def main():
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--draw-markers", action="store_true", default=False,
help="")
ap.add_argument("-c", "--draw-confidence", action="store_true", default=False,
help="")
ap.add_argument("-t", "--confidence-threshold", type=float, default=0.9,
help="")
ap.add_argument("-p", "--draw-pose", action="store_false", default=True,
help="")
ap.add_argument("-u", "--draw-unstable", action="store_true", default=False,
help="")
ap.add_argument("-s", "--draw-segmented", action="store_true", default=False,
help="")
args = vars(ap.parse_args())
confidence_threshold = args["confidence_threshold"]
"""MAIN"""
# Video source from webcam or video file.
video_src = 0
cam = cv2.VideoCapture(video_src)
_, sample_frame = cam.read()
# Introduce mark_detector to detect landmarks.
mark_detector = MarkDetector()
# Setup process and queues for multiprocessing.
img_queue = Queue()
box_queue = Queue()
img_queue.put(sample_frame)
if isWindows():
thread = threading.Thread(target=get_face, args=(mark_detector, confidence_threshold, img_queue, box_queue))
thread.daemon = True
thread.start()
else:
box_process = Process(target=get_face,
args=(mark_detector, confidence_threshold, img_queue, box_queue))
box_process.start()
# Introduce pose estimator to solve pose. Get one frame to setup the
# estimator according to the image size.
height, width = sample_frame.shape[:2]
pose_estimator = PoseEstimator(img_size=(height, width))
# Introduce scalar stabilizers for pose.
pose_stabilizers = [Stabilizer(
state_num=2,
measure_num=1,
cov_process=0.1,
cov_measure=0.1) for _ in range(6)]
while True:
# Read frame, crop it, flip it, suits your needs.
frame_got, frame = cam.read()
if frame_got is False:
break
# Crop it if frame is larger than expected.
# frame = frame[0:480, 300:940]
# If frame comes from webcam, flip it so it looks like a mirror.
if video_src == 0:
frame = cv2.flip(frame, 2)
# Pose estimation by 3 steps:
# 1. detect face;
# 2. detect landmarks;
# 3. estimate pose
# Feed frame to image queue.
img_queue.put(frame)
# Get face from box queue.
result = box_queue.get()
if result is not None:
if args["draw_confidence"]:
mark_detector.face_detector.draw_result(frame, result)
# unpack result
facebox, confidence = result
# fix facebox if needed
if facebox[1] > facebox[3]:
facebox[1] = 0
if facebox[0] > facebox[2]:
facebox[0] = 0
# Detect landmarks from image of 128x128.
face_img = frame[facebox[1]: facebox[3],
facebox[0]: facebox[2]]
face_img = cv2.resize(face_img, (CNN_INPUT_SIZE, CNN_INPUT_SIZE))
face_img = cv2.cvtColor(face_img, cv2.COLOR_BGR2RGB)
marks = mark_detector.detect_marks(face_img)
# Convert the marks locations from local CNN to global image.
marks *= (facebox[2] - facebox[0])
marks[:, 0] += facebox[0]
marks[:, 1] += facebox[1]
# segment the image based on markers and facebox
seg = Segmenter(facebox, marks, frame.shape[1], frame.shape[0])
if args["draw_segmented"]:
mark_detector.draw_box(frame, seg.getSegmentBBs())
cv2.imshow("fg", seg.getSegmentJSON()["faceGrid"])
if args["draw_markers"]:
mark_detector.draw_marks(
frame, marks, color=(0, 255, 0))
# Try pose estimation with 68 points.
pose = pose_estimator.solve_pose_by_68_points(marks)
# Stabilize the pose.
stable_pose = []
pose_np = np.array(pose).flatten()
for value, ps_stb in zip(pose_np, pose_stabilizers):
ps_stb.update([value])
stable_pose.append(ps_stb.state[0])
stable_pose = np.reshape(stable_pose, (-1, 3))
if args["draw_unstable"]:
pose_estimator.draw_annotation_box(
frame, pose[0], pose[1], color=(255, 128, 128))
if args["draw_pose"]:
pose_estimator.draw_annotation_box(
frame, stable_pose[0], stable_pose[1], color=(128, 255, 128))
# Show preview.
cv2.imshow("Preview", frame)
if cv2.waitKey(10) == 27:
break
# Clean up the multiprocessing process.
if not isWindows():
box_process.terminate()
box_process.join()
def thread_func(args, detector, predictor, img_queue, result_queue):
"""Get face from image queue. This function is used for multiprocessing"""
# Introduce mark_detector to detect landmarks.
gaze_model = args["gaze_net"]
eye_size = args["eye_size"]
face_size = args["face_size"]
inputs = args["inputs"]
outputs = args["outputs"]
print("[INFO] loading gaze predictor...")
gaze_detector = GazeEstimator(gaze_model=gaze_model,
eye_image_size=eye_size,
face_image_size=face_size,
inputs=inputs,
outputs=outputs)
# init variables
detectorWidth = 400
faceBoxScale = 0.15
while True:
# get the image
try:
frame = img_queue.get(timeout=1)
except Q.Empty:
print("Image Q empty, thread exiting!")
return
# update factors
originalWidth = frame.shape[1]
factor = originalWidth / detectorWidth
# resize for face detection
image = imutils.resize(frame, width=detectorWidth)
# convert to grayscale for face detection
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# actually run face detection
faceboxes, scores, idx = detector.run(image, 0)
if faceboxes is not None and len(faceboxes) > 0:
facebox = faceboxes[0]
confidence = scores[0]
# get 5 landmarks
marks = predictor(gray, facebox)
# convert marks to np array
marks = face_utils.shape_to_np(marks)
leftEyeMarks = []
rightEyeMarks = []
# pull out left and right eye marks
for (i, (x, y)) in enumerate(marks):
[x, y] = [int(x * factor), int(y * factor)]
if i == 0 or i == 1:
leftEyeMarks.append([x, y])
if i == 2 or i == 3:
rightEyeMarks.append([x, y])
# convert the facebox from dlib format to regular BB and
# rescale it back to original image size
facebox = utils.dlib_to_box(facebox, factor, faceBoxScale)
# segment the image based on markers and facebox
seg = Segmenter(facebox, leftEyeMarks, rightEyeMarks,
frame.shape[1], frame.shape[0])
segments = seg.getSegmentJSON()
# detect gaze
gaze = gaze_detector.detect_gaze(frame, segments["leftEye"],
segments["rightEye"],
segments["face"],
segments["faceGrid"])
# pack result
result = [gaze, frame]
result_queue.put(result)
else:
result_queue.put(None)