def show_card_detect(candis, cap, values):
rect_args = load_model()
for candi in candis:
# k = 20 if 20 < len(candi) else len(candi)
# idxs = topk_idx(candi, k) + candi[0]
idxs = candi
idxs = pick_up_candi(candi, 20, values)
for idx in idxs:
cap.set(cv.CAP_PROP_POS_FRAMES, START_FRAME + idx)
res, frame = cap.read()
frame = frame[250:-250, 500:-500]
print(START_FRAME + idx)
croped_img, rect_score = id_card_detect(frame, rect_args)
if not croped_img.width:
cv.imshow('imgae', put_text(frame, str(rect_score)))
if cv.waitKey(1) == ord('q'):
break
else:
croped_img = cv.cvtColor(np.asarray(croped_img),
cv.COLOR_RGB2BGR)
half = croped_img[int(croped_img.shape[0] *
0.1):int(croped_img.shape[0] * 0.7),
int(croped_img.shape[1] *
0.55):int(croped_img.shape[1] * 0.95)]
half_eye = retina_face_detect(half)
cv.imshow('imgae', put_text(croped_img, str(rect_score)))
if cv.waitKey(1) == ord('q'):
break
cv.waitKey(0)
def find_id_card(candis, cap, values):
id_card_index_candidate = []
face_scores = []
rect_args = load_model()
for candi in candis:
idxs = pick_up_candi(candi, 10, values)
for idx in idxs:
cap.set(cv.CAP_PROP_POS_FRAMES, START_FRAME + idx)
res, frame = cap.read()
frame = frame[250:-250, 500:-500]
print(START_FRAME + idx)
croped_img, rect_score = id_card_detect(frame, rect_args)
if not croped_img.width:
continue
else:
croped_img = cv.cvtColor(np.asarray(croped_img),
cv.COLOR_RGB2BGR)
half = croped_img[int(croped_img.shape[0] *
0.1):int(croped_img.shape[0] * 0.7),
int(croped_img.shape[1] *
0.55):int(croped_img.shape[1] * 0.95)]
has_face, score, _ = retina_face_distinguish(half)
if has_face:
id_card_index_candidate.append(idx)
face_scores.append(score)
best_face_score_idx = np.argmax(np.array(face_scores))
best_idx = id_card_index_candidate[best_face_score_idx]
return best_idx, id_card_index_candidate
def detect_id_card(img):
"""
looking for an id card from a frame, return the confidence score
"""
croped_img, rect_score = id_card_detect(img, rect_args)
if not croped_img.width:
return 0
else:
# looking for a face in the right-middle part of the cropped area
croped_img = cv.cvtColor(np.asarray(croped_img), cv.COLOR_RGB2BGR)
half = croped_img[int(croped_img.shape[0] *
0.1):int(croped_img.shape[0] * 0.7),
int(croped_img.shape[1] *
0.55):int(croped_img.shape[1] * 0.95)]
has_face, score, _ = retina_face_distinguish(half)
# cv.imshow('1', croped_img)
# cv.waitKey(1)
if has_face:
return score
else:
return 0
def find_id_card(candis, cap, values):
id_card_index_candidate = []
face_scores = []
rect_args = load_model()
for candi in candis:
idxs = pick_up_candi(candi, 10, values)
for idx in idxs:
cap.set(cv.CAP_PROP_POS_FRAMES, START_FRAME + idx)
res, frame = cap.read()
frame = frame
print('find id ', START_FRAME + idx)
croped_img, rect_score = id_card_detect(frame, rect_args)
if not croped_img.width:
continue
else:
croped_img = cv.cvtColor(np.asarray(croped_img), cv.COLOR_RGB2BGR)
half = croped_img[int(croped_img.shape[0] * 0.1): int(croped_img.shape[0] * 0.7),
int(croped_img.shape[1] * 0.55): int(croped_img.shape[1] * 0.95)]
has_face, score, _ = retina_face_distinguish(half)
cv.imshow(f'{idx} finding id-{has_face}', croped_img)
cv.waitKey(5)
if has_face:
id_card_index_candidate.append(idx)
face_scores.append(score)
# cv.waitKey(0)
if len(face_scores) == 0:
raise ValueError('id_card_candidates wrong')
best_face_score_idx = np.argmax(np.array(face_scores))
best_idx = id_card_index_candidate[best_face_score_idx]
return best_idx, id_card_index_candidate