def main(): # Load parameters params = get_args() mouse_prec = params['mouse_prec'] mouse_speed = params['mouse_speed'] mouse = MouseController(mouse_prec, mouse_speed) models = load_models(params) # Load input feed input_type = params['input_type'] if input_type=='cam': input_file = None else: input_file = params['input_file_path'] feed=InputFeeder(input_type=input_type, input_file=input_file) feed.load_data() for batch in feed.next_batch(): if batch is not None: image, pos = main_loop(batch, models) cv2.imshow('frame', image) if cv2.waitKey(1) & 0xFF == ord('q'): break mouse.move(pos[0], pos[1]) # break else: break feed.close()
def run_test(args): mc = MouseController('medium', 'fast') model_face = Face_Detector() model_face.load_model(args.model_fd, args.device, CPU_EXTENSION) model_pose = Pose_Estimator() model_pose.load_model(args.model_pe, args.device, CPU_EXTENSION) model_landmark = Facial_Landmarks() model_landmark.load_model(args.model_fl, args.device, CPU_EXTENSION) model_gaze = Gaze_Estimator() model_gaze.load_model(args.model_ge, args.device, CPU_EXTENSION) frame = cv2.imread(args.input) crop_face, face_count, points = model_face.predict(frame, args.thres_fd) if (face_count == 0): print('no face is detected') angles = model_pose.predict(frame, crop_face) left_eye, right_eye, eye_points = model_landmark.predict( frame, crop_face, points) mx, my = model_gaze.predict(frame, left_eye, right_eye, angles, eye_points) cv2.imwrite('images/ne.jpg', frame) mc.move(mx, my)
def __init__(self, device='CPU', mouse_con=False, face_dec=None, fac_land=None, head_pose=None, gaze=None, show_video=False, save_video=False): ''' all models should be put in here ''' if face_dec and fac_land and head_pose and gaze: self.face_dec, self.fac_land, self.head_pose, self.gaze = FaceDetectionModel( face_dec, device=device), FacialLandmarksDetection( fac_land, device=device), Head_Pose_Estimation( head_pose, device=device), Gaze_Estimation(gaze, device=device) self.face_dec.load_model() self.fac_land.load_model() self.head_pose.load_model() self.gaze.load_model() else: raise ValueError('Missing Arguments') if mouse_con: self.mouse_con = MouseController("low", "fast") self.show_video, self.save_video = show_video, save_video
def __init__(self, args): ''' This method instances variables for the Facial Landmarks Detection Model. Args: args = All arguments parsed by the arguments parser function Return: None ''' init_start_time = time.time() self.output_path = args.output_path self.show_output = args.show_output self.total_processing_time = 0 self.count_batch = 0 self.inference_speed = [] self.avg_inference_speed = 0 if args.all_devices != 'CPU': args.face_device = args.all_devices args.face_landmark_device = args.all_devices args.head_pose_device = args.all_devices args.gaze_device = args.all_devices model_init_start = time.time() self.face_model = FaceDetection(args.face_model, args.face_device, args.face_device_ext, args.face_prob_threshold) self.landmarks_model = FacialLandmarksDetection( args.face_landmark_model, args.face_landmark_device, args.face_landmark_device_ext, args.face_landmark_prob_threshold) self.head_pose_model = HeadPoseEstimation( args.head_pose_model, args.head_pose_device, args.head_pose_device_ext, args.head_pose_prob_threshold) self.gaze_model = GazeEstimation(args.gaze_model, args.gaze_device, args.gaze_device_ext, args.gaze_prob_threshold) self.model_init_time = time.time() - model_init_start log.info('[ Main ] All required models initiallized') self.mouse_control = MouseController(args.precision, args.speed) log.info('[ Main ] Mouse controller successfully initialized') self.input_feeder = InputFeeder(args.batch_size, args.input_type, args.input_file) log.info('[ Main ] Initialized input feeder') model_load_start = time.time() self.face_model.load_model() self.landmarks_model.load_model() self.head_pose_model.load_model() self.gaze_model.load_model() self.model_load_time = time.time() - model_load_start self.app_init_time = time.time() - init_start_time log.info('[ Main ] All moadels loaded to Inference Engine\n') return None
def main(): """ Load the network and parse the output. :return: None """ # Grab command line args mc = MouseController("high", "fast") mc.move(randint(10, 109) / 100, -randint(10, 100) / 100)
def __init__(self, args): # load the objects corresponding to the models self.face_detection = Face_Detection(args.face_detection_model, args.device, args.extensions, args.perf_counts) self.gaze_estimation = Gaze_Estimation(args.gaze_estimation_model, args.device, args.extensions, args.perf_counts) self.head_pose_estimation = Head_Pose_Estimation( args.head_pose_estimation_model, args.device, args.extensions, args.perf_counts) self.facial_landmarks_detection = Facial_Landmarks_Detection( args.facial_landmarks_detection_model, args.device, args.extensions, args.perf_counts) start_models_load_time = time.time() self.face_detection.load_model() self.gaze_estimation.load_model() self.head_pose_estimation.load_model() self.facial_landmarks_detection.load_model() logger = logging.getLogger() input_T = args.input_type input_F = args.input_file if input_T.lower() == 'cam': # open the video feed self.feed = InputFeeder(args.input_type, args.input_file) self.feed.load_data() else: if not os.path.isfile(input_F): logger.error('Unable to find specified video file') exit(1) file_extension = input_F.split(".")[-1] if (file_extension in ['jpg', 'jpeg', 'bmp']): self.feed = InputFeeder(args.input_type, args.input_file) self.feed.load_data() elif (file_extension in ['avi', 'mp4']): self.feed = InputFeeder(args.input_type, args.input_file) self.feed.load_data() else: logger.error( "Unsupported file Extension. Allowed ['jpg', 'jpeg', 'bmp', 'avi', 'mp4']" ) exit(1) print("Models total loading time :", time.time() - start_models_load_time) # init mouse controller self.mouse_controller = MouseController('low', 'fast')
def main(args): mouse_controller = MouseController('medium', 'fast') print("Model Loading..") face_detection = Model_FaceDetection(args.face_detection, args.device) face_landmark = Model_FacialLandmarksDetection(args.face_landmark, args.device) head_pose = Model_HeadPoseEstimation(args.head_pose, args.device) gaze_estimation = Model_GazeEstimation(args.gaze_estimation, args.device) print("Model loaded successfully") input_feeder = InputFeeder(input_type='video', input_file=args.input) input_feeder.load_data() face_detection.load_model() head_pose.load_model() face_landmark.load_model() gaze_estimation.load_model() for frame in input_feeder.next_batch(): try: frame.shape except Exception as err: break key = cv2.waitKey(60) face,face_coord = face_detection.predict(frame.copy(), args.prob_threshold) if type(face)==int: print("Unable to detect the face.") if key==27: break continue headPose = head_pose.predict(face.copy()) left_eye, right_eye, eye_coord = face_landmark.predict(face.copy()) mouse_coord, gaze_vector = gaze_estimation.predict(left_eye, right_eye, headPose) cv2.imshow('video',frame) mouse_controller.move(mouse_coord[0], mouse_coord[1]) input_feeder.close() cv2.destroyAllWindows()
def model_instants(args): face_detection_instant = FaceDetectionModel(model_name=args.face_detection, device=args.device, threshold=args.prob_threshold, extensions=args.cpu_extension) head_pose_estimation_instant = HeadPoseEstimationModel( model_name=args.head_pose_estimation, device=args.device, extensions=args.cpu_extension) facial_landmarks_instant = FacialLandmarksDetectionModel( model_name=args.facial_landmarks_detection, device=args.device, extensions=args.cpu_extension) gaze_estimation_instant = GazeEstimationModel( model_name=args.gaze_estimation, device=args.device, extensions=args.cpu_extension) mouse_controller_instant = MouseController('medium', 'fast') return face_detection_instant, head_pose_estimation_instant, facial_landmarks_instant, gaze_estimation_instant, mouse_controller_instant
def main(model_dir, device, precision, input_type, input_file, inspect): mouse_controller = MouseController("medium", "fast") input_feeder = InputFeeder(input_type=input_type, input_file=input_file) input_feeder.load_data() gaze_detect = GazeDetect(model_dir=model_dir, device=device, precision=precision) gaze_detect.load_model() for image in input_feeder.next_batch(): with Timer() as t: outputs = gaze_detect.predict(image) if outputs is not None: angle_y_fc, angle_p_fc, angle_r_fc = outputs.reshape(3) mouse_controller.move(-angle_y_fc, angle_p_fc) print( f"Mouse move x: {-angle_y_fc}, y: {angle_p_fc}, execution time: {t.elapsed}" )
def __init__(self, args): self.log_level = "INFO" if os.environ.get( "LOGLEVEL") == "INFO" or args.verbose_stage else "WARNING" log.basicConfig(level=self.log_level) input_type = 'cam' if args.cam else 'video' self.feed = InputFeeder(input_type, args.video) if not self.feed.load_data(): raise Exception('Input valid image or video file') fps, w, h = self.feed.get_props() self.out_video = cv2.VideoWriter(args.out, cv2.VideoWriter_fourcc(*'MJPG'), fps, (w, h), True) args.head_pose_model = os.path.join( args.head_pose_model, args.precision, os.path.basename(args.head_pose_model)) args.landmarks_model = os.path.join( args.landmarks_model, args.precision, os.path.basename(args.landmarks_model)) args.gaze_model = os.path.join(args.gaze_model, args.precision, os.path.basename(args.gaze_model)) self.fd = FaceDetect(args.face_model, args.device, args.extension, args.threshold) self.fd.load_model() self.fd.set_out_size(w, h) self.hp = HeadPoseEstimate(args.head_pose_model, args.device, args.extension, args.threshold) self.hp.load_model() self.fl = FacialLandMarkDetect(args.landmarks_model, args.device, args.extension, args.threshold) self.fl.load_model() self.gz = GazeEstimate(args.gaze_model, args.device, args.extension, args.threshold) self.gz.load_model() self.mc = MouseController() self.verbose_stage = args.verbose_stage
def main(): """ Load the network and parse the output. :return: None """ # Grab command line args args = build_argparser().parse_args() start_time = time.time() face_detector = FaceDetect(model_name=args.face, device=args.device, output=args.output) face_detector.load_model() print("Time taken to load face detection model (in seconds):", time.time()-start_time) start_time = time.time() eyes_detector = EyesDetect(model_name=args.eyes, device=args.device, output=args.output) eyes_detector.load_model() print("Time taken to load landmark detection model (in seconds):", time.time()-start_time) start_time = time.time() angle_detector = AngleDetect(model_name=args.angle, device=args.device) angle_detector.load_model() print("Time taken to load head pose estimation model (in seconds):", time.time()-start_time) start_time = time.time() gaze_detector = GazeDetect(model_name=args.gaze, device=args.device) gaze_detector.load_model() print("Time taken to load gaze estimation model (in seconds):", time.time()-start_time) mouse_controller = MouseController('medium','medium') feed=InputFeeder(input_type=args.video, input_file=args.input) feed.load_data() for batch in feed.next_batch(): if batch is None: # catch last frame break face = face_detector.predict(batch) left_eye, right_eye = eyes_detector.predict(face) angles = angle_detector.predict(face) x, y = gaze_detector.predict(left_eye, right_eye, angles) mouse_controller.move(x, y) feed.close()
def __init__(self): self.args = None self.feed = None self.face_detection_model = None self.facial_landmark_detection_model = None self.gaze_estimation_model = None self.head_pose_estimation_model = None self.frame = None self.width = None self.Height = None self.mc = MouseController("high", "fast") self.face_detection_load_time = 0 self.facial_landmark_detection_load_time = 0 self.gaze_estimation_load_time = 0 self.head_pose_estimation_load_time = 0 self.face_detection_infer_time = 0 self.facial_landmark_detection_infer_time = 0 self.gaze_estimation_infer_time = 0 self.head_pose_estimation_infer_time = 0 self.frames = 0
def main(args): feed = InputFeeder(input_type=args.it, input_file=args.i) face_model = FaceDetectionModel(args.fm, args.d, args.c, float(args.p)) face_model.load_model() landmarks_model = LandmarksDetectionModel(args.lm, args.d, args.c) landmarks_model.load_model() headpose_model = HeadPoseDetectionModel(args.hpm, args.d, args.c) headpose_model.load_model() gaze_model = GazeEstimationModel(args.gem, args.d, args.c) gaze_model.load_model() mouse = MouseController("medium", "fast") feed.load_data() for batch in feed.next_batch(): # try: cropped_face, coords, _ = face_model.predict(batch) cv2.rectangle(batch, (coords[0], coords[1]), (coords[2], coords[3]), (255, 0, 0), 2) left_eye, right_eye, eyes_coords, _ = landmarks_model.predict( cropped_face) head_pose_angles, _ = headpose_model.predict(cropped_face) x, y, z, _ = gaze_model.predict(left_eye, right_eye, head_pose_angles, cropped_face, eyes_coords) mouse.move(x, y) cv2.imshow("img", batch) if cv2.waitKey(25) & 0xFF == ord('q'): break # except: # print("Frame without prediction. Error: ", sys.exc_info()[0]) # log.error(sys.exc_info()[0]) feed.close()
def main(): mouseController = MouseController() with camera() as cam: while True: ok, read_ = cam.read() if not ok: break frame = cv2.flip(read_, 1) roi = frame[ROI_SLICE].copy() key = cv2.waitKey(2) if key == ord('r'): print('Background reset') setBackground(roi) continue elif key in (27, ord('q')): print('Exit') break mask = getMask(roi) contour = getHandContour(roi, mask) if contour is None or cv2.contourArea(contour) < MIN_HAND_SIZE: continue bottommost, leftmost, topmost, _rightmost = getHandPosition( contour) drawHandPositions(roi, bottommost, leftmost, topmost) angle = getAngle(bottommost, leftmost, topmost) if angle is None: continue mouseController.performActions(roi, topmost, angle) showResult(frame, roi)
def inference_frame(m1,m2,m3,m4,inF,args): """ Funtion to calculate frame count and infernece time. """ visualize = args.visualize mc = MouseController('high','fast') total = 0 fc = 0 inf_time = 0 for ret, frame in inF.next_batch(): if not ret: break; if frame is not None: fc += 1 if fc%5 == 0: cv2.imshow('video', cv2.resize(frame, (500, 500))) key = cv2.waitKey(60) start_inf = time.time() face_crop, face_dim = m1.predict(frame.copy(), args.prob_threshold) if type(face_crop) == int: print("No face detected.") if key == 27: break continue hp_out = m2.predict(face_crop.copy()) le, re, eye_dim = m3.predict(face_crop.copy()) new_dim, gv = m4.predict(le, re, hp_out) end_inf = time.time() inf_time = inf_time + end_inf - start_inf total = total + 1 visualization(visualize, frame, face_crop, face_dim, eye_dim, hp_out, gv, le, re) if fc%5 == 0: mc.move(new_dim[0], new_dim[1]) if key == 27: break return fc,inf_time
def start_pipeline(cla, codec): """ Initializes feeds inputs to models, moving the mouse cursor based on the final gaze estimation. :param cla: Command line arguments for configuring the pipeline. :param codec: Depending on the platform this is run on, OpenCV requires a codec to be specified. Supply it here. :return: None """ preview_flags = cla.preview_flags logger = logging.getLogger() input_file_path = cla.input if input_file_path.lower() == "cam": in_feeder = InputFeeder("cam") elif not os.path.isfile(input_file_path): # top = os.path.dirname(os.path.realpath(__file__)) # walktree(top, visit_file) logger.error("Cannot locate video file provided. Exiting..") sys.exit(1) else: in_feeder = InputFeeder("video", input_file_path) start_model_load_time = time.time() fdm, fldm, hpem, gem = prep_models(cla) total_model_load_time = time.time() - start_model_load_time mc = None if not cla.is_benchmark: mc = MouseController('medium', 'fast') in_feeder.load_data() fps, total_inference_time, total_time = handle_input_feed( logger, preview_flags, fdm, fldm, hpem, gem, mc, in_feeder, cla.frame_out_rate, codec, cla.output_path) with open(os.path.join(cla.output_path, 'stats.txt'), 'w') as f: f.write("Total inference time, " + str(total_inference_time) + '\n') f.write("FPS, " + str(fps) + '\n') f.write("Total model load time, " + str(total_model_load_time) + '\n') f.write("Total time, " + str(total_time) + '\n') logger.error("Video stream ended...") cv2.destroyAllWindows() in_feeder.close()
def init_models(args): global model_face, model_landmarks, model_hpose, model_gaze_estimation, mouse_controller precision = args.precision device = args.device threshold = args.threshold model_face = ModelFaceDetection(model_dir_face[precision], device, threshold) model_landmarks = ModelFacialLandmarksDetection(model_dir_landmarks[precision], device, threshold) model_hpose = ModelHeadPoseEstimation(model_dir_hpose[precision], device, threshold) model_gaze_estimation = ModelGazeEstimation(model_dir_gaze[precision], device, threshold) model_face.load_model() model_landmarks.load_model() model_hpose.load_model() model_gaze_estimation.load_model() mouse_controller = None if args.mouse_precision in ['high', 'low', 'medium'] and args.mouse_speed in ['fast', 'slow', 'medium']: mouse_controller = MouseController(args.mouse_precision, args.mouse_speed)
def setup(args): global input_path, output_path, device, cpu_extension, prob_threshold, flags, mouse_controller, feeder, video_writer, model_dict, model_loading_total_time model_args = [ args.face_detection_model, args.facial_landmarks_detection_model, args.head_pose_estimation_model, args.gaze_estimation_model, ] model_class = [ Model_FaceDetection, Model_FacialLandMarkDetection, Model_HeadPoseEstimation, Model_GazeEstimation, ] input_path = input_path_generator(args.input) if args.input != "CAM" else None output_path = output_path_generator(args.output) device = args.device cpu_extension = args.cpu_extension prob_threshold = args.prob_threshold flags = args.flags if not os.path.exists(output_path): os.mkdir(output_path) mouse_controller = MouseController("low", "fast") if input_path: if input_path.endswith(".jpg"): feeder = InputFeeder("image", input_path) else: feeder = InputFeeder("video", input_path) else: feeder = InputFeeder("cam") feeder.load_data() fps = feeder.fps() initial_w, initial_h, video_len = feeder.frame_initials_and_length() video_writer = cv2.VideoWriter( os.path.join(output_path, "output_video.mp4"), cv2.VideoWriter_fourcc(*"avc1"), fps / 10, (initial_w, initial_h), True, ) model_dict, model_loading_total_time = generate_model_dict(model_args, model_class) return
def init_model(args): global face_model, landmark_model, head_pose_model, gaze_model, mouse_controller device_name = args.device prob_threshold = args.prob_threshold # Initialize variables with the input arguments for easy access model_path_dict = { 'FaceDetectionModel': args.faceDetectionModel, 'LandmarkRegressionModel': args.landmarkRegressionModel, 'HeadPoseEstimationModel': args.headPoseEstimationModel, 'GazeEstimationModel': args.gazeEstimationModel } # Instantiate model face_model = Model_Face(model_path_dict['FaceDetectionModel'], device_name, threshold=prob_threshold) landmark_model = Model_Landmark(model_path_dict['LandmarkRegressionModel'], device_name, threshold=prob_threshold) head_pose_model = Model_Pose(model_path_dict['HeadPoseEstimationModel'], device_name, threshold=prob_threshold) gaze_model = Model_Gaze(model_path_dict['GazeEstimationModel'], device_name, threshold=prob_threshold) mouse_controller = MouseController('medium', 'fast') # Load Models face_model.load_model() landmark_model.load_model() head_pose_model.load_model() gaze_model.load_model() # Check extention of these unsupported layers face_model.check_model() landmark_model.check_model() head_pose_model.check_model() gaze_model.check_model()
def main(): logging.info("Parsing the arguments.") args = get_parser().parse_args() logging.info("Arguments parsed successfully. Now initialing feedreader.") input_feeder = init_feeder(args) logging.info("FeedReader initialized, loading the models.") fd_model, fld_model, ge_model, hpe_model, total_model_load_time = load_all_models( args) mc = MouseController('medium', 'fast') logging.info("Starting the workflow") fps, total_inference_time, effective_fps = run_workflow( fd_model, fld_model, ge_model, hpe_model, input_feeder, mc, args.show_intermediate_visualization) logging.debug("Writing the stats.") with open(os.path.join(args.output_dir, 'stats.txt'), 'w') as f: f.write(str(total_inference_time) + '\n') f.write(str(fps) + '\n') f.write(str(effective_fps) + '\n') f.write(str(total_model_load_time) + '\n')
def gaze_pointer_controller(args, facedetector, facelm, headpose, gaze): mouse_controller = MouseController(precision='high', speed='fast') # Handle input type: inference_time_face = [] inference_time_landmarks = [] inference_time_headpose = [] inference_time_gaze = [] if args.input != 'CAM': try: # It seems that OpenCV can use VideoCapture to treat videos and images: input_stream = cv2.VideoCapture(args.input) length = int(input_stream.get(cv2.CAP_PROP_FRAME_COUNT)) webcamera = False # Check if input is an image or video file: if length > 1: single_image_mode = False else: single_image_mode = True except: print( 'Not supported image or video file format. Please pass a supported one.' ) exit() else: input_stream = cv2.VideoCapture(0) single_image_mode = False webcamera = True if not single_image_mode: count = 0 while (input_stream.isOpened()): # Read the next frame: flag, frame = input_stream.read() if not flag: break if count % args.frame_count == 0: start = time.time() if cv2.waitKey(1) & 0xFF == ord('q'): break # We get a detected face crop and its coordinates: face_crop, detection = facedetector.get_face_crop(frame, args) finish_face_detector_time = time.time() face_detector_time = round(finish_face_detector_time - start, 5) log.info("Face detection took {} seconds.".format( face_detector_time)) inference_time_face.append(face_detector_time) # Obtain eyes coordinates: right_eye, left_eye = facelm.get_eyes_coordinates(face_crop) # Obtain eyes crops: right_eye_crop, left_eye_crop, right_eye_coords, left_eye_coords = utils.get_eyes_crops( face_crop, right_eye, left_eye) finish_eyes_coordinates = time.time() eyes_detector_time = round( finish_eyes_coordinates - finish_face_detector_time, 5) log.info("Eyes detection took {} seconds.".format( eyes_detector_time)) inference_time_landmarks.append(eyes_detector_time) # Obtain headpose angles: headpose_angles = headpose.get_headpose_angles(face_crop) finish_headpose_angles = time.time() headpose_detector_time = round( finish_headpose_angles - finish_eyes_coordinates, 5) log.info("Headpose angles detection took {} seconds.".format( headpose_detector_time)) inference_time_headpose.append(headpose_detector_time) # Obtain gaze vector and mouse movement values: (x_movement, y_movement), gaze_vector = gaze.get_gaze( right_eye_crop, left_eye_crop, headpose_angles) finish_gaze_detection_time = time.time() gaze_detector_time = round( finish_gaze_detection_time - finish_headpose_angles, 5) log.info("Gaze detection took {} seconds.".format( gaze_detector_time)) inference_time_gaze.append(gaze_detector_time) # Optional visualization configuration: if args.view_face: frame = cv2.rectangle(frame, (detection[0], detection[1]), (detection[2], detection[3]), color=(0, 255, 0), thickness=5) if args.view_eyes: right_eye_coords = [ right_eye_coords[0] + detection[1], right_eye_coords[1] + detection[1], right_eye_coords[2] + detection[0], right_eye_coords[3] + detection[0] ] left_eye_coords = [ left_eye_coords[0] + detection[1], left_eye_coords[1] + detection[1], left_eye_coords[2] + detection[0], left_eye_coords[3] + detection[0] ] frame = cv2.rectangle( frame, (right_eye_coords[2], right_eye_coords[1]), (right_eye_coords[3], right_eye_coords[0]), color=(255, 0, 0), thickness=5) frame = cv2.rectangle( frame, (left_eye_coords[2], left_eye_coords[1]), (left_eye_coords[3], left_eye_coords[0]), color=(255, 0, 0), thickness=5) if args.view_gaze: # Right eye: x_r_eye = int(right_eye[0] * face_crop.shape[1] + detection[0]) y_r_eye = int(right_eye[1] * face_crop.shape[0] + detection[1]) x_r_shift, y_r_shift = int(x_r_eye + gaze_vector[0] * 100), int(y_r_eye - gaze_vector[1] * 100) # Left eye: x_l_eye = int(left_eye[0] * face_crop.shape[1] + detection[0]) y_l_eye = int(left_eye[1] * face_crop.shape[0] + detection[1]) x_l_shift, y_l_shift = int(x_l_eye + gaze_vector[0] * 100), int(y_l_eye - gaze_vector[1] * 100) frame = cv2.arrowedLine(frame, (x_r_eye, y_r_eye), (x_r_shift, y_r_shift), (0, 0, 255), 2) frame = cv2.arrowedLine(frame, (x_l_eye, y_l_eye), (x_l_shift, y_l_shift), (0, 0, 255), 2) if args.view_headpose: frame = cv2.putText( frame, 'Yaw: ' + str(headpose_angles[0]) + ' ' + 'Pitch: ' + str(headpose_angles[1]) + ' ' + 'Roll: ' + str(headpose_angles[2]), (15, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 0), 2) # Resizing window for visualization convenience: cv2.namedWindow('Prueba', cv2.WINDOW_NORMAL) cv2.resizeWindow('Prueba', 600, 400) cv2.imshow('Prueba', frame) mouse_controller.move(x_movement, y_movement) count = count + 1 input_stream.release() with open('times.csv', 'w', newline='') as csvfile: writer = csv.writer(csvfile, delimiter=',') writer.writerow([ 'Face Detector', 'Eyes Detector', 'Headpose Detector', 'Gaze Detector' ]) for i in range(len(inference_time_face)): writer.writerow([ inference_time_face[i], inference_time_landmarks[i], inference_time_headpose[i], inference_time_gaze[i] ]) cv2.destroyAllWindows()
def main(): args = build_argparser().parse_args() visual = args.visual_flag log = logging.getLogger() input_source = args.input_source try: video_path = args.input_path except Exception as e: video_path = None feed = None if input_source.lower() == 'cam': feed = InputFeeder('cam') elif input_source.lower() == 'video' and os.path.isfile(video_path): feed = InputFeeder('video', video_path) else: log.error('Wrong input feed. (check the video path).') exit(1) fd = Model_Face(args.face_detection_model, args.device, args.extension) hp = Model_HeadPose(args.head_pose_model, args.device, args.extension) fl = Model_Faciallandmark(args.facial_landmarks_model, args.device, args.extension) ga = Model_Gaze(args.gaze_model, args.device, args.extension) ### You can specify the value of precision and speed directly. ## OR ## 'high'(100),'low'(1000),'medium','low-med' - precision ## 'fast'(1), 'slow'(10), 'medium', 'slow-med' - speed # mouse = MouseController('low-med', 'slow-med') mouse = MouseController(500, 4) feed.load_data() # load models fd.load_model() hp.load_model() fl.load_model() ga.load_model() count = 0 for ret, frame in feed.next_batch(): if not ret: break count += 1 if count % 5 == 0: cv2.imshow('video', cv2.resize(frame, (500, 500))) key = cv2.waitKey(60) frame_cp = frame.copy() face, face_position = fd.predict(frame_cp, args.threshold) if type(face) == int: log.error('Prediction Error: Cant find face.') if key == 27: break continue face_cp = face.copy() hp_output = hp.predict(face_cp) left_eye, right_eye, facial = fl.predict(face_cp) # print('left',left_eye,'\n','right',right_eye,'\n') mouse_coord, gaze_vector = ga.predict(left_eye, right_eye, hp_output) if (not len(visual) == 0): visual_frame = frame.copy() ### Visual FLAGS # face detection if 'fd' in visual: visual_frame = face # Head pose if 'hp' in visual: cv2.putText( visual_frame, "Yaw: {:.2f} Pitch: {:.2f} Roll: {:.2f}".format( hp_output[0], hp_output[1], hp_output[2]), (10, 20), cv2.FONT_HERSHEY_COMPLEX, 0.3, (0, 255, 50), 1) # Facial landmarks if 'fl' in visual: cv2.rectangle(face, (facial[0][0] - 10, facial[0][1] - 10), (facial[0][2] + 10, facial[0][3] + 10), (255, 0, 0), 3) cv2.rectangle(face, (facial[1][0] - 10, facial[1][1] - 10), (facial[1][2] + 10, facial[1][3] + 10), (255, 0, 0), 3) # Gaze estimation if 'ga' in visual: x, y, w = int(gaze_vector[0] * 12), int(gaze_vector[1] * 12), 160 le = cv2.line(left_eye.copy(), (x - w, y - w), (x + w, y + w), (255, 255, 0), 2) cv2.line(le, (x - w, y + w), (x + w, y - w), (255, 50, 150), 2) re = cv2.line(right_eye.copy(), (x - w, y - w), (x + w, y + w), (255, 255, 0), 2) cv2.line(re, (x - w, y + w), (x + w, y - w), (255, 50, 150), 2) face[facial[0][1]:facial[0][3], facial[0][0]:facial[0][2]] = le face[facial[1][1]:facial[1][3], facial[1][0]:facial[1][2]] = re cv2.namedWindow('Visualization', cv2.WINDOW_AUTOSIZE) cv2.moveWindow('Visualization', 900, 900) cv2.imshow('Visualization', cv2.resize(visual_frame, (500, 500))) if args.visual_save.lower() == 'y': if count % 10 == 0: cv2.imwrite(str(count) + '_visual.jpg', visual_frame) if count % 5 == 0: mouse.move(mouse_coord[0], mouse_coord[1]) if key == 27: break log.error('INFO: Ended!') cv2.destroyAllWindows() feed.close()
def main(): args = get_args().parse_args() path_filender = args.input four_flags = args.flags_checker loger = logging.getLogger() feeder_in = None out_path = args.out_path if path_filender.lower() == "cam": feeder_in = InputFeeder("cam") else: if not os.path.isfile(path_filender): loger.error("The video was not found") exit(1) feeder_in = InputFeeder("video", path_filender) model_locations = { 'FaceDetection': args.face_detection_model, 'HeadPoseEstimation': args.head_pose_estimation_model, 'FacialLandmarksDetection': args.facial_landmarks_detection_model, 'GazeEstimation': args.gaze_estimation_model } for key_name in model_locations.keys(): if not os.path.isfile(model_locations[key_name]): loger.error("The system cannot find the " + key_name + " xml file") exit(1) dt = FaceDetection(model_locations['FaceDetection'], args.device, args.cpu_extension) pe = HeadPoseEstimation(model_locations['HeadPoseEstimation'], args.device, args.cpu_extension) ld = FacialLandmarksDetection(model_locations['FacialLandmarksDetection'], args.device, args.cpu_extension) ge = GazeEstimation(model_locations['GazeEstimation'], args.device, args.cpu_extension) cursor = MouseController('medium', 'fast') feeder_in.load_data() model_load_time_start = time.time() dt.load_model() pe.load_model() ld.load_model() ge.load_model() total_load_time = time.time() - model_load_time_start frame_counter = 0 inference_time_start = time.time() for ret, frame in feeder_in.next_batch(): if not ret: break frame_counter = frame_counter + 1 if frame_counter % 1 == 0: cv2.imshow('video', cv2.resize(frame, (600, 600))) key = cv2.waitKey(60) face_detected, coords_face = dt.predict(frame, args.p_th) if type(face_detected) == int: loger.error("The system cannot detect any face.") if key == 27: break continue head_pose_output = pe.predict(face_detected) eye_left_detect, eye_right_detect, eye_coordinates_detect = ld.predict( face_detected) coordi_update_pointer, coordi_gaze = ge.predict( eye_left_detect, eye_right_detect, head_pose_output) if (not len(four_flags) == 0): result_app = frame if 'fad' in four_flags: result_app = face_detected if 'hpe' in four_flags: cv2.putText( result_app, "HP Angles: YAW:{:.3f} * PITCH:{:.3f} * ROLL:{:.3f}". format(head_pose_output[0], head_pose_output[1], head_pose_output[2]), (5, 40), cv2.FONT_HERSHEY_COMPLEX, 0.25, (153, 76, 0), 0) if 'fld' in four_flags: cv2.rectangle(face_detected, (eye_coordinates_detect[0][0] - 4, eye_coordinates_detect[0][1] - 4), (eye_coordinates_detect[0][2] + 4, eye_coordinates_detect[0][3] + 4), (255, 255, 0), 4) cv2.rectangle(face_detected, (eye_coordinates_detect[1][0] - 4, eye_coordinates_detect[1][1] - 4), (eye_coordinates_detect[1][2] + 4, eye_coordinates_detect[1][3] + 4), (255, 255, 0), 4) if 'gae' in four_flags: x = int(coordi_gaze[0] * 2) y = int(coordi_gaze[1] * 2) w = 150 right_E = cv2.line(eye_right_detect, (x - w, y - w), (x + w, y + w), (51, 255, 153), 1) cv2.line(right_E, (x - w, y + w), (x + w, y - w), (51, 255, 253), 1) left_E = cv2.line(eye_left_detect, (x - w, y - w), (x + w, y + w), (51, 255, 153), 1) cv2.line(left_E, (x - w, y + w), (x + w, y - w), (51, 255, 253), 1) face_detected[ eye_coordinates_detect[1][1]:eye_coordinates_detect[1][3], eye_coordinates_detect[1][0]:eye_coordinates_detect[1] [2]] = right_E face_detected[ eye_coordinates_detect[0][1]:eye_coordinates_detect[0][3], eye_coordinates_detect[0][0]:eye_coordinates_detect[0] [2]] = left_E cv2.imshow("Result of the App", cv2.resize(result_app, (600, 600))) if frame_counter % 5 == 0: cursor.move(coordi_update_pointer[0], coordi_update_pointer[1]) if key == 27: break total_time = time.time() - inference_time_start total_time_for_inference = round(total_time, 1) fps = frame_counter / total_time_for_inference with open(out_path + 'stats.txt', 'w') as f: f.write('Inference time: ' + str(total_time_for_inference) + '\n') f.write('FPS: ' + str(fps) + '\n') f.write('Model load time: ' + str(total_load_time) + '\n') loger.error("The video stream is over...") cv2.destroyAllWindows() feeder_in.close()
def main(): # Grab command line args args = build_argparser().parse_args() flags = args.models_outputs_flags logger = logging.getLogger() input_file_path = args.input input_feeder = None if input_file_path.lower() == "cam": input_feeder = InputFeeder("cam") else: if not os.path.isfile(input_file_path): logger.error("Unable to find specified video file") exit(1) input_feeder = InputFeeder("video", input_file_path) model_path_dict = { 'FaceDetection': args.face_detection_model, 'FacialLandmarks': args.facial_landmarks_model, 'GazeEstimation': args.gaze_estimation_model, 'HeadPoseEstimation': args.head_pose_estimation_model } for file_name_key in model_path_dict.keys(): if not os.path.isfile(model_path_dict[file_name_key]): logger.error("Unable to find specified " + file_name_key + " xml file") exit(1) fdm = FaceDetection(model_path_dict['FaceDetection'], args.device, args.cpu_extension) flm = FacialLandmarks(model_path_dict['FacialLandmarks'], args.device, args.cpu_extension) gem = GazeEstimation(model_path_dict['GazeEstimation'], args.device, args.cpu_extension) hpem = HeadPoseEstimation(model_path_dict['HeadPoseEstimation'], args.device, args.cpu_extension) mc = MouseController('medium', 'fast') input_feeder.load_data() fdm.load_model() flm.load_model() hpem.load_model() gem.load_model() frame_count = 0 for ret, frame in input_feeder.next_batch(): if not ret: break frame_count += 1 if frame_count % 5 == 0: cv2.imshow('video', cv2.resize(frame, (500, 500))) key = cv2.waitKey(60) cropped_face, face_coords = fdm.predict(frame, args.prob_threshold) if type(cropped_face) == int: logger.error("Unable to detect any face.") if key == 27: break continue hp_output = hpem.predict(cropped_face) left_eye_img, right_eye_img, eye_coords = flm.predict(cropped_face) new_mouse_coord, gaze_vector = gem.predict(left_eye_img, right_eye_img, hp_output) if (not len(flags) == 0): preview_frame = frame if 'fd' in flags: preview_frame = cropped_face if 'fld' in flags: cv2.rectangle(cropped_face, (eye_coords[0][0] - 10, eye_coords[0][1] - 10), (eye_coords[0][2] + 10, eye_coords[0][3] + 10), (0, 255, 0), 3) cv2.rectangle(cropped_face, (eye_coords[1][0] - 10, eye_coords[1][1] - 10), (eye_coords[1][2] + 10, eye_coords[1][3] + 10), (0, 255, 0), 3) if 'hp' in flags: cv2.putText( preview_frame, "Pose Angles: yaw:{:.2f} | pitch:{:.2f} | roll:{:.2f}". format(hp_output[0], hp_output[1], hp_output[2]), (10, 20), cv2.FONT_HERSHEY_COMPLEX, 0.25, (0, 255, 0), 1) if 'ge' in flags: x, y, w = int(gaze_vector[0] * 12), int(gaze_vector[1] * 12), 160 left_eye = cv2.line(left_eye_img, (x - w, y - w), (x + w, y + w), (255, 0, 255), 2) cv2.line(left_eye, (x - w, y + w), (x + w, y - w), (255, 0, 255), 2) right_eye = cv2.line(right_eye_img, (x - w, y - w), (x + w, y + w), (255, 0, 255), 2) cv2.line(right_eye, (x - w, y + w), (x + w, y - w), (255, 0, 255), 2) cropped_face[eye_coords[0][1]:eye_coords[0][3], eye_coords[0][0]:eye_coords[0][2]] = left_eye cropped_face[eye_coords[1][1]:eye_coords[1][3], eye_coords[1][0]:eye_coords[1][2]] = right_eye cv2.imshow("Visualization", cv2.resize(preview_frame, (500, 500))) if frame_count % 5 == 0: mc.move(new_mouse_coord[0], new_mouse_coord[1]) if key == 27: break logger.error("VideoStream ended...") cv2.destroyAllWindows() input_feeder.close()
def application(args, facedetector, facelm, headpose, gaze): pointer_controller = MouseController(precision='high', speed='fast') if args.input != 'CAM': try: in_stream = cv2.VideoCapture(args.input) l = int(in_stream.get(cv2.CAP_PROP_FRAME_COUNT)) webcam = False if l > 1: single_image_mode = False else: single_image_mode = True except: print('Not supported image or video file format. Please pass a supported one.') exit() else: in_stream = cv2.VideoCapture(0) single_img_mode = False webcam = True if not single_image_mode: count = 0 while(in_stream.isOpened()): flag, frame = in_stream.read() if not flag: break if count % 25 == 0: if cv2.waitKey(1) & 0xFF == ord('q'): break face_crop, detection = facedetector.get_face_crop(frame, args) right_eye, left_eye = facelm.get_eyes_coordinates(face_crop) right_eye_crop, left_eye_crop, right_eye_coords, left_eye_coords = helpers.get_eyes_crops(face_crop, right_eye,left_eye) headpose_angles = headpose.get_headpose_angles(face_crop) (x_movement, y_movement), gaze_vector = gaze.get_gaze(right_eye_crop, left_eye_crop, headpose_angles) if args.show_face: frame = cv2.rectangle(frame, (detection[0],detection[1]), (detection[2],detection[3]), color=(0,255,0), thickness=5) if args.show_headpose: frame = cv2.putText(frame, 'Roll: '+ str(headpose_angles[2])+' '+ 'Pitch: '+str(headpose_angles[1])+' '+ 'Yaw: '+str(headpose_angles[0]),(15,20),cv2.FONT_HERSHEY_SIMPLEX,0.65,(0,0,0),2) if args.show_eyes: right_eye_coords = [right_eye_coords[0]+detection[1], right_eye_coords[1]+detection[1], right_eye_coords[2]+detection[0], right_eye_coords[3]+detection[0]] left_eye_coords = [left_eye_coords[0]+detection[1], left_eye_coords[1]+detection[1], left_eye_coords[2]+detection[0], left_eye_coords[3]+detection[0]] frame = cv2.rectangle(frame, (right_eye_coords[2],right_eye_coords[1]), (right_eye_coords[3],right_eye_coords[0]), color=(255,0,0), thickness=5) frame = cv2.rectangle(frame, (left_eye_coords[2],left_eye_coords[1]), (left_eye_coords[3],left_eye_coords[0]), color=(255,0,0), thickness=5) if args.show_gaze: # Right eye: x_r_eye = int(right_eye[0]*face_crop.shape[1]+detection[0]) y_r_eye = int(right_eye[1]*face_crop.shape[0]+detection[1]) x_r_shift, y_r_shift = int(x_r_eye+gaze_vector[0]*100), int(y_r_eye-gaze_vector[1]*100) # Left eye: x_l_eye = int(left_eye[0]*face_crop.shape[1]+detection[0]) y_l_eye = int(left_eye[1]*face_crop.shape[0]+detection[1]) x_l_shift, y_l_shift = int(x_l_eye+gaze_vector[0]*100), int(y_l_eye-gaze_vector[1]*100) frame = cv2.arrowedLine(frame, (x_r_eye, y_r_eye), (x_r_shift, y_r_shift), (0, 255, 0), 4) frame = cv2.arrowedLine(frame, (x_l_eye, y_l_eye), (x_l_shift, y_l_shift), (0, 255, 0), 4) cv2.namedWindow('Output',cv2.WINDOW_NORMAL) cv2.resizeWindow('Output', 800,600) cv2.imshow('Output', frame) #pointer_controller.move(x_movement,y_movement) count = count + 1 in_stream.release() cv2.destroyAllWindows()
def main(args): # getting the arguments if args.get_perf_counts.lower() == "true": perf_counts = True elif args.get_perf_counts.lower() == "false": perf_counts = False precision = args.precision.lower() speed = args.speed.lower() media_type = args.media_type.lower() media_path = args.media_file toggle_ui = args.show_video print(toggle_ui) batch_size = args.batch_size device = args.device iterations = 1 if media_type == "cam" else int(args.iterations) #initialize the mouse object mouse = MouseController(precision, speed) # Initialize the input feeder feed = InputFeeder(media_type, batch_size, media_path) # Initialize and load the inference models model = Model(face_detection, facial_landmarks, gaze_estimation, head_pose_estimation, device) model.load_models() for _ in range(iterations): feed.load_data() #This will be used as a way to keep track of the average time for the preprocessing and inference of the models times = np.zeros((8, )) counter_frames = 0 if media_type != "image": width = feed.cap.get(3) height = feed.cap.get(4) else: height, width, _ = feed.cap.shape try: for frame in feed.next_batch(media_type): counter_frames += 1 #generates the prediction x, y, gaze_vector, times = model.predict( frame, width, height, times) #generates the movement on the cursor mouse.move(x, y) if perf_counts: cv2.putText( frame, "Preprocess Face Detection: " + str(times[0] / counter_frames * 1000) + " ms", (0, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (209, 80, 0), 3) cv2.putText( frame, "Inference Face Detection: " + str(times[1] / counter_frames * 1000) + " ms", (0, 100), cv2.FONT_HERSHEY_SIMPLEX, 1, (209, 80, 0), 3) cv2.putText( frame, "Preprocess Facial Landmarks: " + str(times[2] / counter_frames * 1000) + " ms", (0, 150), cv2.FONT_HERSHEY_SIMPLEX, 1, (209, 80, 0), 3) cv2.putText( frame, "Inference Facial Landmarks: " + str(times[3] / counter_frames * 1000) + " ms", (0, 200), cv2.FONT_HERSHEY_SIMPLEX, 1, (209, 80, 0), 3) cv2.putText( frame, "Preprocess Head Pose: " + str(times[4] / counter_frames * 1000) + " ms", (0, 250), cv2.FONT_HERSHEY_SIMPLEX, 1, (209, 80, 0), 3) cv2.putText( frame, "Inference Head Pose: " + str(times[5] / counter_frames * 1000) + " ms", (0, 300), cv2.FONT_HERSHEY_SIMPLEX, 1, (209, 80, 0), 3) cv2.putText( frame, "Preprocess Gaze Estimation: " + str(times[6] / counter_frames * 1000) + " ms", (0, 350), cv2.FONT_HERSHEY_SIMPLEX, 1, (209, 80, 0), 3) cv2.putText( frame, "Inference Gaze Estimation: " + str(times[7] / counter_frames * 1000) + " ms", (0, 400), cv2.FONT_HERSHEY_SIMPLEX, 1, (209, 80, 0), 3) print("Preprocess Face Detection: " + str(times[0] / counter_frames * 1000) + " ms") print("Inference Face Detection: " + str(times[1] / counter_frames * 1000) + " ms") print("Preprocess Facial Landmarks: " + str(times[2] / counter_frames * 1000) + " ms") print("Inference Facial Landmarks: " + str(times[3] / counter_frames * 1000) + " ms") print("Preprocess Head Pose: " + str(times[4] / counter_frames * 1000) + " ms") print("Inference Head Pose: " + str(times[5] / counter_frames * 1000) + " ms") print("Preprocess Gaze Estimation: " + str(times[6] / counter_frames * 1000) + " ms") print("Inference Gaze Estimation: " + str(times[7] / counter_frames * 1000) + " ms") if toggle_ui == True: cv2.imshow("Frame", frame) if cv2.waitKey(1) & 0xFF == ord('q'): break if cv2.waitKey(1) & 0xFF == ord('i'): toggle_UI = False if toggle_UI else True except: print("Video has ended or couldn't continue") if perf_counts: print("Final average: ") print("Preprocess Face Detection: " + str(times[0] / counter_frames * 1000) + " ms") print("Inference Face Detection: " + str(times[1] / counter_frames * 1000) + " ms") print("Preprocess Facial Landmarks: " + str(times[2] / counter_frames * 1000) + " ms") print("Inference Facial Landmarks: " + str(times[3] / counter_frames * 1000) + " ms") print("Preprocess Head Pose: " + str(times[4] / counter_frames * 1000) + " ms") print("Inference Head Pose: " + str(times[5] / counter_frames * 1000) + " ms") print("Preprocess Gaze Estimation: " + str(times[6] / counter_frames * 1000) + " ms") print("Inference Gaze Estimation: " + str(times[7] / counter_frames * 1000) + " ms") feed.close() cv2.destroyAllWindows()
def infer(self, args): # Create instances from the models' classes FDM_net = ModelFaceDetection() HPE_net = ModelHeadPoseEstimation() FLD_net = ModelFacialLandmarksDetection() GEM_net = ModelGazeEstimation() mouse_controller = MouseController('high', 'fast') # Load the models start1 = time.time() FDM_net.load_model(args.face_detection_model, args.device) FDM_load_t = time.time() - start1 start2 = time.time() HPE_net.load_model(args.head_pose_estimation_model, args.device) HPE_load_t = time.time() - start2 start3 = time.time() FLD_net.load_model(args.facial_landmarks_detection_model, args.device) FLD_load_t = time.time() - start3 start4 = time.time() GEM_net.load_model(args.gaze_estimation_model, args.device) GEM_load_t = time.time() - start4 print('All models are loaded!') #Check the inputs # To make the mouse moving we need video stream either from camera or video path if args.input.lower() == 'cam': # Initialise the InputFeeder class input_feeder = InputFeeder(input_type='cam', input_file=args.input) else: if not os.path.isfile(args.input): log.error("Please insert valid video path to run the app.") exit() # Initialise the InputFeeder class input_feeder = InputFeeder(input_type='video', input_file=args.input) # Load the video capture input_feeder.load_data() # Inference time inference = time.time() # Read from the video capture for flag, frame in input_feeder.next_batch(): if not flag: break key_pressed = cv2.waitKey(60) # Run inference on the models start5 = time.time() face_coords = FDM_net.predict(frame) FDM_infer_t = time.time() - start5 # crop the face from the frame cropped_face = frame[face_coords[1]:face_coords[3], face_coords[0]:face_coords[2]] #Everything depends on the face detection output, if no face detected then repeat if len(face_coords) == 0: log.error("There is no faces detected.") continue start6 = time.time() HP_angles = HPE_net.predict(cropped_face, face_coords) HPE_infer_t = time.time() - start6 if args.display_flag: #### display the face O_frame = cv2.rectangle(frame.copy(), (face_coords[0], face_coords[1]), (face_coords[2], face_coords[3]), (255, 255, 0), 2) #### display the pose angles # Link for pose estimation output code resource: https://sudonull.com/post/6484-Intel-OpenVINO-on-Raspberry-Pi-2018-harvest cos_r = cos(HP_angles[2] * pi / 180) sin_r = sin(HP_angles[2] * pi / 180) cos_y = cos(HP_angles[0] * pi / 180) sin_y = sin(HP_angles[0] * pi / 180) cos_p = cos(HP_angles[1] * pi / 180) sin_p = sin(HP_angles[1] * pi / 180) x = int((face_coords[0] + face_coords[2]) / 2) y = int((face_coords[1] + face_coords[3]) / 2) cv2.line(O_frame, (x, y), (x + int(65 * (cos_r * cos_y + sin_y * sin_p * sin_r)), y + int(65 * cos_p * sin_r)), (255, 0, 0), thickness=2) cv2.line(O_frame, (x, y), (x + int(65 * (cos_r * sin_y * sin_p + cos_y * sin_r)), y - int(65 * cos_p * cos_r)), (0, 255, 0), thickness=2) cv2.line(O_frame, (x, y), (x + int(65 * sin_y * cos_p), y + int(65 * sin_p)), (0, 0, 255), thickness=2) start7 = time.time() l_e, r_e, l_e_image, r_e_image, e_center = FLD_net.predict( O_frame, cropped_face, face_coords) FLD_infer_t = time.time() - start7 ###display landmarks for both eyes if args.display_flag: cv2.circle(O_frame, (face_coords[0] + l_e[0], face_coords[1] + l_e[1]), 29, (0, 255, 255), 2) cv2.circle(O_frame, (face_coords[0] + r_e[0], face_coords[1] + r_e[1]), 29, (0, 255, 255), 2) start8 = time.time() g_vec = GEM_net.predict(l_e_image, r_e_image, HP_angles) GEM_infer_t = time.time() - start8 ###display gaze model output if args.display_flag: cv2.arrowedLine(O_frame, (int(e_center[0][0]), int(e_center[0][1])), (int(e_center[0][0]) + int(g_vec[0] * 90), int(e_center[0][1]) + int(-g_vec[1] * 90)), (203, 192, 255), 2) cv2.arrowedLine(O_frame, (int(e_center[1][0]), int(e_center[1][1])), (int(e_center[1][0]) + int(g_vec[0] * 90), int(e_center[1][1]) + int(-g_vec[1] * 90)), (203, 192, 255), 2) # change the pointer position according to the estimated gaze direction mouse_controller.move(g_vec[0], g_vec[1]) if key_pressed == 27: break # Display the resulting frame cv2.imshow('Mouse Controller App Results', cv2.resize(O_frame, (750, 550))) inference_time = time.time() - inference print("Loading time: \n1-Face detection: " + str(FDM_load_t) + "\n2- Head pose estimation: " + str(HPE_load_t) + "\n3-Facial landmarks model: " + str(FLD_load_t) + "\n4-Gaze estimation model: " + str(GEM_load_t)) print("Output inference time: \n1-Face detection: " + str(FDM_infer_t) + "\n2- Head pose estimation: " + str(HPE_infer_t) + "\n3-Facial landmarks model: " + str(FLD_infer_t) + "\n4-Gaze estimation model: " + str(GEM_infer_t)) # close the input feeder and destroy all opened windows input_feeder.close() cv2.destroyAllWindows
def main(): args = build_argparser().parse_args() frame_num = 0 inference_time = 0 counter = 0 # Initialize the Inference Engine fd = FaceDetection() fld = Facial_Landmarks_Detection() ge = Gaze_Estimation() hp = Head_Pose_Estimation() # Load Models fd.load_model(args.face_detection_model, args.device, args.cpu_extension) fld.load_model(args.facial_landmark_model, args.device, args.cpu_extension) ge.load_model(args.gaze_estimation_model, args.device, args.cpu_extension) hp.load_model(args.head_pose_model, args.device, args.cpu_extension) # Mouse Controller precision and speed mc = MouseController('medium', 'fast') # feed input from an image, webcam, or video to model if args.input == "cam": feed = InputFeeder("cam") else: assert os.path.isfile(args.input), "Specified input file doesn't exist" feed = InputFeeder("video", args.input) feed.load_data() frame_count = 0 for frame in feed.next_batch(): frame_count += 1 inf_start = time.time() if frame is not None: try: key = cv2.waitKey(60) det_time = time.time() - inf_start # make predictions detected_face, face_coords = fd.predict( frame.copy(), args.prob_threshold) hp_output = hp.predict(detected_face.copy()) left_eye, right_eye, eye_coords = fld.predict( detected_face.copy()) new_mouse_coord, gaze_vector = ge.predict( left_eye, right_eye, hp_output) stop_inference = time.time() inference_time = inference_time + stop_inference - inf_start counter = counter + 1 # Visualization preview = args.visualization if preview: preview_frame = frame.copy() face_frame = detected_face.copy() draw_face_bbox(preview_frame, face_coords) display_hp(preview_frame, hp_output, face_coords) draw_landmarks(face_frame, eye_coords) draw_gaze(face_frame, gaze_vector, left_eye.copy(), right_eye.copy(), eye_coords) if preview: img = np.hstack((cv2.resize(preview_frame, (500, 500)), cv2.resize(face_frame, (500, 500)))) else: img = cv2.resize(frame, (500, 500)) cv2.imshow('Visualization', img) # set speed if frame_count % 5 == 0: mc.move(new_mouse_coord[0], new_mouse_coord[1]) # INFO log.info("NUMBER OF FRAMES: {} ".format(frame_num)) log.info("INFERENCE TIME: {}ms".format(det_time * 1000)) frame_num += 1 if key == 27: break except: print( 'Not supported image or video file format. Please send in a supported video format.' ) exit() feed.close()
def main(): args = build_argparser().parse_args() logger = logging.getLogger('main') model_path_dict = { 'FaceDetectionModel': args.faceDetectionModel, 'FacialLandmarksModel': args.facialLandmarksModel, 'HeadPoseEstimationModel': args.headPoseEstimationModel, 'GazeEstimationModel': args.gazeEstimationModel } bbox_flag = args.bbox_flag input_filename = args.input device_name = args.device prob_threshold = args.prob_threshold output_path = args.output_path if input_filename.lower() == 'cam': feeder = InputFeeder(input_type='cam') else: if not os.path.isfile(input_filename): logger.error("Unable to find specified video file") exit(1) feeder = InputFeeder(input_type='video', input_file=input_filename) for model_path in list(model_path_dict.values()): if not os.path.isfile(model_path): logger.error("Unable to find specified model file" + str(model_path)) exit(1) face_detection_model = Face_detection( model_path_dict['FaceDetectionModel'], device_name, threshold=prob_threshold) facial_landmarks_detection_model = Landmark_Detection( model_path_dict['FacialLandmarksModel'], device_name, threshold=prob_threshold) head_pose_estimation_model = Head_pose( model_path_dict['HeadPoseEstimationModel'], device_name, threshold=prob_threshold) gaze_estimation_model = Gaze_estimation( model_path_dict['GazeEstimationModel'], device_name, threshold=prob_threshold) is_benchmarking = False if not is_benchmarking: mouse_controller = MouseController('medium', 'fast') start_model_load_time = time.time() face_detection_model.load_model() facial_landmarks_detection_model.load_model() head_pose_estimation_model.load_model() gaze_estimation_model.load_model() total_model_load_time = time.time() - start_model_load_time feeder.load_data() out_video = cv2.VideoWriter(os.path.join('output_video.mp4'), cv2.VideoWriter_fourcc(*'avc1'), int(feeder.get_fps() / 10), (1920, 1080), True) frame_count = 0 start_inference_time = time.time() for ret, frame in feeder.next_batch(): if not ret: break frame_count += 1 key = cv2.waitKey(60) try: face_coords, image_copy = face_detection_model.predict(frame) if type(image_copy) == int: logger.warning("Unable to detect the face") if key == 27: break continue left_eye, right_eye, eye_coords = facial_landmarks_detection_model.predict( image_copy) hp_output = head_pose_estimation_model.predict(image_copy) mouse_coords, gaze_coords = gaze_estimation_model.predict( left_eye, right_eye, hp_output) except Exception as e: logger.warning("Could predict using model" + str(e) + " for frame " + str(frame_count)) continue image = cv2.resize(frame, (500, 500)) if not len(bbox_flag) == 0: bbox_frame = draw_bbox(frame, bbox_flag, image_copy, left_eye, right_eye, face_coords, eye_coords, hp_output, gaze_coords) image = np.hstack( (cv2.resize(frame, (500, 500)), cv2.resize(bbox_frame, (500, 500)))) cv2.imshow('preview', image) out_video.write(frame) if frame_count % 5 == 0 and not is_benchmarking: mouse_controller.move(mouse_coords[0], mouse_coords[1]) if key == 27: break total_time = time.time() - start_inference_time total_inference_time = round(total_time, 1) fps = frame_count / total_inference_time try: os.mkdir(output_path) except OSError as error: logger.error(error) with open(output_path + 'stats.txt', 'w') as f: f.write(str(total_inference_time) + '\n') f.write(str(fps) + '\n') f.write(str(total_model_load_time) + '\n') logger.info('Model load time: ' + str(total_model_load_time)) logger.info('Inference time: ' + str(total_inference_time)) logger.info('FPS: ' + str(fps)) logger.info('Video stream ended') cv2.destroyAllWindows() feeder.close()
class MoveMouse: ''' Main Class for the Mouse Controller app. This is the class where all the models are stitched together to control the mouse pointer ''' def __init__(self, args): ''' This method instances variables for the Facial Landmarks Detection Model. Args: args = All arguments parsed by the arguments parser function Return: None ''' init_start_time = time.time() self.output_path = args.output_path self.show_output = args.show_output self.total_processing_time = 0 self.count_batch = 0 self.inference_speed = [] self.avg_inference_speed = 0 if args.all_devices != 'CPU': args.face_device = args.all_devices args.face_landmark_device = args.all_devices args.head_pose_device = args.all_devices args.gaze_device = args.all_devices model_init_start = time.time() self.face_model = FaceDetection(args.face_model, args.face_device, args.face_device_ext, args.face_prob_threshold) self.landmarks_model = FacialLandmarksDetection( args.face_landmark_model, args.face_landmark_device, args.face_landmark_device_ext, args.face_landmark_prob_threshold) self.head_pose_model = HeadPoseEstimation( args.head_pose_model, args.head_pose_device, args.head_pose_device_ext, args.head_pose_prob_threshold) self.gaze_model = GazeEstimation(args.gaze_model, args.gaze_device, args.gaze_device_ext, args.gaze_prob_threshold) self.model_init_time = time.time() - model_init_start log.info('[ Main ] All required models initiallized') self.mouse_control = MouseController(args.precision, args.speed) log.info('[ Main ] Mouse controller successfully initialized') self.input_feeder = InputFeeder(args.batch_size, args.input_type, args.input_file) log.info('[ Main ] Initialized input feeder') model_load_start = time.time() self.face_model.load_model() self.landmarks_model.load_model() self.head_pose_model.load_model() self.gaze_model.load_model() self.model_load_time = time.time() - model_load_start self.app_init_time = time.time() - init_start_time log.info('[ Main ] All moadels loaded to Inference Engine\n') return None def draw_face_box(self, frame, face_coords): ''' Draws face's bounding box on the input frame Args: frame = Input frame from video or camera feed. It could also be an input image Return: frame = Frame with bounding box of faces drawn on it ''' start_point = (face_coords[0][0], face_coords[0][1]) end_point = (face_coords[0][2], face_coords[0][3]) thickness = 5 color = (255, 86, 0) frame = cv2.rectangle(frame, start_point, end_point, color, thickness) return frame def draw_eyes_boxes(self, frame, left_eye_coords, right_eye_coords): ''' Draws face's bounding box on the input frame Args: frame = Input frame from video or camera feed. It could also be an input image Return: frame = Frame with bounding box of left and right eyes drawn on it ''' left_eye_start_point = (left_eye_coords[0], left_eye_coords[1]) left_eye_end_point = (left_eye_coords[2], left_eye_coords[3]) right_eye_start_point = (right_eye_coords[0], right_eye_coords[1]) right_eye_end_point = (right_eye_coords[2], right_eye_coords[3]) thickness = 5 color = (0, 210, 0) frame = cv2.rectangle(frame, left_eye_start_point, left_eye_end_point, color, thickness) frame = cv2.rectangle(frame, right_eye_start_point, right_eye_end_point, color, thickness) return frame def draw_outputs(self, frame): ''' Draws the inference outputs (bounding boxes of the face and both eyes and the 3D head pose directions) of the four models onto the frames. Args: frame = Input frame from video or camera feed. It could also be an input image Return: frame = Frame with all inference outputs drawn on it ''' frame = self.draw_face_box(frame, self.face_coords) frame = self.draw_eyes_boxes(frame, self.left_eye_coords, self.right_eye_coords) frame_id = f'Batch id = {self.count_batch}' avg_inference_speed = f'Avg. inference speed = {self.avg_inference_speed:.3f}fps' total_processing_time = f'Total infer. time = {self.total_processing_time:.3f}s' cv2.putText(frame, frame_id, (15, 15), cv2.FONT_HERSHEY_COMPLEX, 0.45, (255, 86, 0), 1) cv2.putText(frame, avg_inference_speed, (15, 30), cv2.FONT_HERSHEY_COMPLEX, 0.45, (255, 86, 0), 1) cv2.putText(frame, total_processing_time, (15, 45), cv2.FONT_HERSHEY_COMPLEX, 0.45, (255, 86, 0), 1) return frame def run_inference(self, frame): ''' Performs inference on the input video or image by passing it through all four models to get the desired coordinates for moving the mouse pointer. Args: frame = Input image, frame from video or camera feed Return: None ''' self.input_feeder.load_data() for frame in self.input_feeder.next_batch(): if self.input_feeder.frame_flag == True: log.info('[ Main ] Started processing a new batch') start_inference = time.time() self.face_coords, self.face_crop = self.face_model.predict( frame) if self.face_coords == []: log.info( '[ Main ] No face detected.. Waiting for you to stare at the camera' ) f.write('[ Error ] No face was detected') else: self.head_pose_angles = self.head_pose_model.predict( self.face_crop) self.left_eye_coords, self.left_eye_image, self.right_eye_coords, self.right_eye_image = self.landmarks_model.predict( self.face_crop) self.x, self.y = self.gaze_model.predict( self.left_eye_image, self.right_eye_image, self.head_pose_angles) log.info( f'[ Main ] Relative pointer coordinates: [{self.x:.2f}, {self.y:.2f}]' ) batch_process_time = time.time() - start_inference self.total_processing_time += batch_process_time self.count_batch += 1 log.info( f'[ Main ] Finished processing batch. Time taken = {batch_process_time}s\n' ) self.mouse_control.move(self.x, self.y) if self.show_output: self.draw_outputs(frame) cv2.imshow('Computer Pointer Controller Output', frame) self.inference_speed.append(self.count_batch / self.total_processing_time) self.avg_inference_speed = sum(self.inference_speed) / len( self.inference_speed) with open(os.path.join(self.output_path, 'outputs.txt'), 'w+') as f: f.write('INFERENCE STATS\n') f.write( f'Total model initialization time : {self.model_init_time:.2f}s\n' ) f.write( f'Total model load time: {self.model_load_time:.2f}s\n' ) f.write( f'App initialization time: {self.app_init_time:.2f}s\n' ) f.write( f'Total processing time: {self.total_processing_time:.2f}s\n' ) f.write( f'Average inference speed: {self.avg_inference_speed:.2f}FPS\n' ) f.write(f'Batch count: {self.count_batch}\n\n') f.write('LAST OUTPUTS\n') f.write(f'Face coordinates: {self.face_coords}\n') f.write(f'Left eye coordinates: {self.left_eye_coords}\n') f.write( f'Right eye coordinates: {self.right_eye_coords}\n') f.write(f'Head pose angles: {self.head_pose_angles}\n') f.write( f'Relative pointer coordinates/ Gaze vector: [{self.x:.2f}, {self.y:.2f}]' ) else: self.input_feeder.close() cv2.destroyAllWindows() log.info( f'[ Main ] All input Batches processed in {self.total_processing_time:.2f}s' ) log.info('[ Main ] Shutting down app...') log.info('[ Main ] Mouse controller app has been shut down.') break return