def update_image(
self
): # update canvas every time according to time set in the timer.
if self.recognize_face_btn.isChecked():
self.ret, self.image = self.capture.read()
if self.ret:
self.image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)
image_rc = Image.fromarray(self.image)
bboxes, faces = self.face_recognize.align_multi(
image_rc, thresholds=[0.5, 0.7, 0.8])
if len(bboxes) != 0:
bboxes = bboxes[:, :
-1] #shape:[10,4],only keep 10 highest possibiity faces
bboxes = bboxes.astype(int)
bboxes = bboxes + [-1, -1, 1, 1] # personal choice
if self.has_targ:
results, score, embs = self.face_recognize.infer(
faces, self.targets)
for idx, bbox in enumerate(bboxes):
draw_box_name(bbox, self.names[results[idx] + 1],
self.image)
else:
for idx, bbox in enumerate(bboxes):
draw_box_name(bbox, 'Unknow', self.image)
self.display()
else:
QMessageBox().about(self, "Warring", "Video source not found!")
self.recognize_face_btn.setText("Recognize")
self.recognize_face_btn.setCheckable(False)
self.recognize_face_btn.setCheckable(True)
self.stop_timer()
if self.video_recording_btn.isChecked():
self.recording()
def generate(names, targets):
ret, frame = vid_source.read()
# tensor code
while ret:
img_bg = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image = Image.fromarray(img_bg)
try:
bboxes, faces = face_recognize.align_multi(image)
except:
bboxes = []
faces = []
if len(bboxes) != 0:
bboxes = bboxes[:,:-1] #shape:[10,4],only keep 10 highest possibiity faces
bboxes = bboxes.astype(int)
bboxes = bboxes + [-1,-1,1,1] # personal choice
results, score, embs = face_recognize.infer(faces, targets)
for idx, bbox in enumerate(bboxes):
if False:
frame = draw_box_name(bbox, names[results[idx] + 1] + '_{:.2f}'.format(score[idx]), frame)
else:
frame = draw_box_name(bbox, names[results[idx] + 1], frame)
payload = cv2.imencode('.jpg', frame)[1].tobytes()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + payload + b'\r\n')
ret, frame = vid_source.read()
def recognize_webcam(image_np):
# image = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
image = Image.fromarray(image_np)
# try:
bboxes, faces = face_recognize.align_multi(image)
# except:
# # pass
# bboxes = []
# faces = []
if len(bboxes) != 0:
bboxes = bboxes[:,:-1] #shape:[10,4],only keep 10 highest possibiity faces
bboxes = bboxes.astype(int)
bboxes = bboxes + [-1,-1,1,1] # personal choice
results, score, embs = face_recognize.infer(faces, targets)
for idx, bbox in enumerate(bboxes):
is_spoofing = face_recognize.is_spoofing(faces[idx])
status = "attack"
if not is_spoofing:
status = "genuine"
if len(que_name) > 15:
que_name.pop()
que_name.append(names[results[idx] + 1]+ '_{:.7s}'.format(status))
else:
que_name.append(names[results[idx] + 1]+ '_{:.7s}'.format(status))
if False:
image_np = draw_box_name(bbox, names[results[idx] + 1] + '_{:.2f}__{:.7s}'.format(score[idx], status), image_np)
else:
image_np = draw_box_name(bbox, names[results[idx] +1 ], image_np)
return image_np
def face_verify(self, avtive_key):
while 1:
image_np = self.frame_ori[avtive_key].copy()
image = Image.fromarray(image_np)
try:
bboxes, faces = self.face_reg.align_multi(image)
except:
# pass
bboxes = []
faces = []
if len(bboxes) != 0:
bboxes = bboxes[:, :
-1] #shape:[10,4],only keep 10 highest possibiity faces
bboxes = bboxes.astype(int)
bboxes = bboxes + [-1, -1, 1, 1] # personal choice
results, score, embs = self.face_reg.infer(faces, self.targets)
for idx, bbox in enumerate(bboxes):
if False:
image_np = draw_box_name(
bbox, self.names[results[idx] + 1] +
'_{:.2f}__{:.7s}'.format(score[idx], status),
image_np)
else:
image_np = draw_box_name(bbox,
self.names[results[idx] + 1],
image_np)
self.frame_process[avtive_key] = {'image': image_np}
def recognize(image_path, targets, names):
frame = cv2.imread(image_path)
image = Image.fromarray(frame).convert('RGB')
try:
bboxes, faces = face_recognize.align_multi(image)
except:
bboxes = []
faces = []
result = {}
result['original'] = encode_image(image.copy())
if len(bboxes) != 0:
bboxes = bboxes[:,:-1] #shape:[10,4],only keep 10 highest possibiity faces
bboxes = bboxes.astype(int)
bboxes = bboxes + [-1,-1,1,1] # personal choice
results, score, embs = face_recognize.infer(faces, targets)
for idx, bbox in enumerate(bboxes):
result[names[results[idx] + 1]] = encode_image(faces[idx])
if False:
frame = draw_box_name(bbox, names[results[idx]+1] + '_{:.2f}'.format(score[idx]), frame)
else:
frame = draw_box_name(bbox, names[results[idx]+1], frame)
return result
bboxes = bboxes + [-1,-1,1,1] # personal choice
results, score, embs = face_recognize.infer(faces, targets)
for idx, bbox in enumerate(bboxes):
if results[idx] == -1 and args.save_unknow:
new_per = "%s/unknow_%s"%(conf.facebank_path, count_unknow)
if not os.path.exists(new_per):
os.mkdir(new_per)
faces[idx].save('%s/%s.jpg'%(new_per, datetime.now().date().strftime('%Y%m%d')))
targets = torch.cat((targets, embs[idx].unsqueeze(0)), dim=0)
names =np.append(names, 'unknow_%s'%count_unknow)
count_unknow+=1
if args.score:
frame = draw_box_name(bbox, names[results[idx]] + '_{:.2f}'.format(score[idx]), frame)
else:
frame = draw_box_name(bbox, names[results[idx]], frame)
video_writer.write(frame)
cv2.imshow("face_recognize", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
if args.duration != 0:
i += 1
if i % 25 == 0:
print('{} second'.format(i // 25))
if i > 25 * args.duration:
break
cap.release()