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| import numpy as np | |
| from PIL import Image | |
| from tqdm import tqdm | |
| def compute_errors(target, prediction): | |
| thresh = np.maximum((target / prediction), (prediction / target)) | |
| a1 = (thresh < 1.25).mean() | |
| a2 = (thresh < 1.25 ** 2).mean() | |
| a3 = (thresh < 1.25 ** 3).mean() | |
| abs_rel = np.mean(np.abs(target - prediction) / target) | |
| sq_rel = np.mean(((target - prediction) ** 2) / target) | |
| rmse = (target - prediction) ** 2 | |
| rmse = np.sqrt(rmse.mean()) | |
| rmse_log = (np.log(target) - np.log(prediction)) ** 2 | |
| rmse_log = np.sqrt(rmse_log.mean()) | |
| err = np.log(prediction) - np.log(target) | |
| silog = np.sqrt(np.mean(err ** 2) - np.mean(err) ** 2) * 100 | |
| log_10 = (np.abs(np.log10(target) - np.log10(prediction))).mean() | |
| return a1, a2, a3, abs_rel, sq_rel, rmse, rmse_log, silog, log_10 | |
| def compute_eval_metrics(test_files): | |
| min_depth_eval = 1e-3 | |
| max_depth_eval = 10 | |
| num_samples = len(test_files) | |
| a1 = np.zeros(num_samples, np.float32) | |
| a2 = np.zeros(num_samples, np.float32) | |
| a3 = np.zeros(num_samples, np.float32) | |
| abs_rel = np.zeros(num_samples, np.float32) | |
| sq_rel = np.zeros(num_samples, np.float32) | |
| rmse = np.zeros(num_samples, np.float32) | |
| rmse_log = np.zeros(num_samples, np.float32) | |
| silog = np.zeros(num_samples, np.float32) | |
| log10 = np.zeros(num_samples, np.float32) | |
| for i in tqdm(range(num_samples), desc="Calculating metrics for test data", total=num_samples): | |
| sample_path = test_files[i] | |
| target_path = str(sample_path.parent/(sample_path.stem + "_depth.png")) | |
| pred_path = "src/eval/" + str(sample_path.stem) + "_pred.png" | |
| target_image = Image.open(target_path) | |
| pred_image = Image.open(pred_path) | |
| target = np.asarray(target_image) | |
| pred = np.asarray(pred_image) | |
| target = target / 25.0 | |
| pred = pred / 25.0 | |
| pred[pred < min_depth_eval] = min_depth_eval | |
| pred[pred > max_depth_eval] = max_depth_eval | |
| pred[np.isinf(pred)] = max_depth_eval | |
| target[np.isinf(target)] = 0 | |
| target[np.isnan(target)] = 0 | |
| valid_mask = np.logical_and(target > min_depth_eval, target < max_depth_eval) | |
| a1[i], a2[i], a3[i], abs_rel[i], sq_rel[i], rmse[i], rmse_log[i], silog[i], log10[i] = \ | |
| compute_errors(target[valid_mask], pred[valid_mask]) | |
| print("{:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}".format( | |
| 'd1', 'd2', 'd3', 'AbsRel', 'SqRel', 'RMSE', 'RMSElog', 'SILog', 'log10')) | |
| print("{:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}".format( | |
| a1.mean(), a2.mean(), a3.mean(), | |
| abs_rel.mean(), sq_rel.mean(), rmse.mean(), rmse_log.mean(), silog.mean(), log10.mean())) | |
| return dict(a1=a1.mean(), a2=a2.mean(), a3=a3.mean(), | |
| abs_rel=abs_rel.mean(), sq_rel=sq_rel.mean(), | |
| rmse=rmse.mean(), rmse_log=rmse_log.mean(), | |
| log10=log10.mean(), silog=silog.mean()) | |