OpenCV shape matching between two similar shapes

Question

I\'m trying to match a slightly irregular shape to a database of shapes. For example, here the contour I\'m trying to match:

For more information, this is an ou

Answer 1

This answer is based on ZdaR's answer here https://stackoverflow.com/a/55530040/1787145. I have tried some variations in hope of using a single discerning criterion (cv2.matchShapes()) by incorporating more in the pre-processing.

1. Compare images instead of contours

I like the idea of normalization (crop and resize). But after shrinking an image, its originally closed contour might be broken into multiple disconnected parts, due to the low resolution of pixels. The result of cv2.matchShapes() is unreliable. By comparing whole resized images, I get following results. It says the circle is the most similar. Not good!

2. Fill the shape

By filling the shape, we take area into consideration. The result looks better, but DVI still beats HDMI for having a more similar height or Height/Width ratio. We want to ignore that.

3. Resize every image to the same size

By resizing all to the same size, we eliminate some ratio in the dimensions. (300, 300) works well here.

4. Code

def normalize_filled(img):
    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    im, cnt, _ = cv2.findContours(img.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    # fill shape
    cv2.fillPoly(img, pts=cnt, color=(255,255,255))
    bounding_rect = cv2.boundingRect(cnt[0])
    img_cropped_bounding_rect = img[bounding_rect[1]:bounding_rect[1] + bounding_rect[3], bounding_rect[0]:bounding_rect[0] + bounding_rect[2]]
    # resize all to same size
    img_resized = cv2.resize(img_cropped_bounding_rect, (300, 300))
    return img_resized

imgs = [imgQuery, imgHDMI, imgDVI, img5PinDin, imgDB25]
imgs = [normalize_filled(i) for i in imgs]

for i in range(1, 6):
    plt.subplot(2, 3, i), plt.imshow(imgs[i - 1], cmap='gray')
    print(cv2.matchShapes(imgs[0], imgs[i - 1], 1, 0.0))