Page 272 - MDP2020-1

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temp_result = np.zeros((height, width, channel), dtype=np.uint8)
for r in matched_result:
for d in r:
# cv2.drawContours(temp_result, d['contour'], -1, (255, 255, 255))
cv2.rectangle(temp_result, pt1=(d['x'], d['y']), pt2=(d['x']+d['w'], d['y']+d['h']),
color=(255, 255, 255), thickness=2)
plt.figure(figsize=(12, 10))
plt.imshow(temp_result, cmap='gray')
PLATE_WIDTH_PADDING = 1.3 # 1.3
PLATE_HEIGHT_PADDING = 1.5 # 1.5
MIN_PLATE_RATIO = 3
MAX_PLATE_RATIO = 10
plate_imgs = []
plate_infos = []
for i, matched_chars in enumerate(matched_result):
sorted_chars = sorted(matched_chars, key=lambda x: x['cx'])
plate_cx = (sorted_chars[0]['cx'] + sorted_chars[-1]['cx']) / 2
plate_cy = (sorted_chars[0]['cy'] + sorted_chars[-1]['cy']) / 2

plate_width = (sorted_chars[-1]['x'] + sorted_chars[-1]['w'] - sorted_chars[0]['x']) *
PLATE_WIDTH_PADDING

sum_height = 0
for d in sorted_chars:
sum_height += d['h']
plate_height = int(sum_height / len(sorted_chars) * PLATE_HEIGHT_PADDING)

triangle_height = sorted_chars[-1]['cy'] - sorted_chars[0]['cy']
triangle_hypotenus = np.linalg.norm(
np.array([sorted_chars[0]['cx'], sorted_chars[0]['cy']]) -
np.array([sorted_chars[-1]['cx'], sorted_chars[-1]['cy']])
)

angle = np.degrees(np.arcsin(triangle_height / triangle_hypotenus))

rotation_matrix = cv2.getRotationMatrix2D(center=(plate_cx, plate_cy), angle=angle, scale=1.0)

img_rotated = cv2.warpAffine(img_thresh, M=rotation_matrix, dsize=(width, height))

img_cropped = cv2.getRectSubPix(
img_rotated,
patchSize=(int(plate_width), int(plate_height)),
center=(int(plate_cx), int(plate_cy))
)