eft/apps/eval_multicrop.py [194:358]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if eval_pose: # Regressor broadcasting J_regressor_batch = J_regressor[None, :].expand(pred_vertices.shape[0], -1, -1).to(device) # Get 14 ground truth joints if 'h36m' in dataset_name or 'mpi-inf' in dataset_name: gt_keypoints_3d = batch['pose_3d'].cuda() gt_keypoints_3d = gt_keypoints_3d[:, joint_mapper_gt, :-1] # For 3DPW get the 14 common joints from the rendered shape else: gt_vertices = smpl_male(global_orient=gt_pose[:,:3], body_pose=gt_pose[:,3:], betas=gt_betas).vertices gt_vertices_female = smpl_female(global_orient=gt_pose[:,:3], body_pose=gt_pose[:,3:], betas=gt_betas).vertices gt_vertices[gender==1, :, :] = gt_vertices_female[gender==1, :, :] gt_keypoints_3d = torch.matmul(J_regressor_batch, gt_vertices) gt_pelvis = gt_keypoints_3d[:, [0],:].clone() gt_keypoints_3d = gt_keypoints_3d[:, joint_mapper_h36m, :] gt_keypoints_3d = gt_keypoints_3d - gt_pelvis if False: from renderer import glViewer import humanModelViewer batchNum = gt_pose.shape[0] for i in range(batchNum): smpl_face = humanModelViewer.GetSMPLFace() meshes_gt = {'ver': gt_vertices[i].cpu().numpy()*100, 'f': smpl_face} meshes_pred = {'ver': pred_vertices[i].cpu().numpy()*100, 'f': smpl_face} glViewer.setMeshData([meshes_gt, meshes_pred], bComputeNormal= True) glViewer.show(5) # Get 14 predicted joints from the mesh pred_keypoints_3d = torch.matmul(J_regressor_batch, pred_vertices) if save_results: pred_joints[step * batch_size:step * batch_size + curr_batch_size, :, :] = pred_keypoints_3d.cpu().numpy() pred_pelvis = pred_keypoints_3d[:, [0],:].clone() pred_keypoints_3d = pred_keypoints_3d[:, joint_mapper_h36m, :] pred_keypoints_3d = pred_keypoints_3d - pred_pelvis #Visualize GT mesh and SPIN output mesh if False: # from renderer import viewer2D from renderer import glViewer import humanModelViewer gt_keypoints_3d_vis = gt_keypoints_3d.cpu().numpy() gt_keypoints_3d_vis = np.reshape(gt_keypoints_3d_vis, (gt_keypoints_3d_vis.shape[0],-1)) #N,14x3 gt_keypoints_3d_vis = np.swapaxes(gt_keypoints_3d_vis, 0,1) *100 pred_keypoints_3d_vis = pred_keypoints_3d.cpu().numpy() pred_keypoints_3d_vis = np.reshape(pred_keypoints_3d_vis, (pred_keypoints_3d_vis.shape[0],-1)) #N,14x3 pred_keypoints_3d_vis = np.swapaxes(pred_keypoints_3d_vis, 0,1) *100 # output_sample = output_sample[ : , np.newaxis]*0.1 # gt_sample = gt_sample[: , np.newaxis]*0.1 # (skelNum, dim, frames) glViewer.setSkeleton( [gt_keypoints_3d_vis, pred_keypoints_3d_vis] ,jointType='smplcoco')#(skelNum, dim, frames) glViewer.show() # Absolute error (MPJPE) error = torch.sqrt(((pred_keypoints_3d - gt_keypoints_3d) ** 2).sum(dim=-1)).mean(dim=-1).cpu().numpy() error_upper = torch.sqrt(((pred_keypoints_3d - gt_keypoints_3d) ** 2).sum(dim=-1)).mean(dim=-1).cpu().numpy() # mpjpe[step * batch_size:step * batch_size + curr_batch_size] = error # Reconstuction_error r_error = reconstruction_error(pred_keypoints_3d.cpu().numpy(), gt_keypoints_3d.cpu().numpy(), reduction=None) r_error_upper = reconstruction_error(pred_keypoints_3d.cpu().numpy(), gt_keypoints_3d.cpu().numpy(), reduction=None) # recon_err[step * batch_size:step * batch_size + curr_batch_size] = r_error for ii, p in enumerate(batch['imgname'][:len(r_error)]): seqName = os.path.basename( os.path.dirname(p)) # quant_mpjpe[step * batch_size:step * batch_size + curr_batch_size] = error if seqName not in quant_mpjpe.keys(): quant_mpjpe[seqName] = [] quant_recon_err[seqName] = [] quant_mpjpe[seqName].append(error[ii]) quant_recon_err[seqName].append(r_error[ii]) # Reconstuction_error # quant_recon_err[step * batch_size:step * batch_size + curr_batch_size] = r_error list_mpjpe = np.hstack([ quant_mpjpe[k] for k in quant_mpjpe]) list_reconError = np.hstack([ quant_recon_err[k] for k in quant_recon_err]) if bVerbose: print(">>> {} : MPJPE {:.02f} mm, error: {:.02f} mm | Total MPJPE {:.02f} mm, error {:.02f} mm".format(seqName, np.mean(error)*1000, np.mean(r_error)*1000, np.hstack(list_mpjpe).mean()*1000, np.hstack(list_reconError).mean()*1000) ) # print("MPJPE {}, error: {}".format(np.mean(error)*100, np.mean(r_error)*100)) # If mask or part evaluation, render the mask and part images # if eval_masks or eval_parts: # mask, parts = renderer(pred_vertices, pred_camera) # Mask evaluation (for LSP) if eval_masks: center = batch['center'].cpu().numpy() scale = batch['scale'].cpu().numpy() # Dimensions of original image orig_shape = batch['orig_shape'].cpu().numpy() for i in range(curr_batch_size): # After rendering, convert imate back to original resolution pred_mask = uncrop(mask[i].cpu().numpy(), center[i], scale[i], orig_shape[i]) > 0 # Load gt mask gt_mask = cv2.imread(os.path.join(annot_path, batch['maskname'][i]), 0) > 0 # Evaluation consistent with the original UP-3D code accuracy += (gt_mask == pred_mask).sum() pixel_count += np.prod(np.array(gt_mask.shape)) for c in range(2): cgt = gt_mask == c cpred = pred_mask == c tp[c] += (cgt & cpred).sum() fp[c] += (~cgt & cpred).sum() fn[c] += (cgt & ~cpred).sum() f1 = 2 * tp / (2 * tp + fp + fn) # Part evaluation (for LSP) if eval_parts: center = batch['center'].cpu().numpy() scale = batch['scale'].cpu().numpy() orig_shape = batch['orig_shape'].cpu().numpy() for i in range(curr_batch_size): pred_parts = uncrop(parts[i].cpu().numpy().astype(np.uint8), center[i], scale[i], orig_shape[i]) # Load gt part segmentation gt_parts = cv2.imread(os.path.join(annot_path, batch['partname'][i]), 0) # Evaluation consistent with the original UP-3D code # 6 parts + background for c in range(7): cgt = gt_parts == c cpred = pred_parts == c cpred[gt_parts == 255] = 0 parts_tp[c] += (cgt & cpred).sum() parts_fp[c] += (~cgt & cpred).sum() parts_fn[c] += (cgt & ~cpred).sum() gt_parts[gt_parts == 255] = 0 pred_parts[pred_parts == 255] = 0 parts_f1 = 2 * parts_tp / (2 * parts_tp + parts_fp + parts_fn) parts_accuracy += (gt_parts == pred_parts).sum() parts_pixel_count += np.prod(np.array(gt_parts.shape)) # Print intermediate results during evaluation if bVerbose: if step % log_freq == log_freq - 1: if eval_pose: print('MPJPE: ' + str(1000 * mpjpe[:step * batch_size].mean())) print('Reconstruction Error: ' + str(1000 * recon_err[:step * batch_size].mean())) print() if eval_masks: print('Accuracy: ', accuracy / pixel_count) print('F1: ', f1.mean()) print() if eval_parts: print('Parts Accuracy: ', parts_accuracy / parts_pixel_count) print('Parts F1 (BG): ', parts_f1[[0,1,2,3,4,5,6]].mean()) print() # if step==3: #Debug # break # Save reconstructions to a file for further processing if save_results: np.savez(result_file, pred_joints=pred_joints, pose=smpl_pose, betas=smpl_betas, camera=smpl_camera) # Print final results during evaluation if bVerbose: print('*** Final Results ***') print() - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - eft/apps/evalfrompkl_hmr.py [353:518]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - if eval_pose: # Regressor broadcasting J_regressor_batch = J_regressor[None, :].expand(pred_vertices.shape[0], -1, -1).to(device) # Get 14 ground truth joints if 'h36m' in dataset_name or 'mpi-inf' in dataset_name: gt_keypoints_3d = batch['pose_3d'].cuda() gt_keypoints_3d = gt_keypoints_3d[:, joint_mapper_gt, :-1] # For 3DPW get the 14 common joints from the rendered shape else: gt_vertices = smpl_male(global_orient=gt_pose[:,:3], body_pose=gt_pose[:,3:], betas=gt_betas).vertices gt_vertices_female = smpl_female(global_orient=gt_pose[:,:3], body_pose=gt_pose[:,3:], betas=gt_betas).vertices gt_vertices[gender==1, :, :] = gt_vertices_female[gender==1, :, :] gt_keypoints_3d = torch.matmul(J_regressor_batch, gt_vertices) gt_pelvis = gt_keypoints_3d[:, [0],:].clone() gt_keypoints_3d = gt_keypoints_3d[:, joint_mapper_h36m, :] gt_keypoints_3d = gt_keypoints_3d - gt_pelvis if False: from renderer import viewer2D from renderer import glViewer import humanModelViewer batchNum = gt_pose.shape[0] for i in range(batchNum): smpl_face = humanModelViewer.GetSMPLFace() meshes_gt = {'ver': gt_vertices[i].cpu().numpy()*100, 'f': smpl_face} meshes_pred = {'ver': pred_vertices[i].cpu().numpy()*100, 'f': smpl_face} glViewer.setMeshData([meshes_gt, meshes_pred], bComputeNormal= True) glViewer.show(5) # Get 14 predicted joints from the mesh pred_keypoints_3d = torch.matmul(J_regressor_batch, pred_vertices) if save_results: pred_joints[step * batch_size:step * batch_size + curr_batch_size, :, :] = pred_keypoints_3d.cpu().numpy() pred_pelvis = pred_keypoints_3d[:, [0],:].clone() pred_keypoints_3d = pred_keypoints_3d[:, joint_mapper_h36m, :] pred_keypoints_3d = pred_keypoints_3d - pred_pelvis #Visualize GT mesh and SPIN output mesh if False: from renderer import viewer2D from renderer import glViewer import humanModelViewer gt_keypoints_3d_vis = gt_keypoints_3d.cpu().numpy() gt_keypoints_3d_vis = np.reshape(gt_keypoints_3d_vis, (gt_keypoints_3d_vis.shape[0],-1)) #N,14x3 gt_keypoints_3d_vis = np.swapaxes(gt_keypoints_3d_vis, 0,1) *100 pred_keypoints_3d_vis = pred_keypoints_3d.cpu().numpy() pred_keypoints_3d_vis = np.reshape(pred_keypoints_3d_vis, (pred_keypoints_3d_vis.shape[0],-1)) #N,14x3 pred_keypoints_3d_vis = np.swapaxes(pred_keypoints_3d_vis, 0,1) *100 # output_sample = output_sample[ : , np.newaxis]*0.1 # gt_sample = gt_sample[: , np.newaxis]*0.1 # (skelNum, dim, frames) glViewer.setSkeleton( [gt_keypoints_3d_vis, pred_keypoints_3d_vis] ,jointType='smplcoco')#(skelNum, dim, frames) glViewer.show() # Absolute error (MPJPE) error = torch.sqrt(((pred_keypoints_3d - gt_keypoints_3d) ** 2).sum(dim=-1)).mean(dim=-1).cpu().numpy() error_upper = torch.sqrt(((pred_keypoints_3d - gt_keypoints_3d) ** 2).sum(dim=-1)).mean(dim=-1).cpu().numpy() # mpjpe[step * batch_size:step * batch_size + curr_batch_size] = error # Reconstuction_error r_error = reconstruction_error(pred_keypoints_3d.cpu().numpy(), gt_keypoints_3d.cpu().numpy(), reduction=None) r_error_upper = reconstruction_error(pred_keypoints_3d.cpu().numpy(), gt_keypoints_3d.cpu().numpy(), reduction=None) # recon_err[step * batch_size:step * batch_size + curr_batch_size] = r_error for ii, p in enumerate(batch['imgname'][:len(r_error)]): seqName = os.path.basename( os.path.dirname(p)) # quant_mpjpe[step * batch_size:step * batch_size + curr_batch_size] = error if seqName not in quant_mpjpe.keys(): quant_mpjpe[seqName] = [] quant_recon_err[seqName] = [] quant_mpjpe[seqName].append(error[ii]) quant_recon_err[seqName].append(r_error[ii]) # Reconstuction_error # quant_recon_err[step * batch_size:step * batch_size + curr_batch_size] = r_error list_mpjpe = np.hstack([ quant_mpjpe[k] for k in quant_mpjpe]) list_reconError = np.hstack([ quant_recon_err[k] for k in quant_recon_err]) if bVerbose: print(">>> {} : MPJPE {:.02f} mm, error: {:.02f} mm | Total MPJPE {:.02f} mm, error {:.02f} mm".format(seqName, np.mean(error)*1000, np.mean(r_error)*1000, np.hstack(list_mpjpe).mean()*1000, np.hstack(list_reconError).mean()*1000) ) # print("MPJPE {}, error: {}".format(np.mean(error)*100, np.mean(r_error)*100)) # If mask or part evaluation, render the mask and part images # if eval_masks or eval_parts: # mask, parts = renderer(pred_vertices, pred_camera) # Mask evaluation (for LSP) if eval_masks: center = batch['center'].cpu().numpy() scale = batch['scale'].cpu().numpy() # Dimensions of original image orig_shape = batch['orig_shape'].cpu().numpy() for i in range(curr_batch_size): # After rendering, convert imate back to original resolution pred_mask = uncrop(mask[i].cpu().numpy(), center[i], scale[i], orig_shape[i]) > 0 # Load gt mask gt_mask = cv2.imread(os.path.join(annot_path, batch['maskname'][i]), 0) > 0 # Evaluation consistent with the original UP-3D code accuracy += (gt_mask == pred_mask).sum() pixel_count += np.prod(np.array(gt_mask.shape)) for c in range(2): cgt = gt_mask == c cpred = pred_mask == c tp[c] += (cgt & cpred).sum() fp[c] += (~cgt & cpred).sum() fn[c] += (cgt & ~cpred).sum() f1 = 2 * tp / (2 * tp + fp + fn) # Part evaluation (for LSP) if eval_parts: center = batch['center'].cpu().numpy() scale = batch['scale'].cpu().numpy() orig_shape = batch['orig_shape'].cpu().numpy() for i in range(curr_batch_size): pred_parts = uncrop(parts[i].cpu().numpy().astype(np.uint8), center[i], scale[i], orig_shape[i]) # Load gt part segmentation gt_parts = cv2.imread(os.path.join(annot_path, batch['partname'][i]), 0) # Evaluation consistent with the original UP-3D code # 6 parts + background for c in range(7): cgt = gt_parts == c cpred = pred_parts == c cpred[gt_parts == 255] = 0 parts_tp[c] += (cgt & cpred).sum() parts_fp[c] += (~cgt & cpred).sum() parts_fn[c] += (cgt & ~cpred).sum() gt_parts[gt_parts == 255] = 0 pred_parts[pred_parts == 255] = 0 parts_f1 = 2 * parts_tp / (2 * parts_tp + parts_fp + parts_fn) parts_accuracy += (gt_parts == pred_parts).sum() parts_pixel_count += np.prod(np.array(gt_parts.shape)) # Print intermediate results during evaluation if bVerbose: if step % log_freq == log_freq - 1: if eval_pose: print('MPJPE: ' + str(1000 * mpjpe[:step * batch_size].mean())) print('Reconstruction Error: ' + str(1000 * recon_err[:step * batch_size].mean())) print() if eval_masks: print('Accuracy: ', accuracy / pixel_count) print('F1: ', f1.mean()) print() if eval_parts: print('Parts Accuracy: ', parts_accuracy / parts_pixel_count) print('Parts F1 (BG): ', parts_f1[[0,1,2,3,4,5,6]].mean()) print() # if step==3: #Debug # break # Save reconstructions to a file for further processing if save_results: np.savez(result_file, pred_joints=pred_joints, pose=smpl_pose, betas=smpl_betas, camera=smpl_camera) # Print final results during evaluation if bVerbose: print('*** Final Results ***') print() - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -