def webcam_main():
frame = cv2.imread(UPLOAD_PATH)
# frame = cv2.resize(frame, (640, 480))
# frame = cv2.resize(frame, (64,64))
frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
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)
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)
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=1)
print(marks)
if not os.path.exists(OUTPUT_PATH):
os.mkdir(OUTPUT_PATH)
out = ''
for arr in marks:
for num in arr:
out += str(num) + ','
with open(OUTPUT_PATH + '/out.txt', 'w') as f:
f.write(out)
def webcam_main():
print("Camera sensor warming up...")
cv2.namedWindow('face landmarks', cv2.WINDOW_NORMAL)
vs = cv2.VideoCapture(0)
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
# loop over the frames from the video stream
while True:
_, frame = vs.read()
if frame is None:
break
start = cv2.getTickCount()
# frame = cv2.resize(frame, (750,750))
# 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)
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)
mark_detector.draw_democracy(frame, marks)
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 precess_video():
cv2.namedWindow('face landmarks', cv2.WINDOW_NORMAL)
vs = cv2.VideoCapture(VIDEO_PATH)
time.sleep(2.0)
mark_detector = MarkDetector(MODEL_FILE,CNN_INPUT_SIZE)
if MODEL_FILE.split(".")[-1] == "pb":
run_model = 0
elif MODEL_FILE.split(".")[-1] == "hdf5" or MODEL_FILE.split(".")[-1] == "h5":
run_model = 1
else:
print("input model format error !")
return
idx = 0
while True:
_, src = vs.read()
if src is None:
print("The video is precessed over !")
break
start = cv2.getTickCount()
frame = src.copy()
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)
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)
txt_filename = ("%06d" % idx) + ".pts"
txt_file = os.path.join(OUTPUT_DIR, txt_filename)
txt = open(txt_file, 'w')
txt.write("version: 1\n")
txt.write("n_points: %d\n" % marks.size)
txt.write("{\n")
for mark in marks:
txt.write("%.2f %.2f\n" % (mark[0], mark[1]))
txt.write("}\n")
txt.close()
image_filename = ("%06d" % idx) + ".jpg"
image_file = os.path.join(OUTPUT_DIR, image_filename)
cv2.imwrite(image_file, src)
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)
key = cv2.waitKey(1)
if key == ord("q") or key == 0xFF:
break
idx = idx + 1
# do a bit of cleanup
cv2.destroyAllWindows()