updated mp_persondet usage and added .gitattributes

.gitattributes

@@ -0,0 +1,26 @@
+
+# Caffe
+*.caffemodel filter=lfs diff=lfs merge=lfs -text
+
+# Tensorflow
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pbtxt filter=lfs diff=lfs merge=lfs -text
+
+# Torch
+*.t7 filter=lfs diff=lfs merge=lfs -text
+*.net filter=lfs diff=lfs merge=lfs -text
+
+# Darknet
+*.weights filter=lfs diff=lfs merge=lfs -text
+
+# ONNX
+*.onnx filter=lfs diff=lfs merge=lfs -text
+
+# NPY
+*.npy filter=lfs diff=lfs merge=lfs -text
+
+# Images
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,9 @@
+*.pyc
+**/__pycache__
+**/__pycache__/**
+
+.vscode
+
+build/
+**/build
+**/build/**

demo.py

@@ -1,8 +1,8 @@
-import sys
 import argparse
 
 import numpy as np
 import cv2 as cv
+from huggingface_hub import hf_hub_download
 
 # Check OpenCV version
 opencv_python_version = lambda str_version: tuple(map(int, (str_version.split("."))))
@@ -10,10 +10,10 @@ assert opencv_python_version(cv.__version__) >= opencv_python_version("4.10.0"),
        "Please install latest opencv-python for benchmark: python3 -m pip install --upgrade opencv-python"
 
 from mp_pose import MPPose
-
-sys.path.append('../person_detection_mediapipe')
 from mp_persondet import MPPersonDet
 
+mp_model_path = hf_hub_download(repo_id="opencv/person_detection_mediapipe", filename="person_detection_mediapipe_2023mar.onnx")
+
 # Valid combinations of backends and targets
 backend_target_pairs = [
     [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
@@ -171,7 +171,7 @@ if __name__ == '__main__':
     target_id = backend_target_pairs[args.backend_target][1]
 
     # person detector
-    person_detector = MPPersonDet(modelPath='../person_detection_mediapipe/person_detection_mediapipe_2023mar.onnx',
+    person_detector = MPPersonDet(modelPath=mp_model_path,
                                   nmsThreshold=0.3,
                                   scoreThreshold=0.5,
                                   topK=5000, # usually only one person has good performance