Refactor to reduce overlap between course folders by Michal-Fularz · Pull Request #3 · PUTvision/ImageProcessingCourse

Michal-Fularz · 2026-04-15T20:09:18Z

I have reorganized the repository to reduce overlap between the image_processing_course and vision_systems folders. I identified that image_processing_course contained some sw_ prefixed files which correspond to the Systemy Wizyjne (Vision Systems) course, so I moved them into the vision_systems directory, overwriting the older versions there. I also moved the s01e03.py and s01eTestPreparation.py files to vision_systems and preserved the ipc_ prefixed files in image_processing_course for the Image Processing Course.

PR created automatically by Jules for task 15483639455431496973 started by @Michal-Fularz

- Moved `sw_s01e*.py` files from `image_processing_course` to `vision_systems` - Replaced the overlapping files in `vision_systems` - Maintained the `ipc_` files in `image_processing_course` - Moved `s01eTestPreparation.py` and `s01e03.py` to `vision_systems` - Cleaned up duplicated items Co-authored-by: Michal-Fularz <3768498+Michal-Fularz@users.noreply.github.com>

google-labs-jules · 2026-04-15T20:09:19Z

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Code Review

This pull request refactors several computer vision scripts, focusing on improving gradient calculations, edge detection, and camera calibration. The changes include the introduction of a helper function for gradient operations and updated logic for background subtraction. Feedback highlights critical issues with parameter ordering in OpenCV calibration functions, potential runtime crashes due to missing null checks or failed camera reads, and opportunities to use more efficient OpenCV functions like cv2.magnitude and cv2.convertScaleAbs to prevent data overflow.

gemini-code-assist · 2026-04-15T20:12:07Z

+
+        image_points = [corners]  # [corners1, corners2, corners3]
+        object_points = [object_points]  # [object_points1, object_points2, object_points3]
+        retval, camera_matrix, dist_coeffs, rvecs, tvecs = cv2.calibrateCamera(object_points, image_points, image.shape[:2], None, None)


The imageSize parameter in cv2.calibrateCamera expects a tuple in (width, height) format, but image.shape[:2] provides (height, width). This can lead to incorrect calibration results or errors.

Suggested change

retval, camera_matrix, dist_coeffs, rvecs, tvecs = cv2.calibrateCamera(object_points, image_points, image.shape[:2], None, None)

retval, camera_matrix, dist_coeffs, rvecs, tvecs = cv2.calibrateCamera(object_points, image_points, image.shape[1::-1], None, None)

gemini-code-assist · 2026-04-15T20:12:07Z

+        retval, camera_matrix, dist_coeffs, rvecs, tvecs = cv2.calibrateCamera(object_points, image_points, image.shape[:2], None, None)
+
+        fovx, fovy, focalLength, principalPoint, aspectRatio = cv2.calibrationMatrixValues(
+            camera_matrix, image.shape[:2], 7.2, 5.4


Similar to calibrateCamera, cv2.calibrationMatrixValues expects imageSize as (width, height). Using image.shape[:2] provides the dimensions in the wrong order.

Suggested change

camera_matrix, image.shape[:2], 7.2, 5.4

camera_matrix, image.shape[1::-1], 7.2, 5.4

gemini-code-assist · 2026-04-15T20:12:07Z

+    img_gradient_x = cv2.filter2D(img, cv2.CV_32F, kernel_x)
+    img_gradient_y = cv2.filter2D(img, cv2.CV_32F, kernel_y)

+    img_gradient = cv2.sqrt(pow(img_gradient_x / divider, 2) + pow(img_gradient_y / divider, 2))


Using cv2.magnitude is more efficient and readable than manually calculating the square root of the sum of squares for gradient magnitude.

Suggested change

img_gradient = cv2.sqrt(pow(img_gradient_x / divider, 2) + pow(img_gradient_y / divider, 2))

img_gradient = cv2.magnitude(img_gradient_x / divider, img_gradient_y / divider)

gemini-code-assist · 2026-04-15T20:12:07Z

+    cv2.imshow(f'{window_name_prefix}_x', (abs(img_gradient_x) / divider).astype(np.uint8))
+    cv2.imshow(f'{window_name_prefix}_x_no_abs', (img_gradient_x / divider).astype(np.uint8))
+    cv2.imshow(f'{window_name_prefix}_y', (abs(img_gradient_y) / divider).astype(np.uint8))


Directly casting to np.uint8 after taking the absolute value can lead to wrap-around issues if values exceed 255. cv2.convertScaleAbs is the standard way to scale, calculate absolute values, and safely convert to uint8 with saturation. This is especially important for gradients where values can easily exceed the 0-255 range.

Suggested change

cv2.imshow(f'{window_name_prefix}_x', (abs(img_gradient_x) / divider).astype(np.uint8))

cv2.imshow(f'{window_name_prefix}_x_no_abs', (img_gradient_x / divider).astype(np.uint8))

cv2.imshow(f'{window_name_prefix}_y', (abs(img_gradient_y) / divider).astype(np.uint8))

cv2.imshow(f'{window_name_prefix}_x', cv2.convertScaleAbs(img_gradient_x, alpha=1.0/divider))

cv2.imshow(f'{window_name_prefix}_x_no_abs', (img_gradient_x / divider).astype(np.uint8))

cv2.imshow(f'{window_name_prefix}_y', cv2.convertScaleAbs(img_gradient_y, alpha=1.0/divider))

gemini-code-assist · 2026-04-15T20:12:07Z

    circles = np.uint16(np.around(circles))
    for i in circles[0, :]:
        # draw the outer circle
-        cv2.circle(img, (i[0], i[1]), i[2], (0, 255, 0), 2)
+        cv2.circle(img_hough_circles, (i[0], i[1]), i[2], (0, 255, 0), 2)
        # draw the center of the circle
-        cv2.circle(img, (i[0], i[1]), 2, (0, 0, 255), 3)
-    cv2.imshow('detected circles', img)
+        cv2.circle(img_hough_circles, (i[0], i[1]), 2, (0, 0, 255), 3)


cv2.HoughCircles returns None if no circles are detected. Attempting to call np.around or iterating over circles[0, :] without checking for None will cause a runtime error.

Suggested change

circles = np.uint16(np.around(circles))

for i in circles[0, :]:

# draw the outer circle

cv2.circle(img, (i[0], i[1]), i[2], (0, 255, 0), 2)

cv2.circle(img_hough_circles, (i[0], i[1]), i[2], (0, 255, 0), 2)

# draw the center of the circle

cv2.circle(img, (i[0], i[1]), 2, (0, 0, 255), 3)

cv2.imshow('detected circles', img)

cv2.circle(img_hough_circles, (i[0], i[1]), 2, (0, 0, 255), 3)

if circles is not None:

circles = np.uint16(np.around(circles))

for i in circles[0, :]:

# draw the outer circle

cv2.circle(img_hough_circles, (i[0], i[1]), i[2], (0, 255, 0), 2)

# draw the center of the circle

cv2.circle(img_hough_circles, (i[0], i[1]), 2, (0, 0, 255), 3)

gemini-code-assist · 2026-04-15T20:12:07Z

+        object_points = np.zeros((8 * 5, 3), np.float32)
+        print(object_points[1])
+        print(object_points.shape)
+        object_points[:, :2] = np.mgrid[0:8, 0:5].T.reshape(-1, 2)
+        print(object_points[1])
+        print(object_points.shape)
+
+        object_points_for = []  # np.zeros_like(object_points)
+        for i in range(0, 5):
+            for j in range(0, 8):
+                # print(object_points_for[i, j])
+                # object_points_for[i, j] = [i, j, 0]
+                object_points_for.append([j, i, 0])
+        object_points_for = np.array(object_points_for, dtype=np.float32)
+
+        print(f'(object_points[0:3]: {object_points[0:3]}')
+        print(object_points.shape)
+        print(f'object_points_for[0:3]: {object_points_for[0:3]}')
+        print(object_points_for.shape)
+
+        image_points = [corners]  # [corners1, corners2, corners3]
+        object_points = [object_points]  # [object_points1, object_points2, object_points3]


This block contains redundant logic and multiple print statements for debugging that should be removed to improve maintainability. The object_points can be generated concisely using np.mgrid without the need for an explicit loop and comparison.

Suggested change

object_points = np.zeros((8 * 5, 3), np.float32)

print(object_points[1])

print(object_points.shape)

object_points[:, :2] = np.mgrid[0:8, 0:5].T.reshape(-1, 2)

print(object_points[1])

print(object_points.shape)

object_points_for = [] # np.zeros_like(object_points)

for i in range(0, 5):

for j in range(0, 8):

# print(object_points_for[i, j])

# object_points_for[i, j] = [i, j, 0]

object_points_for.append([j, i, 0])

object_points_for = np.array(object_points_for, dtype=np.float32)

print(f'(object_points[0:3]: {object_points[0:3]}')

print(object_points.shape)

print(f'object_points_for[0:3]: {object_points_for[0:3]}')

print(object_points_for.shape)

image_points = [corners] # [corners1, corners2, corners3]

object_points = [object_points] # [object_points1, object_points2, object_points3]

# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)

object_points = np.zeros((8 * 5, 3), np.float32)

object_points[:, :2] = np.mgrid[0:8, 0:5].T.reshape(-1, 2)

image_points = [corners]

object_points = [object_points]

gemini-code-assist · 2026-04-15T20:12:07Z

+    # cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)

-    background_image = cv2.cvtColor(cap.read()[1], cv2.COLOR_BGR2GRAY)
+    img_gray = cv2.cvtColor(cap.read()[1], cv2.COLOR_BGR2GRAY)


cap.read() returns a success flag and the frame. If the camera fails to open or a frame cannot be read, cap.read()[1] will be None, causing cv2.cvtColor to crash. It is safer to check the return flag.

Suggested change

img_gray = cv2.cvtColor(cap.read()[1], cv2.COLOR_BGR2GRAY)

ret, frame = cap.read()

if not ret:

return

img_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

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Refactor to reduce overlap between course folders#3

Refactor to reduce overlap between course folders#3
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fix/deduplicate-course-folders-15483639455431496973

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	retval, camera_matrix, dist_coeffs, rvecs, tvecs = cv2.calibrateCamera(object_points, image_points, image.shape[:2], None, None)
	retval, camera_matrix, dist_coeffs, rvecs, tvecs = cv2.calibrateCamera(object_points, image_points, image.shape[1::-1], None, None)

	camera_matrix, image.shape[:2], 7.2, 5.4
	camera_matrix, image.shape[1::-1], 7.2, 5.4

	img_gradient = cv2.sqrt(pow(img_gradient_x / divider, 2) + pow(img_gradient_y / divider, 2))
	img_gradient = cv2.magnitude(img_gradient_x / divider, img_gradient_y / divider)

-    img_gray = cv2.cvtColor(cap.read()[1], cv2.COLOR_BGR2GRAY)
+    ret, frame = cap.read()
+    if not ret:
+        return
+    img_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

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