add person detection model from MediaPipe (#147)

README.md

@@ -42,6 +42,7 @@ Guidelines:
 | [YoutuReID](./models/person_reid_youtureid)             | Person Re-Identification      | 128x256    | 30.39          | 625.56       | 90.07           | 44.61        | 5.58               | ---         |
 | [MP-PalmDet](./models/palm_detection_mediapipe)         | Palm Detection                | 192x192    | 6.29           | 86.83        | 83.20           | 33.81        | 5.17               | ---         |
 | [MP-HandPose](./models/handpose_estimation_mediapipe)   | Hand Pose Estimation          | 224x224    | 4.68           | 43.57        | 40.10           | 19.47        | 6.27               | ---         |
+| [MP-PersonDet](./models/person_detection_mediapipe)     | Person Detection              | 224x224    | 13.88          | 98.52        | 56.69           | ---          | 16.45              | ---         |
 
 \*: Models are quantized in per-channel mode, which run slower than per-tensor quantized models on NPU.
 

benchmark/README.md

@@ -95,6 +95,7 @@ mean       median     min        input size   model
 29.46      42.21      25.82      [1280, 720]  DaSiamRPN with ['object_tracking_dasiamrpn_kernel_cls1_2021nov.onnx', 'object_tracking_dasiamrpn_kernel_r1_2021nov.onnx', 'object_tracking_dasiamrpn_model_2021nov.onnx']
 6.14       6.02       5.91       [192, 192]   MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx']
 8.51       9.89       5.91       [192, 192]   MPPalmDet with ['palm_detection_mediapipe_2023feb_int8.onnx']
+13.88      14.82      12.39      [224, 224]   MPPersonDet with ['person_detection_mediapipe_2023mar.onnx']
 30.87      30.69      29.85      [128, 256]   YoutuReID with ['person_reid_youtu_2021nov.onnx']
 30.77      30.02      27.97      [128, 256]   YoutuReID with ['person_reid_youtu_2021nov_int8.onnx']
 1.35       1.37       1.30       [100, 100]   WeChatQRCode with ['detect_2021nov.prototxt', 'detect_2021nov.caffemodel', 'sr_2021nov.prototxt', 'sr_2021nov.caffemodel']
@@ -147,6 +148,7 @@ mean       median     min        input size   model
 762.56     738.04     654.25     [1280, 720]  DaSiamRPN with ['object_tracking_dasiamrpn_kernel_cls1_2021nov.onnx', 'object_tracking_dasiamrpn_kernel_r1_2021nov.onnx', 'object_tracking_dasiamrpn_model_2021nov.onnx']
 91.48      91.28      91.15      [192, 192]   MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx']
 115.58     135.17     91.15      [192, 192]   MPPalmDet with ['palm_detection_mediapipe_2023feb_int8.onnx']
+98.52      98.95      97.58      [224, 224]   MPPersonDet with ['person_detection_mediapipe_2023mar.onnx']
 676.15     655.20     636.06     [128, 256]   YoutuReID with ['person_reid_youtu_2021nov.onnx']
 548.93     582.29     443.32     [128, 256]   YoutuReID with ['person_reid_youtu_2021nov_int8.onnx']
 8.18       8.15       8.13       [100, 100]   WeChatQRCode with ['detect_2021nov.prototxt', 'detect_2021nov.caffemodel', 'sr_2021nov.prototxt', 'sr_2021nov.caffemodel']
@@ -200,6 +202,7 @@ mean       median     min        input size   model
 466.19     457.89     442.88     [1280, 720]  DaSiamRPN with ['object_tracking_dasiamrpn_kernel_cls1_2021nov.onnx', 'object_tracking_dasiamrpn_kernel_r1_2021nov.onnx', 'object_tracking_dasiamrpn_model_2021nov.onnx']
 69.60      69.69      69.13      [192, 192]   MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx']
 81.65      82.20      69.13      [192, 192]   MPPalmDet with ['palm_detection_mediapipe_2023feb_int8.onnx']
+98.38      98.20      97.69      [224, 224]   MPPersonDet with ['person_detection_mediapipe_2023mar.onnx']
 411.49     417.53     402.57     [128, 256]   YoutuReID with ['person_reid_youtu_2021nov.onnx']
 372.94     370.17     335.95     [128, 256]   YoutuReID with ['person_reid_youtu_2021nov_int8.onnx']
 5.62       5.64       5.55       [100, 100]   WeChatQRCode with ['detect_2021nov.prototxt', 'detect_2021nov.caffemodel', 'sr_2021nov.prototxt', 'sr_2021nov.caffemodel']
@@ -236,6 +239,7 @@ mean       median     min        input size   model
 1238.91    1244.87    1227.30    [640, 640]   YoloX with ['object_detection_yolox_2022nov.onnx']
 76.54      76.09      74.51      [1280, 720]  DaSiamRPN with ['object_tracking_dasiamrpn_kernel_cls1_2021nov.onnx', 'object_tracking_dasiamrpn_kernel_r1_2021nov.onnx', 'object_tracking_dasiamrpn_model_2021nov.onnx']
 67.34      67.83      62.38      [192, 192]   MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx']
+56.69      55.54      48.96      [224, 224]   MPPersonDet with ['person_detection_mediapipe_2023mar.onnx']
 126.65     126.63     124.96     [128, 256]   YoutuReID with ['person_reid_youtu_2021nov.onnx']
 303.12     302.80     299.30     [640, 480]   DB with ['text_detection_DB_IC15_resnet18_2021sep.onnx']
 302.58     299.78     297.83     [640, 480]   DB with ['text_detection_DB_TD500_resnet18_2021sep.onnx']
@@ -265,6 +269,7 @@ mean       median     min        input size   model
 1223.32    1248.88    1213.25    [640, 640]   YoloX with ['object_detection_yolox_2022nov.onnx']
 52.91      52.96      50.17      [1280, 720]  DaSiamRPN with ['object_tracking_dasiamrpn_kernel_cls1_2021nov.onnx', 'object_tracking_dasiamrpn_kernel_r1_2021nov.onnx', 'object_tracking_dasiamrpn_model_2021nov.onnx']
 212.86     213.21     210.03     [192, 192]   MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx']
+221.12     255.53     217.16     [224, 224]   MPPersonDet with ['person_detection_mediapipe_2023mar.onnx']
 96.68      94.21      89.24      [128, 256]   YoutuReID with ['person_reid_youtu_2021nov.onnx']
 343.38     344.17     337.62     [640, 480]   DB with ['text_detection_DB_IC15_resnet18_2021sep.onnx']
 344.29     345.07     337.62     [640, 480]   DB with ['text_detection_DB_TD500_resnet18_2021sep.onnx']
@@ -310,6 +315,7 @@ mean       median     min        input size   model
 428.66     524.98     391.33     [1280, 720]  DaSiamRPN with ['object_tracking_dasiamrpn_kernel_cls1_2021nov.onnx', 'object_tracking_dasiamrpn_kernel_r1_2021nov.onnx', 'object_tracking_dasiamrpn_model_2021nov.onnx']
 66.91      67.09      64.90      [192, 192]   MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx']
 79.42      81.44      64.90      [192, 192]   MPPalmDet with ['palm_detection_mediapipe_2023feb_int8.onnx']
+84.42      85.99      83.30      [224, 224]   MPPersonDet with ['person_detection_mediapipe_2023mar.onnx']
 439.53     431.92     406.03     [128, 256]   YoutuReID with ['person_reid_youtu_2021nov.onnx']
 358.63     379.93     296.32     [128, 256]   YoutuReID with ['person_reid_youtu_2021nov_int8.onnx']
 5.29       5.30       5.21       [100, 100]   WeChatQRCode with ['detect_2021nov.prototxt', 'detect_2021nov.caffemodel', 'sr_2021nov.prototxt', 'sr_2021nov.caffemodel']
@@ -387,6 +393,7 @@ mean       median     min        input size   model
 701.08     708.52     685.49     [1280, 720]  DaSiamRPN with ['object_tracking_dasiamrpn_kernel_cls1_2021nov.onnx', 'object_tracking_dasiamrpn_kernel_r1_2021nov.onnx', 'object_tracking_dasiamrpn_model_2021nov.onnx']
 105.23     105.14     105.00     [192, 192]   MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx']
 123.41     125.65     105.00     [192, 192]   MPPalmDet with ['palm_detection_mediapipe_2023feb_int8.onnx']
+134.10     134.43     133.62     [224, 224]   MPPersonDet with ['person_detection_mediapipe_2023mar.onnx']
 631.70     631.81     630.61     [128, 256]   YoutuReID with ['person_reid_youtu_2021nov.onnx']
 595.32     599.48     565.32     [128, 256]   YoutuReID with ['person_reid_youtu_2021nov_int8.onnx']
 1452.55    1453.75    1450.98    [640, 480]   DB with ['text_detection_DB_IC15_resnet18_2021sep.onnx']
@@ -422,6 +429,7 @@ mean       median     min        input size   model
 20.62      22.09      19.16      [416, 416]   NanoDet with ['object_detection_nanodet_2022nov.onnx']
 28.59      28.60      27.91      [640, 640]   YoloX with ['object_detection_yolox_2022nov.onnx']
 5.17       5.26       5.09       [192, 192]   MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx']
+16.45      16.44      16.31      [224, 224]   MPPersonDet with ['person_detection_mediapipe_2023mar.onnx']
 5.58       5.57       5.54       [128, 256]   YoutuReID with ['person_reid_youtu_2021nov.onnx']
 17.15      17.18      16.83      [640, 480]   DB with ['text_detection_DB_IC15_resnet18_2021sep.onnx']
 17.95      18.61      16.83      [640, 480]   DB with ['text_detection_DB_TD500_resnet18_2021sep.onnx']

benchmark/config/person_detection_mediapipe.yaml

@@ -0,0 +1,19 @@
+Benchmark:
+  name: "Person Detection Benchmark"
+  type: "Detection"
+  data:
+    path: "data/person_detection"
+    files: ["person1.jpg", "person2.jpg", "person3.jpg"]
+    sizes: # [[w1, h1], ...], Omit to run at original scale
+      - [224, 224]
+  metric:
+    warmup: 30
+    repeat: 10
+  backend: "default"
+  target: "cpu"
+
+Model:
+  name: "MPPersonDet"
+  scoreThreshold: 0.5
+  nmsThreshold: 0.3
+  topK: 1

benchmark/download_data.py

@@ -213,6 +213,10 @@ data_downloaders = dict(
         url='https://drive.google.com/u/0/uc?id=1LUUrQIWYYtiGoNAL_twZvdw5NkC39Swe&export=download',
         sha='4161a5cd3b0be1f51484abacf19dc9a2231e9894',
         filename='object_detection.zip'),
+    person_detection=Downloader(name='person_detection',
+        url='https://drive.google.com/u/0/uc?id=1RbLyetgqFUTt0IHaVmu6c_b7KeXJgKbc&export=download',
+        sha='fbae2fb0a47fe65e316bbd0ec57ba21461967550',
+        filename='person_detection.zip'),
 )
 
 if __name__ == '__main__':

models/__init__.py

@@ -8,6 +8,7 @@ from .text_recognition_crnn.crnn import CRNN
 from .face_recognition_sface.sface import SFace
 from .image_classification_ppresnet.ppresnet import PPResNet
 from .human_segmentation_pphumanseg.pphumanseg import PPHumanSeg
+from .person_detection_mediapipe.mp_persondet import MPPersonDet
 from .qrcode_wechatqrcode.wechatqrcode import WeChatQRCode
 from .object_tracking_dasiamrpn.dasiamrpn import DaSiamRPN
 from .person_reid_youtureid.youtureid import YoutuReID
@@ -80,6 +81,7 @@ MODELS.register(CRNN)
 MODELS.register(SFace)
 MODELS.register(PPResNet)
 MODELS.register(PPHumanSeg)
+MODELS.register(MPPersonDet)
 MODELS.register(WeChatQRCode)
 MODELS.register(DaSiamRPN)
 MODELS.register(YoutuReID)

models/person_detection_mediapipe/LICENSE

@@ -0,0 +1,202 @@
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models/person_detection_mediapipe/README.md

@@ -0,0 +1,35 @@
+# 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

models/person_detection_mediapipe/demo.py

@@ -0,0 +1,139 @@
+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()
+