From abd9b905b6ccc75b20d797694ffa1befa4fc66c6 Mon Sep 17 00:00:00 2001 From: Qing Shuai Date: Tue, 25 Oct 2022 20:57:27 +0800 Subject: [PATCH] update tools --- easymocap/annotator/__init__.py | 1 - easymocap/annotator/basic_annotator.py | 8 +- easymocap/annotator/basic_dataset.py | 14 ++- easymocap/annotator/chessboard.py | 110 ++++++++++++++++++++++-- easymocap/datasets/base.py | 53 ++++++++++-- easymocap/multistage/base.py | 70 ++++++++------- easymocap/multistage/init_cnn.py | 7 +- easymocap/multistage/initialize.py | 4 +- easymocap/multistage/lossbase.py | 27 +++++- easymocap/mytools/camera_utils.py | 4 +- easymocap/mytools/timer.py | 80 +++++++++++++++++ easymocap/mytools/triangulator.py | 11 +++ easymocap/visualize/ffmpeg_wrapper.py | 5 +- easymocap/visualize/pyrender_wrapper.py | 21 ++--- 14 files changed, 342 insertions(+), 73 deletions(-) create mode 100644 easymocap/mytools/timer.py diff --git a/easymocap/annotator/__init__.py b/easymocap/annotator/__init__.py index 85682fb..7032961 100644 --- a/easymocap/annotator/__init__.py +++ b/easymocap/annotator/__init__.py @@ -10,7 +10,6 @@ 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 # create, delete, copy from .bbox_callback import create_bbox, delete_bbox, delete_all_bbox, copy_previous_bbox, copy_previous_missing diff --git a/easymocap/annotator/basic_annotator.py b/easymocap/annotator/basic_annotator.py index bae306f..eafdacf 100644 --- a/easymocap/annotator/basic_annotator.py +++ b/easymocap/annotator/basic_annotator.py @@ -1,5 +1,6 @@ import shutil import cv2 +import os from tqdm import tqdm from .basic_keyboard import print_help, register_keys from .basic_visualize import plot_text, resize_to_screen, merge @@ -203,6 +204,7 @@ class AnnotBase: param['frame'] = nf param['annots'] = annots if not no_img: + assert os.path.exists(imgname), imgname img0 = cv2.imread(imgname) param['img0'] = img0 # param['pid'] = len(annot['annots']) @@ -239,6 +241,7 @@ class AnnotBase: img = func(img, **self.param) if not self.no_window: cv2.imshow(self.name, img) + return img class AnnotMV: def __init__(self, datasets, key_funcs={}, key_funcs_view={}, callbacks=[], vis_funcs=[], vis_funcs_all=[], @@ -602,8 +605,11 @@ def parse_parser(parser): data = read_json(args.from_file) args.sub = sorted([v['vid'] for v in data]) elif len(args.sub) == 0: + if not os.path.exists(join(args.path, args.image)): + print('{} not exists, Please run extract_image first'.format(join(args.path, args.image))) + raise FileNotFoundError subs = sorted(os.listdir(join(args.path, args.image))) - subs = [s for s in subs if os.path.isdir(join(args.path, args.image, s))] + subs = [s for s in subs if os.path.isdir(join(args.path, args.image, s)) and not s.startswith('._')] if len(subs) > 0 and subs[0].isdigit(): subs = sorted(subs, key=lambda x:int(x)) args.sub = subs diff --git a/easymocap/annotator/basic_dataset.py b/easymocap/annotator/basic_dataset.py index 0694d5b..8730cfd 100644 --- a/easymocap/annotator/basic_dataset.py +++ b/easymocap/annotator/basic_dataset.py @@ -2,8 +2,8 @@ @ Date: 2021-04-15 16:57:53 @ Author: Qing Shuai @ LastEditors: Qing Shuai - @ LastEditTime: 2021-10-15 16:43:12 - @ FilePath: /EasyMocap/easymocap/annotator/basic_dataset.py + @ LastEditTime: 2022-09-15 21:58:57 + @ FilePath: /EasyMocapPublic/easymocap/annotator/basic_dataset.py ''' from os.path import join import os @@ -25,13 +25,20 @@ class ImageFolder: self.annot_root_tmp = join(path, self.annot + '_tmp') if os.path.exists(self.annot_root_tmp) and remove_tmp: shutil.rmtree(self.annot_root_tmp) + print('- Load data from {}'.format(path)) if sub is None: + print('- Try to find image names...') self.imgnames = getFileList(self.image_root, ext=ext, max=max_per_folder) + print(' -> find {} images'.format(len(self.imgnames))) if not no_annot: + print('- Try to find annot names...') self.annnames = getFileList(self.annot_root, ext='.json') + print(' -> find {} annots'.format(len(self.annnames))) else: + print('- Try to find image names of camera {}...'.format(sub)) self.imgnames = getFileList(join(self.image_root, sub), ext=ext) self.imgnames = [join(sub, name) for name in self.imgnames] + print(' -> find {} images'.format(len(self.imgnames))) if not no_annot: self.annnames = getFileList(join(self.annot_root, sub), ext='.json') self.annnames = [join(sub, name) for name in self.annnames] @@ -46,6 +53,9 @@ class ImageFolder: self.no_annot = no_annot def __getitem__(self, index): + if index > len(self.imgnames): + print('!!! You are try to read {} image from {} images'.format(index, len(self.imgnames))) + print('!!! Please check image path: {}'.format(self.image_root)) imgname = join(self.image_root, self.imgnames[index]) if self.no_annot: annname = None diff --git a/easymocap/annotator/chessboard.py b/easymocap/annotator/chessboard.py index cc43595..fae53be 100644 --- a/easymocap/annotator/chessboard.py +++ b/easymocap/annotator/chessboard.py @@ -2,8 +2,8 @@ @ Date: 2021-04-13 16:14:36 @ Author: Qing Shuai @ LastEditors: Qing Shuai - @ LastEditTime: 2022-08-17 16:49:40 - @ FilePath: /EasyMocapPublic/easymocap/annotator/chessboard.py + @ LastEditTime: 2022-10-25 20:56:26 + @ FilePath: /EasyMocapRelease/easymocap/annotator/chessboard.py ''' import numpy as np import cv2 @@ -101,14 +101,14 @@ def create_chessboard(path, keypoints3d, out='annots'): annots['keypoints3d'] = template['keypoints3d'] save_json(annname, annots) -ARUCO_DICT = { - "4X4_50": cv2.aruco.DICT_4X4_50, - "4X4_100": cv2.aruco.DICT_4X4_100, - "5X5_100": cv2.aruco.DICT_5X5_100, - "5X5_250": cv2.aruco.DICT_5X5_250, -} def detect_charuco(image, aruco_type, long, short, squareLength, aruco_len): + ARUCO_DICT = { + "4X4_50": cv2.aruco.DICT_4X4_50, + "4X4_100": cv2.aruco.DICT_4X4_100, + "5X5_100": cv2.aruco.DICT_5X5_100, + "5X5_250": cv2.aruco.DICT_5X5_250, + } # 创建ChArUco标定板 dictionary = cv2.aruco.getPredefinedDictionary(dict=ARUCO_DICT[aruco_type]) board = cv2.aruco.CharucoBoard_create( @@ -138,4 +138,96 @@ def detect_charuco(image, aruco_type, long, short, squareLength, aruco_len): keypoints2d[ids, 2] = 1. else: retval = False - return retval, keypoints2d, corners3d \ No newline at end of file + return retval, keypoints2d, corners3d + +class CharucoBoard: + def __init__(self, long, short, squareLength, aruco_len, aruco_type) -> None: + ''' + short,long 分别表示短边、长边的格子数. + squareLength,aruco_len 分别表示棋盘格的边长与aruco的边长. + aruco_type 表示Aruco的类型 4X4表示aruco中的白色格子是4x4的 _50表示aruco字典中有多少种aruco. + ''' + # 定义现有的Aruco类型 + self.ARUCO_DICT = { + "4X4_50": cv2.aruco.DICT_4X4_50, + "4X4_100": cv2.aruco.DICT_4X4_100, + "5X5_100": cv2.aruco.DICT_5X5_100, + "5X5_250": cv2.aruco.DICT_5X5_250, + } + # 创建ChArUco标定板 + dictionary = cv2.aruco.getPredefinedDictionary(dict=self.ARUCO_DICT[aruco_type]) + board = cv2.aruco.CharucoBoard_create( + squaresY=long, + squaresX=short, + squareLength=squareLength, + markerLength=aruco_len, + dictionary=dictionary, + ) + corners = board.chessboardCorners + # ATTN: exchange the XY + corners = corners[:, [1, 0, 2]] + self.template = { + 'keypoints3d': corners, + 'keypoints2d': np.zeros_like(corners), + 'pattern': (long-1, short-1), + 'grid_size': squareLength, + 'visted': False + } + print(corners.shape) + self.dictionary = dictionary + self.board = board + + def detect(self, img_color, annots): + # 查找标志块的左上角点 + corners, ids, _ = cv2.aruco.detectMarkers( + image=img_color, dictionary=self.dictionary, parameters=None + ) + # 棋盘格黑白块内角点 + if ids is not None: + retval, charucoCorners, charucoIds = cv2.aruco.interpolateCornersCharuco( + markerCorners=corners, markerIds=ids, image=img_color, board=self.board + ) + else: + retval = False + if retval: + # 绘制棋盘格黑白块内角点 + cv2.aruco.drawDetectedCornersCharuco( + img_color, charucoCorners, charucoIds, [0, 0, 255] + ) + if False: + cv2.aruco.drawDetectedMarkers( + image=img_color, corners=corners, ids=ids, borderColor=None + ) + + ids = charucoIds[:, 0] + pts = charucoCorners[:, 0] + annots['keypoints2d'][ids, :2] = pts + annots['keypoints2d'][ids, 2] = 1. + # if args.show: + # img_color = cv2.resize(img_color, None, fx=0.5, fy=0.5) + # cv2.imshow('vis', img_color) + # cv2.waitKey(0) + # visname = imgname.replace(images, output) + # os.makedirs(os.path.dirname(visname), exist_ok=True) + # cv2.imwrite(visname, img_color) + else: + # mywarn('Cannot find in {}'.format(imgname)) + pass + + def __call__(self, imgname, images='images', output='output'): + import os + from .file_utils import read_json, save_json + import copy + img_color = cv2.imread(imgname) + annotname = imgname.replace('images', 'chessboard').replace('.jpg', '.json') + if os.path.exists(annotname): + annots = read_json(annotname) + if annots['visited']: + return + else: + annots = copy.deepcopy(self.template) + annots['visited'] = True + self.detect(img_color, annots) + annots['keypoints2d'] = annots['keypoints2d'].tolist() + annots['keypoints3d'] = annots['keypoints3d'].tolist() + save_json(annotname, annots) \ No newline at end of file diff --git a/easymocap/datasets/base.py b/easymocap/datasets/base.py index 6690880..5a375ae 100644 --- a/easymocap/datasets/base.py +++ b/easymocap/datasets/base.py @@ -229,8 +229,13 @@ class Base(BaseData): self.image_names = get_allname(self.root, self.subs, self.ranges, **reader.image) self.reader = reader self.writer = writer - if camera != 'none' and os.path.exists(camera): - cameras = read_cameras(camera) + if camera != 'none': + if not os.path.isabs(camera): + camera = join(self.root, camera) + if os.path.exists(camera): + cameras = read_cameras(camera) + else: + cameras = None else: cameras = None self.cameras = cameras @@ -417,7 +422,7 @@ class ImageFolder(Base): if self.loadmp: for i, data_ in enumerate(data['annots']['annots']): if keyname not in data_.keys(): - if key not in self.cache_shape.keys(): + if key not in self.cache_shape.keys() and self.read_flag[keyname]: cache_shape = { 'handl2d': np.zeros((21, 3)), 'handr2d': np.zeros((21, 3)), @@ -473,6 +478,23 @@ class ImageFolder(Base): data[key+'_distort'] = np.stack([d[key+'_distort'] for d in data['annots']]) else: data.pop('annots') + if not self.loadmp: + if 'depth' in self.reader.keys(): + depthname = join(self.root, self.reader['depth']['root'], self.get_view(index)[1], '{}.png'.format(os.path.basename(data['annname']).replace('.json', ''))) + depthmap = cv2.imread(depthname, cv2.IMREAD_UNCHANGED) + depthmap = depthmap.astype(np.float32)/1000. + depths = np.zeros_like(data['keypoints2d'][:, :2]) + for i, (x, y, c) in enumerate(data['keypoints2d']): + if c < 0.3:continue + if i >= 15:continue + x, y = int(x+0.5), int(y+0.5) + if x > depthmap.shape[0] or y > depthmap.shape[1] or x < 0 or y < 0: + continue + d_value = depthmap[y, x] + if d_value < 0.1:continue + depths[i, 0] = d_value + depths[i, 1] = c + data['depth'] = depths return data def vis_data(self, data, img=None): @@ -503,14 +525,27 @@ class MultiVideo(ImageFolder): camera_for_each_image = False if 'camera' in self.reader.keys(): camera_for_each_image = True - cameras = read_cameras(join(self.root, self.reader['camera'], sub)) + if os.path.exists(join(self.root, self.reader['camera'], sub)): + cameras = read_cameras(join(self.root, self.reader['camera'], sub)) + elif os.path.exists(join(self.root, self.reader['camera'])): + cameras = read_cameras(join(self.root, self.reader['camera'])) + else: + myerror("You must give a valid camera path") + raise NotImplementedError for info in tqdm(self.image_dict[sub], 'Loading {}'.format(sub)): basename = os.path.basename(info['imgname']).split('.')[0] if camera_for_each_image: - K, dist = cameras[basename]['K'], cameras[basename]['dist'] + if basename in cameras.keys(): + camera = cameras[basename] + elif sub+'/'+basename in cameras.keys(): + camera = cameras[sub+'/'+basename] + else: + myerror("You must give a valid camera") + raise NotImplementedError + K, dist = camera['K'], camera['dist'] data = super().__getitem__(info['index'], K=K, dist=dist) for oldkey, newkey in [('K', 'K'), ('dist', 'dist'), ('R', 'Rc'), ('T', 'Tc')]: - data[newkey] = cameras[basename][oldkey].astype(np.float32) + data[newkey] = camera[oldkey].astype(np.float32) else: data = super().__getitem__(info['index']) data_all.append(data) @@ -518,6 +553,12 @@ class MultiVideo(ImageFolder): ret = self.collect_data(data_all) if self.loadmp and self.compose_mp: # 针对镜子的情况，需要load多人的数据 for key in ['keypoints2d', 'keypoints2d_distort', 'keypoints2d_unproj']: + if len(self.pids) > 0: + for i in range(len(ret[key])): + ret[key][i] = ret[key][i][:len(self.pids)] + shapes = set([v.shape for v in ret[key]]) + if len(shapes) > 1: + myerror('The shape is not the same!') ret[key] = np.stack(ret[key]) ret['pid'] = self.pids return ret diff --git a/easymocap/multistage/base.py b/easymocap/multistage/base.py index a54628d..09b9862 100644 --- a/easymocap/multistage/base.py +++ b/easymocap/multistage/base.py @@ -250,6 +250,42 @@ class MultiStage: infos[key] = val.detach().cpu() return body_params + def fit_data(self, data, body_model): + infos = data.copy() + init_params = body_model.init_params(nFrames=infos['nFrames'], nPerson=infos.get('nPerson', 1)) + # first initialize the model + for name, init_func in self.initialize.items(): + if 'loss' in init_func.keys(): + # fitting to initialize + init_params = self.fit_stage(body_model, init_params, infos, init_func, 0) + else: + # use initialize module + init_module = load_object(init_func.module, init_func.args) + init_params = init_module(body_model, init_params, infos) + # if there are multiple initialization params + # then fit each of them + if not isinstance(init_params, list): + init_params = [init_params] + results = [] + for init_param in init_params: + # check the repeat params + body_params = init_param + for stage_name, stage in self.stages.items(): + for irepeat in range(stage.get('repeat', 1)): + with Timer('optimize {}'.format(stage_name), not self.monitor.timer): + body_params = self.fit_stage(body_model, body_params, infos, stage, irepeat) + results.append(body_params) + # select the best results + if len(results) > 1: + # check the result + loss = load_object(self.check.module, self.check.args, **{key:infos[key] for key in self.check.infos}) + metrics = [loss(body_model.keypoints(body_params, return_tensor=True).cpu()).item() for body_params in results] + best_idx = np.argmin(metrics) + else: + best_idx = 0 + body_params = Params(**results[best_idx]) + return body_params, infos + def fit(self, body_model, dataset): batch_size = len(dataset) if self.batch_size == -1 else self.batch_size dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=0, drop_last=False) @@ -257,42 +293,10 @@ class MultiStage: dataloader = tqdm(dataloader, desc='optimizing') for data in dataloader: data = dataset.reshape_data(data) - infos = data.copy() - init_params = body_model.init_params(nFrames=infos['nFrames'], nPerson=infos.get('nPerson', 1)) - # first initialize the model - for name, init_func in self.initialize.items(): - if 'loss' in init_func.keys(): - # fitting to initialize - init_params = self.fit_stage(body_model, init_params, infos, init_func, 0) - else: - # use initialize module - init_module = load_object(init_func.module, init_func.args) - init_params = init_module(body_model, init_params, infos) - # if there are multiple initialization params - # then fit each of them - if not isinstance(init_params, list): - init_params = [init_params] - results = [] - for init_param in init_params: - # check the repeat params - body_params = init_param - for stage_name, stage in self.stages.items(): - for irepeat in range(stage.get('repeat', 1)): - with Timer('optimize {}'.format(stage_name), not self.monitor.timer): - body_params = self.fit_stage(body_model, body_params, infos, stage, irepeat) - results.append(body_params) - # select the best results - if len(results) > 1: - # check the result - loss = load_object(self.check.module, self.check.args, **{key:infos[key] for key in self.check.infos}) - metrics = [loss(body_model.keypoints(body_params, return_tensor=True).cpu()).item() for body_params in results] - best_idx = np.argmin(metrics) - else: - best_idx = 0 + body_params, infos = self.fit_data(data, body_model) if 'sync_offset' in body_params.keys(): offset = body_params.pop('sync_offset') dataset.write_offset(offset) - body_params = Params(**results[best_idx]) if data['nFrames'] != body_params['poses'].shape[0]: for key in body_params.keys(): if body_params[key].shape[0] == 1:continue diff --git a/easymocap/multistage/init_cnn.py b/easymocap/multistage/init_cnn.py index a4e2ce0..b51ac89 100644 --- a/easymocap/multistage/init_cnn.py +++ b/easymocap/multistage/init_cnn.py @@ -3,7 +3,7 @@ @ Author: Qing Shuai @ Mail: s_q@zju.edu.cn @ LastEditors: Qing Shuai - @ LastEditTime: 2022-07-11 22:20:44 + @ LastEditTime: 2022-08-30 19:47:04 @ FilePath: /EasyMocapPublic/easymocap/multistage/init_cnn.py ''' import os @@ -82,7 +82,10 @@ class InitSpin: print('[WARN] not enough joints: {} in first frame'.format(imgname)) else: print('[WARN] not enough joints: {}'.format(imgname)) - result = {'body_params': params_all[-1][pid]} + if self.multi_person: + result = {'body_params': params_all[-1][pid]} + else: + result = {'body_params': params_all[-1]} params = result['body_params'] params['id'] = pid params_current.append(params) diff --git a/easymocap/multistage/initialize.py b/easymocap/multistage/initialize.py index c472c08..97716f4 100644 --- a/easymocap/multistage/initialize.py +++ b/easymocap/multistage/initialize.py @@ -73,7 +73,9 @@ class InitRT: self.torso = torso def __call__(self, body_model, body_params, infos): - keypoints3d = infos['keypoints3d'].detach().cpu().numpy() + keypoints3d = infos['keypoints3d'] + if torch.is_tensor(keypoints3d): + keypoints3d = keypoints3d.detach().cpu().numpy() temp_joints = body_model.keypoints(body_params, return_tensor=False) torso = keypoints3d[..., self.torso, :3].copy() diff --git a/easymocap/multistage/lossbase.py b/easymocap/multistage/lossbase.py index c728711..85fa800 100644 --- a/easymocap/multistage/lossbase.py +++ b/easymocap/multistage/lossbase.py @@ -242,6 +242,8 @@ class AnySmooth(LossBase): return name def check_at_start(self, **kwargs): + if self.key not in kwargs.keys(): + return 0 value = kwargs[self.key] if value.shape[0] < len(self.weight): return 0 @@ -253,6 +255,8 @@ class AnySmooth(LossBase): return super().check_at_start(**kwargs) def check_at_end(self, **kwargs): + if self.key not in kwargs.keys(): + return 0 value = kwargs[self.key] if value.shape[0] < len(self.weight): return 0 @@ -586,4 +590,25 @@ class Keypoints2D(BaseKeypoints): header, contents = self.cache_check header.append('after(pix)') contents.append(err_after.detach().cpu().numpy().tolist()) - print_table(header, contents) \ No newline at end of file + print_table(header, contents) + +class DepthLoss(LossBase): + def __init__(self, K, Rc, Tc, depth, norm, norm_info, index_est=[]): + super().__init__() + P = torch.bmm(K, torch.cat([Rc, Tc], dim=-1)) + self.register_buffer('P', P) + self.index_est = index_est + depth = BaseKeypoints.select(depth, self.index_est, []) + self.register_buffer('depth', depth) + self.einsum = 'fab,fnb->fna' + self.lossfunc = make_loss(norm, norm_info) + + def forward(self, kpts_est, **kwargs): + kpts_est = BaseKeypoints.select(kpts_est, self.index_est, []) + kpts_homo = torch.ones_like(kpts_est[..., -1:]) + kpts_homo = torch.cat([kpts_est, kpts_homo], dim=-1) + point_cam = torch.einsum(self.einsum, self.P, kpts_homo) + depth = point_cam[..., -1] + conf = self.depth[..., 1] + loss = self.lossfunc(depth[..., None], self.depth[..., :1], conf) + return loss \ No newline at end of file diff --git a/easymocap/mytools/camera_utils.py b/easymocap/mytools/camera_utils.py index be14143..7ada0d2 100644 --- a/easymocap/mytools/camera_utils.py +++ b/easymocap/mytools/camera_utils.py @@ -193,7 +193,7 @@ class Undistort: kpts = keypoints[:, None, :2] kpts = np.ascontiguousarray(kpts) kpts = cv2.undistortPoints(kpts, K, dist, P=K) - keypoints[:, :2] = kpts[:, 0] + keypoints = np.hstack([kpts[:, 0], keypoints[:, 2:]]) return keypoints @staticmethod @@ -221,7 +221,7 @@ class UndistortFisheye: kpts = keypoints[:, None, :2] kpts = np.ascontiguousarray(kpts) kpts = cv2.fisheye.undistortPoints(kpts, K, dist, P=Knew) - keypoints[:, :2] = kpts[:, 0] + keypoints = np.hstack([kpts[:, 0], keypoints[:, 2:]]) return keypoints @staticmethod diff --git a/easymocap/mytools/timer.py b/easymocap/mytools/timer.py new file mode 100644 index 0000000..47299da --- /dev/null +++ b/easymocap/mytools/timer.py @@ -0,0 +1,80 @@ +''' + @ Date: 2021-01-15 11:12:00 + @ Author: Qing Shuai + @ LastEditors: Qing Shuai + @ LastEditTime: 2022-09-27 15:50:50 + @ FilePath: /EasyMocapPublic/easymocap/mytools/timer.py +''' +import time +import tabulate +class Timer: + records = {} + tmp = None + indent = -1 + + @classmethod + def tic(cls): + cls.tmp = time.time() + @classmethod + def toc(cls): + res = (time.time() - cls.tmp) * 1000 + cls.tmp = None + return res + + @classmethod + def report(cls): + header = ['', 'Time(ms)'] + contents = [] + for key, val in cls.records.items(): + contents.append(['{:20s}'.format(key), '{:.2f}'.format(sum(val)/len(val))]) + print(tabulate.tabulate(contents, header, tablefmt='fancy_grid')) + + def __init__(self, name, silent=False): + self.name = name + self.silent = silent + if name not in Timer.records.keys(): + Timer.records[name] = [] + + def __enter__(self): + self.start = time.time() + Timer.indent += 1 + + def __exit__(self, exc_type, exc_value, exc_tb): + end = time.time() + Timer.records[self.name].append((end-self.start)*1000) + indent = self.indent * ' ' + if not self.silent: + t = (end - self.start)*1000 + if t > 1000: + print('-> {}[{:20s}]: {:5.1f}s'.format(indent, self.name, t/1000)) + elif t > 1e3*60*60: + print('-> {}[{:20s}]: {:5.1f}min'.format(indent, self.name, t/1e3/60)) + else: + print('-> {}[{:20s}]: {:5.1f}ms'.format(indent, self.name, (end-self.start)*1000)) + Timer.indent -= 1 + + @staticmethod + def timer(name): + from functools import wraps + def decorator(func): + @wraps(func) + def wrapped_function(*args, **kwargs): + with Timer(name): + ret = func(*args, **kwargs) + return ret + return wrapped_function + return decorator + +if __name__ == '__main__': + @Timer.timer('testfunc') + def dummyfunc(): + time.sleep(1) + with Timer('level0'): + with Timer('level1'): + with Timer('level2'): + time.sleep(1) + time.sleep(1) + time.sleep(1) + dummyfunc() + Timer.report() + \ No newline at end of file diff --git a/easymocap/mytools/triangulator.py b/easymocap/mytools/triangulator.py index cba0748..74ccfa3 100644 --- a/easymocap/mytools/triangulator.py +++ b/easymocap/mytools/triangulator.py @@ -7,6 +7,16 @@ from easymocap.mytools.vis_base import merge, plot_keypoints_auto from .debug_utils import log, mywarn, myerror def batch_triangulate(keypoints_, Pall, min_view=2): + """ triangulate the keypoints of whole body + + Args: + keypoints_ (nViews, nJoints, 3): 2D detections + Pall (nViews, 3, 4): projection matrix of each view + min_view (int, optional): min view for visible points. Defaults to 2. + + Returns: + keypoints3d: (nJoints, 4) + """ # keypoints: (nViews, nJoints, 3) # Pall: (nViews, 3, 4) # A: (nJoints, nViewsx2, 4), x: (nJoints, 4, 1); b: (nJoints, nViewsx2, 1) @@ -589,6 +599,7 @@ class SimpleMatchAndTriangulator(SimpleTriangulator): plt.vlines([i-0.5 for i in dimGroups[1:]], -0.5, M-0.5, 'w') plt.ioff() plt.show() + import ipdb;ipdb.set_trace() return aff_svt def _track_add(self, res): diff --git a/easymocap/visualize/ffmpeg_wrapper.py b/easymocap/visualize/ffmpeg_wrapper.py index 28d4314..c6ce406 100644 --- a/easymocap/visualize/ffmpeg_wrapper.py +++ b/easymocap/visualize/ffmpeg_wrapper.py @@ -2,7 +2,7 @@ @ Date: 2021-11-27 16:50:33 @ Author: Qing Shuai @ LastEditors: Qing Shuai - @ LastEditTime: 2022-04-13 18:19:03 + @ LastEditTime: 2022-10-19 21:37:49 @ FilePath: /EasyMocapPublic/easymocap/visualize/ffmpeg_wrapper.py ''' import shutil @@ -41,7 +41,8 @@ class VideoMaker: self.reorder = True if self.reorder: tmpdir = '/tmp/ffmpeg-tmp' - shutil.rmtree(tmpdir) + if os.path.exists(tmpdir): + shutil.rmtree(tmpdir) os.makedirs(tmpdir, exist_ok=True) for nf, imgname in tqdm(enumerate(imgnames), desc='copy to /tmp'): tmpname = join(tmpdir, '{:06d}{}'.format(nf, self.ext)) diff --git a/easymocap/visualize/pyrender_wrapper.py b/easymocap/visualize/pyrender_wrapper.py index 6978339..aa9ba1e 100644 --- a/easymocap/visualize/pyrender_wrapper.py +++ b/easymocap/visualize/pyrender_wrapper.py @@ -2,7 +2,7 @@ @ Date: 2021-05-13 14:20:13 @ Author: Qing Shuai @ LastEditors: Qing Shuai - @ LastEditTime: 2022-06-10 22:47:42 + @ LastEditTime: 2022-09-13 12:24:20 @ FilePath: /EasyMocapPublic/easymocap/visualize/pyrender_wrapper.py ''' import pyrender @@ -97,7 +97,7 @@ class Renderer: # material = pyrender.material.SpecularGlossinessMaterial( # diffuseFactor=1.0, glossinessFactor=0.0 # ) - mesh = pyrender.Mesh.from_trimesh(mesh, material=material, smooth=True) + mesh = pyrender.Mesh.from_trimesh(mesh, material=material, smooth=data.get('smooth', True)) else: mesh = trimesh.Trimesh(vert, faces, vertex_colors=data['colors'], process=False) mesh.apply_transform(rot) @@ -149,17 +149,12 @@ def plot_meshes(img, meshes, K, R, T, mode='image'): # 这个顺序是BGR的。虽然render的使用的是RGB的，但是由于和图像拼接了，所以又变成BGR的了 colors = [ - # (0.5, 0.2, 0.2, 1.), # Defalut BGR - (.5, .5, .7, 1.), # Pink BGR - (.44, .50, .98, 1.), # Red - (.7, .7, .6, 1.), # Neutral - (.5, .5, .7, 1.), # Blue - (.5, .55, .3, 1.), # capsule - (.3, .5, .55, 1.), # Yellow - # (.6, .6, .6, 1.), # gray - (.9, 1., 1., 1.), - (0.95, 0.74, 0.65, 1.), - (.9, .7, .7, 1.) + (94/255, 124/255, 226/255), # 青色 + (255/255, 200/255, 87/255), # yellow + (74/255., 189/255., 172/255.), # green + (8/255, 76/255, 97/255), # blue + (219/255, 58/255, 52/255), # red + (77/255, 40/255, 49/255), # brown ] colors_table = {