def calc_metric(candidate_img_blurred: np.ndarray, candidate_kpts: np.ndarray, candidate_length: int,
url_img_blurred: np.ndarray, url_kpts: np.ndarray, url_length: int, kpt_pos: tuple) -> float:
candidate_kpts, url_kpts = serial.deserialize_keypoints(candidate_kpts), serial.deserialize_keypoints(url_kpts)
m, n = kpt_pos[0], kpt_pos[1]
#print((m,n))
if(m == -1):
return 1
candidate_img_blurred, url_img_blurred = image.adjust_size(candidate_img_blurred, url_img_blurred)
c_x, c_y = candidate_img_blurred.shape
u_x, u_y = url_img_blurred.shape
x, y = np.array(candidate_kpts[n].pt) - np.array(url_kpts[m].pt)
if(x + c_x < 0 or x + u_x < 0):
return 1
if(y + c_y < 0 or y + u_y < 0):
return 1
candidate_img_blurred = image.align_text(candidate_img_blurred, (int(x), int(y)))
candidate_img_blurred, url_img_blurred = candidate_img_blurred.astype(int), url_img_blurred.astype(int)
img_diff = abs(candidate_img_blurred - url_img_blurred)
img_diff = img_diff.astype(int)
divisor = max(candidate_img_blurred.size, url_img_blurred.size)
diff = len(np.where(img_diff > 10)[0]) / float(divisor)
return diff
penalty = abs((float(candidate_length) - url_length)) / max(candidate_length, url_length)
diff = diff / (1.0 - penalty * 10)
return abs(diff)
def get_best_match(matches, candidate_kpts, domain_kpts):
d_accu = float('Inf')
best = (-1, -1)
candidate_kpts, domain_kpts = serial.deserialize_keypoints(candidate_kpts), serial.deserialize_keypoints(domain_kpts)
for match in matches:
n, m = match[0], match[1]
c_x_y = candidate_kpts[m].pt
d_x_y = domain_kpts[n].pt
d = abs(sum(np.array(c_x_y) - np.array(d_x_y)))
if(d < d_accu):
d_accu = d
best = match
return best
