Computer-Vision-Analysis-YHSA/data_generation.py at master · grenn24/Computer-Vision-Analysis-YHSA · GitHub

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import cv2
import numpy as np
import matplotlib.pyplot as plt
import sys
import time

import data_extraction
import oversegmentation
import watershed
import fast_Fourier_transform
import image_processing

# Set image files and segmentation method
if len(sys.argv) > 1:
    image_name = sys.argv[1]
    assert isinstance(image_name, str), 'Incorrect first variable type passed!'
    seg_method = sys.argv[2]
    assert isinstance(seg_method, str), 'Incorrect second variable type passed!'
    try:
        seg_cmap = sys.argv[3]
    except:
        seg_cmap = None
else:
    image_name = 'IHPC_cropped'
    seg_method = 'otsu'

print('Segmenting {} image based on {} method...'.format(image_name, seg_method))

# Set parameters and load image acoording to image_name
if image_name == 'IHPC':
    image = cv2.imread(
        'Data/' + image_name + '.png')
    image_ori = cv2.imread(
        'Data/' + image_name + '.png')

    rectangular_masks = [(-52, 60), (75, 45), (89.9, 30),(60, 25)]  # FFT masks

    if seg_method == 'FFT':
        (thersh, kernel, thresh_pre, dia_iter) = (
            0.24, (3, 3), 25, 3)  # Watershed segmentation

        merge_thresh = 6500  # Merging threshold
    elif seg_method == 'otsu':
        (thersh, kernel, thresh_pre, dia_iter) = (
            0.20, (3, 3), 30, 3)  # Watershed segmentation

        merge_thresh = 6000  # Merging threshold
    else:
        raise ValueError('Incorret seg_method!')
elif image_name == 'IHPC_cropped':
    image = cv2.imread(
        'Data/' + image_name + '.png')
    image_ori = cv2.imread(
        'Data/' + image_name + '.png')

    rectangular_masks = [(70, 30), (-35, 30)]  # FFT masks

    if seg_method == 'FFT':
        (thersh, kernel, thresh_pre, dia_iter) = (
            0.24, (3, 3), 25, 3)  # Watershed segmentation

        merge_thresh = 4000  # Merging threshold
    elif seg_method == 'otsu':
        (thersh, kernel, thresh_pre, dia_iter) = (
            0.20, (3, 3), 30, 3)  # Watershed segmentation

        merge_thresh = 4000  # Merging threshold
    else:
        raise ValueError('Incorret seg_method!')
elif image_name == 'MIPAR':
    image = cv2.imread(
        'Data/' + image_name + '.png')
    image_ori = cv2.imread(
        'Data/' + image_name + '.png')

    rectangular_masks = [(-30, 50), (65, 45), (89.9, 40)]  # FFT masks

    if seg_method == 'FFT':
        (thersh, kernel, thresh_pre, dia_iter) = (
            0.21, (5, 5), 65, 2)  # Watershed segmentation

        merge_thresh = 800  # Merging threshold

    elif seg_method == 'otsu':
        (thersh, kernel, thresh_pre, dia_iter) = (
            0.22, (5, 5), 30, 2)  # Watershed segmentation

        merge_thresh = 1000  # Merging threshold
    else:
        raise ValueError('Incorret seg_method!')
elif image_name == 'MIPAR_cropped':
    image = cv2.imread(
        'Data/' + image_name + '.png')
    image_ori = cv2.imread(
        'Data/' + image_name + '.png')

    rectangular_masks = [(89.9, 30)]  # FFT masks

    if seg_method == 'FFT':
        (thersh, kernel, thresh_pre, dia_iter) = (
            0.21, (5, 5), 65, 2)  # Watershed segmentation

        merge_thresh = 400  # Merging threshold

    elif seg_method == 'otsu':
        (thersh, kernel, thresh_pre, dia_iter) = (
            0.22, (5, 5), 30, 2)  # Watershed segmentation

        merge_thresh = 800  # Merging threshold
    else:
        raise ValueError('Incorret seg_method!')
else:
    raise ValueError('Incorret image name!')

# Measure run time
start = time.time()

# Denoisng
denoised = image_processing.denoise(
    image, method='gaussian', ksize=(5, 5), sigmaX=5)

# Thresholding
thresholded_otsu = image_processing.threshold(denoised, method='Otsu')

# FFT images
fft = fast_Fourier_transform.fft_rectangular(
    thresholded_otsu, r_masks=rectangular_masks)

# Segmentation
if seg_method == 'FFT':
    segmented = watershed.watershed(
        fft, image, thresh=thersh, kernel=kernel, thresh_pre=thresh_pre, dia_iter=dia_iter)
else:
    segmented = watershed.watershed(
        thresholded_otsu, image, thresh=thersh, kernel=kernel, thresh_pre=thresh_pre, dia_iter=dia_iter)

# Reducing oversegmentation
merged = oversegmentation.oversegmentation(segmented['modified markers'], image_ori, merge_thresh)

# Data extraction and saving data
data_extraction.data_extraction(
    merged['merged markers'], 'Data/data_{}_{}'.format(image_name, seg_method))

end = time.time()

# Print run time
print('Data generation took {} seconds to execute.'.format(round(end-start, 1)))