def gen_mesh(res, net, cuda, data, save_path, thresh=0.5, use_octree=True, components=False): image_tensor_global = data['img_512'].to(device=cuda) image_tensor = data['img'].to(device=cuda) calib_tensor = data['calib'].to(device=cuda) net.filter_global(image_tensor_global) net.filter_local(image_tensor[:,None]) try: if net.netG.netF is not None: image_tensor_global = torch.cat([image_tensor_global, net.netG.nmlF], 0) if net.netG.netB is not None: image_tensor_global = torch.cat([image_tensor_global, net.netG.nmlB], 0) except: pass b_min = data['b_min'] b_max = data['b_max'] try: save_img_path = save_path[:-4] + '.png' save_img_list = [] for v in range(image_tensor_global.shape[0]): save_img = (np.transpose(image_tensor_global[v].detach().cpu().numpy(), (1, 2, 0)) * 0.5 + 0.5)[:, :, ::-1] * 255.0 save_img_list.append(save_img) save_img = np.concatenate(save_img_list, axis=1) cv2.imwrite(save_img_path, save_img) verts, faces, _, _ = reconstruction( net, cuda, calib_tensor, res, b_min, b_max, thresh, use_octree=use_octree, num_samples=50000) verts_tensor = torch.from_numpy(verts.T).unsqueeze(0).to(device=cuda).float() # if 'calib_world' in data: # calib_world = data['calib_world'].numpy()[0] # verts = np.matmul(np.concatenate([verts, np.ones_like(verts[:,:1])],1), inv(calib_world).T)[:,:3] color = np.zeros(verts.shape) interval = 50000 for i in range(len(color) // interval + 1): left = i * interval if i == len(color) // interval: right = -1 else: right = (i + 1) * interval net.calc_normal(verts_tensor[:, None, :, left:right], calib_tensor[:,None], calib_tensor) nml = net.nmls.detach().cpu().numpy()[0] * 0.5 + 0.5 color[left:right] = nml.T save_obj_mesh_with_color(save_path, verts, faces, color) except Exception as e: print(e)
def gen_mesh_imgColor(res, netMR, netC, cuda, data, save_path, thresh=0.5, use_octree=True, components=False): image_tensor_global = data['img_512'].to(device=cuda) image_tensor = data['img'].to(device=cuda) calib_tensor = data['calib'].to(device=cuda) netMR.filter_global(image_tensor_global) netMR.filter_local(image_tensor[:,None]) netC.filter(image_tensor_global) netC.attach(netMR.netG.get_im_feat()) try: if netMR.netG.netF is not None: image_tensor_global = torch.cat([image_tensor_global, netMR.netG.nmlF], 0) if netMR.netG.netB is not None: image_tensor_global = torch.cat([image_tensor_global, netMR.netG.nmlB], 0) except: pass b_min = data['b_min'] b_max = data['b_max'] try: save_img_path = save_path[:-4] + '.png' save_img_list = [] for v in range(image_tensor_global.shape[0]): save_img = (np.transpose(image_tensor_global[v].detach().cpu().numpy(), (1, 2, 0)) * 0.5 + 0.5)[:, :, ::-1] * 255.0 save_img_list.append(save_img) save_img = np.concatenate(save_img_list, axis=1) cv2.imwrite(save_img_path, save_img) verts, faces, _, _ = reconstruction( netMR, cuda, calib_tensor, res, b_min, b_max, thresh, use_octree=use_octree, num_samples=100000) verts_tensor = torch.from_numpy(verts.T).unsqueeze(0).to(device=cuda).float() color = np.zeros(verts.shape) interval = 10000 for i in range(len(color) // interval): left = i * interval right = i * interval + interval if i == len(color) // interval - 1: right = -1 netC.query(verts_tensor[:, :, left:right], calib_tensor) rgb = netC.get_preds()[0].detach().cpu().numpy() * 0.5 + 0.5 color[left:right] = rgb.T save_obj_mesh_with_color(save_path, verts, faces, color) except Exception as e: print(e)
def gen_mesh_imgColor(res, net, cuda, data, save_path, thresh=0.5, use_octree=True, components=False): image_tensor_global = data['img_256'].to(device=cuda) image_tensor = data['img'].to(device=cuda) calib_tensor = data['calib'].to(device=cuda) net.filter_global(image_tensor_global) net.filter_local(image_tensor[:,None]) try: if net.netG.netF is not None: image_tensor_global = torch.cat([image_tensor_global, net.netG.nmlF], 0) if net.netG.netB is not None: image_tensor_global = torch.cat([image_tensor_global, net.netG.nmlB], 0) except: pass b_min = data['b_min'] b_max = data['b_max'] try: save_img_path = save_path[:-4] + '.png' save_img_list = [] for v in range(image_tensor_global.shape[0]): save_img = (np.transpose(image_tensor_global[v].detach().cpu().numpy(), (1, 2, 0)) * 0.5 + 0.5)[:, :, ::-1] * 255.0 save_img_list.append(save_img) save_img = np.concatenate(save_img_list, axis=1) cv2.imwrite(save_img_path, save_img) verts, faces, _, _ = reconstruction( net, cuda, calib_tensor, res, b_min, b_max, thresh, use_octree=use_octree, num_samples=100000) verts_tensor = torch.from_numpy(verts.T).unsqueeze(0).to(device=cuda).float() # if this returns error, projection must be defined somewhere else xyz_tensor = net.projection(verts_tensor, calib_tensor[:1]) uv = xyz_tensor[:, :2, :] color = index(image_tensor[:1], uv).detach().cpu().numpy()[0].T color = color * 0.5 + 0.5 if 'calib_world' in data: calib_world = data['calib_world'].numpy()[0] verts = np.matmul(np.concatenate([verts, np.ones_like(verts[:,:1])],1), inv(calib_world).T)[:,:3] save_obj_mesh_with_color(save_path, verts, faces, color) except Exception as e: print(e)