feat: facedetection model in main.py

use face detection model in the main script this script is not finalized yet and need improvements

Author: Ahwar

main.py

@@ -9,13 +9,13 @@
 from pathlib import Path
 from argparse import ArgumentParser
 
-from face_detector import FaceDetector
-from head_pose_estimator import HeadposeEstimator
-from landmark_detector import LandmarkDetector
-from gaze_estimator import GazeEstimator
-from input_feeder import InputFeeder
+from lib.face_detector import FaceDetector
+# from lib.head_pose_estimator import HeadposeEstimator
+# from lib.landmark_detector import LandmarkDetector
+# from lib.gaze_estimator import GazeEstimator
+from lib.input_feeder import InputFeeder
 
-from mouse_controller import MouseController
+# from lib.mouse_controller import MouseController
 import logging
 
 #Create and configure logger
@@ -45,33 +45,25 @@ def build_argparser():
  help="Path to an xml file of Face Detection Model.")
  parser.add_argument("-ldm",
  "--lmar_det_m",
- required=True,
+ required=False,
  type=str,
  help="Path to an xml file of Landmark Detection model")
  parser.add_argument(
  "-hem",
  "--h_pose_m",
- required=True,
+ required=False,
  type=str,
  help="Path to an xml file of Head Pose Estimation model.")
  parser.add_argument("-gem",
  "--g_est_m",
- required=True,
+ required=False,
  type=str,
  help="Path to an xml file of Gaze Estimation Model.")
  parser.add_argument("-i",
  "--input",
  required=True,
  type=str,
  help="Path to image or video file")
- parser.add_argument("-l",
- "--cpu_extension",
- required=False,
- type=str,
- default=None,
- help="MKLDNN (CPU)-targeted custom layers."
- "Absolute path to a shared library with the"
- "kernels impl.")
  parser.add_argument("-d",
  "--device",
  type=str,
@@ -116,7 +108,7 @@ def infer_on_stream(args):
  """
  # Check if all input files are present
  for _ in [
- args.face_det_m, args.lmar_det_m, args.h_pose_m, args.g_est_m,
+ args.face_det_m,# args.lmar_det_m, args.h_pose_m, args.g_est_m,
  args.input
  ]:
  if not Path(_).is_file():
@@ -157,28 +149,27 @@ def infer_on_stream(args):
 
  ### Load All Models
  ## Load Face Detector Model
- face_detector = FaceDetector(args.face_det_m, args.device,
- args.cpu_extension)
+ face_detector = FaceDetector(args.face_det_m, args.device)
  face_detector.load_model()
 
  logger.info("Face Detection model loaded successfully")
  ## Load Headpose Estimator Model
- headpose_estimator = HeadposeEstimator(args.h_pose_m, args.device,
- args.cpu_extension)
- headpose_estimator.load_model()
- logger.info("Headpose Estimator model loaded successfully")
-
- ## Load Landmark Detector Model
- landmark_detector = LandmarkDetector(args.lmar_det_m, args.device,
- args.cpu_extension)
- landmark_detector.load_model()
- logger.info("Landmark Detector model loaded successfully")
-
- ## Load Gaze Estimation Model
- gaze_estimator = GazeEstimator(args.g_est_m, args.device,
- args.cpu_extension)
- gaze_estimator.load_model()
- logger.info("Gaze Estimation model loaded successfully")
+ # headpose_estimator = HeadposeEstimator(args.h_pose_m, args.device,
+ #  args.cpu_extension)
+ # headpose_estimator.load_model()
+ # logger.info("Headpose Estimator model loaded successfully")
+
+ # ## Load Landmark Detector Model
+ # landmark_detector = LandmarkDetector(args.lmar_det_m, args.device,
+ #  args.cpu_extension)
+ # landmark_detector.load_model()
+ # logger.info("Landmark Detector model loaded successfully")
+
+ # ## Load Gaze Estimation Model
+ # gaze_estimator = GazeEstimator(args.g_est_m, args.device,
+ #  args.cpu_extension)
+ # gaze_estimator.load_model()
+ # logger.info("Gaze Estimation model loaded successfully")
  ### Initialize Input Feeder
  input_feeder = InputFeeder(input_type, args.input)
  (initial_w, initial_h) = input_feeder.load_data()
@@ -192,14 +183,15 @@ def infer_on_stream(args):
  break
  f_count += 1
  logger.info("Processing Frame: {}".format(f_count))
- print("\nProcessing Frame: {}".format(f_count))
-
  ### Detect Face in Frame
  output = face_detector.predict(frame)
  ## Crop Face
- face, face_coords = face_detector.preprocess_output(
+ face, face_coords = face_detector.postprocess_output(
  output, args.prob_threshold, frame, initial_w, initial_h)
-
+ if np.any(face):
+ cv2.imshow("face", face)
+ cv2.waitKey()
+ exit()
  # skip frame if face not found
  if not np.any(face):
  print("Face Not found in Frame\tSkipping Frame")