def main():
global face_detector, landmark_dectector, head_pose_estimator, gaze_estimator, mouse_controller
# Load parameters
app_start_time = cv2.getTickCount()
set_logging()
logging.info('start APP')
pyautogui.FAILSAFE = False
cmd_paras = get_comand_line_parameters()
#cmd_paras = get_comand_lineFP16()
# Setup Projects
mouse_controller = createMouseController()
# Setup Classes
logging.info('setUp dectors and estimators started')
# NC2 can only handel one instance of IECore
plugin = IECore()
face_detector = FaceDetector(cmd_paras.fd, cmd_paras.device, plugin)
face_detector.load_model()
landmark_dectector = FacialLandmarksDetector(cmd_paras.lr,
cmd_paras.device, plugin)
landmark_dectector.load_model()
head_pose_estimator = HeadPoseEstimator(cmd_paras.hp, cmd_paras.device,
plugin)
head_pose_estimator.load_model()
gaze_estimator = GazeEstimatior(cmd_paras.ge, cmd_paras.device, plugin)
gaze_estimator.load_model()
logging.info('setUp dectors estimators ends')
logging.info('start inputFeeder read stream')
input_feeder = createInputFeeder(cmd_paras)
input_feeder.load_data()
# RUN the pipline
i = 0
try:
for tmp_image in input_feeder.next_batch():
if tmp_image is not None:
i = i + 1
tmp_image, gaze_position = pipline(tmp_image, i)
cv2.imshow('frame', tmp_image)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
mouse_controller.move(gaze_position[0], gaze_position[1])
else:
break
finally:
input_feeder.close()
logging.info('end inputFeeder end stream')
app_end_time = cv2.getTickCount()
logging.info('App Runtime: {:.4f} seconds pipline runs {:d}'.format(
(app_end_time - app_start_time) / cv2.getTickFrequency(), i))
def main():
# Get command line arguments
args = parser.parse_args()
device = args.device
cpu_extensions = args.extensions
threshold = args.threshold
gaze_estimation_precision = args.gaze_estimation_precision
head_pose_precision = args.head_pose_precision
face_detection_precision = args.face_detection_precision
landmarks_precision = args.landmarks_precision
input_feeder = InputFeeder(args)
control_mouse = MouseController(args)
gaze_model = 'models/intel/gaze-estimation-adas-0002/{}/gaze-estimation-adas-0002'.format(
gaze_estimation_precision)
face_detector_model = 'models/intel/face-detection-adas-binary-0001/{}/face-detection-adas-binary-0001'.format(
face_detection_precision)
facial_landmark_model = 'models/intel/landmarks-regression-retail-0009/{}/landmarks-regression-retail-0009'.format(
landmarks_precision)
head_pose_model = 'models/intel/head-pose-estimation-adas-0001/{}/head-pose-estimation-adas-0001'.format(
head_pose_precision)
# Initialize the models
face_detector = FaceDetector(face_detector_model, args)
facial_landmarks = FacialLandmarksDetector(
model_name=facial_landmark_model,
device=device,
extensions=cpu_extensions)
head_pose_estimation = HeadPoseEstimation(model_name=head_pose_model,
device=device,
extensions=cpu_extensions)
gaze_estimation = GazeEstimation(model_name=gaze_model,
device=device,
extensions=cpu_extensions)
# Load the models
start_time = time.time()
face_detector.load_model()
face_detector_loadtime = time.time() - start_time
start_time = time.time()
facial_landmarks.load_model()
facial_landmark_loadtime = time.time() - start_time
start_time = time.time()
head_pose_estimation.load_model()
head_pose_estimation_loadtime = time.time() - start_time
start_time = time.time()
gaze_estimation.load_model()
gaze_estimation_loadtime = time.time() - start_time
logging.info('FINISH LOADING MODELS')
try:
width, height = input_feeder.load_data()
except TypeError:
logging.error('Invalid file type.')
return
output_handler = OutputHandler(args)
output_handler.initalize_video_writer(width, height)
frame_count = 0
start_time = 0
capture = input_feeder.cap
inputs = args.input
if input_feeder.input_type == 'cam':
inputs = 0
else:
capture.open(inputs)
while capture.isOpened():
flag, frame = capture.read()
if start_time == 0:
start_time = time.time()
if inputs == 0 and time.time() - start_time >= 1:
gaze_estimate = run_inference(frame, face_detector,
facial_landmarks,
head_pose_estimation,
gaze_estimation, output_handler)
if gaze_estimate is None:
break
if gaze_estimate[0][0]:
x, y = gaze_estimate[0][:2]
control_mouse.move(x, y)
start_time = 0
frame_count += 1
elif not inputs == 0:
gaze_estimate = run_inference(frame, face_detector,
facial_landmarks,
head_pose_estimation,
gaze_estimation, output_handler)
if gaze_estimate is None:
break
if gaze_estimate[0][0] and time.time() - start_time >= 0.5:
x, y = gaze_estimate[0][:2]
control_mouse.move(x, y)
start_time = 0
frame_count += 1
input_feeder.close()
logging.info('TOTOAL FRAMES PROCESSED: {}'.format(frame_count))
logging.info('Time to load face detector model is {:.5f}'.format(
face_detector_loadtime))
logging.info('Time to load head pose estimation model is {:.5f}'.format(
head_pose_estimation_loadtime))
logging.info('Time to load facial landmarks model model is {:.5f}'.format(
facial_landmark_loadtime))
logging.info('Time to load gaze estimation model is {:.5f}'.format(
gaze_estimation_loadtime))
def infer_on_stream(args):
try:
log.basicConfig(
level=log.INFO,
format="%(asctime)s [%(levelname)s] %(message)s",
handlers=[log.FileHandler("app.log"),
log.StreamHandler()])
mouse_controller = MouseController(precision="low", speed="fast")
start_model_load_time = time.time()
face_detector = FaceDetector(args.model_face_detection)
facial_landmarks_detector = FacialLandmarksDetector(
args.model_facial_landmarks_detection)
head_pose_estimator = HeadPoseEstimator(
args.model_head_pose_estimation)
gaze_estimator = GazeEstimator(args.model_gaze_estimation)
face_detector.load_model()
facial_landmarks_detector.load_model()
head_pose_estimator.load_model()
gaze_estimator.load_model()
total_model_load_time = time.time() - start_model_load_time
log.info("Model load time: {:.1f}ms".format(1000 *
total_model_load_time))
output_directory = os.path.join(args.output_path + '\\' + args.device)
if not os.path.exists(output_directory):
os.makedirs(output_directory)
feed = InputFeeder(args.input_type, args.input_path)
feed.load_data()
out_video = feed.get_out_video(output_directory)
frame_counter = 0
start_inference_time = time.time()
total_prepocess_time = 0
while True:
try:
frame = next(feed.next_batch())
except StopIteration:
break
frame_counter += 1
face_boxes = face_detector.predict(frame)
for face_box in face_boxes:
face_image = get_crop_image(frame, face_box)
eye_boxes, eye_centers = facial_landmarks_detector.predict(
face_image)
left_eye_image, right_eye_image = [
get_crop_image(face_image, eye_box)
for eye_box in eye_boxes
]
head_pose_angles = head_pose_estimator.predict(face_image)
gaze_x, gaze_y = gaze_estimator.predict(
right_eye_image, head_pose_angles, left_eye_image)
draw_gaze_line(frame, face_box, eye_centers, gaze_x, gaze_y)
if args.show_input:
cv2.imshow('im', frame)
if args.move_mouse:
mouse_controller.move(gaze_x, gaze_y)
total_prepocess_time += face_detector.preprocess_time + facial_landmarks_detector.preprocess_time + \
head_pose_estimator.preprocess_time + gaze_estimator.preprocess_time
break
if out_video is not None:
out_video.write(frame)
if args.input_type == "image":
cv2.imwrite(os.path.join(output_directory, 'output_image.jpg'),
frame)
key_pressed = cv2.waitKey(60)
if key_pressed == 27:
break
total_time = time.time() - start_inference_time
total_inference_time = round(total_time, 1)
fps = frame_counter / total_inference_time
log.info("Inference time:{:.1f}ms".format(1000 * total_inference_time))
log.info("Input/output preprocess time:{:.1f}ms".format(
1000 * total_prepocess_time))
log.info("FPS:{}".format(fps))
with open(os.path.join(output_directory, 'stats.txt'), 'w') as f:
f.write(str(total_inference_time) + '\n')
f.write(str(total_prepocess_time) + '\n')
f.write(str(fps) + '\n')
f.write(str(total_model_load_time) + '\n')
feed.close()
cv2.destroyAllWindows()
except Exception as e:
log.exception("Something wrong when running inference:" + str(e))
def main(args):
print("Main script running...")
log_name = 'stats_' + args.device + '_' + args.hpe + args.fld + args.ge
if not os.path.exists('output'):
os.makedirs('output')
print(f"Logging to: output/{log_name}")
log = open('output/' + log_name, 'w+')
print("Initializing models...")
fd = FaceDetector(
model_name=
'models/intel/face-detection-adas-binary-0001/FP32-INT1/face-detection-adas-binary-0001',
device=args.device,
extensions=None)
fd.load_model()
if args.v: print(f"Face Detection Load Time: {fd.load_time}")
hpe = HeadPoseEstimator(
model_name=
f'models/intel/head-pose-estimation-adas-0001/{args.hpe}/head-pose-estimation-adas-0001',
device=args.device,
extensions=None)
hpe.load_model()
if args.v: print(f"Head Pose Estimation Load Time: {hpe.load_time}")
fld = FacialLandmarkDetector(
model_name=
f'models/intel/landmarks-regression-retail-0009/{args.fld}/landmarks-regression-retail-0009',
device=args.device,
extensions=None)
fld.load_model()
if args.v: print(f"Facial Landmarks Detection Load Time: {fld.load_time}")
ge = GazeEstimator(
model_name=
f'models/intel/gaze-estimation-adas-0002/{args.ge}/gaze-estimation-adas-0002',
device=args.device,
extensions=None)
ge.load_model()
if args.v: print(f"Gaze Estimation Load Time: {ge.load_time}")
image = False
print("Initializing source feed...")
feed = InputFeeder(input_type=args.input_type, input_file=args.input_file)
if args.input_type == 'image':
image = True
feed.load_data()
for batch in feed.next_batch():
if args.v:
print()
cv2.imshow('Batch', batch)
if image:
cv2.imwrite('output/Batch.png', batch)
coords, bounding_face = fd.predict(batch)
if not coords:
print("No face")
continue
if image: cv2.imwrite('output/Face.png', bounding_face)
box = coords[0]
face = bounding_face[box[1]:box[3], box[0]:box[2]]
if args.v:
print(f"Face Time: {fd.infer_time}")
log.write("FD_infer: " + str(fd.infer_time) + "\n")
if image:
cv2.imshow('Cropped Face', face)
# Landmark Detection
coords, landmark_detection, landmark_points = fld.predict(face)
if image: cv2.imwrite('output/Landmarks.png', landmark_detection)
if image: cv2.imshow('Landmark Detection', landmark_detection)
if args.v: print(f"Landmark Time: {fld.infer_time}")
log.write("FLD_infer: " + str(fld.infer_time) + "\n")
right_box, left_box = coords[0:2]
if args.v: print(f"Eye Coords: {coords}")
if left_box == None or right_box == None:
print("No eyes")
continue
left_eye = face[left_box[1]:left_box[3], left_box[0]:left_box[2]]
cv2.putText(face, 'L', (left_box[0], left_box[3]),
cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)
right_eye = face[right_box[1]:right_box[3], right_box[0]:right_box[2]]
cv2.putText(face, 'R', (right_box[0], right_box[3]),
cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)
if args.v:
print(f"Eye Shape: {left_eye.shape} :: {right_eye.shape}")
#Head Pose Estimation
head_yaw, head_pitch, head_roll = hpe.predict(face)
if args.v: print(f"Head Pose Time: {hpe.infer_time}")
log.write("HPE_infer: " + str(hpe.infer_time) + "\n")
head_angles = [head_yaw[0][0], head_pitch[0][0], head_roll[0][0]]
#Gaze Estimation
# expects pose as (yaw, pitch, and roll)
gaze = ge.predict(left_eye, right_eye, head_angles)
if args.v:
print(f"Gaze Time: {ge.infer_time}")
log.write("GE_infer: " + str(ge.infer_time) + "\n")
gaze_point = (int(gaze[0][0] * 50), int(gaze[0][1] * 50))
arrows = cv2.arrowedLine(face, landmark_points[0],
(landmark_points[0][0] + gaze_point[0],
landmark_points[0][1] - gaze_point[1]),
(0, 0, 255), 2)
arrows = cv2.arrowedLine(face, landmark_points[1],
(landmark_points[1][0] + gaze_point[0],
landmark_points[1][1] - gaze_point[1]),
(0, 0, 255), 2)
if image:
cv2.imwrite('output/Gaze.png', arrows)
if not image:
mouse = MouseController(precision='medium', speed='medium')
mouse.move(gaze[0][0], gaze[0][1])
if image:
cv2.imshow('Arrows', arrows)
if image:
log.write("FD_LoadTime: " + str(fd.load_time) + "\n")
log.write("FD_PreprocessTime: " + str(fd.preprocess_input_time) +
"\n")
log.write("FD_PostrocessTime: " + str(fd.preprocess_output_time) +
"\n")
log.write("FLD_LoadTime: " + str(fld.load_time) + "\n")
log.write("FLD_PreprocessTime: " + str(fld.preprocess_input_time) +
"\n")
log.write("FLD_PostprocessTime: " +
str(fld.preprocess_output_time) + "\n")
log.write("HPE_LoadTime: " + str(hpe.load_time) + "\n")
log.write("HPE_PreprocessTime: " + str(hpe.preprocess_input_time) +
"\n")
log.write("GE_LoadTime: " + str(ge.load_time) + "\n")
log.write("GE_PreprocessTime: " + str(ge.preprocess_input_time) +
"\n")
cv2.waitKey(0)
else:
if cv2.waitKey(15) & 0xFF == ord('q'):
break
feed.close()
log.close()
cv2.destroyAllWindows
def main():
args = build_argparser().parse_args()
input_file = args.input
logger = log.getLogger()
if input_file == "CAM":
input_feeder = InputFeeder("cam")
else:
if not os.path.isfile(input_file):
logger.error("Path should be file")
exit(1)
input_feeder = InputFeeder("video", input_file)
face_detector = FaceDetector(
args.face_detection_model,
device=args.device,
threshold=args.threshold,
extensions=args.extensions,
)
face_landmark_detector = FaceLandmarkDetector(
args.face_landmark_model,
device=args.device,
threshold=args.threshold,
extensions=args.extensions,
)
head_pose_estimator = HeadPoseEstimator(
args.head_pose_model,
device=args.device,
threshold=args.threshold,
extensions=args.extensions,
)
gaze_estimator = GazeEstimator(
args.gaze_estimation_model,
device=args.device,
threshold=args.threshold,
extensions=args.extensions,
)
mouse_controller = MouseController("medium", "fast")
face_detector.load_model()
face_landmark_detector.load_model()
head_pose_estimator.load_model()
gaze_estimator.load_model()
input_feeder.load_data()
width = 1000
height = int(width * 9 / 16)
for flag, frame in input_feeder.next_batch():
if not flag:
break
pressed_key = cv2.waitKey(60)
face_detected = face_detector.predict(frame)
if face_detected:
face_coordinates, face_image = face_detected
if not face_coordinates:
continue
else:
continue
if "fd" in args.visualization:
cv2.rectangle(
frame,
(face_coordinates[0], face_coordinates[1]),
(face_coordinates[2], face_coordinates[3]),
(36, 255, 12),
2,
)
cv2.putText(
frame,
"Face Detected",
(face_coordinates[0], face_coordinates[1] - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.9,
(36, 255, 12),
2,
)
left_eye_img, righ_eye_img, eye_coords = face_landmark_detector.predict(
face_image
)
if "fl" in args.visualization:
frame_eye_coords_min = (
np.array(eye_coords)[:, :2] + np.array(face_coordinates)[:2]
)
frame_eye_coords_max = (
np.array(eye_coords)[:, 2:] + np.array(face_coordinates)[:2]
)
cv2.rectangle(
frame,
(frame_eye_coords_min[0][0], frame_eye_coords_min[0][1]),
(frame_eye_coords_max[0][0], frame_eye_coords_max[0][1]),
(36, 255, 12),
2,
)
cv2.rectangle(
frame,
(frame_eye_coords_min[1][0], frame_eye_coords_min[1][1]),
(frame_eye_coords_max[1][0], frame_eye_coords_max[1][1]),
(36, 255, 12),
2,
)
head_pose_estimate = head_pose_estimator.predict(face_image)
if "hp" in args.visualization:
cv2.putText(
frame,
"yaw:{:.1f}|pitch:{:.1f}|roll:{:.1f}".format(*head_pose_estimate),
(20, 35),
cv2.FONT_HERSHEY_COMPLEX,
1.2,
(36, 255, 12),
3,
)
mouse_coordinate, gaze_vector = gaze_estimator.predict(
left_eye_img, righ_eye_img, head_pose_estimate
)
if "ge" in args.visualization:
head_pose_estimate = np.array(head_pose_estimate)
yaw, pitch, roll = head_pose_estimate * np.pi / 180.0
focal_length = 950
scale = 100
origin = (
int(
face_coordinates[0]
+ (face_coordinates[2] - face_coordinates[0]) / 2
),
int(
face_coordinates[1]
+ (face_coordinates[3] - face_coordinates[1]) / 2
),
)
r_x = np.array(
[
[1, 0, 0],
[0, math.cos(pitch), -math.sin(pitch)],
[0, math.sin(pitch), math.cos(pitch)],
]
)
r_y = np.array(
[
[math.cos(yaw), 0, -math.sin(yaw)],
[0, 1, 0],
[math.sin(yaw), 0, math.cos(yaw)],
]
)
r_z = np.array(
[
[math.cos(roll), -math.sin(roll), 0],
[math.sin(roll), math.cos(roll), 0],
[0, 0, 1],
]
)
r = r_z @ r_y @ r_x
zaxis = np.array(([0, 0, -1 * scale]), dtype="float32")
offset = np.array(([0, 0, focal_length]), dtype="float32")
zaxis = np.dot(r, zaxis) + offset
tip = (
int(zaxis[0] / zaxis[2] * focal_length) + origin[0],
int(zaxis[1] / zaxis[2] * focal_length) + origin[1],
)
cv2.arrowedLine(frame, origin, tip, (0, 0, 255), 3, tipLength=0.3)
cv2.imshow("frame", cv2.resize(frame, (width, height)))
mouse_controller.move(mouse_coordinate[0], mouse_coordinate[1])
if pressed_key == 27:
logger.error("exit key is pressed..")
break
def main():
args = get_args()
log.basicConfig(filename='example.log', level=log.DEBUG)
inputFile = args.input
#inputFile = "./bin/demo.mp4"
mouse = MouseController("high", "fast")
frame_count = 0
focal_length = 950.0
scale = 50
#print(f"Visual flag: {args.visual_flag}")
if inputFile.lower() == "cam":
feed = InputFeeder('cam')
log.info("Video source: " + str(inputFile))
else:
if not os.path.isfile(inputFile):
log.error("Unable to find file: " + inputFile)
exit(1)
feed = InputFeeder("video", inputFile)
log.info("Video source: " + str(inputFile))
log.info("InputFeeder initialized")
log.info("Device: " + str(args.device))
log.info("Face detection model: " + str(args.facedetectionmodel))
log.info("Facial landmarks model: " + str(args.faciallandmarksmodel))
log.info("Head pose estimation model: " + str(args.headposemodel))
log.info("Gaze estimation model: " + str(args.gazeestimationmodel))
if args.stats == 1:
print("Running statistics...")
inference_times = []
fdm_inference_times = []
hpm_inference_times = []
flm_inference_times = []
gem_inference_times = []
start_time = time.time()
# Create instances of the different models
fdm = FaceDetector(args.facedetectionmodel, args.device,
args.cpu_extension)
if args.stats == 1:
start_time = time.time()
fdm.load_model()
fdm_load_time = time.time() - start_time
else:
fdm.load_model()
fdm.check_model()
hpm = HeadPoseEstimator(args.headposemodel, args.device,
args.cpu_extension)
if args.stats == 1:
start_time = time.time()
hpm.load_model()
hpm_load_time = time.time() - start_time
else:
hpm.load_model()
hpm.check_model()
flm = FacialLandmarksDetector(args.faciallandmarksmodel, args.device,
args.cpu_extension)
if args.stats == 1:
start_time = time.time()
flm.load_model()
flm_load_time = time.time() - start_time
else:
flm.load_model()
flm.check_model()
gem = GazeEstimator(args.gazeestimationmodel, args.device,
args.cpu_extension)
if args.stats == 1:
start_time = time.time()
gem.load_model()
gem_load_time = time.time() - start_time
else:
gem.load_model()
gem.check_model()
if args.stats == 1:
duration_loading = time.time() - start_time
print(
f"Duration for loading and checking the models: {duration_loading}"
)
log.info(
f"Duration for loading and checking the models: {duration_loading}"
)
cv2.namedWindow('preview', cv2.WINDOW_NORMAL)
cv2.resizeWindow('preview', 600, 600)
feed.load_data()
for ret, frame in feed.next_batch():
if not ret:
break
if frame is not None:
frame_count += 1
key = cv2.waitKey(60)
if args.stats == 1:
start_time = time.time()
# Run face detection
face_crop, face_coords = fdm.predict(frame.copy())
print("Face crop shape: " + str(face_crop.shape))
frame_h, frame_w = frame.shape[:2]
(xmin, ymin, xmax, ymax) = face_coords
face_frame = frame[ymin:ymax, xmin:xmax]
#center_of_face = (xmin + face_frame.shape[1] / 2, ymin + face_frame.shape[0] / 2, 0) # 0 for colour channel
#print("Center of face " + str(center_of_face))
try:
# Check if face was detected
if type(face_coords) == int:
print("Unable to detect face")
if key == 27:
break
continue
# Facial landmark detection
left_eye_crop, right_eye_crop, landmarks, crop_coords = flm.predict(
face_crop.copy())
#print("Landmarks" +str(landmarks))
left_eye = (landmarks[0], landmarks[1])
right_eye = (landmarks[2], landmarks[3])
# Landmark position based on complete frame
landmarks_viz = landmarks
landmarks_viz[0] = landmarks_viz[0] + xmin
landmarks_viz[1] = landmarks_viz[1] + ymin
landmarks_viz[2] = landmarks_viz[2] + xmin
landmarks_viz[3] = landmarks_viz[3] + ymin
crop_coords_viz = (crop_coords[0] + xmin, crop_coords[1] +
ymin, crop_coords[2] + xmin,
crop_coords[3] + ymin, crop_coords[4] +
xmin, crop_coords[5] + ymin,
crop_coords[6] + xmin,
crop_coords[7] + ymin)
left_eye_viz = (landmarks_viz[0], landmarks_viz[1])
right_eye_viz = (landmarks_viz[2], landmarks_viz[3])
third_eye_viz_x = (landmarks_viz[2] -
landmarks_viz[0]) / 2 + landmarks_viz[0]
third_eye_viz_y = (landmarks_viz[3] -
landmarks_viz[1]) / 2 + landmarks_viz[1]
third_eye_viz = (third_eye_viz_x, third_eye_viz_y)
#print(landmarks_viz[0], landmarks_viz[2], third_eye_viz_x)
# Head pose estimation
head_pose = hpm.predict(face_crop.copy())
print("Head pose: " + str(head_pose))
(yaw, pitch, roll) = head_pose
frame = display_head_pose(frame, pitch, roll, yaw)
# Send inputs to GazeEstimator
gaze_vector = gem.predict(head_pose, left_eye_crop,
right_eye_crop)
if args.stats == 1:
inference_time = time.time() - start_time
inference_times.append(inference_time)
print(gaze_vector)
frame = display_gaze(frame, gaze_vector)
# Control the mouse
if frame_count % 5 == 0:
mouse_x, mouse_y = get_mouse_vector(gaze_vector, roll)
print("Mouse vector:" + str(mouse_x) + " - " +
str(mouse_y))
mouse.move(mouse_x, mouse_y)
currentMouseX, currentMouseY = pyautogui.position()
print("Mouse coordinates: " + str(currentMouseX) + ", " +
str(currentMouseY))
if args.visual_flag == 1:
frame = draw_bounding_box(frame, face_coords)
left_eye_frame = crop_coords_viz[0:4]
right_eye_frame = crop_coords_viz[4:]
frame = draw_bounding_box(frame, left_eye_frame)
frame = draw_bounding_box(frame, right_eye_frame)
frame = visualize_landmark(frame, left_eye_viz)
frame = visualize_landmark(frame,
right_eye_viz,
color=(0, 0, 255))
frame = visualize_gaze(frame, gaze_vector, landmarks_viz)
# visualize the axes of the HeadPoseEstimator results
#frame = hpm.draw_axes(frame.copy(), center_of_face, yaw, pitch, roll, scale, focal_length)
frame = hpm.draw_axes(frame.copy(), third_eye_viz, yaw,
pitch, roll, scale, focal_length)
#hdm.draw_axes(frame.copy(), center_of_face, yaw, pitch, roll, scale, focal_length)
cv2.imshow('preview', frame)
cv2.imshow('left eye', left_eye_crop)
cv2.imshow('right eye', right_eye_crop)
except Exception as e:
print("Unable to predict using model" + str(e) +
" for frame " + str(frame_count))
log.error("Unable to predict using model" + str(e) +
" for frame " + str(frame_count))
continue
if args.stats == 1:
avg_inference_time = sum(inference_times) / len(inference_times)
print("Average inference time: " + str(avg_inference_time))
log.info("Average inference time: " + str(avg_inference_time))
log.info("Load time for face detection model: " + str(fdm_load_time))
log.info("Load time for facial landmarks model: " + str(flm_load_time))
log.info("Load time for head pose detection model: " +
str(hpm_load_time))
log.info("Load time for gaze estimation model: " + str(gem_load_time))
cv2.destroyAllWindows()
feed.close()