As the Haar Cascade face detector is no longer used, it is now remoed.

Licenses/haarcascade_frontalface_alt2.txt

@@ -1,41 +0,0 @@
-    Tree-based 20x20 gentle adaboost frontal face detector.
-    Created by Rainer Lienhart.
-
-////////////////////////////////////////////////////////////////////////////////////////
-
-  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-
-  By downloading, copying, installing or using the software you agree to this license.
-  If you do not agree to this license, do not download, install,
-  copy or use the software.
-
-
-                        Intel License Agreement
-                For Open Source Computer Vision Library
-
- Copyright (C) 2000, Intel Corporation, all rights reserved.
- Third party copyrights are property of their respective owners.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
-   * Redistribution's of source code must retain the above copyright notice,
-     this list of conditions and the following disclaimer.
-
-   * Redistribution's in binary form must reproduce the above copyright notice,
-     this list of conditions and the following disclaimer in the documentation
-     and/or other materials provided with the distribution.
-
-   * The name of Intel Corporation may not be used to endorse or promote products
-     derived from this software without specific prior written permission.
-
- This software is provided by the copyright holders and contributors "as is" and
- any express or implied warranties, including, but not limited to, the implied
- warranties of merchantability and fitness for a particular purpose are disclaimed.
- In no event shall the Intel Corporation or contributors be liable for any direct,
- indirect, incidental, special, exemplary, or consequential damages
- (including, but not limited to, procurement of substitute goods or services;
- loss of use, data, or profits; or business interruption) however caused
- and on any theory of liability, whether in contract, strict liability,
- or tort (including negligence or otherwise) arising in any way out of
- the use of this software, even if advised of the possibility of such damage.

README.md

@@ -141,7 +141,7 @@ For all training after the first model, a modified version of [Adaptive Wing Los
 
 For expression detection, [LIBSVM](https://www.csie.ntu.edu.tw/~cjlin/libsvm/) is used.
 
-Face detection is done using OpenCV's Haar Cascade classifier or RetinaFace.
+Face detection is done using a custom heatmap regression based face detection model or RetinaFace.
 
     @inproceedings{deng2019retinaface,
       title={RetinaFace: Single-stage Dense Face Localisation in the Wild},

tracker.py

@@ -458,10 +458,8 @@ class Tracker():
         options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
         options.log_severity_level = 3
         self.gaze_model = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_gaze32_split_opt.onnx"), sess_options=options)
-        self.detection = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_detection_opt.onnx"), sess_options=options)
 
-        self.faceCascade = cv2.CascadeClassifier()
-        self.faceCascade.load(os.path.join(model_base_path, "haarcascade_frontalface_alt2.xml"))
+        self.detection = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_detection_opt.onnx"), sess_options=options)
         self.faces = []
 
         # Image normalization constants
@@ -905,8 +903,6 @@ class Tracker():
                     self.retinaface_scan.background_detect(frame)
                 else:
                     start_fd = time.perf_counter()
-                    #gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-                    #new_faces.extend(list(self.faceCascade.detectMultiScale(gray, 1.3, 4, 0 | cv2.CASCADE_SCALE_IMAGE, (50, 50))))
                     new_faces.extend(self.detect_faces(frame))
                     duration_fd = 1000 * (time.perf_counter() - start_fd)
                     self.wait_count = 0