add person detection model from MediaPipe (#147)

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README.md

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+# Person detector from MediaPipe Pose
+
+This model detects upper body and full body keypoints of a person, and is downloaded from https://github.com/PINTO0309/PINTO_model_zoo/blob/main/053_BlazePose/20_densify_pose_detection/download.sh or converted from TFLite to ONNX using following tools:
+
+- TFLite model to ONNX with MediaPipe custom `densify` op: https://github.com/PINTO0309/tflite2tensorflow
+- simplified by [onnx-simplifier](https://github.com/daquexian/onnx-simplifier)
+
+SSD Anchors are generated from [GenMediaPipePalmDectionSSDAnchors](https://github.com/VimalMollyn/GenMediaPipePalmDectionSSDAnchors)
+
+## Demo
+
+Run the following commands to try the demo:
+
+```bash
+# detect on camera input
+python demo.py
+# detect on an image
+python demo.py -i /path/to/image
+
+# get help regarding various parameters
+python demo.py --help
+```
+
+### Example outputs
+
+![webcam demo](examples/mppersondet_demo.webp)
+
+## License
+
+All files in this directory are licensed under [Apache 2.0 License](LICENSE).
+
+## Reference
+- MediaPipe Pose: https://google.github.io/mediapipe/solutions/pose
+- MediaPipe pose model and model card: https://google.github.io/mediapipe/solutions/models.html#pose
+- BlazePose TFJS: https://github.com/tensorflow/tfjs-models/tree/master/pose-detection/src/blazepose_tfjs

demo.py

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+import argparse
+
+import numpy as np
+import cv2 as cv
+
+from mp_persondet import MPPersonDet
+
+# Check OpenCV version
+assert cv.__version__ >= "4.7.0", \
+       "Please install latest opencv-python to try this demo: python3 -m pip install --upgrade opencv-python"
+
+# Valid combinations of backends and targets
+backend_target_pairs = [
+    [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
+    [cv.dnn.DNN_BACKEND_CUDA,   cv.dnn.DNN_TARGET_CUDA],
+    [cv.dnn.DNN_BACKEND_CUDA,   cv.dnn.DNN_TARGET_CUDA_FP16],
+    [cv.dnn.DNN_BACKEND_TIMVX,  cv.dnn.DNN_TARGET_NPU],
+    [cv.dnn.DNN_BACKEND_CANN,   cv.dnn.DNN_TARGET_NPU]
+]
+
+parser = argparse.ArgumentParser(description='Person Detector from MediaPipe')
+parser.add_argument('--input', '-i', type=str,
+                    help='Usage: Set path to the input image. Omit for using default camera.')
+parser.add_argument('--model', '-m', type=str, default='./person_detection_mediapipe_2023mar.onnx',
+                    help='Usage: Set model path, defaults to person_detection_mediapipe_2023mar.onnx')
+parser.add_argument('--backend_target', '-bt', type=int, default=0,
+                    help='''Choose one of the backend-target pair to run this demo:
+                        {:d}: (default) OpenCV implementation + CPU,
+                        {:d}: CUDA + GPU (CUDA),
+                        {:d}: CUDA + GPU (CUDA FP16),
+                        {:d}: TIM-VX + NPU,
+                        {:d}: CANN + NPU
+                    '''.format(*[x for x in range(len(backend_target_pairs))]))
+parser.add_argument('--score_threshold', type=float, default=0.5,
+                    help='Usage:  Set the minimum needed confidence for the model to identify a person, defaults to 0.5. Smaller values may result in faster detection, but will limit accuracy. Filter out persons of confidence < conf_threshold.')
+parser.add_argument('--nms_threshold', type=float, default=0.3,
+                    help='Usage: Suppress bounding boxes of iou >= nms_threshold. Default = 0.3.')
+parser.add_argument('--top_k', type=int, default=1,
+                    help='Usage: Keep top_k bounding boxes before NMS.')
+parser.add_argument('--save', '-s', action='store_true',
+                    help='Usage: Specify to save file with results (i.e. bounding box, confidence level). Invalid in case of camera input.')
+parser.add_argument('--vis', '-v', action='store_true',
+                    help='Usage: Specify to open a new window to show results. Invalid in case of camera input.')
+args = parser.parse_args()
+
+def visualize(image, results, fps=None):
+    output = image.copy()
+
+    if fps is not None:
+        cv.putText(output, 'FPS: {:.2f}'.format(fps), (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
+
+    for idx, person in enumerate(results):
+        score = person[-1]
+        person_landmarks = person[4:-1].reshape(4, 2).astype(np.int32)
+
+        hip_point = person_landmarks[0]
+        full_body = person_landmarks[1]
+        shoulder_point = person_landmarks[2]
+        upper_body = person_landmarks[3]
+
+        # draw circle for full body
+        radius = np.linalg.norm(hip_point - full_body).astype(np.int32)
+        cv.circle(output, hip_point, radius, (255, 0, 0), 2)
+
+        # draw circle for upper body
+        radius = np.linalg.norm(shoulder_point - upper_body).astype(np.int32)
+        cv.circle(output, shoulder_point, radius, (0, 255, 255), 2)
+
+        # draw points for each keypoint
+        for p in person_landmarks:
+            cv.circle(output, p, 2, (0, 0, 255), 2)
+
+        # put score
+        cv.putText(output, 'Score: {:.4f}'.format(score), (0, output.shape[0] - 48), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 255, 0))
+
+    cv.putText(output, 'Yellow: upper body circle', (0, output.shape[0] - 36), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 255, 255))
+    cv.putText(output, 'Blue: full body circle', (0, output.shape[0] - 24), cv.FONT_HERSHEY_DUPLEX, 0.5, (255, 0, 0))
+    cv.putText(output, 'Red: keypoint', (0, output.shape[0] - 12), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 255))
+
+    return output
+
+if __name__ == '__main__':
+    backend_id = backend_target_pairs[args.backend_target][0]
+    target_id = backend_target_pairs[args.backend_target][1]
+
+    # Instantiate MPPersonDet
+    model = MPPersonDet(modelPath=args.model,
+                      nmsThreshold=args.nms_threshold,
+                      scoreThreshold=args.score_threshold,
+                      topK=args.top_k,
+                      backendId=backend_id,
+                      targetId=target_id)
+
+    # If input is an image
+    if args.input is not None:
+        image = cv.imread(args.input)
+
+        # Inference
+        results = model.infer(image)
+        if len(results) == 0:
+            print('Person not detected')
+
+        # Draw results on the input image
+        image = visualize(image, results)
+
+        # Save results if save is true
+        if args.save:
+            print('Resutls saved to result.jpg\n')
+            cv.imwrite('result.jpg', image)
+
+        # Visualize results in a new window
+        if args.vis:
+            cv.namedWindow(args.input, cv.WINDOW_AUTOSIZE)
+            cv.imshow(args.input, image)
+            cv.waitKey(0)
+    else: # Omit input to call default camera
+        deviceId = 0
+        cap = cv.VideoCapture(deviceId)
+
+        tm = cv.TickMeter()
+        while cv.waitKey(1) < 0:
+            hasFrame, frame = cap.read()
+            if not hasFrame:
+                print('No frames grabbed!')
+                break
+
+            # Inference
+            tm.start()
+            results = model.infer(frame)
+            tm.stop()
+
+            # Draw results on the input image
+            frame = visualize(frame, results, fps=tm.getFPS())
+
+            # Visualize results in a new Window
+            cv.imshow('MPPersonDet Demo', frame)
+
+            tm.reset()
+