update annotator, visualization

apps/calibration/detect_chessboard.py

@@ -2,8 +2,8 @@
   @ Date: 2021-07-16 20:13:57
   @ Author: Qing Shuai
   @ LastEditors: Qing Shuai
-  @ LastEditTime: 2021-07-17 19:25:00
-  @ FilePath: /EasyMocapRelease/apps/calibration/detect_chessboard.py
+  @ LastEditTime: 2021-07-21 19:56:38
+  @ FilePath: /EasyMocap/apps/calibration/detect_chessboard.py
 '''
 # detect the corner of chessboard
 from easymocap.annotator.file_utils import getFileList, read_json, save_json
@@ -60,7 +60,7 @@ def detect_chessboard(path, out, pattern, gridSize, args):
         cv2.imwrite(outname, show)
 
 def detect_chessboard_sequence(path, out, pattern, gridSize, args):
-    # create_chessboard(path, pattern, gridSize, ext=args.ext)
+    create_chessboard(path, pattern, gridSize, ext=args.ext)
     subs = sorted(os.listdir(join(path, 'images')))
     for sub in subs:
         dataset = ImageFolder(path, sub=sub, annot='chessboard', ext=args.ext)
@@ -69,8 +69,8 @@ def detect_chessboard_sequence(path, out, pattern, gridSize, args):
         found = np.zeros(nFrames, dtype=np.bool)
         visited = np.zeros(nFrames, dtype=np.bool)
         proposals = []
-        init_step = 50
-        min_step = 1
+        init_step = args.max_step
+        min_step = args.min_step
         for nf in range(0, nFrames, init_step):
             if nf + init_step < len(dataset):
                 proposals.append([nf, nf+init_step])
@@ -98,6 +98,8 @@ def detect_chessboard_sequence(path, out, pattern, gridSize, args):
             if not found[left] and not found[right]:
                 continue
             mid = (left+right)//2
+            if mid == left or mid == right:
+                continue
             if mid - left > min_step:
                 proposals.append((left, mid))
             if right - mid > min_step:
@@ -113,6 +115,9 @@ if __name__ == "__main__":
         help='The pattern of the chessboard', default=(9, 6))
     parser.add_argument('--grid', type=float, default=0.1, 
         help='The length of the grid size (unit: meter)')
+    parser.add_argument('--max_step', type=int, default=50)
+    parser.add_argument('--min_step', type=int, default=0)
+    
     parser.add_argument('--silent', action='store_true')
     parser.add_argument('--debug', action='store_true')
     parser.add_argument('--seq', action='store_true')

apps/vis/vis_smpl.py

@@ -0,0 +1,50 @@
+'''
+  @ Date: 2021-07-19 20:37:16
+  @ Author: Qing Shuai
+  @ LastEditors: Qing Shuai
+  @ LastEditTime: 2021-08-28 20:42:44
+  @ FilePath: /EasyMocapRelease/apps/vis/vis_smpl.py
+'''
+from easymocap.config import Config, load_object
+import open3d as o3d
+from easymocap.visualize.o3dwrapper import Vector3dVector, create_mesh, create_coord
+import numpy as np
+
+def update_vis(vis, mesh, body_model, params):
+    vertices = body_model(return_verts=True, return_tensor=False, **params)[0]
+    mesh.vertices = Vector3dVector(vertices)
+    vis.update_geometry(model)
+    vis.poll_events()
+    vis.update_renderer()
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--cfg', type=str, 
+        default='config/model/smpl_neutral.yml')
+    parser.add_argument('--key', type=str, 
+        default='poses')
+    parser.add_argument('--num', type=int, default=50)
+    parser.add_argument('--debug', action='store_true')
+    args = parser.parse_args()
+    key = args.key
+    
+    config = Config.load(args.cfg)
+    body_model = load_object(config.module, config.args)
+    params = body_model.init_params(1)
+    vertices = body_model(return_verts=True, return_tensor=False, **params)
+    joints = body_model(return_verts=False, return_smpl_joints=True, return_tensor=False, **params)
+    
+    model = create_mesh(vertices=vertices[0], faces=body_model.faces)
+    vis = o3d.visualization.Visualizer()
+    vis.create_window(width=900, height=900)
+    vis.add_geometry(model)
+    params = body_model.init_params(1)
+    var_ranges = np.linspace(0, np.pi/2, args.num)
+    var_ranges = np.concatenate([-var_ranges, -var_ranges[::-1], var_ranges, var_ranges[::-1]])
+    for npose in range(54, params[key].shape[1]):
+        print('[Vis] {}: {}'.format(key, npose))
+        for i in range(var_ranges.shape[0]):
+            params[key][0, npose] = var_ranges[i]
+            update_vis(vis, model, body_model, params)
+    import ipdb; ipdb.set_trace()

doc/installation.md

@@ -2,7 +2,7 @@
  * @Date: 2021-04-02 11:52:33
  * @Author: Qing Shuai
  * @LastEditors: Qing Shuai
- * @LastEditTime: 2021-06-21 21:18:45
+ * @LastEditTime: 2021-07-22 20:58:33
  * @FilePath: /EasyMocapRelease/doc/installation.md
 -->
 # EasyMocap - Installation
@@ -74,7 +74,7 @@ data
 - torch==1.4.0
 - torchvision==0.5.0
 - opencv-python
-- [pyrender](https://pyrender.readthedocs.io/en/latest/install/index.html#python-installation): for visualization
+- [pyrender](https://pyrender.readthedocs.io/en/latest/install/index.html#python-installation): for visualization, or [pyrender for server without a screen](https://pyrender.readthedocs.io/en/latest/install/index.html#getting-pyrender-working-with-osmesa).
 - chumpy: for loading SMPL model
 - OpenPose[4]: for 2D pose
 

doc/quickstart.md

@@ -2,11 +2,13 @@
  * @Date: 2021-04-02 11:53:16
  * @Author: Qing Shuai
  * @LastEditors: Qing Shuai
- * @LastEditTime: 2021-06-14 14:26:19
+ * @LastEditTime: 2021-07-22 20:57:16
  * @FilePath: /EasyMocapRelease/doc/quickstart.md
 -->
 # Quick Start
 
+First install this project following [install](./installation.md)
+
 ## Demo
 
 We provide an example multiview dataset[[dropbox](https://www.dropbox.com/s/24mb7r921b1g9a7/zju-ls-feng.zip?dl=0)][[BaiduDisk](https://pan.baidu.com/s/1lvAopzYGCic3nauoQXjbPw)(vg1z)], which has 800 frames from 23 synchronized and calibrated cameras. After downloading the dataset, you can run the following example scripts.

easymocap/affinity/plucker.py

@@ -2,8 +2,8 @@
   @ Date: 2021-01-25 21:27:56
   @ Author: Qing Shuai
   @ LastEditors: Qing Shuai
-  @ LastEditTime: 2021-06-25 15:50:40
-  @ FilePath: /EasyMocapRelease/easymocap/affinity/plucker.py
+  @ LastEditTime: 2021-07-28 17:18:20
+  @ FilePath: /EasyMocap/easymocap/affinity/plucker.py
 '''
 import numpy as np
 
@@ -14,9 +14,9 @@ def plucker_from_pl(point, line):
         point {tensor} -- N, 3
         line {tensor} -- N, 3
     """
-    norm = np.linalg.norm(line, axis=1, keepdims=True)
+    norm = np.linalg.norm(line, axis=-1, keepdims=True)
     lunit = line/norm
-    moment = np.cross(point, lunit, axis=1)
+    moment = np.cross(point, lunit, axis=-1)
     return lunit, moment
 
 def plucker_from_pp(point1, point2):
@@ -70,3 +70,16 @@ def computeRay(keypoints2d, invK, R, T):
     res = np.hstack((l, m, conf))
     # 兼容cpp版本，所以补一个维度
     return res[None, :, :]
+
+def computeRaynd(keypoints2d, invK, R, T):
+    # keypoints2d: (..., 3)
+    conf = keypoints2d[..., 2:]
+    # cam_center: (1, 3)
+    cam_center = - (R.T @ T).T
+    kp_pixel = np.concatenate([keypoints2d[..., :2], np.ones_like(conf)], axis=-1)
+    kp_all_3d = (kp_pixel @ invK.T - T.T) @ R
+    while len(cam_center.shape) < len(kp_all_3d.shape):
+        cam_center = cam_center[None]
+    l, m = plucker_from_pp(cam_center, kp_all_3d)
+    res = np.concatenate((l, m, conf), axis=-1)
+    return res

easymocap/annotator/__init__.py

@@ -1,7 +1,19 @@
-from .basic_dataset import ImageFolder
-from .basic_visualize import vis_point, vis_line
-from .basic_visualize import plot_bbox_body, plot_skeleton, plot_skeleton_simple, plot_text, vis_active_bbox
-from .basic_annotator import AnnotBase
+'''
+  @ Date: 2021-04-15 16:56:18
+  @ Author: Qing Shuai
+  @ LastEditors: Qing Shuai
+  @ LastEditTime: 2021-06-09 10:16:29
+  @ FilePath: /EasyMocap/easymocap/annotator/__init__.py
+'''
+from .basic_annotator import load_parser, parse_parser
+from .basic_dataset import ImageFolder, MVBase
+from .basic_visualize import vis_point, vis_line, vis_bbox
+from .basic_visualize import plot_bbox_body, plot_skeleton, plot_text, vis_active_bbox, plot_bbox_factory
+from .basic_annotator import AnnotBase, AnnotMV
 from .chessboard import findChessboardCorners
 # bbox callbacks
-from .bbox_callback import callback_select_bbox_center, callback_select_bbox_corner, auto_pose_track
+# create, delete, copy
+from .bbox_callback import create_bbox, delete_bbox, delete_all_bbox, copy_previous_bbox, copy_previous_missing
+# track
+from .bbox_callback import get_auto_track
+from .bbox_callback import callback_select_bbox_center, callback_select_bbox_corner

easymocap/annotator/basic_annotator.py

@@ -1,9 +1,10 @@
 import shutil
 import cv2
+from tqdm import tqdm
 from .basic_keyboard import register_keys
-from .basic_visualize import resize_to_screen
+from .basic_visualize import plot_text, resize_to_screen, merge
 from .basic_callback import point_callback, CV_KEY, get_key
-from .file_utils import load_annot_to_tmp, save_annot
+from .file_utils import load_annot_to_tmp, read_json, save_annot
 
 class ComposedCallback:
     def __init__(self, callbacks=[point_callback], processes=[]) -> None:
@@ -12,7 +13,7 @@ class ComposedCallback:
     
     def call(self, event, x, y, flags, param):
         scale = param['scale']
-        x, y = int(x/scale), int(y/scale)
+        x, y = int(round(x/scale)), int(round(y/scale))
         for callback in self.callbacks:
             callback(event, x, y, flags, param)
         for key in ['click', 'start', 'end']:
@@ -23,25 +24,50 @@ class ComposedCallback:
         for process in self.processes:
             process(**param)
 
+def get_valid_yn():
+    while True:
+        key = input('Saving this annotations? [y/n]')
+        if key in ['y', 'n']:
+            break
+        print('Please specify [y/n]')
+    return key
+
+restore_key = {
+    'body25': ('bbox', 'keypoints'),
+    'handl': ('bbox_handl2d', 'handl2d'),
+    'handr': ('bbox_handr2d', 'handr2d'),
+}
 class AnnotBase:
     def __init__(self, dataset, key_funcs={}, callbacks=[], vis_funcs=[],
-        name = 'main',
-        step=1) -> None:
+        name = 'main', body='body25',
+        start=0, end=100000, step=10, no_window=False) -> None:
         self.name = name
         self.dataset = dataset
         self.nFrames = len(dataset)
         self.step = step
         self.register_keys = register_keys.copy()
         self.register_keys.update(key_funcs)
+        self.no_img = False
+        if resize_to_screen not in vis_funcs:
+            vis_funcs += [resize_to_screen]
+        self.vis_funcs = vis_funcs
+        self.start = start
+        self.end = end
 
-        self.vis_funcs = vis_funcs + [resize_to_screen]
         self.isOpen = True
-        self._frame = 0
+        self._frame = self.start
         self.visited_frames = set([self._frame])
-        self.param = {'select': {'bbox': -1, 'corner': -1}, 
-            'start': None, 'end': None, 'click': None, 
+        bbox_name, kpts_name = restore_key[body]
+        self.param = {
+            'frame': 0, 'nFrames': self.nFrames,
+            'kpts_name': kpts_name, 'bbox_name': bbox_name,
+            'select': {bbox_name: -1, 'corner': -1}, 
+            'click': None, 
+            'name': name,
             'capture_screen':False}
-        self.set_frame(0)
+        self.set_frame(self.start)
+        self.no_window = no_window
+        if not no_window:
             cv2.namedWindow(self.name)
             callback = ComposedCallback(processes=callbacks)
             cv2.setMouseCallback(self.name, callback.call, self.param)
@@ -57,32 +83,29 @@ class AnnotBase:
                 flag = True
         return flag
 
-    def clear_working(self):
-        self.param['click'] = None
-        self.param['start'] = None
-        self.param['end'] = None
-        for key in self.param['select']:
-            self.param['select'][key] = -1
+    @staticmethod
+    def clear_working(param):
+        param['click'] = None
+        param['start'] = None
+        param['end'] = None
+        for key in param['select']:
+            param['select'][key] = -1
 
-    def save_and_quit(self):
+    def save_and_quit(self, key=None):
         self.frame = self.frame
         self.isOpen = False
         cv2.destroyWindow(self.name)
         # get the input
-        while True:
-            key = input('Saving this annotations? [y/n]')
-            if key in ['y', 'n']:
-                break
-            print('Please specify [y/n]')
+        if key is None:
+            key = get_valid_yn()
         if key == 'n':
             return 0
-        if key == 'n':
-            return 0
-        for frame in self.visited_frames:
+        for frame in tqdm(self.visited_frames, desc='writing'):
             self.dataset.isTmp = True
             _, annname = self.dataset[frame]
             self.dataset.isTmp = False
             _, annname_ = self.dataset[frame]
+            if annname is not None:
                 shutil.copy(annname, annname_)
         
     @property
@@ -97,24 +120,33 @@ class AnnotBase:
         annots = load_annot_to_tmp(annname)
         return annots
 
-    def set_frame(self, nf):
-        self.clear_working()
-        imgname, annname = self.dataset[nf]
-        img0 = cv2.imread(imgname)
+    @staticmethod
+    def set_param(param, imgname, annname, nf, no_img=False):
         annots = load_annot_to_tmp(annname)
         # 清空键盘
         for key in ['click', 'start', 'end']:
-            self.param[key] = None
+            param[key] = None
         # 清空选中
-        for key in self.param['select']:
-            self.param['select'][key] = -1
-        self.param['imgname'] = imgname
-        self.param['annname'] = annname
-        self.param['frame'] = nf
-        self.param['annots'] = annots
-        self.param['img0'] = img0
-        # self.param['pid'] = len(annot['annots'])
-        self.param['scale'] = min(CV_KEY.WINDOW_HEIGHT/img0.shape[0], CV_KEY.WINDOW_WIDTH/img0.shape[1])
+        for key in param['select']:
+            param['select'][key] = -1
+        param['imgname'] = imgname
+        param['annname'] = annname
+        param['frame'] = nf
+        param['annots'] = annots
+        if not no_img:
+            img0 = cv2.imread(imgname)
+            param['img0'] = img0
+            # param['pid'] = len(annot['annots'])
+            param['scale'] = min(CV_KEY.WINDOW_HEIGHT/img0.shape[0], CV_KEY.WINDOW_WIDTH/img0.shape[1])
+            # param['scale'] = 1
+
+    def set_frame(self, nf):
+        param = self.param
+        if 'annots' in param.keys():
+            save_annot(param['annname'], param['annots'])
+        self.clear_working(param)
+        imgname, annname = self.dataset[nf]
+        self.set_param(param, imgname, annname, nf, no_img=self.no_img)
         
     @frame.setter
     def frame(self, value):
@@ -124,14 +156,232 @@ class AnnotBase:
         save_annot(self.param['annname'], self.param['annots'])
         self.set_frame(value)
         
-    def run(self, key=None):
+    def run(self, key=None, noshow=False):
         if key is None:
             key = chr(get_key())
         if key in self.register_keys.keys():
             self.register_keys[key](self, param=self.param)
         if not self.isOpen:
             return 0
+        if noshow:
+            return 0
         img = self.param['img0'].copy()
         for func in self.vis_funcs:
             img = func(img, **self.param)
+        if not self.no_window:
             cv2.imshow(self.name, img)
+
+class AnnotMV:
+    def __init__(self, datasets, key_funcs={}, key_funcs_view={}, callbacks=[], vis_funcs=[], vis_funcs_all=[], 
+        name='main', step=100, body='body25', start=0, end=100000) -> None:
+        self.subs = list(datasets.keys())
+        self.annotdict = {}
+        self.nFrames = end
+        for sub, dataset in datasets.items():
+            annot = AnnotBase(dataset, key_funcs={}, callbacks=callbacks, vis_funcs=vis_funcs,
+                name=sub, step=step, body=body, start=start, end=end)
+            self.annotdict[sub] = annot
+            self.nFrames = min(self.nFrames, annot.nFrames)
+        self.isOpen = True
+        # self.register_keys_view = {key:register_keys[key] for key in 'q'}
+        self.register_keys_view = {}
+        if 'w' not in key_funcs:
+            for key in 'wasd':
+                self.register_keys_view[key] = register_keys[key]
+        self.register_keys_view.update(key_funcs_view)
+        self.register_keys = {
+            'Q': register_keys['q'],
+            'h': register_keys['H'],
+            'A': register_keys['A']
+        }
+        self.register_keys.update(key_funcs)
+        self.vis_funcs_all = vis_funcs_all
+        self.name = name
+        self.param = {}
+    
+    @property
+    def frame(self):
+        sub = list(self.annotdict.keys())[0]
+        return self.annotdict[sub].frame
+
+    @property
+    def working(self):
+        return False
+    
+    def save_and_quit(self):
+        key = get_valid_yn()
+        for sub, annot in self.annotdict.items():
+            annot.save_and_quit(key)
+        self.isOpen = False
+
+    def run(self, key=None, noshow=False):
+        if key is None:
+            key = chr(get_key())
+        for sub, annot in self.annotdict.items():
+            if key in self.register_keys_view.keys():
+                self.register_keys_view[key](annot, param=annot.param)
+            else:
+                annot.run(key='')
+        if key in self.register_keys.keys():
+            self.register_keys[key](self, param=self.param)
+        if len(self.vis_funcs_all) > 0 or True:
+            imgs = []
+            for sub in self.subs:
+                img = self.annotdict[sub].param['img0'].copy()
+                for func in self.vis_funcs_all:
+                    img = func(img, sub, param=self.annotdict[sub].param)
+                imgs.append(img)
+            for func in [merge, resize_to_screen]:
+                imgs = func(imgs, scale=0.1)
+            cv2.imshow(self.name, imgs)
+
+import numpy as np
+def callback_select_image(click, select, ranges, **kwargs):
+    if click is None:
+        return 0
+    ranges = np.array(ranges)
+    click = np.array(click).reshape(1, -1)
+    res = (click[:, 0]>ranges[:, 0])&(click[:, 0]<ranges[:, 2])&(click[:, 1]>ranges[:, 1])&(click[:, 1]<ranges[:, 3])
+    if res.any():
+        select['camera'] = int(np.where(res)[0])
+
+class AnnotMVMain:
+    def __init__(self, datasets, key_funcs={}, key_funcs_view={}, callbacks=[], vis_funcs=[], vis_funcs_all=[], 
+        name='main', step=100, body='body25', start=0, end=100000) -> None:
+        self.subs = list(datasets.keys())
+        self.annotdict = {}
+        self.nFrames = end
+        for sub, dataset in datasets.items():
+            annot = AnnotBase(dataset, key_funcs={}, callbacks=callbacks, vis_funcs=vis_funcs,
+                name=sub, step=step, body=body, start=start, end=end, no_window=True)
+            self.annotdict[sub] = annot
+            self.nFrames = min(self.nFrames, annot.nFrames)
+        self.isOpen = True
+        self.register_keys_view = {}
+        self.register_keys = {
+            'Q': register_keys['q'],
+            'h': register_keys['H'],
+            'A': register_keys['A']
+        }
+        self.register_keys.update(key_funcs)
+        self.vis_funcs_all = vis_funcs_all
+        self.name = name
+        imgs = self.load_images()
+        imgs, ranges = merge(imgs, ret_range=True)
+        self.param = {
+            'scale': 0.45, 'ranges': ranges,
+            'click': None, 'start': None, 'end': None,
+            'select': {'camera': -1}}
+        callbacks = [callback_select_image]
+        cv2.namedWindow(self.name)
+        callback = ComposedCallback(processes=callbacks)
+        cv2.setMouseCallback(self.name, callback.call, self.param)
+    
+    @property
+    def frame(self):
+        sub = list(self.annotdict.keys())[0]
+        return self.annotdict[sub].frame
+
+    @property
+    def working(self):
+        return False
+    
+    def save_and_quit(self, key=None):
+        if key is None:
+            key = get_valid_yn()
+        for sub, annot in self.annotdict.items():
+            annot.save_and_quit(key)
+        self.isOpen = False
+
+    def load_images(self):
+        imgs = []
+        for sub in self.subs:
+            img = self.annotdict[sub].param['img0'].copy()
+            imgs.append(img)
+        return imgs
+
+    def run(self, key=None, noshow=False):
+        if key is None:
+            key = chr(get_key())
+        active_v = self.param['select']['camera']
+        if active_v == -1:
+            # run the key for all cameras
+            if key in self.register_keys.keys():
+                self.register_keys[key](self, param=self.param)
+            else:
+                for sub in self.subs:
+                    self.annotdict[sub].run(key)
+        else:
+            # run the key for the selected cameras
+            self.annotdict[self.subs[active_v]].run(key=key)
+        if len(self.vis_funcs_all) > 0:
+            imgs = []
+            for nv, sub in enumerate(self.subs):
+                img = self.annotdict[sub].param['img0'].copy()
+                for func in self.vis_funcs_all:
+                    # img = func(img, sub, param=self.annotdict[sub].param)
+                    img = func(img, **self.annotdict[sub].param)
+                if self.param['select']['camera'] == nv:
+                    cv2.rectangle(img, (0, 0), (img.shape[1], img.shape[0]), (0, 0, 255), img.shape[1]//100)
+                # img = plot_text(img, self.annotdict[sub].param['annots'], self.annotdict[sub].param['imgname'])
+                imgs.append(img)
+            for func in [merge, resize_to_screen]:
+                imgs = func(imgs, scale=0.45)
+            cv2.imshow(self.name, imgs)
+
+def load_parser():
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument('path', type=str)
+    parser.add_argument('--out', type=str)
+    parser.add_argument('--sub', type=str, nargs='+', default=[],
+        help='the sub folder lists when in video mode')
+    parser.add_argument('--from_file', type=str, default=None)
+    parser.add_argument('--image', type=str, default='images')
+    parser.add_argument('--annot', type=str, default='annots')
+    parser.add_argument('--body', type=str, default='handl')
+    parser.add_argument('--step', type=int, default=100)
+    parser.add_argument('--vis', action='store_true')
+    parser.add_argument('--debug', action='store_true')
+    
+    # new arguments
+    parser.add_argument('--start', type=int, default=0, help='frame start')
+    parser.add_argument('--end', type=int, default=100000, help='frame end')
+    return parser
+
+def parse_parser(parser):
+    import os
+    from os.path import join
+    args = parser.parse_args()
+    if args.from_file is not None and args.from_file.endswith('.txt'):
+        assert os.path.exists(args.from_file), args.from_file
+        with open(args.from_file) as f:
+            datas = f.readlines()
+            subs = [d for d in datas if not d.startswith('#')]
+            subs = [d.rstrip().replace('https://www.youtube.com/watch?v=', '') for d in subs]
+        newsubs = sorted(os.listdir(join(args.path, 'images')))
+        clips = []
+        for newsub in newsubs:
+            if newsub in subs:
+                continue
+            if newsub.split('+')[0] in subs:
+                clips.append(newsub)
+        for sub in subs:
+            if os.path.exists(join(args.path, 'images', sub)):
+                clips.append(sub)
+        args.sub = sorted(clips)
+    elif args.from_file is not None and args.from_file.endswith('.json'):
+        data = read_json(args.from_file)
+        args.sub = sorted([v['vid'] for v in data])
+    elif len(args.sub) == 0:
+        args.sub = sorted(os.listdir(join(args.path, 'images')))
+        if args.sub[0].isdigit():
+            args.sub = sorted(args.sub, key=lambda x:int(x))
+    helps = """
+    Demo code for annotation:
+    - Input : {}
+    -      => {}
+    -      => {}
+""".format(args.path, ', '.join(args.sub), args.annot)
+    print(helps)
+    return args

easymocap/annotator/basic_callback.py

@@ -1,3 +1,10 @@
+'''
+  @ Date: 2021-04-21 14:18:50
+  @ Author: Qing Shuai
+  @ LastEditors: Qing Shuai
+  @ LastEditTime: 2021-07-11 16:56:39
+  @ FilePath: /EasyMocap/easymocap/annotator/basic_callback.py
+'''
 import cv2
 
 class CV_KEY:
@@ -38,11 +45,13 @@ def point_callback(event, x, y, flags, param):
         return 0
     # 判断出了选择了的点的位置，直接写入这个位置
     if event == cv2.EVENT_LBUTTONDOWN:
+        # 如果选中了框，那么在按下的时候，就不能清零
         param['click'] = None
         param['start'] = (x, y)
         param['end'] = (x, y)
-        # 清除所有选择项
+        # 清除所有选择项：需要操作吗?
         for key in param['select'].keys():
+            if key != 'bbox':
                 param['select'][key] = -1
     elif event == cv2.EVENT_MOUSEMOVE and flags == cv2.EVENT_FLAG_LBUTTON:
         param['end'] = (x, y)

easymocap/annotator/basic_keyboard.py

@@ -1,3 +1,11 @@
+'''
+  @ Date: 2021-04-15 17:39:34
+  @ Author: Qing Shuai
+  @ LastEditors: Qing Shuai
+  @ LastEditTime: 2021-07-24 17:01:18
+  @ FilePath: /EasyMocap/easymocap/annotator/basic_keyboard.py
+'''
+from glob import glob
 from tqdm import tqdm
 from .basic_callback import get_key
 
@@ -6,16 +14,36 @@ def print_help(annotator, **kwargs):
     print('Here is the help:')
     print(  '------------------')
     for key, val in annotator.register_keys.items():
-        # print('    {}: {}'.format(key, ': ', str(val.__doc__)))
         print('    {}: '.format(key, ': '), str(val.__doc__))
 
-def close(annotator, param, **kwargs):
+def print_help_mv(annotator, **kwargs):
+    print_help(annotator)
+    print(  '------------------')
+    print('Here is the help for each view:')
+    print(  '------------------')
+    for key, val in annotator.register_keys_view.items():
+        print('    {}: '.format(key, ': '), str(val.__doc__))
+
+def close(annotator, **kwargs):
     """quit the annotation"""
     if annotator.working:
-        annotator.clear_working()
+        annotator.set_frame(annotator.frame)
     else:
         annotator.save_and_quit()
         # annotator.pbar.close()
+def skip(annotator, **kwargs):
+    """skip the annotation"""
+    annotator.save_and_quit(key='y')
+
+def get_any_move(df):
+    get_frame = lambda x, f: f + df
+    clip_frame = lambda x, f: max(0, min(x.nFrames-1, f))
+    def move(annotator, **kwargs):
+        newframe = get_frame(annotator, annotator.frame)
+        newframe = clip_frame(annotator, newframe)
+        annotator.frame = newframe
+    move.__doc__ = '{} frames'.format(df)
+    return move
 
 def get_move(wasd):
     get_frame = {
@@ -30,7 +58,7 @@ def get_move(wasd):
         'w': 'Move to last step frame',
         's': 'Move to next step frame'
     }
-    clip_frame = lambda x, f: max(0, min(x.nFrames-1, f))
+    clip_frame = lambda x, f: max(x.start, min(x.nFrames-1, min(x.end-1, f)))
     def move(annotator, **kwargs):
         newframe = get_frame(annotator, annotator.frame)
         newframe = clip_frame(annotator, newframe)
@@ -41,7 +69,7 @@ def get_move(wasd):
 def set_personID(i):
     def func(self, param, **kwargs):
         active = param['select']['bbox']
-        if active == -1:
+        if active == -1 and active >= len(param['annots']['annots']):
             return 0
         else:
             param['annots']['annots'][active]['personID'] = i
@@ -49,15 +77,14 @@ def set_personID(i):
     func.__doc__ = "set the bbox ID to {}".format(i)
     return func
 
-def delete_bbox(self, param, **kwargs):
-    "delete the person"
-    active = param['select']['bbox']
-    if active == -1:
-        return 0
-    else:
-        param['annots']['annots'].pop(active)
-        param['select']['bbox'] = -1
+def choose_personID(i):
+    def func(self, param, **kwargs):
+        for idata, data in enumerate(param['annots']['annots']):
+            if data['personID'] == i:
+                param['select']['bbox'] = idata
         return 0
+    func.__doc__ = "choose the bbox of ID {}".format(i)
+    return func
 
 def capture_screen(self, param):
     "capture the screen"
@@ -66,27 +93,97 @@ def capture_screen(self, param):
     else:
         param['capture_screen'] = True
 
-def automatic(self, param):
-    "Automatic running"
+remain = 0
+keys_pre = []
+
+def cont_automatic(self, param):
+    "continue automatic"
+    global remain, keys_pre
+    if remain > 0:
+        keys = keys_pre
+        repeats = remain
+    else:
+        print('Examples: ')
+        print('  - noshow r t: automatic removing and tracking')
+        print('  - noshow nostop r t r c: automatic removing and tracking, if missing, just copy')
         keys = input('Enter the ordered key(separate with blank): ').split(' ')
-    repeats = int(input('Input the repeat times: (0->{})'.format(len(self.dataset)-self.frame)))
+        keys_pre = keys
+        try:
+            repeats = int(input('Input the repeat times(0->{}): '.format(len(self.dataset)-self.frame)))
+        except:
+            repeats = 0
+        if repeats == -1:
+            repeats = len(self.dataset)
+        repeats = min(repeats, len(self.dataset)-self.frame+1)
+    if len(keys) < 1:
+        return 0
+    noshow = 'noshow' in keys
+    if noshow:
+        self.no_img = True
+    nostop = 'nostop' in keys
+    param['stop'] = False
     for nf in tqdm(range(repeats), desc='auto {}'.format('->'.join(keys))):
         for key in keys:
-            self.run(key=key)
-        if chr(get_key()) == 'q':
+            self.run(key=key, noshow=noshow)
+        if chr(get_key()) == 'q' or (param['stop'] and not nostop):
+            remain = repeats - nf
             break
-        self.run(key='d')
+        self.run(key='d', noshow=noshow)
+    else:
+        remain = 0
+        keys_pre = []
+    self.no_img = False
+
+def automatic(self, param):
+    "Automatic running"
+    global remain, keys_pre
+    print('Examples: ')
+    print('  - noshow r t: automatic removing and tracking')
+    print('  - noshow nostop r t r c: automatic removing and tracking, if missing, just copy')
+    keys = input('Enter the ordered key(separate with blank): ').split(' ')
+    keys_pre = keys
+    try:
+        repeats = int(input('Input the repeat times(0->{}): '.format(self.nFrames-self.frame)))
+    except:
+        repeats = 0
+    repeats = min(repeats, self.nFrames-self.frame+1)
+    if len(keys) < 1:
+        return 0
+    noshow = 'noshow' in keys
+    if noshow:
+        self.no_img = True
+    nostop = 'nostop' in keys
+    param['stop'] = False
+    for nf in tqdm(range(repeats), desc='auto {}'.format('->'.join(keys))):
+        for key in keys:
+            self.run(key=key, noshow=noshow)
+        if chr(get_key()) == 'q' or (param['stop'] and not nostop):
+            remain = repeats - nf
+            break
+        self.run(key='d', noshow=noshow)
+    else:
+        remain = 0
+        keys_pre = []
+    self.no_img = False
+
+def set_keyframe(self, param):
+    "set/unset the key-frame"
+    param['annots']['isKeyframe'] = not param['annots']['isKeyframe']
 
 register_keys = {
     'h': print_help,
+    'H': print_help_mv,
     'q': close,
-    'x': delete_bbox,
+    ' ': skip,
     'p': capture_screen,
-    'A': automatic
+    'A': automatic,
+    'z': cont_automatic,
+    'k': set_keyframe
 }
 
 for key in 'wasd':
     register_keys[key] = get_move(key)
 
-for i in range(10):
+for i in range(5):
     register_keys[str(i)] = set_personID(i)
+    register_keys['s'+str(i)] = choose_personID(i)

easymocap/annotator/basic_visualize.py

@@ -2,7 +2,8 @@ import numpy as np
 import cv2
 import os
 from os.path import join
-from ..mytools import plot_cross, plot_line, plot_bbox, plot_keypoints, get_rgb
+from ..mytools import plot_cross, plot_line, plot_bbox, plot_keypoints, get_rgb, merge
+from ..mytools.file_utils import get_bbox_from_pose
 from ..dataset import CONFIG
 
 # click and (start, end) is the output of the OpenCV callback
@@ -13,11 +14,23 @@ def vis_point(img, click, **kwargs):
 
 def vis_line(img, start, end, **kwargs):
     if start is not None and end is not None:
+        lw = max(2, img.shape[0]//500)
         cv2.line(img, (int(start[0]), int(start[1])), 
-            (int(end[0]), int(end[1])), (0, 255, 0), 1)
+            (int(end[0]), int(end[1])), (0, 255, 0), lw)
     return img
 
-def resize_to_screen(img, scale=1, capture_screen=False, **kwargs):
+def vis_bbox(img, start, end, **kwargs):
+    if start is not None and end is not None:
+        lw = max(2, img.shape[0]//500)
+        cv2.rectangle(img, (int(start[0]), int(start[1])), 
+            (int(end[0]), int(end[1])), (0, 255, 0), lw)
+    return img
+
+def resize_to_screen(img, scale=1, **kwargs):
+    img = cv2.resize(img, None, fx=scale, fy=scale)
+    return img
+
+def capture_screen(img, capture_screen=False, **kwargs):
     if capture_screen:
         from datetime import datetime
         time_now = datetime.now().strftime("%m-%d-%H:%M:%S")
@@ -25,18 +38,33 @@ def resize_to_screen(img, scale=1, capture_screen=False, **kwargs):
         os.makedirs('capture', exist_ok=True)
         cv2.imwrite(outname, img)
         print('Capture current screen to {}'.format(outname))
-    img = cv2.resize(img, None, fx=scale, fy=scale)
     return img
 
-def plot_text(img, annots, **kwargs):
+def plot_text(img, annots, imgname, **kwargs):
+    if 'isKeyframe' in annots.keys():
         if annots['isKeyframe']: # 关键帧使用红框表示
             cv2.rectangle(img, (0, 0), (img.shape[1], img.shape[0]), (0, 0, 255), img.shape[1]//100)
         else: # 非关键帧使用绿框表示
             cv2.rectangle(img, (0, 0), (img.shape[1], img.shape[0]), (0, 255, 0), img.shape[1]//100)
+    imgname = '/'.join(imgname.split(os.sep)[-3:])
     text_size = int(max(1, img.shape[0]//1500))
     border = 20 * text_size
     width = 2 * text_size
-    cv2.putText(img, '{}'.format(annots['filename']), (border, img.shape[0]-border), cv2.FONT_HERSHEY_SIMPLEX, text_size, (0, 0, 255), width)
+    cv2.putText(img, '{}'.format(imgname), (border, img.shape[0]-border), cv2.FONT_HERSHEY_SIMPLEX, text_size, (0, 0, 255), width)
+    # 显示标注进度条:
+    if 'frame' in kwargs.keys():
+        width = img.shape[1]
+        frame, nFrames = kwargs['frame'], kwargs['nFrames']
+        lw = 12
+        pos = lambda x: int(width*(x+1)/nFrames)
+        COL_ALL = (0, 255, 0)
+        COL_CUR = (255, 0, 0)
+        COL_PIN = (255, 128, 128)
+        plot_line(img, (0, lw/2), (width, lw/2), lw, COL_ALL)
+        plot_line(img, (0, lw/2), (pos(frame), lw/2), lw, COL_CUR)
+        top = pos(frame)
+        pts = np.array([[top, lw], [top-lw, lw*4], [top+lw, lw*4]])
+        cv2.fillPoly(img, [pts], COL_PIN)
     return img
 
 def plot_bbox_body(img, annots, **kwargs):
@@ -52,8 +80,33 @@ def plot_bbox_body(img, annots, **kwargs):
         plot_line(img, (x1, y2), (x2, y1), lw, color)
         # border
         cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), color, lw+1)
+        ratio = (y2-y1)/(x2-x1)
+        w = 10*lw
+        cv2.rectangle(img, 
+            (int((x1+x2)/2-w), int((y1+y2)/2-w*ratio)), 
+            (int((x1+x2)/2+w), int((y1+y2)/2+w*ratio)), 
+            color, -1)
+        cv2.putText(img, '{}'.format(pid), (int(x1), int(y1)+20), cv2.FONT_HERSHEY_SIMPLEX, 5, color, 2)
+    return img
+
+def plot_bbox_sp(img, annots, bbox_type='handl_bbox', add_center=False):
+    assert bbox_type in ('bbox', 'bbox_handl2d', 'bbox_handr2d', 'bbox_face2d')
+    for data in annots['annots']:
+        if bbox_type not in data.keys():
+            continue
+        bbox = data[bbox_type]
+        # 画一个X形
+        x1, y1, x2, y2 = bbox[:4]
+        pid = data['personID']
+        color = get_rgb(pid)
+        lw = max(1, int((x2 - x1)//100))
+        plot_line(img, (x1, y1), (x2, y2), lw, color)
+        plot_line(img, (x1, y2), (x2, y1), lw, color)
+        # border
+        cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), color, lw+1)
         ratio = (y2-y1)/(x2-x1)/2
         w = 10*lw
+        if add_center:
             cv2.rectangle(img,
                 (int((x1+x2)/2-w), int((y1+y2)/2-w*ratio)),
                 (int((x1+x2)/2+w), int((y1+y2)/2+w*ratio)),
@@ -61,51 +114,45 @@ def plot_bbox_body(img, annots, **kwargs):
         cv2.putText(img, '{}'.format(pid), (int(x1), int(y1)+20), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2)
     return img
 
-def plot_skeleton(img, annots, **kwargs):
+def plot_bbox_factory(bbox_type, add_center=False):
+    def ret_foo(img, annots, **kwargs):
+        return plot_bbox_sp(img, annots, bbox_type=bbox_type, add_center=add_center)
+    return ret_foo
+
+def plot_skeleton(img, annots, body='body25', bbox_name='bbox', kpts_name='keypoints', **kwargs):
     annots = annots['annots']
     vis_conf = False
     for data in annots:
-        bbox, keypoints = data['bbox'], data['keypoints']
-        if False:
-            pid = data.get('matchID', -1)
-        else:
         pid = data.get('personID', -1)
+        if kpts_name in data.keys():
+            keypoints = data[kpts_name]
+            plot_keypoints(img, keypoints, pid, CONFIG[body], vis_conf=vis_conf, use_limb_color=True)
+        if bbox_name in data.keys():
+            bbox = data[bbox_name]
+            plot_bbox(img, bbox, pid)
+        elif kpts_name in data.keys():
+            bbox = get_bbox_from_pose(np.array(data[kpts_name]))
             plot_bbox(img, bbox, pid)
-        if True:
-            plot_keypoints(img, keypoints, pid, CONFIG['body25'], vis_conf=vis_conf, use_limb_color=True)
-            if 'handl2d' in data.keys():
-                plot_keypoints(img, data['handl2d'], pid, CONFIG['hand'], vis_conf=vis_conf, lw=1, use_limb_color=False)
-                plot_keypoints(img, data['handr2d'], pid, CONFIG['hand'], vis_conf=vis_conf, lw=1, use_limb_color=False)
-                plot_keypoints(img, data['face2d'], pid, CONFIG['face'], vis_conf=vis_conf, lw=1, use_limb_color=False)
     return img
 
-def plot_keypoints_whole(img, points, kintree):
-    for ii, (i, j) in enumerate(kintree):
-        if i >= len(points) or j >= len(points):
-            continue
-        col = (255, 240, 160)
-        lw = 4
-        pt1, pt2 = points[i], points[j]
-        if pt1[-1] > 0.01 and pt2[-1] > 0.01:
-            image = cv2.line(
-                img, (int(pt1[0]+0.5), int(pt1[1]+0.5)), (int(pt2[0]+0.5), int(pt2[1]+0.5)),
-                col, lw)
+def plot_skeleton_factory(body):
+    restore_key = {
+        'body25': ('bbox', 'keypoints'),
+        'handl': ('bbox_handl2d', 'handl2d'),
+        'handr': ('bbox_handr2d', 'handr2d'),
+        'face': ('bbox_face2d', 'face2d'),
+    }
+    bbox_name, kpts_name = restore_key[body]
+    def ret_foo(img, annots, **kwargs):
+        return plot_skeleton(img, annots, body, bbox_name, kpts_name)
+    return ret_foo
 
-def plot_skeleton_simple(img, annots, **kwargs):
-    annots = annots['annots']
-    vis_conf = False
-    for data in annots:
-        bbox, keypoints = data['bbox'], data['keypoints']
-        pid = data.get('personID', -1)
-        plot_keypoints_whole(img, keypoints, CONFIG['body25']['kintree'])
-    return img
-
-def vis_active_bbox(img, annots, select, **kwargs):
-    active = select['bbox']
-    if active == -1:
+def vis_active_bbox(img, annots, select, bbox_name, **kwargs):
+    active = select[bbox_name]
+    if active == -1 or active >= len(annots['annots']):
         return img
     else:
-        bbox = annots['annots'][active]['bbox']
+        bbox = annots['annots'][active][bbox_name]
         pid = annots['annots'][active]['personID']
         mask = np.zeros_like(img, dtype=np.uint8)
         cv2.rectangle(mask, 

easymocap/annotator/bbox_callback.py

@@ -1,8 +1,22 @@
 import numpy as np
-import cv2
 
 MIN_PIXEL = 50
-def callback_select_bbox_corner(start, end, annots, select, **kwargs):
+def findNearestPoint(points, click):
+    # points: (N, 2)
+    # click : [x, y]
+    click = np.array(click)
+    if len(points.shape) == 2:
+        click = click[None, :]
+    elif len(points.shape) == 3:
+        click = click[None, None, :]
+    dist = np.linalg.norm(points - click, axis=-1)
+    if dist.min() < MIN_PIXEL:
+        idx = np.unravel_index(dist.argmin(), dist.shape)
+        return True, idx
+    else:
+        return False, (-1, -1)
+
+def callback_select_bbox_corner(start, end, annots, select, bbox_name, **kwargs):
     if start is None or end is None:
         select['corner'] = -1
         return 0
@@ -10,63 +24,109 @@ def callback_select_bbox_corner(start, end, annots, select, **kwargs):
         return 0
     # 判断选择了哪个角点
     annots = annots['annots']
-    start = np.array(start)[None, :]
-    if select['bbox'] == -1 and select['corner'] == -1:
+    # not select a bbox
+    if select[bbox_name] == -1 and select['corner'] == -1:
+        corners = []
         for i in range(len(annots)):
-            l, t, r, b = annots[i]['bbox'][:4]
-            corners = np.array([(l, t), (l, b), (r, t), (r, b)])
-            dist = np.linalg.norm(corners - start, axis=1)
-            mindist = dist.min()
-            if mindist < MIN_PIXEL:
-                mincor = dist.argmin()
-                select['bbox'] = i
-                select['corner'] = mincor
-                break
+            l, t, r, b = annots[i][bbox_name][:4]
+            corner = np.array([(l, t), (l, b), (r, t), (r, b), ((l+r)/2, (t+b)/2)])
+            corners.append(corner)
+        corners = np.stack(corners)
+        flag, minid = findNearestPoint(corners, start)
+        if flag:
+            select[bbox_name] = minid[0]
+            select['corner'] = minid[1]
         else:
             select['corner'] = -1
-    elif select['bbox'] != -1 and select['corner'] == -1:
-        i = select['bbox']
-        l, t, r, b = annots[i]['bbox'][:4]
-        corners = np.array([(l, t), (l, b), (r, t), (r, b)])
-        dist = np.linalg.norm(corners - start, axis=1)
-        mindist = dist.min()
-        if mindist < MIN_PIXEL:
-            mincor = dist.argmin()
-            select['corner'] = mincor
-    elif select['bbox'] != -1 and select['corner'] != -1:
-        # Move the corner
+    # have selected a bbox, not select a corner
+    elif select[bbox_name] != -1 and select['corner'] == -1:
+        i = select[bbox_name]
+        l, t, r, b = annots[i][bbox_name][:4]
+        corners = np.array([(l, t), (l, b), (r, t), (r, b), ((l+r)/2, (t+b)/2)])
+        flag, minid = findNearestPoint(corners, start)
+        if flag:
+            select['corner'] = minid[0]
+    # have selected a bbox, and select a corner
+    elif select[bbox_name] != -1 and select['corner'] != -1:
         x, y = end
+        # Move the corner
+        if select['corner'] < 4:
             (i, j) = [(0, 1), (0, 3), (2, 1), (2, 3)][select['corner']]
-        data = annots[select['bbox']]
-        data['bbox'][i] = x
-        data['bbox'][j] = y
-    elif select['bbox'] == -1 and select['corner'] != -1:
+            data = annots[select[bbox_name]]
+            data[bbox_name][i] = x
+            data[bbox_name][j] = y
+        # Move the center
+        else:
+            bbox = annots[select[bbox_name]][bbox_name]
+            w = (bbox[2] - bbox[0])/2
+            h = (bbox[3] - bbox[1])/2
+            bbox[0] = x - w
+            bbox[1] = y - h
+            bbox[2] = x + w
+            bbox[3] = y + h
+
+    elif select[bbox_name] == -1 and select['corner'] != -1:
         select['corner'] = -1
 
-def callback_select_bbox_center(click, annots, select, **kwargs):
+def callback_select_bbox_center(click, annots, select, bbox_name, **kwargs):
     if click is None:
         return 0
     annots = annots['annots']
-    bboxes = np.array([d['bbox'] for d in annots])
+    bboxes = np.array([d[bbox_name] for d in annots])
     center = (bboxes[:, [2, 3]] + bboxes[:, [0, 1]])/2
     click = np.array(click)[None, :]
     dist = np.linalg.norm(click - center, axis=1)
     mindist, minid = dist.min(), dist.argmin()
     if mindist < MIN_PIXEL:
-        select['bbox'] = minid
+        select[bbox_name] = minid
 
-def auto_pose_track(self, param, **kwargs):
-    "auto tracking with poses"
+def get_auto_track(mode='kpts'):
     MAX_SPEED = 100
+    if mode == 'bbox':
+        MAX_SPEED = 0.2
+    def auto_track(self, param, **kwargs):
         if self.frame == 0:
             return 0
         previous = self.previous()
         annots = param['annots']['annots']
-    keypoints_pre = np.array([d['keypoints'] for d in previous['annots']])
-    keypoints_now = np.array([d['keypoints'] for d in annots])
+        bbox_name = param['bbox_name']
+        kpts_name = param['kpts_name']
+        if len(annots) == 0:
+            return 0
+        if len(previous['annots']) == 0:
+            return 0
+        if mode == 'kpts':
+            keypoints_pre = np.array([d[kpts_name] for d in previous['annots']])
+            keypoints_now = np.array([d[kpts_name] for d in annots])
             conf = np.sqrt(keypoints_now[:, None, :, -1] * keypoints_pre[None, :, :, -1])
-    diff = np.linalg.norm(keypoints_now[:, None, :, :] - keypoints_pre[None, :, :, :], axis=-1)
+            diff = np.linalg.norm(keypoints_now[:, None, :, :2] - keypoints_pre[None, :, :, :2], axis=-1)
             dist = np.sum(diff * conf, axis=-1)/np.sum(conf, axis=-1)
+        elif mode == bbox_name:
+            # 计算IoU
+            bbox_pre = np.array([d[bbox_name] for d in previous['annots']])
+            bbox_now = np.array([d[bbox_name] for d in annots])
+            bbox_pre = bbox_pre[None]
+            bbox_now = bbox_now[:, None]
+            areas_pre = (bbox_pre[..., 2] - bbox_pre[..., 0]) * (bbox_pre[..., 3] - bbox_pre[..., 1])
+            areas_now = (bbox_now[..., 2] - bbox_now[..., 0]) * (bbox_now[..., 3] - bbox_now[..., 1])
+            # 左边界的大值
+            xx1 = np.maximum(bbox_pre[..., 0], bbox_now[..., 0])
+            yy1 = np.maximum(bbox_pre[..., 1], bbox_now[..., 1])
+            # 右边界的小值
+            xx2 = np.minimum(bbox_pre[..., 2], bbox_now[..., 2])
+            yy2 = np.minimum(bbox_pre[..., 3], bbox_now[..., 3])
+            
+            w = np.maximum(0.0, xx2 - xx1)
+            h = np.maximum(0.0, yy2 - yy1)
+            inter = w * h
+            over = inter / (areas_pre + areas_now - inter)
+            dist = 1 - over
+            # diff = np.linalg.norm(bbox_now[:, None, :4] - bbox_pre[None, :, :4], axis=-1)
+            # bbox_size = np.max(bbox_pre[:, [2, 3]] - bbox_pre[:, [0, 1]], axis=1)[None, :]
+            # diff = diff / bbox_size
+            # dist = diff
+        else:
+            raise NotImplementedError
         nows, pres = np.where(dist < MAX_SPEED)
         edges = []
         for n, p in zip(nows, pres):
@@ -80,4 +140,74 @@ def auto_pose_track(self, param, **kwargs):
             used_n.append(n)
             used_p.append(p)
         # TODO:stop when missing
+        pre_ids = [d['personID'] for d in previous['annots']]
+        if len(used_p) != len(pre_ids):
+            param['stop'] = True
+            print('>>> Stop because missing key: {}'.format(
+                [i for i in pre_ids if i not in used_p]))
+            print(dist)
+        max_id = max(pre_ids) + 1
+        for i in range(len(annots)):
+            if i in used_n:
+                continue
+            annots[i]['personID'] = max_id
+            max_id += 1
+    auto_track.__doc__ = 'auto track the {}'.format(mode)
+    return auto_track
 
+def copy_previous_missing(self, param, **kwargs):
+    "copy the missing person of previous frame"
+    if self.frame == 0:
+        return 0
+    previous = self.previous()
+    annots = param['annots']['annots']
+    pre_ids = [d['personID'] for d in previous['annots']]
+    now_ids = [d['personID'] for d in annots]
+    for i in range(len(pre_ids)):
+        if pre_ids[i] not in now_ids:
+            annots.append(previous['annots'][i])
+
+def copy_previous_bbox(self, param, **kwargs):
+    "copy the annots of previous frame"
+    if self.frame == 0:
+        return 0
+    previous = self.previous()
+    annots = param['annots']['annots'] = previous['annots']
+
+def create_bbox(self, param, **kwargs):
+    "add new boundbox"
+    start, end = param['start'], param['end']
+    if start is None or end is None:
+        return 0
+    annots = param['annots']['annots']
+    nowids = [d['personID'] for d in annots]
+    bbox_name, kpts_name = param['bbox_name'], param['kpts_name']
+    if len(nowids) == 0:
+        maxID = 0
+    else:
+        maxID = max(nowids) + 1
+    data = {
+        'personID': maxID,
+        bbox_name: [start[0], start[1], end[0], end[1], 1],
+        kpts_name: [[0., 0., 0.] for _ in range(25)]
+    }
+    annots.append(data)
+    param['start'], param['end'] = None, None
+
+def delete_bbox(self, param, **kwargs):
+    "delete the person"
+    bbox_name = param['bbox_name']
+    active = param['select'][bbox_name]
+    if active == -1:
+        return 0
+    else:
+        param['annots']['annots'].pop(active)
+        param['select'][bbox_name] = -1
+    return 0
+
+def delete_all_bbox(self, param, **kwargs):
+    "delete the person"
+    bbox_name = param['bbox_name']
+    param['annots']['annots'] = []
+    param['select'][bbox_name] = -1
+    return 0

easymocap/annotator/chessboard.py

@@ -2,7 +2,7 @@
   @ Date: 2021-04-13 16:14:36
   @ Author: Qing Shuai
   @ LastEditors: Qing Shuai
-  @ LastEditTime: 2021-05-26 15:42:02
+  @ LastEditTime: 2021-07-17 16:00:17
   @ FilePath: /EasyMocap/easymocap/annotator/chessboard.py
 '''
 import numpy as np
@@ -53,7 +53,10 @@ def _findChessboardCornersAdapt(img, pattern):
     return _findChessboardCorners(img, pattern)
 
 def findChessboardCorners(img, annots, pattern):
-    if annots['visited']:
+    conf = sum([v[2] for v in annots['keypoints2d']])
+    if annots['visited'] and conf > 0:
+        return True
+    elif annots['visited']:
         return None
     annots['visited'] = True
     gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

easymocap/annotator/keypoints_callback.py

@@ -0,0 +1,59 @@
+'''
+  @ Date: 2021-04-22 11:40:31
+  @ Author: Qing Shuai
+  @ LastEditors: Qing Shuai
+  @ LastEditTime: 2021-06-10 16:00:15
+  @ FilePath: /EasyMocap/easymocap/annotator/keypoints_callback.py
+'''
+import numpy as np
+from .bbox_callback import findNearestPoint
+
+def callback_select_joints(start, end, annots, select, bbox_name='bbox', kpts_name='keypoints', **kwargs):
+    if start is None or end is None:
+        select['joints'] = -1
+        return 0
+    if start[0] == end[0] and start[1] == end[1]:
+        select['joints'] = -1
+        return 0
+    if select['corner'] != -1:
+        return 0
+    # 判断选择了哪个角点
+    annots = annots['annots']
+    # not select a bbox
+    if select[bbox_name] == -1 and select['joints'] == -1:
+        corners = []
+        for annot in annots:
+            corners.append(np.array(annot[kpts_name]))
+        corners = np.stack(corners)
+        flag, minid = findNearestPoint(corners[..., :2], start)
+        if flag:
+            select[bbox_name] = minid[0]
+            select['joints'] = minid[1]
+        else:
+            select['joints'] = -1
+    # have selected a bbox, not select a corner
+    elif select[bbox_name] != -1 and select['joints'] == -1:
+        i = select[bbox_name]
+        corners = np.array(annots[i][kpts_name])[:, :2]
+        flag, minid = findNearestPoint(corners, start)
+        if flag:
+            select['joints'] = minid[0]
+    # have selected a bbox, and select a corner
+    elif select[bbox_name] != -1 and select['joints'] != -1:
+        x, y = end
+        # Move the corner
+        data = annots[select[bbox_name]]
+        nj = select['joints']
+        data[kpts_name][nj][0] = x
+        data[kpts_name][nj][1] = y
+        if kpts_name == 'keypoints': # for body
+            if nj in [1, 8]:
+                return 0
+            if nj in [2, 5]:
+                data[kpts_name][1][0] = (data[kpts_name][2][0] + data[kpts_name][5][0])/2
+                data[kpts_name][1][1] = (data[kpts_name][2][1] + data[kpts_name][5][1])/2
+            if nj in [9, 12]:
+                data[kpts_name][8][0] = (data[kpts_name][9][0] + data[kpts_name][12][0])/2
+                data[kpts_name][8][1] = (data[kpts_name][9][1] + data[kpts_name][12][1])/2
+    elif select[bbox_name] == -1 and select['joints'] != -1:
+        select['joints'] = -1

easymocap/annotator/keypoints_keyboard.py

@@ -0,0 +1,89 @@
+'''
+  @ Date: 2021-06-10 15:39:55
+  @ Author: Qing Shuai
+  @ LastEditors: Qing Shuai
+  @ LastEditTime: 2021-06-10 16:03:13
+  @ FilePath: /EasyMocap/easymocap/annotator/keypoints_keyboard.py
+'''
+import numpy as np
+
+def set_unvisible(self, param, **kwargs):
+    "set the selected joints unvisible"
+    bbox_name, kpts_name = param['bbox_name'], param['kpts_name']
+    select = param['select']
+    if select[bbox_name] == -1:
+        return 0
+    if select['joints'] == -1:
+        return 0
+    param['annots']['annots'][select[bbox_name]][kpts_name][select['joints']][-1] = 0.
+
+def set_unvisible_according_previous(self, param, **kwargs):
+    "set the selected joints unvisible if previous unvisible"
+    previous = self.previous()
+    select = param['select']
+    bbox_name, kpts_name = param['bbox_name'], param['kpts_name']
+    if select[bbox_name] == -1:
+        return 0
+    pid = param['annots']['annots'][select[bbox_name]]['personID']
+    kpts_now = param['annots']['annots'][select[bbox_name]][kpts_name]
+    for annots in previous['annots']:
+        if annots['personID'] == pid:
+            kpts_old = annots[kpts_name]
+            for nj in range(len(kpts_old)):
+                kpts_now[nj][2] = min(kpts_old[nj][2], kpts_now[nj][2])
+
+def set_face_unvisible(self, param, **kwargs):
+    "set the face unvisible"
+    select = param['select']
+    bbox_name, kpts_name = param['bbox_name'], param['kpts_name']
+    if select[bbox_name] == -1:
+        return 0
+    for i in [15, 16, 17, 18]:
+        param['annots']['annots'][select[bbox_name]][kpts_name][i][-1] = 0.
+
+def mirror_keypoints2d(self, param, **kwargs):
+    "mirror the keypoints2d"
+    select = param['select']
+    bbox_name, kpts_name = param['bbox_name'], param['kpts_name']
+    if select[bbox_name] == -1:
+        return 0
+    kpts = param['annots']['annots'][select[bbox_name]][kpts_name]
+    for pairs in [[(2, 5), (3, 6), (4, 7)], [(15, 16), (17, 18)], [(9, 12), (10, 13), (11, 14), (21, 24), (19, 22), (20, 23)]]:
+        for i, j in pairs:
+            kpts[i], kpts[j] = kpts[j], kpts[i]
+
+def mirror_keypoints2d_leg(self, param, **kwargs):
+    "mirror the keypoints2d of legs and feet"
+    select = param['select']
+    bbox_name, kpts_name = param['bbox_name'], param['kpts_name']
+    if select[bbox_name] == -1:
+        return 0
+    kpts = param['annots']['annots'][select[bbox_name]][kpts_name]
+    for pairs in [[(9, 12), (10, 13), (11, 14), (21, 24), (19, 22), (20, 23)]]:
+        for i, j in pairs:
+            kpts[i], kpts[j] = kpts[j], kpts[i]
+
+def check_track(self, param):
+    "check the tracking keypoints"
+    if self.frame == 0:
+        return 0
+    bbox_name, kpts_name = param['bbox_name'], param['kpts_name']
+    annots_pre = self.previous()['annots']
+    annots = param['annots']['annots']
+    if len(annots) == 0 or len(annots_pre) == 0 or len(annots) != len(annots_pre):
+        param['stop'] = True
+        return 0
+    for data in annots:
+        for data_pre in annots_pre:
+            if data_pre['personID'] != data['personID']:
+                continue
+            l, t, r, b, c = data_pre[bbox_name][:5]
+            bbox_size = max(r-l, b-t)
+            keypoints_now = np.array(data[kpts_name])
+            keypoints_pre = np.array(data_pre[kpts_name])
+            conf = np.sqrt(keypoints_now[:, -1] * keypoints_pre[:, -1])
+            diff = np.linalg.norm(keypoints_now[:, :2] - keypoints_pre[:, :2], axis=-1)
+            dist = np.sum(diff * conf, axis=-1)/np.sum(conf, axis=-1)/bbox_size
+            print('{}: {:.2f}'.format(data['personID'], dist))
+            if dist > 0.05:
+                param['stop'] = True

easymocap/annotator/vanish_callback.py

@@ -1,6 +1,7 @@
 import numpy as np
 from easymocap.dataset.mirror import flipPoint2D
 
+CONF_VANISHING_ANNOT = 2.
 def clear_vanish_points(self, param):
     "remove all vanishing points"
     annots = param['annots']
@@ -67,12 +68,16 @@ def get_record_vanish_lines(index):
             annots['vanish_point'] = [[], [], []]
         start, end = param['start'], param['end']
         if start is not None and end is not None:
-            annots['vanish_line'][index].append([[start[0], start[1], 2], [end[0], end[1], 2]])
+            annots['vanish_line'][index].append([[start[0], start[1], CONF_VANISHING_ANNOT], [end[0], end[1], CONF_VANISHING_ANNOT]])
             # 更新vanish point
-            if len(annots['vanish_line'][index]) > 1:
-                annots['vanish_point'][index] = update_vanish_points(annots['vanish_line'][index])
             param['start'] = None
             param['end'] = None
+        if len(annots['vanish_line'][index]) > 1:
+            for val in annots['vanish_line'][index]:
+                if len(val[0]) == 2:
+                    val[0].append(CONF_VANISHING_ANNOT)
+                    val[1].append(CONF_VANISHING_ANNOT)
+            annots['vanish_point'][index] = update_vanish_points(annots['vanish_line'][index])
     func = record_vanish_lines
     text = ['parallel to mirror edges', 'vertical to mirror', 'vertical to ground']
     func.__doc__ = 'vanish line of ' + text[index]
@@ -135,8 +140,10 @@ def get_calc_intrinsic(mode='xy'):
         K = np.eye(3)
         K[0, 2] = W/2
         K[1, 2] = H/2
+        print(vanish_point)
         vanish_point[:, 0] -= W/2
         vanish_point[:, 1] -= H/2
+        print(vanish_point)
         focal = np.sqrt(-(vanish_point[0][0]*vanish_point[1][0] + vanish_point[0][1]*vanish_point[1][1]))
         
         K[0, 0] = focal

easymocap/annotator/vanish_visualize.py

@@ -1,3 +1,10 @@
+'''
+  @ Date: 2021-07-13 21:12:15
+  @ Author: Qing Shuai
+  @ LastEditors: Qing Shuai
+  @ LastEditTime: 2021-07-13 21:12:46
+  @ FilePath: /EasyMocap/easymocap/annotator/vanish_visualize.py
+'''
 import cv2
 import numpy as np
 from .basic_visualize import plot_cross
@@ -17,7 +24,7 @@ def vis_vanish_lines(img, annots, **kwargs):
         plot_cross(img, x, y, colors[i])
         points = np.array(annots['vanish_line'][i]).reshape(-1, 3)
         for (xx, yy, conf) in points:
-            plot_cross(img, xx, yy, colors[i])
+            plot_cross(img, xx, yy, col=colors[i])
             cv2.line(img, (int(x), int(y)), (int(xx), int(yy)), colors[i], 2)
 
     for i in range(3):

easymocap/config/yacs.py

@@ -78,6 +78,16 @@ class CfgNode(dict):
             if type(v) is dict:
                 # Convert dict to CfgNode
                 init_dict[k] = CfgNode(v, key_list=key_list + [k])
+                if '_parent_' in v.keys():
+                    init_dict[k].merge_from_file(v['_parent_'])
+                    init_dict[k].pop('_parent_')
+                if '_const_' in v.keys() and v['_const_']:
+                    init_dict[k].__dict__[CfgNode.IMMUTABLE] = True
+                    init_dict[k].pop('_const_')
+            elif type(v) is str and v.startswith('_file_/'):
+                filename = v.replace('_file_/', '')
+                init_dict[k] = CfgNode()
+                init_dict[k].merge_from_file(filename)
             else:
                 # Check for valid leaf type or nested CfgNode
                 _assert_with_logging(
@@ -385,7 +395,6 @@ def _merge_a_into_b(a, b, root, key_list):
     if '_no_merge_' in a.keys() and a['_no_merge_']:
         b.clear()
         a.pop('_no_merge_')
-
     for k, v_ in a.items():
         full_key = ".".join(key_list + [k])
         # a must specify keys that are in b

easymocap/mytools/reader.py

@@ -2,8 +2,8 @@
   @ Date: 2021-04-21 15:19:21
   @ Author: Qing Shuai
   @ LastEditors: Qing Shuai
-  @ LastEditTime: 2021-06-28 11:55:27
-  @ FilePath: /EasyMocapRelease/easymocap/mytools/reader.py
+  @ LastEditTime: 2021-07-29 16:12:37
+  @ FilePath: /EasyMocap/easymocap/mytools/reader.py
 '''
 # function to read data
 """
@@ -27,7 +27,7 @@ def read_keypoints3d(filename):
     res_ = []
     for d in data:
         pid = d['id'] if 'id' in d.keys() else d['personID']
-        pose3d = np.array(d['keypoints3d'])
+        pose3d = np.array(d['keypoints3d'], dtype=np.float32)
         if pose3d.shape[0] > 25:
             # 对于有手的情况，把手的根节点赋值成body25上的点
             pose3d[25, :] = pose3d[7, :]
@@ -40,13 +40,27 @@ def read_keypoints3d(filename):
         })
     return res_
 
+def read_keypoints3d_dict(filename):
+    data = read_json(filename)
+    res_ = {}
+    for d in data:
+        pid = d['id'] if 'id' in d.keys() else d['personID']
+        pose3d = np.array(d['keypoints3d'], dtype=np.float32)
+        if pose3d.shape[1] == 3:
+            pose3d = np.hstack([pose3d, np.ones((pose3d.shape[0], 1))])
+        res_[pid] = {
+            'id': pid,
+            'keypoints3d': pose3d
+        }
+    return res_
+
 def read_smpl(filename):
     datas = read_json(filename)
     outputs = []
     for data in datas:
         for key in ['Rh', 'Th', 'poses', 'shapes', 'expression']:
             if key in data.keys():
-                data[key] = np.array(data[key])
+                data[key] = np.array(data[key], dtype=np.float32)
         # for smplx results
         outputs.append(data)
     return outputs
@@ -68,7 +82,7 @@ def read_keypoints3d_a4d(outname):
             pose3d = np.fromstring(content, dtype=float, sep=' ').reshape((nJoints, 4))
             # association4d 的关节顺序和正常的定义不一样
             pose3d = pose3d[[4, 1, 5, 9, 13, 6, 10, 14, 0, 2, 7, 11, 3, 8, 12], :]
-            res_.append({'id':trackId, 'keypoints3d':np.array(pose3d)})
+            res_.append({'id':trackId, 'keypoints3d':np.array(pose3d, dtype=np.float32)})
     return res_
 
 def read_keypoints3d_all(path, key='keypoints3d', pids=[]):

easymocap/mytools/vis_base.py

@@ -2,7 +2,7 @@
   @ Date: 2020-11-28 17:23:04
   @ Author: Qing Shuai
   @ LastEditors: Qing Shuai
-  @ LastEditTime: 2021-06-03 22:31:31
+  @ LastEditTime: 2021-08-22 16:11:25
   @ FilePath: /EasyMocap/easymocap/mytools/vis_base.py
 '''
 import cv2
@@ -75,7 +75,7 @@ def plot_line(img, pt1, pt2, lw, col):
 
 def plot_cross(img, x, y, col, width=-1, lw=-1):
     if lw == -1:
-        lw = int(round(img.shape[0]/1000))
+        lw = max(1, int(round(img.shape[0]/1000)))
         width = lw * 5
     cv2.line(img, (int(x-width), int(y)), (int(x+width), int(y)), col, lw)
     cv2.line(img, (int(x), int(y-width)), (int(x), int(y+width)), col, lw)
@@ -170,7 +170,7 @@ def merge(images, row=-1, col=-1, resize=False, ret_range=False, **kwargs):
             ret_img[height * i: height * (i+1), width * j: width * (j+1)] = img
             ranges.append((width*j, height*i, width*(j+1), height*(i+1)))
     if resize:
-        min_height = 3000
+        min_height = 1000
         if ret_img.shape[0] > min_height:
             scale = min_height/ret_img.shape[0]
             ret_img = cv2.resize(ret_img, None, fx=scale, fy=scale)

easymocap/smplmodel/body_model.py

@@ -2,12 +2,13 @@
   @ Date: 2020-11-18 14:04:10
   @ Author: Qing Shuai
   @ LastEditors: Qing Shuai
-  @ LastEditTime: 2021-06-28 11:55:00
-  @ FilePath: /EasyMocapRelease/easymocap/smplmodel/body_model.py
+  @ LastEditTime: 2021-08-28 16:37:55
+  @ FilePath: /EasyMocap/easymocap/smplmodel/body_model.py
 '''
 import torch
 import torch.nn as nn
-from .lbs import lbs, batch_rodrigues
+from .lbs import batch_rodrigues
+from .lbs import lbs, dqs
 import os.path as osp
 import pickle
 import numpy as np
@@ -59,7 +60,7 @@ class SMPLlayer(nn.Module):
     def __init__(self, model_path, model_type='smpl', gender='neutral', device=None,
         regressor_path=None,
         use_pose_blending=True, use_shape_blending=True, use_joints=True,
-        with_color=False,
+        with_color=False, use_lbs=True,
         **kwargs) -> None:
         super(SMPLlayer, self).__init__()
         dtype = torch.float32
@@ -72,7 +73,12 @@ class SMPLlayer(nn.Module):
             device = torch.device(device)
         self.device = device
         self.model_type = model_type
+        self.NUM_POSES = NUM_POSES[model_type]
         # create the SMPL model
+        if use_lbs:
+            self.lbs = lbs
+        else:
+            self.lbs = dqs
         data = load_bodydata(model_type, model_path, gender)
         if with_color:
             self.color = data['vertex_colors']
@@ -151,35 +157,30 @@ class SMPLlayer(nn.Module):
             self.register_buffer('j_J_regressor', j_J_regressor)
         if self.model_type == 'smplh':
             # load smplh data
-            self.num_pca_comps = 6
+            self.num_pca_comps = kwargs['num_pca_comps']
             from os.path import join
             for key in ['LEFT', 'RIGHT']:
-                left_file = join(os.path.dirname(smpl_path), 'MANO_{}.pkl'.format(key))
+                left_file = join(kwargs['mano_path'], 'MANO_{}.pkl'.format(key))
                 with open(left_file, 'rb') as f:
                     data = pickle.load(f, encoding='latin1')
                 val = to_tensor(to_np(data['hands_mean'].reshape(1, -1)), dtype=dtype)
                 self.register_buffer('mHandsMean'+key[0], val)
                 val = to_tensor(to_np(data['hands_components'][:self.num_pca_comps, :]), dtype=dtype)
                 self.register_buffer('mHandsComponents'+key[0], val)
-            self.use_pca = True
-            self.use_flat_mean = True
-        elif self.model_type == 'mano':
-            # TODO:write this into config file
-            # self.num_pca_comps = 12
-            # self.use_pca = True
-            # if self.use_pca:
-            #     NUM_POSES['mano'] = self.num_pca_comps + 3
-            # else:
-            #     NUM_POSES['mano'] = 45 + 3
-            # self.use_flat_mean = True
-
-            self.num_pca_comps = 12
-            self.use_pca = True
-            self.use_flat_mean = True
+            self.use_pca = kwargs['use_pca']
+            self.use_flat_mean = kwargs['use_flat_mean']
             if self.use_pca:
-                NUM_POSES['mano'] = self.num_pca_comps + 3
+                self.NUM_POSES = 66 + self.num_pca_comps * 2
             else:
-                NUM_POSES['mano'] = 45 + 3
+                self.NUM_POSES = 66 + 15 * 3 * 2
+        elif self.model_type == 'mano':
+            self.num_pca_comps = kwargs['num_pca_comps']
+            self.use_pca = kwargs['use_pca']
+            self.use_flat_mean = kwargs['use_flat_mean']
+            if self.use_pca:
+                self.NUM_POSES = self.num_pca_comps + 3
+            else:
+                self.NUM_POSES = 45 + 3
 
             val = to_tensor(to_np(data['hands_mean'].reshape(1, -1)), dtype=dtype)
             self.register_buffer('mHandsMean', val)
@@ -202,19 +203,21 @@ class SMPLlayer(nn.Module):
     def extend_hand(poses, use_pca, use_flat_mean, coeffs, mean):
         if use_pca:
             poses = poses @ coeffs
-        if use_flat_mean:
+        if not use_flat_mean:
             poses = poses + mean
         return poses
 
     def extend_pose(self, poses):
+        # skip SMPL or already extend
         if self.model_type not in ['smplh', 'smplx', 'mano']:
             return poses
-        elif self.model_type == 'smplh' and poses.shape[-1] == 156:
+        elif self.model_type == 'smplh' and poses.shape[-1] == 156 and self.use_flat_mean:
             return poses
-        elif self.model_type == 'smplx' and poses.shape[-1] == 165:
+        elif self.model_type == 'smplx' and poses.shape[-1] == 165 and self.use_flat_mean:
             return poses
-        elif self.model_type == 'mano' and poses.shape[-1] == 48:
+        elif self.model_type == 'mano' and poses.shape[-1] == 48 and self.use_flat_mean:
             return poses
+        # skip mano
         if self.model_type == 'mano':
             poses_hand = self.extend_hand(poses[..., 3:], self.use_pca, self.use_flat_mean,
                 self.mHandsComponents, self.mHandsMean)
@@ -231,7 +234,7 @@ class SMPLlayer(nn.Module):
         if self.use_pca:
             poses_lh = poses_lh @ self.mHandsComponentsL
             poses_rh = poses_rh @ self.mHandsComponentsR
-        if self.use_flat_mean:
+        if not self.use_flat_mean:
             poses_lh = poses_lh + self.mHandsMeanL
             poses_rh = poses_rh + self.mHandsMeanR
         if self.model_type == 'smplh':
@@ -299,7 +302,9 @@ class SMPLlayer(nn.Module):
         return poses.detach().cpu().numpy()
 
     def forward(self, poses, shapes, Rh=None, Th=None, expression=None, 
-        return_verts=True, return_tensor=True, return_smpl_joints=False, only_shape=False, **kwargs):
+        v_template=None,
+        return_verts=True, return_tensor=True, return_smpl_joints=False, 
+        only_shape=False, pose2rot=True, **kwargs):
         """ Forward pass for SMPL model
 
         Args:
@@ -338,20 +343,23 @@ class SMPLlayer(nn.Module):
         if expression is not None and self.model_type == 'smplx':
             shapes = torch.cat([shapes, expression], dim=1)
         # process poses
+        if pose2rot: # if given rotation matrix, no need for this
             poses = self.extend_pose(poses)
         if return_verts or not self.use_joints:
-            vertices, joints = lbs(shapes, poses, self.v_template,
+            if v_template is None:
+                v_template = self.v_template
+            vertices, joints = self.lbs(shapes, poses, v_template,
                                 self.shapedirs, self.posedirs,
                                 self.J_regressor, self.parents,
-                                self.weights, pose2rot=True, dtype=self.dtype,
+                                self.weights, pose2rot=pose2rot, dtype=self.dtype,
                                 use_pose_blending=self.use_pose_blending, use_shape_blending=self.use_shape_blending, J_shaped=self.J_shaped)
             if not self.use_joints and not return_verts:
                 vertices = joints
         else:
-            vertices, joints = lbs(shapes, poses, self.j_v_template,
+            vertices, joints = self.lbs(shapes, poses, self.j_v_template,
                                 self.j_shapedirs, self.j_posedirs,
                                 self.j_J_regressor, self.parents,
-                                self.j_weights, pose2rot=True, dtype=self.dtype, only_shape=only_shape,
+                                self.j_weights, pose2rot=pose2rot, dtype=self.dtype, only_shape=only_shape,
                                 use_pose_blending=self.use_pose_blending, use_shape_blending=self.use_shape_blending, J_shaped=self.J_shaped)
             if return_smpl_joints:
                 vertices = vertices[:, :self.J_regressor.shape[0], :]
@@ -364,13 +372,13 @@ class SMPLlayer(nn.Module):
     
     def init_params(self, nFrames=1, nShapes=1, ret_tensor=False):
         params = {
-            'poses': np.zeros((nFrames, NUM_POSES[self.model_type])),
+            'poses': np.zeros((nFrames, self.NUM_POSES)),
             'shapes': np.zeros((nShapes, NUM_SHAPES)),
             'Rh': np.zeros((nFrames, 3)),
             'Th': np.zeros((nFrames, 3)),
         }
         if self.model_type == 'smplx':
-            params['expression'] = np.zeros((nFrames, NUM_EXPR))
+            params['expression'] = np.zeros((nFrames, self.NUM_EXPR))
         if ret_tensor:
             for key in params.keys():
                 params[key] = to_tensor(params[key], self.dtype, self.device)
@@ -379,10 +387,10 @@ class SMPLlayer(nn.Module):
     def check_params(self, body_params):
         model_type = self.model_type
         nFrames = body_params['poses'].shape[0]
-        if body_params['poses'].shape[1] != NUM_POSES[model_type]:
-            body_params['poses'] = np.hstack((body_params['poses'], np.zeros((nFrames, NUM_POSES[model_type] - body_params['poses'].shape[1]))))
+        if body_params['poses'].shape[1] != self.NUM_POSES:
+            body_params['poses'] = np.hstack((body_params['poses'], np.zeros((nFrames, self.NUM_POSES - body_params['poses'].shape[1]))))
         if model_type == 'smplx' and 'expression' not in body_params.keys():
-            body_params['expression'] = np.zeros((nFrames, NUM_EXPR))
+            body_params['expression'] = np.zeros((nFrames, self.NUM_EXPR))
         return body_params
 
     @staticmethod    
@@ -393,4 +401,22 @@ class SMPLlayer(nn.Module):
                 output[key] = np.vstack([v[key] for v in param_list])
         if share_shape:
             output['shapes'] = output['shapes'].mean(axis=0, keepdims=True)
+        # add other keys
+        for key in param_list[0].keys():
+            if key in output.keys():
+                continue
+            output[key] = np.stack([v[key] for v in param_list])
+        return output
+    
+    @staticmethod
+    def select_nf(params_all, nf):
+        output = {}
+        for key in ['poses', 'Rh', 'Th']:
+            output[key] = params_all[key][nf:nf+1, :]
+        if 'expression' in params_all.keys():
+            output['expression'] = params_all['expression'][nf:nf+1, :]
+        if params_all['shapes'].shape[0] == 1:
+            output['shapes'] = params_all['shapes']
+        else:
+            output['shapes'] = params_all['shapes'][nf:nf+1, :]
         return output

easymocap/smplmodel/lbs.py

@@ -379,3 +379,115 @@ def batch_rigid_transform(rot_mats, joints, parents, dtype=torch.float32):
         torch.matmul(transforms, joints_homogen), [3, 0, 0, 0, 0, 0, 0, 0])
 
     return posed_joints, rel_transforms
+
+def dqs(betas, pose, v_template, shapedirs, posedirs, J_regressor, parents,
+        lbs_weights, pose2rot=True, dtype=torch.float32, only_shape=False,
+        use_shape_blending=True, use_pose_blending=True, J_shaped=None):
+    ''' Performs Linear Blend Skinning with the given shape and pose parameters
+
+        Parameters
+        ----------
+        betas : torch.tensor BxNB
+            The tensor of shape parameters
+        pose : torch.tensor Bx(J + 1) * 3
+            The pose parameters in axis-angle format
+        v_template torch.tensor BxVx3
+            The template mesh that will be deformed
+        shapedirs : torch.tensor 1xNB
+            The tensor of PCA shape displacements
+        posedirs : torch.tensor Px(V * 3)
+            The pose PCA coefficients
+        J_regressor : torch.tensor JxV
+            The regressor array that is used to calculate the joints from
+            the position of the vertices
+        parents: torch.tensor J
+            The array that describes the kinematic tree for the model
+        lbs_weights: torch.tensor N x V x (J + 1)
+            The linear blend skinning weights that represent how much the
+            rotation matrix of each part affects each vertex
+        pose2rot: bool, optional
+            Flag on whether to convert the input pose tensor to rotation
+            matrices. The default value is True. If False, then the pose tensor
+            should already contain rotation matrices and have a size of
+            Bx(J + 1)x9
+        dtype: torch.dtype, optional
+
+        Returns
+        -------
+        verts: torch.tensor BxVx3
+            The vertices of the mesh after applying the shape and pose
+            displacements.
+        joints: torch.tensor BxJx3
+            The joints of the model
+    '''
+
+    batch_size = max(betas.shape[0], pose.shape[0])
+    device = betas.device
+
+    # Add shape contribution
+    if use_shape_blending:
+        v_shaped = v_template + blend_shapes(betas, shapedirs)
+        # Get the joints
+        # NxJx3 array
+        J = vertices2joints(J_regressor, v_shaped)
+    else:
+        v_shaped = v_template.unsqueeze(0).expand(batch_size, -1, -1)
+        assert J_shaped is not None
+        J = J_shaped[None].expand(batch_size, -1, -1)
+
+    if only_shape:
+        return v_shaped, J
+    # 3. Add pose blend shapes
+    # N x J x 3 x 3
+    if pose2rot:
+        rot_mats = batch_rodrigues(
+            pose.view(-1, 3), dtype=dtype).view([batch_size, -1, 3, 3])
+    else:
+        rot_mats = pose.view(batch_size, -1, 3, 3)
+
+    if use_pose_blending:
+        ident = torch.eye(3, dtype=dtype, device=device)
+        pose_feature = (rot_mats[:, 1:, :, :] - ident).view([batch_size, -1])
+        pose_offsets = torch.matmul(pose_feature, posedirs) \
+            .view(batch_size, -1, 3)
+        v_posed = pose_offsets + v_shaped
+    else:
+        v_posed = v_shaped
+    # 4. Get the global joint location
+    J_transformed, A = batch_rigid_transform(rot_mats, J, parents, dtype=dtype)
+
+
+    # 5. Do skinning:
+    # W is N x V x (J + 1)
+    W = lbs_weights.unsqueeze(dim=0).expand([batch_size, -1, -1])
+
+    verts=batch_dqs_blending(A,W,v_posed)
+
+    return verts, J_transformed
+
+#A: B,J,4,4 W: B,V,J
+def batch_dqs_blending(A,W,Vs):
+    Bnum,Jnum,_,_=A.shape
+    _,Vnum,_=W.shape
+    A = A.view(Bnum*Jnum,4,4)
+    Rs=A[:,:3,:3]
+    ws=torch.sqrt(torch.clamp(Rs[:,0,0]+Rs[:,1,1]+Rs[:,2,2]+1.,min=1.e-6))/2.
+    xs=(Rs[:,2,1]-Rs[:,1,2])/(4.*ws)
+    ys=(Rs[:,0,2]-Rs[:,2,0])/(4.*ws)
+    zs=(Rs[:,1,0]-Rs[:,0,1])/(4.*ws)
+    Ts=A[:,:3,3]
+    vDw=-0.5*( Ts[:,0]*xs + Ts[:,1]*ys + Ts[:,2]*zs)
+    vDx=0.5*( Ts[:,0]*ws + Ts[:,1]*zs - Ts[:,2]*ys)
+    vDy=0.5*(-Ts[:,0]*zs + Ts[:,1]*ws + Ts[:,2]*xs)
+    vDz=0.5*( Ts[:,0]*ys - Ts[:,1]*xs + Ts[:,2]*ws)
+    b0=W.unsqueeze(-2)@torch.cat([ws[:,None],xs[:,None],ys[:,None],zs[:,None]],dim=-1).reshape(Bnum, 1, Jnum, 4) #B,V,J,4
+    be=W.unsqueeze(-2)@torch.cat([vDw[:,None],vDx[:,None],vDy[:,None],vDz[:,None]],dim=-1).reshape(Bnum, 1, Jnum, 4) #B,V,J,4
+    b0 = b0.reshape(-1, 4)
+    be = be.reshape(-1, 4)
+
+    ns=torch.norm(b0,dim=-1,keepdim=True)
+    be=be/ns
+    b0=b0/ns
+    Vs=Vs.view(Bnum*Vnum,3)
+    Vs=Vs+2.*b0[:,1:].cross(b0[:,1:].cross(Vs)+b0[:,:1]*Vs)+2.*(b0[:,:1]*be[:,1:]-be[:,:1]*b0[:,1:]+b0[:,1:].cross(be[:,1:]))
+    return Vs.reshape(Bnum,Vnum,3)

easymocap/visualize/assets/sphere_faces_2.txt

@@ -0,0 +1,8 @@
+     0      2      3
+     1      3      2
+     0      3      4
+     1      4      3
+     0      4      5
+     1      5      4
+     0      5      2
+     1      2      5

easymocap/visualize/assets/sphere_faces_4.txt

@@ -0,0 +1,48 @@
+     0      2      3
+     1     19     18
+     0      3      4
+     1     20     19
+     0      4      5
+     1     21     20
+     0      5      6
+     1     22     21
+     0      6      7
+     1     23     22
+     0      7      8
+     1     24     23
+     0      8      9
+     1     25     24
+     0      9      2
+     1     18     25
+    10      3      2
+    10     11      3
+    11      4      3
+    11     12      4
+    12      5      4
+    12     13      5
+    13      6      5
+    13     14      6
+    14      7      6
+    14     15      7
+    15      8      7
+    15     16      8
+    16      9      8
+    16     17      9
+    17      2      9
+    17     10      2
+    18     11     10
+    18     19     11
+    19     12     11
+    19     20     12
+    20     13     12
+    20     21     13
+    21     14     13
+    21     22     14
+    22     15     14
+    22     23     15
+    23     16     15
+    23     24     16
+    24     17     16
+    24     25     17
+    25     10     17
+    25     18     10

easymocap/visualize/assets/sphere_faces_8.txt

@@ -0,0 +1,224 @@
+     0      2      3
+     1     99     98
+     0      3      4
+     1    100     99
+     0      4      5
+     1    101    100
+     0      5      6
+     1    102    101
+     0      6      7
+     1    103    102
+     0      7      8
+     1    104    103
+     0      8      9
+     1    105    104
+     0      9     10
+     1    106    105
+     0     10     11
+     1    107    106
+     0     11     12
+     1    108    107
+     0     12     13
+     1    109    108
+     0     13     14
+     1    110    109
+     0     14     15
+     1    111    110
+     0     15     16
+     1    112    111
+     0     16     17
+     1    113    112
+     0     17      2
+     1     98    113
+    18      3      2
+    18     19      3
+    19      4      3
+    19     20      4
+    20      5      4
+    20     21      5
+    21      6      5
+    21     22      6
+    22      7      6
+    22     23      7
+    23      8      7
+    23     24      8
+    24      9      8
+    24     25      9
+    25     10      9
+    25     26     10
+    26     11     10
+    26     27     11
+    27     12     11
+    27     28     12
+    28     13     12
+    28     29     13
+    29     14     13
+    29     30     14
+    30     15     14
+    30     31     15
+    31     16     15
+    31     32     16
+    32     17     16
+    32     33     17
+    33      2     17
+    33     18      2
+    34     19     18
+    34     35     19
+    35     20     19
+    35     36     20
+    36     21     20
+    36     37     21
+    37     22     21
+    37     38     22
+    38     23     22
+    38     39     23
+    39     24     23
+    39     40     24
+    40     25     24
+    40     41     25
+    41     26     25
+    41     42     26
+    42     27     26
+    42     43     27
+    43     28     27
+    43     44     28
+    44     29     28
+    44     45     29
+    45     30     29
+    45     46     30
+    46     31     30
+    46     47     31
+    47     32     31
+    47     48     32
+    48     33     32
+    48     49     33
+    49     18     33
+    49     34     18
+    50     35     34
+    50     51     35
+    51     36     35
+    51     52     36
+    52     37     36
+    52     53     37
+    53     38     37
+    53     54     38
+    54     39     38
+    54     55     39
+    55     40     39
+    55     56     40
+    56     41     40
+    56     57     41
+    57     42     41
+    57     58     42
+    58     43     42
+    58     59     43
+    59     44     43
+    59     60     44
+    60     45     44
+    60     61     45
+    61     46     45
+    61     62     46
+    62     47     46
+    62     63     47
+    63     48     47
+    63     64     48
+    64     49     48
+    64     65     49
+    65     34     49
+    65     50     34
+    66     51     50
+    66     67     51
+    67     52     51
+    67     68     52
+    68     53     52
+    68     69     53
+    69     54     53
+    69     70     54
+    70     55     54
+    70     71     55
+    71     56     55
+    71     72     56
+    72     57     56
+    72     73     57
+    73     58     57
+    73     74     58
+    74     59     58
+    74     75     59
+    75     60     59
+    75     76     60
+    76     61     60
+    76     77     61
+    77     62     61
+    77     78     62
+    78     63     62
+    78     79     63
+    79     64     63
+    79     80     64
+    80     65     64
+    80     81     65
+    81     50     65
+    81     66     50
+    82     67     66
+    82     83     67
+    83     68     67
+    83     84     68
+    84     69     68
+    84     85     69
+    85     70     69
+    85     86     70
+    86     71     70
+    86     87     71
+    87     72     71
+    87     88     72
+    88     73     72
+    88     89     73
+    89     74     73
+    89     90     74
+    90     75     74
+    90     91     75
+    91     76     75
+    91     92     76
+    92     77     76
+    92     93     77
+    93     78     77
+    93     94     78
+    94     79     78
+    94     95     79
+    95     80     79
+    95     96     80
+    96     81     80
+    96     97     81
+    97     66     81
+    97     82     66
+    98     83     82
+    98     99     83
+    99     84     83
+    99    100     84
+   100     85     84
+   100    101     85
+   101     86     85
+   101    102     86
+   102     87     86
+   102    103     87
+   103     88     87
+   103    104     88
+   104     89     88
+   104    105     89
+   105     90     89
+   105    106     90
+   106     91     90
+   106    107     91
+   107     92     91
+   107    108     92
+   108     93     92
+   108    109     93
+   109     94     93
+   109    110     94
+   110     95     94
+   110    111     95
+   111     96     95
+   111    112     96
+   112     97     96
+   112    113     97
+   113     82     97
+   113     98     82

easymocap/visualize/assets/sphere_vertices_2.txt

@@ -0,0 +1,6 @@
+  0.000   0.000   1.000
+  0.000   0.000  -1.000
+  1.000   0.000   0.000
+  0.000   1.000   0.000
+ -1.000   0.000   0.000
+ -0.000  -1.000   0.000

easymocap/visualize/assets/sphere_vertices_20.txt

@@ -0,0 +1,762 @@
+  0.000   0.000   1.000
+  0.000   0.000  -1.000
+  0.156   0.000   0.988
+  0.155   0.024   0.988
+  0.149   0.048   0.988
+  0.139   0.071   0.988
+  0.127   0.092   0.988
+  0.111   0.111   0.988
+  0.092   0.127   0.988
+  0.071   0.139   0.988
+  0.048   0.149   0.988
+  0.024   0.155   0.988
+  0.000   0.156   0.988
+ -0.024   0.155   0.988
+ -0.048   0.149   0.988
+ -0.071   0.139   0.988
+ -0.092   0.127   0.988
+ -0.111   0.111   0.988
+ -0.127   0.092   0.988
+ -0.139   0.071   0.988
+ -0.149   0.048   0.988
+ -0.155   0.024   0.988
+ -0.156   0.000   0.988
+ -0.155  -0.024   0.988
+ -0.149  -0.048   0.988
+ -0.139  -0.071   0.988
+ -0.127  -0.092   0.988
+ -0.111  -0.111   0.988
+ -0.092  -0.127   0.988
+ -0.071  -0.139   0.988
+ -0.048  -0.149   0.988
+ -0.024  -0.155   0.988
+ -0.000  -0.156   0.988
+  0.024  -0.155   0.988
+  0.048  -0.149   0.988
+  0.071  -0.139   0.988
+  0.092  -0.127   0.988
+  0.111  -0.111   0.988
+  0.127  -0.092   0.988
+  0.139  -0.071   0.988
+  0.149  -0.048   0.988
+  0.155  -0.024   0.988
+  0.309   0.000   0.951
+  0.305   0.048   0.951
+  0.294   0.095   0.951
+  0.275   0.140   0.951
+  0.250   0.182   0.951
+  0.219   0.219   0.951
+  0.182   0.250   0.951
+  0.140   0.275   0.951
+  0.095   0.294   0.951
+  0.048   0.305   0.951
+  0.000   0.309   0.951
+ -0.048   0.305   0.951
+ -0.095   0.294   0.951
+ -0.140   0.275   0.951
+ -0.182   0.250   0.951
+ -0.219   0.219   0.951
+ -0.250   0.182   0.951
+ -0.275   0.140   0.951
+ -0.294   0.095   0.951
+ -0.305   0.048   0.951
+ -0.309   0.000   0.951
+ -0.305  -0.048   0.951
+ -0.294  -0.095   0.951
+ -0.275  -0.140   0.951
+ -0.250  -0.182   0.951
+ -0.219  -0.219   0.951
+ -0.182  -0.250   0.951
+ -0.140  -0.275   0.951
+ -0.095  -0.294   0.951
+ -0.048  -0.305   0.951
+ -0.000  -0.309   0.951
+  0.048  -0.305   0.951
+  0.095  -0.294   0.951
+  0.140  -0.275   0.951
+  0.182  -0.250   0.951
+  0.219  -0.219   0.951
+  0.250  -0.182   0.951
+  0.275  -0.140   0.951
+  0.294  -0.095   0.951
+  0.305  -0.048   0.951
+  0.454   0.000   0.891
+  0.448   0.071   0.891
+  0.432   0.140   0.891
+  0.405   0.206   0.891
+  0.367   0.267   0.891
+  0.321   0.321   0.891
+  0.267   0.367   0.891
+  0.206   0.405   0.891
+  0.140   0.432   0.891
+  0.071   0.448   0.891
+  0.000   0.454   0.891
+ -0.071   0.448   0.891
+ -0.140   0.432   0.891
+ -0.206   0.405   0.891
+ -0.267   0.367   0.891
+ -0.321   0.321   0.891
+ -0.367   0.267   0.891
+ -0.405   0.206   0.891
+ -0.432   0.140   0.891
+ -0.448   0.071   0.891
+ -0.454   0.000   0.891
+ -0.448  -0.071   0.891
+ -0.432  -0.140   0.891
+ -0.405  -0.206   0.891
+ -0.367  -0.267   0.891
+ -0.321  -0.321   0.891
+ -0.267  -0.367   0.891
+ -0.206  -0.405   0.891
+ -0.140  -0.432   0.891
+ -0.071  -0.448   0.891
+ -0.000  -0.454   0.891
+  0.071  -0.448   0.891
+  0.140  -0.432   0.891
+  0.206  -0.405   0.891
+  0.267  -0.367   0.891
+  0.321  -0.321   0.891
+  0.367  -0.267   0.891
+  0.405  -0.206   0.891
+  0.432  -0.140   0.891
+  0.448  -0.071   0.891
+  0.588   0.000   0.809
+  0.581   0.092   0.809
+  0.559   0.182   0.809
+  0.524   0.267   0.809
+  0.476   0.345   0.809
+  0.416   0.416   0.809
+  0.345   0.476   0.809
+  0.267   0.524   0.809
+  0.182   0.559   0.809
+  0.092   0.581   0.809
+  0.000   0.588   0.809
+ -0.092   0.581   0.809
+ -0.182   0.559   0.809
+ -0.267   0.524   0.809
+ -0.345   0.476   0.809
+ -0.416   0.416   0.809
+ -0.476   0.345   0.809
+ -0.524   0.267   0.809
+ -0.559   0.182   0.809
+ -0.581   0.092   0.809
+ -0.588   0.000   0.809
+ -0.581  -0.092   0.809
+ -0.559  -0.182   0.809
+ -0.524  -0.267   0.809
+ -0.476  -0.345   0.809
+ -0.416  -0.416   0.809
+ -0.345  -0.476   0.809
+ -0.267  -0.524   0.809
+ -0.182  -0.559   0.809
+ -0.092  -0.581   0.809
+ -0.000  -0.588   0.809
+  0.092  -0.581   0.809
+  0.182  -0.559   0.809
+  0.267  -0.524   0.809
+  0.345  -0.476   0.809
+  0.416  -0.416   0.809
+  0.476  -0.345   0.809
+  0.524  -0.267   0.809
+  0.559  -0.182   0.809
+  0.581  -0.092   0.809
+  0.707   0.000   0.707
+  0.698   0.111   0.707
+  0.672   0.219   0.707
+  0.630   0.321   0.707
+  0.572   0.416   0.707
+  0.500   0.500   0.707
+  0.416   0.572   0.707
+  0.321   0.630   0.707
+  0.219   0.672   0.707
+  0.111   0.698   0.707
+  0.000   0.707   0.707
+ -0.111   0.698   0.707
+ -0.219   0.672   0.707
+ -0.321   0.630   0.707
+ -0.416   0.572   0.707
+ -0.500   0.500   0.707
+ -0.572   0.416   0.707
+ -0.630   0.321   0.707
+ -0.672   0.219   0.707
+ -0.698   0.111   0.707
+ -0.707   0.000   0.707
+ -0.698  -0.111   0.707
+ -0.672  -0.219   0.707
+ -0.630  -0.321   0.707
+ -0.572  -0.416   0.707
+ -0.500  -0.500   0.707
+ -0.416  -0.572   0.707
+ -0.321  -0.630   0.707
+ -0.219  -0.672   0.707
+ -0.111  -0.698   0.707
+ -0.000  -0.707   0.707
+  0.111  -0.698   0.707
+  0.219  -0.672   0.707
+  0.321  -0.630   0.707
+  0.416  -0.572   0.707
+  0.500  -0.500   0.707
+  0.572  -0.416   0.707
+  0.630  -0.321   0.707
+  0.672  -0.219   0.707
+  0.698  -0.111   0.707
+  0.809   0.000   0.588
+  0.799   0.127   0.588
+  0.769   0.250   0.588
+  0.721   0.367   0.588
+  0.655   0.476   0.588
+  0.572   0.572   0.588
+  0.476   0.655   0.588
+  0.367   0.721   0.588
+  0.250   0.769   0.588
+  0.127   0.799   0.588
+  0.000   0.809   0.588
+ -0.127   0.799   0.588
+ -0.250   0.769   0.588
+ -0.367   0.721   0.588
+ -0.476   0.655   0.588
+ -0.572   0.572   0.588
+ -0.655   0.476   0.588
+ -0.721   0.367   0.588
+ -0.769   0.250   0.588
+ -0.799   0.127   0.588
+ -0.809   0.000   0.588
+ -0.799  -0.127   0.588
+ -0.769  -0.250   0.588
+ -0.721  -0.367   0.588
+ -0.655  -0.476   0.588
+ -0.572  -0.572   0.588
+ -0.476  -0.655   0.588
+ -0.367  -0.721   0.588
+ -0.250  -0.769   0.588
+ -0.127  -0.799   0.588
+ -0.000  -0.809   0.588
+  0.127  -0.799   0.588
+  0.250  -0.769   0.588
+  0.367  -0.721   0.588
+  0.476  -0.655   0.588
+  0.572  -0.572   0.588
+  0.655  -0.476   0.588
+  0.721  -0.367   0.588
+  0.769  -0.250   0.588
+  0.799  -0.127   0.588
+  0.891   0.000   0.454
+  0.880   0.139   0.454
+  0.847   0.275   0.454
+  0.794   0.405   0.454
+  0.721   0.524   0.454
+  0.630   0.630   0.454
+  0.524   0.721   0.454
+  0.405   0.794   0.454
+  0.275   0.847   0.454
+  0.139   0.880   0.454
+  0.000   0.891   0.454
+ -0.139   0.880   0.454
+ -0.275   0.847   0.454
+ -0.405   0.794   0.454
+ -0.524   0.721   0.454
+ -0.630   0.630   0.454
+ -0.721   0.524   0.454
+ -0.794   0.405   0.454
+ -0.847   0.275   0.454
+ -0.880   0.139   0.454
+ -0.891   0.000   0.454
+ -0.880  -0.139   0.454
+ -0.847  -0.275   0.454
+ -0.794  -0.405   0.454
+ -0.721  -0.524   0.454
+ -0.630  -0.630   0.454
+ -0.524  -0.721   0.454
+ -0.405  -0.794   0.454
+ -0.275  -0.847   0.454
+ -0.139  -0.880   0.454
+ -0.000  -0.891   0.454
+  0.139  -0.880   0.454
+  0.275  -0.847   0.454
+  0.405  -0.794   0.454
+  0.524  -0.721   0.454
+  0.630  -0.630   0.454
+  0.721  -0.524   0.454
+  0.794  -0.405   0.454
+  0.847  -0.275   0.454
+  0.880  -0.139   0.454
+  0.951   0.000   0.309
+  0.939   0.149   0.309
+  0.905   0.294   0.309
+  0.847   0.432   0.309
+  0.769   0.559   0.309
+  0.672   0.672   0.309
+  0.559   0.769   0.309
+  0.432   0.847   0.309
+  0.294   0.905   0.309
+  0.149   0.939   0.309
+  0.000   0.951   0.309
+ -0.149   0.939   0.309
+ -0.294   0.905   0.309
+ -0.432   0.847   0.309
+ -0.559   0.769   0.309
+ -0.672   0.672   0.309
+ -0.769   0.559   0.309
+ -0.847   0.432   0.309
+ -0.905   0.294   0.309
+ -0.939   0.149   0.309
+ -0.951   0.000   0.309
+ -0.939  -0.149   0.309
+ -0.905  -0.294   0.309
+ -0.847  -0.432   0.309
+ -0.769  -0.559   0.309
+ -0.672  -0.672   0.309
+ -0.559  -0.769   0.309
+ -0.432  -0.847   0.309
+ -0.294  -0.905   0.309
+ -0.149  -0.939   0.309
+ -0.000  -0.951   0.309
+  0.149  -0.939   0.309
+  0.294  -0.905   0.309
+  0.432  -0.847   0.309
+  0.559  -0.769   0.309
+  0.672  -0.672   0.309
+  0.769  -0.559   0.309
+  0.847  -0.432   0.309
+  0.905  -0.294   0.309
+  0.939  -0.149   0.309
+  0.988   0.000   0.156
+  0.976   0.155   0.156
+  0.939   0.305   0.156
+  0.880   0.448   0.156
+  0.799   0.581   0.156
+  0.698   0.698   0.156
+  0.581   0.799   0.156
+  0.448   0.880   0.156
+  0.305   0.939   0.156
+  0.155   0.976   0.156
+  0.000   0.988   0.156
+ -0.155   0.976   0.156
+ -0.305   0.939   0.156
+ -0.448   0.880   0.156
+ -0.581   0.799   0.156
+ -0.698   0.698   0.156
+ -0.799   0.581   0.156
+ -0.880   0.448   0.156
+ -0.939   0.305   0.156
+ -0.976   0.155   0.156
+ -0.988   0.000   0.156
+ -0.976  -0.155   0.156
+ -0.939  -0.305   0.156
+ -0.880  -0.448   0.156
+ -0.799  -0.581   0.156
+ -0.698  -0.698   0.156
+ -0.581  -0.799   0.156
+ -0.448  -0.880   0.156
+ -0.305  -0.939   0.156
+ -0.155  -0.976   0.156
+ -0.000  -0.988   0.156
+  0.155  -0.976   0.156
+  0.305  -0.939   0.156
+  0.448  -0.880   0.156
+  0.581  -0.799   0.156
+  0.698  -0.698   0.156
+  0.799  -0.581   0.156
+  0.880  -0.448   0.156
+  0.939  -0.305   0.156
+  0.976  -0.155   0.156
+  1.000   0.000   0.000
+  0.988   0.156   0.000
+  0.951   0.309   0.000
+  0.891   0.454   0.000
+  0.809   0.588   0.000
+  0.707   0.707   0.000
+  0.588   0.809   0.000
+  0.454   0.891   0.000
+  0.309   0.951   0.000
+  0.156   0.988   0.000
+  0.000   1.000   0.000
+ -0.156   0.988   0.000
+ -0.309   0.951   0.000
+ -0.454   0.891   0.000
+ -0.588   0.809   0.000
+ -0.707   0.707   0.000
+ -0.809   0.588   0.000
+ -0.891   0.454   0.000
+ -0.951   0.309   0.000
+ -0.988   0.156   0.000
+ -1.000   0.000   0.000
+ -0.988  -0.156   0.000
+ -0.951  -0.309   0.000
+ -0.891  -0.454   0.000
+ -0.809  -0.588   0.000
+ -0.707  -0.707   0.000
+ -0.588  -0.809   0.000
+ -0.454  -0.891   0.000
+ -0.309  -0.951   0.000
+ -0.156  -0.988   0.000
+ -0.000  -1.000   0.000
+  0.156  -0.988   0.000
+  0.309  -0.951   0.000
+  0.454  -0.891   0.000
+  0.588  -0.809   0.000
+  0.707  -0.707   0.000
+  0.809  -0.588   0.000
+  0.891  -0.454   0.000
+  0.951  -0.309   0.000
+  0.988  -0.156   0.000
+  0.988   0.000  -0.156
+  0.976   0.155  -0.156
+  0.939   0.305  -0.156
+  0.880   0.448  -0.156
+  0.799   0.581  -0.156
+  0.698   0.698  -0.156
+  0.581   0.799  -0.156
+  0.448   0.880  -0.156
+  0.305   0.939  -0.156
+  0.155   0.976  -0.156
+  0.000   0.988  -0.156
+ -0.155   0.976  -0.156
+ -0.305   0.939  -0.156
+ -0.448   0.880  -0.156
+ -0.581   0.799  -0.156
+ -0.698   0.698  -0.156
+ -0.799   0.581  -0.156
+ -0.880   0.448  -0.156
+ -0.939   0.305  -0.156
+ -0.976   0.155  -0.156
+ -0.988   0.000  -0.156
+ -0.976  -0.155  -0.156
+ -0.939  -0.305  -0.156
+ -0.880  -0.448  -0.156
+ -0.799  -0.581  -0.156
+ -0.698  -0.698  -0.156
+ -0.581  -0.799  -0.156
+ -0.448  -0.880  -0.156
+ -0.305  -0.939  -0.156
+ -0.155  -0.976  -0.156
+ -0.000  -0.988  -0.156
+  0.155  -0.976  -0.156
+  0.305  -0.939  -0.156
+  0.448  -0.880  -0.156
+  0.581  -0.799  -0.156
+  0.698  -0.698  -0.156
+  0.799  -0.581  -0.156
+  0.880  -0.448  -0.156
+  0.939  -0.305  -0.156
+  0.976  -0.155  -0.156
+  0.951   0.000  -0.309
+  0.939   0.149  -0.309
+  0.905   0.294  -0.309
+  0.847   0.432  -0.309
+  0.769   0.559  -0.309
+  0.672   0.672  -0.309
+  0.559   0.769  -0.309
+  0.432   0.847  -0.309
+  0.294   0.905  -0.309
+  0.149   0.939  -0.309
+  0.000   0.951  -0.309
+ -0.149   0.939  -0.309
+ -0.294   0.905  -0.309
+ -0.432   0.847  -0.309
+ -0.559   0.769  -0.309
+ -0.672   0.672  -0.309
+ -0.769   0.559  -0.309
+ -0.847   0.432  -0.309
+ -0.905   0.294  -0.309
+ -0.939   0.149  -0.309
+ -0.951   0.000  -0.309
+ -0.939  -0.149  -0.309
+ -0.905  -0.294  -0.309
+ -0.847  -0.432  -0.309
+ -0.769  -0.559  -0.309
+ -0.672  -0.672  -0.309
+ -0.559  -0.769  -0.309
+ -0.432  -0.847  -0.309
+ -0.294  -0.905  -0.309
+ -0.149  -0.939  -0.309
+ -0.000  -0.951  -0.309
+  0.149  -0.939  -0.309
+  0.294  -0.905  -0.309
+  0.432  -0.847  -0.309
+  0.559  -0.769  -0.309
+  0.672  -0.672  -0.309
+  0.769  -0.559  -0.309
+  0.847  -0.432  -0.309
+  0.905  -0.294  -0.309
+  0.939  -0.149  -0.309
+  0.891   0.000  -0.454
+  0.880   0.139  -0.454
+  0.847   0.275  -0.454
+  0.794   0.405  -0.454
+  0.721   0.524  -0.454
+  0.630   0.630  -0.454
+  0.524   0.721  -0.454
+  0.405   0.794  -0.454
+  0.275   0.847  -0.454
+  0.139   0.880  -0.454
+  0.000   0.891  -0.454
+ -0.139   0.880  -0.454
+ -0.275   0.847  -0.454
+ -0.405   0.794  -0.454
+ -0.524   0.721  -0.454
+ -0.630   0.630  -0.454
+ -0.721   0.524  -0.454
+ -0.794   0.405  -0.454
+ -0.847   0.275  -0.454
+ -0.880   0.139  -0.454
+ -0.891   0.000  -0.454
+ -0.880  -0.139  -0.454
+ -0.847  -0.275  -0.454
+ -0.794  -0.405  -0.454
+ -0.721  -0.524  -0.454
+ -0.630  -0.630  -0.454
+ -0.524  -0.721  -0.454
+ -0.405  -0.794  -0.454
+ -0.275  -0.847  -0.454
+ -0.139  -0.880  -0.454
+ -0.000  -0.891  -0.454
+  0.139  -0.880  -0.454
+  0.275  -0.847  -0.454
+  0.405  -0.794  -0.454
+  0.524  -0.721  -0.454
+  0.630  -0.630  -0.454
+  0.721  -0.524  -0.454
+  0.794  -0.405  -0.454
+  0.847  -0.275  -0.454
+  0.880  -0.139  -0.454
+  0.809   0.000  -0.588
+  0.799   0.127  -0.588
+  0.769   0.250  -0.588
+  0.721   0.367  -0.588
+  0.655   0.476  -0.588
+  0.572   0.572  -0.588
+  0.476   0.655  -0.588
+  0.367   0.721  -0.588
+  0.250   0.769  -0.588
+  0.127   0.799  -0.588
+  0.000   0.809  -0.588
+ -0.127   0.799  -0.588
+ -0.250   0.769  -0.588
+ -0.367   0.721  -0.588
+ -0.476   0.655  -0.588
+ -0.572   0.572  -0.588
+ -0.655   0.476  -0.588
+ -0.721   0.367  -0.588
+ -0.769   0.250  -0.588
+ -0.799   0.127  -0.588
+ -0.809   0.000  -0.588
+ -0.799  -0.127  -0.588
+ -0.769  -0.250  -0.588
+ -0.721  -0.367  -0.588
+ -0.655  -0.476  -0.588
+ -0.572  -0.572  -0.588
+ -0.476  -0.655  -0.588
+ -0.367  -0.721  -0.588
+ -0.250  -0.769  -0.588
+ -0.127  -0.799  -0.588
+ -0.000  -0.809  -0.588
+  0.127  -0.799  -0.588
+  0.250  -0.769  -0.588
+  0.367  -0.721  -0.588
+  0.476  -0.655  -0.588
+  0.572  -0.572  -0.588
+  0.655  -0.476  -0.588
+  0.721  -0.367  -0.588
+  0.769  -0.250  -0.588
+  0.799  -0.127  -0.588
+  0.707   0.000  -0.707
+  0.698   0.111  -0.707
+  0.672   0.219  -0.707
+  0.630   0.321  -0.707
+  0.572   0.416  -0.707
+  0.500   0.500  -0.707
+  0.416   0.572  -0.707
+  0.321   0.630  -0.707
+  0.219   0.672  -0.707
+  0.111   0.698  -0.707
+  0.000   0.707  -0.707
+ -0.111   0.698  -0.707
+ -0.219   0.672  -0.707
+ -0.321   0.630  -0.707
+ -0.416   0.572  -0.707
+ -0.500   0.500  -0.707
+ -0.572   0.416  -0.707
+ -0.630   0.321  -0.707
+ -0.672   0.219  -0.707
+ -0.698   0.111  -0.707
+ -0.707   0.000  -0.707
+ -0.698  -0.111  -0.707
+ -0.672  -0.219  -0.707
+ -0.630  -0.321  -0.707
+ -0.572  -0.416  -0.707
+ -0.500  -0.500  -0.707
+ -0.416  -0.572  -0.707
+ -0.321  -0.630  -0.707
+ -0.219  -0.672  -0.707
+ -0.111  -0.698  -0.707
+ -0.000  -0.707  -0.707
+  0.111  -0.698  -0.707
+  0.219  -0.672  -0.707
+  0.321  -0.630  -0.707
+  0.416  -0.572  -0.707
+  0.500  -0.500  -0.707
+  0.572  -0.416  -0.707
+  0.630  -0.321  -0.707
+  0.672  -0.219  -0.707
+  0.698  -0.111  -0.707
+  0.588   0.000  -0.809
+  0.581   0.092  -0.809
+  0.559   0.182  -0.809
+  0.524   0.267  -0.809
+  0.476   0.345  -0.809
+  0.416   0.416  -0.809
+  0.345   0.476  -0.809
+  0.267   0.524  -0.809
+  0.182   0.559  -0.809
+  0.092   0.581  -0.809
+  0.000   0.588  -0.809
+ -0.092   0.581  -0.809
+ -0.182   0.559  -0.809
+ -0.267   0.524  -0.809
+ -0.345   0.476  -0.809
+ -0.416   0.416  -0.809
+ -0.476   0.345  -0.809
+ -0.524   0.267  -0.809
+ -0.559   0.182  -0.809
+ -0.581   0.092  -0.809
+ -0.588   0.000  -0.809
+ -0.581  -0.092  -0.809
+ -0.559  -0.182  -0.809
+ -0.524  -0.267  -0.809
+ -0.476  -0.345  -0.809
+ -0.416  -0.416  -0.809
+ -0.345  -0.476  -0.809
+ -0.267  -0.524  -0.809
+ -0.182  -0.559  -0.809
+ -0.092  -0.581  -0.809
+ -0.000  -0.588  -0.809
+  0.092  -0.581  -0.809
+  0.182  -0.559  -0.809
+  0.267  -0.524  -0.809
+  0.345  -0.476  -0.809
+  0.416  -0.416  -0.809
+  0.476  -0.345  -0.809
+  0.524  -0.267  -0.809
+  0.559  -0.182  -0.809
+  0.581  -0.092  -0.809
+  0.454   0.000  -0.891
+  0.448   0.071  -0.891
+  0.432   0.140  -0.891
+  0.405   0.206  -0.891
+  0.367   0.267  -0.891
+  0.321   0.321  -0.891
+  0.267   0.367  -0.891
+  0.206   0.405  -0.891
+  0.140   0.432  -0.891
+  0.071   0.448  -0.891
+  0.000   0.454  -0.891
+ -0.071   0.448  -0.891
+ -0.140   0.432  -0.891
+ -0.206   0.405  -0.891
+ -0.267   0.367  -0.891
+ -0.321   0.321  -0.891
+ -0.367   0.267  -0.891
+ -0.405   0.206  -0.891
+ -0.432   0.140  -0.891
+ -0.448   0.071  -0.891
+ -0.454   0.000  -0.891
+ -0.448  -0.071  -0.891
+ -0.432  -0.140  -0.891
+ -0.405  -0.206  -0.891
+ -0.367  -0.267  -0.891
+ -0.321  -0.321  -0.891
+ -0.267  -0.367  -0.891
+ -0.206  -0.405  -0.891
+ -0.140  -0.432  -0.891
+ -0.071  -0.448  -0.891
+ -0.000  -0.454  -0.891
+  0.071  -0.448  -0.891
+  0.140  -0.432  -0.891
+  0.206  -0.405  -0.891
+  0.267  -0.367  -0.891
+  0.321  -0.321  -0.891
+  0.367  -0.267  -0.891
+  0.405  -0.206  -0.891
+  0.432  -0.140  -0.891
+  0.448  -0.071  -0.891
+  0.309   0.000  -0.951
+  0.305   0.048  -0.951
+  0.294   0.095  -0.951
+  0.275   0.140  -0.951
+  0.250   0.182  -0.951
+  0.219   0.219  -0.951
+  0.182   0.250  -0.951
+  0.140   0.275  -0.951
+  0.095   0.294  -0.951
+  0.048   0.305  -0.951
+  0.000   0.309  -0.951
+ -0.048   0.305  -0.951
+ -0.095   0.294  -0.951
+ -0.140   0.275  -0.951
+ -0.182   0.250  -0.951
+ -0.219   0.219  -0.951
+ -0.250   0.182  -0.951
+ -0.275   0.140  -0.951
+ -0.294   0.095  -0.951
+ -0.305   0.048  -0.951
+ -0.309   0.000  -0.951
+ -0.305  -0.048  -0.951
+ -0.294  -0.095  -0.951
+ -0.275  -0.140  -0.951
+ -0.250  -0.182  -0.951
+ -0.219  -0.219  -0.951
+ -0.182  -0.250  -0.951
+ -0.140  -0.275  -0.951
+ -0.095  -0.294  -0.951
+ -0.048  -0.305  -0.951
+ -0.000  -0.309  -0.951
+  0.048  -0.305  -0.951
+  0.095  -0.294  -0.951
+  0.140  -0.275  -0.951
+  0.182  -0.250  -0.951
+  0.219  -0.219  -0.951
+  0.250  -0.182  -0.951
+  0.275  -0.140  -0.951
+  0.294  -0.095  -0.951
+  0.305  -0.048  -0.951
+  0.156   0.000  -0.988
+  0.155   0.024  -0.988
+  0.149   0.048  -0.988
+  0.139   0.071  -0.988
+  0.127   0.092  -0.988
+  0.111   0.111  -0.988
+  0.092   0.127  -0.988
+  0.071   0.139  -0.988
+  0.048   0.149  -0.988
+  0.024   0.155  -0.988
+  0.000   0.156  -0.988
+ -0.024   0.155  -0.988
+ -0.048   0.149  -0.988
+ -0.071   0.139  -0.988
+ -0.092   0.127  -0.988
+ -0.111   0.111  -0.988
+ -0.127   0.092  -0.988
+ -0.139   0.071  -0.988
+ -0.149   0.048  -0.988
+ -0.155   0.024  -0.988
+ -0.156   0.000  -0.988
+ -0.155  -0.024  -0.988
+ -0.149  -0.048  -0.988
+ -0.139  -0.071  -0.988
+ -0.127  -0.092  -0.988
+ -0.111  -0.111  -0.988
+ -0.092  -0.127  -0.988
+ -0.071  -0.139  -0.988
+ -0.048  -0.149  -0.988
+ -0.024  -0.155  -0.988
+ -0.000  -0.156  -0.988
+  0.024  -0.155  -0.988
+  0.048  -0.149  -0.988
+  0.071  -0.139  -0.988
+  0.092  -0.127  -0.988
+  0.111  -0.111  -0.988
+  0.127  -0.092  -0.988
+  0.139  -0.071  -0.988
+  0.149  -0.048  -0.988
+  0.155  -0.024  -0.988

easymocap/visualize/assets/sphere_vertices_4.txt

@@ -0,0 +1,26 @@
+  0.000   0.000   1.000
+  0.000   0.000  -1.000
+  0.707   0.000   0.707
+  0.500   0.500   0.707
+  0.000   0.707   0.707
+ -0.500   0.500   0.707
+ -0.707   0.000   0.707
+ -0.500  -0.500   0.707
+ -0.000  -0.707   0.707
+  0.500  -0.500   0.707
+  1.000   0.000   0.000
+  0.707   0.707   0.000
+  0.000   1.000   0.000
+ -0.707   0.707   0.000
+ -1.000   0.000   0.000
+ -0.707  -0.707   0.000
+ -0.000  -1.000   0.000
+  0.707  -0.707   0.000
+  0.707   0.000  -0.707
+  0.500   0.500  -0.707
+  0.000   0.707  -0.707
+ -0.500   0.500  -0.707
+ -0.707   0.000  -0.707
+ -0.500  -0.500  -0.707
+ -0.000  -0.707  -0.707
+  0.500  -0.500  -0.707

easymocap/visualize/assets/sphere_vertices_8.txt

@@ -0,0 +1,114 @@
+  0.000   0.000   1.000
+  0.000   0.000  -1.000
+  0.383   0.000   0.924
+  0.354   0.146   0.924
+  0.271   0.271   0.924
+  0.146   0.354   0.924
+  0.000   0.383   0.924
+ -0.146   0.354   0.924
+ -0.271   0.271   0.924
+ -0.354   0.146   0.924
+ -0.383   0.000   0.924
+ -0.354  -0.146   0.924
+ -0.271  -0.271   0.924
+ -0.146  -0.354   0.924
+ -0.000  -0.383   0.924
+  0.146  -0.354   0.924
+  0.271  -0.271   0.924
+  0.354  -0.146   0.924
+  0.707   0.000   0.707
+  0.653   0.271   0.707
+  0.500   0.500   0.707
+  0.271   0.653   0.707
+  0.000   0.707   0.707
+ -0.271   0.653   0.707
+ -0.500   0.500   0.707
+ -0.653   0.271   0.707
+ -0.707   0.000   0.707
+ -0.653  -0.271   0.707
+ -0.500  -0.500   0.707
+ -0.271  -0.653   0.707
+ -0.000  -0.707   0.707
+  0.271  -0.653   0.707
+  0.500  -0.500   0.707
+  0.653  -0.271   0.707
+  0.924   0.000   0.383
+  0.854   0.354   0.383
+  0.653   0.653   0.383
+  0.354   0.854   0.383
+  0.000   0.924   0.383
+ -0.354   0.854   0.383
+ -0.653   0.653   0.383
+ -0.854   0.354   0.383
+ -0.924   0.000   0.383
+ -0.854  -0.354   0.383
+ -0.653  -0.653   0.383
+ -0.354  -0.854   0.383
+ -0.000  -0.924   0.383
+  0.354  -0.854   0.383
+  0.653  -0.653   0.383
+  0.854  -0.354   0.383
+  1.000   0.000   0.000
+  0.924   0.383   0.000
+  0.707   0.707   0.000
+  0.383   0.924   0.000
+  0.000   1.000   0.000
+ -0.383   0.924   0.000
+ -0.707   0.707   0.000
+ -0.924   0.383   0.000
+ -1.000   0.000   0.000
+ -0.924  -0.383   0.000
+ -0.707  -0.707   0.000
+ -0.383  -0.924   0.000
+ -0.000  -1.000   0.000
+  0.383  -0.924   0.000
+  0.707  -0.707   0.000
+  0.924  -0.383   0.000
+  0.924   0.000  -0.383
+  0.854   0.354  -0.383
+  0.653   0.653  -0.383
+  0.354   0.854  -0.383
+  0.000   0.924  -0.383
+ -0.354   0.854  -0.383
+ -0.653   0.653  -0.383
+ -0.854   0.354  -0.383
+ -0.924   0.000  -0.383
+ -0.854  -0.354  -0.383
+ -0.653  -0.653  -0.383
+ -0.354  -0.854  -0.383
+ -0.000  -0.924  -0.383
+  0.354  -0.854  -0.383
+  0.653  -0.653  -0.383
+  0.854  -0.354  -0.383
+  0.707   0.000  -0.707
+  0.653   0.271  -0.707
+  0.500   0.500  -0.707
+  0.271   0.653  -0.707
+  0.000   0.707  -0.707
+ -0.271   0.653  -0.707
+ -0.500   0.500  -0.707
+ -0.653   0.271  -0.707
+ -0.707   0.000  -0.707
+ -0.653  -0.271  -0.707
+ -0.500  -0.500  -0.707
+ -0.271  -0.653  -0.707
+ -0.000  -0.707  -0.707
+  0.271  -0.653  -0.707
+  0.500  -0.500  -0.707
+  0.653  -0.271  -0.707
+  0.383   0.000  -0.924
+  0.354   0.146  -0.924
+  0.271   0.271  -0.924
+  0.146   0.354  -0.924
+  0.000   0.383  -0.924
+ -0.146   0.354  -0.924
+ -0.271   0.271  -0.924
+ -0.354   0.146  -0.924
+ -0.383   0.000  -0.924
+ -0.354  -0.146  -0.924
+ -0.271  -0.271  -0.924
+ -0.146  -0.354  -0.924
+ -0.000  -0.383  -0.924
+  0.146  -0.354  -0.924
+  0.271  -0.271  -0.924
+  0.354  -0.146  -0.924

easymocap/visualize/geometry.py

@@ -2,7 +2,7 @@
   @ Date: 2021-01-17 22:44:34
   @ Author: Qing Shuai
   @ LastEditors: Qing Shuai
-  @ LastEditTime: 2021-06-20 17:24:12
+  @ LastEditTime: 2021-08-24 16:28:15
   @ FilePath: /EasyMocap/easymocap/visualize/geometry.py
 '''
 import numpy as np
@@ -30,11 +30,13 @@ def create_point(points, r=0.01):
         points (array): (N, 3)/(N, 4)
         r (float, optional): radius. Defaults to 0.01.
     """
+    points = np.array(points)
     nPoints = points.shape[0]
     vert, face = load_sphere()
+    vert = vert * r
     nVerts = vert.shape[0]
     vert = vert[None, :, :].repeat(points.shape[0], 0)
-    vert = vert + points[:, None, :]
+    vert = vert + points[:, None, :3]
     verts = np.vstack(vert)
     face = face[None, :, :].repeat(points.shape[0], 0)
     face = face + nVerts * np.arange(nPoints).reshape(nPoints, 1, 1)
@@ -148,6 +150,17 @@ def create_plane(normal, center, dx=1, dy=1, dz=0.005, color=[0.8, 0.8, 0.8]):
     vertices += np.array(center).reshape(-1, 3)
     return {'vertices': vertices, 'faces': PLANE_FACES.copy(), 'name': 'plane'}
 
+def merge_meshes(meshes):
+    verts = []
+    faces = []
+    # TODO:add colors
+    nVerts = 0
+    for mesh in meshes:
+        verts.append(mesh['vertices'])
+        faces.append(mesh['faces'] + nVerts)
+        nVerts += mesh['vertices'].shape[0]
+    return {'vertices': np.vstack(verts), 'faces':np.vstack(faces), 'name': 'compose_{}'.format(meshes[0]['name'])}
+
 def create_cameras(cameras):
     vertex = np.array([[0.203982,0.061435,0.00717595],[-0.116019,0.061435,0.00717595],[-0.116019,-0.178565,0.00717595],[0.203982,-0.178565,0.00717595],[0.203982,0.061435,-0.092824],[-0.116019,0.061435,-0.092824],[-0.116019,-0.178565,-0.092824],[0.203982,-0.178565,-0.092824],[0.131154,-0.0361827,0.00717595],[0.131154,-0.0361827,0.092176],[0.122849,-0.015207,0.00717595],[0.122849,-0.015207,0.092176],[0.109589,0.00304419,0.00717595],[0.109589,0.00304419,0.092176],[0.092206,0.0174247,0.00717595],[0.092206,0.0174247,0.092176],[0.071793,0.0270302,0.00717595],[0.071793,0.0270302,0.092176],[0.0496327,0.0312577,0.00717595],[0.0496327,0.0312577,0.092176],[0.0271172,0.0298412,0.00717595],[0.0271172,0.0298412,0.092176],[0.00566135,0.0228697,0.00717595],[0.00566135,0.0228697,0.092176],[-0.0133865,0.0107812,0.00717595],[-0.0133865,0.0107812,0.092176],[-0.02883,-0.0056643,0.00717595],[-0.02883,-0.0056643,0.092176],[-0.0396985,-0.0254336,0.00717595],[-0.0396985,-0.0254336,0.092176],[-0.045309,-0.0472848,0.00717595],[-0.045309,-0.0472848,0.092176],[-0.045309,-0.069845,0.00717595],[-0.045309,-0.069845,0.092176],[-0.0396985,-0.091696,0.00717595],[-0.0396985,-0.091696,0.092176],[-0.02883,-0.111466,0.00717595],[-0.02883,-0.111466,0.092176],[-0.0133865,-0.127911,0.00717595],[-0.0133865,-0.127911,0.092176],[0.00566135,-0.14,0.00717595],[0.00566135,-0.14,0.092176],[0.0271172,-0.146971,0.00717595],[0.0271172,-0.146971,0.092176],[0.0496327,-0.148388,0.00717595],[0.0496327,-0.148388,0.092176],[0.071793,-0.14416,0.00717595],[0.071793,-0.14416,0.092176],[0.092206,-0.134554,0.00717595],[0.092206,-0.134554,0.092176],[0.109589,-0.120174,0.00717595],[0.109589,-0.120174,0.092176],[0.122849,-0.101923,0.00717595],[0.122849,-0.101923,0.092176],[0.131154,-0.080947,0.00717595],[0.131154,-0.080947,0.092176],[0.133982,-0.058565,0.00717595],[0.133982,-0.058565,0.092176],[-0.0074325,0.061435,-0.0372285],[-0.0074325,0.074435,-0.0372285],[-0.0115845,0.061435,-0.0319846],[-0.0115845,0.074435,-0.0319846],[-0.018215,0.061435,-0.0274218],[-0.018215,0.074435,-0.0274218],[-0.0269065,0.061435,-0.0238267],[-0.0269065,0.074435,-0.0238267],[-0.0371125,0.061435,-0.0214253],[-0.0371125,0.074435,-0.0214253],[-0.048193,0.061435,-0.0203685],[-0.048193,0.074435,-0.0203685],[-0.0594505,0.061435,-0.0207226],[-0.0594505,0.074435,-0.0207226],[-0.0701785,0.061435,-0.0224655],[-0.0701785,0.074435,-0.0224655],[-0.0797025,0.061435,-0.0254875],[-0.0797025,0.074435,-0.0254875],[-0.0874245,0.061435,-0.0295989],[-0.0874245,0.074435,-0.0295989],[-0.0928585,0.061435,-0.0345412],[-0.0928585,0.074435,-0.0345412],[-0.0956635,0.061435,-0.040004],[-0.0956635,0.074435,-0.040004],[-0.0956635,0.061435,-0.045644],[-0.0956635,0.074435,-0.045644],[-0.0928585,0.061435,-0.051107],[-0.0928585,0.074435,-0.051107],[-0.0874245,0.061435,-0.056049],[-0.0874245,0.074435,-0.056049],[-0.0797025,0.061435,-0.0601605],[-0.0797025,0.074435,-0.0601605],[-0.0701785,0.061435,-0.0631825],[-0.0701785,0.074435,-0.0631825],[-0.0594505,0.061435,-0.0649255],[-0.0594505,0.074435,-0.0649255],[-0.048193,0.061435,-0.0652795],[-0.048193,0.074435,-0.0652795],[-0.0371125,0.061435,-0.064223],[-0.0371125,0.074435,-0.064223],[-0.0269065,0.061435,-0.0618215],[-0.0269065,0.074435,-0.0618215],[-0.018215,0.061435,-0.0582265],[-0.018215,0.074435,-0.0582265],[-0.0115845,0.061435,-0.0536635],[-0.0115845,0.074435,-0.0536635],[-0.0074325,0.061435,-0.0484195],[-0.0074325,0.074435,-0.0484195],[-0.0060185,0.061435,-0.0428241],[-0.0060185,0.074435,-0.0428241]])*0.5
     tri = [[4,3,2],[1,4,2],[6,1,2],[6,5,1],[8,4,1],[5,8,1],[3,7,2],[7,6,2],[4,7,3],[8,7,4],[6,7,5],[7,8,5],[43,42,44],[42,43,41],[43,46,45],[46,43,44],[58,9,57],[9,58,10],[55,58,57],[56,58,55],[53,54,55],[54,56,55],[12,11,9],[12,9,10],[21,20,22],[20,21,19],[34,33,32],[32,33,31],[35,36,37],[37,36,38],[33,36,35],[36,33,34],[29,30,31],[30,32,31],[40,39,37],[40,37,38],[39,40,41],[40,42,41],[47,48,49],[49,48,50],[48,47,45],[46,48,45],[49,52,51],[52,49,50],[52,53,51],[52,54,53],[14,15,13],[15,14,16],[11,14,13],[12,14,11],[18,17,15],[18,15,16],[17,18,19],[18,20,19],[27,35,37],[17,27,15],[27,53,55],[27,49,51],[11,27,9],[27,47,49],[27,33,35],[23,27,21],[27,39,41],[27,55,57],[9,27,57],[15,27,13],[39,27,37],[47,27,45],[53,27,51],[27,11,13],[43,27,41],[27,29,31],[27,43,45],[27,17,19],[21,27,19],[33,27,31],[27,23,25],[23,24,25],[25,24,26],[24,21,22],[24,23,21],[28,36,34],[42,28,44],[28,58,56],[54,28,56],[52,28,54],[28,34,32],[28,46,44],[18,28,20],[20,28,22],[30,28,32],[40,28,42],[58,28,10],[28,48,46],[28,12,10],[28,14,12],[36,28,38],[28,24,22],[28,40,38],[48,28,50],[28,52,50],[14,28,16],[28,18,16],[24,28,26],[28,27,25],[28,25,26],[28,30,29],[27,28,29],[108,59,60],[59,108,107],[62,59,61],[59,62,60],[103,102,101],[102,103,104],[64,61,63],[64,62,61],[70,67,69],[67,70,68],[70,71,72],[71,70,69],[83,84,82],[83,82,81],[86,85,87],[86,87,88],[86,83,85],[83,86,84],[77,78,75],[75,78,76],[105,106,103],[103,106,104],[108,106,107],[106,105,107],[97,96,95],[96,97,98],[96,93,95],[93,96,94],[93,92,91],[92,93,94],[79,105,103],[59,79,61],[79,93,91],[83,79,85],[85,79,87],[61,79,63],[79,103,101],[65,79,67],[79,99,97],[89,79,91],[79,77,75],[79,59,107],[67,79,69],[79,89,87],[79,73,71],[105,79,107],[79,97,95],[79,71,69],[79,83,81],[99,79,101],[93,79,95],[79,65,63],[73,79,75],[99,100,97],[97,100,98],[102,100,101],[100,99,101],[89,90,87],[87,90,88],[90,89,91],[92,90,91],[66,67,68],[66,65,67],[66,64,63],[65,66,63],[74,75,76],[74,73,75],[71,74,72],[73,74,71],[80,106,108],[74,80,72],[86,80,84],[84,80,82],[64,80,62],[80,108,60],[80,100,102],[62,80,60],[66,80,64],[80,70,72],[80,102,104],[96,80,94],[80,90,92],[70,80,68],[80,86,88],[78,80,76],[106,80,104],[80,96,98],[80,92,94],[100,80,98],[90,80,88],[80,66,68],[80,74,76],[82,80,81],[80,79,81],[80,78,77],[79,80,77]]
@@ -159,6 +172,7 @@ def create_cameras(cameras):
         meshes.append({
             'vertices': vertices, 'faces': triangles, 'name': 'camera_{}'.format(nv), 'vid': nv
         })
+    meshes = merge_meshes(meshes)
     return meshes
 
 import os
@@ -203,3 +217,6 @@ def create_mesh_pyrender(vert, faces, col):
         mesh,
         material=material)
     return mesh
+
+if __name__ == "__main__":
+    pass

easymocap/visualize/skelmodel.py

@@ -2,8 +2,8 @@
   @ Date: 2021-01-17 21:38:19
   @ Author: Qing Shuai
   @ LastEditors: Qing Shuai
-  @ LastEditTime: 2021-06-28 11:43:00
-  @ FilePath: /EasyMocapRelease/easymocap/visualize/skelmodel.py
+  @ LastEditTime: 2021-08-24 16:42:22
+  @ FilePath: /EasyMocap/easymocap/visualize/skelmodel.py
 '''
 import numpy as np
 import cv2
@@ -39,7 +39,7 @@ def calTransformation(v_i, v_j, r, adaptr=False, ratio=10):
     return T, r, length
 
 class SkelModel:
-    def __init__(self, nJoints=None, kintree=None, body_type=None, joint_radius=0.02, **kwargs) -> None:
+    def __init__(self, nJoints=None, kintree=None, body_type=None, joint_radius=0.02, res=20, **kwargs) -> None:
         if nJoints is not None:
             self.nJoints = nJoints
             self.kintree = kintree
@@ -50,8 +50,8 @@ class SkelModel:
         self.body_type = body_type
         self.device = 'none'
         cur_dir = os.path.dirname(__file__)
-        faces = np.loadtxt(join(cur_dir, 'sphere_faces_20.txt'), dtype=np.int)
-        self.vertices = np.loadtxt(join(cur_dir, 'sphere_vertices_20.txt'))
+        faces = np.loadtxt(join(cur_dir, 'assets', 'sphere_faces_{}.txt'.format(res)), dtype=np.int)
+        self.vertices = np.loadtxt(join(cur_dir, 'assets', 'sphere_vertices_{}.txt'.format(res)))
         # compose faces
         faces_all = []
         for nj in range(self.nJoints):
@@ -69,7 +69,7 @@ class SkelModel:
         if not return_verts:
             return keypoints3d
         if keypoints3d.shape[-1] == 3: # add confidence
-            keypoints3d = np.hstack((keypoints3d, np.ones((keypoints3d.shape[0], 1))))
+            keypoints3d = np.dstack((keypoints3d, np.ones((keypoints3d.shape[0], keypoints3d.shape[1], 1))))
         r = self.joint_radius
         # joints 
         min_conf = 0.1