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| import torch | |
| import numpy as np | |
| import os | |
| import cv2 | |
| import glob | |
| import trimesh | |
| import pyrender | |
| from PIL import Image | |
| from lib.Renderer import Renderer_pyr | |
| def get_cam_ssns(dataset_fpath): | |
| cam_ssns = [] # sorted according to their spatial position | |
| with open(os.path.join(dataset_fpath, 'cam_ssns.txt'), 'r') as fp: | |
| lns = fp.readlines() | |
| for ln in lns: | |
| ln = ln.strip().split(' ') | |
| if len(ln) > 0: | |
| cam_ssns.append(ln[0]) | |
| return cam_ssns | |
| class CalibRecorder(): | |
| def __init__(self, data_folder, param_folder): | |
| self.data_folder = data_folder | |
| self.camera_ids = ['01', '02', '04', '05'] | |
| self.param_folder = param_folder | |
| os.makedirs(self.param_folder, exist_ok=True) | |
| # self.camera_ids = ['220700191', '221501007', '222200036', '222200037', '222200038', '222200039', '222200040', '222200041', | |
| # '222200042', '222200043', '222200044', '222200045', '222200046', '222200047', '222200048', '222200049'] | |
| def log(self, log_data): | |
| eids = log_data['eids'] | |
| landmarks_gt = log_data['landmarks_gt'] | |
| landmarks_2d = log_data['landmarks_2d'] | |
| bfm = log_data['bfm'] | |
| intrinsics = log_data['intrinsics'] | |
| extrinsics = log_data['extrinsics'] | |
| with torch.no_grad(): | |
| vertices0, landmarks0 = log_data['bfm']() | |
| # landmarks = torch.cat([landmarks0, vertices0[:, ::100, :]], 1) | |
| for n, eid in enumerate(eids): | |
| os.makedirs(os.path.join(self.param_folder, eid), exist_ok=True) | |
| bfm.save('%s/params.npz' % (os.path.join(self.param_folder, eid)), batch_id=n) | |
| np.save('%s/lmk_3d.npy' % (os.path.join(self.param_folder, eid)), landmarks0[n].cpu().numpy()) | |
| np.save('%s/vertices.npy' % (os.path.join(self.param_folder, eid)), vertices0[n].cpu().numpy()) | |
| # np.save('%s/keypoints_face.npy' % (os.path.join(self.param_folder, eid)), landmarks[n].cpu().numpy()) | |
| # for v in range(0, 1): | |
| # vertices = vertices0[n].cpu().numpy() | |
| # faces = log_data['bfm'].faces.cpu().numpy() | |
| # mesh_trimesh = trimesh.Trimesh(vertices=vertices, faces=faces) | |
| # mesh = pyrender.Mesh.from_trimesh(mesh_trimesh) | |
| # renderer = Renderer_pyr(intrinsic=intrinsics[n, v], extrinsic=extrinsics[n, v]) | |
| # render_image = renderer.render(mesh) | |
| # cv2.imwrite('%s/vis_%d.jpg' % (os.path.join(self.param_folder, eid), v+1), render_image[:,:,::-1]) | |