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 get_face(img_queue, box_queue):
'''
Get face from image queue. this function is used for multiprocessing.
introduce mark_detector to detect landmarks
'''
mark_detector = MarkDetector()
while True:
image = img_queue.get()
boxes = mark_detector.extract_cnn_facebox(image)
box_queue.put(boxes) # the boxes of faces.
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 = cv.VideoCapture(video_src)
if video_src == 0:
cap.set(cv.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()
while True:
# Read frame, crop it, flip it, suits your needs.
frame_got, frame = cap.read()
if frame_got is False:
break
# If frame comes from webcam, flip it so it looks like a mirror.
if video_src == 0:
frame = cv.flip(frame, 2)
# Feed frame to image queue.
img_queue.put(frame)
# Get face from box queue.
facebox = box_queue.get()
if facebox is not None:
mark_detector.draw_box(frame, [facebox], box_color=(255, 125, 0))
# Show preview.
cv.imshow("Preview", frame)
if cv.waitKey(10) == 27: # press ESC key
break
# Clean up the multiprocessing process.
box_process.terminate()
box_process.join()
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 gen3(camera):
"""Video streaming generator function."""
mark_detector = None
flag1 = True
if mark_detector == None:
mark_detector = MarkDetector()
# 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:
frame, flag1 = camera.get_frame3(mark_detector, pose_stabilizers,
flag1)
# print (frame)
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
def init_model(transform):
global mark_detector, box_process, img_queue, box_queue, pose_estimator, pose_stabilizers, tm
# 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 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()
return None, None
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 __init__(self):
# Load the parameters
self.conf = config()
# initialize dlib's face detector (HOG-based) and then create the
# facial landmark predictor
print("[INFO] loading facial landmark predictor...")
self.detector = dlib.get_frontal_face_detector()
self.predictor = dlib.shape_predictor(self.conf.shape_predictor_path)
# grab the indexes of the facial landmarks for the left and
# right eye, respectively
(self.lStart, self.lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
(self.rStart, self.rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
# initialize the video stream and sleep for a bit, allowing the
# camera sensor to warm up
self.cap = cv2.VideoCapture(0)
if self.conf.vedio_path == 0:
self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
_, sample_frame = self.cap.read()
# Introduce mark_detector to detect landmarks.
self.mark_detector = MarkDetector()
# Setup process and queues for multiprocessing.
self.img_queue = Queue()
self.box_queue = Queue()
self.img_queue.put(sample_frame)
self.box_process = Process(target=get_face, args=(
self.mark_detector, self.img_queue, self.box_queue,))
self.box_process.start()
# Introduce pose estimator to solve pose. Get one frame to setup the
# estimator according to the image size.
self.height, self.width = sample_frame.shape[:2]
self.pose_estimator = PoseEstimator(img_size=(self.height, self.width))
# Introduce scalar stabilizers for pose.
self.pose_stabilizers = [Stabilizer(
state_num=2,
measure_num=1,
cov_process=0.1,
cov_measure=0.1) for _ in range(6)]
self.tm = cv2.TickMeter()
# Gaze tracking
self.gaze = GazeTracking()
def init():
"""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()
return cap, video_src, img_queue, box_queue, tm, mark_detector, pose_estimator
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 headPoseEstimation():
print("HEAD POSE ESTIMATION...")
"""MAIN"""
# Video source from webcam or video file.
#video_src = 0
#video_src = 'EWSN.avi'
#cam = cv2.VideoCapture(video_src)
#_, sample_frame = cam.read()
# 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()
print("1")
# 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()
print("2")
# 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)
]
print("3")
noseMarks = [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
[0, 0], [0, 0], [0, 0]]
counter = 0
font = cv2.FONT_HERSHEY_SIMPLEX
numXPoints = 0
numYPoints = 0
sumX = 0
sumY = 0
# start = time.time()
# previousTime1 = start
# previousTime2 = start
currentTime = 0
previousTime1 = 0
previousTime2 = 0
directionArray = []
moveSequence = []
moves = []
classifyMoves = 0
headPoseDirection = 'emtpy'
while True:
# Read frame, crop it, flip it, suits your needs.
frame_got, frame = cap.read()
if frame_got is False:
break
#print("4")
#print(frame.shape)
#print("5")
# 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:
# print("6")
# frame = cv2.flip(frame, 2)
# cv2.imwrite("Preview.png", frame)
# print("7")
# Pose estimation by 3 steps:
# 1. detect face;
# 2. detect landmarks;
# 3. estimate pose
# Feed frame to image queue.
img_queue.put(frame)
#print("8")
# Get face from box queue.
facebox = box_queue.get()
#print(type(facebox)
#print(facebox
#print("9")
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)
print(face_img.shape)
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]
# # Get average position of nose
noseMarksTemp = []
noseMarksTemp.append(marks[30][0])
noseMarksTemp.append(marks[30][1])
noseMarks[0] = noseMarksTemp
for i in range(9, 0, -1):
noseMarks[i] = noseMarks[i - 1]
# Get the direction of head movement
headPoseDirection = calculateDirection(noseMarks)
#if headPoseDirection != 'still':
directionArray.append(headPoseDirection)
#print(directionArray
#print(len(directionArray)
print("To capture a movement press 'a' and perform a movement.")
#currentTime1 = time.time()
if cv2.waitKey(5) == ord('a') and not classifyMoves:
classifyMoves = 1
print("Start classifying movement...")
timer = time.time()
currentTime = timer
previousTime1 = timer
previousTime2 = timer
if cv2.waitKey(5) == ord('b') and classifyMoves:
classifyMoves = 0
print("Stopped classifying movement...")
currentTime = time.time()
if currentTime - previousTime1 > 2 and classifyMoves:
print("------------------------------------------------")
print("Elapsed timer 1: " + str(currentTime - previousTime1))
#print(len(directionArray)
# Get most common direction
moveClass = mostCommon(directionArray)
#moveSequence.append(moveClass)
print(moveClass)
# Get a sequence of head movements
# if currentTime - previousTime2 > 10 and classifyMoves == 1 and len(moves) == 0:
# print("Elapsed timer 2: " + str(currentTime - previousTime2)
# numMoves = len(moveSequence)
# moves = moveSequence[(numMoves-5):(numMoves-1)]
# print(moves
# moveSequence = []
# previousTime2 = currentTime
# classifyMoves = 0
classifyMoves = 0
directionArray = []
previousTime1 = currentTime
print(
"To continue type 'c' or to recapture a movement type 'a'."
)
if cv2.waitKey(5) == ord('c'):
break
#print(previousTime
# 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))
# if len(moves) > 1:
# cv2.putText(frame, moves[0], (450,70), font, 1, (0,0,0), 4)
# cv2.putText(frame, moves[1], (450,100), font, 1, (0,0,0), 4)
# cv2.putText(frame, moves[2], (450,130), font, 1, (0,0,0), 4)
# cv2.putText(frame, moves[3], (450,160), font, 1, (0,0,0), 4)
cv2.putText(frame, headPoseDirection, (20, 70), font, 1, (0, 255, 0),
4)
# Show preview.
#cv2.namedWindow("", cv2.WINDOW_NORMAL)
cv2.imshow("Preview", frame)
#cv2.resizeWindow("preview", 5000,5000)
if cv2.waitKey(5) == 27:
break
# Clean up the multiprocessing process.
box_process.terminate()
box_process.join()
return moveClass
x = self.avgpool(x)
x = x.view(x.size(0), -1)
pre_yaw = self.fc_yaw(x)
pre_pitch = self.fc_pitch(x)
pre_roll = self.fc_roll(x)
return pre_yaw, pre_pitch, pre_roll
gpu = 0
if __name__ == '__main__':
args = parse_args()
cuda0 = torch.device('cuda:0')
cudnn.enabled = True
mark_detector = MarkDetector()
batch_size = 1
gpu = args.gpu_id
snapshot_path = args.snapshot
out_dir = 'output/video'
video_path = args.video_path
# initialize the NN
model = ConvAutoencoder(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3])
estimation_model = pose_estimation(torchvision.models.resnet.Bottleneck,
[3, 4, 6, 3], 67)
estimation_model.cuda(gpu)
hopenet = Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3],
67).cuda()
hopenet.load_state_dict(
torch.load(
def Camera_Capture():
mark_detector = MarkDetector()
name = input("Enter student id:")
directory = os.path.join(facepath, name)
if not os.path.exists(facepath):
os.makedirs(facepath, exist_ok = 'True')
if not os.path.exists(directory):
try:
os.makedirs(directory, exist_ok = 'True')
except OSError as e:
if e.errno != errno.EEXIST:
print('invalid student id or access denied')
return
poses=['frontal','right','left','up','down']
file=0
cap = cv2.VideoCapture(file)
ret, sample_frame = cap.read()
i = 0
count = 0
if ret==False:
return
# 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)]
images_saved_per_pose=0
number_of_images = 0
shape_predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
face_aligner = FaceAligner(shape_predictor, desiredFaceWidth=FACE_WIDTH)
while i<5:
saveit = False
# Read frame, crop it, flip it, suits your needs.
ret, frame = cap.read()
if ret is False:
break
if count % 5 !=0: # skip 5 frames
count+=1
continue
if images_saved_per_pose==IMAGE_PER_POSE:
i+=1
images_saved_per_pose=0
# If frame comes from webcam, flip it so it looks like a mirror.
if file == 0:
frame = cv2.flip(frame, 2)
original_frame=frame.copy()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
facebox = mark_detector.extract_cnn_facebox(frame)
if facebox is not None:
# Detect landmarks from image of 128x128.
x1=max(facebox[0]-0,0)
x2=min(facebox[2]+0,width)
y1=max(facebox[1]-0,0)
y2=min(facebox[3]+0,height)
face = frame[y1: y2,x1:x2]
face_img = cv2.resize(face, (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]
# 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))
# print(steady_pose[0][0])
# if steady_pose[0][0]>0.1:
# print('right')
# else:
# if steady_pose[0][0]<-0.1:
# print('left')
# if steady_pose[0][1]>0.1:
# print('down')
# else:
# if steady_pose[0][1]<-0.1:
# print('up')
# print(steady_pose[0])
if i==0:
if abs(steady_pose[0][0])<ANGLE_THRESHOLD and abs(steady_pose[0][1])<ANGLE_THRESHOLD:
images_saved_per_pose+=1
saveit = True
if i==1:
if steady_pose[0][0]>ANGLE_THRESHOLD:
images_saved_per_pose+=1
saveit = True
if i==2:
if steady_pose[0][0]<-ANGLE_THRESHOLD:
images_saved_per_pose+=1
saveit = True
if i==3:
if steady_pose[0][1]<-ANGLE_THRESHOLD:
images_saved_per_pose+=1
saveit = True
if i==4:
if steady_pose[0][1]>ANGLE_THRESHOLD:
images_saved_per_pose+=1
saveit = True
# Show preview.
if i>=5:
print ('Thank you')
break
frame = cv2.putText(frame,poses[i] +' : '+ str(images_saved_per_pose)+'/'+str(IMAGE_PER_POSE),(10,50),cv2.FONT_HERSHEY_SIMPLEX,2,(255,255,255),1,cv2.LINE_AA)
frame = cv2.rectangle(frame, (x1,y1), (x2,y2),(255,255,0),2)
cv2.imshow("Preview", frame)
if saveit:
face = dlib.rectangle(x1, y1, x2, y2)
face_aligned = face_aligner.align(original_frame, frame_gray, face)
cv2.imwrite(os.path.join(directory, str(name)+'_'+str(number_of_images)+'.jpg'), face_aligned)
print(images_saved_per_pose)
number_of_images+=1
if cv2.waitKey(10) == 27:
break
cap.release()
cv2.destroyAllWindows()
def main():
instructions = 0
print "--- Intention classification for communication between a human and a robot ---"
if instructions == 1:
print "First you will be required to present a facial expression before you will do a head movement."
print "If done correctly these gestures will be detected by the robot and it will perform the desired task."
raw_input("Press Enter to continue...")
if instructions == 1:
print "This is the module for facial expression recognition."
print "This program can detect the emotions: Happy and Angry."
print "The program will look for the expression for 3 seconds."
raw_input("To proceed to Facial Expression Recognition press Enter...")
predictExp = 0
# Load Facial Expression Recognition trained model
print "- Loading FER model... -"
#faceExpModel = tf.keras.models.load_model("/home/bjornar/ML_models/FER/Good models(80+)/tf_keras_weights_ninthRev-88percent/tf_keras_weights_ninthRev.hdf5")
faceExpModel = tf.keras.models.load_model(
"/home/project/Bjorn/tf_keras_weights_ninthRev-88percent/tf_keras_weights_ninthRev.hdf5"
)
# Load Face Cascade for Face Detection
print "- Loading Face Cascade for Face Detection... -"
#cascPath = "/home/bjornar/MScDissertation/TrainingData/FaceDetection/haarcascade_frontalface_default.xml"
cascPath = "/home/project/Bjorn/IntentionClassification-Repository/haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascPath)
## Initializing Head Movement variables
# Introduce mark_detector to detect landmarks.
mark_detector = MarkDetector()
sample_frame = cv2.imread("sample_frame.png")
# 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)
]
noseMarks = [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
[0, 0], [0, 0], [0, 0]]
counter = 0
font = cv2.FONT_HERSHEY_SIMPLEX
numXPoints = 0
numYPoints = 0
sumX = 0
sumY = 0
currentTime = 0
previousTime1 = 0
previousTime2 = 0
directionArray = []
moveSequence = []
moves = []
classifyMoves = 0
headPoseDirection = 'emtpy'
if camera == 'kinect':
## Initialize Kinect camera
print "Initializing camera..."
try:
from pylibfreenect2 import OpenGLPacketPipeline
pipeline = OpenGLPacketPipeline()
except:
try:
from pylibfreenect2 import OpenCLPacketPipeline
pipeline = OpenCLPacketPipeline()
except:
from pylibfreenect2 import CpuPacketPipeline
pipeline = CpuPacketPipeline()
#print("Packet pipeline:", type(pipeline).__name__)
# Create and set logger
#logger = createConsoleLogger(LoggerLevel.Debug)
setGlobalLogger()
fn = Freenect2()
num_devices = fn.enumerateDevices()
if num_devices == 0:
print("- No device connected! -")
sys.exit(1)
serial = fn.getDeviceSerialNumber(0)
device = fn.openDevice(serial, pipeline=pipeline)
listener = SyncMultiFrameListener(FrameType.Color | FrameType.Ir
| FrameType.Depth)
# Register listeners
device.setColorFrameListener(listener)
device.setIrAndDepthFrameListener(listener)
device.start()
# NOTE: must be called after device.start()
registration = Registration(device.getIrCameraParams(),
device.getColorCameraParams())
elif camera == 'webcam':
#video_capture = cv2.VideoCapture(0)
video_capture = cv2.VideoCapture(-1)
elif camera == 'video':
#video_capture = cv2.VideoCapture(0)
video_capture = cv2.VideoCapture(
"/home/project/Bjorn/SonyHandycamTest.mp4")
## Facial Expression Recognition variables
FER_prediction = []
FERclass = ''
FERstart = 0
classifyMoves = 0
## Head movement variables
predictHeadMov = 3
HeadMov = []
HMCclass = ''
detectedFaces = []
mark = []
notDone = 0
progressStatus = [0, 0]
while notDone in progressStatus: # This waits for each module to finsih
if camera == 'kinect':
frames = listener.waitForNewFrame()
color = frames["color"]
color = color.asarray()
color = cv2.resize(color, (int(873), int(491)))
color = color[0:480, 150:790]
color = np.delete(color, np.s_[3::], 2)
elif camera == 'webcam':
# Capture frame-by-frame
ret, color = video_capture.read()
elif camera == 'video':
# Capture frame-by-frame
ret, color = video_capture.read()
## Detect facial expression
predictExpNums = [1, 2]
if predictExp == 0:
while predictExp not in predictExpNums:
predictExp = int(
raw_input(
"\nPress 1 to detect Facial Expression or press 2 to do Head Movement classification."
))
if predictExp == 1:
predictExp = 1
print "------ Facial Expression Recognition ------"
elif predictExp == 2:
predictHeadMov = 0
progressStatus[0] = 1
FERstart = time.time()
elif predictExp == 1:
currentTime = time.time()
if currentTime - FERstart < 10:
FER_prediction.append(
facialExpressionRecogntion(color, faceExpModel,
faceCascade))
else:
predictExp = 2
FER_prediction = filter(None, FER_prediction)
FERclass = mostCommon(FER_prediction)
FERclass = FERclass[2:7]
predictHeadMov = 0
if FERclass == '':
print("Did not detect an expression, try again.")
predictExp = 0
else:
progressStatus[0] = 1
print "Detected expression: " + str(FERclass)
print "Progress: FER DONE"
# else:
# #cv2.imwrite("sample_frame.png", color)
# break
## Detect head movement class
if predictHeadMov == 0:
predictHeadMov = int(
raw_input(
"\nPress 1 to do Head Movement classification or 2 to skip."
))
if predictHeadMov == 1:
predictHeadMov = 1
print "------ Head Movement Classification ------"
moveTime = int(
raw_input(
"How many seconds should I record your movement?"))
#moveTime = 2
else:
predictHeadMov = 2
timer = time.time()
previousTime1 = timer
previousTime2 = timer
if predictHeadMov == 1:
print color.shape
color = color[0:480, 0:480]
color = cv2.cvtColor(color, cv2.COLOR_BGR2GRAY)
cv2.imshow("", color)
raw_input()
print color.shape
# Feed frame to image queue.
img_queue.put(color)
#pdb.set_trace()
# Get face from box queue.
facebox = box_queue.get()
print color.shape
if facebox is not None:
# Detect landmarks from image of 128x128.
face_img = color[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]
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(color, headPoseDirection, (20, 70), font, 1,
(0, 255, 0), 4)
# # Get average position of nose
noseMarksTemp = []
noseMarksTemp.append(marks[30][0])
noseMarksTemp.append(marks[30][1])
noseMarks[0] = noseMarksTemp
for i in range(9, 0, -1):
noseMarks[i] = noseMarks[i - 1]
# Get the direction of head movement
headPoseDirection = calculateDirection(noseMarks)
directionArray.append(headPoseDirection)
if classifyMoves == 0:
classifyMoves = 1
timer = time.time()
previousTime1 = timer
previousTime2 = timer
currentTime = time.time()
if currentTime - previousTime1 > moveTime and classifyMoves == 1:
print "------------------------------------------------"
print "Elapsed timer 1: " + str(currentTime -
previousTime1)
# Get most common direction
HMCclass = mostCommon(directionArray)
classifyMoves = 2
directionArray = []
previousTime1 = currentTime
progressStatus[1] = 1
print "Progress: HMC DONE"
else:
print "Did not detect a face"
elif predictHeadMov == 2:
progressStatus[1] = 1
print "Skipped Head Movement Estimation."
break
# if notDone in progressStatus and predictHeadMov == 2 and predictExp == 2:
# print "You seem to have skipped one or more tasks."
# inpt = ''
# while inpt == '':
# inpt = raw_input("To do FER press 1 and to do HMC press 2...")
# if input == '1':
# predictExp = 1
# elif input == '2':
# predictHeadMov
cv2.imshow("", color)
if camera == 'kinect':
listener.release(frames)
key = cv2.waitKey(delay=1)
if key == ord('q'):
break
if camera == 'kinect':
listener.release(frames)
device.stop()
device.close()
elif camera == 'webcam' or camera == 'video':
video_capture.release()
cv2.destroyAllWindows()
# Clean up the multiprocessing process.
box_process.terminate()
box_process.join()
print "---------------- RESULT ----------------"
if FERclass != '':
print "Detected facial expression: " + str(FERclass)
else:
print "Did not detect any expression."
if HMCclass != '':
print "Detected head movement: " + str(HMCclass)
else:
print "Did not detect a head movement."
print "----------------------------------------"
intentionClassification = [FERclass, HMCclass]
return intentionClassification
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"""
# 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 Image_Database_Generator():
mark_detector = MarkDetector()
video_capture = cv2.VideoCapture(file)
name = input("Enter student id:")
directory = os.path.join(facepath, name)
if not os.path.exists(facepath):
os.makedirs(facepath, exist_ok='True')
if not os.path.exists(directory):
try:
os.makedirs(directory, exist_ok='True')
except OSError as e:
if e.errno != errno.EEXIST:
print('invalid student id or access denied')
return
number_of_images = 0
MAX_NUMBER_OF_IMAGES = 50
count = 0
count = 0
while number_of_images < MAX_NUMBER_OF_IMAGES:
ret, frame = video_capture.read()
count += 1
if ret == False:
break
if count % 5 != 0:
continue
frame = cv2.flip(frame, 1)
height, width, _ = frame.shape
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
original_frame = frame.copy()
height, width = frame.shape[:2]
shape_predictor = dlib.shape_predictor(
"shape_predictor_68_face_landmarks.dat")
face_aligner = FaceAligner(shape_predictor,
desiredFaceWidth=FACE_WIDTH)
facebox = mark_detector.extract_cnn_facebox(frame)
if facebox is not None:
# Detect landmarks from image of 128x128.
x1 = max(facebox[0] - 0, 0)
x2 = min(facebox[2] + 0, width)
y1 = max(facebox[1] - 0, 0)
y2 = min(facebox[3] + 0, height)
face = frame[y1:y2, x1:x2]
face_img = cv2.resize(face, (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]
# Show preview.
if showit:
frame = cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 255, 0),
2)
cv2.imshow("Preview", frame)
face = dlib.rectangle(x1, y1, x2, y2)
face_aligned = face_aligner.align(original_frame, frame_gray, face)
cv2.imwrite(
os.path.join(directory,
str(name) + '_' + str(number_of_images) + '.jpg'),
face_aligned)
number_of_images += 1
print(number_of_images)
if cv2.waitKey(10) == 27:
break
print('Thank you')
video_capture.release()
cv2.destroyAllWindows()
def main():
''' main function'''
img_dir = "img_dir/" # image file dir
# setup process and queues for multiprocessing.
img_queue = Queue()
box_queue = Queue()
img_list = []
for filename in os.listdir(img_dir):
frame = cv2.imread(img_dir + filename)
img_list.append(frame)
img_queue.put(frame)
box_process = Process(target=get_face, args=(img_queue, box_queue))
box_process.start()
# introduce mark_detector to detect landmarks
mark_detector = MarkDetector()
while True:
# Crop it if frame is larger than expected.
# frame = frame[0:480, 300:940]
# flipcode > 0: horizontal flip; flipcode = 0: vertical flip; flipcode < 0: horizental and vertical flip
# frame = cv2.flip(frame, 2)
if len(img_list) != 0:
frame = img_list.pop(0)
raw_frame = frame.copy()
# introduce pos estimator to solve pose. Get one frames to setup the
# estimator according to the images size.
height, width = frame.shape[:2]
pose_estimator = PoseEstimator(img_size=(height, width))
else:
break
# Pose estimation by 3 steps.
# 1. detect faces.
# 2. detect landmarks.
# 3. estimate pose.
# get face from box queue.
faceboxes = box_queue.get()
# print("the length of facebox is " + str(len(faceboxes)))
for facebox in faceboxes:
# detect landmarks from image 128 * 128
face_img = frame[facebox[1]: facebox[3], facebox[0]: facebox[2]] # cut off the area of face.
face_img = cv2.resize(face_img, (CNN_INPUT_SIZE, CNN_INPUT_SIZE))
face_img = cv2.cvtColor(face_img, cv2.COLOR_BGR2RGB) # BGR -> RGB
marks = mark_detector.detect_marks(face_img)
# covert the marks locations from local CNN to global image.
marks[:, 0] *= (facebox[2] - facebox[0]) # the width of rectangle.
marks[:, 1] *= (facebox[3] - facebox[1]) # the length of rectangle.
marks[:, 0] += facebox[0]
marks[:, 1] += facebox[1]
# uncomment following line to show raw marks.
# util.draw_marks(frame, marks, color=(0, 255, 0))
# util.draw_faceboxes(frame, facebox)
# try pose estimation with 68 points.
pose = pose_estimator.solve_pose_by_68_points(marks)
# pose[0] is rotation_vector, and pose[1] is translation_vector
pose_np = np.array(pose).flatten()
pose = np.reshape(pose_np, (-1, 3))
angle = util.get_angle(pose[0])
pose_estimator.get_warp_affined_image(frame, facebox, marks, angle[2])
# show preview
cv2.imshow("Preview", frame)
cv2.imshow("raw_frame", raw_frame)
if cv2.waitKey(0) == 27:
continue
# clean up the multiprocessing process.
box_process.terminate()
box_process.join()
name="logits")
# print(tf.all_variables())
sess = tf.Session()
INPUT_FILE = '/private/var/py/ReadHeart/static/datas/1532335528.0497806/1532335528.0497806.mp4'
# cam = cv2.VideoCapture(video_src)
cam = cv2.VideoCapture(INPUT_FILE)
# _, sample_frame = cam.read()
#
#
# print('sample_frame', sample_frame)
#
# # Introduce mark_detector to detect landmarks.
mark_detector = MarkDetector()
# facebox = mark_detector.extract_cnn_facebox(sample_frame)
# print('Facebox', facebox)
#
# 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)
]
# sess =
frame = None
count = 0
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 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 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():
# Initiate Class for Face & Mark Detector
face_detector = FaceDetector()
mark_detector = MarkDetector()
# Landmark 3D for projection and landmark 2d index of corresponding mark
landmarks_3d = np.array(
[
[0.000000, 0.000000, 6.763430], # 33 nose bottom edge
[6.825897, 6.760612, 4.402142], # 17 left brow left corner
[1.330353, 7.122144, 6.903745], # 21 left brow right corner
[-1.330353, 7.122144, 6.903745], # 22 right brow left corner
[-6.825897, 6.760612, 4.402142], # 26 right brow right corner
[5.311432, 5.485328, 3.987654], # 36 left eye left corner
[1.789930, 5.393625, 4.413414], # 39 left eye right corner
[-1.789930, 5.393625, 4.413414], # 42 right eye left corner
[-5.311432, 5.485328, 3.987654], # 45 right eye right corner
[2.005628, 1.409845, 6.165652], # 31 nose left corner
[-2.005628, 1.409845, 6.165652], # 35 nose right corner
[2.774015, -2.080775, 5.048531], # 48 mouth left corner
[-2.774015, -2.080775, 5.048531], # 54 mouth right corner
[0.000000, -3.116408, 6.097667], # 57 mouth central bottom corner
[0.000000, -7.415691, 4.070434] # 8 chin corner
],
dtype=np.double)
lm_2d_index = [33, 17, 21, 22, 26, 36, 39, 42, 45, 31, 35, 48, 54, 57, 8]
# Define color for facebox
color = (244, 134, 66)
# Initiate Video Streaming
file_name = 'assets/trump.gif'
vs = FileVideoStream(file_name).start()
time.sleep(2.0)
frame = vs.read()
while frame is not None:
frame = imutils.resize(frame, width=800, height=600)
(H, W) = frame.shape[:2]
faceboxes = face_detector.extract_square_facebox(frame)
if faceboxes is not None:
for facebox in faceboxes:
face_img = frame[facebox[1]:facebox[3], facebox[0]:facebox[2]]
marks = mark_detector.detect_marks(face_img)
marks *= facebox[2] - facebox[0]
marks[:, 0] += facebox[0]
marks[:, 1] += facebox[1]
rvec, tvec, cm, dc = get_headpose(h=H,
w=W,
landmarks_2d=marks,
landmarks_3d=landmarks_3d,
lm_2d_index=lm_2d_index)
draw_front_box(frame, color, rvec, tvec, cm, dc)
cv2.imshow("3D Face Box", frame)
# writer.write(frame)
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
if key == ord("w"):
frame = vs.read()
video_src = args.cam if args.cam is not None else args.video
if video_src is None:
print("Video source not assigned, default webcam will be used.")
video_src = 0
cap = cv2.VideoCapture(video_src)
# Get the frame size. This will be used by the pose estimator.
width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
# 2. Introduce a pose estimator to solve pose.
pose_estimator = PoseEstimator(img_size=(height, width))
# 3. Introduce a mark detector to detect landmarks.
mark_detector = MarkDetector()
# 4. Measure the performance with a tick meter.
tm = cv2.TickMeter()
# Now, let the frames flow.
while True:
# Read a frame.
frame_got, frame = cap.read()
if frame_got is False:
break
# If the frame comes from webcam, flip it so it looks like a mirror.
if video_src == 0:
frame = cv2.flip(frame, 2)
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(strargument):
shutil.rmtree("./test")
os.mkdir("./test")
os.remove("result.txt")
f = open("result.txt", "a")
cap = cv2.VideoCapture(strargument)
# cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
#cap = cv2.VideoCapture("NTQ.mkv")
#cap = cv2.VideoCapture("/home/fitmta/Real-Time-Face-Detection-OpenCV-GPU/videos/video/out1.1.avi")
#cap = cv2.VideoCapture("http://*****:*****@#[email protected]:8932/mjpg/video.mjpg")
# cap = cv2.VideoCapture("http://*****:*****@[email protected]:8933/Streaming/channels/102/preview")
success, frame = cap.read()
startId = countIdFolder("./face_db/")
# quit if unable to read the video file
if not success:
print('Failed to read video')
sys.exit(1)
#The color of the rectangle we draw around the face
rectangleColor = (0, 165, 255)
#variables holding the current frame number and the current faceid
frameCounter = 0
currentFaceID = 0
#Variables holding the correlation trackers and the name per faceid
conf = configparser.ConfigParser()
conf.read("config/main.cfg")
mtcnn_detector = load_mtcnn(conf)
MODEL_PATH = conf.get("MOBILEFACENET", "MODEL_PATH")
VERIFICATION_THRESHOLD = float(
conf.get("MOBILEFACENET", "VERIFICATION_THRESHOLD"))
FACE_DB_PATH = conf.get("MOBILEFACENET", "FACE_DB_PATH")
BLUR_THRESH = int(conf.get("CUSTOM", "BLUR_THRESH"))
MIN_FACE_SIZE = int(conf.get("MTCNN", "MIN_FACE_SIZE"))
MAX_BLACK_PIXEL = int(conf.get("CUSTOM", "MAX_BLACK_PIXEL"))
YAWL = int(conf.get("CUSTOM", "YAWL"))
YAWR = int(conf.get("CUSTOM", "YAWR"))
PITCHL = int(conf.get("CUSTOM", "PITCHL"))
PITCHR = int(conf.get("CUSTOM", "PITCHR"))
ROLLL = int(conf.get("CUSTOM", "ROLLL"))
ROLLR = int(conf.get("CUSTOM", "ROLLR"))
MAXDISAPPEARED = int(conf.get("CUSTOM", "MAXDISAPPEARED"))
IS_FACE_THRESH = float(conf.get("CUSTOM", "IS_FACE_THRESH"))
EXTEND_Y = int(conf.get("CUSTOM", "EXTEND_Y"))
EXTEND_X = int(conf.get("CUSTOM", "EXTEND_X"))
SIMILAR_THRESH = float(conf.get("CUSTOM", "SIMILAR_THRESH"))
MAX_LIST_LEN = int(conf.get("CUSTOM", "MAX_LIST_LEN"))
MIN_FACE_FOR_SAVE = int(conf.get("CUSTOM", "MIN_FACE_FOR_SAVE"))
LIVE_TIME = int(conf.get("CUSTOM", "LIVE_TIME"))
ROIXL = int(conf.get("CUSTOM", "ROIXL"))
ROIXR = int(conf.get("CUSTOM", "ROIXR"))
ROIYB = int(conf.get("CUSTOM", "ROIYB"))
ROIYA = int(conf.get("CUSTOM", "ROIYA"))
maxDisappeared = MAXDISAPPEARED ## khong xuat hien toi da 100 frame
faces_db = load_faces(FACE_DB_PATH, mtcnn_detector)
# load_face_db = ThreadingUpdatefacedb(FACE_DB_PATH,mtcnn_detector)
time.sleep(10)
for item in faces_db:
print(item["name"])
listTrackedFace = []
mark_detector = MarkDetector()
tm = cv2.TickMeter()
_, sample_frame = cap.read()
height, width = sample_frame.shape[:2]
pose_estimator = PoseEstimator(img_size=(height, width))
with tf.Graph().as_default():
with tf.Session() as sess:
load_mobilefacenet(MODEL_PATH)
inputs_placeholder = tf.get_default_graph().get_tensor_by_name(
"input:0")
embeddings = tf.get_default_graph().get_tensor_by_name(
"embeddings:0")
try:
start = time.time()
while True:
start1 = time.time()
retval, frame = cap.read()
#Increase the framecounter
frameCounter += 1
if retval:
_frame = frame[ROIYA:ROIYB, ROIXL:ROIXR]
cv2.rectangle(frame, (ROIXL, ROIYA), (ROIXR, ROIYB),
(0, 0, 255), 2)
good_face_index = []
# faces_db = load_face_db.face_db
if (frameCounter % 1) == 0:
### embed and compare name
for i, face_db in enumerate(faces_db):
if not os.path.isdir(
"./face_db/" +
face_db["name"].split("_")[0]):
faces_db.pop(i)
faces, landmarks = mtcnn_detector.detect(_frame)
if faces.shape[0] is not 0:
input_images = np.zeros(
(faces.shape[0], 112, 112, 3))
save_images = np.zeros(
(faces.shape[0], 112, 112, 3))
(yaw, pitch, roll) = (0, 0, 0)
for i, face in enumerate(faces):
if round(faces[i, 4], 6) > IS_FACE_THRESH:
bbox = faces[i, 0:4]
points = landmarks[i, :].reshape(
(5, 2))
nimg = face_preprocess.preprocess(
_frame,
bbox,
points,
image_size='112,112')
save_images[i, :] = nimg
nimg = nimg - 127.5
nimg = nimg * 0.0078125
input_images[i, :] = nimg
(x1, y1, x2, y2) = bbox.astype("int")
if x1 < 0 or y1 < 0 or x2 < 0 or y2 < 0 or x1 >= x2 or y1 >= y2:
continue
temp = int((y2 - y1) / EXTEND_Y)
y1 = y1 + temp
y2 = y2 + temp
temp = int((x2 - x1) / EXTEND_X)
if x1 > temp:
x1 = x1 - temp
x2 = x2 + temp
# cv2.imshow("mainframe",frame)
# cv2.imwrite("temp2.jpg",frame[y1:y2,x1:x2])
face_img = cv2.resize(
_frame[y1:y2, x1:x2], (128, 128))
# cv2.imshow("ok",face_img)
face_img = cv2.cvtColor(
face_img, cv2.COLOR_BGR2RGB)
tm.start()
marks = mark_detector.detect_marks(
[face_img])
tm.stop()
marks *= (x2 - x1)
marks[:, 0] += x1
marks[:, 1] += y1
# mark_detector.draw_marks(
# frame, marks, color=(0, 255, 0))
pose, (
yaw, pitch, roll
) = pose_estimator.solve_pose_by_68_points(
marks)
# temp = frame
# cv2.putText(temp,"yaw: "+str(yaw),(x2,y1),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), thickness=2)
# cv2.putText(temp,"pitch: "+str(pitch),(x2,y1+25),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), thickness=2)
# cv2.putText(temp,"roll: "+str(roll),(x2,y1+50),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), thickness=2)
# cv2.imshow("frame",temp)
# if measureGoodFace(MIN_FACE_SIZE,MAX_BLACK_PIXEL,frame[y1:y2,x1:x2],yaw,pitch,roll,BLUR_THRESH,YAWL,YAWR,PITCHL,PITCHR,ROLLL,ROLLR):
# good_face_index.append(i)
# cv2.waitKey(0)
# print(good_face_index)
feed_dict = {inputs_placeholder: input_images}
emb_arrays = sess.run(embeddings,
feed_dict=feed_dict)
emb_arrays = sklearn.preprocessing.normalize(
emb_arrays)
names = []
sims = []
for i, embedding in enumerate(emb_arrays):
# if len(listTrackedFace)>i and RepresentsInt(listTrackedFace[i].name)==False:
# names.append(listTrackedFace[i].name)
# continue
embedding = embedding.flatten()
temp_dict = {}
for com_face in faces_db:
ret, sim = feature_compare(
embedding, com_face["feature"],
0.65)
temp_dict[com_face["name"]] = sim
# print(temp_dict)
dictResult = sorted(temp_dict.items(),
key=lambda d: d[1],
reverse=True)
# print(dictResult[:5])
name = ""
if len(dictResult) > 0 and dictResult[0][
1] > VERIFICATION_THRESHOLD:
name = dictResult[0][
0] #.split("_")[0]
sim = dictResult[0][1]
## wite log
t = time.time()
f.write(name + "___" +
str((t - start) // 60) + ":" +
str(int(t - start) % 60) +
"\n")
else:
name = "unknown"
sim = 0
names.append(name)
sims.append(sim)
# cv2.imwrite("./test/"+name+"_"+str(frameCounter//60)+":"+str(frameCounter%60)+".jpg",save_images[i,:])
# if len(dictResult)>0 :
# cv2.imwrite("./test/"+names[i]+"_"+str(frameCounter//60)+":"+str(frameCounter%60)+"_"+str(dictResult[0][1])+".jpg",save_images[i,:])
################################ tracker
for i, embedding in enumerate(emb_arrays):
embedding = embedding.flatten()
ResultDict = {}
for objectTrackFace in listTrackedFace:
tempList = []
(x1, y1, x2,
y2) = objectTrackFace.latestBox
for com_face in objectTrackFace.listEmbedding:
ret, sim = feature_compare(
embedding, com_face, 0.65)
tempList.append(sim)
tempList.sort(reverse=True)
if len(tempList) > 0:
if tempList[0] > 0.9 or (
similarIOU(
faces[i, :4].astype(
"int"),
objectTrackFace.
latestBox) and
(frameCounter - objectTrackFace
.latestFrameCounter) < 3):
ResultDict[objectTrackFace.
name] = tempList[0]
dictResult = sorted(ResultDict.items(),
key=lambda d: d[1],
reverse=True)
if True:
if len(
ResultDict
) > 0 and dictResult[0][
1] > SIMILAR_THRESH: ## neu khop -- 0.5
# for ik in range(len(dict)):
# if dict[ik][1]>SIMILAR_THRESH:
nameTrackCurrent = dictResult[0][0]
for index, tempFaceTrack in enumerate(
listTrackedFace):
if tempFaceTrack.name == nameTrackCurrent:
if len(tempFaceTrack.
listImage
) > MAX_LIST_LEN:
tempFaceTrack.listImage.pop(
0)
tempFaceTrack.listEmbedding.pop(
0)
if measureGoodFace(
MIN_FACE_SIZE,
MAX_BLACK_PIXEL,
save_images[
i, :], yaw,
pitch, roll,
BLUR_THRESH,
YAWL, YAWR,
PITCHL, PITCHR,
ROLLL, ROLLR):
tempFaceTrack.listImage.append(
save_images[
i, :])
tempFaceTrack.listEmbedding.append(
emb_arrays[i])
else:
if measureGoodFace(
MIN_FACE_SIZE,
MAX_BLACK_PIXEL,
save_images[
i, :], yaw,
pitch, roll,
BLUR_THRESH,
YAWL, YAWR,
PITCHL, PITCHR,
ROLLL, ROLLR):
tempFaceTrack.listImage.append(
save_images[
i, :])
tempFaceTrack.listEmbedding.append(
emb_arrays[i])
if names[i] != "unknown":
if RepresentsInt(
nameTrackCurrent
):
tempFaceTrack.name = names[
i]
# else: #################
# names[i] = nameTrackCurrent
else:
if not RepresentsInt(
nameTrackCurrent
):
names[
i] = nameTrackCurrent
tempFaceTrack.countDisappeared = 0
tempFaceTrack.latestBox = faces[
i, 0:4].astype("int")
tempFaceTrack.latestFrameCounter = frameCounter
tempFaceTrack.liveTime = 0
tempFaceTrack.justAdded = True ## but we still action with it
break
else: ## neu khong khop thi tao moi nhung chi them anh khi mat du tot
if len(ResultDict) > 0:
print(dictResult[0][1])
if names[i] != "unknown":
newTrackFace = trackedFace(
names[i])
else:
newTrackFace = trackedFace(
str(currentFaceID))
currentFaceID = currentFaceID + 1
if measureGoodFace(
MIN_FACE_SIZE,
MAX_BLACK_PIXEL,
save_images[i, :], yaw,
pitch, roll, BLUR_THRESH,
YAWL, YAWR, PITCHL, PITCHR,
ROLLL, ROLLR):
newTrackFace.listImage.append(
save_images[i, :])
newTrackFace.listEmbedding.append(
emb_arrays[i])
newTrackFace.latestBox = faces[
i, 0:4].astype("int")
newTrackFace.latestFrameCounter = frameCounter
# print(newTrackFace.latestBox)
newTrackFace.justAdded = True
listTrackedFace.append(
newTrackFace) ## add list
### disappeared
for index, trackFace in enumerate(
listTrackedFace):
if trackFace.justAdded == False:
trackFace.countDisappeared = trackFace.countDisappeared + 1
trackFace.liveTime = trackFace.liveTime + 1
else:
trackFace.justAdded = False
if trackFace.liveTime > LIVE_TIME:
t = listTrackedFace.pop(index)
del t
if trackFace.countDisappeared > maxDisappeared:
if len(
trackFace.listImage
) < MIN_FACE_FOR_SAVE: ## neu chua duoc it nhat 5 mat thi xoa luon
trackedFace.countDisappeared = 0
continue
if trackFace.saveTrackedFace(
"./temp/", startId):
startId = startId + 1
t = listTrackedFace.pop(index)
del t
for i, face in enumerate(faces):
x1, y1, x2, y2 = faces[i][0], faces[i][
1], faces[i][2], faces[i][3]
x1 = max(int(x1), 0)
y1 = max(int(y1), 0)
x2 = min(int(x2), _frame.shape[1])
y2 = min(int(y2), _frame.shape[0])
cv2.rectangle(frame,
(x1 + ROIXL, y1 + ROIYA),
(x2 + ROIXL, y2 + ROIYA),
(0, 255, 0), 2)
# if i in good_face_index:
# if not RepresentsInt(names[i]):
cv2.putText(frame, names[i].split("_")[0],
(int(x1 / 2 + x2 / 2 + ROIXL),
int(y1 + ROIYA)),
cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(255, 255, 255), 2)
else:
for index, trackFace in enumerate(
listTrackedFace):
trackFace.countDisappeared = trackFace.countDisappeared + 1
trackFace.liveTime = trackFace.liveTime + 1
if trackFace.liveTime > LIVE_TIME:
t = listTrackedFace.pop(index)
del t
continue
if trackFace.countDisappeared > maxDisappeared:
if len(
trackFace.listImage
) < MIN_FACE_FOR_SAVE: ## neu chua duoc it nhat 5 mat thi xoa luon
trackedFace.countDisappeared = 0
continue
if trackFace.saveTrackedFace(
"./temp/", startId):
startId = startId + 1
t = listTrackedFace.pop(index)
del t
end = time.time()
cv2.putText(frame,
"FPS: " + str(1 // (end - start1)),
(400, 30), cv2.FONT_HERSHEY_SIMPLEX, 1,
(0, 0, 0), 3)
cv2.putText(
frame, "Time: " + str((end - start) // 60) +
":" + str(int(end - start) % 60), (200, 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 3)
cv2.imshow("frame", frame)
key = cv2.waitKey(30)
if key & 0xFF == ord('q'):
break
if key == 32:
cv2.waitKey(0)
else:
break
except KeyboardInterrupt as e:
pass
gc.collect()
cv2.destroyAllWindows()
exit(0)
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 Test_Video():
# model=load_model(model_path)
with open(model_json, 'r') as json_file:
loaded_json = json_file.read()
model = model_from_json(loaded_json)
model.load_weights(model_weights)
trainX = []
ids = []
for filename in os.listdir(embedfolder):
if filename.endswith('.npz'):
f = filename.split('.npz')
foldername = f[0]
data = np.load(os.path.join(embedfolder, filename))
trainX.append(data['arr_0'])
idc = [foldername] * data['arr_0'].shape[0]
ids.append(idc)
trainX = np.concatenate(trainX, axis=0)
ids = np.concatenate(ids, axis=0)
grid = pickle.load(open(classifierfilename, 'rb'))
# get target mapping
en = TolerantLabelEncoder(ignore_unknown=True)
en.fit(ids)
video_capture = cv2.VideoCapture(file)
count = 0
mark_detector = MarkDetector()
while True:
ret, frame = video_capture.read()
count += 1
if ret == False:
break
if count % 5 == 0:
frame = cv2.flip(frame, 1)
height, width = frame.shape[:2]
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
facebox = mark_detector.extract_cnn_facebox(frame)
if facebox is not None:
# if len(faces)> 0:
x1 = max(facebox[0] - 0, 0)
x2 = min(facebox[2] + 0, width)
y1 = max(facebox[1] - 0, 0)
y2 = min(facebox[3] + 0, height)
face = dlib.rectangle(x1, y1, x2, y2)
face_aligned = face_aligner.align(frame, frame_gray, face)
embedding = model.predict(
prewhiten(face_aligned).reshape(-1, FACE_WIDTH,
FACE_HEIGHT, 3))
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
yhat_test = grid.predict(embedding)
yhat_prob = grid.predict_proba(embedding)
id = np.argmax(yhat_prob)
# print(yhat_prob[0][id])
if yhat_prob[0][id] < th:
print(yhat_prob[0][id], 'unknown')
else:
print(yhat_prob[0][id], en.inverse_transform(id))
cv2.imshow('Video', frame)
if cv2.waitKey(10) == 27:
break
video_capture.release()
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()
if __name__ == '__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)
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))
