++++++ .../ex4b_simplex_noise_anisotrpic.py | 120 ++++++++++++++ src/pyvale/specklegen/__init__.py | 8 + .../specklegen/runfiles/main_specklegen.py | 143 ----------------- .../runfiles/main_specklegen_perlin_noise.py | 128 --------------- .../runfiles/main_specklegen_simplex_noise.py | 125 --------------- src/pyvale/specklegen/scripts/base/case1.sh | 27 ---- src/pyvale/specklegen/scripts/base/case2.sh | 27 ---- .../specklegen/scripts/perlin_noise/case1.sh | 25 --- .../specklegen/scripts/perlin_noise/case2.sh | 29 ---- .../scripts/reduce_overlap/case1.sh | 27 ---- .../scripts/reduce_overlap/case2.sh | 27 ---- .../specklegen/scripts/simplex_noise/case1.sh | 25 --- .../{utils => }/speckle_generation.py | 2 +- .../speckle_pattern_diagnostics.py | 9 +- tests/specklegen/generate_speckles_test.py | 139 +++++++++++++++++ 27 files changed, 1229 insertions(+), 588 deletions(-) create mode 100644 src/pyvale/examples/specklegen/ex1a_random_disks_overlap.py create mode 100644 src/pyvale/examples/specklegen/ex1b_random_disks_reduce_overlap.py create mode 100644 src/pyvale/examples/specklegen/ex2a_perlin_noise_isotropic.py create mode 100644 src/pyvale/examples/specklegen/ex2b_perlin_noise_anisotropic.py create mode 100644 src/pyvale/examples/specklegen/ex3a_fractal_noise_isotropic.py create mode 100644 src/pyvale/examples/specklegen/ex3b_fractal_noise_anisotropic.py create mode 100644 src/pyvale/examples/specklegen/ex4a_simplex_noise_isotropic.py create mode 100644 src/pyvale/examples/specklegen/ex4b_simplex_noise_anisotrpic.py create mode 100644 src/pyvale/specklegen/__init__.py delete mode 100644 src/pyvale/specklegen/runfiles/main_specklegen.py delete mode 100644 src/pyvale/specklegen/runfiles/main_specklegen_perlin_noise.py delete mode 100644 src/pyvale/specklegen/runfiles/main_specklegen_simplex_noise.py delete mode 100644 src/pyvale/specklegen/scripts/base/case1.sh delete mode 100644 src/pyvale/specklegen/scripts/base/case2.sh delete mode 100644 src/pyvale/specklegen/scripts/perlin_noise/case1.sh delete mode 100644 src/pyvale/specklegen/scripts/perlin_noise/case2.sh delete mode 100644 src/pyvale/specklegen/scripts/reduce_overlap/case1.sh delete mode 100644 src/pyvale/specklegen/scripts/reduce_overlap/case2.sh delete mode 100644 src/pyvale/specklegen/scripts/simplex_noise/case1.sh rename src/pyvale/specklegen/{utils => }/speckle_generation.py (98%) rename src/pyvale/specklegen/{utils => }/speckle_pattern_diagnostics.py (95%) create mode 100644 tests/specklegen/generate_speckles_test.py diff --git a/docs/apidoc.sh b/docs/apidoc.sh index c2257774..6e1c1927 100644 --- a/docs/apidoc.sh +++ b/docs/apidoc.sh @@ -27,7 +27,7 @@ echo "Updating generated RST files..." # sed -i '0,/dataset.dataset/s/dataset.dataset/pyvale.dataset/' source/pyvale.dataset.dataset.rst || error_exit "Failed to update dataset title" # Modules to process -modules=("dic" "blender" "mooseherder" "sensorsim" "verif" "dataset") +modules=("dic" "blender" "mooseherder" "sensorsim" "verif" "dataset" "specklegen") for mod in "${modules[@]}"; do rst="source/pyvale.${mod}.rst" diff --git a/docs/source/api_py.rst b/docs/source/api_py.rst index 1600d894..1f3f01e0 100644 --- a/docs/source/api_py.rst +++ b/docs/source/api_py.rst @@ -13,4 +13,6 @@ Detailed Python API pyvale.mooseherder pyvale.verif pyvale.dataset + pyvale.specklegen + diff --git a/docs/source/conf.py b/docs/source/conf.py index e8be0f67..7e204e30 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -115,6 +115,7 @@ '../../src/pyvale/examples/dic', '../../src/pyvale/examples/renderblender', '../../src/pyvale/examples/mooseherder', + '../../src/pyvale/examples/specklegen', ], # Path to where to save gallery generated output 'gallery_dirs': [ @@ -122,6 +123,7 @@ 'examples/dic/', 'examples/renderblender', 'examples/mooseherder', + 'examples/specklegen', ], # Pattern to identify example files 'filename_pattern': '/plot_', diff --git a/docs/source/examples/examples.rst b/docs/source/examples/examples.rst index fa3c6b54..9ab126e6 100644 --- a/docs/source/examples/examples.rst +++ b/docs/source/examples/examples.rst @@ -30,3 +30,10 @@ Mooseherder :maxdepth: 2 examples_mooseherder + +Specklegen +------------------------------------- +.. toctree:: + :maxdepth: 2 + + examples_specklegen diff --git a/pyproject.toml b/pyproject.toml index d3135dcd..5493cf1a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,7 +32,11 @@ dependencies = [ "pytest>=8.3.5", "pybind11>=2.13.6", "pyqtgraph>=0.13.7", - "opencv-python<=4.9.0.80" + "opencv-python<=4.9.0.80", + "seaborn>=0.13.2", + "skimage>=0.0", + "perlin_numpy>=0.0.0", + "opensimplex>=0.4.5.1" ] [tool.pytest.ini_options] diff --git a/src/pyvale/examples/specklegen/ex1a_random_disks_overlap.py b/src/pyvale/examples/specklegen/ex1a_random_disks_overlap.py new file mode 100644 index 00000000..21b41209 --- /dev/null +++ b/src/pyvale/examples/specklegen/ex1a_random_disks_overlap.py @@ -0,0 +1,146 @@ +""" +Specklegen: Speckle pattern generation using random disk placement without checking for overlap +================================================================================ +Script to generate a synthetic speckle pattern made from randomly placed circular +speckles (disks), run diagnostics on the generated image, and save both the image +and diagnostics to the selected folder. +""" + +import numpy as np +import argparse +import json +import sys +import os +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +import pyvale.specklegen as specklegen + +#%% +# Here we parse command line arguments to set the speckle pattern parameters. +# For ease of use in this example script we set parameter values directly in the +# code rather than via bash script. +# The parameter responsible for reducing overlap is set to 'False' in this example. +parser = argparse.ArgumentParser(description='Generate random speckle patterns with specified parameters.') +args = parser.parse_args() +args.speckle_size = 25 +args.screen_size_width = 1000 +args.screen_size_height = 800 +args.bit_depth = 8 +args.theme = 'white_on_black' +args.random_seed = 10 +args.sigma = 4.0 +args.reduce_overlap = 'False' +args.output_path = "src/pyvale/examples/specklegen/output/ex1a" +args.attempts = None + +print('Args in simulation:') +print(args) +print('') +print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') +print('') + +print('Start') + +# Extract parameteres and revert to default values if not provided by user +speckle_size = args.speckle_size if args.speckle_size is not None else 5.0 +screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 +screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 +bit_depth = args.bit_depth if args.bit_depth is not None else 8 +theme = args.theme if args.theme is not None else 'white_on_black' +random_seed = args.random_seed if args.random_seed is not None else 10 +sigma = args.sigma if args.sigma is not None else 1.0 +reduce_overlap = args.reduce_overlap +attempts = args.attempts if args.attempts is not None else 100 + +assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." +assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." +assert reduce_overlap in ['True', 'False'], "reduce_overlap should be either 'True' or 'False' (a string)." + +reduce_overlap_bool = True if reduce_overlap == "True" else False +if reduce_overlap_bool: + print("Reducing overlap between speckles") +else: + print("Not reducing overlap between speckles") + +np.random.seed(random_seed) + +subfolder = f"/{speckle_size}_{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{random_seed}_{sigma}_{reduce_overlap}" +print(subfolder) +save_path = args.output_path + subfolder +if not os.path.exists(save_path): + os.makedirs(save_path) + +#%% +# We calculate parameteres aiming for approximately 50/50 black-to-white ratio. +# We now generate the speckle pattern using the specified parameters. +# The background and foreground colours are set based on the chosen theme and bit depth. + +speckle_area = np.pi * (speckle_size / 2) ** 2 +total_area = screen_size_width * screen_size_height +total_speckles = int((0.5 * total_area) / speckle_area) +print(f"Total number of speckles = {total_speckles}") +dynamic_range: int = 2**bit_depth - 1 +background_colour = 0 if theme == 'white_on_black' else dynamic_range +foreground_colour = dynamic_range if theme == 'white_on_black' else 0 + +# Generate speckle pattern +image, results = specklegen.generate_speckles(screen_size_width, screen_size_height, + speckle_size, foreground_colour, + total_speckles, reduce_overlap_bool, + bit_depth, background_colour, + sigma, attempts) + +# save the speckle placement results +np.savetxt(f"{save_path}/speckle_placement_results.csv", results, delimiter=",", + header="speckle_number, attempts, overlap(1/0/2), cent_x, cent_y", comments='', fmt=['%d', '%d', '%d', '%.3f', '%.3f']) + +#%% +# Now we run diagnostics on the generated speckle pattern and save the results. +# Finally we print out the key statistics to the console. +print("") +print('Starting speckle pattern diagnostics...') +results = specklegen.speckle_pattern_diagnostics(image, dynamic_range, save_path) + +# save the diagnostics results +with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: + json.dump(results, f, indent=4) + +ratio = results.get("black_white_ratio", None) +mean_gradient = results.get("mean_intensity_gradient", None) +std_dev = results.get("std_dev_irradiance", None) +avg = results.get("avg_irradiance", None) +contrast = results.get("contrast", None) +entropy = results.get("shannon_entropy", None) +peak_to_mean = results.get("peak_to_mean_ratio", None) +skew = results.get("skewness", None) +kurt = results.get("kurtosis", None) +avg_speckle_size_fwhm = results.get("avg_speckle_size_fwhm", None) +avg_speckle_size_e2 = results.get("avg_speckle_size_e2", None) +H_fit_stats = results.get("H_fit_stats", None) +V_fit_stats = results.get("V_fit_stats", None) + +print("") +print("Speckle statistics:") + +print(f"Black/White ratio: {np.round(ratio, 3)}") +print(f"Mean intensity gradient: {np.round(mean_gradient, 3)}") +print(f"Standard deviation of irradiance values: {np.round(std_dev, 3)}") +print(f"Average irradiance value: {np.round(avg, 3)}") +print(f"Contrast (std/mean): {np.round(contrast, 3)}") +print(f"Skewness: {np.round(skew, 3)}") +print(f"Kurtosis: {np.round(kurt, 3)}") +print(f"Shannon entropy: {np.round(entropy, 3)}") +print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") +print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") +print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") +print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") + +error = np.abs(avg_speckle_size_fwhm - speckle_size) * 100 / speckle_size +print(f"Percentage error between requested speckle size and measured speckle size from FWHM: {np.round(error, 3)} %") +error = np.abs(avg_speckle_size_e2 - speckle_size) * 100 / speckle_size +print(f"Percentage error between requested speckle size and measured speckle size from 1/e^2: {np.round(error, 3)} %") +np.save(f"{save_path}/image.npy", image) +print("") +print('End :)') +print("") +print("") +print("") \ No newline at end of file diff --git a/src/pyvale/examples/specklegen/ex1b_random_disks_reduce_overlap.py b/src/pyvale/examples/specklegen/ex1b_random_disks_reduce_overlap.py new file mode 100644 index 00000000..99b1fb23 --- /dev/null +++ b/src/pyvale/examples/specklegen/ex1b_random_disks_reduce_overlap.py @@ -0,0 +1,146 @@ +""" +Specklegen: Speckle pattern generation using random disk placement with checking for overlap +================================================================================ +Script to generate a synthetic speckle pattern made from randomly placed circular +speckles (disks), run diagnostics on the generated image, and save both the image +and diagnostics to the selected folder. +""" + +import numpy as np +import argparse +import json +import sys +import os +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +import pyvale.specklegen as specklegen + +#%% +# Here we parse command line arguments to set the speckle pattern parameters. +# For ease of use in this example script we set parameter values directly in the +# code rather than via bash script. +# The parameter responsible for reducing overlap is set to 'True' in this example. +parser = argparse.ArgumentParser(description='Generate random speckle patterns with specified parameters.') +args = parser.parse_args() +args.speckle_size = 25 +args.screen_size_width = 1000 +args.screen_size_height = 800 +args.bit_depth = 8 +args.theme = 'white_on_black' +args.random_seed = 10 +args.sigma = 4.0 +args.reduce_overlap = 'True' +args.output_path = "src/pyvale/examples/specklegen/output/ex1b" +args.attempts = None + +print('Args in simulation:') +print(args) +print('') +print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') +print('') + +print('Start') + +# Extract parameteres and revert to default values if not provided by user +speckle_size = args.speckle_size if args.speckle_size is not None else 5.0 +screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 +screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 +bit_depth = args.bit_depth if args.bit_depth is not None else 8 +theme = args.theme if args.theme is not None else 'white_on_black' +random_seed = args.random_seed if args.random_seed is not None else 10 +sigma = args.sigma if args.sigma is not None else 1.0 +reduce_overlap = args.reduce_overlap +attempts = args.attempts if args.attempts is not None else 100 + +assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." +assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." +assert reduce_overlap in ['True', 'False'], "reduce_overlap should be either 'True' or 'False' (a string)." + +reduce_overlap_bool = True if reduce_overlap == "True" else False +if reduce_overlap_bool: + print("Reducing overlap between speckles") +else: + print("Not reducing overlap between speckles") + +np.random.seed(random_seed) + +subfolder = f"/{speckle_size}_{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{random_seed}_{sigma}_{reduce_overlap}" +print(subfolder) +save_path = args.output_path + subfolder +if not os.path.exists(save_path): + os.makedirs(save_path) + +#%% +# We calculate parameteres aiming for approximately 50/50 black-to-white ratio. +# We now generate the speckle pattern using the specified parameters. +# The background and foreground colours are set based on the chosen theme and bit depth. + +speckle_area = np.pi * (speckle_size / 2) ** 2 +total_area = screen_size_width * screen_size_height +total_speckles = int((0.5 * total_area) / speckle_area) +print(f"Total number of speckles = {total_speckles}") +dynamic_range: int = 2**bit_depth - 1 +background_colour = 0 if theme == 'white_on_black' else dynamic_range +foreground_colour = dynamic_range if theme == 'white_on_black' else 0 + +# Generate speckle pattern +image, results = specklegen.generate_speckles(screen_size_width, screen_size_height, + speckle_size, foreground_colour, + total_speckles, reduce_overlap_bool, + bit_depth, background_colour, + sigma, attempts) + +# save the speckle placement results +np.savetxt(f"{save_path}/speckle_placement_results.csv", results, delimiter=",", + header="speckle_number, attempts, overlap(1/0/2), cent_x, cent_y", comments='', fmt=['%d', '%d', '%d', '%.3f', '%.3f']) + +#%% +# Now we run diagnostics on the generated speckle pattern and save the results. +# Finally we print out the key statistics to the console. +print("") +print('Starting speckle pattern diagnostics...') +results = specklegen.speckle_pattern_diagnostics(image, dynamic_range, save_path) + +# save the diagnostics results +with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: + json.dump(results, f, indent=4) + +ratio = results.get("black_white_ratio", None) +mean_gradient = results.get("mean_intensity_gradient", None) +std_dev = results.get("std_dev_irradiance", None) +avg = results.get("avg_irradiance", None) +contrast = results.get("contrast", None) +entropy = results.get("shannon_entropy", None) +peak_to_mean = results.get("peak_to_mean_ratio", None) +skew = results.get("skewness", None) +kurt = results.get("kurtosis", None) +avg_speckle_size_fwhm = results.get("avg_speckle_size_fwhm", None) +avg_speckle_size_e2 = results.get("avg_speckle_size_e2", None) +H_fit_stats = results.get("H_fit_stats", None) +V_fit_stats = results.get("V_fit_stats", None) + +print("") +print("Speckle statistics:") + +print(f"Black/White ratio: {np.round(ratio, 3)}") +print(f"Mean intensity gradient: {np.round(mean_gradient, 3)}") +print(f"Standard deviation of irradiance values: {np.round(std_dev, 3)}") +print(f"Average irradiance value: {np.round(avg, 3)}") +print(f"Contrast (std/mean): {np.round(contrast, 3)}") +print(f"Skewness: {np.round(skew, 3)}") +print(f"Kurtosis: {np.round(kurt, 3)}") +print(f"Shannon entropy: {np.round(entropy, 3)}") +print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") +print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") +print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") +print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") + +error = np.abs(avg_speckle_size_fwhm - speckle_size) * 100 / speckle_size +print(f"Percentage error between requested speckle size and measured speckle size from FWHM: {np.round(error, 3)} %") +error = np.abs(avg_speckle_size_e2 - speckle_size) * 100 / speckle_size +print(f"Percentage error between requested speckle size and measured speckle size from 1/e^2: {np.round(error, 3)} %") +np.save(f"{save_path}/image.npy", image) +print("") +print('End :)') +print("") +print("") +print("") \ No newline at end of file diff --git a/src/pyvale/examples/specklegen/ex2a_perlin_noise_isotropic.py b/src/pyvale/examples/specklegen/ex2a_perlin_noise_isotropic.py new file mode 100644 index 00000000..f2b399d5 --- /dev/null +++ b/src/pyvale/examples/specklegen/ex2a_perlin_noise_isotropic.py @@ -0,0 +1,130 @@ +""" +Specklegen: Speckle pattern generation using isotropic Perlin noise +================================================================================ +Script to generate a synthetic speckle pattern made using isotropic Perlin noise, run diagnostics on the generated image, and save both the image +and diagnostics to the selected folder. Isotroic Perlin noise in this case means that the speckle size is the same in both horisontal and vertical directions. +""" + +import numpy as np +import argparse +import json +import sys +import os +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +import pyvale.specklegen as specklegen + +#%% +# Here we parse command line arguments to set the speckle pattern parameters. +# For ease of use in this example script we set parameter values directly in the +# code rather than via bash script. +# The parameters are set to generate isotropic Perlin noise in this example. The number of periods width- and height-wise are the same. +parser = argparse.ArgumentParser(description='Generate random speckle patterns with specified parameters.') +args = parser.parse_args() + +args.screen_size_width = 1000 +args.screen_size_height = 800 +args.bit_depth = 8 +args.theme = 'white_on_black' +args.octaves = None +args.type_gen = "perlin" +args.res_width = 25 +args.res_height = 25 +args.output_path = "src/pyvale/examples/specklegen/output/ex2a" + +print('Args in simulation:') +print(args) +print('') +print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') +print('') + +print('Start') + +# Extract parameteres and revert to default values if not provided by user +screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 +screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 +bit_depth = args.bit_depth if args.bit_depth is not None else 8 +theme = args.theme if args.theme is not None else 'white_on_black' +octaves = args.octaves if args.octaves is not None else "Not defined" + +type_gen = args.type_gen +res_width = args.res_width +res_height = args.res_height + +assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." +assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." + +if type_gen == "perlin": + subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{type_gen}_{res_width}_{res_height}" +elif type_gen == "fractal": + subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{type_gen}_{res_width}_{res_height}_{octaves}" +print(subfolder) +save_path = args.output_path + subfolder +if not os.path.exists(save_path): + os.makedirs(save_path) + + +#%% +# We now generate the speckle pattern using the specified parameters. +# The background and foreground colours are set based on the chosen theme and bit depth. +dynamic_range: int = 2**bit_depth - 1 +background_colour = 0 if theme == 'white_on_black' else dynamic_range +foreground_colour = dynamic_range if theme == 'white_on_black' else 0 + +# Generate speckle pattern +if type_gen == "perlin": + image = specklegen.generate_speckles_perlin_noise(screen_size_width, screen_size_height, + res_height, res_width, + foreground_colour, bit_depth, background_colour, type_gen) +elif type_gen == "fractal": + image = specklegen.generate_speckles_perlin_noise(screen_size_width, screen_size_height, + res_height, res_width, + foreground_colour, bit_depth, background_colour, type_gen, + octaves=octaves) + +#%% +# Now we run diagnostics on the generated speckle pattern and save the results. +# Finally we print out the key statistics to the console. +print("") +print('Starting speckle pattern diagnostics...') +results = specklegen.speckle_pattern_diagnostics(image, dynamic_range, save_path) + +# save the diagnostics results +with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: + json.dump(results, f, indent=4) + +ratio = results.get("black_white_ratio", None) +mean_gradient = results.get("mean_intensity_gradient", None) +std_dev = results.get("std_dev_irradiance", None) +avg = results.get("avg_irradiance", None) +contrast = results.get("contrast", None) +entropy = results.get("shannon_entropy", None) +peak_to_mean = results.get("peak_to_mean_ratio", None) +skew = results.get("skewness", None) +kurt = results.get("kurtosis", None) +avg_speckle_size_fwhm = results.get("avg_speckle_size_fwhm", None) +avg_speckle_size_e2 = results.get("avg_speckle_size_e2", None) +H_fit_stats = results.get("H_fit_stats", None) +V_fit_stats = results.get("V_fit_stats", None) + +print("") +print("Speckle statistics:") + +print(f"Black/White ratio: {np.round(ratio, 3)}") +print(f"Mean intensity gradient: {np.round(mean_gradient, 3)}") +print(f"Standard deviation of irradiance values: {np.round(std_dev, 3)}") +print(f"Average irradiance value: {np.round(avg, 3)}") +print(f"Contrast (std/mean): {np.round(contrast, 3)}") +print(f"Skewness: {np.round(skew, 3)}") +print(f"Kurtosis: {np.round(kurt, 3)}") +print(f"Shannon entropy: {np.round(entropy, 3)}") +print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") +print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") +print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") +print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") + +np.save(f"{save_path}/image.npy", image) +print("") +print('End :)') +print("") +print("") +print("") \ No newline at end of file diff --git a/src/pyvale/examples/specklegen/ex2b_perlin_noise_anisotropic.py b/src/pyvale/examples/specklegen/ex2b_perlin_noise_anisotropic.py new file mode 100644 index 00000000..b6b24ed7 --- /dev/null +++ b/src/pyvale/examples/specklegen/ex2b_perlin_noise_anisotropic.py @@ -0,0 +1,130 @@ +""" +Specklegen: Speckle pattern generation using anisotropic Perlin noise +================================================================================ +Script to generate a synthetic speckle pattern made using isotropic Perlin noise, run diagnostics on the generated image, and save both the image +and diagnostics to the selected folder. Anisotroic Perlin noise in this case means that the speckle size is different in horiasontal and vertical directions. +""" + +import numpy as np +import argparse +import json +import sys +import os +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +import pyvale.specklegen as specklegen + +#%% +# Here we parse command line arguments to set the speckle pattern parameters. +# For ease of use in this example script we set parameter values directly in the +# code rather than via bash script. +# The parameters are set to generate anisotropic Perlin noise in this example. The number of periods width- and height-wise are not the same. +parser = argparse.ArgumentParser(description='Generate random speckle patterns with specified parameters.') +args = parser.parse_args() + +args.screen_size_width = 1000 +args.screen_size_height = 800 +args.bit_depth = 8 +args.theme = 'white_on_black' +args.octaves = None +args.type_gen = "perlin" +args.res_width = 25 +args.res_height = 100 +args.output_path = "src/pyvale/examples/specklegen/output/ex2b" + +print('Args in simulation:') +print(args) +print('') +print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') +print('') + +print('Start') + +# Extract parameteres and revert to default values if not provided by user +screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 +screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 +bit_depth = args.bit_depth if args.bit_depth is not None else 8 +theme = args.theme if args.theme is not None else 'white_on_black' +octaves = args.octaves if args.octaves is not None else "Not defined" + +type_gen = args.type_gen +res_width = args.res_width +res_height = args.res_height + +assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." +assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." + +if type_gen == "perlin": + subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{type_gen}_{res_width}_{res_height}" +elif type_gen == "fractal": + subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{type_gen}_{res_width}_{res_height}_{octaves}" +print(subfolder) +save_path = args.output_path + subfolder +if not os.path.exists(save_path): + os.makedirs(save_path) + + +#%% +# We now generate the speckle pattern using the specified parameters. +# The background and foreground colours are set based on the chosen theme and bit depth. +dynamic_range: int = 2**bit_depth - 1 +background_colour = 0 if theme == 'white_on_black' else dynamic_range +foreground_colour = dynamic_range if theme == 'white_on_black' else 0 + +# Generate speckle pattern +if type_gen == "perlin": + image = specklegen.generate_speckles_perlin_noise(screen_size_width, screen_size_height, + res_height, res_width, + foreground_colour, bit_depth, background_colour, type_gen) +elif type_gen == "fractal": + image = specklegen.generate_speckles_perlin_noise(screen_size_width, screen_size_height, + res_height, res_width, + foreground_colour, bit_depth, background_colour, type_gen, + octaves=octaves) + +#%% +# Now we run diagnostics on the generated speckle pattern and save the results. +# Finally we print out the key statistics to the console. +print("") +print('Starting speckle pattern diagnostics...') +results = specklegen.speckle_pattern_diagnostics(image, dynamic_range, save_path) + +# save the diagnostics results +with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: + json.dump(results, f, indent=4) + +ratio = results.get("black_white_ratio", None) +mean_gradient = results.get("mean_intensity_gradient", None) +std_dev = results.get("std_dev_irradiance", None) +avg = results.get("avg_irradiance", None) +contrast = results.get("contrast", None) +entropy = results.get("shannon_entropy", None) +peak_to_mean = results.get("peak_to_mean_ratio", None) +skew = results.get("skewness", None) +kurt = results.get("kurtosis", None) +avg_speckle_size_fwhm = results.get("avg_speckle_size_fwhm", None) +avg_speckle_size_e2 = results.get("avg_speckle_size_e2", None) +H_fit_stats = results.get("H_fit_stats", None) +V_fit_stats = results.get("V_fit_stats", None) + +print("") +print("Speckle statistics:") + +print(f"Black/White ratio: {np.round(ratio, 3)}") +print(f"Mean intensity gradient: {np.round(mean_gradient, 3)}") +print(f"Standard deviation of irradiance values: {np.round(std_dev, 3)}") +print(f"Average irradiance value: {np.round(avg, 3)}") +print(f"Contrast (std/mean): {np.round(contrast, 3)}") +print(f"Skewness: {np.round(skew, 3)}") +print(f"Kurtosis: {np.round(kurt, 3)}") +print(f"Shannon entropy: {np.round(entropy, 3)}") +print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") +print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") +print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") +print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") + +np.save(f"{save_path}/image.npy", image) +print("") +print('End :)') +print("") +print("") +print("") \ No newline at end of file diff --git a/src/pyvale/examples/specklegen/ex3a_fractal_noise_isotropic.py b/src/pyvale/examples/specklegen/ex3a_fractal_noise_isotropic.py new file mode 100644 index 00000000..aab87efb --- /dev/null +++ b/src/pyvale/examples/specklegen/ex3a_fractal_noise_isotropic.py @@ -0,0 +1,132 @@ +""" +Specklegen: Speckle pattern generation using isotropic fractal noise +================================================================================ +Script to generate a synthetic speckle pattern made using isotropic fractal noise, run diagnostics on the generated image, and save both the image +and diagnostics to the selected folder. Isotroic fractal noise in this case means that the speckle size is the same in both horisontal and vertical directions. +""" + +import numpy as np +import argparse +import json +import sys +import os +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +import pyvale.specklegen as specklegen + +#%% +# Here we parse command line arguments to set the speckle pattern parameters. +# For ease of use in this example script we set parameter values directly in the +# code rather than via bash script. +# The parameters are set to generate isotropic fractal noise in this example. The number of periods width- and height-wise are the same. +# The number of octaves is set to 3 to generate fractal noise. This is an additioal parameter compared to the Perlin noise examples. +# This noise involves layering multiple frequencies of Perlin noise (which are referred to as octaves) to create a more complex pattern. +parser = argparse.ArgumentParser(description='Generate random speckle patterns with specified parameters.') +args = parser.parse_args() + +args.screen_size_width = 1000 +args.screen_size_height = 800 +args.bit_depth = 8 +args.theme = 'white_on_black' +args.octaves = 3 +args.type_gen = "fractal" +args.res_width = 25 +args.res_height = 25 +args.output_path = "src/pyvale/examples/specklegen/output/ex3a" + +print('Args in simulation:') +print(args) +print('') +print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') +print('') + +print('Start') + +# Extract parameteres and revert to default values if not provided by user +screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 +screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 +bit_depth = args.bit_depth if args.bit_depth is not None else 8 +theme = args.theme if args.theme is not None else 'white_on_black' +octaves = args.octaves if args.octaves is not None else "Not defined" + +type_gen = args.type_gen +res_width = args.res_width +res_height = args.res_height + +assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." +assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." + +if type_gen == "perlin": + subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{type_gen}_{res_width}_{res_height}" +elif type_gen == "fractal": + subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{type_gen}_{res_width}_{res_height}_{octaves}" +print(subfolder) +save_path = args.output_path + subfolder +if not os.path.exists(save_path): + os.makedirs(save_path) + + +#%% +# We now generate the speckle pattern using the specified parameters. +# The background and foreground colours are set based on the chosen theme and bit depth. +dynamic_range: int = 2**bit_depth - 1 +background_colour = 0 if theme == 'white_on_black' else dynamic_range +foreground_colour = dynamic_range if theme == 'white_on_black' else 0 + +# Generate speckle pattern +if type_gen == "perlin": + image = specklegen.generate_speckles_perlin_noise(screen_size_width, screen_size_height, + res_height, res_width, + foreground_colour, bit_depth, background_colour, type_gen) +elif type_gen == "fractal": + image = specklegen.generate_speckles_perlin_noise(screen_size_width, screen_size_height, + res_height, res_width, + foreground_colour, bit_depth, background_colour, type_gen, + octaves=octaves) + +#%% +# Now we run diagnostics on the generated speckle pattern and save the results. +# Finally we print out the key statistics to the console. +print("") +print('Starting speckle pattern diagnostics...') +results = specklegen.speckle_pattern_diagnostics(image, dynamic_range, save_path) + +# save the diagnostics results +with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: + json.dump(results, f, indent=4) + +ratio = results.get("black_white_ratio", None) +mean_gradient = results.get("mean_intensity_gradient", None) +std_dev = results.get("std_dev_irradiance", None) +avg = results.get("avg_irradiance", None) +contrast = results.get("contrast", None) +entropy = results.get("shannon_entropy", None) +peak_to_mean = results.get("peak_to_mean_ratio", None) +skew = results.get("skewness", None) +kurt = results.get("kurtosis", None) +avg_speckle_size_fwhm = results.get("avg_speckle_size_fwhm", None) +avg_speckle_size_e2 = results.get("avg_speckle_size_e2", None) +H_fit_stats = results.get("H_fit_stats", None) +V_fit_stats = results.get("V_fit_stats", None) + +print("") +print("Speckle statistics:") + +print(f"Black/White ratio: {np.round(ratio, 3)}") +print(f"Mean intensity gradient: {np.round(mean_gradient, 3)}") +print(f"Standard deviation of irradiance values: {np.round(std_dev, 3)}") +print(f"Average irradiance value: {np.round(avg, 3)}") +print(f"Contrast (std/mean): {np.round(contrast, 3)}") +print(f"Skewness: {np.round(skew, 3)}") +print(f"Kurtosis: {np.round(kurt, 3)}") +print(f"Shannon entropy: {np.round(entropy, 3)}") +print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") +print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") +print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") +print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") + +np.save(f"{save_path}/image.npy", image) +print("") +print('End :)') +print("") +print("") +print("") \ No newline at end of file diff --git a/src/pyvale/examples/specklegen/ex3b_fractal_noise_anisotropic.py b/src/pyvale/examples/specklegen/ex3b_fractal_noise_anisotropic.py new file mode 100644 index 00000000..aa1d1343 --- /dev/null +++ b/src/pyvale/examples/specklegen/ex3b_fractal_noise_anisotropic.py @@ -0,0 +1,131 @@ +""" +Specklegen: Speckle pattern generation using anisotropic fractal noise +================================================================================ +Script to generate a synthetic speckle pattern made using isotropic fractal noise, run diagnostics on the generated image, and save both the image +and diagnostics to the selected folder. Anisotroic fractal noise in this case means that the speckle size is different in horisontal and vertical directions. +""" + +import numpy as np +import argparse +import json +import sys +import os +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +import pyvale.specklegen as specklegen + +#%% +# Here we parse command line arguments to set the speckle pattern parameters. +# For ease of use in this example script we set parameter values directly in the +# code rather than via bash script. +# The parameters are set to generate anisotropic fractal noise in this example. The number of periods width- and height-wise are different. +# The number of octaves is set to 3 to generate fractal noise. This is an additioal parameter compared to the Perlin noise examples. +parser = argparse.ArgumentParser(description='Generate random speckle patterns with specified parameters.') +args = parser.parse_args() + +args.screen_size_width = 1000 +args.screen_size_height = 800 +args.bit_depth = 8 +args.theme = 'white_on_black' +args.octaves = 3 +args.type_gen = "fractal" +args.res_width = 10 +args.res_height = 25 +args.output_path = "src/pyvale/examples/specklegen/output/ex3b" + +print('Args in simulation:') +print(args) +print('') +print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') +print('') + +print('Start') + +# Extract parameteres and revert to default values if not provided by user +screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 +screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 +bit_depth = args.bit_depth if args.bit_depth is not None else 8 +theme = args.theme if args.theme is not None else 'white_on_black' +octaves = args.octaves if args.octaves is not None else "Not defined" + +type_gen = args.type_gen +res_width = args.res_width +res_height = args.res_height + +assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." +assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." + +if type_gen == "perlin": + subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{type_gen}_{res_width}_{res_height}" +elif type_gen == "fractal": + subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{type_gen}_{res_width}_{res_height}_{octaves}" +print(subfolder) +save_path = args.output_path + subfolder +if not os.path.exists(save_path): + os.makedirs(save_path) + + +#%% +# We now generate the speckle pattern using the specified parameters. +# The background and foreground colours are set based on the chosen theme and bit depth. +dynamic_range: int = 2**bit_depth - 1 +background_colour = 0 if theme == 'white_on_black' else dynamic_range +foreground_colour = dynamic_range if theme == 'white_on_black' else 0 + +# Generate speckle pattern +if type_gen == "perlin": + image = specklegen.generate_speckles_perlin_noise(screen_size_width, screen_size_height, + res_height, res_width, + foreground_colour, bit_depth, background_colour, type_gen) +elif type_gen == "fractal": + image = specklegen.generate_speckles_perlin_noise(screen_size_width, screen_size_height, + res_height, res_width, + foreground_colour, bit_depth, background_colour, type_gen, + octaves=octaves) + +#%% +# Now we run diagnostics on the generated speckle pattern and save the results. +# Finally we print out the key statistics to the console. +print("") +print('Starting speckle pattern diagnostics...') +results = specklegen.speckle_pattern_diagnostics(image, dynamic_range, save_path) + +# save the diagnostics results +with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: + json.dump(results, f, indent=4) + +ratio = results.get("black_white_ratio", None) +mean_gradient = results.get("mean_intensity_gradient", None) +std_dev = results.get("std_dev_irradiance", None) +avg = results.get("avg_irradiance", None) +contrast = results.get("contrast", None) +entropy = results.get("shannon_entropy", None) +peak_to_mean = results.get("peak_to_mean_ratio", None) +skew = results.get("skewness", None) +kurt = results.get("kurtosis", None) +avg_speckle_size_fwhm = results.get("avg_speckle_size_fwhm", None) +avg_speckle_size_e2 = results.get("avg_speckle_size_e2", None) +H_fit_stats = results.get("H_fit_stats", None) +V_fit_stats = results.get("V_fit_stats", None) + +print("") +print("Speckle statistics:") + +print(f"Black/White ratio: {np.round(ratio, 3)}") +print(f"Mean intensity gradient: {np.round(mean_gradient, 3)}") +print(f"Standard deviation of irradiance values: {np.round(std_dev, 3)}") +print(f"Average irradiance value: {np.round(avg, 3)}") +print(f"Contrast (std/mean): {np.round(contrast, 3)}") +print(f"Skewness: {np.round(skew, 3)}") +print(f"Kurtosis: {np.round(kurt, 3)}") +print(f"Shannon entropy: {np.round(entropy, 3)}") +print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") +print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") +print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") +print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") + +np.save(f"{save_path}/image.npy", image) +print("") +print('End :)') +print("") +print("") +print("") \ No newline at end of file diff --git a/src/pyvale/examples/specklegen/ex4a_simplex_noise_isotropic.py b/src/pyvale/examples/specklegen/ex4a_simplex_noise_isotropic.py new file mode 100644 index 00000000..0be9ccbe --- /dev/null +++ b/src/pyvale/examples/specklegen/ex4a_simplex_noise_isotropic.py @@ -0,0 +1,122 @@ +""" +Specklegen: Speckle pattern generation using isotropic Simplex noise +================================================================================ +Script to generate a synthetic speckle pattern made using isotropic Simplex noise, run diagnostics on the generated image, and save both the image +and diagnostics to the selected folder. Isotroic Simplex noise in this case means that the speckle size is the same in both horisontal and vertical directions. +""" + +import numpy as np +import argparse +import json +import sys +import os +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +import pyvale.specklegen as specklegen + +#%% +# Here we parse command line arguments to set the speckle pattern parameters. +# For ease of use in this example script we set parameter values directly in the +# code rather than via bash script. +# The parameters are set to generate isotropic Simplex noise in this example. +# For the Simplex noise, the speckle size is defined directly via speckles size width and speckle size height parameters. +# This is in contrast to the Perlin and fractal noise examples where the speckle size is defined via the number of periods width- and height-wise. +parser = argparse.ArgumentParser(description='Generate random speckle patterns with specified parameters.') +args = parser.parse_args() + +args.screen_size_width = 1000 +args.screen_size_height = 800 +args.bit_depth = 8 +args.theme = 'white_on_black' +args.speckle_size_width = 20 +args.speckle_size_height = 20 +args.seed = 1234 +args.output_path = "src/pyvale/examples/specklegen/output/ex4a" + +print('Args in simulation:') +print(args) +print('') +print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') +print('') + +print('Start') + +# Extract parameteres and revert to default values if not provided by user +speckle_size_width = args.speckle_size_width if args.speckle_size_width is not None else 5.0 +speckle_size_height = args.speckle_size_height if args.speckle_size_height is not None else 5.0 +screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 +screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 +bit_depth = args.bit_depth if args.bit_depth is not None else 8 +theme = args.theme if args.theme is not None else 'white_on_black' +seed = args.seed if args.seed is not None else 1234 + +assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." +assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." + +subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{speckle_size_width}_{speckle_size_height}_{seed}" +print(subfolder) +save_path = args.output_path + subfolder +if not os.path.exists(save_path): + os.makedirs(save_path) + +dynamic_range: int = 2**bit_depth - 1 +background_colour = 0 if theme == 'white_on_black' else dynamic_range +foreground_colour = dynamic_range if theme == 'white_on_black' else 0 + +# Generate speckle pattern +image = specklegen.generate_speckles_simplex_noise(screen_size_width, screen_size_height, + foreground_colour, + bit_depth, background_colour, + feature_size_width=speckle_size_width, + feature_size_height=speckle_size_height, + seed=seed) + +# Diagnostics +print("") +print('Starting speckle pattern diagnostics...') +results = specklegen.speckle_pattern_diagnostics(image, dynamic_range, save_path) + +# save the diagnostics results +with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: + json.dump(results, f, indent=4) + +ratio = results.get("black_white_ratio", None) +mean_gradient = results.get("mean_intensity_gradient", None) +std_dev = results.get("std_dev_irradiance", None) +avg = results.get("avg_irradiance", None) +contrast = results.get("contrast", None) +entropy = results.get("shannon_entropy", None) +peak_to_mean = results.get("peak_to_mean_ratio", None) +skew = results.get("skewness", None) +kurt = results.get("kurtosis", None) +avg_speckle_size_fwhm = results.get("avg_speckle_size_fwhm", None) +avg_speckle_size_e2 = results.get("avg_speckle_size_e2", None) +H_fit_stats = results.get("H_fit_stats", None) +V_fit_stats = results.get("V_fit_stats", None) + +print("") +print("Speckle statistics:") + +print(f"Black/White ratio: {np.round(ratio, 3)}") +print(f"Mean intensity gradient: {np.round(mean_gradient, 3)}") +print(f"Standard deviation of irradiance values: {np.round(std_dev, 3)}") +print(f"Average irradiance value: {np.round(avg, 3)}") +print(f"Contrast (std/mean): {np.round(contrast, 3)}") +print(f"Skewness: {np.round(skew, 3)}") +print(f"Kurtosis: {np.round(kurt, 3)}") +print(f"Shannon entropy: {np.round(entropy, 3)}") +print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") +print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") +print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") +print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") + +speckle_size = (speckle_size_width + speckle_size_height) / 2 +error = np.abs(avg_speckle_size_fwhm - speckle_size) * 100 / speckle_size +print(f"Percentage error between requested speckle size and measured speckle size from FWHM: {np.round(error, 3)} %") +error = np.abs(avg_speckle_size_e2 - speckle_size) * 100 / speckle_size +print(f"Percentage error between requested speckle size and measured speckle size from 1/e^2: {np.round(error, 3)} %") +np.save(f"{save_path}/image.npy", image) +print("") +print('End :)') +print("") +print("") +print("") diff --git a/src/pyvale/examples/specklegen/ex4b_simplex_noise_anisotrpic.py b/src/pyvale/examples/specklegen/ex4b_simplex_noise_anisotrpic.py new file mode 100644 index 00000000..1fac6f8f --- /dev/null +++ b/src/pyvale/examples/specklegen/ex4b_simplex_noise_anisotrpic.py @@ -0,0 +1,120 @@ +""" +Specklegen: Speckle pattern generation using anisotropic Simplex noise +================================================================================ +Script to generate a synthetic speckle pattern made using anisotropic Simplex noise, run diagnostics on the generated image, and save both the image +and diagnostics to the selected folder. Anisotroic Simplex noise in this case means that the speckle size is different in horisontal and vertical directions. +""" + +import numpy as np +import argparse +import json +import sys +import os +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +import pyvale.specklegen as specklegen + +#%% +# Here we parse command line arguments to set the speckle pattern parameters. +# For ease of use in this example script we set parameter values directly in the +# code rather than via bash script. +# The parameters are set to generate anisotropic Simplex noise in this example. +parser = argparse.ArgumentParser(description='Generate random speckle patterns with specified parameters.') +args = parser.parse_args() + +args.screen_size_width = 1000 +args.screen_size_height = 800 +args.bit_depth = 8 +args.theme = 'white_on_black' +args.speckle_size_width = 10 +args.speckle_size_height = 30 +args.seed = 1234 +args.output_path = "src/pyvale/examples/specklegen/output/ex4b" + +print('Args in simulation:') +print(args) +print('') +print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') +print('') + +print('Start') + +# Extract parameteres and revert to default values if not provided by user +speckle_size_width = args.speckle_size_width if args.speckle_size_width is not None else 5.0 +speckle_size_height = args.speckle_size_height if args.speckle_size_height is not None else 5.0 +screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 +screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 +bit_depth = args.bit_depth if args.bit_depth is not None else 8 +theme = args.theme if args.theme is not None else 'white_on_black' +seed = args.seed if args.seed is not None else 1234 + +assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." +assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." + +subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{speckle_size_width}_{speckle_size_height}_{seed}" +print(subfolder) +save_path = args.output_path + subfolder +if not os.path.exists(save_path): + os.makedirs(save_path) + +dynamic_range: int = 2**bit_depth - 1 +background_colour = 0 if theme == 'white_on_black' else dynamic_range +foreground_colour = dynamic_range if theme == 'white_on_black' else 0 + +# Generate speckle pattern +image = specklegen.generate_speckles_simplex_noise(screen_size_width, screen_size_height, + foreground_colour, + bit_depth, background_colour, + feature_size_width=speckle_size_width, + feature_size_height=speckle_size_height, + seed=seed) + +# Diagnostics +print("") +print('Starting speckle pattern diagnostics...') +results = specklegen.speckle_pattern_diagnostics(image, dynamic_range, save_path) + +# save the diagnostics results +with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: + json.dump(results, f, indent=4) + +ratio = results.get("black_white_ratio", None) +mean_gradient = results.get("mean_intensity_gradient", None) +std_dev = results.get("std_dev_irradiance", None) +avg = results.get("avg_irradiance", None) +contrast = results.get("contrast", None) +entropy = results.get("shannon_entropy", None) +peak_to_mean = results.get("peak_to_mean_ratio", None) +skew = results.get("skewness", None) +kurt = results.get("kurtosis", None) +avg_speckle_size_fwhm = results.get("avg_speckle_size_fwhm", None) +avg_speckle_size_e2 = results.get("avg_speckle_size_e2", None) +H_fit_stats = results.get("H_fit_stats", None) +V_fit_stats = results.get("V_fit_stats", None) + +print("") +print("Speckle statistics:") + +print(f"Black/White ratio: {np.round(ratio, 3)}") +print(f"Mean intensity gradient: {np.round(mean_gradient, 3)}") +print(f"Standard deviation of irradiance values: {np.round(std_dev, 3)}") +print(f"Average irradiance value: {np.round(avg, 3)}") +print(f"Contrast (std/mean): {np.round(contrast, 3)}") +print(f"Skewness: {np.round(skew, 3)}") +print(f"Kurtosis: {np.round(kurt, 3)}") +print(f"Shannon entropy: {np.round(entropy, 3)}") +print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") +print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") +print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") +print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") + +speckle_size = (speckle_size_width + speckle_size_height) / 2 +error = np.abs(avg_speckle_size_fwhm - speckle_size) * 100 / speckle_size +print(f"Percentage error between requested speckle size and measured speckle size from FWHM: {np.round(error, 3)} %") +error = np.abs(avg_speckle_size_e2 - speckle_size) * 100 / speckle_size +print(f"Percentage error between requested speckle size and measured speckle size from 1/e^2: {np.round(error, 3)} %") +np.save(f"{save_path}/image.npy", image) +print("") +print('End :)') +print("") +print("") +print("") diff --git a/src/pyvale/specklegen/__init__.py b/src/pyvale/specklegen/__init__.py new file mode 100644 index 00000000..d55ebac2 --- /dev/null +++ b/src/pyvale/specklegen/__init__.py @@ -0,0 +1,8 @@ +#=============================================================================== +# pyvale: the python validation engine +# License: MIT +# Copyright (C) 2025 The Computer Aided Validation Team +#=============================================================================== + +from .speckle_generation import * +from .speckle_pattern_diagnostics import * \ No newline at end of file diff --git a/src/pyvale/specklegen/runfiles/main_specklegen.py b/src/pyvale/specklegen/runfiles/main_specklegen.py deleted file mode 100644 index 32c70e89..00000000 --- a/src/pyvale/specklegen/runfiles/main_specklegen.py +++ /dev/null @@ -1,143 +0,0 @@ -import numpy as np -import argparse -import json -import sys -import os -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) -from utils.speckle_pattern_diagnostics import speckle_pattern_diagnostics -from utils.speckle_generation import generate_speckles - -def main() -> None: - - parser = argparse.ArgumentParser() - - # Load parameter definitions from the JSON file - with open('./scripts/configs/config_input_params_spec.json') as f: - parameters_input = json.load(f) - - # Merge all dictionaries - parameters = {**parameters_input} - - # Automatically add arguments from the JSON dictionary - for param, param_args in parameters.items(): - # Convert the "type" and "required" from string to actual Python type - # Check if "type" and "required" are present in the JSON file - if "type" in param_args.keys(): - param_args["type"] = eval(param_args["type"]) - if "required" in param_args.keys(): - param_args["required"] = eval(param_args["required"]) - parser.add_argument(param, **param_args) - - # Parse arguments - args = parser.parse_args() - - print('Args in simulation:') - print(args) - print('') - print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - print('') - - print('Start') - - # Extract parameteres and revert to default values if not provided by user - speckle_size = args.speckle_size if args.speckle_size is not None else 5.0 - screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 - screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 - bit_depth = args.bit_depth if args.bit_depth is not None else 8 - theme = args.theme if args.theme is not None else 'white_on_black' - random_seed = args.random_seed if args.random_seed is not None else 10 - sigma = args.sigma if args.sigma is not None else 1.0 - reduce_overlap = args.reduce_overlap - attempts = args.attempts if args.attempts is not None else 100 - - assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." - assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." - assert reduce_overlap in ['True', 'False'], "reduce_overlap should be either 'True' or 'False' (a string)." - - reduce_overlap_bool = True if reduce_overlap == "True" else False - if reduce_overlap_bool: - print("Reducing overlap between speckles") - else: - print("Not reducing overlap between speckles") - - np.random.seed(random_seed) - - subfolder = f"/{speckle_size}_{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{random_seed}_{sigma}_{reduce_overlap}" - print(subfolder) - save_path = args.output_path + subfolder - if not os.path.exists(save_path): - os.makedirs(save_path) - - # Calculate parameteres aiming for approximately 50/50 black/white ratio - speckle_area = np.pi * (speckle_size / 2) ** 2 - total_area = screen_size_width * screen_size_height - total_speckles = int((0.5 * total_area) / speckle_area) - print(f"Total number of speckles = {total_speckles}") - dynamic_range: int = 2**bit_depth - 1 - background_colour = 0 if theme == 'white_on_black' else dynamic_range - foreground_colour = dynamic_range if theme == 'white_on_black' else 0 - - # Generate speckle pattern - image, results = generate_speckles(screen_size_width, screen_size_height, - speckle_size, foreground_colour, - total_speckles, reduce_overlap_bool, - bit_depth, background_colour, - sigma, attempts) - - # save the speckle placement results - np.savetxt(f"{save_path}/speckle_placement_results.csv", results, delimiter=",", - header="speckle_number, attempts, overlap(1/0/2), cent_x, cent_y", comments='', fmt=['%d', '%d', '%d', '%.3f', '%.3f']) - - # Diagnostics - print("") - print('Starting speckle pattern diagnostics...') - results = speckle_pattern_diagnostics(image, dynamic_range, save_path) - - # save the diagnostics results - with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: - json.dump(results, f, indent=4) - - ratio = results.get("black_white_ratio", None) - mean_gradient = results.get("mean_intensity_gradient", None) - std_dev = results.get("std_dev_irradiance", None) - avg = results.get("avg_irradiance", None) - contrast = results.get("contrast", None) - entropy = results.get("shannon_entropy", None) - peak_to_mean = results.get("peak_to_mean_ratio", None) - skew = results.get("skewness", None) - kurt = results.get("kurtosis", None) - avg_speckle_size_fwhm = results.get("avg_speckle_size_fwhm", None) - avg_speckle_size_e2 = results.get("avg_speckle_size_e2", None) - H_fit_stats = results.get("H_fit_stats", None) - V_fit_stats = results.get("V_fit_stats", None) - - print("") - print("Speckle statistics:") - - print(f"Black/White ratio: {np.round(ratio, 3)}") - print(f"Mean intensity gradient: {np.round(mean_gradient, 3)}") - print(f"Standard deviation of irradiance values: {np.round(std_dev, 3)}") - print(f"Average irradiance value: {np.round(avg, 3)}") - print(f"Contrast (std/mean): {np.round(contrast, 3)}") - print(f"Skewness: {np.round(skew, 3)}") - print(f"Kurtosis: {np.round(kurt, 3)}") - print(f"Shannon entropy: {np.round(entropy, 3)}") - print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") - print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") - print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") - print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") - - error = np.abs(avg_speckle_size_fwhm - speckle_size) * 100 / speckle_size - print(f"Percentage error between requested speckle size and measured speckle size from FWHM: {np.round(error, 3)} %") - error = np.abs(avg_speckle_size_e2 - speckle_size) * 100 / speckle_size - print(f"Percentage error between requested speckle size and measured speckle size from 1/e^2: {np.round(error, 3)} %") - np.save(f"{save_path}/image.npy", image) - print("") - print('End :)') - print("") - print("") - print("") - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/src/pyvale/specklegen/runfiles/main_specklegen_perlin_noise.py b/src/pyvale/specklegen/runfiles/main_specklegen_perlin_noise.py deleted file mode 100644 index 1f6dff6b..00000000 --- a/src/pyvale/specklegen/runfiles/main_specklegen_perlin_noise.py +++ /dev/null @@ -1,128 +0,0 @@ -import numpy as np -import argparse -import json -import sys -import os -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) -from utils.speckle_pattern_diagnostics import speckle_pattern_diagnostics -from utils.speckle_generation import generate_speckles_perlin_noise - -def main() -> None: - - parser = argparse.ArgumentParser() - - # Load parameter definitions from the JSON file - with open('./scripts/configs/config_input_params_spec_perlin.json') as f: - parameters_input = json.load(f) - - # Merge all dictionaries - parameters = {**parameters_input} - - # Automatically add arguments from the JSON dictionary - for param, param_args in parameters.items(): - # Convert the "type" and "required" from string to actual Python type - # Check if "type" and "required" are present in the JSON file - if "type" in param_args.keys(): - param_args["type"] = eval(param_args["type"]) - if "required" in param_args.keys(): - param_args["required"] = eval(param_args["required"]) - parser.add_argument(param, **param_args) - - # Parse arguments - args = parser.parse_args() - - print('Args in simulation:') - print(args) - print('') - print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - print('') - - print('Start') - - # Extract parameteres and revert to default values if not provided by user - screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 - screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 - bit_depth = args.bit_depth if args.bit_depth is not None else 8 - theme = args.theme if args.theme is not None else 'white_on_black' - octaves = args.octaves if args.octaves is not None else "Not defined" - - type_gen = args.type_gen - res_width = args.res_width - res_height = args.res_height - - assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." - assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." - - if type_gen == "perlin": - subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{type_gen}_{res_width}_{res_height}" - elif type_gen == "fractal": - subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{type_gen}_{res_width}_{res_height}_{octaves}" - print(subfolder) - save_path = args.output_path + subfolder - if not os.path.exists(save_path): - os.makedirs(save_path) - - dynamic_range: int = 2**bit_depth - 1 - background_colour = 0 if theme == 'white_on_black' else dynamic_range - foreground_colour = dynamic_range if theme == 'white_on_black' else 0 - - # Generate speckle pattern - if type_gen == "perlin": - image = generate_speckles_perlin_noise(screen_size_width, screen_size_height, - res_height, res_width, - foreground_colour, bit_depth, background_colour, type_gen) - elif type_gen == "fractal": - image = generate_speckles_perlin_noise(screen_size_width, screen_size_height, - res_height, res_width, - foreground_colour, bit_depth, background_colour, type_gen, - octaves=octaves) - - # Diagnostics - print("") - print('Starting speckle pattern diagnostics...') - results = speckle_pattern_diagnostics(image, dynamic_range, save_path) - - # save the diagnostics results - with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: - json.dump(results, f, indent=4) - - ratio = results.get("black_white_ratio", None) - mean_gradient = results.get("mean_intensity_gradient", None) - std_dev = results.get("std_dev_irradiance", None) - avg = results.get("avg_irradiance", None) - contrast = results.get("contrast", None) - entropy = results.get("shannon_entropy", None) - peak_to_mean = results.get("peak_to_mean_ratio", None) - skew = results.get("skewness", None) - kurt = results.get("kurtosis", None) - avg_speckle_size_fwhm = results.get("avg_speckle_size_fwhm", None) - avg_speckle_size_e2 = results.get("avg_speckle_size_e2", None) - H_fit_stats = results.get("H_fit_stats", None) - V_fit_stats = results.get("V_fit_stats", None) - - print("") - print("Speckle statistics:") - - print(f"Black/White ratio: {np.round(ratio, 3)}") - print(f"Mean intensity gradient: {np.round(mean_gradient, 3)}") - print(f"Standard deviation of irradiance values: {np.round(std_dev, 3)}") - print(f"Average irradiance value: {np.round(avg, 3)}") - print(f"Contrast (std/mean): {np.round(contrast, 3)}") - print(f"Skewness: {np.round(skew, 3)}") - print(f"Kurtosis: {np.round(kurt, 3)}") - print(f"Shannon entropy: {np.round(entropy, 3)}") - print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") - print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") - print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") - print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") - - np.save(f"{save_path}/image.npy", image) - print("") - print('End :)') - print("") - print("") - print("") - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/src/pyvale/specklegen/runfiles/main_specklegen_simplex_noise.py b/src/pyvale/specklegen/runfiles/main_specklegen_simplex_noise.py deleted file mode 100644 index 50e0f4cb..00000000 --- a/src/pyvale/specklegen/runfiles/main_specklegen_simplex_noise.py +++ /dev/null @@ -1,125 +0,0 @@ -import numpy as np -import argparse -import json -import sys -import os -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) -from utils.speckle_pattern_diagnostics import speckle_pattern_diagnostics -from utils.speckle_generation import generate_speckles_simplex_noise - -def main() -> None: - - parser = argparse.ArgumentParser() - - # Load parameter definitions from the JSON file - with open('./scripts/configs/config_input_params_spec_simplex.json') as f: - parameters_input = json.load(f) - - # Merge all dictionaries - parameters = {**parameters_input} - - # Automatically add arguments from the JSON dictionary - for param, param_args in parameters.items(): - # Convert the "type" and "required" from string to actual Python type - # Check if "type" and "required" are present in the JSON file - if "type" in param_args.keys(): - param_args["type"] = eval(param_args["type"]) - if "required" in param_args.keys(): - param_args["required"] = eval(param_args["required"]) - parser.add_argument(param, **param_args) - - # Parse arguments - args = parser.parse_args() - - print('Args in simulation:') - print(args) - print('') - print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - print('') - - print('Start') - - # Extract parameteres and revert to default values if not provided by user - speckle_size_width = args.speckle_size_width if args.speckle_size_width is not None else 5.0 - speckle_size_height = args.speckle_size_height if args.speckle_size_height is not None else 5.0 - screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 - screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 - bit_depth = args.bit_depth if args.bit_depth is not None else 8 - theme = args.theme if args.theme is not None else 'white_on_black' - seed = args.seed if args.seed is not None else 1234 - - assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." - assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." - - subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{speckle_size_width}_{speckle_size_height}_{seed}" - print(subfolder) - save_path = args.output_path + subfolder - if not os.path.exists(save_path): - os.makedirs(save_path) - - dynamic_range: int = 2**bit_depth - 1 - background_colour = 0 if theme == 'white_on_black' else dynamic_range - foreground_colour = dynamic_range if theme == 'white_on_black' else 0 - - # Generate speckle pattern - image = generate_speckles_simplex_noise(screen_size_width, screen_size_height, - foreground_colour, - bit_depth, background_colour, - feature_size_width=speckle_size_width, - feature_size_height=speckle_size_height, - seed=seed) - - # Diagnostics - print("") - print('Starting speckle pattern diagnostics...') - results = speckle_pattern_diagnostics(image, dynamic_range, save_path) - - # save the diagnostics results - with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: - json.dump(results, f, indent=4) - - ratio = results.get("black_white_ratio", None) - mean_gradient = results.get("mean_intensity_gradient", None) - std_dev = results.get("std_dev_irradiance", None) - avg = results.get("avg_irradiance", None) - contrast = results.get("contrast", None) - entropy = results.get("shannon_entropy", None) - peak_to_mean = results.get("peak_to_mean_ratio", None) - skew = results.get("skewness", None) - kurt = results.get("kurtosis", None) - avg_speckle_size_fwhm = results.get("avg_speckle_size_fwhm", None) - avg_speckle_size_e2 = results.get("avg_speckle_size_e2", None) - H_fit_stats = results.get("H_fit_stats", None) - V_fit_stats = results.get("V_fit_stats", None) - - print("") - print("Speckle statistics:") - - print(f"Black/White ratio: {np.round(ratio, 3)}") - print(f"Mean intensity gradient: {np.round(mean_gradient, 3)}") - print(f"Standard deviation of irradiance values: {np.round(std_dev, 3)}") - print(f"Average irradiance value: {np.round(avg, 3)}") - print(f"Contrast (std/mean): {np.round(contrast, 3)}") - print(f"Skewness: {np.round(skew, 3)}") - print(f"Kurtosis: {np.round(kurt, 3)}") - print(f"Shannon entropy: {np.round(entropy, 3)}") - print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") - print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") - print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") - print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") - - speckle_size = (speckle_size_width + speckle_size_height) / 2 - error = np.abs(avg_speckle_size_fwhm - speckle_size) * 100 / speckle_size - print(f"Percentage error between requested speckle size and measured speckle size from FWHM: {np.round(error, 3)} %") - error = np.abs(avg_speckle_size_e2 - speckle_size) * 100 / speckle_size - print(f"Percentage error between requested speckle size and measured speckle size from 1/e^2: {np.round(error, 3)} %") - np.save(f"{save_path}/image.npy", image) - print("") - print('End :)') - print("") - print("") - print("") - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/src/pyvale/specklegen/scripts/base/case1.sh b/src/pyvale/specklegen/scripts/base/case1.sh deleted file mode 100644 index 0cb70955..00000000 --- a/src/pyvale/specklegen/scripts/base/case1.sh +++ /dev/null @@ -1,27 +0,0 @@ -SIMULATION="base" -CASE="case1" - -if [ ! -d logs/$SIMULATION ]; then - mkdir -p logs/$SIMULATION -fi - -> logs/$SIMULATION/$CASE.log - -for SPECKLE_SIZE in $(seq 10 5 25) -do -for SIGMA_COEF in 1 3 5 -do - python -u runfiles/main_specklegen.py \ - --output_path ./output/$SIMULATION/$CASE/ \ - --speckle_size $SPECKLE_SIZE \ - --screen_size_width 1000 \ - --screen_size_height 800 \ - --bit_depth 8 \ - --theme "white_on_black" \ - --random_seed 10 \ - --sigma $(($SPECKLE_SIZE/$SIGMA_COEF)) \ - --reduce_overlap "False" \ - >> logs/$SIMULATION/$CASE.log 2>&1 # Append to the log file - -done -done \ No newline at end of file diff --git a/src/pyvale/specklegen/scripts/base/case2.sh b/src/pyvale/specklegen/scripts/base/case2.sh deleted file mode 100644 index 5f0904fb..00000000 --- a/src/pyvale/specklegen/scripts/base/case2.sh +++ /dev/null @@ -1,27 +0,0 @@ -SIMULATION="base" -CASE="case2" - -if [ ! -d logs/$SIMULATION ]; then - mkdir -p logs/$SIMULATION -fi - -> logs/$SIMULATION/$CASE.log - -for SPECKLE_SIZE in $(seq 10 5 25) -do -for SIGMA_COEF in 1 3 5 -do - python -u runfiles/main_specklegen.py \ - --output_path ./output/$SIMULATION/$CASE/ \ - --speckle_size $SPECKLE_SIZE \ - --screen_size_width 1000 \ - --screen_size_height 800 \ - --bit_depth 8 \ - --theme "black_on_white" \ - --random_seed 10 \ - --sigma $(($SPECKLE_SIZE/$SIGMA_COEF)) \ - --reduce_overlap "False" \ - >> logs/$SIMULATION/$CASE.log 2>&1 # Append to the log file - -done -done \ No newline at end of file diff --git a/src/pyvale/specklegen/scripts/perlin_noise/case1.sh b/src/pyvale/specklegen/scripts/perlin_noise/case1.sh deleted file mode 100644 index 48aa5e3c..00000000 --- a/src/pyvale/specklegen/scripts/perlin_noise/case1.sh +++ /dev/null @@ -1,25 +0,0 @@ -SIMULATION="perlin_noise" -CASE="case1" - -if [ ! -d logs/$SIMULATION ]; then - mkdir -p logs/$SIMULATION -fi - -> logs/$SIMULATION/$CASE.log - -for RES_WIDTH in 10 25 100 -do -for RES_HEIGHT in 10 25 100 -do - python -u runfiles/main_specklegen_perlin_noise.py \ - --output_path ./output/$SIMULATION/$CASE/ \ - --screen_size_width 1000 \ - --screen_size_height 800 \ - --res_width $RES_WIDTH \ - --res_height $RES_HEIGHT \ - --bit_depth 8 \ - --theme "white_on_black" \ - --type_gen "perlin" \ - >> logs/$SIMULATION/$CASE.log 2>&1 # Append to the log file -done -done \ No newline at end of file diff --git a/src/pyvale/specklegen/scripts/perlin_noise/case2.sh b/src/pyvale/specklegen/scripts/perlin_noise/case2.sh deleted file mode 100644 index 225ed28e..00000000 --- a/src/pyvale/specklegen/scripts/perlin_noise/case2.sh +++ /dev/null @@ -1,29 +0,0 @@ -SIMULATION="perlin_noise" -CASE="case2" - -if [ ! -d logs/$SIMULATION ]; then - mkdir -p logs/$SIMULATION -fi - -> logs/$SIMULATION/$CASE.log - -for RES_WIDTH in 10 25 100 -do -for RES_HEIGHT in 10 25 100 -do -for OCTAVES in 1 2 3 -do - python -u runfiles/main_specklegen_perlin_noise.py \ - --output_path ./output/$SIMULATION/$CASE/ \ - --screen_size_width 1000 \ - --screen_size_height 800 \ - --res_width $RES_WIDTH \ - --res_height $RES_HEIGHT \ - --bit_depth 8 \ - --theme "white_on_black" \ - --type_gen "fractal" \ - --octaves $OCTAVES \ - >> logs/$SIMULATION/$CASE.log 2>&1 # Append to the log file -done -done -done \ No newline at end of file diff --git a/src/pyvale/specklegen/scripts/reduce_overlap/case1.sh b/src/pyvale/specklegen/scripts/reduce_overlap/case1.sh deleted file mode 100644 index f273b507..00000000 --- a/src/pyvale/specklegen/scripts/reduce_overlap/case1.sh +++ /dev/null @@ -1,27 +0,0 @@ -SIMULATION="reduce_overlap" -CASE="case1" - -if [ ! -d logs/$SIMULATION ]; then - mkdir -p logs/$SIMULATION -fi - -> logs/$SIMULATION/$CASE.log - -for SPECKLE_SIZE in $(seq 10 5 25) -do -for SIGMA_COEF in 1 3 5 -do - python -u runfiles/main_specklegen.py \ - --output_path ./output/$SIMULATION/$CASE/ \ - --speckle_size $SPECKLE_SIZE \ - --screen_size_width 1000 \ - --screen_size_height 800 \ - --bit_depth 8 \ - --theme "white_on_black" \ - --random_seed 10 \ - --sigma $(($SPECKLE_SIZE/$SIGMA_COEF)) \ - --reduce_overlap "True" \ - >> logs/$SIMULATION/$CASE.log 2>&1 # Append to the log file - -done -done \ No newline at end of file diff --git a/src/pyvale/specklegen/scripts/reduce_overlap/case2.sh b/src/pyvale/specklegen/scripts/reduce_overlap/case2.sh deleted file mode 100644 index e1b8b2ee..00000000 --- a/src/pyvale/specklegen/scripts/reduce_overlap/case2.sh +++ /dev/null @@ -1,27 +0,0 @@ -SIMULATION="reduce_overlap" -CASE="case2" - -if [ ! -d logs/$SIMULATION ]; then - mkdir -p logs/$SIMULATION -fi - -> logs/$SIMULATION/$CASE.log - -for SPECKLE_SIZE in $(seq 10 5 25) -do -for SIGMA_COEF in 1 3 5 -do - python -u runfiles/main_specklegen.py \ - --output_path ./output/$SIMULATION/$CASE/ \ - --speckle_size $SPECKLE_SIZE \ - --screen_size_width 1000 \ - --screen_size_height 800 \ - --bit_depth 8 \ - --theme "black_on_white" \ - --random_seed 10 \ - --sigma $(($SPECKLE_SIZE/$SIGMA_COEF)) \ - --reduce_overlap "True" \ - >> logs/$SIMULATION/$CASE.log 2>&1 # Append to the log file - -done -done \ No newline at end of file diff --git a/src/pyvale/specklegen/scripts/simplex_noise/case1.sh b/src/pyvale/specklegen/scripts/simplex_noise/case1.sh deleted file mode 100644 index 88f50229..00000000 --- a/src/pyvale/specklegen/scripts/simplex_noise/case1.sh +++ /dev/null @@ -1,25 +0,0 @@ -SIMULATION="simplex_noise" -CASE="case1" - -if [ ! -d logs/$SIMULATION ]; then - mkdir -p logs/$SIMULATION -fi - -> logs/$SIMULATION/$CASE.log - -for SPECKLE_SIZE_WIDTH in 10 15 20 30 40 -do -for SPECKLE_SIZE_HEIGHT in 10 15 20 30 40 -do - python -u runfiles/main_specklegen_simplex_noise.py \ - --output_path ./output/$SIMULATION/$CASE/ \ - --screen_size_width 1000 \ - --screen_size_height 800 \ - --speckle_size_width $SPECKLE_SIZE_WIDTH \ - --speckle_size_height $SPECKLE_SIZE_HEIGHT \ - --bit_depth 8 \ - --theme "white_on_black" \ - --seed 1234 \ - >> logs/$SIMULATION/$CASE.log 2>&1 # Append to the log file -done -done \ No newline at end of file diff --git a/src/pyvale/specklegen/utils/speckle_generation.py b/src/pyvale/specklegen/speckle_generation.py similarity index 98% rename from src/pyvale/specklegen/utils/speckle_generation.py rename to src/pyvale/specklegen/speckle_generation.py index a2029a3a..b1411e37 100644 --- a/src/pyvale/specklegen/utils/speckle_generation.py +++ b/src/pyvale/specklegen/speckle_generation.py @@ -17,7 +17,7 @@ def pixelsInDisk(cent_x: int, cent_y: int, " foreground_colour: colour value to set for the disk pixels" " image: 2D numpy array representing the image" " check_overlap: if True, check for overlap with existing foreground pixels and does not modify the image" - " Output: modifies the image array" + " Output: 0 if overlap detected, 1 if no overlap (when check_overlap is True), otherwise modifies the image" box_max_x = int(np.ceil(cent_x + speckle_size / 2)) box_min_x = int(np.floor(cent_x - speckle_size / 2)) diff --git a/src/pyvale/specklegen/utils/speckle_pattern_diagnostics.py b/src/pyvale/specklegen/speckle_pattern_diagnostics.py similarity index 95% rename from src/pyvale/specklegen/utils/speckle_pattern_diagnostics.py rename to src/pyvale/specklegen/speckle_pattern_diagnostics.py index 57fec9f0..7be15e44 100644 --- a/src/pyvale/specklegen/utils/speckle_pattern_diagnostics.py +++ b/src/pyvale/specklegen/speckle_pattern_diagnostics.py @@ -3,15 +3,20 @@ import matplotlib import seaborn as sns import scipy.fftpack -from scipy.signal import correlate from scipy.optimize import curve_fit -from scipy.signal import fftconvolve from scipy.stats import skew, kurtosis from skimage.measure import shannon_entropy def speckle_pattern_diagnostics(image: np.ndarray, dynamic_range: int, save_path: str) -> dict: + """ Perform diagnostics on the speckle pattern image.""" + """Input: """ + """ image: 2D numpy array representing the speckle pattern """ + """ dynamic_range: maximum pixel value based on bit depth """ + """ save_path: path to save the diagnostic plots """ + """ Output: dictionary with diagnostic results """ + HFWHM, HeSquared, H_fit_stats, VFWHM, VeSquared, V_fit_stats, popt_H, popt_V, h_profile, v_profile = speckle_size(image) avg_speckle_size_fwhm = np.mean([HFWHM, VFWHM]) avg_speckle_size_e2 = np.mean([HeSquared, VeSquared]) diff --git a/tests/specklegen/generate_speckles_test.py b/tests/specklegen/generate_speckles_test.py new file mode 100644 index 00000000..60fec02d --- /dev/null +++ b/tests/specklegen/generate_speckles_test.py @@ -0,0 +1,139 @@ +import numpy as np +import argparse +import json +import pytest +import sys +import os +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +import pyvale.specklegen as specklegen + +width_range = np.arange(10, 1000, 250) +height_range = np.arange(10, 1000, 250) +speckle_size_range = np.arange(4, 25, 5) +total_speckles_range = np.arange(1, 10, 1) +sizes = list(zip(width_range, height_range, speckle_size_range)) + +print(width_range.shape) +print(height_range.shape) +print(speckle_size_range.shape) + +pytestmark = pytest.mark.parametrize("width,height,speckle_size", sizes) + +def test_pixelsInDisk_out_of_bounds(width: int, height: int, speckle_size: int) -> None: + img = np.zeros((height, width), dtype=np.uint8) + cantre_x, centre_y = -speckle_size * 2, -speckle_size * 2 # Out of bounds + fg_colour = 1 + check_overlap = False + + specklegen.pixelsInDisk(cantre_x, centre_y, width, height, speckle_size, fg_colour, img, check_overlap) + + assert np.all(img == 0) + +def test_pixelsInDisk_basic_circle(width: int, height: int, speckle_size: int) -> None: + img = np.zeros((height, width), dtype=np.uint8) + cantre_x, centre_y = 5, 5 + fg_colour = 1 + check_overlap = False + + specklegen.pixelsInDisk(cantre_x, centre_y, width, height, speckle_size, fg_colour, img, check_overlap) + + # Check that the centre pixel is set + assert img[centre_y, cantre_x] == fg_colour + + # Check that pixels outside the radius are not set + radius_sq = (speckle_size / 2) ** 2 + for y in range(height): + for x in range(width): + dist_sq = (x + 0.5 - cantre_x) ** 2 + (y + 0.5 - centre_y) ** 2 + if dist_sq > radius_sq: + assert img[y, x] == 0 + +def test_pixelsInDisk_near_edges(width: int, height: int, speckle_size: int) -> None: + img = np.zeros((height, width), dtype=np.uint8) + cantre_x, centre_y = 0, 0 + fg_colour = 1 + check_overlap = False + + specklegen.pixelsInDisk(cantre_x, centre_y, width, height, speckle_size, fg_colour, img, check_overlap) + + # Pixels outside bounds should not be accessed => Mo error => Passed test + assert img[0, 0] == fg_colour # Centre pixel should be set + + radius_sq = (speckle_size / 2) ** 2 + for y in range(height): + for x in range(width): + dist_sq = (x + 0.5 - cantre_x) ** 2 + (y + 0.5 - centre_y) ** 2 + if dist_sq <= radius_sq: + assert img[y, x] == fg_colour + +@pytest.mark.parametrize("total_speckles", total_speckles_range) +def test_generate_speckles_basic_no_overlap(width: int, height: int, speckle_size: int, + total_speckles: int) -> None: + fg_colour = 255 + bg_colour = 0 + total_speckles = 5 + sigma = 1.0 + bit_depth = 8 + + image, results = specklegen.generate_speckles(width, height, speckle_size, fg_colour, total_speckles, + reduce_overlap=False, bit_depth=bit_depth, + background_colour=bg_colour, sigma=sigma) + + assert image.shape == (height, width) + assert results.shape == (total_speckles, 5) + + # All speckles should have attempt count = 1 and overlap flag = 2 (not checked) + assert np.all(results[:, 1] == 1) + assert np.all(results[:, 2] == 2) + + +@pytest.mark.parametrize("total_speckles", total_speckles_range) +def test_generate_speckles_with_overlap_reduction(width: int, height: int, speckle_size: int, + total_speckles: int) -> None: + fg_colour = 255 + bg_colour = 0 + sigma = 1.0 + bit_depth = 8 + + image, results = specklegen.generate_speckles(width, height, speckle_size, fg_colour, total_speckles, + reduce_overlap=True, bit_depth=bit_depth, + background_colour=bg_colour, sigma=sigma, attempts_tot=50) + + assert image.shape == (height, width) + assert results.shape == (total_speckles, 5) + + # Overlap flag should be either 0 or 1 (placed with or without overlap) + assert np.all(np.isin(results[:, 2], [0, 1])) + +@pytest.mark.parametrize("total_speckles", total_speckles_range) +def test_generate_speckles_bit_depth(width: int, height: int, speckle_size: int, + total_speckles: int) -> None: + fg_colour = 65535 + bg_colour = 0 + sigma = 0 + bit_depth = 16 + + image, results = specklegen.generate_speckles(width, height, speckle_size, fg_colour, total_speckles, + reduce_overlap=False, bit_depth=bit_depth, + background_colour=bg_colour, sigma=sigma) + + assert image.dtype == np.uint16 + assert image.max() <= fg_colour + assert results.shape == (total_speckles, 5) + +@pytest.mark.parametrize("total_speckles", total_speckles_range) +def test_generate_speckles_blur(width: int, height: int, speckle_size: int, + total_speckles: int) -> None: + fg_colour = 255 + bg_colour = 0 + sigma = 1.5 + bit_depth = 8 + + image, results = specklegen.generate_speckles(width, height, speckle_size, fg_colour, total_speckles, + reduce_overlap=False, bit_depth=bit_depth, + background_colour=bg_colour, sigma=sigma) + + assert np.any(image != 0) + assert np.any(image != fg_colour) + assert image.shape == (height, width) + assert results.shape == (total_speckles, 5) From 179f6ecceb8d93143a931cb9fc9bd2bf77de92fc Mon Sep 17 00:00:00 2001 From: WieraB Date: Mon, 27 Oct 2025 11:10:45 +0000 Subject: [PATCH 07/16] Added README.md file to example forlder for doxygen. --- src/pyvale/examples/specklegen/README.md | 2 ++ .../examples/specklegen/ex1a_random_disks_overlap.py | 6 ++++++ .../specklegen/ex1b_random_disks_reduce_overlap.py | 6 ++++++ .../examples/specklegen/ex2a_perlin_noise_isotropic.py | 6 ++++++ .../examples/specklegen/ex2b_perlin_noise_anisotropic.py | 6 ++++++ .../examples/specklegen/ex3a_fractal_noise_isotropic.py | 6 ++++++ .../examples/specklegen/ex3b_fractal_noise_anisotropic.py | 7 +++++++ .../examples/specklegen/ex4a_simplex_noise_isotropic.py | 7 +++++++ .../examples/specklegen/ex4b_simplex_noise_anisotrpic.py | 6 ++++++ 9 files changed, 52 insertions(+) create mode 100644 src/pyvale/examples/specklegen/README.md diff --git a/src/pyvale/examples/specklegen/README.md b/src/pyvale/examples/specklegen/README.md new file mode 100644 index 00000000..fbbd16fc --- /dev/null +++ b/src/pyvale/examples/specklegen/README.md @@ -0,0 +1,2 @@ +Specklegen Examples +================== diff --git a/src/pyvale/examples/specklegen/ex1a_random_disks_overlap.py b/src/pyvale/examples/specklegen/ex1a_random_disks_overlap.py index 21b41209..9b2c0bcd 100644 --- a/src/pyvale/examples/specklegen/ex1a_random_disks_overlap.py +++ b/src/pyvale/examples/specklegen/ex1a_random_disks_overlap.py @@ -1,3 +1,9 @@ +# ============================================================================== +# pyvale: the python validation engine +# License: MIT +# Copyright (C) 2025 The Computer Aided Validation Team +# ============================================================================== + """ Specklegen: Speckle pattern generation using random disk placement without checking for overlap ================================================================================ diff --git a/src/pyvale/examples/specklegen/ex1b_random_disks_reduce_overlap.py b/src/pyvale/examples/specklegen/ex1b_random_disks_reduce_overlap.py index 99b1fb23..968db695 100644 --- a/src/pyvale/examples/specklegen/ex1b_random_disks_reduce_overlap.py +++ b/src/pyvale/examples/specklegen/ex1b_random_disks_reduce_overlap.py @@ -1,3 +1,9 @@ +# ============================================================================== +# pyvale: the python validation engine +# License: MIT +# Copyright (C) 2025 The Computer Aided Validation Team +# ============================================================================== + """ Specklegen: Speckle pattern generation using random disk placement with checking for overlap ================================================================================ diff --git a/src/pyvale/examples/specklegen/ex2a_perlin_noise_isotropic.py b/src/pyvale/examples/specklegen/ex2a_perlin_noise_isotropic.py index f2b399d5..fdd4081d 100644 --- a/src/pyvale/examples/specklegen/ex2a_perlin_noise_isotropic.py +++ b/src/pyvale/examples/specklegen/ex2a_perlin_noise_isotropic.py @@ -1,3 +1,9 @@ +# ============================================================================== +# pyvale: the python validation engine +# License: MIT +# Copyright (C) 2025 The Computer Aided Validation Team +# ============================================================================== + """ Specklegen: Speckle pattern generation using isotropic Perlin noise ================================================================================ diff --git a/src/pyvale/examples/specklegen/ex2b_perlin_noise_anisotropic.py b/src/pyvale/examples/specklegen/ex2b_perlin_noise_anisotropic.py index b6b24ed7..f72c2c01 100644 --- a/src/pyvale/examples/specklegen/ex2b_perlin_noise_anisotropic.py +++ b/src/pyvale/examples/specklegen/ex2b_perlin_noise_anisotropic.py @@ -1,3 +1,9 @@ +# ============================================================================== +# pyvale: the python validation engine +# License: MIT +# Copyright (C) 2025 The Computer Aided Validation Team +# ============================================================================== + """ Specklegen: Speckle pattern generation using anisotropic Perlin noise ================================================================================ diff --git a/src/pyvale/examples/specklegen/ex3a_fractal_noise_isotropic.py b/src/pyvale/examples/specklegen/ex3a_fractal_noise_isotropic.py index aab87efb..85077fbc 100644 --- a/src/pyvale/examples/specklegen/ex3a_fractal_noise_isotropic.py +++ b/src/pyvale/examples/specklegen/ex3a_fractal_noise_isotropic.py @@ -1,3 +1,9 @@ +# ============================================================================== +# pyvale: the python validation engine +# License: MIT +# Copyright (C) 2025 The Computer Aided Validation Team +# ============================================================================== + """ Specklegen: Speckle pattern generation using isotropic fractal noise ================================================================================ diff --git a/src/pyvale/examples/specklegen/ex3b_fractal_noise_anisotropic.py b/src/pyvale/examples/specklegen/ex3b_fractal_noise_anisotropic.py index aa1d1343..f2869f52 100644 --- a/src/pyvale/examples/specklegen/ex3b_fractal_noise_anisotropic.py +++ b/src/pyvale/examples/specklegen/ex3b_fractal_noise_anisotropic.py @@ -1,3 +1,10 @@ +# ============================================================================== +# pyvale: the python validation engine +# License: MIT +# Copyright (C) 2025 The Computer Aided Validation Team +# ============================================================================== + + """ Specklegen: Speckle pattern generation using anisotropic fractal noise ================================================================================ diff --git a/src/pyvale/examples/specklegen/ex4a_simplex_noise_isotropic.py b/src/pyvale/examples/specklegen/ex4a_simplex_noise_isotropic.py index 0be9ccbe..c8b4cd57 100644 --- a/src/pyvale/examples/specklegen/ex4a_simplex_noise_isotropic.py +++ b/src/pyvale/examples/specklegen/ex4a_simplex_noise_isotropic.py @@ -1,3 +1,10 @@ +# ============================================================================== +# pyvale: the python validation engine +# License: MIT +# Copyright (C) 2025 The Computer Aided Validation Team +# ============================================================================== + + """ Specklegen: Speckle pattern generation using isotropic Simplex noise ================================================================================ diff --git a/src/pyvale/examples/specklegen/ex4b_simplex_noise_anisotrpic.py b/src/pyvale/examples/specklegen/ex4b_simplex_noise_anisotrpic.py index 1fac6f8f..de3cb3cf 100644 --- a/src/pyvale/examples/specklegen/ex4b_simplex_noise_anisotrpic.py +++ b/src/pyvale/examples/specklegen/ex4b_simplex_noise_anisotrpic.py @@ -1,3 +1,9 @@ +# ============================================================================== +# pyvale: the python validation engine +# License: MIT +# Copyright (C) 2025 The Computer Aided Validation Team +# ============================================================================== + """ Specklegen: Speckle pattern generation using anisotropic Simplex noise ================================================================================ From 4b73939969453120d60bbf9060e9c36cfa25f930 Mon Sep 17 00:00:00 2001 From: WieraB Date: Fri, 31 Oct 2025 11:36:35 +0000 Subject: [PATCH 08/16] - Refactored functions: 1. Grouped all specklegen options into one function, 2.Converted number of periods into a speckle size, add random seed setting in Perlin/fractal noise, 3. Split diagnostics function into just stats and everything chart-related, let the diagnostics function return the plot handle as well. - Created function descriptions in a standard format. - Added an option to create a speckle pattern by first creating a regular circle pattern and then randomly perturbating speckles. --- src/pyvale/specklegen/speckle_generation.py | 432 ++++++++++++++---- .../specklegen/speckle_pattern_diagnostics.py | 167 ++++++- 2 files changed, 496 insertions(+), 103 deletions(-) diff --git a/src/pyvale/specklegen/speckle_generation.py b/src/pyvale/specklegen/speckle_generation.py index b1411e37..642b9527 100644 --- a/src/pyvale/specklegen/speckle_generation.py +++ b/src/pyvale/specklegen/speckle_generation.py @@ -8,17 +8,34 @@ def pixelsInDisk(cent_x: int, cent_y: int, screen_size_width: int, screen_size_height: int, speckle_size: float, foreground_colour: int, image: np.ndarray, check_overlap: bool) -> int: - - """ Set pixels in a disk shape on the image array, disk centered at (cent_x, cent_y) with given speckle_size """ - "Inputs:" - " cent_x, cent_y: center coordinates of the disk" - " screen_size_width, screen_size_height: dimensions of the image" - " speckle_size: diameter of the disk" - " foreground_colour: colour value to set for the disk pixels" - " image: 2D numpy array representing the image" - " check_overlap: if True, check for overlap with existing foreground pixels and does not modify the image" - " Output: 0 if overlap detected, 1 if no overlap (when check_overlap is True), otherwise modifies the image" - + """A function to set pixels in a disk shape on the image array + + Parameters + ---------- + cent_x : int + Center x coordinate of the disk + cent_y : int + Center y coordinate of the disk + screen_size_width : int + Dimension of the image width-wise (pixels) + screen_size_height : int + Dimension of the image height-wise (pixels) + speckle_size : float + Diameter of the speckle disk (pixels) + foreground_colour : int + Colour value to set for the speckle pixels + image : np.ndarray + 2D numpy array representing the image + check_overlap : bool + If True, check for overlap with existing foreground pixels and does not modify the image + If False, sets the pixels in the disk shape to the foreground colour + + Returns + ------- + int + 0 if overlap detected, 1 if no overlap (when check_overlap is True), otherwise a function modifies the image + """ + box_max_x = int(np.ceil(cent_x + speckle_size / 2)) box_min_x = int(np.floor(cent_x - speckle_size / 2)) @@ -52,26 +69,62 @@ def pixelsInDisk(cent_x: int, cent_y: int, image[yy, xx] = foreground_colour -def generate_speckles(screen_size_width: int, screen_size_height: int, - speckle_size: float, foreground_colour: int, - total_speckles: int, reduce_overlap: bool, - bit_depth: int, background_colour: int, - sigma: float, attempts_tot: int = 100) -> np.ndarray: - - """ Generate a speckle pattern image with given parameters """ - """ The speckles are disks of given speckle_size, placed randomly on the image, based on the uniform probability distribution """ - """ Inputs: """ - """ screen_size_width, screen_size_height: dimensions of the image """ - """ speckle_size: diameter of each speckle disk """ - """ foreground_colour: colour value for the speckles """ - """ total_speckles: total number of speckles to place """ - """ reduce_overlap: if True, attempts to reduce overlap between speckles """ - """ bit_depth: bit depth of the image (8 or 16) """ - """ background_colour: colour value for the background """ - """ sigma: standard deviation for Gaussian blur applied after speckle placement """ - """ attempts_tot: maximum number of attempts to place each speckle without overlap """ - """ Output: speckle pattern image as a 2D numpy array and speckle generation stats """ +def generate_speckles_random_disks(screen_size_width: int, screen_size_height: int, + feature_size_width: float, feature_size_height: float, + foreground_colour: int, background_colour: int, + bit_depth: int, type_gen: str, seed: int, **kwargs) -> np.ndarray: + """A function to generate a speckle pattern image by randomly placing disk-shaped speckles + based on uniform probability distribution. + + Parameters + ---------- + screen_size_width : int + Dimension of the image width-wise (pixels) + screen_size_height : int + Dimension of the image height-wise (pixels) + feature_size_width : float + Speckle size width-wise + feature_size_height : float + Speckle size height-wise + foreground_colour : int + Colour value for the speckles + background_colour : int + Colour value for the background + bit_depth : int + Bit depth of the image (8 or 16) + type_gen : str + Type of noise to generate (must be 'random_disks' for this function) + seed : int + Random seed for the noise generation + total_speckles : int + Total number of speckles to place + reduce_overlap : bool + If True, attempts to reduce overlap between speckles + sigma : float + Standard deviation for Gaussian blur applied after speckle placement + attempts_tot : int (optional) + Maximum number of attempts to place each speckle without overlap (if reduce_overlap is True) + + Returns + ------- + np.ndarray + Speckle pattern image as a 2D numpy array and speckle generation stats + """ + + speckle_size = (feature_size_width + feature_size_height) / 2 + total_speckles = kwargs.get("total_speckles") + reduce_overlap = kwargs.get("reduce_overlap") + sigma = kwargs.get("sigma") + attempts_tot = kwargs.get("attempts_tot", 100) + # check that non of the required parameters are None + assert speckle_size is not None, "speckle_size parameter is required." + assert total_speckles is not None, "total_speckles parameter is required." + assert reduce_overlap is not None, "reduce_overlap parameter is required." + assert sigma is not None, "sigma parameter is required." + assert type_gen == "random_disks", "Type_gen must be 'random_disks' for this function." + + np.random.seed(seed) image: np.ndarray = np.ones((screen_size_height,screen_size_width), dtype=np.uint16 if bit_depth == 16 else np.uint8) * background_colour @@ -127,7 +180,6 @@ def generate_speckles(screen_size_width: int, screen_size_height: int, results[i, 3] = cent_x results[i, 4] = cent_y - # Apply Gaussian blur image_blur = np.copy(image) image_blur = ndimage.gaussian_filter(image_blur, sigma=sigma) @@ -135,38 +187,170 @@ def generate_speckles(screen_size_width: int, screen_size_height: int, return image_blur, results -def generate_speckles_perlin_noise(screen_size_width: int, screen_size_height: int, - res_width: int, res_height: int, - foreground_colour: int, - bit_depth: int, background_colour: int, type_gen: str, - **kwargs) -> np.ndarray: +def generate_speckles_random_disks_grid(screen_size_width: int, screen_size_height: int, + feature_size_width: float, feature_size_height: float, + foreground_colour: int, background_colour: int, + bit_depth: int, type_gen: str, seed: int, **kwargs) -> np.ndarray: + """A function to generate a speckle pattern image by randomly perturbating a grid of regularly-placed + disk-shaped speckles based on disrete uniform probability distribution. - """This function uses noise implementation from perlin-numpy package - (https://github.com/pvigier/perlin-numpy/tree/master)""" - - """Generate a speckle pattern image using Perlin or fractal noise. """ - """ Inputs: """ - """ screen_size_width, screen_size_height: dimensions of the image """ - """ res_width, res_height: number of periods of noise to generate along 2 axes """ - """ foreground_colour: colour value for the speckles """ - """ bit_depth: bit depth of the image (8 or 16) """ - """ background_colour: colour value for the background """ - """ type_gen: type of noise to generate ('perlin' or 'fractal')""" - """ kwargs: Additional keyword arguments for fractal noise generation (e.g., octaves)""" - """ Output: speckle pattern image as a 2D numpy array """ + Parameters + ---------- + screen_size_width : int + Dimension of the image width-wise (pixels) + screen_size_height : int + Dimension of the image height-wise (pixels) + feature_size_width : float + Speckle size width-wise + feature_size_height : float + Speckle size height-wise + foreground_colour : int + Colour value for the speckles + background_colour : int + Colour value for the background + bit_depth : int + Bit depth of the image (8 or 16) + type_gen : str + Type of noise to generate (must be 'random_disks_grid' for this function) + seed : int + Random seed for the noise generation + total_speckles : int + Total number of speckles to place + sigma : float + Standard deviation for Gaussian blur applied after speckle placement + perturbation_max : float + Maximum amount to move speckles by during grid perturbation + + Returns + ------- + np.ndarray + Speckle pattern image as a 2D numpy array and speckle generation stats + """ + + speckle_size = (feature_size_width + feature_size_height) / 2 + total_speckles = kwargs.get("total_speckles") + sigma = kwargs.get("sigma") + perturbation_max = kwargs.get("perturbation_max") + + # check that none of the required parameters are None + assert speckle_size is not None, "speckle_size parameter is required." + assert total_speckles is not None, "total_speckles parameter is required." + assert sigma is not None, "sigma parameter is required." + assert type_gen == "random_disks_grid", "Type_gen must be 'random_disks_grid' for this function." + + np.random.seed(seed) + image: np.ndarray = np.ones((screen_size_height,screen_size_width), + dtype=np.uint16 if bit_depth == 16 else np.uint8) * background_colour + + results = np.zeros([total_speckles, 5]) # speckle number, attempts, with/without/not checked overlap (1/0/2), cent_x, cent_y + + # Calculate the size of each grid cell and create initial speckle positions + grid_cols = int(np.sqrt(total_speckles)) + grid_rows = int(np.ceil(total_speckles / grid_cols)) + grid_cell_width = screen_size_width // grid_cols + grid_cell_height = screen_size_height // grid_rows + + i = np.arange(grid_rows) + j = np.arange(grid_cols) + speckle_positions_x = j * grid_cell_width + grid_cell_width // 2 + speckle_size // 2 + speckle_positions_y = i * grid_cell_height + grid_cell_height // 2 + speckle_size // 2 + speckle_positions = np.array(np.meshgrid(speckle_positions_x, speckle_positions_y)).T.reshape(-1, 2) + + # Remove extra speckle positions randomly + total_speckles_diff = len(speckle_positions) - total_speckles + + if total_speckles_diff > 0: + remove_indices = np.random.choice(len(speckle_positions), total_speckles_diff, replace=False) + mask = np.ones(len(speckle_positions), dtype=bool) + mask[remove_indices] = False + speckle_positions = speckle_positions[mask] + + # Perturb each speckle's position + perturbations = np.random.randint(-perturbation_max, perturbation_max, size=(total_speckles, 2)) + perturbed_positions = [] + check_overlap = False + for idx, (x, y) in enumerate(speckle_positions): + # Add random perturbation to the x and y coordinates + x_offset, y_offset = perturbations[idx] + new_x = np.clip(x + x_offset, 0, screen_size_width - 1) + new_y = np.clip(y + y_offset, 0, screen_size_height - 1) + perturbed_positions.append((new_x, new_y)) + + pixelsInDisk(new_x, new_y, + screen_size_width, screen_size_height, + speckle_size, foreground_colour, image, check_overlap) + + results[idx, 0] = idx + 1 + results[idx, 1] = 1 + results[idx, 2] = 2 + results[idx, 3] = new_x + results[idx, 4] = new_y + + # Apply Gaussian blur + image_blur = np.copy(image) + image_blur = ndimage.gaussian_filter(image_blur, sigma=sigma) + + return image_blur, results + +def generate_speckles_perlin_noise(screen_size_width: int, screen_size_height: int, + feature_size_width: float, feature_size_height: float, + foreground_colour: int, background_colour: int, + bit_depth: int, type_gen: str, seed: int, **kwargs) -> np.ndarray: + """A function to generate a speckle pattern image using Perlin noise or fractal noise. + This function uses noise implementation from perlin-numpy package + (https://github.com/pvigier/perlin-numpy/tree/master) + + Parameters + ---------- + screen_size_width : int + Dimension of the image width-wise (pixels) + screen_size_height : int + Dimension of the image height-wise (pixels) + feature_size_width : float + Speckle size width-wise + feature_size_height : float + Speckle size height-wise + foreground_colour : int + Colour value for the speckles + background_colour : int + Colour value for the background + bit_depth : int + Bit depth of the image (8 or 16) + type_gen : str + Type of noise to generate (must be 'perlin' or 'fractal' for this function) + seed : int + Random seed for the noise generation + lacunarity : float (optional) + Lacunarity parameter for fractal noise (required if type_gen is 'fractal') + octaves : int (optional) + Number of octaves for fractal noise (required if type_gen is 'fractal') + + Returns + ------- + np.ndarray + Speckle pattern image as a 2D numpy array + """ + + res_width = int(screen_size_width / feature_size_width) + res_height = int(screen_size_height / feature_size_height) + + np.random.seed(seed) + + assert type_gen in ["perlin", "fractal"], "type_gen must be either 'perlin' or 'fractal' for this function." if type_gen == "perlin": - # screen size must be a multiple of res - assert screen_size_width % res_width == 0, "The screen size must be a multiple of res." - assert screen_size_height % res_height == 0, "The screen size must be a multiple of res." + assert screen_size_width % res_width == 0, "The screen width must be a multiple of res_width." + assert screen_size_height % res_height == 0, "The screen height must be a multiple of res_height." image = generate_perlin_noise_2d(shape = (screen_size_height, screen_size_width), res = (res_height, res_width)) - if type_gen == "fractal": - # screen size must be a multiple of lacunarity^(octaves-1)*res - assert screen_size_width % res_width == 0, "The screen size must be a multiple of lacunarity^(octaves-1)*res." - assert screen_size_height % res_height == 0, "The screen size must be a multiple of lacunarity^(octaves-1)*res." - octaves = kwargs["octaves"] + elif type_gen == "fractal": + lacunarity = kwargs.get("lacunarity", None) + octaves = kwargs.get("octaves", None) + assert lacunarity is not None, "lacunarity parameter is required for fractal noise." + assert octaves is not None, "octaves parameter is required for fractal noise." + assert screen_size_width % (lacunarity ** (octaves - 1) * res_width) == 0, "The screen width must be a multiple of lacunarity^(octaves-1)*res_width." + assert screen_size_height % (lacunarity ** (octaves - 1) * res_height) == 0, "The screen height must be a multiple of lacunarity^(octaves-1)*res_height." image = generate_fractal_noise_2d(shape = (screen_size_height, screen_size_width), res = (res_height, res_width), octaves = octaves) @@ -175,40 +359,56 @@ def generate_speckles_perlin_noise(screen_size_width: int, screen_size_height: i min_val = np.min(image) max_val = np.max(image) image = (image - min_val) / (max_val - min_val) # Normalise to [0, 1] - image = image * (foreground_colour - background_colour) + background_colour + + if foreground_colour >= background_colour: + # Black speckles on white background + image = image * (foreground_colour - background_colour) + background_colour + else: + # White speckles on black background + image = (1 - image) * (background_colour - foreground_colour) + foreground_colour + image = image.astype(np.uint16 if bit_depth == 16 else np.uint8) return image -def generate_speckles_simplex_noise(screen_size_width: int, screen_size_height: int, - foreground_colour: int, - bit_depth: int, background_colour: int, +def generate_speckles_simplex_noise(screen_size_width: int, screen_size_height: int, feature_size_width: float, feature_size_height: float, - seed: int) -> np.ndarray: - - """This function uses noise implementation from opensimplex package + foreground_colour: int, background_colour: int, + bit_depth: int, type_gen: str, seed: int, **kwargs) -> np.ndarray: + """A function to generate a speckle pattern image using Simplex noise. + This function uses noise implementation from opensimplex package (https://pypi.org/project/opensimplex/) - (https://code.larus.se/lmas/opensimplex)""" - - """Generate a speckle pattern image using OpenSimplex noise (patent-free). """ - """ Inputs: """ - """ screen_size_width, screen_size_height: dimensions of the image """ - """ foreground_colour: colour value for the speckles """ - """ bit_depth: bit depth of the image (8 or 16) """ - """ background_colour: colour value for the background """ - """ feature_size_width: controls the size of features in the noise pattern (speckle size width-wise) """ - """ feature_size_height: controls the size of features in the noise pattern (speckle size height-wise) """ - """ seed: seed for the noise generation """ - """ Output: speckle pattern image as a 2D numpy array """ + (https://code.larus.se/lmas/opensimplex) - simplex.seed(seed) + Parameters + ---------- + screen_size_width : int + Dimension of the image width-wise (pixels) + screen_size_height : int + Dimension of the image height-wise (pixels) + feature_size_width : float + Speckle size width-wise + feature_size_height : float + Speckle size height-wise + foreground_colour : int + Colour value for the speckles + background_colour : int + Colour value for the background + bit_depth : int + Bit depth of the image (8 or 16) + type_gen : str + Type of noise to generate (must be 'simplex' for this function) + seed : int + Random seed for the noise generation - # image: np.ndarray = np.zeros((screen_size_height,screen_size_width)) + Returns + ------- + np.ndarray + Speckle pattern image as a 2D numpy array + """ - # for y in range(0, screen_size_height): - # for x in range(0, screen_size_width): - # value = simplex.noise2(x/feature_size, y/feature_size) - # image[y, x] = value + assert type_gen == "simplex", "Type_gen must be 'simplex' for this function." + simplex.seed(seed) ix, iy = np.arange(screen_size_width), np.arange(screen_size_height) image = simplex.noise2array(ix/feature_size_width, iy/feature_size_height) @@ -216,10 +416,74 @@ def generate_speckles_simplex_noise(screen_size_width: int, screen_size_height: # scale to background and foreground colours min_val = np.min(image) max_val = np.max(image) - # print(f"Min and max values before scaling: {min_val}, {max_val}") image = (image - min_val) / (max_val - min_val) # Normalise to [0, 1] - image = image * (foreground_colour - background_colour) + background_colour + + if foreground_colour >= background_colour: + # Black speckles on white background + image = image * (foreground_colour - background_colour) + background_colour + else: + # White speckles on black background + image = (1 - image) * (background_colour - foreground_colour) + foreground_colour + image = image.astype(np.uint16 if bit_depth == 16 else np.uint8) return image + +def generate_speckles(screen_size_width: int, screen_size_height: int, + feature_size_width: float, feature_size_height: float, + foreground_colour: int, background_colour: int, + bit_depth: int, type_gen: str, + seed: int, **kwargs) -> np.ndarray: + """A function to generate a speckle pattern image using specified generation method. + Speckle generation methods include 'random_disks', "random_disks_grid", 'perlin', 'fractal', and 'simplex'. + + Parameters + ---------- + screen_size_width : int + Dimension of the image width-wise (pixels) + screen_size_height : int + Dimension of the image height-wise (pixels) + feature_size_width : float + Speckle size width-wise + feature_size_height : float + Speckle size height-wise + foreground_colour : int + Colour value for the speckles + background_colour : int + Colour value for the background + bit_depth : int + Bit depth of the image (8 or 16) + type_gen : str + Type of noise to generate (must be one of 'random_disks', "random_disks_grid", 'perlin', 'fractal', 'simplex') + seed : int + Random seed for the noise generation + + Returns + ------- + np.ndarray + Speckle pattern image as a 2D numpy array and speckle generation stats (if applicable) + + Raises + ------ + ValueError + Unknown speckle generation type + """ + + assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." + dispatch = { + "random_disks": generate_speckles_random_disks, + "random_disks_grid": generate_speckles_random_disks_grid, + "perlin": generate_speckles_perlin_noise, + "fractal": generate_speckles_perlin_noise, + "simplex": generate_speckles_simplex_noise + } + + generate = dispatch.get(type_gen) + if generate: + return generate(screen_size_width, screen_size_height, + feature_size_width, feature_size_height, + foreground_colour, background_colour, + bit_depth, type_gen, seed, **kwargs) + else: + raise ValueError(f"Unknown speckle generation type: {type_gen}") diff --git a/src/pyvale/specklegen/speckle_pattern_diagnostics.py b/src/pyvale/specklegen/speckle_pattern_diagnostics.py index 7be15e44..58977444 100644 --- a/src/pyvale/specklegen/speckle_pattern_diagnostics.py +++ b/src/pyvale/specklegen/speckle_pattern_diagnostics.py @@ -7,15 +7,22 @@ from scipy.stats import skew, kurtosis from skimage.measure import shannon_entropy -def speckle_pattern_diagnostics(image: np.ndarray, dynamic_range: int, - save_path: str) -> dict: - - """ Perform diagnostics on the speckle pattern image.""" - """Input: """ - """ image: 2D numpy array representing the speckle pattern """ - """ dynamic_range: maximum pixel value based on bit depth """ - """ save_path: path to save the diagnostic plots """ - """ Output: dictionary with diagnostic results """ + +def speckle_pattern_statistics(image: np.ndarray, dynamic_range: int) -> dict: + """A function to perform diagnostics on the speckle pattern image. + + Parameters + ---------- + image : np.ndarray + 2D numpy array representing the speckle pattern + dynamic_range : int + Maximum pixel value (irradiance) based on bit depth + + Returns + ------- + dict + Dictionary with diagnostic results + """ HFWHM, HeSquared, H_fit_stats, VFWHM, VeSquared, V_fit_stats, popt_H, popt_V, h_profile, v_profile = speckle_size(image) avg_speckle_size_fwhm = np.mean([HFWHM, VFWHM]) @@ -53,6 +60,30 @@ def speckle_pattern_diagnostics(image: np.ndarray, dynamic_range: int, "V_fit_stats": V_fit_stats } + return results + +def speckle_pattern_plots(image: np.ndarray, dynamic_range: int, + save_path: str = None) -> dict: + """A function to generate and save diagnostic plots for the speckle pattern. + + Parameters + ---------- + image : np.ndarray + 2D numpy array representing the speckle pattern + dynamic_range : int + Maximum pixel value (irradiance) based on bit depth + save_path : str (optional) + Path to save the generated plots using example formatting to the folder if provided + + Returns + ------- + dict + Dictionary containing figures and axes of the generated plots + """ + + HFWHM, HeSquared, H_fit_stats, VFWHM, VeSquared, V_fit_stats, popt_H, popt_V, h_profile, v_profile = speckle_size(image) + plots = {} + # Set Seaborn theme sns.set_theme(style="darkgrid") matplotlib.rcParams['font.family'] = 'Sans-serif' @@ -69,8 +100,12 @@ def speckle_pattern_diagnostics(image: np.ndarray, dynamic_range: int, ax.set_xlabel("Position [pixel]", fontsize=fontsize1-2) ax.set_ylabel("Position [pixel]", fontsize=fontsize1-2) ax.set_title("Speckle pattern", fontsize=fontsize1) - # plt.savefig(f"{save_path}/speckle_pattern.tiff", dpi=300, format='tiff', bbox_inches='tight') - plt.savefig(f"{save_path}/speckle_pattern.jpg", dpi=300, format='jpg', bbox_inches='tight') + + if save_path is not None: + plt.savefig(f"{save_path}/speckle_pattern.jpg", dpi=300, format='jpg', bbox_inches='tight') + + plots['speckle_pattern_fig'] = fig + plots['speckle_pattern_ax'] = ax f_image = scipy.fftpack.fft2(image) f_image_shifted = scipy.fftpack.fftshift(f_image) @@ -85,8 +120,12 @@ def speckle_pattern_diagnostics(image: np.ndarray, dynamic_range: int, ax.set_title("Spatial frequency (log scale)", fontsize=fontsize1) ax.set_xlabel("Frequency [1/pixel]", fontsize=fontsize1-2) ax.set_ylabel("Frequency [1/pixel]", fontsize=fontsize1-2) - # plt.savefig(f"{save_path}/PSD_spectrum.tiff", dpi=300, format='tiff', bbox_inches='tight') - plt.savefig(f"{save_path}/frequency_spectrum.jpg", dpi=300, format='jpg', bbox_inches='tight') + + if save_path is not None: + plt.savefig(f"{save_path}/frequency_spectrum.jpg", dpi=300, format='jpg', bbox_inches='tight') + + plots['frequency_spectrum_fig'] = fig + plots['frequency_spectrum_ax'] = ax fig, axes = plt.subplots(1, 1, figsize=(7.0, 5.0)) ax = axes @@ -95,11 +134,14 @@ def speckle_pattern_diagnostics(image: np.ndarray, dynamic_range: int, ax.set_xlabel("Pixel value", fontsize=fontsize1-2) ax.set_ylabel("Density (log scale)", fontsize=fontsize1-2) ax.tick_params(axis='both', which='major', labelsize=fontsize1-2) - # plt.savefig(f"{save_path}/pixel_value_histogram.tiff", dpi=300, format='tiff', bbox_inches='tight') - plt.savefig(f"{save_path}/pixel_value_histogram.jpg", dpi=300, format='jpg', bbox_inches='tight') + + if save_path is not None: + plt.savefig(f"{save_path}/pixel_value_histogram.jpg", dpi=300, format='jpg', bbox_inches='tight') + + plots['pixel_value_histogram_fig'] = fig + plots['pixel_value_histogram_ax'] = ax plt.figure(figsize=(7.0, 5.0)) - fontsize1= 17 x_H = np.arange(1, h_profile.size + 1) x_V = np.arange(1, v_profile.size + 1) plt.subplot(2, 1, 1) @@ -121,11 +163,51 @@ def speckle_pattern_diagnostics(image: np.ndarray, dynamic_range: int, plt.legend(fontsize=fontsize1-2) plt.tick_params(axis='both', which='major', labelsize=fontsize1-2) plt.tight_layout() - plt.savefig(f"{save_path}/autocovariance.jpg", dpi=300, format='jpg', bbox_inches='tight') + + if save_path is not None: + plt.savefig(f"{save_path}/autocovariance.jpg", dpi=300, format='jpg', bbox_inches='tight') - return results + # Extract figure and axis + fig = plt.gcf() + ax = plt.gca() + + plots['autocovariance_fig'] = fig + plots['autocovariance_ax'] = ax + + return plots def speckle_size(image: np.ndarray) -> tuple: + """ A function to calculate speckle size from the autocovariance of the speckle pattern. + + Parameters + ---------- + image : np.ndarray + 2D numpy array representing the speckle pattern + + Returns + ------- + tuple + HFWHM : float + Full width at half maximum for horisontal profile. + HeSquared : float + 1/e^2 width for horisontal profile. + H_fit_stats : dict + R-squared goodness of fit for horisontal profile. + VFWHM : float + Full width at half maximum for vertical profile. + VeSquared : float + 1/e^2 width for vertical profile. + V_fit_stats : dict + R-squared goodness of fit for vertical profile. + popt_H : array + Optimal parameters for horisontal Gaussian fit. + popt_V : array + Optimal parameters for vertical Gaussian fit. + h_profile : np.ndarray + Horisontal autocovariance profile. + v_profile : np.ndarray + Vertical autocovariance profile. + """ # image = (image - np.mean(image)) / np.std(image) image = (image - np.mean(image)) @@ -144,9 +226,56 @@ def speckle_size(image: np.ndarray) -> tuple: return HFWHM, HeSquared, H_fit_stats, VFWHM, VeSquared, V_fit_stats, popt_H, popt_V, h_profile, v_profile def gaussian(x: np.ndarray, a1: float, b1: float, c1: float) -> np.ndarray: - return a1 * np.exp(-((x - b1)/c1) ** 2) + """A function to model Gaussian distribution. + + Parameters + ---------- + x : np.ndarray + Input array. + a1 : float + Peak amplitude (height) + b1 : float + Center position (mean) + c1 : float + Standard deviation (width, spread) + + Returns + ------- + np.ndarray + _description_ + """ + return a1 * np.exp(-((x - b1)/c1) ** 2) def fit_gaussian(H: np.ndarray, V: np.ndarray) -> tuple: + """ Fit Gaussian functions to the horisontal and vertical autocovariance profiles. + + Parameters + ---------- + H : np.ndarray + Horisontal autocovariance profile. + V : np.ndarray + Vertical autocovariance profile. + + Returns + ------- + tuple + HFWHM : float + Full width at half maximum for horisontal profile. + HeSquared : float + 1/e^2 width for horisontal profile. + H_fit_stats : dict + R-squared goodness of fit for horisontal profile. + VFWHM : float + Full width at half maximum for vertical profile. + VeSquared : float + 1/e^2 width for vertical profile. + V_fit_stats : dict + R-squared goodness of fit for vertical profile. + popt_H : array + Optimal parameters for horisontal Gaussian fit. + popt_V : array + Optimal parameters for vertical Gaussian fit. + """ range_H = np.arange(1, H.size + 1) range_V = np.arange(1, V.size + 1) From a046e77ecadfd3cc462ce0f9ceabc2482fe5c4af Mon Sep 17 00:00:00 2001 From: WieraB Date: Sun, 2 Nov 2025 09:48:19 +0000 Subject: [PATCH 09/16] Amended examples in line with the new function structure, added more comments to the examples. --- src/pyvale/examples/specklegen/__init__.py | 8 + .../specklegen/ex1a_random_disks_overlap.py | 98 ++-- .../ex1b_random_disks_reduce_overlap.py | 98 ++-- .../specklegen/ex1c_random_disks_grid.py | 151 ++++++ ...oise_isotropic.py => ex2a_perlin_noise.py} | 106 ++-- .../ex2b_perlin_noise_anisotropic.py | 136 ----- .../examples/specklegen/ex3a_fractal_noise.py | 145 ++++++ .../ex3a_fractal_noise_isotropic.py | 138 ----- .../ex3b_fractal_noise_anisotropic.py | 138 ----- ...ise_isotropic.py => ex4a_simplex_noise.py} | 95 ++-- .../ex4b_simplex_noise_anisotrpic.py | 126 ----- .../autocovariance.jpg | Bin 0 -> 178275 bytes .../frequency_spectrum.jpg | Bin 0 -> 303909 bytes .../pixel_value_histogram.jpg | Bin 0 -> 107478 bytes .../speckle_pattern.jpg | Bin 0 -> 264171 bytes .../autocovariance.jpg | Bin 0 -> 178347 bytes .../frequency_spectrum.jpg | Bin 0 -> 310886 bytes .../pixel_value_histogram.jpg | Bin 0 -> 166066 bytes .../speckle_pattern.jpg | Bin 0 -> 296173 bytes .../autocovariance.jpg | Bin 0 -> 186302 bytes .../frequency_spectrum.jpg | Bin 0 -> 314167 bytes .../pixel_value_histogram.jpg | Bin 0 -> 116266 bytes .../speckle_pattern.jpg | Bin 0 -> 286322 bytes .../autocovariance.jpg | Bin 0 -> 177581 bytes .../frequency_spectrum.jpg | Bin 0 -> 315036 bytes .../pixel_value_histogram.jpg | Bin 0 -> 157794 bytes .../speckle_pattern.jpg | Bin 0 -> 249731 bytes .../autocovariance.jpg | Bin 0 -> 177598 bytes .../frequency_spectrum.jpg | Bin 0 -> 319938 bytes .../pixel_value_histogram.jpg | Bin 0 -> 157290 bytes .../speckle_pattern.jpg | Bin 0 -> 291326 bytes .../autocovariance.jpg | Bin 0 -> 178777 bytes .../frequency_spectrum.jpg | Bin 0 -> 312960 bytes .../pixel_value_histogram.jpg | Bin 0 -> 157480 bytes .../speckle_pattern.jpg | Bin 0 -> 253331 bytes .../examples/specklegen/speckle_generation.py | 489 ++++++++++++++++++ .../specklegen/speckle_pattern_diagnostics.py | 301 +++++++++++ src/pyvale/specklegen/speckle_generation.py | 8 +- 38 files changed, 1297 insertions(+), 740 deletions(-) create mode 100644 src/pyvale/examples/specklegen/__init__.py create mode 100644 src/pyvale/examples/specklegen/ex1c_random_disks_grid.py rename src/pyvale/examples/specklegen/{ex2a_perlin_noise_isotropic.py => ex2a_perlin_noise.py} (54%) delete mode 100644 src/pyvale/examples/specklegen/ex2b_perlin_noise_anisotropic.py create mode 100644 src/pyvale/examples/specklegen/ex3a_fractal_noise.py delete mode 100644 src/pyvale/examples/specklegen/ex3a_fractal_noise_isotropic.py delete mode 100644 src/pyvale/examples/specklegen/ex3b_fractal_noise_anisotropic.py rename src/pyvale/examples/specklegen/{ex4a_simplex_noise_isotropic.py => ex4a_simplex_noise.py} (61%) delete mode 100644 src/pyvale/examples/specklegen/ex4b_simplex_noise_anisotrpic.py create mode 100644 src/pyvale/examples/specklegen/output/ex1a/20_1000_800_8_white_on_black_10_4.0_False/autocovariance.jpg create mode 100644 src/pyvale/examples/specklegen/output/ex1a/20_1000_800_8_white_on_black_10_4.0_False/frequency_spectrum.jpg create mode 100644 src/pyvale/examples/specklegen/output/ex1a/20_1000_800_8_white_on_black_10_4.0_False/pixel_value_histogram.jpg create mode 100644 src/pyvale/examples/specklegen/output/ex1a/20_1000_800_8_white_on_black_10_4.0_False/speckle_pattern.jpg create mode 100644 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100644 src/pyvale/examples/specklegen/output/ex2a/perlin_20_1000_800_8_white_on_black_10/autocovariance.jpg create mode 100644 src/pyvale/examples/specklegen/output/ex2a/perlin_20_1000_800_8_white_on_black_10/frequency_spectrum.jpg create mode 100644 src/pyvale/examples/specklegen/output/ex2a/perlin_20_1000_800_8_white_on_black_10/pixel_value_histogram.jpg create mode 100644 src/pyvale/examples/specklegen/output/ex2a/perlin_20_1000_800_8_white_on_black_10/speckle_pattern.jpg create mode 100644 src/pyvale/examples/specklegen/output/ex3a/fractal_20_1000_800_8_white_on_black_10/autocovariance.jpg create mode 100644 src/pyvale/examples/specklegen/output/ex3a/fractal_20_1000_800_8_white_on_black_10/frequency_spectrum.jpg create mode 100644 src/pyvale/examples/specklegen/output/ex3a/fractal_20_1000_800_8_white_on_black_10/pixel_value_histogram.jpg create mode 100644 src/pyvale/examples/specklegen/output/ex3a/fractal_20_1000_800_8_white_on_black_10/speckle_pattern.jpg create mode 100644 src/pyvale/examples/specklegen/output/ex4a/simplex_20_1000_800_8_white_on_black_10/autocovariance.jpg create mode 100644 src/pyvale/examples/specklegen/output/ex4a/simplex_20_1000_800_8_white_on_black_10/frequency_spectrum.jpg create mode 100644 src/pyvale/examples/specklegen/output/ex4a/simplex_20_1000_800_8_white_on_black_10/pixel_value_histogram.jpg create mode 100644 src/pyvale/examples/specklegen/output/ex4a/simplex_20_1000_800_8_white_on_black_10/speckle_pattern.jpg create mode 100644 src/pyvale/examples/specklegen/speckle_generation.py create mode 100644 src/pyvale/examples/specklegen/speckle_pattern_diagnostics.py diff --git a/src/pyvale/examples/specklegen/__init__.py b/src/pyvale/examples/specklegen/__init__.py new file mode 100644 index 00000000..d55ebac2 --- /dev/null +++ b/src/pyvale/examples/specklegen/__init__.py @@ -0,0 +1,8 @@ +#=============================================================================== +# pyvale: the python validation engine +# License: MIT +# Copyright (C) 2025 The Computer Aided Validation Team +#=============================================================================== + +from .speckle_generation import * +from .speckle_pattern_diagnostics import * \ No newline at end of file diff --git a/src/pyvale/examples/specklegen/ex1a_random_disks_overlap.py b/src/pyvale/examples/specklegen/ex1a_random_disks_overlap.py index 9b2c0bcd..45e082fd 100644 --- a/src/pyvale/examples/specklegen/ex1a_random_disks_overlap.py +++ b/src/pyvale/examples/specklegen/ex1a_random_disks_overlap.py @@ -13,65 +13,44 @@ """ import numpy as np -import argparse +import time import json import sys import os sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) -import pyvale.specklegen as specklegen +# import pyvale.specklegen as specklegen + +import specklegen as specklegen #%% # Here we parse command line arguments to set the speckle pattern parameters. # For ease of use in this example script we set parameter values directly in the # code rather than via bash script. # The parameter responsible for reducing overlap is set to 'False' in this example. -parser = argparse.ArgumentParser(description='Generate random speckle patterns with specified parameters.') -args = parser.parse_args() -args.speckle_size = 25 -args.screen_size_width = 1000 -args.screen_size_height = 800 -args.bit_depth = 8 -args.theme = 'white_on_black' -args.random_seed = 10 -args.sigma = 4.0 -args.reduce_overlap = 'False' -args.output_path = "src/pyvale/examples/specklegen/output/ex1a" -args.attempts = None - -print('Args in simulation:') -print(args) -print('') -print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') -print('') + +speckle_size = 20 +screen_size_width = 1000 +screen_size_height = 800 +bit_depth = 8 +theme = 'white_on_black' +seed = 10 +sigma = 4.0 +reduce_overlap = False +type_gen = "random_disks" +output_path = "src/pyvale/examples/specklegen/output/ex1a" print('Start') -# Extract parameteres and revert to default values if not provided by user -speckle_size = args.speckle_size if args.speckle_size is not None else 5.0 -screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 -screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 -bit_depth = args.bit_depth if args.bit_depth is not None else 8 -theme = args.theme if args.theme is not None else 'white_on_black' -random_seed = args.random_seed if args.random_seed is not None else 10 -sigma = args.sigma if args.sigma is not None else 1.0 -reduce_overlap = args.reduce_overlap -attempts = args.attempts if args.attempts is not None else 100 - -assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." -assert reduce_overlap in ['True', 'False'], "reduce_overlap should be either 'True' or 'False' (a string)." -reduce_overlap_bool = True if reduce_overlap == "True" else False -if reduce_overlap_bool: +if reduce_overlap: print("Reducing overlap between speckles") else: print("Not reducing overlap between speckles") -np.random.seed(random_seed) - -subfolder = f"/{speckle_size}_{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{random_seed}_{sigma}_{reduce_overlap}" +subfolder = f"/{type_gen}_{speckle_size}_{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{seed}_{sigma}_{reduce_overlap}" print(subfolder) -save_path = args.output_path + subfolder +save_path = output_path + subfolder if not os.path.exists(save_path): os.makedirs(save_path) @@ -87,24 +66,46 @@ dynamic_range: int = 2**bit_depth - 1 background_colour = 0 if theme == 'white_on_black' else dynamic_range foreground_colour = dynamic_range if theme == 'white_on_black' else 0 + +feature_size_width = speckle_size +feature_size_height = speckle_size # Generate speckle pattern -image, results = specklegen.generate_speckles(screen_size_width, screen_size_height, - speckle_size, foreground_colour, - total_speckles, reduce_overlap_bool, - bit_depth, background_colour, - sigma, attempts) +time_start = time.time() +image, results = specklegen.generate_speckles(screen_size_width, screen_size_height, + feature_size_width, feature_size_height, + foreground_colour, background_colour, + bit_depth, type_gen, seed, + total_speckles=total_speckles, + reduce_overlap=reduce_overlap, + sigma=sigma) +time_end = time.time() +time_taken = time_end - time_start +print(f"Time taken for speckle generation: {np.round(time_taken, 3)} seconds") # save the speckle placement results np.savetxt(f"{save_path}/speckle_placement_results.csv", results, delimiter=",", header="speckle_number, attempts, overlap(1/0/2), cent_x, cent_y", comments='', fmt=['%d', '%d', '%d', '%.3f', '%.3f']) #%% -# Now we run diagnostics on the generated speckle pattern and save the results. -# Finally we print out the key statistics to the console. +# Now we run diagnostics on the generated speckle pattern and save the results. + +# Finally, we print out the key statistics to the console. + +# The plots are saved in the provided output folder. However, the diagnostic function outputs the matplotlib figures and axes, + +# so the plot formatting could be changed from the default one used by the function. + +# We aim to achieve black-to-white ratio as close to unity as possible. Unity ratio means 50/50 distribution of black and white colours. +# However, in this example, black-to-white ratio considerably deviates from unity. +# It is also visible in the irradiance value histogram. +# The proportion of 0 irradiance values, corresponding to black colour, overweighs the 255 values, corresponding to white colour. +# This is a result of speckle overlap. + print("") print('Starting speckle pattern diagnostics...') -results = specklegen.speckle_pattern_diagnostics(image, dynamic_range, save_path) +results = specklegen.speckle_pattern_statistics(image, dynamic_range) +plots = specklegen.speckle_pattern_plots(image, dynamic_range, save_path) # save the diagnostics results with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: @@ -140,6 +141,7 @@ print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") +# The relative errors beetween the specified speckle size and the speckle size approximated using cautocovariance are calculated. error = np.abs(avg_speckle_size_fwhm - speckle_size) * 100 / speckle_size print(f"Percentage error between requested speckle size and measured speckle size from FWHM: {np.round(error, 3)} %") error = np.abs(avg_speckle_size_e2 - speckle_size) * 100 / speckle_size @@ -147,6 +149,4 @@ np.save(f"{save_path}/image.npy", image) print("") print('End :)') -print("") -print("") print("") \ No newline at end of file diff --git a/src/pyvale/examples/specklegen/ex1b_random_disks_reduce_overlap.py b/src/pyvale/examples/specklegen/ex1b_random_disks_reduce_overlap.py index 968db695..270c5856 100644 --- a/src/pyvale/examples/specklegen/ex1b_random_disks_reduce_overlap.py +++ b/src/pyvale/examples/specklegen/ex1b_random_disks_reduce_overlap.py @@ -13,65 +13,46 @@ """ import numpy as np -import argparse +import time import json import sys import os sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) -import pyvale.specklegen as specklegen +# import pyvale.specklegen as specklegen + +import specklegen as specklegen #%% # Here we parse command line arguments to set the speckle pattern parameters. # For ease of use in this example script we set parameter values directly in the # code rather than via bash script. # The parameter responsible for reducing overlap is set to 'True' in this example. -parser = argparse.ArgumentParser(description='Generate random speckle patterns with specified parameters.') -args = parser.parse_args() -args.speckle_size = 25 -args.screen_size_width = 1000 -args.screen_size_height = 800 -args.bit_depth = 8 -args.theme = 'white_on_black' -args.random_seed = 10 -args.sigma = 4.0 -args.reduce_overlap = 'True' -args.output_path = "src/pyvale/examples/specklegen/output/ex1b" -args.attempts = None - -print('Args in simulation:') -print(args) -print('') -print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') -print('') +# Additionally, we have an extra parameter specifying the maximum number of attempts to place each speckle without overlap. + +speckle_size = 20 +screen_size_width = 1000 +screen_size_height = 800 +bit_depth = 8 +theme = 'white_on_black' +seed = 10 +sigma = 4.0 +reduce_overlap = True +type_gen = "random_disks" +attempts_tot = 300 +output_path = "src/pyvale/examples/specklegen/output/ex1b" print('Start') -# Extract parameteres and revert to default values if not provided by user -speckle_size = args.speckle_size if args.speckle_size is not None else 5.0 -screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 -screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 -bit_depth = args.bit_depth if args.bit_depth is not None else 8 -theme = args.theme if args.theme is not None else 'white_on_black' -random_seed = args.random_seed if args.random_seed is not None else 10 -sigma = args.sigma if args.sigma is not None else 1.0 -reduce_overlap = args.reduce_overlap -attempts = args.attempts if args.attempts is not None else 100 - -assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." -assert reduce_overlap in ['True', 'False'], "reduce_overlap should be either 'True' or 'False' (a string)." -reduce_overlap_bool = True if reduce_overlap == "True" else False -if reduce_overlap_bool: +if reduce_overlap: print("Reducing overlap between speckles") else: print("Not reducing overlap between speckles") -np.random.seed(random_seed) - -subfolder = f"/{speckle_size}_{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{random_seed}_{sigma}_{reduce_overlap}" +subfolder = f"/{type_gen}_{speckle_size}_{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{seed}_{sigma}_{reduce_overlap}" print(subfolder) -save_path = args.output_path + subfolder +save_path = output_path + subfolder if not os.path.exists(save_path): os.makedirs(save_path) @@ -79,6 +60,7 @@ # We calculate parameteres aiming for approximately 50/50 black-to-white ratio. # We now generate the speckle pattern using the specified parameters. # The background and foreground colours are set based on the chosen theme and bit depth. +# We simply pass on one additional parameter to the function. speckle_area = np.pi * (speckle_size / 2) ** 2 total_area = screen_size_width * screen_size_height @@ -88,23 +70,44 @@ background_colour = 0 if theme == 'white_on_black' else dynamic_range foreground_colour = dynamic_range if theme == 'white_on_black' else 0 +feature_size_width = speckle_size +feature_size_height = speckle_size + # Generate speckle pattern -image, results = specklegen.generate_speckles(screen_size_width, screen_size_height, - speckle_size, foreground_colour, - total_speckles, reduce_overlap_bool, - bit_depth, background_colour, - sigma, attempts) +time_start = time.time() +image, results = specklegen.generate_speckles(screen_size_width, screen_size_height, + feature_size_width, feature_size_height, + foreground_colour, background_colour, + bit_depth, type_gen, seed, + total_speckles=total_speckles, + reduce_overlap=reduce_overlap, + sigma=sigma, attempts_tot=attempts_tot) +time_end = time.time() +time_taken = time_end - time_start +print(f"Time taken for speckle generation: {np.round(time_taken, 3)} seconds") # save the speckle placement results np.savetxt(f"{save_path}/speckle_placement_results.csv", results, delimiter=",", header="speckle_number, attempts, overlap(1/0/2), cent_x, cent_y", comments='', fmt=['%d', '%d', '%d', '%.3f', '%.3f']) #%% -# Now we run diagnostics on the generated speckle pattern and save the results. -# Finally we print out the key statistics to the console. +# Now we run diagnostics on the generated speckle pattern and save the results. + +# Finally, we print out the key statistics to the console. + +# The plots are saved in the provided output folder. However, the diagnostic function outputs the matplotlib figures and axes, + +# so the plot formatting could be changed from the default one used by the function. + +# The speckles are placed in such a way as to reduce the overlap between them as much as possible. + +# As a consequence of this, it can be seen that the black-to-white ratio in this example is considerably closer to unity than in the previous example (ex1a). +# This can also be supported by the irradiance value histogram. +# The number of 0 irradiance values, corresponding to black colour, and the 255 values, corresponding to white colour, became much more equal. print("") print('Starting speckle pattern diagnostics...') -results = specklegen.speckle_pattern_diagnostics(image, dynamic_range, save_path) +results = specklegen.speckle_pattern_statistics(image, dynamic_range) +plots = specklegen.speckle_pattern_plots(image, dynamic_range, save_path) # save the diagnostics results with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: @@ -140,6 +143,7 @@ print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") +# The relative errors beetween the specified speckle size and the speckle size approximated using cautocovariance are calculated. error = np.abs(avg_speckle_size_fwhm - speckle_size) * 100 / speckle_size print(f"Percentage error between requested speckle size and measured speckle size from FWHM: {np.round(error, 3)} %") error = np.abs(avg_speckle_size_e2 - speckle_size) * 100 / speckle_size diff --git a/src/pyvale/examples/specklegen/ex1c_random_disks_grid.py b/src/pyvale/examples/specklegen/ex1c_random_disks_grid.py new file mode 100644 index 00000000..fd2d77c5 --- /dev/null +++ b/src/pyvale/examples/specklegen/ex1c_random_disks_grid.py @@ -0,0 +1,151 @@ +# ============================================================================== +# pyvale: the python validation engine +# License: MIT +# Copyright (C) 2025 The Computer Aided Validation Team +# ============================================================================== + +""" +Specklegen: Speckle pattern generation using random disk-shaped speckle placement perturbation from a grid of regularly-placed disk-shaped speckles +================================================================================ +Script to generate a synthetic speckle pattern made from by randomly perturbating a grid of regularly-placed +disk-shaped speckles based on disrete uniform probability distribution, run diagnostics on the generated image, +and save both the image and diagnostics to the selected folder. +""" + +import numpy as np +import time +import json +import sys +import os +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +# import pyvale.specklegen as specklegen + +import specklegen as specklegen + +#%% +# Here we parse command line arguments to set the speckle pattern parameters. +# For ease of use in this example script we set parameter values directly in the +# code rather than via bash script. +# The parameter responsible for reducing overlap is set to 'False' in this example. +# Here we select a different type of speckle generation compared with the previous examples (ex1a and ex1b). +# Additionally, we have an extra parameter specifying the maximum amount to move speckles by during grid perturbation (in pixels). + +speckle_size = 20 +screen_size_width = 1000 +screen_size_height = 800 +bit_depth = 8 +theme = 'white_on_black' +seed = 10 +sigma = 4.0 +reduce_overlap = False +perturbation_max = 12 +type_gen = "random_disks_grid" +output_path = "src/pyvale/examples/specklegen/output/ex1c" + +print('Start') + +assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." + +if reduce_overlap: + print("Reducing overlap between speckles") +else: + print("Not reducing overlap between speckles") + +subfolder = f"/{type_gen}_{speckle_size}_{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{seed}" +print(subfolder) +save_path = output_path + subfolder +if not os.path.exists(save_path): + os.makedirs(save_path) + +#%% +# We calculate parameteres aiming for approximately 50/50 black-to-white ratio. +# We now generate the speckle pattern using the specified parameters. +# The background and foreground colours are set based on the chosen theme and bit depth. +# We simply pass on one additional parameter to the function. + +speckle_area = np.pi * (speckle_size / 2) ** 2 +total_area = screen_size_width * screen_size_height +total_speckles = int((0.5 * total_area) / speckle_area) +print(f"Total number of speckles = {total_speckles}") +dynamic_range: int = 2**bit_depth - 1 +background_colour = 0 if theme == 'white_on_black' else dynamic_range +foreground_colour = dynamic_range if theme == 'white_on_black' else 0 + +feature_size_width = speckle_size +feature_size_height = speckle_size + +# Generate speckle pattern +time_start = time.time() +image, results = specklegen.generate_speckles(screen_size_width, screen_size_height, + feature_size_width, feature_size_height, + foreground_colour, background_colour, + bit_depth, type_gen, seed, + total_speckles=total_speckles, + reduce_overlap=reduce_overlap, + sigma=sigma, perturbation_max=perturbation_max) +time_end = time.time() +time_taken = time_end - time_start +print(f"Time taken for speckle generation: {np.round(time_taken, 3)} seconds") + +# save the speckle placement results +np.savetxt(f"{save_path}/speckle_placement_results.csv", results, delimiter=",", + header="speckle_number, attempts, overlap(1/0/2), cent_x, cent_y", comments='', fmt=['%d', '%d', '%d', '%.3f', '%.3f']) + +#%% +# Now we run diagnostics on the generated speckle pattern and save the results. +# Finally, we print out the key statistics to the console. +# The plots are saved in the provided output folder. However, the diagnostic function outputs the matplotlib figures and axes, +# so the plot formatting could be changed from the default one used by the function. +# The black-to-white ratio is better than achieved in ex1a, when we don’t check for speckle overlap. +# However, it is worse than the value achieved in ex1b, when we do check for speckle overlap. +# On the other hand, we still get the benefit of the improved black-to-white ratio at the reduced computational cost, +# as the runtime in this example is shorter than in ex1b. + +print("") +print('Starting speckle pattern diagnostics...') +results = specklegen.speckle_pattern_statistics(image, dynamic_range) +plots = specklegen.speckle_pattern_plots(image, dynamic_range, save_path) + +# save the diagnostics results +with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: + json.dump(results, f, indent=4) + +ratio = results.get("black_white_ratio", None) +mean_gradient = results.get("mean_intensity_gradient", None) +std_dev = results.get("std_dev_irradiance", None) +avg = results.get("avg_irradiance", None) +contrast = results.get("contrast", None) +entropy = results.get("shannon_entropy", None) +peak_to_mean = results.get("peak_to_mean_ratio", None) +skew = results.get("skewness", None) +kurt = results.get("kurtosis", None) +avg_speckle_size_fwhm = results.get("avg_speckle_size_fwhm", None) +avg_speckle_size_e2 = results.get("avg_speckle_size_e2", None) +H_fit_stats = results.get("H_fit_stats", None) +V_fit_stats = results.get("V_fit_stats", None) + +print("") +print("Speckle statistics:") + +print(f"Black/White ratio: {np.round(ratio, 3)}") +print(f"Mean intensity gradient: {np.round(mean_gradient, 3)}") +print(f"Standard deviation of irradiance values: {np.round(std_dev, 3)}") +print(f"Average irradiance value: {np.round(avg, 3)}") +print(f"Contrast (std/mean): {np.round(contrast, 3)}") +print(f"Skewness: {np.round(skew, 3)}") +print(f"Kurtosis: {np.round(kurt, 3)}") +print(f"Shannon entropy: {np.round(entropy, 3)}") +print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") +print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") +print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") +print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") + +# The relative errors beetween the specified speckle size and the speckle size approximated using cautocovariance are calculated. +error = np.abs(avg_speckle_size_fwhm - speckle_size) * 100 / speckle_size +print(f"Percentage error between requested speckle size and measured speckle size from FWHM: {np.round(error, 3)} %") +error = np.abs(avg_speckle_size_e2 - speckle_size) * 100 / speckle_size +print(f"Percentage error between requested speckle size and measured speckle size from 1/e^2: {np.round(error, 3)} %") +np.save(f"{save_path}/image.npy", image) +print("") +print('End :)') +print("") \ No newline at end of file diff --git a/src/pyvale/examples/specklegen/ex2a_perlin_noise_isotropic.py b/src/pyvale/examples/specklegen/ex2a_perlin_noise.py similarity index 54% rename from src/pyvale/examples/specklegen/ex2a_perlin_noise_isotropic.py rename to src/pyvale/examples/specklegen/ex2a_perlin_noise.py index fdd4081d..725bd6b3 100644 --- a/src/pyvale/examples/specklegen/ex2a_perlin_noise_isotropic.py +++ b/src/pyvale/examples/specklegen/ex2a_perlin_noise.py @@ -9,90 +9,85 @@ ================================================================================ Script to generate a synthetic speckle pattern made using isotropic Perlin noise, run diagnostics on the generated image, and save both the image and diagnostics to the selected folder. Isotroic Perlin noise in this case means that the speckle size is the same in both horisontal and vertical directions. + +This is a gradient-based noise algorithm that generates smooth and continuous random patterns. +It produces a texture with gradually occurring transitions. +Perlin noise achieves this by assigning random gradient vectors to grid points and then smoothly interpolating between them to create natural-looking transitions. """ import numpy as np -import argparse +import time import json import sys import os sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) -import pyvale.specklegen as specklegen +# import pyvale.specklegen as specklegen + +import specklegen as specklegen #%% # Here we parse command line arguments to set the speckle pattern parameters. # For ease of use in this example script we set parameter values directly in the # code rather than via bash script. -# The parameters are set to generate isotropic Perlin noise in this example. The number of periods width- and height-wise are the same. -parser = argparse.ArgumentParser(description='Generate random speckle patterns with specified parameters.') -args = parser.parse_args() - -args.screen_size_width = 1000 -args.screen_size_height = 800 -args.bit_depth = 8 -args.theme = 'white_on_black' -args.octaves = None -args.type_gen = "perlin" -args.res_width = 25 -args.res_height = 25 -args.output_path = "src/pyvale/examples/specklegen/output/ex2a" - -print('Args in simulation:') -print(args) -print('') -print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') -print('') +# The parameters are set to generate isotropic Perlin noise in this example. +# Perlin noise is defined by the number of noise periods width- and height-wise. +# They can be calculated from the corresponding width and height of a feature, which is a speckle size in our case, together with a screen size. +# The noise period is obtained by dividing a screen size by a speckle size. +# It should be noted that the screen size should be a multiple of the noise period number, otherwise the function would produce an error. + +speckle_size = 20 +screen_size_width = 1000 +screen_size_height = 800 +bit_depth = 8 +theme = 'white_on_black' +seed = 10 +type_gen = "perlin" +output_path = "src/pyvale/examples/specklegen/output/ex2a" print('Start') -# Extract parameteres and revert to default values if not provided by user -screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 -screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 -bit_depth = args.bit_depth if args.bit_depth is not None else 8 -theme = args.theme if args.theme is not None else 'white_on_black' -octaves = args.octaves if args.octaves is not None else "Not defined" - -type_gen = args.type_gen -res_width = args.res_width -res_height = args.res_height - -assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." -if type_gen == "perlin": - subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{type_gen}_{res_width}_{res_height}" -elif type_gen == "fractal": - subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{type_gen}_{res_width}_{res_height}_{octaves}" +subfolder = f"/{type_gen}_{speckle_size}_{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{seed}" print(subfolder) -save_path = args.output_path + subfolder +save_path = output_path + subfolder if not os.path.exists(save_path): os.makedirs(save_path) - #%% # We now generate the speckle pattern using the specified parameters. # The background and foreground colours are set based on the chosen theme and bit depth. +# It should be noted that there is no need to calculate the total number of speckles to generate like we did in the previous examples. + dynamic_range: int = 2**bit_depth - 1 background_colour = 0 if theme == 'white_on_black' else dynamic_range foreground_colour = dynamic_range if theme == 'white_on_black' else 0 + +feature_size_width = speckle_size +feature_size_height = speckle_size # Generate speckle pattern -if type_gen == "perlin": - image = specklegen.generate_speckles_perlin_noise(screen_size_width, screen_size_height, - res_height, res_width, - foreground_colour, bit_depth, background_colour, type_gen) -elif type_gen == "fractal": - image = specklegen.generate_speckles_perlin_noise(screen_size_width, screen_size_height, - res_height, res_width, - foreground_colour, bit_depth, background_colour, type_gen, - octaves=octaves) - +time_start = time.time() +image = specklegen.generate_speckles(screen_size_width, screen_size_height, + feature_size_width, feature_size_height, + foreground_colour, background_colour, + bit_depth, type_gen, seed) +time_end = time.time() +time_taken = time_end - time_start +print(f"Time taken for speckle generation: {np.round(time_taken, 3)} seconds") + #%% -# Now we run diagnostics on the generated speckle pattern and save the results. -# Finally we print out the key statistics to the console. +# Now we run diagnostics on the generated speckle pattern and save the results. +# Finally, we print out the key statistics to the console. +# The plots are saved in the provided output folder. However, the diagnostic function outputs the matplotlib figures and axes, +# so the plot formatting could be changed from the default one used by the function. +# The black-to-white ratio is already close to unity, +# so there is no need to perform any additional operations related to speckle overlap reduction, like we did in ex1a, ex1b, and ex1c. + print("") print('Starting speckle pattern diagnostics...') -results = specklegen.speckle_pattern_diagnostics(image, dynamic_range, save_path) +results = specklegen.speckle_pattern_statistics(image, dynamic_range) +plots = specklegen.speckle_pattern_plots(image, dynamic_range, save_path) # save the diagnostics results with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: @@ -128,9 +123,12 @@ print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") +# The relative errors beetween the specified speckle size and the speckle size approximated using cautocovariance are calculated. +error = np.abs(avg_speckle_size_fwhm - speckle_size) * 100 / speckle_size +print(f"Percentage error between requested speckle size and measured speckle size from FWHM: {np.round(error, 3)} %") +error = np.abs(avg_speckle_size_e2 - speckle_size) * 100 / speckle_size +print(f"Percentage error between requested speckle size and measured speckle size from 1/e^2: {np.round(error, 3)} %") np.save(f"{save_path}/image.npy", image) print("") print('End :)') -print("") -print("") print("") \ No newline at end of file diff --git a/src/pyvale/examples/specklegen/ex2b_perlin_noise_anisotropic.py b/src/pyvale/examples/specklegen/ex2b_perlin_noise_anisotropic.py deleted file mode 100644 index f72c2c01..00000000 --- a/src/pyvale/examples/specklegen/ex2b_perlin_noise_anisotropic.py +++ /dev/null @@ -1,136 +0,0 @@ -# ============================================================================== -# pyvale: the python validation engine -# License: MIT -# Copyright (C) 2025 The Computer Aided Validation Team -# ============================================================================== - -""" -Specklegen: Speckle pattern generation using anisotropic Perlin noise -================================================================================ -Script to generate a synthetic speckle pattern made using isotropic Perlin noise, run diagnostics on the generated image, and save both the image -and diagnostics to the selected folder. Anisotroic Perlin noise in this case means that the speckle size is different in horiasontal and vertical directions. -""" - -import numpy as np -import argparse -import json -import sys -import os -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) -import pyvale.specklegen as specklegen - -#%% -# Here we parse command line arguments to set the speckle pattern parameters. -# For ease of use in this example script we set parameter values directly in the -# code rather than via bash script. -# The parameters are set to generate anisotropic Perlin noise in this example. The number of periods width- and height-wise are not the same. -parser = argparse.ArgumentParser(description='Generate random speckle patterns with specified parameters.') -args = parser.parse_args() - -args.screen_size_width = 1000 -args.screen_size_height = 800 -args.bit_depth = 8 -args.theme = 'white_on_black' -args.octaves = None -args.type_gen = "perlin" -args.res_width = 25 -args.res_height = 100 -args.output_path = "src/pyvale/examples/specklegen/output/ex2b" - -print('Args in simulation:') -print(args) -print('') -print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') -print('') - -print('Start') - -# Extract parameteres and revert to default values if not provided by user -screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 -screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 -bit_depth = args.bit_depth if args.bit_depth is not None else 8 -theme = args.theme if args.theme is not None else 'white_on_black' -octaves = args.octaves if args.octaves is not None else "Not defined" - -type_gen = args.type_gen -res_width = args.res_width -res_height = args.res_height - -assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." -assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." - -if type_gen == "perlin": - subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{type_gen}_{res_width}_{res_height}" -elif type_gen == "fractal": - subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{type_gen}_{res_width}_{res_height}_{octaves}" -print(subfolder) -save_path = args.output_path + subfolder -if not os.path.exists(save_path): - os.makedirs(save_path) - - -#%% -# We now generate the speckle pattern using the specified parameters. -# The background and foreground colours are set based on the chosen theme and bit depth. -dynamic_range: int = 2**bit_depth - 1 -background_colour = 0 if theme == 'white_on_black' else dynamic_range -foreground_colour = dynamic_range if theme == 'white_on_black' else 0 - -# Generate speckle pattern -if type_gen == "perlin": - image = specklegen.generate_speckles_perlin_noise(screen_size_width, screen_size_height, - res_height, res_width, - foreground_colour, bit_depth, background_colour, type_gen) -elif type_gen == "fractal": - image = specklegen.generate_speckles_perlin_noise(screen_size_width, screen_size_height, - res_height, res_width, - foreground_colour, bit_depth, background_colour, type_gen, - octaves=octaves) - -#%% -# Now we run diagnostics on the generated speckle pattern and save the results. -# Finally we print out the key statistics to the console. -print("") -print('Starting speckle pattern diagnostics...') -results = specklegen.speckle_pattern_diagnostics(image, dynamic_range, save_path) - -# save the diagnostics results -with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: - json.dump(results, f, indent=4) - -ratio = results.get("black_white_ratio", None) -mean_gradient = results.get("mean_intensity_gradient", None) -std_dev = results.get("std_dev_irradiance", None) -avg = results.get("avg_irradiance", None) -contrast = results.get("contrast", None) -entropy = results.get("shannon_entropy", None) -peak_to_mean = results.get("peak_to_mean_ratio", None) -skew = results.get("skewness", None) -kurt = results.get("kurtosis", None) -avg_speckle_size_fwhm = results.get("avg_speckle_size_fwhm", None) -avg_speckle_size_e2 = results.get("avg_speckle_size_e2", None) -H_fit_stats = results.get("H_fit_stats", None) -V_fit_stats = results.get("V_fit_stats", None) - -print("") -print("Speckle statistics:") - -print(f"Black/White ratio: {np.round(ratio, 3)}") -print(f"Mean intensity gradient: {np.round(mean_gradient, 3)}") -print(f"Standard deviation of irradiance values: {np.round(std_dev, 3)}") -print(f"Average irradiance value: {np.round(avg, 3)}") -print(f"Contrast (std/mean): {np.round(contrast, 3)}") -print(f"Skewness: {np.round(skew, 3)}") -print(f"Kurtosis: {np.round(kurt, 3)}") -print(f"Shannon entropy: {np.round(entropy, 3)}") -print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") -print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") -print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") -print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") - -np.save(f"{save_path}/image.npy", image) -print("") -print('End :)') -print("") -print("") -print("") \ No newline at end of file diff --git a/src/pyvale/examples/specklegen/ex3a_fractal_noise.py b/src/pyvale/examples/specklegen/ex3a_fractal_noise.py new file mode 100644 index 00000000..6e0d298c --- /dev/null +++ b/src/pyvale/examples/specklegen/ex3a_fractal_noise.py @@ -0,0 +1,145 @@ +# ============================================================================== +# pyvale: the python validation engine +# License: MIT +# Copyright (C) 2025 The Computer Aided Validation Team +# ============================================================================== + +""" +Specklegen: Speckle pattern generation using isotropic fractal noise +================================================================================ +Script to generate a synthetic speckle pattern made using isotropic fractal noise, run diagnostics on the generated image, and save both the image +and diagnostics to the selected folder. Isotroic fractal noise in this case means that the speckle size is the same in both horisontal and vertical directions. + +The fractal noise pattern is usually characterised by self-similarity across multiple scales. +It is commonly used to produce realistic, organic textures. +Fractal noise combines smooth but irregular variations at different levels of detail by layering several frequencies of Perlin noise (octaves), +each with its own amplitude. +""" + +import numpy as np +import time +import json +import sys +import os +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +# import pyvale.specklegen as specklegen + +import specklegen as specklegen + +#%% +# Here we parse command line arguments to set the speckle pattern parameters. +# For ease of use in this example script we set parameter values directly in the +# code rather than via bash script. +# The parameters are set to generate isotropic fractal noise in this example. +# Fractal noise is defined by the number of noise periods width- and height-wise. +# They can be calculated from the corresponding width and height of a feature, which is a speckle size in our case, together with a screen size. +# The noise period (res) is obtained by dividing a screen size by a speckle size. + +# Additionally, we see two new parameters here: octaves and lacunarity. +# Octaves define a number of detail levels in the generated pattern. +# Lacunarity is a frequency factor between two octaves. It essentially defines the amount of detail added or removed at each octave. +# It should be noted that the screen size should be a multiple of the lacunarity^(octaves-1)*res, otherwise the function would produce an error. +# Consequently, the restrictions placed on the relationship between octaves, lacunarity, speckle and screen sizes make the parameter definition more complex. + +speckle_size = 20 +screen_size_width = 1000 +screen_size_height = 800 +bit_depth = 8 +theme = 'white_on_black' +seed = 10 +type_gen = "fractal" +octaves = 3 +lacunarity = 2 +output_path = "src/pyvale/examples/specklegen/output/ex3a" + +print('Start') + +assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." + +subfolder = f"/{type_gen}_{speckle_size}_{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{seed}" +print(subfolder) +save_path = output_path + subfolder +if not os.path.exists(save_path): + os.makedirs(save_path) + +#%% +# We now generate the speckle pattern using the specified parameters. +# The background and foreground colours are set based on the chosen theme and bit depth. +# We simply pass on the two additional parameters to the function. + +dynamic_range: int = 2**bit_depth - 1 +background_colour = 0 if theme == 'white_on_black' else dynamic_range +foreground_colour = dynamic_range if theme == 'white_on_black' else 0 + +feature_size_width = speckle_size +feature_size_height = speckle_size + +# Generate speckle pattern +time_start = time.time() +image = specklegen.generate_speckles(screen_size_width, screen_size_height, + feature_size_width, feature_size_height, + foreground_colour, background_colour, + bit_depth, type_gen, seed, + octaves=octaves, lacunarity=lacunarity) +time_end = time.time() +time_taken = time_end - time_start +print(f"Time taken for speckle generation: {np.round(time_taken, 3)} seconds") + +#%% +# Now we run diagnostics on the generated speckle pattern and save the results. +# Finally, we print out the key statistics to the console. +# The plots are saved in the provided output folder. However, the diagnostic function outputs the matplotlib figures and axes, +# so the plot formatting could be changed from the default one used by the function. +# The black-to-white ratio is already close to unity, +# so there is no need to perform any additional operations related to speckle overlap reduction, like we did in ex1a, ex1b, and ex1c. +# Compared with the previous example (ex2a), the generated speckle pattern does exhibit more textured appearance. +# However, this makes the pattern less realistic and hence less suitable for our applications. + +print("") +print('Starting speckle pattern diagnostics...') +results = specklegen.speckle_pattern_statistics(image, dynamic_range) +plots = specklegen.speckle_pattern_plots(image, dynamic_range, save_path) + +# save the diagnostics results +with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: + json.dump(results, f, indent=4) + +ratio = results.get("black_white_ratio", None) +mean_gradient = results.get("mean_intensity_gradient", None) +std_dev = results.get("std_dev_irradiance", None) +avg = results.get("avg_irradiance", None) +contrast = results.get("contrast", None) +entropy = results.get("shannon_entropy", None) +peak_to_mean = results.get("peak_to_mean_ratio", None) +skew = results.get("skewness", None) +kurt = results.get("kurtosis", None) +avg_speckle_size_fwhm = results.get("avg_speckle_size_fwhm", None) +avg_speckle_size_e2 = results.get("avg_speckle_size_e2", None) +H_fit_stats = results.get("H_fit_stats", None) +V_fit_stats = results.get("V_fit_stats", None) + +print("") +print("Speckle statistics:") + +print(f"Black/White ratio: {np.round(ratio, 3)}") +print(f"Mean intensity gradient: {np.round(mean_gradient, 3)}") +print(f"Standard deviation of irradiance values: {np.round(std_dev, 3)}") +print(f"Average irradiance value: {np.round(avg, 3)}") +print(f"Contrast (std/mean): {np.round(contrast, 3)}") +print(f"Skewness: {np.round(skew, 3)}") +print(f"Kurtosis: {np.round(kurt, 3)}") +print(f"Shannon entropy: {np.round(entropy, 3)}") +print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") +print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") +print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") +print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") + +# The relative errors beetween the specified speckle size and the speckle size approximated using cautocovariance are calculated. +error = np.abs(avg_speckle_size_fwhm - speckle_size) * 100 / speckle_size +print(f"Percentage error between requested speckle size and measured speckle size from FWHM: {np.round(error, 3)} %") +error = np.abs(avg_speckle_size_e2 - speckle_size) * 100 / speckle_size +print(f"Percentage error between requested speckle size and measured speckle size from 1/e^2: {np.round(error, 3)} %") +np.save(f"{save_path}/image.npy", image) +print("") +print('End :)') +print("") \ No newline at end of file diff --git a/src/pyvale/examples/specklegen/ex3a_fractal_noise_isotropic.py b/src/pyvale/examples/specklegen/ex3a_fractal_noise_isotropic.py deleted file mode 100644 index 85077fbc..00000000 --- a/src/pyvale/examples/specklegen/ex3a_fractal_noise_isotropic.py +++ /dev/null @@ -1,138 +0,0 @@ -# ============================================================================== -# pyvale: the python validation engine -# License: MIT -# Copyright (C) 2025 The Computer Aided Validation Team -# ============================================================================== - -""" -Specklegen: Speckle pattern generation using isotropic fractal noise -================================================================================ -Script to generate a synthetic speckle pattern made using isotropic fractal noise, run diagnostics on the generated image, and save both the image -and diagnostics to the selected folder. Isotroic fractal noise in this case means that the speckle size is the same in both horisontal and vertical directions. -""" - -import numpy as np -import argparse -import json -import sys -import os -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) -import pyvale.specklegen as specklegen - -#%% -# Here we parse command line arguments to set the speckle pattern parameters. -# For ease of use in this example script we set parameter values directly in the -# code rather than via bash script. -# The parameters are set to generate isotropic fractal noise in this example. The number of periods width- and height-wise are the same. -# The number of octaves is set to 3 to generate fractal noise. This is an additioal parameter compared to the Perlin noise examples. -# This noise involves layering multiple frequencies of Perlin noise (which are referred to as octaves) to create a more complex pattern. -parser = argparse.ArgumentParser(description='Generate random speckle patterns with specified parameters.') -args = parser.parse_args() - -args.screen_size_width = 1000 -args.screen_size_height = 800 -args.bit_depth = 8 -args.theme = 'white_on_black' -args.octaves = 3 -args.type_gen = "fractal" -args.res_width = 25 -args.res_height = 25 -args.output_path = "src/pyvale/examples/specklegen/output/ex3a" - -print('Args in simulation:') -print(args) -print('') -print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') -print('') - -print('Start') - -# Extract parameteres and revert to default values if not provided by user -screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 -screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 -bit_depth = args.bit_depth if args.bit_depth is not None else 8 -theme = args.theme if args.theme is not None else 'white_on_black' -octaves = args.octaves if args.octaves is not None else "Not defined" - -type_gen = args.type_gen -res_width = args.res_width -res_height = args.res_height - -assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." -assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." - -if type_gen == "perlin": - subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{type_gen}_{res_width}_{res_height}" -elif type_gen == "fractal": - subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{type_gen}_{res_width}_{res_height}_{octaves}" -print(subfolder) -save_path = args.output_path + subfolder -if not os.path.exists(save_path): - os.makedirs(save_path) - - -#%% -# We now generate the speckle pattern using the specified parameters. -# The background and foreground colours are set based on the chosen theme and bit depth. -dynamic_range: int = 2**bit_depth - 1 -background_colour = 0 if theme == 'white_on_black' else dynamic_range -foreground_colour = dynamic_range if theme == 'white_on_black' else 0 - -# Generate speckle pattern -if type_gen == "perlin": - image = specklegen.generate_speckles_perlin_noise(screen_size_width, screen_size_height, - res_height, res_width, - foreground_colour, bit_depth, background_colour, type_gen) -elif type_gen == "fractal": - image = specklegen.generate_speckles_perlin_noise(screen_size_width, screen_size_height, - res_height, res_width, - foreground_colour, bit_depth, background_colour, type_gen, - octaves=octaves) - -#%% -# Now we run diagnostics on the generated speckle pattern and save the results. -# Finally we print out the key statistics to the console. -print("") -print('Starting speckle pattern diagnostics...') -results = specklegen.speckle_pattern_diagnostics(image, dynamic_range, save_path) - -# save the diagnostics results -with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: - json.dump(results, f, indent=4) - -ratio = results.get("black_white_ratio", None) -mean_gradient = results.get("mean_intensity_gradient", None) -std_dev = results.get("std_dev_irradiance", None) -avg = results.get("avg_irradiance", None) -contrast = results.get("contrast", None) -entropy = results.get("shannon_entropy", None) -peak_to_mean = results.get("peak_to_mean_ratio", None) -skew = results.get("skewness", None) -kurt = results.get("kurtosis", None) -avg_speckle_size_fwhm = results.get("avg_speckle_size_fwhm", None) -avg_speckle_size_e2 = results.get("avg_speckle_size_e2", None) -H_fit_stats = results.get("H_fit_stats", None) -V_fit_stats = results.get("V_fit_stats", None) - -print("") -print("Speckle statistics:") - -print(f"Black/White ratio: {np.round(ratio, 3)}") -print(f"Mean intensity gradient: {np.round(mean_gradient, 3)}") -print(f"Standard deviation of irradiance values: {np.round(std_dev, 3)}") -print(f"Average irradiance value: {np.round(avg, 3)}") -print(f"Contrast (std/mean): {np.round(contrast, 3)}") -print(f"Skewness: {np.round(skew, 3)}") -print(f"Kurtosis: {np.round(kurt, 3)}") -print(f"Shannon entropy: {np.round(entropy, 3)}") -print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") -print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") -print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") -print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") - -np.save(f"{save_path}/image.npy", image) -print("") -print('End :)') -print("") -print("") -print("") \ No newline at end of file diff --git a/src/pyvale/examples/specklegen/ex3b_fractal_noise_anisotropic.py b/src/pyvale/examples/specklegen/ex3b_fractal_noise_anisotropic.py deleted file mode 100644 index f2869f52..00000000 --- a/src/pyvale/examples/specklegen/ex3b_fractal_noise_anisotropic.py +++ /dev/null @@ -1,138 +0,0 @@ -# ============================================================================== -# pyvale: the python validation engine -# License: MIT -# Copyright (C) 2025 The Computer Aided Validation Team -# ============================================================================== - - -""" -Specklegen: Speckle pattern generation using anisotropic fractal noise -================================================================================ -Script to generate a synthetic speckle pattern made using isotropic fractal noise, run diagnostics on the generated image, and save both the image -and diagnostics to the selected folder. Anisotroic fractal noise in this case means that the speckle size is different in horisontal and vertical directions. -""" - -import numpy as np -import argparse -import json -import sys -import os -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) -import pyvale.specklegen as specklegen - -#%% -# Here we parse command line arguments to set the speckle pattern parameters. -# For ease of use in this example script we set parameter values directly in the -# code rather than via bash script. -# The parameters are set to generate anisotropic fractal noise in this example. The number of periods width- and height-wise are different. -# The number of octaves is set to 3 to generate fractal noise. This is an additioal parameter compared to the Perlin noise examples. -parser = argparse.ArgumentParser(description='Generate random speckle patterns with specified parameters.') -args = parser.parse_args() - -args.screen_size_width = 1000 -args.screen_size_height = 800 -args.bit_depth = 8 -args.theme = 'white_on_black' -args.octaves = 3 -args.type_gen = "fractal" -args.res_width = 10 -args.res_height = 25 -args.output_path = "src/pyvale/examples/specklegen/output/ex3b" - -print('Args in simulation:') -print(args) -print('') -print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') -print('') - -print('Start') - -# Extract parameteres and revert to default values if not provided by user -screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 -screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 -bit_depth = args.bit_depth if args.bit_depth is not None else 8 -theme = args.theme if args.theme is not None else 'white_on_black' -octaves = args.octaves if args.octaves is not None else "Not defined" - -type_gen = args.type_gen -res_width = args.res_width -res_height = args.res_height - -assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." -assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." - -if type_gen == "perlin": - subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{type_gen}_{res_width}_{res_height}" -elif type_gen == "fractal": - subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{type_gen}_{res_width}_{res_height}_{octaves}" -print(subfolder) -save_path = args.output_path + subfolder -if not os.path.exists(save_path): - os.makedirs(save_path) - - -#%% -# We now generate the speckle pattern using the specified parameters. -# The background and foreground colours are set based on the chosen theme and bit depth. -dynamic_range: int = 2**bit_depth - 1 -background_colour = 0 if theme == 'white_on_black' else dynamic_range -foreground_colour = dynamic_range if theme == 'white_on_black' else 0 - -# Generate speckle pattern -if type_gen == "perlin": - image = specklegen.generate_speckles_perlin_noise(screen_size_width, screen_size_height, - res_height, res_width, - foreground_colour, bit_depth, background_colour, type_gen) -elif type_gen == "fractal": - image = specklegen.generate_speckles_perlin_noise(screen_size_width, screen_size_height, - res_height, res_width, - foreground_colour, bit_depth, background_colour, type_gen, - octaves=octaves) - -#%% -# Now we run diagnostics on the generated speckle pattern and save the results. -# Finally we print out the key statistics to the console. -print("") -print('Starting speckle pattern diagnostics...') -results = specklegen.speckle_pattern_diagnostics(image, dynamic_range, save_path) - -# save the diagnostics results -with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: - json.dump(results, f, indent=4) - -ratio = results.get("black_white_ratio", None) -mean_gradient = results.get("mean_intensity_gradient", None) -std_dev = results.get("std_dev_irradiance", None) -avg = results.get("avg_irradiance", None) -contrast = results.get("contrast", None) -entropy = results.get("shannon_entropy", None) -peak_to_mean = results.get("peak_to_mean_ratio", None) -skew = results.get("skewness", None) -kurt = results.get("kurtosis", None) -avg_speckle_size_fwhm = results.get("avg_speckle_size_fwhm", None) -avg_speckle_size_e2 = results.get("avg_speckle_size_e2", None) -H_fit_stats = results.get("H_fit_stats", None) -V_fit_stats = results.get("V_fit_stats", None) - -print("") -print("Speckle statistics:") - -print(f"Black/White ratio: {np.round(ratio, 3)}") -print(f"Mean intensity gradient: {np.round(mean_gradient, 3)}") -print(f"Standard deviation of irradiance values: {np.round(std_dev, 3)}") -print(f"Average irradiance value: {np.round(avg, 3)}") -print(f"Contrast (std/mean): {np.round(contrast, 3)}") -print(f"Skewness: {np.round(skew, 3)}") -print(f"Kurtosis: {np.round(kurt, 3)}") -print(f"Shannon entropy: {np.round(entropy, 3)}") -print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") -print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") -print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") -print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") - -np.save(f"{save_path}/image.npy", image) -print("") -print('End :)') -print("") -print("") -print("") \ No newline at end of file diff --git a/src/pyvale/examples/specklegen/ex4a_simplex_noise_isotropic.py b/src/pyvale/examples/specklegen/ex4a_simplex_noise.py similarity index 61% rename from src/pyvale/examples/specklegen/ex4a_simplex_noise_isotropic.py rename to src/pyvale/examples/specklegen/ex4a_simplex_noise.py index c8b4cd57..f1ca62da 100644 --- a/src/pyvale/examples/specklegen/ex4a_simplex_noise_isotropic.py +++ b/src/pyvale/examples/specklegen/ex4a_simplex_noise.py @@ -4,83 +4,84 @@ # Copyright (C) 2025 The Computer Aided Validation Team # ============================================================================== - """ Specklegen: Speckle pattern generation using isotropic Simplex noise ================================================================================ Script to generate a synthetic speckle pattern made using isotropic Simplex noise, run diagnostics on the generated image, and save both the image and diagnostics to the selected folder. Isotroic Simplex noise in this case means that the speckle size is the same in both horisontal and vertical directions. + +Simplex noise is an enhanced version of Perlin noise that aims to produce more consistent and isotropic noise patterns. """ import numpy as np -import argparse +import time import json import sys import os sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) -import pyvale.specklegen as specklegen +# import pyvale.specklegen as specklegen + +import specklegen as specklegen #%% # Here we parse command line arguments to set the speckle pattern parameters. # For ease of use in this example script we set parameter values directly in the # code rather than via bash script. -# The parameters are set to generate isotropic Simplex noise in this example. -# For the Simplex noise, the speckle size is defined directly via speckles size width and speckle size height parameters. -# This is in contrast to the Perlin and fractal noise examples where the speckle size is defined via the number of periods width- and height-wise. -parser = argparse.ArgumentParser(description='Generate random speckle patterns with specified parameters.') -args = parser.parse_args() - -args.screen_size_width = 1000 -args.screen_size_height = 800 -args.bit_depth = 8 -args.theme = 'white_on_black' -args.speckle_size_width = 20 -args.speckle_size_height = 20 -args.seed = 1234 -args.output_path = "src/pyvale/examples/specklegen/output/ex4a" - -print('Args in simulation:') -print(args) -print('') -print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') -print('') +# The parameters are set to generate isotropic Simplex noise in this example. +# Contrary to the Perlin (ex2a) and fractal (ex3a) noise, there are no restrictions placed on the parameters defining Simplex noise. + +speckle_size = 20 +screen_size_width = 1000 +screen_size_height = 800 +bit_depth = 8 +theme = 'white_on_black' +seed = 10 +type_gen = "simplex" +output_path = "src/pyvale/examples/specklegen/output/ex4a" print('Start') -# Extract parameteres and revert to default values if not provided by user -speckle_size_width = args.speckle_size_width if args.speckle_size_width is not None else 5.0 -speckle_size_height = args.speckle_size_height if args.speckle_size_height is not None else 5.0 -screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 -screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 -bit_depth = args.bit_depth if args.bit_depth is not None else 8 -theme = args.theme if args.theme is not None else 'white_on_black' -seed = args.seed if args.seed is not None else 1234 - -assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." -subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{speckle_size_width}_{speckle_size_height}_{seed}" +subfolder = f"/{type_gen}_{speckle_size}_{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{seed}" print(subfolder) -save_path = args.output_path + subfolder +save_path = output_path + subfolder if not os.path.exists(save_path): os.makedirs(save_path) +#%% +# We now generate the speckle pattern using the specified parameters. +# The background and foreground colours are set based on the chosen theme and bit depth. + dynamic_range: int = 2**bit_depth - 1 background_colour = 0 if theme == 'white_on_black' else dynamic_range foreground_colour = dynamic_range if theme == 'white_on_black' else 0 + +feature_size_width = speckle_size +feature_size_height = speckle_size # Generate speckle pattern -image = specklegen.generate_speckles_simplex_noise(screen_size_width, screen_size_height, - foreground_colour, - bit_depth, background_colour, - feature_size_width=speckle_size_width, - feature_size_height=speckle_size_height, - seed=seed) - -# Diagnostics +time_start = time.time() +image = specklegen.generate_speckles(screen_size_width, screen_size_height, + feature_size_width, feature_size_height, + foreground_colour, background_colour, + bit_depth, type_gen, seed) +time_end = time.time() +time_taken = time_end - time_start +print(f"Time taken for speckle generation: {np.round(time_taken, 3)} seconds") + +#%% +# Now we run diagnostics on the generated speckle pattern and save the results. +# Finally, we print out the key statistics to the console. +# The plots are saved in the provided output folder. However, the diagnostic function outputs the matplotlib figures and axes, +# so the plot formatting could be changed from the default one used by the function. +# The black-to-white ratio is already close to unity, +# so there is no need to perform any additional operations related to speckle overlap reduction, like we did in ex1a, ex1b, and ex1c. + print("") print('Starting speckle pattern diagnostics...') -results = specklegen.speckle_pattern_diagnostics(image, dynamic_range, save_path) +results = specklegen.speckle_pattern_statistics(image, dynamic_range) +plots = specklegen.speckle_pattern_plots(image, dynamic_range, save_path) # save the diagnostics results with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: @@ -116,7 +117,7 @@ print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") -speckle_size = (speckle_size_width + speckle_size_height) / 2 +# The relative errors beetween the specified speckle size and the speckle size approximated using cautocovariance are calculated. error = np.abs(avg_speckle_size_fwhm - speckle_size) * 100 / speckle_size print(f"Percentage error between requested speckle size and measured speckle size from FWHM: {np.round(error, 3)} %") error = np.abs(avg_speckle_size_e2 - speckle_size) * 100 / speckle_size @@ -124,6 +125,4 @@ np.save(f"{save_path}/image.npy", image) print("") print('End :)') -print("") -print("") -print("") +print("") \ No newline at end of file diff --git a/src/pyvale/examples/specklegen/ex4b_simplex_noise_anisotrpic.py b/src/pyvale/examples/specklegen/ex4b_simplex_noise_anisotrpic.py deleted file mode 100644 index de3cb3cf..00000000 --- a/src/pyvale/examples/specklegen/ex4b_simplex_noise_anisotrpic.py +++ /dev/null @@ -1,126 +0,0 @@ -# ============================================================================== -# pyvale: the python validation engine -# License: MIT -# Copyright (C) 2025 The Computer Aided Validation Team -# ============================================================================== - -""" -Specklegen: Speckle pattern generation using anisotropic Simplex noise -================================================================================ -Script to generate a synthetic speckle pattern made using anisotropic Simplex noise, run diagnostics on the generated image, and save both the image -and diagnostics to the selected folder. Anisotroic Simplex noise in this case means that the speckle size is different in horisontal and vertical directions. -""" - -import numpy as np -import argparse -import json -import sys -import os -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) -import pyvale.specklegen as specklegen - -#%% -# Here we parse command line arguments to set the speckle pattern parameters. -# For ease of use in this example script we set parameter values directly in the -# code rather than via bash script. -# The parameters are set to generate anisotropic Simplex noise in this example. -parser = argparse.ArgumentParser(description='Generate random speckle patterns with specified parameters.') -args = parser.parse_args() - -args.screen_size_width = 1000 -args.screen_size_height = 800 -args.bit_depth = 8 -args.theme = 'white_on_black' -args.speckle_size_width = 10 -args.speckle_size_height = 30 -args.seed = 1234 -args.output_path = "src/pyvale/examples/specklegen/output/ex4b" - -print('Args in simulation:') -print(args) -print('') -print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') -print('') - -print('Start') - -# Extract parameteres and revert to default values if not provided by user -speckle_size_width = args.speckle_size_width if args.speckle_size_width is not None else 5.0 -speckle_size_height = args.speckle_size_height if args.speckle_size_height is not None else 5.0 -screen_size_width = args.screen_size_width if args.screen_size_width is not None else 500 -screen_size_height = args.screen_size_height if args.screen_size_height is not None else 400 -bit_depth = args.bit_depth if args.bit_depth is not None else 8 -theme = args.theme if args.theme is not None else 'white_on_black' -seed = args.seed if args.seed is not None else 1234 - -assert bit_depth in [8, 16], "Bit depth should be either 8 or 16." -assert theme in ['black_on_white', 'white_on_black'], "Theme should be either 'black_on_white' or 'white_on_black'." - -subfolder = f"/{screen_size_width}_{screen_size_height}_{bit_depth}_{theme}_{speckle_size_width}_{speckle_size_height}_{seed}" -print(subfolder) -save_path = args.output_path + subfolder -if not os.path.exists(save_path): - os.makedirs(save_path) - -dynamic_range: int = 2**bit_depth - 1 -background_colour = 0 if theme == 'white_on_black' else dynamic_range -foreground_colour = dynamic_range if theme == 'white_on_black' else 0 - -# Generate speckle pattern -image = specklegen.generate_speckles_simplex_noise(screen_size_width, screen_size_height, - foreground_colour, - bit_depth, background_colour, - feature_size_width=speckle_size_width, - feature_size_height=speckle_size_height, - seed=seed) - -# Diagnostics -print("") -print('Starting speckle pattern diagnostics...') -results = specklegen.speckle_pattern_diagnostics(image, dynamic_range, save_path) - -# save the diagnostics results -with open(f"{save_path}/speckle_pattern_diagnostics.json", 'w') as f: - json.dump(results, f, indent=4) - -ratio = results.get("black_white_ratio", None) -mean_gradient = results.get("mean_intensity_gradient", None) -std_dev = results.get("std_dev_irradiance", None) -avg = results.get("avg_irradiance", None) -contrast = results.get("contrast", None) -entropy = results.get("shannon_entropy", None) -peak_to_mean = results.get("peak_to_mean_ratio", None) -skew = results.get("skewness", None) -kurt = results.get("kurtosis", None) -avg_speckle_size_fwhm = results.get("avg_speckle_size_fwhm", None) -avg_speckle_size_e2 = results.get("avg_speckle_size_e2", None) -H_fit_stats = results.get("H_fit_stats", None) -V_fit_stats = results.get("V_fit_stats", None) - -print("") -print("Speckle statistics:") - -print(f"Black/White ratio: {np.round(ratio, 3)}") -print(f"Mean intensity gradient: {np.round(mean_gradient, 3)}") -print(f"Standard deviation of irradiance values: {np.round(std_dev, 3)}") -print(f"Average irradiance value: {np.round(avg, 3)}") -print(f"Contrast (std/mean): {np.round(contrast, 3)}") -print(f"Skewness: {np.round(skew, 3)}") -print(f"Kurtosis: {np.round(kurt, 3)}") -print(f"Shannon entropy: {np.round(entropy, 3)}") -print(f"Peak to mean ratio: {np.round(peak_to_mean, 3)}") -print(f"Average speckle size (full width at half maximum): {np.round(avg_speckle_size_fwhm, 3)} pixels") -print(f"Average speckle size (1/e^2): {np.round(avg_speckle_size_e2, 3)} pixels") -print(f"R_squared: Horisontal fit: {np.round(H_fit_stats['R_squared'], 3)}, Vertical fit: {np.round(V_fit_stats['R_squared'], 3)}") - -speckle_size = (speckle_size_width + speckle_size_height) / 2 -error = np.abs(avg_speckle_size_fwhm - speckle_size) * 100 / speckle_size -print(f"Percentage error between requested speckle size and measured speckle size from FWHM: {np.round(error, 3)} %") -error = np.abs(avg_speckle_size_e2 - speckle_size) * 100 / speckle_size -print(f"Percentage error between requested speckle size and measured speckle size from 1/e^2: {np.round(error, 3)} %") -np.save(f"{save_path}/image.npy", image) -print("") -print('End :)') -print("") -print("") -print("") diff --git a/src/pyvale/examples/specklegen/output/ex1a/20_1000_800_8_white_on_black_10_4.0_False/autocovariance.jpg b/src/pyvale/examples/specklegen/output/ex1a/20_1000_800_8_white_on_black_10_4.0_False/autocovariance.jpg new file mode 100644 index 0000000000000000000000000000000000000000..58fda66241cee41963c0af35a94340531106c08b GIT binary patch literal 178275 zcmeGE2Ut_vwgwC@YLFH{kWNslRHaA}2#SD+RHaClq7-QY(gHycQ93A~hy*DDQXeefK%{+;Yx6|Nq?keLK&yR+72aO6DAM%rV|E<`{$cg*Xi` zT+`6f07$@>7>O7F5XS*EfQph5LPgSJ;P@OnIOGC>>N5>|0n(MUGKl~y# z0gO}>g;eBZBxe9pMiMec5@H(w0{{|=pS>j^0sitLAtfWHpoCDJpr!$DC}#jjNyx}Z z$;l`v$jQOGgTU(mIU@zr>GPK;Pa52YobhCqdYJHritlnsGmGH>mS5WTe&`A6Q><+4 z90Gzu!e>QfWG~3cD=4a6QPpv<#AnZwl2cy1OwG*7&Uu@gm;dfVX<2ziWmR=eOKV$uM`u^}$HAfDkx|T-uVXW_ zbMxO9ek?96Z*Fbx?C$L!9O8bCiv%G1by&ZT>~F@!2#$-CoScjt@^f4yq`u%m#z;x6qy0FS)NJKyF{ROJN=EtVd19ay^hkOW9@Ono|^n^Nud?Db(M 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