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I am working on an image segmentation project to identify playing cards on varying backgrounds. For my use case I care most that I accurately extract all the cards in the image; false-positives are inconvenient but not fatal. I also care that I can run the segmentation without user interaction (i.e., an algorithm like grabcut does not work for me).

So far I have the following simple algorithm based on Canny edge detection and contour selection:

# Pre-processing: Convert frame to standard size, 1024x768
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.medianBlur(gray, 9)
edges = cv2.Canny(gray, 10, 25)
_, contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
    rect = cv2.minAreaRect(c)
    box = cv2.boxPoints(rect)
    area = cv2.contourArea(box)
    ratio = area / size
    if ratio < 0.015:
        continue
    # Mark box as segment

For some (many) inputs it works well enough, or even very well: goo.gl/photos/1NgkjWfpYamy3M2L6

However for other inputs the contour detection does not successfully disambiguate cards. The contours are grouped in ways that link cards or result in undesirable bounding boxes: goo.gl/photos/hdxD4KmsQVtL53EC6

I've tried various improvements such as altering all the parameters, adding a thresholding step, etc. but without much success. I would love to hear your suggestions on what else I should try!

For reference (or, better yet, if you want to play along at home) I have a set of example inputs and the outputs of my current algorithm: goo.gl/photos/HCYeiFqhG5pVNWnY9

(Apologies for all the awkward links, because this is my first post I cannot post real links or photos...)

I am working on an image segmentation project to identify playing cards on varying backgrounds. For my use case I care most that I accurately extract all the cards in the image; false-positives are inconvenient but not fatal. I also care that I can run the segmentation without user interaction (i.e., an algorithm like grabcut does not work for me).

So far I have the following simple algorithm based on Canny edge detection and contour selection:

# Pre-processing: Convert frame to standard size, 1024x768
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.medianBlur(gray, 9)
edges = cv2.Canny(gray, 10, 25)
_, contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
    rect = cv2.minAreaRect(c)
    box = cv2.boxPoints(rect)
    area = cv2.contourArea(box)
    ratio = area / size
    if ratio < 0.015:
        continue
    # Mark box as segment

For some (many) inputs it works well enough, or even very well: goo.gl/photos/1NgkjWfpYamy3M2L6

However for other inputs the contour detection does not successfully disambiguate cards. The contours are grouped in ways that link cards or result in undesirable bounding boxes: goo.gl/photos/hdxD4KmsQVtL53EC6

I've tried various improvements such as altering all the parameters, adding a thresholding step, etc. but without much success. I would love to hear your suggestions on what else I should try!

For reference (or, better yet, if you want to play along at home) I have a set of example inputs and the outputs of my current algorithm: goo.gl/photos/HCYeiFqhG5pVNWnY9

(Apologies for all the awkward links, because this is my first post I cannot post real links or photos...)https://goo.gl/photos/HCYeiFqhG5pVNWnY9

I am working on an image segmentation project to identify playing cards on varying backgrounds. For my use case I care most that I accurately extract all the cards in the image; false-positives are inconvenient but not fatal. I also care that I can run the segmentation without user interaction (i.e., an algorithm like grabcut does not work for me).

So far I have the following simple algorithm based on Canny edge detection and contour selection:

# Pre-processing: Convert frame to standard size, 1024x768
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.medianBlur(gray, 9)
edges = cv2.Canny(gray, 10, 25)
_, contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
    rect = cv2.minAreaRect(c)
    box = cv2.boxPoints(rect)
    area = cv2.contourArea(box)
    ratio = area / size
    if ratio < 0.015:
        continue
    # Mark box as segment

For some (many) inputs it works well enough, or even very well:

However for other inputs the contour detection does not successfully disambiguate cards. The contours are grouped in ways that link cards or result in undesirable bounding boxes:

I've tried various improvements such as altering all the parameters, adding a thresholding step, etc. but without much success. I would love to hear your suggestions on what else I should try!

For reference (or, better yet, if you want to play along at home) I have a set of example inputs and the outputs of my current algorithm: https://goo.gl/photos/HCYeiFqhG5pVNWnY9

def auto_canny(image, sigma=0.5):
    v = np.median(image)
    lower = int(max(0, (1.0 - sigma) * v))
    upper = int(min(255, (1.0 + sigma) * v))
    return cv2.Canny(image, lower, upper)