| | |
|---|
| | | if card_img is None: |
|---|
| | | print('WARNING: card %s is not found!' % img_name) |
|---|
| | | continue |
|---|
| | | |
|---|
| | | set_img = card_img[575:638, 567:700] |
|---|
| | | """ |
|---|
| | | img_cc = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY) |
|---|
| | | img_thresh = cv2.adaptiveThreshold(img_cc, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 5) |
|---|
| | | # Dilute the image, then erode them to remove minor noises |
|---|
| | | kernel = np.ones((3, 3), np.uint8) |
|---|
| | | img_dilate = cv2.dilate(img_thresh, kernel, iterations=1) |
|---|
| | | img_erode = cv2.erode(img_dilate, kernel, iterations=1) |
|---|
| | | cnts, hier = cv2.findContours(img_erode, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) |
|---|
| | | cnts2 = sorted(cnts, key=cv2.contourArea, reverse=True) |
|---|
| | | cnts2 = cnts2[:10] |
|---|
| | | if True: |
|---|
| | | cv2.drawContours(img_cc, cnts2, -1, (0, 255, 0), 3) |
|---|
| | | #cv2.imshow('Contours', card_img) |
|---|
| | | #cv2.waitKey(10000) |
|---|
| | | """ |
|---|
| | | set_img = card_img[595:635, 600:690] |
|---|
| | | #cv2.imshow(card_info['name'], set_img) |
|---|
| | | # Compute value of the card's perceptual hash, then store it to the database |
|---|
| | | #img_art = Image.fromarray(card_img[121:580, 63:685]) # For 745*1040 size card image |
|---|
