def webcam_main():
print("Camera sensor warming up...")
vs = cv2.VideoCapture(0)
time.sleep(2.0)
mark_detector = MarkDetector()
# loop over the frames from the video stream
while True:
_, frame = vs.read()
start = cv2.getTickCount()
frame = imutils.resize(frame, width=750, height=750)
frame = cv2.flip(frame, 1)
faceboxes = mark_detector.extract_cnn_facebox(frame)
if faceboxes is not None:
for facebox in faceboxes:
# Detect landmarks from image of 64X64 with grayscale.
face_img = frame[facebox[1]: facebox[3],
facebox[0]: facebox[2]]
# cv2.rectangle(frame, (facebox[0], facebox[1]), (facebox[2], facebox[3]), (0, 255, 0), 2)
face_img = cv2.resize(face_img, (CNN_INPUT_SIZE, CNN_INPUT_SIZE))
face_img = cv2.cvtColor(face_img, cv2.COLOR_BGR2GRAY)
face_img0 = face_img.reshape(1, CNN_INPUT_SIZE, CNN_INPUT_SIZE, 1)
land_start_time = time.time()
marks = mark_detector.detect_marks_keras(face_img0)
# marks *= 255
marks *= facebox[2] - facebox[0]
marks[:, 0] += facebox[0]
marks[:, 1] += facebox[1]
# Draw Predicted Landmarks
mark_detector.draw_marks(frame, marks, color=(255, 255, 255), thick=2)
fps_time = (cv2.getTickCount() - start)/cv2.getTickFrequency()
cv2.putText(frame, '%.1ffps'%(1/fps_time) , (frame.shape[1]-65,15), cv2.FONT_HERSHEY_DUPLEX, 0.5, (0,255,0))
# show the frame
cv2.imshow("Frame", frame)
# writer.write(frame)
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()
def webcam_main():
print("Camera sensor warming up...")
cv2.namedWindow('face landmarks', cv2.WINDOW_NORMAL)
frame = cv2.imread(IMAGE_PATH)
time.sleep(2.0)
mark_detector = MarkDetector(current_model, CNN_INPUT_SIZE)
if current_model.split(".")[-1] == "pb":
run_model = 0
elif current_model.split(".")[-1] == "hdf5" or current_model.split(
".")[-1] == "h5":
run_model = 1
else:
print("input model format error !")
return
faceboxes = mark_detector.extract_cnn_facebox(frame)
print(faceboxes)
if faceboxes is not None:
facebox = faceboxes[0]
# Detect landmarks from image of 64X64 with grayscale.
face_img = frame[facebox[1]:facebox[3], facebox[0]:facebox[2]]
cv2.rectangle(frame, (facebox[0], facebox[1]),
(facebox[2], facebox[3]), (0, 255, 0), 2)
face_img = cv2.resize(face_img, (CNN_INPUT_SIZE, CNN_INPUT_SIZE))
face_img = cv2.cvtColor(face_img, cv2.COLOR_BGR2GRAY)
face_img0 = face_img.reshape(1, CNN_INPUT_SIZE, CNN_INPUT_SIZE, 1)
if run_model == 1:
marks = mark_detector.detect_marks_keras(face_img0)
else:
marks = mark_detector.detect_marks_tensor(face_img0, 'input_2:0',
'output/BiasAdd:0')
# marks *= 255
marks *= facebox[2] - facebox[0]
marks[:, 0] += facebox[0]
marks[:, 1] += facebox[1]
# Draw Predicted Landmarks
mark_detector.draw_marks(frame, marks, color=(255, 255, 255), thick=2)
# loop over the frames from the video stream
while True:
start = cv2.getTickCount()
# frame = cv2.resize(frame, (750,750))
# frame = cv2.flip(frame, 1)
# fps_time = (cv2.getTickCount() - start)/cv2.getTickFrequency()
# cv2.putText(frame, '%.1ffps'%(1/fps_time), (frame.shape[1]-65,15), cv2.FONT_HERSHEY_DUPLEX, 0.5, (0,255,0))
# show the frame
cv2.imshow("face landmarks", frame)
# writer.write(frame)
key = cv2.waitKey(1)
# if the `q` key was pressed, break from the loop
if key == ord("q") or key == 0xFF:
break
# do a bit of cleanup
cv2.destroyAllWindows()
def single_main():
"""MAIN"""
cam = cv2.VideoCapture(VIDEO_PATH)
_, sample_frame = cam.read()
# Load Landmark detector
mark_detector = MarkDetector()
# Setup process and queues for multiprocessing
img_queue = Queue()
box_queue = Queue()
img_queue.put(sample_frame)
box_process = Process(target=get_face,
args=(
mark_detector,
img_queue,
box_queue,
))
box_process.start()
while True:
frame_got, frame = cam.read()
if frame_got is False:
break
img_queue.put(frame)
start = cv2.getTickCount()
# Get face from box queue.
faceboxes = box_queue.get()
if faceboxes is not None:
for facebox in faceboxes:
# if facebox is not None:
# Detect landmarks from image of 64x64.
face_img = frame[facebox[1]:facebox[3], facebox[0]:facebox[2]]
# cv2.rectangle(frame, (facebox[0], facebox[1]), (facebox[2], facebox[3]), (0, 255, 0), 2)
face_img = cv2.resize(face_img,
(CNN_INPUT_SIZE, CNN_INPUT_SIZE))
face_img = cv2.cvtColor(face_img, cv2.COLOR_BGR2GRAY)
face_img0 = face_img.reshape(1, CNN_INPUT_SIZE, CNN_INPUT_SIZE,
1)
marks = mark_detector.detect_marks_keras(
face_img0) # landmark predictor
marks *= facebox[2] - facebox[0]
marks[:, 0] += facebox[0]
marks[:, 1] += facebox[1]
# Draw Predicted Landmarks
mark_detector.draw_marks(frame, marks, color=(255, 255, 255))
fps_time = (cv2.getTickCount() - start) / cv2.getTickFrequency()
cv2.putText(frame, '%.1ffps' % (1 / fps_time),
(frame.shape[1] - 65, 15), cv2.FONT_HERSHEY_DUPLEX, 0.5,
(127, 127, 127))
# Show preview.
cv2.imshow("Preview", frame)
if cv2.waitKey(10) == ord('q'):
break
# Clean up the multiprocessing process.
box_process.terminate()
box_process.join()
# out.release()
cam.release()
cv2.destroyAllWindows()
