RTMPose关键点检测全流程-
- MMPose预训练模型预测-命令行 - 预测单张图 -
# HRNetpython .\demo\topdown_demo_with_mmdet.py .\demo\mmdetection_cfg\faster_rcnn_r50_fpn_coco.py https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth .\configs\body_2d_keypoint\topdown_heatmap\coco\td-hm_hrnet-w32_8xb64-210e_coco-256x192.py https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth --input .\data\test\multi-person.jpeg --output-root .\outputs\B1_HRNet_1 --dev cuda:0 --bbox-thr 0.5 --kpt-thr 0.2 --nms-thr 0.3 --radius 8 --thickness 4 --draw-bbox --draw-heatmap --show-kpt-idx- Loads checkpoint by http backend from path: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth Loads checkpoint by http backend from path: https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth 06/03 20:47:33 - mmengine - WARNING -Visualizerbackend is not initialized because save_dir is None. -
- #RTMPosepython .\demo\topdown_demo_with_mmdet.py .\demo\mmdetection_cfg\faster_rcnn_r50_fpn_coco.py https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth .\projects\rtmpose\rtmpose\body_2d_keypoint\rtmpose-s_8xb256-420e_coco-256x192.py https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmpose-s_simcc-aic-coco_pt-aic-coco_420e-256x192-fcb2599b_20230126.pth --input .\data\test\multi-person.jpeg --output-root .\outputs\B1_RTM_1 --device cuda:0 --bbox-thr 0.5 --kpt-thr 0.5 --nms-thr 0.3 --radius 8 --thickness 4 --draw-bbox --draw-heatmap --show-kpt-idx- Loads checkpoint by http backend from path: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth Loads checkpoint by http backend from path: https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmpose-s_simcc-aic-coco_pt-aic-coco_420e-256x192-fcb2599b_20230126.pth Downloading: "https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmpose-s_simcc-aic-coco_pt-aic-coco_420e-256x192-fcb2599b_20230126.pth" to C:\Users\16798/.cache\torch\hub\checkpoints\rtmpose-s_simcc-aic-coco_pt-aic-coco_420e-256x192-fcb2599b_20230126.pth 100%|██████████████████████████████████████████████████████████████████| 60.8M/60.8M [00:37<00:00, 1.71MB/s] 06/03 23:04:00 - mmengine - WARNING -Visualizerbackend is not initialized because save_dir is None. d:\workspace.git\mmpose\mmpose\models\heads\coord_cls_heads\rtmcc_head.py:217: UserWarning: The predicted si mcc values are normalized for visualization. This may cause discrepancy between the keypoint scores and the 1D heatmaps. warnings.warn('The predicted simcc values are normalized for '-
- 预测视频：直接将--input 换成视频路径即可 - #HRNet python .\demo\topdown_demo_with_mmdet.py .\demo\mmdetection_cfg\faster_rcnn_r50_fpn_coco.py https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth .\configs\body_2d_keypoint\topdown_heatmap\coco\td-hm_hrnet-w32_8xb64-210e_coco-256x192.py https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth --input .\data\test\mother_wx.mp4 --output-root .\outputs\B1_HRNet_2 --device cuda:0 --bbox-thr 0.5 --kpt-thr 0.2 --nms-thr 0.3 --radius 5 --thickness 2 --draw-bbox --draw-heatmap --show-kpt-idx- Loads checkpoint by http backend from path: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth Loads checkpoint by http backend from path: https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth 06/03 23:11:03 - mmengine - WARNING -Visualizerbackend is not initialized because save_dir is None. -#TRMPosepython .\demo\topdown_demo_with_mmdet.py .\demo\mmdetection_cfg\faster_rcnn_r50_fpn_coco.py https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth projects/rtmpose/rtmpose/body_2d_keypoint/rtmpose-s_8xb256-420e_coco-256x192.py https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmpose-s_simcc-aic-coco_pt-aic-coco_420e-256x192-fcb2599b_20230126.pth --input data/test/mother_wx.mp4 --output-root outputs/B1_RTM_2 --device cuda:0 --bbox-thr 0.5 --kpt-thr 0.5 --nms-thr 0.3 --radius 5 --thickness 2 --draw-bbox --draw-heatmap --show-kpt-idx- Loads checkpoint by http backend from path: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth Loads checkpoint by http backend from path: https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmpose-s_simcc-aic-coco_pt-aic-coco_420e-256x192-fcb2599b_20230126.pth 06/03 23:14:39 - mmengine - WARNING -Visualizerbackend is not initialized because save_dir is None. d:\workspace.git\mmpose\mmpose\models\heads\coord_cls_heads\rtmcc_head.py:217: UserWarning: The predicted si mcc values are normalized for visualization. This may cause discrepancy between the keypoint scores and the 1D heatmaps. warnings.warn('The predicted simcc values are normalized for ' - MMPose预测训练模型预测-Python API -
import cv2import numpy as npfrom PIL import Imageimport matplotlib.pyplot as pltimport torchimport mmcvfrom mmcv import imreadimport mmenginefrom mmengine.registry import init_default_scopefrom mmpose.apis import inference_topdownfrom mmpose.apis import init_model as init_pose_estimatorfrom mmpose.evaluation.functional import nmsfrom mmpose.registry import VISUALIZERSfrom mmpose.structures import merge_data_samplesfrom mmdet.apis import inference_detector, init_detector# 设备device = torch.device('cuda') if torch.cuda.is_available() else torch.device( 'cpu')print('device:', device)# 载入待测图片img_path = 'data/test/multi-person.jpeg'# 打开图片Image.open(img_path)# 构建目标检测模型# Faster RCNNdetector = init_detector( 'demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py', 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth', device=device)# RTMPose-Tinydetector = init_detector( 'projects/rtmpose/rtmdet/person/rtmdet_m_640-8xb32_coco-person.py', 'https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_m_8xb32-100e_coco-obj365-person-235e8209.pth', device=device)# 构建人体姿态估计模型pose_estimator = init_pose_estimator( 'configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w32_8xb64-210e_coco-256x192.py', 'https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth', device=device, cfg_options={'model': { 'test_cfg': { 'output_heatmaps': True } }})# 预测-目标检测init_default_scope(detector.cfg.get('default_scope', 'mmdet'))#获取目标检测预测结果detect_result = inference_detector(detector, img_path)detect_result.keys()# 预测类别print(detect_result.pred_instances.labels)# 置信度print(detect_result.pred_instances.scores)# 框坐标：左上角X坐标、左上角Y坐标、右下角X坐标、右下角Y坐标print(detect_result.pred_instances.bboxes)# 置信度阈值过滤，获得最终目标检测预测结果# 置信度阈值CONF_THRES = 0.5pred_instance = detect_result.pred_instances.cpu().numpy()bboxes = np.concatenate((pred_instance.bboxes, pred_instance.scores[:, None]), axis=1)bboxes = bboxes[np.logical_and(pred_instance.labels == 0, pred_instance.scores > CONF_THRES)]bboxes = bboxes[nms(bboxes, 0.3)][:, :4]print(bboxes)# 预测-关键点# 获取每个 bbox 的关键点预测结果pose_results = inference_topdown(pose_estimator, img_path, bboxes)print(len(pose_results))# 把多个bbox的pose结果打包到一起data_samples = merge_data_samples(pose_results)print(data_samples.keys())# 预测结果-关键点坐标print(data_samples.pred_instances.keypoints.shape)# 索引为 0 的人，每个关键点的坐标print(data_samples.pred_instances.keypoints[0,:,:])# 预测结果-关键点热力图# 每一类关键点的预测热力图print(data_samples.pred_fields.heatmaps.shape)idx_point = 13heatmap = data_samples.pred_fields.heatmaps[idx_point,:,:]print(heatmap.shape)# 索引为 idx 的关键点，在全图上的预测热力图plt.imshow(heatmap)plt.show()
- ## MMPose官方可视化工具visualizer-# 半径pose_estimator.cfg.visualizer.radius = 10# 线宽pose_estimator.cfg.visualizer.line_width = 8visualizer = VISUALIZERS.build(pose_estimator.cfg.visualizer)# 元数据visualizer.set_dataset_meta(pose_estimator.dataset_meta)# 可视化img = mmcv.imread(img_path)img = mmcv.imconvert(img, 'bgr', 'rgb')img_output = visualizer.add_datasample( 'result', img, data_sample=data_samples, draw_gt=False, draw_heatmap=True, draw_bbox=True, show_kpt_idx=True, show=False, wait_time=0, out_file='outputs/B2.jpg')print(img_output.shape)plt.figure(figsize=(10, 10))plt.imshow(img_output)plt.show()
- 多人检测问题 -
代码实现：- GitHub - guwuyue/OpenMMLabCamp: OpenMMLabCamp
本文转载自: https://blog.csdn.net/guwuyue/article/details/131025343
版权归原作者 guwuyue 所有， 如有侵权，请联系我们删除。
版权归原作者 guwuyue 所有， 如有侵权，请联系我们删除。