Updated to include command line arguments

Author: whizkid42

GEMstack/offboard/calibration/test_transforms.py

@@ -1,60 +1,70 @@
 import numpy as np
 import cv2
 import matplotlib.pyplot as plt
+import argparse
 
-# Load LiDAR points
-lidar_data = np.load("./data/lidar1.npz")  # Update filename if needed
-lidar_points = lidar_data['arr_0']  # Ensure the correct key
+def load_lidar_data(lidar_path):
+    lidar_data = np.load(lidar_path)
+    return lidar_data['arr_0']  # Ensure the correct key
 
-# Load Camera Image
-image = cv2.imread("./data/color1.png")  # Update filename if needed
-image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # Convert BGR to RGB
+def load_image(image_path):
+    image = cv2.imread(image_path)
+    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB) #convert BGR2RGB
 
-# Transformation Matrix
-import numpy as np
+def main(lidar_path, image_path):
+    # Load Data
+    lidar_points = load_lidar_data(lidar_path)
+    image = load_image(image_path)
+    
+    # Transformation Matrix
+    T_lidar_camera = np.array([
+        [ 2.89748006e-02, -9.99580136e-01,  3.68439439e-05, -3.07300513e-02],
+        [-9.49930618e-03, -3.12215512e-04, -9.99954834e-01, -3.86689354e-01],
+        [ 9.99534999e-01,  2.89731321e-02, -9.50437214e-03, -6.71425124e-01],
+        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  1.00000000e+00]
+    ])
+
+    # Convert LiDAR points to homogeneous coordinates
+    num_points = lidar_points.shape[0]
+    lidar_homogeneous = np.hstack((lidar_points, np.ones((num_points, 1))))
+
+    # Transform LiDAR points to Camera Frame
+    lidar_points_camera = (T_lidar_camera @ lidar_homogeneous.T).T
+
+    # Intrinsic Camera Matrix
+    K = np.array([
+        [684.83331299,   0.        , 573.37109375],
+        [  0.        , 684.60968018, 363.70092773],
+        [  0.        ,   0.        ,   1.        ]
+    ], dtype=np.float32)
+
+    # Extract 3D points in camera frame
+    X_c, Y_c, Z_c = lidar_points_camera[:, 0], lidar_points_camera[:, 1], lidar_points_camera[:, 2]
+
+    # Avoid division by zero
+    valid = Z_c > 0
+    X_c, Y_c, Z_c = X_c[valid], Y_c[valid], Z_c[valid]
+
+    # Project points onto image plane
+    u = (K[0, 0] * X_c / Z_c) + K[0, 2]
+    v = (K[1, 1] * Y_c / Z_c) + K[1, 2]
+
+    # Filter points within image bounds
+    img_h, img_w, _ = image.shape
+    valid_pts = (u >= 0) & (u < img_w) & (v >= 0) & (v < img_h)
+    u, v = u[valid_pts], v[valid_pts]
+
+    # Plot projected points on camera image
+    plt.imshow(image)
+    plt.scatter(u, v, s=2, c='r')
+    plt.title("Projected LiDAR Points on Camera Image")
+    plt.show()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Project LiDAR points onto a camera image.")
+    parser.add_argument("--lidar", type=str, required=True, help="Path to the LiDAR .npz file")
+    parser.add_argument("--image", type=str, required=True, help="Path to the camera image")
+    args = parser.parse_args()
+    
+    main(args.lidar, args.image)
 
-T_lidar_camera = np.array([
-    [ 2.89748006e-02, -9.99580136e-01,  3.68439439e-05, -3.07300513e-02],
-    [-9.49930618e-03, -3.12215512e-04, -9.99954834e-01, -3.86689354e-01],
-    [ 9.99534999e-01,  2.89731321e-02, -9.50437214e-03, -6.71425124e-01],
-    [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  1.00000000e+00]
-])
-
-
-# Convert LiDAR points to homogeneous coordinates
-num_points = lidar_points.shape[0]
-lidar_homogeneous = np.hstack((lidar_points, np.ones((num_points, 1))))  # (N, 4)
-
-# Transform LiDAR points to Camera Frame
-lidar_points_camera = (T_lidar_camera @ lidar_homogeneous.T).T  # (N, 4)
-
-# intrinsic matrix
-K = np.array([
-    [684.83331299,   0.        , 573.37109375],
-    [  0.        , 684.60968018, 363.70092773],
-    [  0.        ,   0.        ,   1.        ]
-], dtype=np.float32)
-
-# Extract 3D points in camera frame
-X_c = lidar_points_camera[:, 0]
-Y_c = lidar_points_camera[:, 1]
-Z_c = lidar_points_camera[:, 2]  # Depth
-
-# Avoid division by zero
-valid = Z_c > 0
-X_c, Y_c, Z_c = X_c[valid], Y_c[valid], Z_c[valid]
-
-# Project points onto image plane
-u = (K[0, 0] * X_c / Z_c) + K[0, 2]
-v = (K[1, 1] * Y_c / Z_c) + K[1, 2]
-
-# Filter points within image bounds
-img_h, img_w, _ = image.shape
-valid_pts = (u >= 0) & (u < img_w) & (v >= 0) & (v < img_h)
-u, v = u[valid_pts], v[valid_pts]
-
-# Plot projected points on camera image
-plt.imshow(image)
-plt.scatter(u, v, s=2, c='r')  # Red dots for projected points
-plt.title("Projected LiDAR Points on Camera Image")
-plt.show()