def infer_on_stream(args):
"""
Initialize the inference network, stream video to network,
and output stats and video.
:param args: Command line arguments parsed by `build_argparser()`
:return: None
"""
try:
logging.basicConfig(level=logging.DEBUG,
format="%(asctime)s [%(levelname)s] %(message)s",
handlers=[
logging.FileHandler("debug.log"),
logging.StreamHandler()
])
# Initialise the class
mc = MouseController("low", "fast")
fdnet = FaceDetectionModel(args.fdmodel)
lmnet = FacialLandMarksDetectionModel(args.lmmodel)
hpnet = HeadPoseEstimationModel(args.hpmodel)
genet = GazeEstimationModel(args.gemodel)
start_time = time.time()
fdnet.load_model()
logging.info(
f"Face Detection Model: {1000 * (time.time() - start_time):.1f}ms")
start_time = time.time()
lmnet.load_model()
logging.info(
f"Facial Landmarks Detection Model: {1000 * (time.time() - start_time):.1f}ms"
)
start_time = time.time()
hpnet.load_model()
logging.info(
f"Headpose Estimation Model: {1000 * (time.time() - start_time):.1f}ms"
)
start_time = time.time()
genet.load_model()
logging.info(
f"Gaze Estimation Model: {1000 * (time.time() - start_time):.1f}ms"
)
# Get and open video capture
feeder = InputFeeder('video', args.input)
feeder.load_data()
frame_count = 0
fd_infertime = 0
lm_infertime = 0
hp_infertime = 0
ge_infertime = 0
while True:
# Read the next frame
try:
frame = next(feeder.next_batch())
except StopIteration:
break
key_pressed = cv2.waitKey(60)
frame_count += 1
# face detection
p_frame = fdnet.preprocess_input(frame)
start_time = time.time()
fd_output = fdnet.predict(p_frame)
fd_infertime += time.time() - start_time
out_frame, bboxes = fdnet.preprocess_output(
fd_output, frame, args.print)
for bbox in bboxes:
face = frame[bbox[1]:bbox[3], bbox[0]:bbox[2]]
p_frame = lmnet.preprocess_input(face)
start_time = time.time()
lm_output = lmnet.predict(p_frame)
lm_infertime += time.time() - start_time
out_frame, left_eye_point, right_eye_point = lmnet.preprocess_output(
lm_output, bbox, out_frame, args.print)
# get head pose estimation
p_frame = hpnet.preprocess_input(face)
start_time = time.time()
hp_output = hpnet.predict(p_frame)
hp_infertime += time.time() - start_time
out_frame, headpose_angles = hpnet.preprocess_output(
hp_output, out_frame, face, bbox, args.print)
# get gaze estimation
out_frame, left_eye, right_eye = genet.preprocess_input(
out_frame, face, left_eye_point, right_eye_point,
args.print)
start_time = time.time()
ge_output = genet.predict(left_eye, right_eye, headpose_angles)
ge_infertime += time.time() - start_time
out_frame, gaze_vector = genet.preprocess_output(
ge_output, out_frame, bbox, left_eye_point,
right_eye_point, args.print)
if not args.no_video:
cv2.imshow('image', out_frame)
if not args.no_move:
mc.move(gaze_vector[0], gaze_vector[1])
break
if key_pressed == 27:
break
if frame_count > 0:
logging.info(
f"Face Detection:{1000* fd_infertime/frame_count:.1f}ms")
logging.info(
f"Facial Landmarks Detection:{1000* lm_infertime/frame_count:.1f}ms"
)
logging.info(
f"Headpose Estimation:{1000* hp_infertime/frame_count:.1f}ms")
logging.info(
f"Gaze Estimation:{1000* ge_infertime/frame_count:.1f}ms")
feeder.close()
cv2.destroyAllWindows()
except Exception as ex:
logging.exception(f"Error during inference:{str(ex)}")
def infer_on_stream(args):
"""
Initialize the inference network, stream video to network,
and output stats and video.
:param args: Command line arguments parsed by `build_argparser()`
:return: None
"""
try:
logging.basicConfig(level=logging.INFO,
format="%(asctime)s [%(levelname)s] %(message)s",
handlers=[
logging.FileHandler("gaze-app.log"),
logging.StreamHandler()
])
# Initialise the class
mc = MouseController("low", "fast")
#mc.move(100,100)
fdnet = FaceDetectionModel(args.fdmodel)
lmnet = FacialLandMarksDetectionModel(args.lmmodel)
hpnet = HeadPoseEstimationModel(args.hpmodel)
genet = GazeEstimationModel(args.gemodel)
### Load the model through ###
logging.info("============== Models Load time ===============")
start_time = time.time()
fdnet.load_model()
logging.info("Face Detection Model: {:.1f}ms".format(
1000 * (time.time() - start_time)))
start_time = time.time()
lmnet.load_model()
logging.info("Facial Landmarks Detection Model: {:.1f}ms".format(
1000 * (time.time() - start_time)))
start_time = time.time()
hpnet.load_model()
logging.info("Headpose Estimation Model: {:.1f}ms".format(
1000 * (time.time() - start_time)))
start_time = time.time()
genet.load_model()
logging.info("Gaze Estimation Model: {:.1f}ms".format(
1000 * (time.time() - start_time)))
logging.info("============== End =====================")
# Get and open video capture
feeder = InputFeeder('video', args.input)
feeder.load_data()
# FPS = feeder.get_fps()
# Grab the shape of the input
# width = feeder.get_width()
# height = feeder.get_height()
# init scene variables
frame_count = 0
### Loop until stream is over ###
fd_infertime = 0
lm_infertime = 0
hp_infertime = 0
ge_infertime = 0
while True:
# Read the next frame
try:
frame = next(feeder.next_batch())
except StopIteration:
break
key_pressed = cv2.waitKey(60)
frame_count += 1
#print(int((frame_count) % int(FPS)))
# face detection
p_frame = fdnet.preprocess_input(frame)
start_time = time.time()
fnoutput = fdnet.predict(p_frame)
fd_infertime += time.time() - start_time
out_frame, fboxes = fdnet.preprocess_output(
fnoutput, frame, args.print)
#for each face
for fbox in fboxes:
# fbox = (xmin,ymin,xmax,ymax)
# get face landmarks
# crop face from frame
face = frame[fbox[1]:fbox[3], fbox[0]:fbox[2]]
p_frame = lmnet.preprocess_input(face)
start_time = time.time()
lmoutput = lmnet.predict(p_frame)
lm_infertime += time.time() - start_time
out_frame, left_eye_point, right_eye_point = lmnet.preprocess_output(
lmoutput, fbox, out_frame, args.print)
# get head pose estimation
p_frame = hpnet.preprocess_input(face)
start_time = time.time()
hpoutput = hpnet.predict(p_frame)
hp_infertime += time.time() - start_time
out_frame, headpose_angels = hpnet.preprocess_output(
hpoutput, out_frame, face, fbox, args.print)
# get gaze estimation
out_frame, left_eye, right_eye = genet.preprocess_input(
out_frame, face, left_eye_point, right_eye_point,
args.print)
start_time = time.time()
geoutput = genet.predict(left_eye, right_eye, headpose_angels)
ge_infertime += time.time() - start_time
out_frame, gazevector = genet.preprocess_output(
geoutput, out_frame, fbox, left_eye_point, right_eye_point,
args.print)
if (not args.no_video):
cv2.imshow('im', out_frame)
if (not args.no_move):
mc.move(gazevector[0], gazevector[1])
#consider only first detected face in the frame
break
# Break if escape key pressed
if key_pressed == 27:
break
#logging inference times
if (frame_count > 0):
logging.info(
"============== Models Inference time ===============")
logging.info("Face Detection:{:.1f}ms".format(1000 * fd_infertime /
frame_count))
logging.info("Facial Landmarks Detection:{:.1f}ms".format(
1000 * lm_infertime / frame_count))
logging.info("Headpose Estimation:{:.1f}ms".format(
1000 * hp_infertime / frame_count))
logging.info("Gaze Estimation:{:.1f}ms".format(
1000 * ge_infertime / frame_count))
logging.info("============== End ===============================")
# Release the capture and destroy any OpenCV windows
feeder.close()
cv2.destroyAllWindows()
except Exception as ex:
logging.exception("Error in inference:" + str(ex))
def infer_on_stream(args):
face_detection_model_file = args.faceDetectionModel
facial_landmarks_detection_model_file = args.facialLandmarksModel
head_pose_estimation_model_file = args.headPoseModel
gaze_estimation_model_file = args.gazeModel
video_file = args.input
device_name = args.device
cpu_extension = args.cpu_extension
prob_threshold = args.prob_threshold
preview_flag = args.preview_flag
output_path = args.output_path
if not os.path.exists(output_path):
os.mkdir(output_path)
mouse_control = MouseController("low", "fast")
try:
logging.info("*********** Model Load Time ***************")
start_model_load_time = time.time()
start_time = time.time()
face_detection_model = FaceDetectionModel(face_detection_model_file,
device_name, cpu_extension)
logging.info("Face Detection Model: {:.1f} ms.".format(
1000 * (time.time() - start_time)))
start_time = time.time()
facial_landmarks_detection_model = FacialLandmarksDetectionModel(
facial_landmarks_detection_model_file, device_name, cpu_extension)
logging.info("Facial Landmarks Detection Model: {:.1f} ms.".format(
1000 * (time.time() - start_time)))
start_time = time.time()
head_pose_estimation_model = HeadPoseEstimationModel(
head_pose_estimation_model_file, device_name, cpu_extension)
logging.info("Head Pose Estimation Model: {:.1f} ms.".format(
1000 * (time.time() - start_time)))
start_time = time.time()
gaze_estimation_model = GazeEstimationModel(gaze_estimation_model_file,
device_name, cpu_extension)
logging.info("Gaze Estimation Model: {:.1f} ms.".format(
1000 * (time.time() - start_time)))
total_model_load_time = time.time() - start_model_load_time
logging.info("*********** Model Load Completed ***********")
except Exception as e:
logging.error("ERROR in model loading: " + str(e))
sys.exit(1)
feeder = InputFeeder('video', video_file)
feeder.load_data()
out_video = cv2.VideoWriter(os.path.join(output_path, 'output_video.mp4'),
cv2.VideoWriter_fourcc(*'avc1'),
int(feeder.fps() / 10), (1920, 1080), True)
start_inference_time = 0
frame_count = 0
face_detect_infer_time = 0
facial_landmarks_infer_time = 0
head_pose_infer_time = 0
gaze_infer_time = 0
while True:
try:
frame = next(feeder.next_batch())
except StopIteration:
break
key_pressed = cv2.waitKey(60)
frame_count += 1
## Face Detecton Model
image = face_detection_model.preprocess_input(frame)
start_time = time.time()
outputs = face_detection_model.predict(image)
face_detect_infer_time += (time.time() - start_time)
out_frame, faces = face_detection_model.preprocess_output(
outputs, frame, preview_flag, prob_threshold)
for face in faces:
crop_image = frame[face[1]:face[3], face[0]:face[2]]
## Facial Landmarks Detecton Model
image = facial_landmarks_detection_model.preprocess_input(
crop_image)
start_time = time.time()
outputs = facial_landmarks_detection_model.predict(image)
facial_landmarks_infer_time += (time.time() - start_time)
out_frame, left_eye_point, right_eye_point = facial_landmarks_detection_model.preprocess_output(
outputs, out_frame, face, preview_flag)
## Head Pose Estimation Model
image = head_pose_estimation_model.preprocess_input(crop_image)
start_time = time.time()
outputs = head_pose_estimation_model.predict(image)
head_pose_infer_time += (time.time() - start_time)
out_frame, headpose_angels_list = head_pose_estimation_model.preprocess_output(
outputs, out_frame, preview_flag)
## Gaze Estimation Model
out_frame, left_eye, right_eye = gaze_estimation_model.preprocess_input(
out_frame, crop_image, left_eye_point, right_eye_point)
start_time = time.time()
outputs = gaze_estimation_model.predict(left_eye, right_eye,
headpose_angels_list)
gaze_infer_time += (time.time() - start_time)
out_frame, gazevector = gaze_estimation_model.preprocess_output(
outputs, out_frame, face, left_eye_point, right_eye_point,
preview_flag)
cv2.imshow("Computer Pointer Control", out_frame)
out_video.write(out_frame)
mouse_control.move(gazevector[0], gazevector[1])
if key_pressed == 27:
break
if frame_count > 0:
logging.info("*********** Model Inference Time ****************")
logging.info("Face Detection Model: {:.1f} ms.".format(
1000 * face_detect_infer_time / frame_count))
logging.info("Facial Landmarks Detection Model: {:.1f} ms.".format(
1000 * facial_landmarks_infer_time / frame_count))
logging.info("Head Pose Detection Model: {:.1f} ms.".format(
1000 * head_pose_infer_time / frame_count))
logging.info("Gaze Detection Model: {:.1f} ms.".format(
1000 * gaze_infer_time / frame_count))
logging.info("*********** Model Inference Completed ***********")
total_infer_time = time.time() - start_inference_time
total_inference_time = round(total_infer_time, 1)
fps = frame_count / total_inference_time
with open(os.path.join(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')
logging.info("*********** Total Summary ****************")
logging.info(f"Total Model Load Time: {total_model_load_time}")
logging.info(f"Total Inference Time: {total_inference_time}")
logging.info(f"FPS: {fps}")
logging.info("*********** Total Summary ***********")
logging.info("*********** ************************* ***********")
feeder.close()
cv2.destroyAllWindows()
