diff --git a/README.md b/README.md index 0ff5d2239bdeebc17d0c4f69fd0d817f438fe571..0d6251b0e31094739b7eacf4a6ca6672ec06d9df 100644 --- a/README.md +++ b/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. diff --git a/benchmark/README.md b/benchmark/README.md index 75a1cc395e02539b2b61e73213b1b5b7e43b064a..b8f8367c108d71f5e0acd3462d4eaf1439f0a691 100644 --- a/benchmark/README.md +++ b/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'] diff --git a/benchmark/config/person_detection_mediapipe.yaml b/benchmark/config/person_detection_mediapipe.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ba95446c787190c38bd67ba92ebacb18a5032586 --- /dev/null +++ b/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 diff --git a/benchmark/download_data.py b/benchmark/download_data.py index 628c3f80ea28a727b14c1f39a75e0a82ac4bcd98..68033302c0754c9ac58d532214d3d912004af174 100644 --- a/benchmark/download_data.py +++ b/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__': diff --git a/models/__init__.py b/models/__init__.py index 5c031938a70b150849d96975e323b967dac302a0..019cad0bd074e2ee213a3b739aa47ae556f11d6c 100644 --- a/models/__init__.py +++ b/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) diff --git a/models/person_detection_mediapipe/LICENSE b/models/person_detection_mediapipe/LICENSE new file mode 100644 index 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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() + diff --git a/models/person_detection_mediapipe/examples/mppersondet_demo.webp b/models/person_detection_mediapipe/examples/mppersondet_demo.webp new file mode 100644 index 0000000000000000000000000000000000000000..8b6978ba9b5655e5652d73905b8bdb64c8761eca Binary files /dev/null and b/models/person_detection_mediapipe/examples/mppersondet_demo.webp differ diff --git a/models/person_detection_mediapipe/mp_persondet.py b/models/person_detection_mediapipe/mp_persondet.py new file mode 100644 index 0000000000000000000000000000000000000000..63422ef2b4488d8b082960c53061255457accded --- /dev/null +++ b/models/person_detection_mediapipe/mp_persondet.py @@ -0,0 +1,2366 @@ +import numpy as np +import cv2 as cv + +class MPPersonDet: + def __init__(self, modelPath, nmsThreshold=0.3, scoreThreshold=0.5, topK=1, backendId=0, targetId=0): + self.model_path = modelPath + self.nms_threshold = nmsThreshold + self.score_threshold = scoreThreshold + self.topK = topK + self.backend_id = backendId + self.target_id = targetId + + self.input_size = np.array([224, 224]) # wh + + self.model = cv.dnn.readNet(self.model_path) + self.model.setPreferableBackend(self.backend_id) + self.model.setPreferableTarget(self.target_id) + + self.anchors = self._load_anchors() + + @property + def name(self): + return self.__class__.__name__ + + def setBackendAndTarget(self, backendId, targetId): + self.backend_id = backendId + self.target_id = targetId + self.model.setPreferableBackend(self.backend_id) + self.model.setPreferableTarget(self.target_id) + + def _preprocess(self, image): + pad_bias = np.array([0., 0.]) # left, top + image = cv.cvtColor(image, cv.COLOR_BGR2RGB) + image = image.astype(np.float32) / 255.0 # norm + image = (image - 0.5) * 2 # [0, 1] -> [-1, 1] + ratio = min(self.input_size / image.shape[:2]) + if image.shape[0] != self.input_size[0] or image.shape[1] != self.input_size[1]: + # keep aspect ratio when resize + ratio_size = (np.array(image.shape[:2]) * ratio).astype(np.int32) + image = cv.resize(image, (ratio_size[1], ratio_size[0])) + pad_h = self.input_size[0] - ratio_size[0] + pad_w = self.input_size[1] - ratio_size[1] + pad_bias[0] = left = pad_w // 2 + pad_bias[1] = top = pad_h // 2 + right = pad_w - left + bottom = pad_h - top + image = cv.copyMakeBorder(image, top, bottom, left, right, cv.BORDER_CONSTANT, None, (0, 0, 0)) + + blob = np.transpose(image, [2, 0, 1]) + pad_bias = (pad_bias / ratio).astype(np.int32) + return blob[np.newaxis, :, :, :], pad_bias # chw -> nchw + + def infer(self, image): + h, w, _ = image.shape + + # Preprocess + input_blob, pad_bias = self._preprocess(image) + + # Forward + self.model.setInput(input_blob) + output_blob = self.model.forward(self.model.getUnconnectedOutLayersNames()) + + # Postprocess + results = self._postprocess(output_blob, np.array([w, h]), pad_bias) + + return results + + def _postprocess(self, output_blob, original_shape, pad_bias): + score = output_blob[1][0, :, 0] + box_delta = output_blob[0][0, :, 0:4] + landmark_delta = output_blob[0][0, :, 4:] + scale = max(original_shape) + + # get scores + score = score.astype(np.float64) + score = np.clip(score, -100, 100) + score = 1 / (1 + np.exp(-score)) + + # get boxes + cxy_delta = box_delta[:, :2] / self.input_size + wh_delta = box_delta[:, 2:] / self.input_size + xy1 = (cxy_delta - wh_delta / 2 + self.anchors) * scale + xy2 = (cxy_delta + wh_delta / 2 + self.anchors) * scale + boxes = np.concatenate([xy1, xy2], axis=1) + boxes -= [pad_bias[0], pad_bias[1], pad_bias[0], pad_bias[1]] + # NMS + keep_idx = cv.dnn.NMSBoxes(boxes, score, self.score_threshold, self.nms_threshold, top_k=self.topK) + if len(keep_idx) == 0: + return np.empty(shape=(0, 13)) + selected_score = score[keep_idx] + selected_box = boxes[keep_idx] + + # get landmarks + selected_landmarks = landmark_delta[keep_idx].reshape(-1, 4, 2) + selected_landmarks = selected_landmarks / self.input_size + selected_anchors = self.anchors[keep_idx] + for idx, landmark in enumerate(selected_landmarks): + landmark += selected_anchors[idx] + selected_landmarks *= scale + selected_landmarks -= pad_bias + + # TODO: still don't know the meaning of face bbox + # each landmark: hip center point; full body point; shoulder center point; upper body point; + # + # [ + # [face_bbox, landmarks, score] + # ... + # [face_bbox, landmarks, score] + # ] + return np.c_[selected_box.reshape(-1, 4), selected_landmarks.reshape(-1, 8), selected_score.reshape(-1, 1)] + + def _load_anchors(self): + return np.array([[0.017857142857142856, 0.017857142857142856], + [0.017857142857142856, 0.017857142857142856], + [0.05357142857142857, 0.017857142857142856], + [0.05357142857142857, 0.017857142857142856], + [0.08928571428571429, 0.017857142857142856], + [0.08928571428571429, 0.017857142857142856], + [0.125, 0.017857142857142856], + [0.125, 0.017857142857142856], + [0.16071428571428573, 0.017857142857142856], + [0.16071428571428573, 0.017857142857142856], + [0.19642857142857142, 0.017857142857142856], + [0.19642857142857142, 0.017857142857142856], + [0.23214285714285715, 0.017857142857142856], + 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