def main():
"""MAIN"""
# Video source from webcam or video file.
video_src = args.cam if args.cam is not None else args.video
if video_src is None:
print("Warning: video source not assigned, default webcam will be used.")
video_src = 0
cap = cv2.VideoCapture(video_src)
if video_src == 0:
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
_, sample_frame = cap.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)
box_process = Process(target=get_face, args=(
mark_detector, 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))
if args.out != None:
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
output_movie = cv2.VideoWriter(args.out, fourcc, 30, (width, height))
# 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)]
tm = cv2.TickMeter()
cnt = 0
input_path = args.input_path
listdir = os.listdir(input_path)
for v_name in listdir:
v_path = os.path.join(input_path, v_name)
cap = cv2.VideoCapture(v_path)
while True:
# Read frame, crop it, flip it, suits your needs.
frame_got, frame = cap.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.
facebox = box_queue.get()
if facebox is not None:
# 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)
tm.start()
marks = mark_detector.detect_marks(face_img)
tm.stop()
# Convert the marks locations from local CNN to global image.
marks *= (facebox[2] - facebox[0])
marks[:, 0] += facebox[0]
marks[:, 1] += facebox[1]
# Uncomment following line to show raw marks.
# mark_detector.draw_marks(frame, marks, color=(0, 255, 0))
# Uncomment following line to show facebox.
# mark_detector.draw_box(frame, [facebox])
# Try pose estimation with 68 points.
pose = pose_estimator.solve_pose_by_68_points(marks)
# Stabilize the pose.
steady_pose = []
pose_np = np.array(pose).flatten()
for value, ps_stb in zip(pose_np, pose_stabilizers):
ps_stb.update([value])
steady_pose.append(ps_stb.state[0])
steady_pose = np.reshape(steady_pose, (-1, 3))
# Uncomment following line to draw pose annotation on frame.
# pose_estimator.draw_annotation_box(
# frame, pose[0], pose[1], color=(255, 128, 128))
# Uncomment following line to draw stabile pose annotation on frame.
pose_estimator.draw_annotation_box(
frame, steady_pose[0], steady_pose[1], color=(128, 255, 128))
# Uncomment following line to draw head axes on frame.
# pose_estimator.draw_axes(frame, steady_pose[0], steady_pose[1])
# Show preview.
# cv2.imshow("Preview", frame)
# if cv2.waitKey(10) == 27:
# break
if args.out != None:
output_movie.write(frame)
else:
cv2.imshow("Preview", frame)
cnt = cnt + 1
if cnt % 100 == 0:
print(str(cnt), flush=True)
# Clean up the multiprocessing process.
box_process.terminate()
box_process.join()
cv2.destroyAllWindows()
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()
[355.48022, 222.92123], [342.7749, 221.94604]])
pose = pose_estimator.solve_pose_by_68_points(marks)
# Stabilize the pose.
steady_pose = []
pose_np = np.array(pose).flatten()
for value, ps_stb in zip(pose_np, pose_stabilizers):
ps_stb.update([value])
steady_pose.append(ps_stb.state[0])
steady_pose = np.reshape(steady_pose, (-1, 3))
# Uncomment following line to draw pose annotation on frame.
# pose_estimator.draw_annotation_box(
# frame, pose[0], pose[1], color=(255, 128, 128))
# Uncomment following line to draw stabile pose annotation on frame.
pose_estimator.draw_annotation_box(frame,
steady_pose[0],
steady_pose[1],
color=(128, 255, 128))
# Uncomment following line to draw head axes on frame.
# pose_estimator.draw_axes(frame, steady_pose[0], steady_pose[1])
# Show preview.
cv2.imshow("Preview", frame)
if cv2.waitKey(10) == 27:
break
# Clean up the multiprocessing process.
def main():
#bagreader = BagFileReader(args.video, 640,480,848,480,30,30)
bagreader = BagFileReader(args.video, 640, 480, 640, 480, 15, 15)
# Introduce mark_detector to detect landmarks.
mark_detector = MarkDetector()
sample_frame = bagreader.get_color_frame()
sample_frame = cv2.cvtColor(sample_frame, cv2.COLOR_BGR2RGB)
height, width, _ = sample_frame.shape
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
out = cv2.VideoWriter('output-%s.avi' % args.name_output, fourcc, args.fps,
(width, height))
# 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()
# 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)
]
tm = cv2.TickMeter()
while True:
t1 = time.time()
# Read frame, crop it, flip it, suits your needs.
frame = bagreader.get_color_frame()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
if frame 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.
faceboxes = box_queue.get()
print(faceboxes)
mess = "Not detect pose"
if faceboxes is not None:
if isinstance(faceboxes[1], int):
faceboxes = [faceboxes]
for facebox in faceboxes:
# 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)
tm.start()
marks = mark_detector.detect_marks([face_img])
tm.stop()
# Convert the marks locations from local CNN to global image.
marks *= (facebox[2] - facebox[0])
marks[:, 0] += facebox[0]
marks[:, 1] += facebox[1]
# Uncomment following line to show raw marks.
# mark_detector.draw_marks(
# frame, marks, color=(0, 255, 0))
# Uncomment following line to show facebox.
# mark_detector.draw_box(frame, [facebox])
# Try pose estimation with 68 points.
pose = pose_estimator.solve_pose_by_68_points(marks)
# Stabilize the pose.
steady_pose = []
pose_np = np.array(pose).flatten()
for value, ps_stb in zip(pose_np, pose_stabilizers):
ps_stb.update([value])
steady_pose.append(ps_stb.state[0])
steady_pose = np.reshape(steady_pose, (-1, 3))
# Uncomment following line to draw pose annotation on frame.
pose_estimator.draw_annotation_box(frame,
pose[0],
pose[1],
color=(255, 128, 128))
# Uncomment following line to draw stabile pose annotation on frame.
t2 = time.time()
mess = round(1 / (t2 - t1), 2)
# pose_estimator.draw_annotation_box(
# frame, steady_pose[0], steady_pose[1], color=(128, 255, 128))
# Uncomment following line to draw head axes on frame.
# pose_estimator.draw_axes(frame, stabile_pose[0], stabile_pose[1])
cv2.putText(frame,
"FPS: " + "{}".format(mess), (20, 20),
cv2.FONT_HERSHEY_SIMPLEX,
0.75, (0, 255, 0),
thickness=2)
# Show preview.
cv2.imshow("Preview", frame)
out.write(frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
out.release()
# Clean up the multiprocessing process.
box_process.terminate()
box_process.join()
def main():
"""MAIN"""
cv2.namedWindow("Test") # Create a named window
cv2.moveWindow("Test", 900, 600) # Move it to (40,30)
screenWidth, screenHeight = pyautogui.size()
st = 'Last command'
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
_, sample_frame = cap.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)
box_process = Process(target=get_face,
args=(
mark_detector,
img_queue,
box_queue,
))
box_process.start()
# Setting up process for listening to audio commands
voice_command_queue = Q()
stt_process = Thread(target=get_voice_command,
args=(voice_command_queue, ))
stt_process.setDaemon(True)
stt_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)
]
tm = cv2.TickMeter()
while True:
# Read frame, crop it, flip it, suits your needs.
frame_got, frame = cap.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.
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.
facebox = box_queue.get()
if facebox is not None:
# 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)
tm.start()
marks = mark_detector.detect_marks([face_img])
tm.stop()
# Convert the marks locations from local CNN to global image.
marks *= (facebox[2] - facebox[0])
marks[:, 0] += facebox[0]
marks[:, 1] += facebox[1]
# Uncomment following line to show raw marks.
# mark_detector.draw_marks(
# frame, marks, color=(0, 255, 0))
# Uncomment following line to show facebox.
# mark_detector.draw_box(frame, [facebox])
# Try pose estimation with 68 points.
pose = pose_estimator.solve_pose_by_68_points(marks)
# Stabilize the pose.
steady_pose = []
pose_np = np.array(pose).flatten()
for value, ps_stb in zip(pose_np, pose_stabilizers):
ps_stb.update([value])
steady_pose.append(ps_stb.state[0])
steady_pose = np.reshape(steady_pose, (-1, 3))
# Uncomment following line to draw pose annotation on frame.
# pose_estimator.draw_annotation_box(
# frame, pose[0], pose[1], color=(255, 128, 128))
# Uncomment following line to draw stabile pose annotation on frame.
pose_estimator.draw_annotation_box(frame,
steady_pose[0],
steady_pose[1],
color=(255, 128, 128))
# Uncomment following line to draw head axes on frame.
endpoints = pose_estimator.getEndPoints(frame, steady_pose[0],
steady_pose[1])
deltax = endpoints[1][0] - endpoints[0][0]
deltay = endpoints[1][1] - endpoints[0][1]
xpos = math.floor((deltax + 44) * screenWidth / 88)
ypos = math.floor((deltay + 14) * screenHeight / 58)
# print(xpos, ypos)
pyautogui.moveTo(xpos, ypos)
if not voice_command_queue.empty():
command = voice_command_queue.get_nowait()
if 'click' in command or 'select' in command:
pyautogui.click()
st = 'Click'
elif 'double' in command or 'in' in command:
pyautogui.doubleClick()
st = 'Double Click'
elif 'right' in command or 'menu' in command or 'light' in command:
pyautogui.rightClick()
st = 'Right Click'
print(command)
cv2.putText(frame, st, (0, 100), cv2.FONT_HERSHEY_SIMPLEX, 20, 255)
scale_percent = 30
# calculate the 50 percent of original dimensions
width = int(frame.shape[1] * scale_percent / 100)
height = int(frame.shape[0] * scale_percent / 100)
# dsize
dsize = (width, height)
# resize image
output = cv2.resize(frame, dsize)
cv2.moveWindow("Test", screenWidth - width, screenHeight - height)
# Show preview.
cv2.imshow("Test", output)
if cv2.waitKey(10) == 27:
break
# Clean up the multiprocessing process.
box_process.terminate()
box_process.join()
def main():
"""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)
box_process = Process(target=get_face, args=(
mark_detector, 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.
facebox = box_queue.get()
if facebox is not None:
# 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]
# Uncomment following line to show raw marks.
# 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.
stabile_pose = []
pose_np = np.array(pose).flatten()
for value, ps_stb in zip(pose_np, pose_stabilizers):
ps_stb.update([value])
stabile_pose.append(ps_stb.state[0])
stabile_pose = np.reshape(stabile_pose, (-1, 3))
# Uncomment following line to draw pose annotaion on frame.
# pose_estimator.draw_annotation_box(
# frame, pose[0], pose[1], color=(255, 128, 128))
# Uncomment following line to draw stabile pose annotaion on frame.
pose_estimator.draw_annotation_box(
frame, stabile_pose[0], stabile_pose[1], color=(128, 255, 128))
# Show preview.
cv2.imshow("Preview", frame)
if cv2.waitKey(10) == 27:
break
# Clean up the multiprocessing process.
box_process.terminate()
box_process.join()
def process_image(transform, processing_model, img):
global mark_detector, box_process, img_queue, box_queue, pose_estimator, pose_stabilizers, tm, width, height
tracks = []
try:
frame = img
h, w, d = frame.shape
if pose_estimator is None or w != width or h != height:
# sample_frame = frame
# img_queue.put(sample_frame)
# Introduce pose estimator to solve pose. Get one frame to setup the
# estimator according to the image size.
height, width = h, w
# (height, width) = (1062 , 485)
# (height, width) = (720 , 1280)
pose_estimator = PoseEstimator(img_size=(height, width))
# Feed frame to image queue.
img_queue.put(frame)
# Get face from box queue.
facebox = box_queue.get()
if facebox is not None:
# 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)
tm.start()
marks = mark_detector.detect_marks(face_img)
tm.stop()
# Convert the marks locations from local CNN to global image.
marks *= (facebox[2] - facebox[0])
marks[:, 0] += facebox[0]
marks[:, 1] += facebox[1]
# Uncomment following line to show raw marks.
# mark_detector.draw_marks(frame, marks, color=(0, 255, 0))
# Uncomment following line to show facebox.
# mark_detector.draw_box(frame, [facebox])
# Try pose estimation with 68 points.
pose = pose_estimator.solve_pose_by_68_points(marks)
# Stabilize the pose.
steady_pose = []
pose_np = np.array(pose).flatten()
for value, ps_stb in zip(pose_np, pose_stabilizers):
ps_stb.update([value])
steady_pose.append(ps_stb.state[0])
steady_pose = np.reshape(steady_pose, (-1, 3))
# Uncomment following line to draw pose annotation on frame.
# pose_estimator.draw_annotation_box(
# frame, pose[0], pose[1], color=(255, 128, 128))
# Uncomment following line to draw stabile pose annotation on frame.
pose_estimator.draw_annotation_box(frame,
steady_pose[0],
steady_pose[1],
color=(128, 255, 128))
# Uncomment following line to draw head axes on frame.
# pose_estimator.draw_axes(frame, steady_pose[0], steady_pose[1])
img = frame
except Exception as e:
track = traceback.format_exc()
print(track)
print("HandPose Exception", e)
pass
return tracks, img
marks = mark_detector.detect_marks(face_img)
tm.stop()
# Convert the locations from local face area to the global image.
marks *= (x2 - x1)
marks[:, 0] += x1
marks[:, 1] += y1
# Try pose estimation with 68 points.
pose = pose_estimator.solve_pose_by_68_points(marks)
# All done. The best way to show the result would be drawing the
# pose on the frame in realtime.
# Do you want to see the pose annotation?
pose_estimator.draw_annotation_box(
frame, pose[0], pose[1], color=(0, 255, 0))
# Do you want to see the head axes?
# pose_estimator.draw_axes(frame, pose[0], pose[1])
# Do you want to see the marks?
# mark_detector.draw_marks(frame, marks, color=(0, 255, 0))
# Do you want to see the facebox?
# mark_detector.draw_box(frame, [facebox])
# Show preview.
cv2.imshow("Preview", frame)
if cv2.waitKey(1) == 27:
break
class PoseHeadNode(object):
def __init__(self):
# subscribe
rospy.Subscriber("/pose_estimator/pose", Persons, self.pose_cb)
rospy.Subscriber("/camera/color/image_raw", Image, self.color_callback)
# publish
self.pub_headpose_info = rospy.Publisher("/headpose_info", GazingInfo, \
queue_size=10)
#data
self.humans = None
self.color_img = None
self.init()
def init(self):
self.detector = RetinaFace('./model/R50', 0, 0, 'net3')
self.mark_detector = MarkDetector()
self.pose_stabilizers = [
Stabilizer(state_num=2,
measure_num=1,
cov_process=0.1,
cov_measure=0.1) for _ in range(6)
]
sample_img = None
#if self.color_img is None:
# print("None")
# time.sleep(0.2)
#
#height, width, _ = self.color_img.shape
self.pose_estimator = PoseEstimator(img_size=(480, 640))
def pose_cb(self, data):
if self.color_img is None:
return
h, w = self.color_img.shape[:2]
# ros topic to Person instance
humans = []
for p_idx, person in enumerate(data.persons):
human = Human([])
for body_part in person.body_part:
part = BodyPart('', body_part.part_id, body_part.x,
body_part.y, body_part.confidence)
human.body_parts[body_part.part_id] = part
humans.append(human)
self.humans = humans
#self.image_test_pose = TfPoseEstimator.draw_humans(self.color_img, humans, imgcopy=False)
def draw_pose(self, img, humans, parts=[4, 7]):
if img is None or humans is None:
#print("Cannot draw pose on {} image and {} humans".format(img, humans))
return None
image_h, image_w, _ = img.shape
if parts is None or len(parts) == 0:
return TfPoseEstimator.draw_humans(img, humans, imgcopy=False)
else:
for human in humans:
for part in parts:
if part in human.body_parts.keys():
body_part = human.body_parts[part]
coord = (int(body_part.x * image_w + 0.5), \
int(body_part.y * image_h + 0.5))
img = cv2.circle(img, coord, 2, (0, 255, 0))
return img
def color_callback(
self, data
): # Need semaphore to protect self.color_img, because it is also used by is_waving_hand function
#print("Having color image")
#cv_image = self.bridge.imgmsg_to_cv2(data, data.encoding)
#print(cv_image)
decoded = np.frombuffer(data.data, np.uint8)
img = np.reshape(decoded, (480, 640, 3))
#print("COLOR_CALLBACK", img.shape)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
self.color_img = img
def get_top_vertice_human(self, human, img_w, img_h):
sorted_id = sorted(human["pose"].body_parts.keys())
body_part = human["pose"].body_parts[sorted_id[0]]
return (int(body_part.x * image_w + 0.5), \
int(body_part.y * image_h + 0.5))
def is_gazing(self, face_direction, human, img):
def get_angle_direct_with_peakhand(face_direction, peak):
d0, d1 = face_direction
peak_direction = (peak[0] - d0[0], peak[1] - d0[1])
face_direct = (d1[0] - d0[0], d1[1] - d0[1])
d_peak_direction = np.sqrt(peak_direction[0]**2 +
peak_direction[1]**2)
d_face_direct = np.sqrt(face_direct[0]**2 + face_direct[1]**2)
return (peak_direction[0]*face_direct[0] + \
peak_direction[1]*face_direct[1])/(d_peak_direction*\
d_face_direct)
thresh = 0.1 #0.5 #cos(PI/3)
image_h, image_w, _ = img.shape
if 4 in human.body_parts.keys():
body_part = human.body_parts[4]
coord = (int(body_part.x * image_w + 0.5), \
int(body_part.y * image_h + 0.5))
print("angle left = ",
get_angle_direct_with_peakhand(face_direction, coord))
return get_angle_direct_with_peakhand(face_direction,
coord) > thresh
if 7 in human.body_parts.keys():
body_part = human.body_parts[7]
coord = (int(body_part.x * image_w + 0.5), \
int(body_part.y * image_h + 0.5))
print("angle right = ",
get_angle_direct_with_peakhand(face_direction, coord))
return get_angle_direct_with_peakhand(face_direction,
coord) > thresh
return False
def get_gazing_status(self, face_directions, face_coords, humans, img):
def get_dist_to_humans(human_coords, coord):
result = []
for _coord in human_coords:
result.append(np.sqrt((coord[0]-_coord[0])**2) + \
(coord[1]-_coord[1])**2)
return result
image_h, image_w, _ = img.shape
gazing_status = []
human_coords = []
for human in humans:
sorted_id = sorted(human.body_parts.keys())
body_part = human.body_parts[sorted_id[0]]
coord = (int(body_part.x * image_w + 0.5), \
int(body_part.y * image_h + 0.5))
human_coords.append(coord)
for fdirection, fcoord in zip(face_directions, face_coords):
dists = get_dist_to_humans(human_coords, fcoord)
idx = np.argmin(dists)
gazing_status.append(self.is_gazing(fdirection, humans[idx], img))
del human_coords[idx]
return gazing_status
def publish_headpose_info(self, viz=True):
thresh = 0.8
time.sleep(0.1)
if self.color_img is None or self.humans is None:
print("Color img None")
return
im_shape = self.color_img.shape
#scales = [1024, 1980]
scales = [480 * 2, 640 * 2]
target_size = scales[0]
max_size = scales[1]
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
im_scale = float(target_size) / float(im_size_min)
thresh = 0.8
if np.round(im_scale * im_size_max) > max_size:
im_scale = float(max_size) / float(im_size_max)
scales = [im_scale]
frame = copy.deepcopy(self.color_img)
t1 = time.time()
faceboxes = self.mark_detector.extract_cnn_facebox(frame)
mess = "Not detect pose"
face_directions = []
face_coords = []
if faceboxes is not None and len(faceboxes) > 0:
if isinstance(faceboxes[1], int):
faceboxes = [faceboxes]
for facebox in faceboxes:
#facebox = facebox.astype(np.int)
# Detect landmarks from image of 128x128.
face_img = frame[facebox[1]:facebox[3], facebox[0]:facebox[2]]
face_coords.append((int((facebox[0]+facebox[2])/2), \
int((facebox[1]+facebox[3])/2)))
face_img = cv2.resize(face_img,
(CNN_INPUT_SIZE, CNN_INPUT_SIZE))
face_img = cv2.cvtColor(face_img, cv2.COLOR_BGR2RGB)
marks = self.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]
# Uncomment following line to show raw marks.
self.mark_detector.draw_marks(frame, marks, color=(0, 255, 0))
# Uncomment following line to show facebox.
# mark_detector.draw_box(frame, [facebox])
# Try pose estimation with 68 points.
pose = self.pose_estimator.solve_pose_by_68_points(marks)
# Stabilize the pose.
steady_pose = []
pose_np = np.array(pose).flatten()
for value, ps_stb in zip(pose_np, self.pose_stabilizers):
ps_stb.update([value])
steady_pose.append(ps_stb.state[0])
steady_pose = np.reshape(steady_pose, (-1, 3))
# Uncomment following line to draw pose annotation on frame.
face_direction = self.pose_estimator.draw_annotation_box(
frame, pose[0], pose[1], color=(255, 128, 128))
face_directions.append(face_direction)
# Uncomment following line to draw stabile pose annotation on frame.
t2 = time.time()
mess = round(1 / (t2 - t1), 2)
#self.pose_estimator.draw_annotation_box(
# frame, steady_pose[0], steady_pose[1], color=(128, 255, 128))
# Uncomment following line to draw head axes on frame.
#self.pose_estimator.draw_axes(frame, stabile_pose[0], stabile_pose[1])
gazing_status = self.get_gazing_status(face_directions, face_coords, \
copy.deepcopy(self.humans), self.color_img)
msg = GazingInfo()
msg.is_gazing = []
msg.coords = []
for coord, gazestatus in zip(face_coords, gazing_status):
_coord = Coord2D()
_coord.x = coord[0]
_coord.y = coord[1]
msg.coords.append(_coord)
msg.is_gazing.append(gazestatus)
self.pub_headpose_info.publish(msg)
print(gazing_status)
cv2.putText(frame, "FPS: " + "{}".format(mess), (20, 20), \
cv2.FONT_HERSHEY_SIMPLEX,0.75, (0, 255, 0), thickness=2)
# Show preview.
if viz:
frame = self.draw_pose(frame, self.humans)
if frame is None:
return
cv2.imshow("Preview", frame)
cv2.waitKey(1)
def main():
"""MAIN"""
# Video source from webcam or video file.
video_src = args.cam if args.cam is not None else args.video
if video_src is None:
engine.say(
"Warning: video source not assigned, default webcam will be used")
engine.runAndWait()
video_src = 0
cap = cv2.VideoCapture(video_src)
if video_src == 0:
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
_, sample_frame = cap.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)
box_process = Process(target=get_face,
args=(
mark_detector,
img_queue,
box_queue,
))
box_process.start()
gaze = GazeTracking()
# 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)
]
tm = cv2.TickMeter()
head_flag = 0
gaze_flag = 0
while True:
# Read frame, crop it, flip it, suits your needs.
frame_got, frame = cap.read()
if frame_got is False:
break
# Crop it if frame is larger than expected.
# frame = frame[0:480, 300:940]
#audio_record(AUDIO_OUTPUT, 3)
#sphinx_recog(AUDIO_OUTPUT)
# 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.
facebox = box_queue.get()
gaze.refresh(frame)
frame = gaze.annotated_frame()
text = ""
if facebox is not None:
# 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))
gray = cv2.cvtColor(face_img, cv2.COLOR_BGR2GRAY)
face_img = cv2.cvtColor(face_img, cv2.COLOR_BGR2RGB)
rects = detector(gray, 0)
tm.start()
marks = mark_detector.detect_marks([face_img])
tm.stop()
#
for rect in rects:
# determine the facial landmarks for the face region, then
# convert the facial landmark (x, y)-coordinates to a NumPy
# array
shape = predictor(gray, rect)
shape = face_utils.shape_to_np(
shape) #converting to NumPy Array矩阵运算
mouth = shape[Start:End]
leftEye = shape[lStart:lEnd]
rightEye = shape[rStart:rEnd]
leftEAR = eye_aspect_ratio(leftEye) #眼睛长宽比
rightEAR = eye_aspect_ratio(rightEye)
ear = (leftEAR + rightEAR) / 2.0 #长宽比平均值
lipdistance = lip_distance(shape)
if (lipdistance > YAWN_THRESH):
#print(lipdistance)
flag0 += 1
print("yawning time: ", flag0)
if flag0 >= 40:
cv2.putText(frame, "Yawn Alert", (10, 150),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255),
2)
cv2.putText(frame, "Yawn Alert", (220, 150),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255),
2)
engine.say("don't yawn")
engine.runAndWait()
flag0 = 0
else:
flag0 = 0
if (ear < thresh):
flag += 1
print("eyes closing time: ", flag)
if flag >= frame_check:
cv2.putText(frame,
"****************ALERT!****************",
(10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7,
(0, 0, 255), 2)
cv2.putText(frame,
"****************ALERT!****************",
(10, 250), cv2.FONT_HERSHEY_SIMPLEX, 0.7,
(0, 0, 255), 2)
engine.say("open your eyes")
engine.runAndWait()
flag = 0
else:
flag = 0
if gaze.is_right():
print("Looking right")
text = "Looking right"
elif gaze.is_left():
print("Looking left")
text = "Looking left"
elif gaze.is_up():
text = "Looking up"
else:
text = "Looking center"
if text is not "Looking center":
gaze_flag += 1
if gaze_flag >= 20:
engine.say("look forward")
engine.runAndWait()
gaze_flag = 0
else:
gaze_flag = 0
marks *= (facebox[2] - facebox[0])
marks[:, 0] += facebox[0]
marks[:, 1] += facebox[1]
# Uncomment following line to show raw marks.
# mark_detector.draw_marks(
# frame, marks, color=(0, 255, 0))
# Uncomment following line to show facebox.
# mark_detector.draw_box(frame, [facebox])
# Try pose estimation with 68 points.
pose = pose_estimator.solve_pose_by_68_points(marks)
# get angles
angles = pose_estimator.get_angles(pose[0], pose[1])
if ((-8 > angles[0] or angles[0] > 8)
or (-8 > angles[1] or angles[1] > 8)):
head_flag += 1
if head_flag >= 40:
print(angles[0])
engine.say("please look ahead")
engine.runAndWait()
else:
head_flag = 0
# pose_estimator.draw_info(frame, angles)
# Stabilize the pose.
steady_pose = []
pose_np = np.array(pose).flatten()
for value, ps_stb in zip(pose_np, pose_stabilizers):
ps_stb.update([value])
steady_pose.append(ps_stb.state[0])
steady_pose = np.reshape(steady_pose, (-1, 3))
# Uncomment following line to draw pose annotation on frame.
pose_estimator.draw_annotation_box(frame,
pose[0],
pose[1],
color=(255, 128, 128))
# Uncomment following line to draw stabile pose annotation on frame.
pose_estimator.draw_annotation_box(frame,
steady_pose[0],
steady_pose[1],
color=(128, 255, 128))
# Uncomment following line to draw head axes on frame.
pose_estimator.draw_axes(frame, steady_pose[0], steady_pose[1])
#pose_estimator.show_3d_model
# Show preview.
cv2.imshow("Preview", frame)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
# Clean up the multiprocessing process.
box_process.terminate()
box_process.join()
def main(video_src):
"""MAIN
:param video_src: Source of video to analyze
:type video_src: str or int"""
cam = cv2.VideoCapture(video_src)
_, sample_frame = cam.read()
# Introduce mark_detector to detect landmarks.
mark_detector = MarkDetector()
face_detector = FaceDetector()
# 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 = Process(target=get_faces, args=(face_detector, 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)]
#face expression recognizer initialization
from keras.models import model_from_json
model = model_from_json(open("./model/facial_expression_model_structure.json", "r").read())
model.load_weights('./model/facial_expression_model_weights.h5') #load weights
#-----------------------------
emotions = ('angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral')
while True:
input()
# 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.
#facebox = box_queue.get()
faceboxes = dump_queue(box_queue)
print("{} FACEBOXES".format(len(faceboxes)))
for facebox in faceboxes:
if min(facebox) < 0:
continue
# Detect landmarks from image of 128x128.
face_img = frame[facebox[1]: facebox[3],
facebox[0]: facebox[2]]
if not face_img.shape[0] or not face_img.shape[1]:
continue
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]
# Uncomment following line to show raw marks.
# mark_detector.draw_marks(
# frame, marks, color=(0, 255, 0))
detected_face = frame[facebox[1]: facebox[3],facebox[0]: facebox[2]] #crop detected face
detected_face = cv2.cvtColor(detected_face, cv2.COLOR_BGR2GRAY) #transform to gray scale
detected_face = cv2.resize(detected_face, (48, 48)) #resize to 48x48
# emotion estimation
img_pixels = image.img_to_array(detected_face)
img_pixels = np.expand_dims(img_pixels, axis = 0)
img_pixels /= 255 #pixels are in scale of [0, 255]. normalize all pixels in scale of [0, 1]
predictions = model.predict(img_pixels) #store probabilities of 7 expressions
#find max indexed array 0: angry, 1:disgust, 2:fear, 3:happy, 4:sad, 5:surprise, 6:neutral
max_index = np.argmax(predictions[0])
emotion = emotions[max_index]
#write emotion text above rectangle
#V_FONT_HERSHEY_SIMPLEX normal size sans-serif font
#CV_FONT_HERSHEY_PLAIN small size sans-serif font
#CV_FONT_HERSHEY_DUPLEX normal size sans-serif font (more complex than CV_FONT_HERSHEY_SIMPLEX )
#CV_FONT_HERSHEY_COMPLEX normal size serif font
#CV_FONT_HERSHEY_TRIPLEX normal size serif font (more complex than CV_FONT_HERSHEY_COMPLEX )
#CV_FONT_HERSHEY_COMPLEX_SMALL smaller version of CV_FONT_HERSHEY_COMPLEX
#CV_FONT_HERSHEY_SCRIPT_SIMPLEX hand-writing style font
#CV_FONT_HERSHEY_SCRIPT_COMPLEX more complex variant of CV_FONT_HERSHEY_SCRIPT_SIMPLEX
image_text = ""
for index in range(len(emotions)):
if predictions[0][index]>0.3:
image_text += "{0} : {1} %\n".format(emotions[index], int(predictions[0][index]*100))
space = 0
for text in image_text.strip().split("\n"):
cv2.putText(
img = frame,
text= text,
org =(int(facebox[0]), int(facebox[1])-space),
fontFace = cv2.FONT_HERSHEY_PLAIN,
fontScale = 0.8,
color = (255,255,255),
thickness = 1
)
space += int(0.25*48)
# Try pose estimation with 68 points.
pose = pose_estimator.solve_pose_by_68_points(marks)
#pose = pose_estimator.solve_pose(marks)
# Stabilize the pose.
#stabile_pose = []
#pose_np = np.array(pose).flatten()
#for value, ps_stb in zip(pose_np, pose_stabilizers):
# ps_stb.update([value])
# stabile_pose.append(ps_stb.state[0])
#stabile_pose = np.reshape(stabile_pose, (-1, 3))
# Uncomment following line to draw pose annotaion on frame.
pose_estimator.draw_annotation_box(
frame, pose[0], pose[1], color=(255, 128, 128))
# Uncomment following line to draw stabile pose annotaion on frame.
#pose_estimator.draw_annotation_box(
# frame, stabile_pose[0], stabile_pose[1], color=(128, 255, 128))
# Show preview.
cv2.imshow("Preview", frame)
if cv2.waitKey(10) == 27:
break
# Clean up the multiprocessing process.
box_process.terminate()
box_process.join()
class HeadPoseEstimator():
def __init__(self):
print "OpenCV version: {}".format(cv2.__version__)
self.CNN_INPUT_SIZE = 128
self.mark_detector = MarkDetector()
height = 480
width = 640
self.pose_estimator = PoseEstimator(img_size=(height, width))
self.pose_stabilizers = [
Stabilizer(state_num=2,
measure_num=1,
cov_process=0.1,
cov_measure=0.1) for _ in range(8)
]
self.img_queue = Queue()
self.box_queue = Queue()
self.box_process = Process(target=self.get_face,
args=(
self.mark_detector,
self.img_queue,
self.box_queue,
))
self.process_ctrl_flag = rospy.get_param("process_ctrl", True)
self.process_ctrl_subscriber = rospy.Subscriber(
"/head_pose_estimator/process_ctrl", Bool,
self.process_ctrl_callback)
self.sub_img_name = rospy.get_param("sub_img_name",
"/usb_cam/image_raw")
self.img_subscriber = rospy.Subscriber(self.sub_img_name, Image,
self.img_callback)
self.show_result_img_flag = rospy.get_param("show_result_img", True)
self.show_axis_flag = rospy.get_param("show_axis", True)
self.show_annotation_box_flag = rospy.get_param(
"show_annotation_box", True)
self.pub_result_img_flag = rospy.get_param("pub_result_img", True)
self.img_publisher = rospy.Publisher(
"/head_pose_estimator/result_image", Image, queue_size=10)
self.result_publisher = rospy.Publisher(
"/head_pose_estimator/head_pose", HeadPose, queue_size=10)
self.box_process.start()
def get_face(self, detector, img_queue, box_queue):
"""
画像キューから顔を取り出します。
処理はマルチプロセッシングで行います。
"""
while not rospy.is_shutdown():
image = img_queue.get()
box = detector.extract_cnn_facebox(image)
box_queue.put(box)
def process_ctrl_callback(self, msg):
self.process_ctrl_flag = msg.data
def euler_to_quaternion(self, euler):
# オイラー角からクォータニオンに変換
# 鏡写しになるように値を調整
q = tf.transformations.quaternion_from_euler(euler.x, euler.y, euler.z)
return Quaternion(x=q[1], y=-q[2], z=-q[3], w=q[0])
#return Quaternion(x=q[0], y=q[1], z=q[2], w=q[3])
def img_callback(self, img_msg):
if self.process_ctrl_flag == True:
try:
cv_img = CvBridge().imgmsg_to_cv2(img_msg, "bgr8")
flip_frame = cv2.flip(cv_img, 2) # 画像を鏡写しにする
frame = cv_img
img_height, img_width, _ = frame.shape[:3]
self.img_queue.put(frame)
facebox = self.box_queue.get()
# 顔の検出に成功
if facebox is not None:
# 画像から顔をトリミングしてCNNでlandmarksを見つける
# img[top : bottom, left : right]
face_img = frame[facebox[1]:facebox[3],
facebox[0]:facebox[2]]
face_img = cv2.resize(
face_img, (self.CNN_INPUT_SIZE, self.CNN_INPUT_SIZE))
face_img = cv2.cvtColor(face_img, cv2.COLOR_BGR2RGB)
face_rect = FaceRect()
face_rect.x = facebox[0]
face_rect.y = facebox[1]
face_rect.width = facebox[2] - facebox[0]
face_rect.height = facebox[3] - facebox[1]
#face_rect.x = img_width - facebox[0] - (facebox[2] - facebox[0])
#face_rect.y = facebox[1]
#face_rect.width = facebox[2] - facebox[0]
#face_rect.height = facebox[3] - facebox[1]
marks = self.mark_detector.detect_marks([face_img])
marks *= (facebox[2] - facebox[0])
marks[:, 0] += facebox[0]
marks[:, 1] += facebox[1]
# Uncomment following line to show raw marks.
#self.mark_detector.draw_marks(frame, marks, color=(0, 255, 0))
# Uncomment following line to show facebox.
#self.mark_detector.draw_box(frame, [facebox])
pose = self.pose_estimator.solve_pose_by_68_points(marks)
#rotation = pose[0]
#euler = Vector3(x=rotation[0], y=rotation[1], z=rotation[2])
#quaternion = self.euler_to_quaternion(euler)
# stabilize the pose
steady_pose = []
pose_np = np.array(pose).flatten()
for value, ps_stb in zip(pose_np, self.pose_stabilizers):
ps_stb.update([value])
steady_pose.append(ps_stb.state[0])
steady_pose = np.reshape(steady_pose, (-1, 3))
rotation = steady_pose[0]
euler = Vector3(x=rotation[0],
y=rotation[1],
z=rotation[2])
head_rotation = self.euler_to_quaternion(euler)
#print head_rotation
#print "---"
head_pose = HeadPose()
head_pose.face_rect = face_rect
head_pose.head_rotation = head_rotation
self.result_publisher.publish(head_pose)
# Uncomment following line to draw pose annotation on frame.
#self.pose_estimator.draw_annotation_box(frame, pose[0], pose[1], color=(255, 128, 128))
# Uncomment following line to draw stabile pose annotation on frame.
if self.show_annotation_box_flag == True:
self.pose_estimator.draw_annotation_box(frame,
steady_pose[0],
steady_pose[1],
color=(128,
255,
128))
# Uncomment following line to draw head axes on frame.
# openCV3.4.0では動かない
#self.pose_estimator.draw_axes(frame, steady_pose[0], steady_pose[1])
if self.show_axis_flag == True:
self.pose_estimator.draw_axis(frame, steady_pose[0],
steady_pose[1])
if self.show_result_img_flag == True:
cv2.imshow("result", frame)
cv2.waitKey(1)
if self.pub_result_img_flag == True:
try:
pub_img = CvBridge().cv2_to_imgmsg(frame, "bgr8")
self.img_publisher.publish(pub_img)
except CvBridgeError, e:
rospy.logerr(str(e))
except CvBridgeError, e:
rospy.logerr(str(e))
class PoseHeadNode(object):
def __init__(self):
# subscribe
rospy.Subscriber("/pose_estimator/pose", Persons, self.pose_cb)
rospy.Subscriber("/camera/color/image_raw", Image, self.color_callback)
# publish
#self.pub_headpose_info = rospy.Publisher("/headpose_info", HeadPoseInfo, \
# queue_size=10)
#data
self.humans = None
self.color_img = None
self.init()
def init(self):
self.detector = RetinaFace('./model/R50', 0, 0, 'net3')
self.mark_detector = MarkDetector()
self.pose_stabilizers = [Stabilizer(
state_num=2,
measure_num=1,
cov_process=0.1,
cov_measure=0.1) for _ in range(6)]
sample_img = None
#if self.color_img is None:
# print("None")
# time.sleep(0.2)
#
#height, width, _ = self.color_img.shape
self.pose_estimator = PoseEstimator(img_size=(480, 640))
def pose_cb(self, data):
if self.color_img is None:
return
h, w = self.color_img.shape[:2]
# ros topic to Person instance
humans = []
for p_idx, person in enumerate(data.persons):
human = Human([])
for body_part in person.body_part:
part = BodyPart('', body_part.part_id, body_part.x, body_part.y, body_part.confidence)
human.body_parts[body_part.part_id] = part
humans.append(human)
self.humans = humans
#self.image_test_pose = TfPoseEstimator.draw_humans(self.color_img, humans, imgcopy=False)
def draw_pose(self, img, humans):
if img is None or humans is None:
#print("Cannot draw pose on {} image and {} humans".format(img, humans))
return None
return TfPoseEstimator.draw_humans(img, humans, imgcopy=False)
def color_callback(self, data): # Need semaphore to protect self.color_img, because it is also used by is_waving_hand function
#print("Having color image")
#cv_image = self.bridge.imgmsg_to_cv2(data, data.encoding)
#print(cv_image)
decoded = np.frombuffer(data.data, np.uint8)
img = np.reshape(decoded, (480,640, 3))
print("COLOR_CALLBACK", img.shape)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
self.color_img = img
def publish_headpose_info(self, viz=True):
thresh = 0.8
time.sleep(0.1)
if self.color_img is None:
print("Color img None")
return
im_shape = self.color_img.shape
#scales = [1024, 1980]
scales = [480*2, 640*2]
target_size = scales[0]
max_size = scales[1]
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
im_scale = float(target_size) / float(im_size_min)
thresh = 0.8
if np.round(im_scale * im_size_max) > max_size:
im_scale = float(max_size) / float(im_size_max)
scales = [im_scale]
frame = copy.deepcopy(self.color_img)
t1 = time.time()
faceboxes, landmark = self.detector.detect(frame, \
thresh, scales=scales, do_flip=False)
mess = "Not detect pose"
if faceboxes is not None and len(faceboxes) > 0:
#if isinstance(faceboxes[1], int):
# faceboxes = [faceboxes]
for facebox in faceboxes:
facebox = facebox.astype(np.int)
# 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 = self.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]
# Uncomment following line to show raw marks.
self.mark_detector.draw_marks(
frame, marks, color=(0, 255, 0))
# Uncomment following line to show facebox.
# mark_detector.draw_box(frame, [facebox])
# Try pose estimation with 68 points.
pose = self.pose_estimator.solve_pose_by_68_points(marks)
# Stabilize the pose.
steady_pose = []
pose_np = np.array(pose).flatten()
for value, ps_stb in zip(pose_np, self.pose_stabilizers):
ps_stb.update([value])
steady_pose.append(ps_stb.state[0])
steady_pose = np.reshape(steady_pose, (-1, 3))
# Uncomment following line to draw pose annotation on frame.
self.pose_estimator.draw_annotation_box(
frame, pose[0], pose[1], color=(255, 128, 128))
# Uncomment following line to draw stabile pose annotation on frame.
t2 = time.time()
mess=round(1/(t2-t1), 2)
#self.pose_estimator.draw_annotation_box(
# frame, steady_pose[0], steady_pose[1], color=(128, 255, 128))
# Uncomment following line to draw head axes on frame.
#self.pose_estimator.draw_axes(frame, stabile_pose[0], stabile_pose[1])
cv2.putText(frame, "FPS: " + "{}".format(mess), (20, 20), \
cv2.FONT_HERSHEY_SIMPLEX,0.75, (0, 255, 0), thickness=2)
# Show preview.
if viz:
frame = self.draw_pose(frame, self.humans)
if frame is None:
return
cv2.imshow("Preview", frame)
cv2.waitKey(1)