[Enhance] Improve control of 3D visualisation of pose lifter (open-mm…

…lab#1481)

* Add some missing parameter documentation

* Expose vis_height / axis_azimuth

* Fix interhand axis_azimuth

* keep original default behavior

Co-authored-by: ly015 <liyining0712@gmail.com>

demo/interhand3d_img_demo.py

@@ -0,0 +1,259 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+import os.path as osp
+from argparse import ArgumentParser
+
+import mmcv
+import numpy as np
+from xtcocotools.coco import COCO
+
+from mmpose.apis import inference_interhand_3d_model, vis_3d_pose_result
+from mmpose.apis.inference import init_pose_model
+from mmpose.core import SimpleCamera
+
+
+def _transform_interhand_camera_param(interhand_camera_param):
+    """Transform the camera parameters in interhand2.6m dataset to the format
+    of SimpleCamera.
+
+    Args:
+        interhand_camera_param (dict): camera parameters including:
+            - camrot: 3x3, camera rotation matrix (world-to-camera)
+            - campos: 3x1, camera location in world space
+            - focal: 2x1, camera focal length
+            - princpt: 2x1, camera center
+
+    Returns:
+        param (dict): camera parameters including:
+            - R: 3x3, camera rotation matrix (camera-to-world)
+            - T: 3x1, camera translation (camera-to-world)
+            - f: 2x1, camera focal length
+            - c: 2x1, camera center
+    """
+    camera_param = {}
+    camera_param['R'] = np.array(interhand_camera_param['camrot']).T
+    camera_param['T'] = np.array(interhand_camera_param['campos'])[:, None]
+    camera_param['f'] = np.array(interhand_camera_param['focal'])[:, None]
+    camera_param['c'] = np.array(interhand_camera_param['princpt'])[:, None]
+    return camera_param
+
+
+def main():
+    parser = ArgumentParser()
+    parser.add_argument('pose_config', help='Config file for pose network')
+    parser.add_argument('pose_checkpoint', help='Checkpoint file')
+    parser.add_argument('--img-root', type=str, default='', help='Image root')
+    parser.add_argument(
+        '--json-file',
+        type=str,
+        default='',
+        help='Json file containing image info.')
+    parser.add_argument(
+        '--camera-param-file',
+        type=str,
+        default=None,
+        help='Camera parameter file for converting 3D pose predictions from '
+        ' the pixel space to camera space. If None, keypoints in pixel space'
+        'will be visualized')
+    parser.add_argument(
+        '--gt-joints-file',
+        type=str,
+        default=None,
+        help='Optional argument. Ground truth 3D keypoint parameter file. '
+        'If None, gt keypoints will not be shown and keypoints in pixel '
+        'space will be visualized.')
+    parser.add_argument(
+        '--rebase-keypoint-height',
+        action='store_true',
+        help='Rebase the predicted 3D pose so its lowest keypoint has a '
+        'height of 0 (landing on the ground). This is useful for '
+        'visualization when the model do not predict the global position '
+        'of the 3D pose.')
+    parser.add_argument(
+        '--show-ground-truth',
+        action='store_true',
+        help='If True, show ground truth keypoint if it is available.')
+    parser.add_argument(
+        '--show',
+        action='store_true',
+        default=False,
+        help='whether to show img')
+    parser.add_argument(
+        '--out-img-root',
+        type=str,
+        default=None,
+        help='Root of the output visualization images. '
+        'Default not saving the visualization images.')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device for inference')
+    parser.add_argument(
+        '--kpt-thr', type=float, default=0.3, help='Keypoint score threshold')
+    parser.add_argument(
+        '--radius',
+        type=int,
+        default=4,
+        help='Keypoint radius for visualization')
+    parser.add_argument(
+        '--thickness',
+        type=int,
+        default=1,
+        help='Link thickness for visualization')
+
+    args = parser.parse_args()
+    assert args.show or (args.out_img_root != '')
+
+    coco = COCO(args.json_file)
+
+    # 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.lower())
+    dataset = pose_model.cfg.data['test']['type']
+
+    # load camera parameters
+    camera_params = None
+    if args.camera_param_file is not None:
+        camera_params = mmcv.load(args.camera_param_file)
+    # load ground truth joints parameters
+    gt_joint_params = None
+    if args.gt_joints_file is not None:
+        gt_joint_params = mmcv.load(args.gt_joints_file)
+
+    # load hand bounding boxes
+    det_results_list = []
+    for image_id, image in coco.imgs.items():
+        image_name = osp.join(args.img_root, image['file_name'])
+
+        ann_ids = coco.getAnnIds(image_id)
+        det_results = []
+
+        capture_key = str(image['capture'])
+        camera_key = image['camera']
+        frame_idx = image['frame_idx']
+
+        for ann_id in ann_ids:
+            ann = coco.anns[ann_id]
+            if camera_params is not None:
+                camera_param = {
+                    key: camera_params[capture_key][key][camera_key]
+                    for key in camera_params[capture_key].keys()
+                }
+                camera_param = _transform_interhand_camera_param(camera_param)
+            else:
+                camera_param = None
+            if gt_joint_params is not None:
+                joint_param = gt_joint_params[capture_key][str(frame_idx)]
+                gt_joint = np.concatenate([
+                    np.array(joint_param['world_coord']),
+                    np.array(joint_param['joint_valid'])
+                ],
+                                          axis=-1)
+            else:
+                gt_joint = None
+
+            det_result = {
+                'image_name': image_name,
+                'bbox': ann['bbox'],  # bbox format is 'xywh'
+                'camera_param': camera_param,
+                'keypoints_3d_gt': gt_joint
+            }
+            det_results.append(det_result)
+        det_results_list.append(det_results)
+
+    for i, det_results in enumerate(
+            mmcv.track_iter_progress(det_results_list)):
+
+        image_name = det_results[0]['image_name']
+
+        pose_results = inference_interhand_3d_model(
+            pose_model, image_name, det_results, dataset=dataset)
+
+        # Post processing
+        pose_results_vis = []
+        for idx, res in enumerate(pose_results):
+            keypoints_3d = res['keypoints_3d']
+            # normalize kpt score
+            if keypoints_3d[:, 3].max() > 1:
+                keypoints_3d[:, 3] /= 255
+            # get 2D keypoints in pixel space
+            res['keypoints'] = keypoints_3d[:, [0, 1, 3]]
+
+            # For model-predicted keypoints, channel 0 and 1 are coordinates
+            # in pixel space, and channel 2 is the depth (in mm) relative
+            # to root joints.
+            # If both camera parameter and absolute depth of root joints are
+            # provided, we can transform keypoint to camera space for better
+            # visualization.
+            camera_param = res['camera_param']
+            keypoints_3d_gt = res['keypoints_3d_gt']
+            if camera_param is not None and keypoints_3d_gt is not None:
+                # build camera model
+                camera = SimpleCamera(camera_param)
+                # transform gt joints from world space to camera space
+                keypoints_3d_gt[:, :3] = camera.world_to_camera(
+                    keypoints_3d_gt[:, :3])
+
+                # transform relative depth to absolute depth
+                keypoints_3d[:21, 2] += keypoints_3d_gt[20, 2]
+                keypoints_3d[21:, 2] += keypoints_3d_gt[41, 2]
+
+                # transform keypoints from pixel space to camera space
+                keypoints_3d[:, :3] = camera.pixel_to_camera(
+                    keypoints_3d[:, :3])
+
+            # rotate the keypoint to make z-axis correspondent to height
+            # for better visualization
+            vis_R = np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]])
+            keypoints_3d[:, :3] = keypoints_3d[:, :3] @ vis_R
+            if keypoints_3d_gt is not None:
+                keypoints_3d_gt[:, :3] = keypoints_3d_gt[:, :3] @ vis_R
+
+            # rebase height (z-axis)
+            if args.rebase_keypoint_height:
+                valid = keypoints_3d[..., 3] > 0
+                keypoints_3d[..., 2] -= np.min(
+                    keypoints_3d[valid, 2], axis=-1, keepdims=True)
+            res['keypoints_3d'] = keypoints_3d
+            res['keypoints_3d_gt'] = keypoints_3d_gt
+
+            # Add title
+            instance_id = res.get('track_id', idx)
+            res['title'] = f'Prediction ({instance_id})'
+            pose_results_vis.append(res)
+            # Add ground truth
+            if args.show_ground_truth:
+                if keypoints_3d_gt is None:
+                    print('Fail to show ground truth. Please make sure that'
+                          ' gt-joints-file is provided.')
+                else:
+                    gt = res.copy()
+                    if args.rebase_keypoint_height:
+                        valid = keypoints_3d_gt[..., 3] > 0
+                        keypoints_3d_gt[..., 2] -= np.min(
+                            keypoints_3d_gt[valid, 2], axis=-1, keepdims=True)
+                    gt['keypoints_3d'] = keypoints_3d_gt
+                    gt['title'] = f'Ground truth ({instance_id})'
+                    pose_results_vis.append(gt)
+
+        # Visualization
+        if args.out_img_root is None:
+            out_file = None
+        else:
+            os.makedirs(args.out_img_root, exist_ok=True)
+            out_file = osp.join(args.out_img_root, f'vis_{i}.jpg')
+
+        vis_3d_pose_result(
+            pose_model,
+            result=pose_results_vis,
+            img=det_results[0]['image_name'],
+            out_file=out_file,
+            dataset=dataset,
+            show=args.show,
+            kpt_score_thr=args.kpt_thr,
+            radius=args.radius,
+            thickness=args.thickness,
+            axis_azimuth=-115,
+        )
+
+
+if __name__ == '__main__':
+    main()