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 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 main():
args = build_argparser().parse_args()
logger = logging.getLogger()
# video file
inputFilePath = args.input
inputFeeder = None
# check to see if the user wants to use a video or camera feed
if inputFilePath.lower() == "cam":
inputFeeder = InputFeeder("cam")
else:
if not os.path.isfile(inputFilePath):
logger.error("Unable to find specified video file")
exit(1)
inputFeeder = InputFeeder("video", inputFilePath)
# get model files from the command line
modelPathDict = {
'FaceDetectionModel': args.facedetectionmodel,
'FacialLandmarksDetectionModel': args.faciallandmarkmodel,
'GazeEstimationModel': args.gazeestimationmodel,
'HeadPoseEstimationModel': args.headposemodel
}
# check if all files are accessible and correct
for fileNameKey in modelPathDict.keys():
if not os.path.isfile(modelPathDict[fileNameKey]):
logger.error("Unable to find specified " + fileNameKey +
" xml file")
exit(1)
# initializing the 4 models
fdm = FaceDetectionModel(modelPathDict['FaceDetectionModel'], args.device,
args.cpu_extension)
fldm = FacialLandmarksDetectionModel(
modelPathDict['FacialLandmarksDetectionModel'], args.device,
args.cpu_extension)
gem = GazeEstimationModel(modelPathDict['GazeEstimationModel'],
args.device, args.cpu_extension)
hpem = HeadPoseEstimationModel(modelPathDict['HeadPoseEstimationModel'],
args.device, args.cpu_extension)
mc = MouseController('medium', 'fast')
# loading models
inputFeeder.load_data()
fdm.load_model()
fldm.load_model()
hpem.load_model()
gem.load_model()
# starting frame by frame inference
frame_count = 0
for ret, frame in inputFeeder.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)
# getting the cropped face from the Face Detecction model
croppedFace, face_coords = fdm.predict(frame.copy(),
args.prob_threshold)
if type(croppedFace) == int:
logger.error("Unable to detect the face.")
if key == 27:
break
continue
# getting the head post estimation output
hp_out = hpem.predict(croppedFace.copy())
# getting the coordinated for the facial landmarks using the cropped image from the FacialRecognition model as input
left_eye, right_eye, eye_coords = fldm.predict(croppedFace.copy())
# getting the mouse coordinated by feeding the outputs from landmark and head-pose as inputs to the gaze detection
new_mouse_coord, gaze_vector = gem.predict(left_eye, right_eye, hp_out)
# moving the mouse to the new position
if frame_count % 5 == 0:
mc.move(new_mouse_coord[0], new_mouse_coord[1])
if key == 27:
break
logger.error("Video ended, exiting...")
cv2.destroyAllWindows()
inputFeeder.close()
def main():
# Grab command line args
args = build_args().parse_args()
# Config Logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger()
#os.system('clear')
print("\n")
logger.info("starting app ...")
print("\n==========<COMPUTER POINTER CONTROLLER>==========")
print("============>(c) Ibrahim Ishaka 2020<============\n")
# initialize model object for each class
FDModel = FaceDetectionModel(model=args.face_detection_model,
device=args.device,
extensions=args.extension,
threshold=args.prob_threshold)
FLDModel = FacialLandmarksDetectionModel(model=args.facial_landmark_model,
device=args.device,
extensions=args.extension)
HPEModel = HeadPoseEstimationModel(model=args.head_pose_model,
device=args.device,
extensions=args.extension)
GEModel = GazeEstimationModel(model=args.gaze_estimation_model,
device=args.device,
extensions=args.extension)
models = {'fd': FDModel, 'fl': FLDModel, 'hp': HPEModel, 'ge': GEModel}
models_loading_time = 0
for k in models:
# load model
logger.info("Loading {} Model".format(models[k].model_name))
model_loading_start = time.time()
models[k].load_model()
model_loading_finish = (time.time() - model_loading_start)
models_loading_time = models_loading_time + model_loading_finish
logger.info("time taken to load Model: {:.3f}secs".format(
model_loading_finish))
# check if model output visualization is specified in sh arg
if k in args.show_output or args.show_output == 'all':
models[k].show = True
logger.info("show {} outputs: {} \n".format(models[k].model_name,
models[k].show))
logger.info("time taken to load All Models: {:.3f}secs\n".format(
models_loading_time))
# setting for mouse controller
_precision = "medium"
_speed = "fast"
mouse_controller = MouseController(precision=_precision, speed=_speed)
# verify and handle input stream
input_source = args.input
input_feeder = None
input_type = ""
if input_source.lower() != "cam":
# check if input file exist
if os.path.exists(input_source) and os.path.isfile(input_source):
image_formats = [".png", ".jpg", ".bmp", ".jpeg"]
is_image = [
True for x in image_formats if input_source.endswith(x)
]
if is_image:
input_type = "image"
else:
input_type = "video"
input_feeder = InputFeeder(input_type=input_type,
input_file=input_source)
else:
logger.error("Input file is not a file, or does't exist")
sys.exit(1)
elif input_source.lower() == "cam":
input_type = "cam"
input_feeder = InputFeeder(input_type=input_type)
input_feeder.load_data()
frame_count = 0
total_inference_time_all = 0
window_closed = False
for flag, frame in input_feeder.next_batch():
if flag is False:
# no frame to read
break
frame_count = frame_count + 1
key_pressed = cv2.waitKey(60)
if input_source == 'cam':
# preprocess frame as webcam is backwards/inverted
frame = cv2.flip(frame, 1)
face_detection_result = FDModel.predict(frame)
# The prediction result should return None, if no face detected
if face_detection_result is None:
if not window_closed:
cv2.imshow(input_type, cv2.resize(frame, (500, 500)))
logger.info("NO FACE DETECTED... skipping")
continue
cropped_face = face_detection_result[0]
face_coords = face_detection_result[1]
hp_result = HPEModel.predict(cropped_face)
left_eye, right_eye = FLDModel.predict(cropped_face)
new_mouse_coords, gaze_vector = GEModel.predict(
left_eye, right_eye, hp_result)
total_inference_time = 0
for key in models:
total_inference_time = total_inference_time + models[
key].inference_time
total_inference_time_all = total_inference_time_all + total_inference_time
#uncomment the following line to see the inference time for each frame
#logger.info("Inference Time : {:.3f}".format(total_inference_time))
try:
x, y = new_mouse_coords
except:
logger.error(
"unable to get mouse coordinates for current frame\nReading Next Frame..."
)
continue
if GEModel.show == True:
GEModel.show_gaze(left_eye, right_eye, gaze_vector)
if HPEModel.show == True:
frame = HPEModel.show_hp(frame, hp_result)
if new_mouse_coords is None:
# Error during LR_eyes processing
continue
'''
wait on before moving mouse again
this is recomended to avoid failsafe exception
but you change this setting
'''
if input_type == "image":
cv2.imshow(input_type, cv2.resize(frame, (500, 500)))
mouse_controller.move(x, y)
break
if frame_count % 5 == 0:
try:
logger.info("changing mouse position... moving")
mouse_controller.move(x, y)
except pyautogui.FailSafeException:
logger.error("safe exception From pyautogui")
continue
if not window_closed:
cv2.imshow(input_type, cv2.resize(frame, (500, 500)))
# Break if escape key pressed
if key_pressed == 27:
break
# close the OpenCV window if q key pressed
if key_pressed == ord('q'):
window_closed = True
cv2.destroyWindow(input_type)
logger.info(input_type +
" window closed... to exit app, press CTRL+Z")
if frame_count != 0:
# Release the capture and destroy any OpenCV window
input_feeder.close()
cv2.destroyAllWindows()
logger.info("Stream ended !")
fps = round(frame_count / total_inference_time_all, 2)
print("\n==========SUMMARY===========")
print("models loading time : ", round(models_loading_time, 2))
print("frames per seconds : ", fps)
print("total inference time : ", round(total_inference_time_all, 2))
print("============================")
else:
logger.error("Unable to handle Unsupported file ")
sys.exit(1)
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 main():
# Grab command line args
args = build_argparser().parse_args()
inputFilePath = args.input
inputFeeder = None
if args.input == "CAM":
inputFeeder = InputFeeder("cam")
else:
if not os.path.isfile(args.input):
log.info("Unable to find specified video file")
sys.exit(1)
inputFeeder = InputFeeder("video", args.input)
modelPathDict = {
'FaceDetectionModel': args.face_detection_model,
'FacialLandmarksDetectionModel': args.facial_landmark_model,
'GazeEstimationModel': args.gaze_estimation_model,
'HeadPoseEstimationModel': args.head_pose_model
}
for fileNameKey in modelPathDict.keys():
if not os.path.isfile(modelPathDict[fileNameKey]):
log.info("Unable to find specified " + fileNameKey + " xml file")
sys.exit(1)
fdm = FaceDetectionModel(modelPathDict['FaceDetectionModel'], args.device,
args.cpu_extension)
fldm = FacialLandmarksDetectionModel(
modelPathDict['FacialLandmarksDetectionModel'], args.device,
args.cpu_extension)
gem = GazeEstimationModel(modelPathDict['GazeEstimationModel'],
args.device, args.cpu_extension)
hpem = HeadPoseEstimationModel(modelPathDict['HeadPoseEstimationModel'],
args.device, args.cpu_extension)
mc = MouseController('medium', 'fast')
inputFeeder.load_data()
fdm.load_model()
fldm.load_model()
hpem.load_model()
gem.load_model()
frame_count = 0
for ret, frame in inputFeeder.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)
croppedFace, face_coords = fdm.predict(frame.copy(),
args.prob_threshold)
if type(croppedFace) == int:
log.info("Unable to detect the face.")
if key == 27:
break
continue
hp_out = hpem.predict(croppedFace.copy())
left_eye, right_eye, eye_coords = fldm.predict(croppedFace.copy())
new_mouse_coord, gaze_vector = gem.predict(left_eye, right_eye, hp_out)
'''
if (not len(previewFlags)==0):
preview_frame = frame.copy()
if 'fd' in previewFlags:
#cv2.rectangle(preview_frame, (face_coords[0], face_coords[1]), (face_coords[2], face_coords[3]), (255,0,0), 3)
preview_frame = croppedFace
if 'fld' in previewFlags:
cv2.rectangle(croppedFace, (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(croppedFace, (eye_coords[1][0]-10, eye_coords[1][1]-10), (eye_coords[1][2]+10, eye_coords[1][3]+10), (0,255,0), 3)
#preview_frame[face_coords[1]:face_coords[3], face_coords[0]:face_coords[2]] = croppedFace
if 'hp' in previewFlags:
cv2.putText(preview_frame, "Pose Angles: yaw:{:.2f} | pitch:{:.2f} | roll:{:.2f}".format(hp_out[0],hp_out[1],hp_out[2]), (10, 20), cv2.FONT_HERSHEY_COMPLEX, 0.25, (0, 255, 0), 1)
if 'ge' in previewFlags:
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), (255,0,255), 2)
cv2.line(le, (x-w, y+w), (x+w, y-w), (255,0,255), 2)
re = cv2.line(right_eye.copy(), (x-w, y-w), (x+w, y+w), (255,0,255), 2)
cv2.line(re, (x-w, y+w), (x+w, y-w), (255,0,255), 2)
croppedFace[eye_coords[0][1]:eye_coords[0][3],eye_coords[0][0]:eye_coords[0][2]] = le
croppedFace[eye_coords[1][1]:eye_coords[1][3],eye_coords[1][0]:eye_coords[1][2]] = re
#preview_frame[face_coords[1]:face_coords[3], face_coords[0]:face_coords[2]] = croppedFace
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
log.info("VideoStream ended...")
cv2.destroyAllWindows()
inputFeeder.close()
def main(args):
# models
face_detection_model = args.face_detection_model
head_pose_estimation_model = args.head_pose_estimation_model
facial_landmarks_detection_model = args.facial_landmarks_detection_model
gaze_estimation_model = args.gaze_estimation_model
# toggles
toggle_face_detect = int(args.toggle_face_detect)
toggle_eye_detection = int(args.toggle_eye_detection)
toggle_head_pose_euler_angles = int(args.toggle_head_pose_euler_angles)
toggle_gaze_estimation_direction_lines = int(
args.toggle_gaze_estimation_direction_lines)
device = args.device
video_file = args.video
threshold = args.threshold
output_path = args.output_path
# model load times
fd_start_model_load_time = time.time()
fd = FaceDetectionModel(face_detection_model, device, threshold)
fd.load_model()
fd_total_model_load_time = time.time() - fd_start_model_load_time
fld_start_model_load_time = time.time()
fld = FacialLandmarksDetectionModel(facial_landmarks_detection_model,
device)
fld.load_model()
fld_total_model_load_time = time.time() - fld_start_model_load_time
hpe_start_model_load_time = time.time()
hpe = HeadPoseEstimationModel(head_pose_estimation_model, device)
hpe.load_model()
hpe_total_model_load_time = time.time() - hpe_start_model_load_time
ge_start_model_load_time = time.time()
ge = GazeEstimationModel(gaze_estimation_model, device)
ge.load_model()
ge_total_model_load_time = time.time() - ge_start_model_load_time
# mouse controller
mouse_controller = MouseController('medium', 'fast')
# Handle the input stream
# see https://github.com/anvillasoto/people-counter-edge-application/blob/master/main.py
if video_file == 'CAM':
input_stream = 0
single_image_mode = False
# Checks for input image
elif video_file.endswith('.jpg') or video_file.endswith('.bmp'):
single_image_mode = True
input_stream = video_file
elif (not video_file.endswith('.jpg')) or (
not (video_file.endswith('.bmp'))):
input_stream = video_file
assert os.path.isfile(video_file), "Input file does not exist"
else:
input_stream = video_file
log.error("The file is unsupported.please pass a supported file")
try:
cap = cv2.VideoCapture(input_stream)
except Exception as e:
log.error(f"Something else went wrong with the video file: {e}")
initial_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
initial_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
video_len = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
out_video = cv2.VideoWriter(os.path.join(output_path, 'output.mp4'),
cv2.VideoWriter_fourcc(*'avc1'), fps,
(initial_w, initial_h), True)
counter = 0
start_inference_time = time.time()
try:
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
counter += 1
# detect face
face_location, image = fd.predict(frame, toggle_face_detect)
xmin, ymin, xmax, ymax = face_location[0]
face_image = image[ymin:ymax, xmin:xmax].copy()
# detect eyes
eye_locations, eye_images, face_image_drawn = fld.predict(
face_image, toggle_eye_detection)
# detect head pose
head_pose_angles, face_image_drawn = hpe.predict(
face_image, face_image_drawn, toggle_head_pose_euler_angles)
# gaze estimation
gaze_vector, face_image_drawn = ge.predict(
face_image_drawn, eye_images, head_pose_angles, eye_locations,
toggle_gaze_estimation_direction_lines)
# replace face with face image drawn (depending on toggle)
image[ymin:ymax, xmin:xmax] = face_image_drawn
x, y, z = gaze_vector
if toggle_gaze_estimation_direction_lines == 1:
# frame message to add gaze vector x, y, and z
frame_message = "Gaze Coordinates: {:.2f}, {:.2f}, {:.2f}".format(
x, y, z)
image = cv2.putText(image, frame_message, (20, 20),
cv2.FONT_HERSHEY_COMPLEX, 1,
COLOR_WHITE_BGR, 2)
out_video.write(image)
# move mouse after five frames
if counter % 5 == 0:
mouse_controller.move(x, y)
total_time = time.time() - start_inference_time
total_inference_time = round(total_time, 1)
fps = counter / total_inference_time
with open(os.path.join(output_path, 'stats.txt'), 'w') as f:
f.write("Total Inference Time for Four Models: " +
str(total_inference_time) + '\n')
f.write("Frames Per Second for Four Models: " + str(fps) + '\n\n')
f.write("Model Load Time (Face Detection): " +
str(fd_total_model_load_time) + '\n')
f.write("Model Load Time (Facial Landmark Detection): " +
str(fld_total_model_load_time) + '\n')
f.write("Model Load Time (Head Pose Estimation): " +
str(hpe_total_model_load_time) + '\n')
f.write("Model Load Time (Gaze Estimation): " +
str(ge_total_model_load_time) + '\n')
cap.release()
cv2.destroyAllWindows()
except Exception as e:
print("Could not run Inference: ", e)
def main():
args = build_argparser().parse_args()
previewFlags = args.previewFlags
logger = logging.getLogger()
inputFilePath = args.input
inputFeeder = None
if inputFilePath.lower() == "cam":
inputFeeder = InputFeeder("cam")
else:
if not os.path.isfile(inputFilePath):
logger.error("Unable to find specified video file")
exit(1)
inputFeeder = InputFeeder("video", inputFilePath)
modelPathDict = {
'FaceDetectionModel': args.facedetectionmodel,
'FacialLandmarksDetectionModel': args.faciallandmarkmodel,
'GazeEstimationModel': args.gazeestimationmodel,
'HeadPoseEstimationModel': args.headposemodel
}
for fileNameKey in modelPathDict.keys():
if not os.path.isfile(modelPathDict[fileNameKey]):
logger.error("Unable to find specified " + fileNameKey +
" xml file")
exit(1)
fdm = FaceDetectionModel(modelPathDict['FaceDetectionModel'], args.device,
args.cpu_extension)
fldm = FacialLandmarksDetectionModel(
modelPathDict['FacialLandmarksDetectionModel'], args.device,
args.cpu_extension)
gem = GazeEstimationModel(modelPathDict['GazeEstimationModel'],
args.device, args.cpu_extension)
hpem = HeadPoseEstimationModel(modelPathDict['HeadPoseEstimationModel'],
args.device, args.cpu_extension)
mc = MouseController('medium', 'fast')
inputFeeder.load_data()
start = time.time()
fdm.load_model()
fdmload_time = time.time() - start
load_time_message = "Loading time for Face Detection Model: {:.3f}ms".format(
fdmload_time * 1000)
print(load_time_message)
start = time.time()
fldm.load_model()
fldmload_time = time.time() - start
load_time_message = "Loading time for Facial Landmark Model: {:.3f}ms".format(
fldmload_time * 1000)
print(load_time_message)
start = time.time()
hpem.load_model()
hpemload_time = time.time() - start
load_time_message = "Loading time for Head Pose Estimation Model: {:.3f}ms".format(
hpemload_time * 1000)
print(load_time_message)
start = time.time()
gem.load_model()
gemload_time = time.time() - start
load_time_message = "Loading time for Gaze Estimation Model: {:.3f}ms".format(
gemload_time * 1000)
print(load_time_message)
total_load_time = gemload_time + fdmload_time + hpemload_time + fldmload_time
load_time_message = "Loading time for all the Models: {:.3f}ms".format(
total_load_time * 1000)
print(load_time_message)
frame_count = 0
inf_start = time.time()
for ret, frame in inputFeeder.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)
croppedFace, face_coords = fdm.predict(frame.copy(),
args.prob_threshold)
if type(croppedFace) == int:
logger.error("Unable to detect the face.")
if key == 27:
break
continue
hp_out = hpem.predict(croppedFace.copy())
left_eye, right_eye, eye_coords = fldm.predict(croppedFace.copy())
total_inf_time = time.time() - inf_start
inf_time_message = "Total Inference Time: {:.3f}s".format(
total_inf_time)
new_mouse_coord, gaze_vector = gem.predict(left_eye, right_eye, hp_out)
if (not len(previewFlags) == 0):
preview_frame = frame.copy()
if 'fd' in previewFlags:
#cv2.rectangle(preview_frame, (face_coords[0], face_coords[1]), (face_coords[2], face_coords[3]), (255,0,0), 3)
preview_frame = croppedFace
if 'fld' in previewFlags:
cv2.rectangle(croppedFace,
(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(croppedFace,
(eye_coords[1][0] - 10, eye_coords[1][1] - 10),
(eye_coords[1][2] + 10, eye_coords[1][3] + 10),
(0, 255, 0), 3)
#preview_frame[face_coords[1]:face_coords[3], face_coords[0]:face_coords[2]] = croppedFace
if 'hp' in previewFlags:
cv2.putText(
preview_frame,
"Pose Angles: yaw:{:.2f} | pitch:{:.2f} | roll:{:.2f}".
format(hp_out[0], hp_out[1], hp_out[2]), (10, 20),
cv2.FONT_HERSHEY_COMPLEX, 0.25, (0, 255, 0), 1)
if 'ge' in previewFlags:
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, 0, 255), 2)
cv2.line(le, (x - w, y + w), (x + w, y - w), (255, 0, 255), 2)
re = cv2.line(right_eye.copy(), (x - w, y - w), (x + w, y + w),
(255, 0, 255), 2)
cv2.line(re, (x - w, y + w), (x + w, y - w), (255, 0, 255), 2)
croppedFace[eye_coords[0][1]:eye_coords[0][3],
eye_coords[0][0]:eye_coords[0][2]] = le
croppedFace[eye_coords[1][1]:eye_coords[1][3],
eye_coords[1][0]:eye_coords[1][2]] = re
#preview_frame[face_coords[1]:face_coords[3], face_coords[0]:face_coords[2]] = croppedFace
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()
inputFeeder.close()
print(inf_time_message)
fps = frame_count / total_inf_time
fps_message = "Total FPS: {:.3f} fps".format(fps)
print(fps_message)
def main_benchmark(args):
feed = InputFeeder(input_type=args.it, input_file=args.i)
face_model = FaceDetectionModel(args.fm, args.d, args.c, float(args.p))
start_time = time.time()
face_model.load_model()
face_load_model_time = time.time() - start_time
landmarks_model = LandmarksDetectionModel(args.lm, args.d, args.c)
start_time = time.time()
landmarks_model.load_model()
landmarks_model_time = time.time() - start_time
headpose_model = HeadPoseDetectionModel(args.hpm, args.d, args.c)
start_time = time.time()
headpose_model.load_model()
headpose_model_time = time.time() - start_time
gaze_model = GazeEstimationModel(args.gem, args.d, args.c)
start_time = time.time()
gaze_model.load_model()
gaze_model_time = time.time() - start_time
feed.load_data()
for batch in feed.next_batch():
try:
start_time = time.time()
cropped_face, coords, face_time_prediction = face_model.predict(
batch)
cv2.rectangle(batch, (coords[0], coords[1]),
(coords[2], coords[3]), (255, 0, 0), 2)
io_face_model_time = time.time() - start_time
start_time = time.time()
left_eye, right_eye, eyes_coords, landmarks_time_prediction = landmarks_model.predict(
cropped_face)
io_landmarks_model_time = time.time() - start_time
start_time = time.time()
head_pose_angles, headpose_time_prediction = headpose_model.predict(
cropped_face)
io_head_pose_model_time = time.time() - start_time
start_time = time.time()
x, y, z, gaze_time_prediction = gaze_model.predict(
left_eye, right_eye, head_pose_angles, cropped_face,
eyes_coords)
io_gaze_model_time = time.time() - start_time
print("Graphing loading time...")
graph_loading_time(face_load_model_time, landmarks_model_time,
headpose_model_time, gaze_model_time, args.bm)
print("Graphing io processing time...")
graph_io_processing_time(io_face_model_time,
io_landmarks_model_time,
io_head_pose_model_time,
io_gaze_model_time, args.bm)
print("Graphing inference time...")
graph_model_inference_time(face_time_prediction,
landmarks_time_prediction,
headpose_time_prediction,
gaze_time_prediction, args.bm)
print("Done")
break
except:
print("Frame without prediction. Error: ", sys.exc_info()[0])
log.error(sys.exc_info()[0])
feed.close()
def main():
# Grab command line args
args = build_argparser().parse_args()
logger = logging.getLogger()
inputFilePath = args.input
inputFeeder = None
inference_time = None
if inputFilePath.lower() == "cam":
inputFeeder = InputFeeder("cam")
else:
if not os.path.isfile(inputFilePath):
logger.error("Unable to find specified video file")
exit(1)
inputFeeder = InputFeeder("video", inputFilePath)
#else:
# if not os.path.isfile(inputFilePath):
# logger.error("Unable to find specified image file")
# exit(1)
# inputFeeder = InputFeeder("image",inputFilePath)
# Initialize variables with the input arguments
modelPathDict = {
'FaceDetectionModel': args.faceDetectionModel,
'FacialLandmarksDetectionModel': args.FacialLandmarksDetectionModel,
'GazeEstimationModel': args.gazeEstimationModel,
'HeadPoseEstimationModel': args.HeadPoseEstimationModel
}
for fileNameKey in modelPathDict.keys():
if not os.path.isfile(modelPathDict[fileNameKey]):
logger.error("Unable to find specified " + fileNameKey +
" xml file")
exit(1)
fdm = FaceDetectionModel(modelPathDict['FaceDetectionModel'], args.device,
args.cpu_extension)
flm = FacialLandmarksDetectionModel(
modelPathDict['FacialLandmarksDetectionModel'], args.device,
args.cpu_extension)
gem = GazeEstimationModel(modelPathDict['GazeEstimationModel'],
args.device, args.cpu_extension)
hpe = HeadPoseEstimationModel(modelPathDict['HeadPoseEstimationModel'],
args.device, args.cpu_extension)
mc = MouseController('high', 'fast')
inputFeeder.load_data()
# Load Models and generate load times
start_time = time.time()
fdm.load_model()
logger.error("Face detection model loaded: time: {:.3f} ms".format(
(time.time() - start_time) * 1000))
first_mark = time.time()
flm.load_model()
logger.error(
"Facial landmarks detection model loaded: time: {:.3f} ms".format(
(time.time() - first_mark) * 1000))
second_mark = time.time()
hpe.load_model()
logger.error("Head pose estimation model loaded: time: {:.3f} ms".format(
(time.time() - second_mark) * 1000))
third_mark = time.time()
gem.load_model()
logger.error("Gaze estimation model loaded: time: {:.3f} ms".format(
(time.time() - third_mark) * 1000))
load_total_time = time.time() - start_time
logger.error("Total loading time: time: {:.3f} ms".format(load_total_time *
1000))
logger.error("Required models have been loaded..")
frame_count = 0
start_inf_time = time.time()
for ret, frame in inputFeeder.next_batch():
if not ret:
break
frame_count += 1
if frame_count % 5 == 0:
cv2.imshow('video', cv2.resize(frame, (600, 800)))
key = cv2.waitKey(60)
croppedFace, face_coords = fdm.predict(frame.copy(),
args.prob_threshold)
if type(croppedFace) == int:
logger.error("Unable to detect the face.")
if key == 27:
break
continue
if frame_count % 5 == 0:
mc.move(new_mouse_coord[0], new_mouse_coord[1])
hp_out = hpe.predict(croppedFace.copy())
left_eye, right_eye, eye_coords = flm.predict(croppedFace.copy())
new_mouse_coord, gaze_vector = gem.predict(left_eye, right_eye, hp_out)
inference_time = round(time.time() - start_inf_time, 1)
total_frames = int(frame_count)
fps = int(frame_count) / (inference_time)
logger.error("count {} seconds".format(frame_count))
logger.error("total inference time {} seconds".format(inference_time))
logger.error("total frames {} frames".format(frame_count))
logger.error("fps {} frame/second".format(fps))
with open(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
'RunReport.txt'), 'w') as R:
R.write('Load Time: ' + str(load_total_time) + '\n')
R.write('Inference Time :' + str(inference_time) + '\n')
R.write('total frames processed' + str(total_frames) + '\n')
R.write('fps: ' + str(fps) + '\n')
logger.error("VideoStream ended...")
cv2.destroyAllWindows()
inputFeeder.close()
atexit.register(profile.print_stats)
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()
def main():
# Grabing command line args
args = build_argparser().parse_args()
# Getting Input File Path
inputFilePath = args.input
# For Visualization
visual_flag = args.visualization_flag
# Initialize inputfeeder
inputFeeder = None
# Handle video file or CAM (like webcam)
if args.input =="CAM":
inputFeeder = InputFeeder("cam")
else:
if not os.path.isfile(args.input):
log.info("Unable to find specified video file")
sys.exit(1)
inputFeeder = InputFeeder("video",args.input)
# Now define model path dictionary for all 04 intel pre trained models
modelPathDict = {'FaceDetectionModel':args.face_detection_model, 'FacialLandmarksDetectionModel':args.facial_landmark_model,
'GazeEstimationModel':args.gaze_estimation_model, 'HeadPoseEstimationModel':args.head_pose_model}
# Check model XML file
for fileNameKey in modelPathDict.keys():
if not os.path.isfile(modelPathDict[fileNameKey]):
log.info("Unable to find specified "+fileNameKey+" xml file")
sys.exit(1)
# Defining Intel Pre Trained Models Objects
fdm = FaceDetectionModel(modelPathDict['FaceDetectionModel'], args.device, args.cpu_extension)
fldm = FacialLandmarksDetectionModel(modelPathDict['FacialLandmarksDetectionModel'], args.device, args.cpu_extension)
gem = GazeEstimationModel(modelPathDict['GazeEstimationModel'], args.device, args.cpu_extension)
hpem = HeadPoseEstimationModel(modelPathDict['HeadPoseEstimationModel'], args.device, args.cpu_extension)
# Determining Precision and Speed for mouse controller
mc = MouseController('medium','fast')
# Loading Input Feeder
inputFeeder.load_data()
# Loading our four pre trained models and calculate the total models loading time
# This will help us to find different model time for different models precison like F32,F16 & INT8
start_time_1= time.time()
fdm.load_model()
fldm.load_model()
hpem.load_model()
gem.load_model()
total_model_load_time= (time.time()-start_time_1)
print("Total Model Load Time for All our Intel Pre Trained Models is (in seconds): {:.3f}".format(total_model_load_time))
# Above print statement will give total model load time for our 04 models for different precisions as well
frame_count = 0
start_time = time.time()
# Start Loop till break through input feeder
for ret, frame in inputFeeder.next_batch():
if not ret:
break
frame_count+=1
if frame_count%5==0:
cv2.imshow('video',cv2.resize(frame,(450,450)))
key = cv2.waitKey(60)
# Extracting face detection features
croppedFace, face_coords = fdm.predict(frame.copy(), args.prob_threshold)
if type(croppedFace)==int:
log.info("Unable to detect the face.")
if key==27:
break
continue
# Head position detection
hp_out = hpem.predict(croppedFace.copy())
# Landmarks detection (left_eye, right_eye, eyes coordinates)
left_eye, right_eye, eye_coords = fldm.predict(croppedFace.copy())
# Mouse coordinates and gaze vector Detection
new_mouse_coord, gaze_vector = gem.predict(left_eye, right_eye, hp_out)
# Creating variables for visualisation
# Extracting four face coordinates for rectangle (xmin,ymin,xmax,ymax)
x_minimum= face_coords[0]
y_minimum=face_coords[1]
x_maximum=face_coords[2]
y_maximum=face_coords[3]
# Take eye surrounding area
eye_surrounding_area=10
# Now extracting few features from eye coordinates
# Extracting four coordinates of left eye from eye coordinates
l_l1= eye_coords[0][0]
l_l2=eye_coords[0][1]
l_l3=eye_coords[0][2]
l_l4=eye_coords[0][3]
# Extracting four coordinates of left eye from eye coordinates
r_r1=eye_coords[1][0]
r_r2=eye_coords[1][1]
r_r3=eye_coords[1][2]
r_r4=eye_coords[1][3]
# Extracting pose angle, pitch and roll from head pose output
pose_angle= hp_out[0]
pitch=hp_out[1]
roll=hp_out[2]
# Visualizing face, landmarks, head pose and gaze
if (not len(visual_flag)==0):
preview_frame = frame.copy()
if 'fd' in visual_flag:
# Drawing a rectangle with our four face coordiantes (xmin,ymin,xmax,ymax)
cv2.rectangle(preview_frame, (x_minimum, y_minimum), (x_maximum, y_maximum), (20,20,150), 3)
if 'fld' in visual_flag:
# Drawing a rectangle for each eyes with the help of eye coordinates and eye surrounding area
# Left Eye
cv2.rectangle(preview_frame, (l_l1-eye_surrounding_area, l_l2-eye_surrounding_area), (l_l3+eye_surrounding_area, l_l4+eye_surrounding_area), (60,255,0), 2)
# Right Eye
cv2.rectangle(preview_frame, (r_r1-eye_surrounding_area, r_r2-eye_surrounding_area), (r_r3+eye_surrounding_area, r_r4+eye_surrounding_area), (60,255,0), 2)
if 'hp' in visual_flag:
# We have extracted pose angle, pitch and roll from head pose output, now we put text on preview_frame
cv2.putText(preview_frame, "Pose Angles:{:.2f} | pitch:{:.2f} | roll:{:.2f}".format(pose_angle, pitch, roll), (20, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 60), 1)
if 'ge' in visual_flag:
# Calculating coordinates for left eye to obtain left eye center
le_x= (l_l1 + l_l3)/2
le_y= (l_l2 + l_l4)/2
# Calculating coordinates for right eye to obtain right eye center
re_x= (r_r1 + r_r3)/2
re_y= (r_r2 + r_r4)/2
# Calculating left eye center
le_center= int(x_minimum + le_x), int(y_minimum + le_y)
# Calculating right eye center
re_center= int(x_minimum + re_x), int(y_minimum + re_y)
# Now put both eyes center in a list
eyes_center = [le_center, re_center ]
# Extracting left eye x and y coordinates from eyes_center
le_center_x = int(eyes_center[0][0])
le_center_y = int(eyes_center[0][1])
# Extracting right eye x and y coordinates from eyes_center
re_center_x = int(eyes_center[1][0])
re_center_y = int(eyes_center[1][1])
# Extracting x and y (first and second) value from gaze_vector
g_x, g_y = gaze_vector[0:2]
# With the help of above parameters, draw arrowed lines for gaze on left and right eyes
cv2.arrowedLine(preview_frame, (le_center_x, le_center_y), (le_center_x + int(g_x * 100), le_center_y + int(-g_y * 100)), (0,50,160), 1)
cv2.arrowedLine(preview_frame, (re_center_x, re_center_y), (re_center_x + int(g_x * 100), re_center_y + int(-g_y * 100)), (0,50,160), 1)
cv2.imshow("visualization",cv2.resize(preview_frame,(450,450)))
if frame_count%5==0:
mc.move(new_mouse_coord[0],new_mouse_coord[1])
if key==27:
break
log.info("VideoStream has been ended")
cv2.destroyAllWindows()
inputFeeder.close()
# Calculating Inference time and frame per seconds
total_time = time.time() - start_time
total_inference_time=total_time
fps=frame_count/total_inference_time
print("Inference time: {:.3f}".format(total_inference_time))
print("FPS: {}".format(fps))
def main():
args = build_argparser().parse_args()
inputFilePath = args.input
inputFeeder = None
if args.input == "CAM":
inputFeeder = InputFeeder("cam")
else:
if not os.path.isfile(args.input):
log.info("Unable to find specified video file")
sys.exit(1)
inputFeeder = InputFeeder("video", args.input)
modelPathDict = {
'FaceDetectionModel': args.face_detection_model,
'FacialLandmarksDetectionModel': args.facial_landmark_model,
'GazeEstimationModel': args.gaze_estimation_model,
'HeadPoseEstimationModel': args.head_pose_model
}
for fileNameKey in modelPathDict.keys():
if not os.path.isfile(modelPathDict[fileNameKey]):
log.info("Unable to find specified " + fileNameKey + " xml file")
sys.exit(1)
fdm = FaceDetectionModel(modelPathDict['FaceDetectionModel'], args.device,
args.cpu_extension)
fldm = FacialLandmarksDetectionModel(
modelPathDict['FacialLandmarksDetectionModel'], args.device,
args.cpu_extension)
gem = GazeEstimationModel(modelPathDict['GazeEstimationModel'],
args.device, args.cpu_extension)
hpem = HeadPoseEstimationModel(modelPathDict['HeadPoseEstimationModel'],
args.device, args.cpu_extension)
mc = MouseController('medium', 'fast')
start_time_1 = time.time()
inputFeeder.load_data()
fdm.load_model()
fldm.load_model()
hpem.load_model()
gem.load_model()
total_model_load_time = (time.time() - start_time_1)
print("Model Load Time: {:.3f}".format(total_model_load_time))
frame_count = 0
start_time = time.time()
for ret, frame in inputFeeder.next_batch():
if not ret:
break
frame_count += 1
if frame_count % 5 == 0:
cv2.imshow('video', cv2.resize(frame, (450, 450)))
key = cv2.waitKey(60)
croppedFace, face_coords = fdm.predict(frame.copy(),
args.prob_threshold)
if type(croppedFace) == int:
log.info("Unable to detect the face.")
if key == 27:
break
continue
hp_out = hpem.predict(croppedFace.copy())
left_eye, right_eye, eye_coords = fldm.predict(croppedFace.copy())
new_mouse_coord, gaze_vector = gem.predict(left_eye, right_eye, hp_out)
if frame_count % 5 == 0:
mc.move(new_mouse_coord[0], new_mouse_coord[1])
if key == 27:
break
log.info("VideoStream has ended.")
cv2.destroyAllWindows()
inputFeeder.close()
total_time = time.time() - start_time
total_inference_time = total_time
fps = frame_count / total_inference_time
print("Inference Time: {:.3f}".format(total_inference_time))
print("FPS: {}".format(fps))
def main():
"""
"""
# Grab command line args
args = build_argparser().parse_args()
input_src = args.input
device = args.device
extension = args.cpu_extension
prob_threshold = args.prob_threshold
face_detection_model = args.facedetectionmodel
head_pose_model = args.headposemodel
landmarks_model = args.facelandmarksnmodel
gaze_estimation_model = args.gazeestimationmodel
# Create log object set for console output and set log level
log_obj = log.getLogger()
log_obj.setLevel(LOGLEVEL)
console_handler = log.StreamHandler()
console_handler.setLevel(LOGLEVEL)
log_obj.addHandler(console_handler)
# Create detection objects
face_detection_obj = FaceDetectionModel(face_detection_model, device,
extension)
head_pose_obj = HeadPoseModel(head_pose_model, device, extension)
landmarks_obj = LandmarksModel(landmarks_model, device, extension)
gaze_estimation_obj = GazeEstimationModel(gaze_estimation_model, device,
extension)
# Create mouse controller object
mouse_controller = MouseController('medium', 'fast')
# Place mouse at the center of the screen
mouse_controller.init_position()
log_obj.info("[Info]: Place mouse at the center of the screen")
# Place holder for total inferencing time
total_inference_time = 0
# Load models and get the model loading times
start_time = time.time()
face_detection_obj.load_model()
end_time = time.time()
face_detection_loading_time = end_time - start_time
start_time = time.time()
head_pose_obj.load_model()
end_time = time.time()
head_pose_loading_time = end_time - start_time
start_time = time.time()
landmarks_obj.load_model()
end_time = time.time()
landmarks_detection_loading_time = end_time - start_time
start_time = time.time()
gaze_estimation_obj.load_model()
end_time = time.time()
gaze_estimation_loading_time = end_time - start_time
# Configure input video source
if input_src.lower() == 'cam':
input_channel = InputFeeder(input_type='cam')
elif not os.path.exists(input_src):
log.error("Video file not found! Exiting....")
exit(1)
else:
input_channel = InputFeeder(input_type='video', input_file=input_src)
log_obj.info("[Info]: Opening video file ...")
input_channel.load_data()
video_width = int(input_channel.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
video_height = int(input_channel.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(input_channel.cap.get(cv2.CAP_PROP_FPS))
frame_counter = 0
total_face_inf_time = 0
total_head_inf_time = 0
total_lanmarks_inf_time = 0
total_gaze_inf_time = 0
frame_processing_time = 0
# Process each frame
try:
for frame in input_channel.next_batch():
frame_processing_start_time = time.time()
frame_counter = frame_counter + 1
key = cv2.waitKey(60)
# Use face detection to find cropped face and provide face coordinates
cropped_face, face_coords, face_inference_time = face_detection_obj.predict(
frame, prob_threshold)
total_face_inf_time = total_face_inf_time + face_inference_time
# Now use cropped face for head pose detection
head_pose_estimate, head_inference_time = head_pose_obj.predict(
cropped_face, prob_threshold)
total_head_inf_time = total_head_inf_time + head_inference_time
# Now use cropped face for landmarks detection
cropped_left_eye, cropped_right_eye, eyes_coords, converted_landmarks, landmarks_inference_time = landmarks_obj.predict(
cropped_face, prob_threshold)
total_lanmarks_inf_time = total_lanmarks_inf_time + landmarks_inference_time
# Finally gaze estimation
gaze_vector, gaze_estimate_time = gaze_estimation_obj.predict(
cropped_left_eye, cropped_right_eye, head_pose_estimate)
total_gaze_inf_time = total_gaze_inf_time + gaze_estimate_time
# Move the mouse
#mouse_controller.move(gaze_vector[0], gaze_vector[1])
# Show size-reduced frame for visual comparison
# Check potential visualize flags: 'F', 'H', 'L', 'G'
# If flag exist, process image to show inference results
if args.visualize is not None:
visualize_flag = str(args.visualize)
# Draw bounding box around detected face
if 'F' in visualize_flag:
cv2.rectangle(frame,
(face_coords[0][0], face_coords[0][1]),
(face_coords[0][2], face_coords[0][3]),
(0, 255, 0), 2)
# Show head pose parameters
if 'H' in visualize_flag:
cv2.putText(
frame,
"Head pose: yaw: {:.3f}, pitch: {:.3f}, roll: {:.3f}".
format(head_pose_estimate[0], head_pose_estimate[1],
head_pose_estimate[2]), (10, 20),
cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 5)
# Draw dots on detected facial landmarks
if 'L' in visualize_flag:
cv2.circle(frame,
(converted_landmarks[0] + face_coords[0][0],
converted_landmarks[1] + face_coords[0][1]),
10, (0, 255, 0), 5)
cv2.circle(frame,
(converted_landmarks[2] + face_coords[0][0],
converted_landmarks[3] + face_coords[0][1]),
10, (0, 255, 0), 5)
cv2.circle(frame,
(converted_landmarks[4] + face_coords[0][0],
converted_landmarks[5] + face_coords[0][1]),
10, (0, 255, 0), 5)
cv2.circle(frame,
(converted_landmarks[6] + face_coords[0][0],
converted_landmarks[7] + face_coords[0][1]),
10, (0, 255, 0), 5)
cv2.circle(frame,
(converted_landmarks[8] + face_coords[0][0],
converted_landmarks[9] + face_coords[0][1]),
10, (0, 255, 0), 5)
# Display gaze parameters
if 'G' in visualize_flag:
cv2.putText(
frame,
"Gaze estimate: x: {:.3f}, y: {:.3f}, z: {:.3f}".
format(gaze_vector[0], gaze_vector[1], gaze_vector[2]),
(10, 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 5)
resized_frame = cv2.resize(frame, (640, 360))
cv2.imshow('frame', resized_frame)
if frame_counter % 4 == 0:
mouse_controller.move(gaze_vector[0], gaze_vector[1])
frame_processing_time = frame_processing_time + (
time.time() - frame_processing_start_time) * 1000
if key == 27:
break
except Exception as e:
#traceback.print_exc()
if 'shape' in str(e):
log_obj.info("Video feed finished")
else:
log_obj.error("[ERROR]: " + str(e))
pass
# All done, cleaning up
cv2.destroyAllWindows()
input_channel.close()
# Print out statistics
log_obj.info("[Info]: Video source FPS: " + str(fps))
log_obj.info("[Info]: Total frame count: " + str(frame_counter))
log_obj.info("")
log_obj.info("[Info]: Face detection model loading time: {:.3f} ms".format(
face_detection_loading_time * 1000))
log_obj.info("[Info]: Head pose model loading time: {:.3f} ms".format(
head_pose_loading_time * 1000))
log_obj.info(
"[Info]: Facial landmarks detection model loading time: {:.3f} ms".
format(landmarks_detection_loading_time * 1000))
log_obj.info(
"[Info]: Gaze estimation model loading time: {:.3f} ms".format(
gaze_estimation_loading_time * 1000))
log_obj.info("")
log_obj.info(
"[Info]: Average per frame total processing time : {:.3f} ms".format(
frame_processing_time / frame_counter))
log_obj.info("[Info]: Average face inferencing time: {:.3f} ms".format(
total_face_inf_time / frame_counter))
log_obj.info(
"[Info]: Average head pose inferencing time: {:.3f} ms".format(
total_head_inf_time / frame_counter))
log_obj.info(
"[Info]: Average facial landmarks inferencing time: {:.3f} ms".format(
total_lanmarks_inf_time / frame_counter))
log_obj.info("[Info]: Average gaze estimate time: {:.3f} ms".format(
total_gaze_inf_time / frame_counter))
def main():
args = build_argparser().parse_args()
preview_flags = args.preview_flags
logger = logging.getLogger()
input_path = args.input
if input_path.lower() == 'cam':
input_feed = InputFeeder('cam')
else:
if not os.path.isfile(input_path):
logger.error('Unable to find specified video file')
exit(1)
file_extension = input_path.split(".")[-1]
if (file_extension in ['jpg', 'jpeg', 'bmp']):
input_feed = InputFeeder('image', input_path)
elif (file_extension in ['avi', 'mp4']):
input_feed = InputFeeder('video', input_path)
else:
logger.error(
"Unsupported file Extension. Allowed ['jpg', 'jpeg', 'bmp', 'avi', 'mp4']"
)
exit(1)
if sys.platform == "linux" or sys.platform == "linux2":
#CODEC = 0x00000021
CODEC = cv2.VideoWriter_fourcc(*"mp4v")
elif sys.platform == "darwin":
CODEC = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G')
else:
print("Unsupported OS.")
exit(1)
file_flag = False
if args.output_file.lower() == 'y':
file_flag = True
out = cv2.VideoWriter('output.mp4', CODEC, 30,
(FRAME_WIDTH, FRAME_HEIGHT))
modelPathDict = {
'face_detect': args.face_detection_model,
'face_landmark_regress': args.facial_landmark_model,
'head_pose': args.head_pose_model,
'gaze_estimate': args.gaze_estimation_model
}
for pathname in modelPathDict:
if not os.path.isfile(modelPathDict[pathname]):
logger.error('Unable to find specified ' + pathname + ' xml file')
exit(1)
#initializing models
fdm = FaceDetectionModel(modelPathDict['face_detect'], args.device,
args.cpu_extension)
fldm = FacialLandmarksDetectionModel(
modelPathDict['face_landmark_regress'], args.device,
args.cpu_extension)
hpem = HeadPoseEstimationModel(modelPathDict['head_pose'], args.device,
args.cpu_extension)
gem = GazeEstimationModel(modelPathDict['gaze_estimate'], args.device,
args.cpu_extension)
#initializing mouse controller
mouse_controller = MouseController('medium', 'fast')
input_feed.load_data()
#checking models
fdm.check_model()
fldm.check_model()
hpem.check_model()
gem.check_model()
#loading models / creating executable network
fdm.load_model()
fldm.load_model()
hpem.load_model()
gem.load_model()
frame_count = 0
for ret, frame in input_feed.next_batch():
if not ret:
break
frame_count += 1
key = cv2.waitKey(60)
"""
Sequence of model execution:-
1. Predict from each model.
2. Preprocess of outputs from each model.
3. Send the processed output to the next model.
Model Sequence:-
- Head Pose Estimation Model -
Face Detection Model <(First Head Pose and Then Facial Landmark)>Gaze Estimation Model
- Facial Landmark Detection Model -
"""
cropped_face, face_coords = fdm.preprocess_output(
frame.copy(), fdm.predict(frame.copy()), args.prob_threshold)
if type(cropped_face) == int:
logger.error('Unable to detect the face.')
if key == 27:
break
continue
hp_out = hpem.preprocess_output(hpem.predict(cropped_face.copy()))
left_eye, right_eye, eye_coords = fldm.preprocess_output(
cropped_face.copy(), fldm.predict(cropped_face.copy()))
new_mouse_coord, gaze_vector = gem.preprocess_output(
gem.predict(left_eye, right_eye, hp_out), hp_out)
if (not len(preview_flags) == 0) or file_flag:
preview_frame = frame.copy()
if 'fd' in preview_flags:
preview_frame = cv2.rectangle(preview_frame,
(face_coords[0], face_coords[1]),
(face_coords[2], face_coords[3]),
(0, 0, 255), 3)
cropped_face = preview_frame[face_coords[1]:face_coords[3],
face_coords[0]:face_coords[2]]
if 'fld' in preview_flags:
cropped_face = 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)
cropped_face = 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)
preview_frame[face_coords[1]:face_coords[3],
face_coords[0]:face_coords[2]] = cropped_face
if 'hp' in preview_flags:
cv2.putText(
preview_frame,
'Pose Angles: yaw: {:.2f} | pitch: {:.2f} | roll: {:.2f}'.
format(hp_out[0], hp_out[1], hp_out[2]), (20, 40),
cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)
if 'ge' in preview_flags:
x, y = int(gaze_vector[0] * GAZE_ARROW_LENGTH), -int(
gaze_vector[1] * GAZE_ARROW_LENGTH)
le_mid_x = int((eye_coords[0][0] + eye_coords[0][2]) / 2)
le_mid_y = int((eye_coords[0][1] + eye_coords[0][3]) / 2)
re_mid_x = int((eye_coords[1][0] + eye_coords[1][2]) / 2)
re_mid_y = int((eye_coords[1][1] + eye_coords[1][3]) / 2)
cv2.arrowedLine(cropped_face, (le_mid_x, le_mid_y),
((le_mid_x + x), (le_mid_y + y)), (255, 0, 0),
GAZE_ARROW_WIDTH)
cv2.arrowedLine(cropped_face, (re_mid_x, re_mid_y),
((re_mid_x + x), (re_mid_y + y)), (255, 0, 0),
GAZE_ARROW_WIDTH)
preview_frame[face_coords[1]:face_coords[3],
face_coords[0]:face_coords[2]] = cropped_face
if (not len(preview_flags) == 0) and frame_count % 2 == 0:
if args.zoomed:
cv2.imshow(
'Cropped Face',
cv2.resize(cropped_face, (FRAME_WIDTH, FRAME_HEIGHT)))
else:
cv2.imshow(
'Preview',
cv2.resize(preview_frame, (FRAME_WIDTH, FRAME_HEIGHT)))
if file_flag:
out.write(
cv2.resize(preview_frame, (FRAME_WIDTH, FRAME_HEIGHT)))
#move the mouse pointer
try:
mouse_controller.move(new_mouse_coord[0], new_mouse_coord[1])
except pyautogui.FailSafeException:
pass
if frame_count % 2 == 0 and len(preview_flags) == 0:
cv2.imshow('Video', cv2.resize(frame, (FRAME_WIDTH, FRAME_HEIGHT)))
if key == 27:
break
logger.error('VideoStream ended.')
if args.output_file.lower() == 'y':
out.release()
input_feed.close()
cv2.destroyAllWindows()
def main():
"""
Initialise the inference network, stream video to network
and output stats and video
:param args: Command line arguments parsed by build_argsparser()
:return: None
"""
# mouse movement ("low", "medium", "fast")
global POSE_CHECKED
mouse_movement = MouseController("low", "fast")
logging.basicConfig(format="[ %(levelname)s ] %(message)s",
level=logging.INFO,
stream=sys.stdout)
args = args_parser().parse_args()
logging_message = logging.getLogger()
if args.input == 'cam':
input_feed = 0
else:
input_feed = args.input
assert os.path.isfile(
args.input
), "Missing files or Specified input file doesn't exist or entered correctly"
# Ref: source code: https://stackoverflow.com/questions/33834708/cant-write-video-by-opencv-in-python/33836463
# Ref: source code: https://knowledge.udacity.com/questions/275173
cap = cv2.VideoCapture(input_feed)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
duration = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
vout = cv2.VideoWriter(os.path.join(args.out_dir, "vout.mp4"),
cv2.VideoWriter_fourcc(*"MP4V"), fps,
(width, height), True)
if args.save_output == 'yes':
vout_fd = cv2.VideoWriter(os.path.join(args.out_dir, "vout_fd.mp4"),
cv2.VideoWriter_fourcc(*"MP4V"), fps,
(width, height), True)
vout_fl = cv2.VideoWriter(os.path.join(args.out_dir, "vout_fl.mp4"),
cv2.VideoWriter_fourcc(*"MP4V"), fps,
(width, height), True)
vout_hp = cv2.VideoWriter(os.path.join(args.out_dir, "vout_hp.mp4"),
cv2.VideoWriter_fourcc(*"MP4V"), fps,
(width, height), True)
vout_ge = cv2.VideoWriter(os.path.join(args.out_dir, "vout_g.mp4"),
cv2.VideoWriter_fourcc(*"MP4V"), fps,
(width, height), True)
box_count = 0
working = 1
infer_time_start = time.time()
if input_feed:
cap.open(args.input)
# Adjust delays to match the number of Frame Per Seconds in the video file
if not cap.isOpened():
logging_message.error("ERROR MESSAGE! Corrupt video file")
return
if args.mode == 'sync':
async_mode = False
else:
async_mode = True
# Initialising the class variables
# ref: https://github.com/gauravshelangia/computer-pointer-controller/blob/master/src/main.py
if args.cpu_extension:
fd_model = FaceDetectionModel(args.fdmodel,
args.threshold,
extensions=args.cpu_extension,
async_mode=async_mode)
hp_model = HeadPoseEstimationModel(args.hpmodel,
args.threshold,
extensions=args.cpu_extension,
async_mode=async_mode)
fl_model = FaceLandmarksDetectionModel(args.flmodel,
args.threshold,
extensions=args.cpu_extension,
async_mode=async_mode)
ge_model = GazeEstimationModel(args.gemodel,
args.threshold,
extensions=args.cpu_extension,
async_mode=async_mode)
else:
fd_model = FaceDetectionModel(args.fdmodel,
args.threshold,
async_mode=async_mode)
hp_model = HeadPoseEstimationModel(args.hpmodel,
args.threshold,
async_mode=async_mode)
fl_model = FaceLandmarksDetectionModel(args.flmodel,
args.threshold,
async_mode=async_mode)
ge_model = GazeEstimationModel(args.gemodel,
args.threshold,
async_mode=async_mode)
# Load the model through ##
# And infer network
logging_message.info(
"================ Models loading time ======================")
start_time = time.time()
fd_model.load_model()
logging_message.info("Face Detection Model: {:.1f}ms".format(
1000 * (time.time() - start_time)))
start_time = time.time()
hp_model.load_model()
logging_message.info("Headpose Estimation Model: {:.1f}ms".format(
1000 * (time.time() - start_time)))
start_time = time.time()
fl_model.load_model()
logging_message.info("Facial Landmarks Detection Model: {:.1f}ms".format(
1000 * (time.time() - start_time)))
start_time = time.time()
ge_model.load_model()
logging_message.info("Gaze Estimation Model: {:.1f}ms".format(
1000 * (time.time() - start_time)))
logging_message.info(
"========================== End ============================")
model_load_time = time.time() - infer_time_start
logging.info("All models are loaded successfully")
while cap.isOpened():
flag, img_frame = cap.read()
if not flag:
print("checkpoint *UNRECORDED")
break
box_count += 1
gazing = 0
POSE_CHECKED = False
if img_frame is None:
logging.error("checkpoint ERROR! EMPTY FRAME")
break
width = int(cap.get(3))
height = int(cap.get(4))
# Asynchronous Request
inf_start_fd = time.time()
# Display the results of the output layer of the model network
# ref source code: https://knowledge.udacity.com/questions/285095
values, img_frame = fd_model.predict(img_frame)
if args.save_output == 'yes':
vout_fd.write(img_frame)
fd_dur_time = time.time() - inf_start_fd
if len(values) > 0:
[xmin, ymin, xmax, ymax] = values[0]
head_is_moving = img_frame[ymin:ymax, xmin:xmax]
inf_start_hp = time.time()
person_in_frame, target_gaze = hp_model.predict(head_is_moving)
if args.save_output == 'yes':
p = "Target Gaze {}, Person in Frame? {}".format(
target_gaze, person_in_frame)
cv2.putText(frame, p, (50, 15), cv2.FONT_HERSHEY_COMPLEX, 0.5,
(0, 0, 255), 2)
vout_hp.write(img_frame)
if person_in_frame:
hp_dur_time = time.time() - inf_start_hp
POSE_CHECKED = True
inf_start_fl = time.time()
values, marking = fl_model.predict(head_is_moving)
img_frame[ymin:ymax, xmin:xmax] = marking
if args.save_output == "yes":
vout_fl.write(img_frame)
fl_dur_time = time.time() - inf_start_fl
[[xlmin, ylmin, xlmax, ylmax], [xrmin, yrmin, xrmax,
yrmax]] = values
l_eye_img = marking[ylmin:ylmax, xlmin:xlmax]
r_eye_img = marking[yrmin:yrmax, xrmin:xrmax]
output, gaze_vector = ge_model.predict(l_eye_img, r_eye_img,
target_gaze)
#ref: source code: https://knowledge.udacity.com/questions/264973
if args.save_output == 'yes':
p = "Gaze Vector {}".format(gaze_vector)
cv2.putText(frame, p, (50, 15), cv2.FONT_HERSHEY_COMPLEX,
0.5, (255, 0, 0), 1)
left_frame = draw_gaze(l_eye_img, gaze_vector)
right_frame = draw_gaze(r_eye_img, gaze_vector)
marking[ylmin:ylmax, xlmin:xlmax] = left_frame
marking[yrmin:yrmax, xrmin:xrmax] = right_frame
# cv2.arrowedLine(f, (xlmin, ylmin), (xrmin, yrmin), (0,0,255), 5)
vout_ge.write(img_frame)
if box_count % 10 == 0:
mouse_movement.move(output[0], output[1])
# Drawing and documenting performance stat
# ref: https://github.com/gauravshelangia/computer-pointer-controller/blob/master/src/main.py
# ref source code: https://knowledge.udacity.com/questions/257795
inf_time_message = "Face Detection Inference time: {:.3f} ms.".format(
fd_dur_time * 1000)
#
if POSE_CHECKED:
cv2.putText(
frame,
"Head Pose Estimation Inference time: {:.3f} ms.".format(
hp_dur_time * 1000), (0, 35), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 255, 0), 2)
cv2.putText(img_frame, inf_time_message, (0, 15),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 255), 2)
vout.write(img_frame)
if box_count % 10 == 0:
print("Inference time = ", int(time.time() - infer_time_start))
print('Box count {} and duration {}'.format(box_count, duration))
if args.out_dir:
final_infer_time = time.time() - infer_time_start
with open(os.path.join(args.out_dir, 'stats.txt'), 'w') as marking:
marking.write(str(round(final_infer_time, 1)) + '\n')
marking.write(str(box_count) + '\n')
if args.out_dir:
with open(os.path.join(args.out_dir, 'stats.txt'), 'a') as marking:
marking.write(str(round(model_load_time)) + '\n')
# Clean all models
fd_model.clean()
hp_model.clean()
fl_model.clean()
ge_model.clean()
# release cv2 cap
cap.release()
cv2.destroyAllWindows()
# release all resulting ouputs writer
vout.release()
if args.save_output == 'yes':
vout_fd.release()
vout_hp.release()
vout_fl.release()
vout_ge.release()
def main():
args = build_argparser().parse_args()
Flags = args.Flags
logger = logging.getLogger()
inputFilePath = args.input
inputFeeder = None
if inputFilePath.lower() == "cam":
inputFeeder = InputFeeder("cam")
else:
if not os.path.isfile(inputFilePath):
logger.error("Unable to find specified video file")
exit(1)
inputFeeder = InputFeeder("video", inputFilePath)
Dir = {
'FaceDetectionModel': args.facedetectionmodel,
'FacialLandmarksDetectionModel': args.faciallandmarkmodel,
'GazeEstimationModel': args.gazeestimationmodel,
'HeadPoseEstimationModel': args.headposemodel
}
for fileKey in Dir.keys():
if not os.path.isfile(Dir[fileKey]):
logger.error("Unable to find specified " + fileKey + " xml file")
exit(1)
Fd = FaceDetectionModel(Dir['FaceDetectionModel'], args.device,
args.cpu_extension)
Fl = FacialLandmarksDetectionModel(Dir['FacialLandmarksDetectionModel'],
args.device, args.cpu_extension)
Ge = GazeEstimationModel(Dir['GazeEstimationModel'], args.device,
args.cpu_extension)
Hp = HeadPoseEstimationModel(Dir['HeadPoseEstimationModel'], args.device,
args.cpu_extension)
Mc = MouseController('medium', 'fast')
## Loading part starts here
#start_model_load_time=time.time()
inputFeeder.load_data()
Fd.load_model()
Fl.load_model()
Hp.load_model()
Ge.load_model()
#total_model_load_time = time.time() - start_model_load_time
count = 0
#start_inference_time=time.time()
for ret, frame in inputFeeder.next_batch():
if not ret:
break
count += 1
if count % 5 == 0:
cv2.imshow('video', cv2.resize(frame, (500, 500)))
key = cv2.waitKey(60)
croppedFace, face_coords = Fd.predict(frame.copy(),
args.prob_threshold)
if type(croppedFace) == int:
logger.error("Unable to detect the face.")
if key == 27:
break
continue
hp_out = Hp.predict(croppedFace.copy())
l_eye, r_eye, eye_coords = Fl.predict(
croppedFace.copy()) # Main funcn's doing all our task
new_coord, gaze_vector = Ge.predict(l_eye, r_eye, hp_out)
#total_time=time.time()-start_inference_time
#total_inference_time=round(total_time, 1)
#fps=count/total_inference_time
## Now comes the importance of all the flags
if (not len(Flags) == 0):
new_frame = frame.copy()
if 'fd' in Flags:
new_frame = croppedFace
if 'fl' in Flags:
cv2.rectangle(croppedFace,
(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(croppedFace,
(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(
new_frame,
"Pose Angles: yaw:{:.2f} | pitch:{:.2f} | roll:{:.2f}".
format(hp_out[0], hp_out[1], hp_out[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
le = cv2.line(l_eye.copy(), (x - w, y - w), (x + w, y + w),
(255, 0, 255), 2)
cv2.line(le, (x - w, y + w), (x + w, y - w), (255, 0, 255), 2)
re = cv2.line(r_eye.copy(), (x - w, y - w), (x + w, y + w),
(255, 0, 255), 2)
cv2.line(re, (x - w, y + w), (x + w, y - w), (255, 0, 255), 2)
croppedFace[eye_coords[0][1]:eye_coords[0][3],
eye_coords[0][0]:eye_coords[0][2]] = le
croppedFace[eye_coords[1][1]:eye_coords[1][3],
eye_coords[1][0]:eye_coords[1][2]] = re
cv2.imshow("visualization", cv2.resize(new_frame, (500, 500)))
if count % 5 == 0:
Mc.move(new_coord[0], new_coord[1])
if key == 27:
break
logger.error("Video Done...")
# print(total_inference_time)
# print(fps)
#print(total_model_load_time)
cv2.destroyAllWindows()
inputFeeder.close()
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
"""
# --- INPUT ---
# Initialize the input_type
input_type = None
# Check if the input is a webcam
if args.input == 'CAM':
input_type = 'cam'
# Check if the input is an image
elif args.input.endswith(('.jpg', '.bmp', '.png')):
input_type = 'image'
# Check if the input is a video
elif args.input.endswith(('.mp4', '.avi')):
input_type = 'video'
else:
sys.exit(
f"[ ERRO ] The format of the input file '{args.input.endswith}' is not supported."
)
# Initialize the InputFeeder
input_feeder = InputFeeder(input_type, args.input)
input_feeder.load_data()
# --- MODELS ---
# Load the Face Detection Model
face_detection_model = FaceDetectionModel(
model_xml_path=args.model_face_detection,
device=args.device,
extensions_path=args.cpu_extension,
)
face_detection_model.load_model()
# Load the Head Pose Estimation Model
head_pose_estimation_model = HeadPoseEstimationModel(
model_xml_path=args.model_head_pose,
device=args.device,
extensions_path=args.cpu_extension,
)
head_pose_estimation_model.load_model()
# Load the Facial Landmarks Detection Model
facial_landmarks_detection_model = FacialLandmarksDetectionModel(
model_xml_path=args.model_face_landmark,
device=args.device,
extensions_path=args.cpu_extension,
)
facial_landmarks_detection_model.load_model()
# Load the Gaze Estimation Model
gaze_estimation_model = GazeEstimationModel(
model_xml_path=args.model_gaze_estimation,
device=args.device,
extensions_path=args.cpu_extension,
)
gaze_estimation_model.load_model()
# --- POINTER CONTROLLER ---
pointer_controller = MouseController(
precision='medium',
speed='medium',
)
# --- WINDOW ---
# Set the window to fullscreen
# cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)
# cv2.setWindowProperty(WINDOW_NAME, cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)
# Initialize list to track the inference time
list_inference_time = []
#Loop until stream is over
for frame in input_feeder.next_batch():
# If there is no frame break the loop
if frame is None:
break
# start the timer
start_time = time.time()
# Initialize the frame to be displayed
display_frame = frame
# --- DETECT HEAD ---
# Detect the head on the frame
list_heads = face_detection_model.predict(frame)
# Draw the outputs of the head detection algorithm
if args.display_outputs:
display_frame = face_detection_model.display_output(
frame, list_heads)
# --- HEAD POSE ESTIMATION ---
# Extract the roi of the head with the highest confidence score
head = list_heads[0]
head_x_max = head.x + head.w
head_y_max = head.y + head.h
head_roi = frame[head.y:head_y_max, head.x:head_x_max, :]
# Estimate the pose of the best head
head_angles = head_pose_estimation_model.predict(head_roi)
# Draw the pose of the best head
if args.display_outputs:
display_head_pose = head_pose_estimation_model.display_output(
head_roi, head_angles)
display_frame[head.y:head_y_max,
head.x:head_x_max, :] = display_head_pose
# --- FACIAL LANDMARKS DETECTION ---
# Detect the facial landmarks on the head with the highest confidence score
face_landmarks = facial_landmarks_detection_model.predict(head_roi)
# Draw the facial landmarks of the best head
if args.display_outputs:
# Set display_name to True to display the name of the landmarks
display_facial_landmarks = facial_landmarks_detection_model.display_output(
display_head_pose, face_landmarks, display_name=True)
display_frame[head.y:head_y_max,
head.x:head_x_max, :] = display_facial_landmarks
# --- GAZE ESTIMATION ---
# Calculate the eye ROI size
eye_roi_size = int(head_roi.shape[1] / 3)
# Extract the roi of the left eyes
left_eye_roi, left_eye_bbox = extract_landmark_roi(
name='left_eye',
landmarks=face_landmarks,
roi_size=eye_roi_size,
image=frame,
origin_x=head.x,
origin_y=head.y,
)
# Extract the roi of the Rigth eyes
right_eye_roi, right_eye_bbox = extract_landmark_roi(
name='right_eye',
landmarks=face_landmarks,
roi_size=eye_roi_size,
image=frame,
origin_x=head.x,
origin_y=head.y,
)
# Predict the gaze
gaze_vector = gaze_estimation_model.predict(
left_eye_image=left_eye_roi,
right_eye_image=right_eye_roi,
head_angles=head_angles,
)
# normalize the gaze vector based on the left eye
left_eye_x_center = left_eye_bbox.x + int(left_eye_bbox.w / 2)
left_eye_y_center = left_eye_bbox.y + int(left_eye_bbox.h / 2)
start_vector = np.array([left_eye_x_center, left_eye_y_center, 0])
end_vector = np.array([
left_eye_x_center + gaze_vector.x,
left_eye_y_center - gaze_vector.y, 0 + gaze_vector.z
])
vector = end_vector - start_vector
norm_gaze_vector = vector / np.sqrt(np.dot(vector, vector))
# Draw the gaze output and the eyes ROI
if args.display_outputs:
# draw the bbox around each eyes
display_frame = face_detection_model.display_output(
display_frame,
[left_eye_bbox, right_eye_bbox],
color=(255, 255, 255),
display_conf=False,
)
# draw the gaze from both eyes
display_frame = gaze_estimation_model.display_output(
display_frame,
norm_gaze_vector,
[left_eye_bbox, right_eye_bbox],
)
# Update position of the Computer Pointer
if not args.disable_pointer_controller:
pointer_controller.move(gaze_vector.x, gaze_vector.y)
# Calculate the inference time
stop_time = time.time()
list_inference_time.append(stop_time - start_time)
# Calculate and print the FPS
fps = round(1 / (stop_time - start_time), 2)
cv2.rectangle(display_frame, (10, 2), (120, 20), (255, 255, 255), -1)
cv2.putText(display_frame, f"{fps} FPS", (15, 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
# Display the frame
cv2.imshow(WINDOW_NAME, display_frame)
# Wait for 'ESC' or 'q' to exit the program
keyboard = cv2.waitKey(30)
if keyboard == 'q' or keyboard == 27:
break
# Release the input feeder
input_feeder.close()
# Destroy any OpenCV windows
cv2.destroyAllWindows()
# Display the average inference time and fps
average_fps = round(1 / (mean(list_inference_time)), 2)
print(
f"[ INFO ] Average inference time was {mean(list_inference_time)}s ({average_fps} FPS)."
)
print(f"[ INFO ] Successfully exited the program.")
def main():
args = build_argparser().parse_args()
logging.basicConfig(filename=args.output+'/app.log', filemode='w')
print("Begin: Try not to move mouse with your hands")
mc = MouseController("low", "fast")
if args.input == "cam":
frames = InputFeeder("cam")
else:
frames = InputFeeder("video", args.input)
cap = frames.load_data()
if args.display:
initial_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
initial_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
video_len = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
out_video = cv2.VideoWriter(os.path.join(args.output, 'output_video.mp4'), cv2.VideoWriter_fourcc('m','p','4','v'), fps, (initial_w, initial_h))
face_model = FaceDetectionModel(args.face_model, args.output, args.device)
pose_model = HeadPoseEstimationModel(args.pose_model, args.output, args.device)
landmarks_model = FacialLandmarksDetectionModel(args.landmarks_model, args.output, args.device)
gaze_model = GazeEstimationModel(args.gaze_model, args.output, args.device)
avg_out = 0
avg = 0
tmlt_face_avg = 0
tinpt_face_avg = 0
tint_face_avg = 0
toutt_face_avg = 0
tmlt_pose_avg = 0
tinpt_pose_avg = 0
tint_pose_avg = 0
toutt_pose_avg = 0
tmlt_landmarks_avg = 0
tinpt_landmarks_avg = 0
tint_landmarks_avg = 0
toutt_landmarks_avg = 0
tmlt_gaze_avg = 0
tinpt_gaze_avg = 0
tint_gaze_avg = 0
toutt_gaze_avg = 0
logging.info("Frames starting")
for frame in frames.next_batch():
if frame is None:
logging.error("Frame: " + frame + "failed")
continue
output_image = frame.copy()
cropped_faces, tmlt_face, tinpt_face, tint_face, toutt_face = face_model.predict(frame)
try:
largest_face = cropped_faces[0]
for face in cropped_faces:
if largest_face.size < face.size:
largest_face = face
pose, tmlt_pose, tinpt_pose, tint_pose, toutt_pose = pose_model.predict(largest_face)
landmarks, tmlt_landmarks, tinpt_landmarks, tint_landmarks, toutt_landmarks = landmarks_model.predict(largest_face)
gaze_vector, tmlt_gaze, tinpt_gaze, tint_gaze, toutt_gaze = gaze_model.predict(largest_face, landmarks, pose)
except Exception as e:
logging.error("Model inference failed: " + str(e))
# print(e)
continue
if args.display:
output_image, xmin, ymin = face_model.draw_crop_outputs(output_image, args.display)
output_image = gaze_model.display_eye_boxes(output_image, landmarks, xmin, ymin, args.display)
out_video.write(output_image)
cv2.imshow("output_image", output_image)
cv2.waitKey(15)
face_model.coords = []
tmlt_face_avg += tmlt_face
tinpt_face_avg += tinpt_face
tint_face_avg += tint_face
toutt_face_avg += toutt_face
tmlt_pose_avg += tmlt_pose
tinpt_pose_avg += tinpt_pose
tint_pose_avg += tint_pose
toutt_pose_avg += toutt_pose
tmlt_landmarks_avg += tmlt_landmarks
tinpt_landmarks_avg+= tinpt_landmarks
tint_landmarks_avg += tint_landmarks
toutt_landmarks_avg += toutt_landmarks
if gaze_vector is None:
avg_out += 1
continue
tmlt_gaze_avg += tmlt_gaze
tinpt_gaze_avg += tinpt_gaze
tint_gaze_avg += tint_gaze
toutt_gaze_avg += toutt_gaze
avg += 1
gaze_vector_norm = gaze_vector / np.linalg.norm(gaze_vector)
try:
mc.move(gaze_vector_norm[0], gaze_vector_norm[1])
except Exception as e:
logging.error("Gaze failed: " + str(e))
# print(e)
continue
file_name = "stats_"+args.precision+".txt"
save_path = os.path.join(os.getcwd(), args.output)
f = open(os.path.join(save_path, file_name), "w")
f.write("Benchmark Start:"+"\n\n")
f.write("Face Detection Model stats"+"\n")
f.write("Total model Load Time:"+str(tmlt_face_avg/avg)+"\n")
f.write("Total Input Time:"+str(tinpt_face_avg/avg)+"\n")
f.write("Total Inference Time:"+str(tint_face_avg/avg)+"\n")
f.write("Total Output Time:"+str(toutt_face_avg/avg)+"\n\n")
f.write("Head Pose Estimation Model stats"+"\n")
f.write("Total model Load Time:"+str(tmlt_pose_avg/avg)+"\n")
f.write("Total Input Time:"+str(tinpt_pose_avg/avg)+"\n")
f.write("Total Inference Time:"+str(tint_pose_avg/avg)+"\n")
f.write("Total Output Time:"+str(toutt_pose_avg/avg)+"\n\n")
f.write("Facial Landmarks Detection Model stats"+"\n")
f.write("Total model Load Time:"+str(tmlt_landmarks_avg/avg)+"\n")
f.write("Total Input Time:"+str(tinpt_landmarks_avg/avg)+"\n")
f.write("Total Inference Time:"+str(tint_landmarks_avg/avg)+"\n")
f.write("Total Output Time:"+str(toutt_landmarks_avg/avg)+"\n\n")
f.write("Gaze Estimation Model stats"+"\n")
f.write("Total model Load Time:"+str(tmlt_gaze_avg/(avg-avg_out))+"\n")
f.write("Total Input Time:"+str(tinpt_gaze_avg/(avg-avg_out))+"\n")
f.write("Total Inference Time:"+str(tint_gaze_avg/(avg-avg_out))+"\n")
f.write("Total Output Time:"+str(toutt_gaze_avg/(avg-avg_out))+"\n\n")
f.write("Benchmark end"+"\n")
f.close()
print("Thank you, Goodbye")
frames.close()
def main():
# get command line args
args = build_argparser().parse_args()
logger = log.getLogger()
type_input = args.input
if type_input == "CAM":
inputFeeder = InputFeeder("cam")
else:
inputFeeder = InputFeeder("video", args.input)
inputFeeder.load_data()
mc = MouseController("medium", "fast")
fdm = FaceDetectionModel(model_name=args.face_dectection_model,
device=args.device,
extensions=args.cpu_extension,
threshold=args.prob_threshold)
fldm = FacialLandmarksModel(model_name=args.face_landmarks_model,
device=args.device,
extensions=args.cpu_extension)
gem = GazeEstimationModel(model_name=args.gaze_estimation_model,
device=args.device,
extensions=args.cpu_extension)
hpem = HeadPoseEstimationModel(model_name=args.head_pose_model,
device=args.device,
extensions=args.cpu_extension)
data_capture = {}
start_time = time.time()
fdm.load_model()
fdm_load_time = time.time()
fldm.load_model()
fldm_load_time = time.time()
hpem.load_model()
hpem_load_time = time.time()
gem.load_model()
gem_load_time = time.time()
data_capture['FaceDetectionModel_loadtime'] = round(
(fdm_load_time - start_time) * 1000, 3)
data_capture['FacialLandmarksModel_loadtime'] = round(
(fldm_load_time - fdm_load_time) * 1000, 3)
data_capture['HeadPoseEstimationModel_loadtime'] = round(
(hpem_load_time - fldm_load_time) * 1000, 3)
data_capture['GazeEstimationModel_loadtime'] = round(
(gem_load_time - hpem_load_time) * 1000, 3)
for flag, frame in inputFeeder.next_batch():
if not flag:
break
pressedKey = cv2.waitKey(60)
start_infer_time = time.time() # time to start inference
face_coords, face_img = fdm.predict(frame)
fdm_infertime = time.time()
if face_coords == 0: # if face not detected
continue
hpem_out = hpem.predict(face_img)
hpem_infertime = time.time()
left_eye, right_eye, eye_coord = fldm.predict(face_img)
fldm_infertime = time.time()
if left_eye.all() == 0 or right_eye.all(
) == 0: # if eye are not detected
continue
mouse_coord, gaze_vector = gem.predict(left_eye, right_eye, hpem_out)
gem_infertime = time.time()
if args.preview:
output_boxes(frame, (face_coords[0], face_coords[1]),
(face_coords[2], face_coords[3]))
bound_boxes(frame, eye_coord, 45, 25, face_coords[0],
face_coords[1])
text = "Yaw: {:.2f}, Pitch: {:+.2f}, Roll: {:.2f}".format(
hpem_out[0], hpem_out[1], hpem_out[2])
output_text(frame, text, (100, 100))
h = frame.shape[0]
w = frame.shape[1]
center_of_face = (h / 2, w / 2, 0)
draw_axes(frame,
center_of_face,
hpem_out[0],
hpem_out[1],
hpem_out[2],
scale=50,
focal_length=950)
cv2.imshow('video', cv2.resize(frame, (500, 500)))
cv2.imshow('video', cv2.resize(frame, (500, 500)))
mc.move(mouse_coord[0], mouse_coord[1])
if pressedKey == 27:
break
data_capture['FaceDetectionModel_Inferencetime'] = round(
(fdm_infertime - start_infer_time) * 1000, 3)
data_capture['HeadPoseEstimationModel_Inferencetime'] = round(
(hpem_infertime - fdm_infertime) * 1000, 3)
data_capture['FacialLandmarksModel_Inferencetime'] = round(
(fldm_infertime - hpem_infertime) * 1000, 3)
data_capture['GazeEstimationModel_Inferencetime'] = round(
(gem_infertime - fldm_infertime) * 1000, 3)
total_time = round((time.time() - start_infer_time) * 1000, 3)
data_capture['Total_time'] = total_time
df = pd.DataFrame.from_dict(data_capture,
orient='index',
columns=['time(msecs)'])
df.to_csv("results.csv")
logger.error("Video has ended...")
cv2.destroyAllWindows()
inputFeeder.close()