add pose tracking demo (#380)

demo/2d_pose_tracking_demo.md

@@ -0,0 +1,38 @@
+## 2D Pose Tracking Demo
+
+### 2D Top-Down Video Human Pose Tracking Demo
+
+We provide a video demo to illustrate the pose tracking results.
+
+Assume that you have already installed [mmdet](https://github.com/open-mmlab/mmdetection).
+
+```shell
+python demo/top_down_video_demo_with_mmdet.py \
+    ${MMDET_CONFIG_FILE} ${MMDET_CHECKPOINT_FILE} \
+    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
+    --video-path ${VIDEO_FILE} \
+    --output-video-root ${OUTPUT_VIDEO_ROOT} \
+    [--show --device ${GPU_ID}] \
+    [--bbox-thr ${BBOX_SCORE_THR} --kpt-thr ${KPT_SCORE_THR} --iou-thr ${IOU_SCORE_THR}]
+```
+
+Examples:
+
+```shell
+python demo/top_down_pose_tracking_demo_with_mmdet.py \
+    demo/mmdetection_cfg/faster_rcnn_r50_fpn_1x_coco.py \
+    http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
+    configs/top_down/resnet/coco/res50_coco_256x192.py \
+    https://download.openmmlab.com/mmpose/top_down/resnet/res50_coco_256x192-ec54d7f3_20200709.pth \
+    --video-path demo/demo_video.mp4 \
+    --out-video-root vis_results
+```
+
+### Speed Up Inference
+
+Some tips to speed up MMPose inference:
+
+For top-down 2D human pose models, try to edit the config file. For example,
+
+1. set `flip_test=False` in [topdown-res50](/configs/top_down/resnet/coco/res50_coco_256x192.py#L51).
+2. set `unbiased_decoding=False` in [topdown-res50](/configs/top_down/resnet/coco/res50_coco_256x192.py#L54).

demo/README.md

@@ -6,5 +6,6 @@ This page provides tutorials about running demos.
 
 - [2D human pose demo](2d_human_pose_demo.md)
 - [2D hand demo](2d_hand_demo.md)
+- [2D pose tracking demo](2d_pose_tracking_demo.md)
 
 <!-- TOC -->

demo/top_down_pose_tracking_demo_with_mmdet.py

@@ -0,0 +1,150 @@
+import os
+from argparse import ArgumentParser
+
+import cv2
+from mmdet.apis import inference_detector, init_detector
+
+from mmpose.apis import (get_track_id, inference_top_down_pose_model,
+                         init_pose_model, vis_pose_tracking_result)
+
+
+def process_mmdet_results(mmdet_results, cat_id=0):
+    """Process mmdet results, and return a list of bboxes.
+
+    :param mmdet_results:
+    :param cat_id: category id (default: 0 for human)
+    :return: a list of detected bounding boxes
+    """
+    if isinstance(mmdet_results, tuple):
+        det_results = mmdet_results[0]
+    else:
+        det_results = mmdet_results
+    return det_results[cat_id]
+
+
+def main():
+    """Visualize the demo images.
+
+    Using mmdet to detect the human.
+    """
+    parser = ArgumentParser()
+    parser.add_argument('det_config', help='Config file for detection')
+    parser.add_argument('det_checkpoint', help='Checkpoint file for detection')
+    parser.add_argument('pose_config', help='Config file for pose')
+    parser.add_argument('pose_checkpoint', help='Checkpoint file for pose')
+    parser.add_argument('--video-path', type=str, help='Video path')
+    parser.add_argument(
+        '--show',
+        action='store_true',
+        default=False,
+        help='whether to show visualizations.')
+    parser.add_argument(
+        '--out-video-root',
+        default='',
+        help='Root of the output video file. '
+        'Default not saving the visualization video.')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    parser.add_argument(
+        '--bbox-thr',
+        type=float,
+        default=0.3,
+        help='Bounding box score threshold')
+    parser.add_argument(
+        '--kpt-thr', type=float, default=0.3, help='Keypoint score threshold')
+    parser.add_argument(
+        '--iou-thr', type=float, default=0.3, help='IoU score threshold')
+
+    args = parser.parse_args()
+
+    assert args.show or (args.out_video_root != '')
+    assert args.det_config is not None
+    assert args.det_checkpoint is not None
+
+    det_model = init_detector(
+        args.det_config, args.det_checkpoint, device=args.device)
+    # build the pose model from a config file and a checkpoint file
+    pose_model = init_pose_model(
+        args.pose_config, args.pose_checkpoint, device=args.device)
+
+    dataset = pose_model.cfg.data['test']['type']
+
+    cap = cv2.VideoCapture(args.video_path)
+
+    if args.out_video_root == '':
+        save_out_video = False
+    else:
+        os.makedirs(args.out_video_root, exist_ok=True)
+        save_out_video = True
+
+    if save_out_video:
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
+                int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        videoWriter = cv2.VideoWriter(
+            os.path.join(args.out_video_root,
+                         f'vis_{os.path.basename(args.video_path)}'), fourcc,
+            fps, size)
+
+    # optional
+    return_heatmap = False
+
+    # e.g. use ('backbone', ) to return backbone feature
+    output_layer_names = None
+
+    next_id = 0
+    pose_results = []
+    while (cap.isOpened()):
+        pose_results_last = pose_results
+
+        flag, img = cap.read()
+        if not flag:
+            break
+        # test a single image, the resulting box is (x1, y1, x2, y2)
+        mmdet_results = inference_detector(det_model, img)
+
+        # keep the person class bounding boxes.
+        person_bboxes = process_mmdet_results(mmdet_results)
+
+        # test a single image, with a list of bboxes.
+        pose_results, returned_outputs = inference_top_down_pose_model(
+            pose_model,
+            img,
+            person_bboxes,
+            bbox_thr=args.bbox_thr,
+            format='xyxy',
+            dataset=dataset,
+            return_heatmap=return_heatmap,
+            outputs=output_layer_names)
+
+        # get track id for each person instance
+        pose_results, next_id = get_track_id(
+            pose_results, pose_results_last, next_id, iou_thr=args.iou_thr)
+
+        # show the results
+        vis_img = vis_pose_tracking_result(
+            pose_model,
+            img,
+            pose_results,
+            dataset=dataset,
+            kpt_score_thr=args.kpt_thr,
+            show=False)
+
+        if args.show:
+            cv2.imshow('Image', vis_img)
+
+        if save_out_video:
+            videoWriter.write(vis_img)
+
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    cap.release()
+    if save_out_video:
+        videoWriter.release()
+    cv2.destroyAllWindows()
+
+
+if __name__ == '__main__':
+    main()

mmpose/apis/__init__.py

@@ -1,11 +1,12 @@
 from .inference import (inference_bottom_up_pose_model,
                         inference_top_down_pose_model, init_pose_model,
                         vis_pose_result)
+from .inference_tracking import get_track_id, vis_pose_tracking_result
 from .test import multi_gpu_test, single_gpu_test
 from .train import train_model
 
 __all__ = [
     'train_model', 'init_pose_model', 'inference_top_down_pose_model',
     'inference_bottom_up_pose_model', 'multi_gpu_test', 'single_gpu_test',
-    'vis_pose_result'
+    'vis_pose_result', 'get_track_id', 'vis_pose_tracking_result'
 ]