def get_all_poses(self, image, mark_detector=None, face_detector=None, draw=False, draw_marks=False):
"""
Get all the poses in an image
"""
CNN_INPUT_SIZE = 128
print("Initializing detectors")
# Introduce mark_detector to detect landmarks.
if mark_detector is None:
from .mark_detector import MarkDetector
mark_detector = MarkDetector()
if face_detector is None:
from .mark_detector import FaceDetector
face_detector = FaceDetector()
confidences, faceboxes = face_detector.get_faceboxes(
image, threshold=0.2)
poses = []
print("{} FACEBOXES".format(len(faceboxes)))
for facebox in faceboxes:
if min(facebox) < 0:
continue
# Detect landmarks from image of 128x128.
face_img = image[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]
if draw_marks:
mark_detector.draw_marks(image, marks, color=(0, 0, 255))
detected_face = image[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
# Try pose estimation with 68 points.
pose = self.solve_pose_by_68_points(marks)
#pose = pose_estimator.solve_pose(marks)
poses.append((pose[0].copy(), pose[1].copy()))
if draw:
self.draw_annotation_box(
image, pose[0], pose[1], color=(255, 128, 128))
return poses
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 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 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()
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))
# 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.
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))
right_corner = tuple([int(i) for i in marks[36]])
left_corner = tuple([int(i) for i in marks[45]])
# print(marks[36], marks[45])
cv2.line(frame, right_corner, left_corner, (255, 0, 0), 2)
pixel_distance = int(
math.sqrt((right_corner[0] - left_corner[0])**2 +
(right_corner[1] - left_corner[1])**2))
estimated_distance = (real_width * focal_length) / pixel_distance
cv2.putText(frame, str(round(estimated_distance, 2)), (100, 100),
cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 0, 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.
print(steady_pose[1])
pose_estimator.draw_axes(frame, steady_pose[0], steady_pose[1])
angles = process_eyes(frame, marks)
if bool(angles) is True:
# print(angles)
angles = cvt_to_radians(angles)
rotated_vector = rotate_vector(steady_pose[0],
angles['right'][0],
angles['right'][1])
shifted_translation_vector = np.copy(steady_pose[1])
shifted_translation_vector[0] += 50
shifted_translation_vector[1] += 50
pose_estimator.draw_axes(frame, rotated_vector,
shifted_translation_vector)
# Show preview.
cv2.imshow("Preview", frame)
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)
#cam.set(3, 640)
#cam.set(4, 480)
sF = 1.05
_, 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)
img_queue.put(sample_frame)
thread = threading.Thread(target=get_face,
args=(mark_detector, img_queue, box_queue))
thread.daemon = True
thread.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()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 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.f
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(facebox,
frame,
stabile_pose[0],
stabile_pose[1],
color=(128, 255, 128))
ret, img = cap.read()
# gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# blur = cv2.GaussianBlur(gray,(5,5),0)
# thresh = cv2.adaptiveThreshold(blur,255,1,1,11,2)
x = width / 2 + 275
y = height / 2 + 275
text_color = (255, 0, 0)
cv2.imshow("input", cv2.resize(frame, (800, 600)))
# Show preview.
if cv2.waitKey(10) == 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)
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)
