from multiprocessing import Process, Queue
import numpy as np
import cv2
import imutils
from imutils.video import VideoStream
from mark_detector import MarkDetector
import time
from kalman_filter import Stabilizer
from optical_flow_tracker import Tracker
import dlib
CNN_INPUT_SIZE = 64
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()
if __name__ == "__main__":
webcam_main()
