CODEWITHSUNDEEP
pythonopencvimage-processing
Pillow
Pillow

Reputation: 11

OpenCV: Find similar object with contours matching

I have a bundle of different images with birds but some of them contains feathers, slightly visible birds etc. I need to find images where the bird visible well and remove images with feathers, far distant birds, etc.

I already tried ORB, simple template matching, Canny edge detection. And I cannot use neural nets.

Now i try with such algorithm:

  1. Binarize template image to get shapes

  2. Slide window over another binarized image with sliding window and calculate matchShape with template in every window

  3. Find best match

As you can see this method gives me strange result

Binary template

img.

Shape on the other binary image, for example:

img

I calculated matchShapes in different parts of this image and the best result ~ 0.05 I got in this part:

jpg

which is obviously not similar to original shape.

Code for sliding window:

import cv2

OFFSET = 5
SCALE_RATIO = [0.5, 1]

def get_scaled_list(img_path, template):
    matcher_list = []

    img = cv2.imread(img_path)
    #JUST BINARIZATION AND RESIZING
    img = preprocess(resize_image(img))

    height, width = img.shape

    # building size of scale window
    for scaler in SCALE_RATIO:
        x_point = 0
        y_point = 0

        x1_point = int(width * scaler)
        y1_point = x1_point

        if x1_point > height:
            y1_point = height

        while y1_point <= height:
            while x1_point <= width:
                img1 = img[y_point:y1_point, x_point:x1_point]

                #Comparing template and part of image
                diff = cv2.matchShapes(template, img1, cv2.CONTOURS_MATCH_I1, 0)

                data_tuple = (img_path, x_point, y_point, int(width * scaler), diff)
                matcher_list.append(data_tuple)

                x_point += OFFSET
                x1_point += OFFSET

            x_point = 0
            x1_point = int(width * scaler)

            y_point += OFFSET
            y1_point += OFFSET
    return matcher_list

How can I perform correct shape matching and why is the best result performs here?

Upvotes: 1

Views: 3351

Answers (1)

user1196549
user1196549

Reputation:

The naive window sliding method with a rigid template will work very poorly. In particular, the sizes are different making correct overlap impossible.

What you are trying to achieve is difficult because you only have edge information and the edges are complex, broken in several independent arcs and with junctions.

You can find many solutions when you have a single closed curve (lookup "elastic contour matching" for instance), but not for your case. This would be a case of "approximate elastic graph matching".

Other possible approaches are by special distance functions such as the chamfer or Hausdorff distances, but you can still be stuck because of the size mismatch.

Upvotes: 2

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