diff --git a/apps/calibration/detect_chessboard.py b/apps/calibration/detect_chessboard.py index 057f6e6..ad45cd4 100644 --- a/apps/calibration/detect_chessboard.py +++ b/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') diff --git a/apps/vis/vis_smpl.py b/apps/vis/vis_smpl.py new file mode 100644 index 0000000..e2e0ee2 --- /dev/null +++ b/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() \ No newline at end of file diff --git a/doc/installation.md b/doc/installation.md index 8c773cf..cfd91b5 100644 --- a/doc/installation.md +++ b/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 diff --git a/doc/quickstart.md b/doc/quickstart.md index eae3453..72d2ab6 100644 --- a/doc/quickstart.md +++ b/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. diff --git a/easymocap/affinity/plucker.py b/easymocap/affinity/plucker.py index 6bc18a1..b39d868 100644 --- a/easymocap/affinity/plucker.py +++ b/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): @@ -69,4 +69,17 @@ def computeRay(keypoints2d, invK, R, T): l, m = plucker_from_pp(cam_center.T, kp_all_3d) res = np.hstack((l, m, conf)) # 兼容cpp版本，所以补一个维度 - return res[None, :, :] \ No newline at end of file + 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 \ No newline at end of file diff --git a/easymocap/annotator/__init__.py b/easymocap/annotator/__init__.py index 5874c0d..85682fb 100644 --- a/easymocap/annotator/__init__.py +++ b/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 diff --git a/easymocap/annotator/basic_annotator.py b/easymocap/annotator/basic_annotator.py index ae15326..730b902 100644 --- a/easymocap/annotator/basic_annotator.py +++ b/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']: @@ -23,28 +24,53 @@ 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) - cv2.namedWindow(self.name) - callback = ComposedCallback(processes=callbacks) - cv2.setMouseCallback(self.name, callback.call, self.param) + 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) @property def working(self): @@ -57,33 +83,30 @@ 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] - shutil.copy(annname, annname_) + if annname is not None: + shutil.copy(annname, annname_) @property def frame(self): @@ -97,25 +120,34 @@ 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): self.visited_frames.add(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) - cv2.imshow(self.name, img) \ No newline at end of file + 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[:, 1])&(click[:, 1] 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 \ No newline at end of file diff --git a/easymocap/annotator/basic_callback.py b/easymocap/annotator/basic_callback.py index daa28d5..372be2c 100644 --- a/easymocap/annotator/basic_callback.py +++ b/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: @@ -38,12 +45,14 @@ 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(): - param['select'][key] = -1 + if key != 'bbox': + param['select'][key] = -1 elif event == cv2.EVENT_MOUSEMOVE and flags == cv2.EVENT_FLAG_LBUTTON: param['end'] = (x, y) elif event == cv2.EVENT_LBUTTONUP: diff --git a/easymocap/annotator/basic_keyboard.py b/easymocap/annotator/basic_keyboard.py index db790db..6c8b478 100644 --- a/easymocap/annotator/basic_keyboard.py +++ b/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 = { @@ -30,18 +58,18 @@ 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) annotator.frame = newframe - move.__doc__ = text[wasd] + move.__doc__ = text[wasd] return move 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: +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 - else: - param['annots']['annots'].pop(active) - param['select']['bbox'] = -1 - 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" - keys = input('Enter the ordered key(separate with blank): ').split(' ') - repeats = int(input('Input the repeat times: (0->{})'.format(len(self.dataset)-self.frame))) +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(' ') + 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): - register_keys[str(i)] = set_personID(i) \ No newline at end of file +for i in range(5): + register_keys[str(i)] = set_personID(i) + register_keys['s'+str(i)] = choose_personID(i) \ No newline at end of file diff --git a/easymocap/annotator/basic_visualize.py b/easymocap/annotator/basic_visualize.py index 2f14449..5d5234e 100644 --- a/easymocap/annotator/basic_visualize.py +++ b/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): - 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) +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): @@ -52,60 +80,79 @@ 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)/2 + 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)), + color, -1) 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) - 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_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']) + 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) return img -def vis_active_bbox(img, annots, select, **kwargs): - active = select['bbox'] - if active == -1: +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 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, diff --git a/easymocap/annotator/bbox_callback.py b/easymocap/annotator/bbox_callback.py index 9184c15..807940c 100644 --- a/easymocap/annotator/bbox_callback.py +++ b/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 @@ -10,74 +24,190 @@ 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 - (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: + # Move the corner + if select['corner'] < 4: + (i, j) = [(0, 1), (0, 3), (2, 1), (2, 3)][select['corner']] + 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'] + 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, :, :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): + edges.append((n, p, dist[n, p])) + edges.sort(key=lambda x:x[2]) + used_n, used_p = [], [] + for n, p, _ in edges: + if n in used_n or p in used_p: + continue + annots[n]['personID'] = previous['annots'][p]['personID'] + 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'] - keypoints_pre = np.array([d['keypoints'] for d in previous['annots']]) - keypoints_now = np.array([d['keypoints'] 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) - dist = np.sum(diff * conf, axis=-1)/np.sum(conf, axis=-1) - nows, pres = np.where(dist < MAX_SPEED) - edges = [] - for n, p in zip(nows, pres): - edges.append((n, p, dist[n, p])) - edges.sort(key=lambda x:x[2]) - used_n, used_p = [], [] - for n, p, _ in edges: - if n in used_n or p in used_p: - continue - annots[n]['personID'] = previous['annots'][p]['personID'] - used_n.append(n) - used_p.append(p) - # TODO:stop when missing - \ No newline at end of file + 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 \ No newline at end of file diff --git a/easymocap/annotator/chessboard.py b/easymocap/annotator/chessboard.py index 50628d2..4290c2c 100644 --- a/easymocap/annotator/chessboard.py +++ b/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) diff --git a/easymocap/annotator/keypoints_callback.py b/easymocap/annotator/keypoints_callback.py new file mode 100644 index 0000000..0d75887 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/easymocap/annotator/keypoints_keyboard.py b/easymocap/annotator/keypoints_keyboard.py new file mode 100644 index 0000000..18f105d --- /dev/null +++ b/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 \ No newline at end of file diff --git a/easymocap/annotator/vanish_callback.py b/easymocap/annotator/vanish_callback.py index e87fadb..a62041a 100644 --- a/easymocap/annotator/vanish_callback.py +++ b/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 diff --git a/easymocap/annotator/vanish_visualize.py b/easymocap/annotator/vanish_visualize.py index 388bd71..1872af8 100644 --- a/easymocap/annotator/vanish_visualize.py +++ b/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): diff --git a/easymocap/config/yacs.py b/easymocap/config/yacs.py index 39d3989..31d9b70 100644 --- a/easymocap/config/yacs.py +++ b/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 diff --git a/easymocap/mytools/reader.py b/easymocap/mytools/reader.py index 6333c26..b200881 100644 --- a/easymocap/mytools/reader.py +++ 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=[]): diff --git a/easymocap/mytools/vis_base.py b/easymocap/mytools/vis_base.py index e1b4da3..325548c 100644 --- a/easymocap/mytools/vis_base.py +++ b/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) diff --git a/easymocap/smplmodel/body_model.py b/easymocap/smplmodel/body_model.py index 3524302..ea88066 100644 --- a/easymocap/smplmodel/body_model.py +++ b/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 - poses = self.extend_pose(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 \ No newline at end of file diff --git a/easymocap/smplmodel/lbs.py b/easymocap/smplmodel/lbs.py index c544911..4c82dd2 100644 --- a/easymocap/smplmodel/lbs.py +++ b/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) \ No newline at end of file diff --git a/easymocap/visualize/assets/sphere_faces_2.txt b/easymocap/visualize/assets/sphere_faces_2.txt new file mode 100644 index 0000000..295c081 --- /dev/null +++ b/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 diff --git a/easymocap/visualize/assets/sphere_faces_20.txt b/easymocap/visualize/assets/sphere_faces_20.txt new file mode 100644 index 0000000..ab11027 --- /dev/null +++ b/easymocap/visualize/assets/sphere_faces_20.txt @@ -0,0 +1,1520 @@ + 0 2 3 + 1 723 722 + 0 3 4 + 1 724 723 + 0 4 5 + 1 725 724 + 0 5 6 + 1 726 725 + 0 6 7 + 1 727 726 + 0 7 8 + 1 728 727 + 0 8 9 + 1 729 728 + 0 9 10 + 1 730 729 + 0 10 11 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b/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 diff --git a/easymocap/visualize/assets/sphere_vertices_8.txt b/easymocap/visualize/assets/sphere_vertices_8.txt new file mode 100644 index 0000000..41918dd --- /dev/null +++ b/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 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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 diff --git a/easymocap/visualize/geometry.py b/easymocap/visualize/geometry.py index e2ad0e8..10f54a5 100644 --- a/easymocap/visualize/geometry.py +++ b/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 @@ -202,4 +216,7 @@ def create_mesh_pyrender(vert, faces, col): mesh = pyrender.Mesh.from_trimesh( mesh, material=material) - return mesh \ No newline at end of file + return mesh + +if __name__ == "__main__": + pass \ No newline at end of file diff --git a/easymocap/visualize/skelmodel.py b/easymocap/visualize/skelmodel.py index fd39ce4..32d04c0 100644 --- a/easymocap/visualize/skelmodel.py +++ b/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