单目深度估计常用数据集之一make3d。

下载链接：

Make3D --- Range Image Dataset

由于一般该数据集只用来测试在KITTI数据集上训练好的权重的性能，也就是只作为测试集，因此只下载134张图片与对应的深度mat。

下载之后得到两个文件夹，将文件夹解压到一个目录下，并使用一个提取名字的脚本来获得包含所有测试集名字的txt文件。

# 获得make3d图片的名称 import glob imglist = glob.glob('E:/dataset/make3d/Test134/*.jpg') with open('make3d_test_files.txt', 'a+') as f: for i in imglist: _, name = i.split('\\') f.write('{}\n'.format(name[:-4]))

最终数据集包含如下：

现在就可以使用验证脚本来验证模型在make3d数据集上的精度了。

# make3d 数据集评估， 以Monodepth2为例 from layers import disp_to_depth import networks import cv2 import os import torch import scipy.misc from scipy import io import numpy as np load_weights_folder = './models/mono_resnet50_640x192' main_path = 'E:/dataset/make3d' encoder_path = os.path.join(load_weights_folder, "encoder.pth") decoder_path = os.path.join(load_weights_folder, "depth.pth") encoder_dict = torch.load(encoder_path) encoder = networks.ResnetEncoder(50, False) depth_decoder = networks.DepthDecoder(encoder.num_ch_enc) model_dict = encoder.state_dict() encoder.load_state_dict({k: v for k, v in encoder_dict.items() if k in model_dict}) depth_decoder.load_state_dict(torch.load(decoder_path)) #encoder.cuda() encoder.eval() #depth_decoder.cuda() depth_decoder.eval() def compute_errors(gt, pred): rmse = (gt - pred) 2 rmse = np.sqrt(rmse.mean()) rmse_log = (np.log10(gt) - np.log10(pred)) 2 rmse_log = np.sqrt(rmse_log.mean()) abs_rel = np.mean(np.abs(gt - pred) / gt) sq_rel = np.mean(((gt - pred)2) / gt) return abs_rel, sq_rel, rmse, rmse_log with open(os.path.join(main_path, "make3d_test_files.txt")) as f: test_filenames = f.read().splitlines() test_filenames = map(lambda x: x[4:], test_filenames) depths_gt = [] images = [] ratio = 2 h_ratio = 1 / (1.33333 * ratio) color_new_height = int(1704 / 2) depth_new_height = 21 for filename in test_filenames: mat = io.loadmat(os.path.join(main_path, "Gridlaserdata", "depth_sph_corr-{}.mat".format(filename))) depths_gt.append(mat["Position3DGrid"][:,:,3]) image = cv2.imread(os.path.join(main_path, "Test134", "img-{}.jpg".format(filename))) image = image[ int((2272 - color_new_height)/2):int((2272 + color_new_height)/2),:,:] images.append(image[:,:,::-1]) depths_gt_resized = map(lambda x: cv2.resize(x, (305, 407), interpolation=cv2.INTER_NEAREST), depths_gt) depths_gt_cropped = map(lambda x: x[int((55 - 21)/2):int((55 + 21)/2),:], depths_gt) depths_gt_cropped = list(depths_gt_cropped) errors = [] with torch.no_grad(): for i in range(len( images)): input_color = images[i] input_color = cv2.resize(input_color/255.0, (640, 192), interpolation=cv2.INTER_NEAREST)#<----1 input_color = torch.tensor(input_color, dtype = torch.float).permute(2,0,1)[None,:,:,:] output = depth_decoder(encoder(input_color)) pred_disp,_ = disp_to_depth(output[("disp", 0)], 0.1, 100) #<---2 pred_disp = pred_disp.squeeze().cpu().numpy() depth_gt = depths_gt_cropped[i] depth_pred = 1 / pred_disp depth_pred = cv2.resize(depth_pred, depth_gt.shape[::-1], interpolation=cv2.INTER_NEAREST) mask = np.logical_and(depth_gt > 0, depth_gt < 70) depth_gt = depth_gt[mask] depth_pred = depth_pred[mask] depth_pred *= np.median(depth_gt) / np.median(depth_pred) depth_pred[depth_pred > 70] = 70 errors.append(compute_errors(depth_gt, depth_pred)) mean_errors = np.mean(errors, 0) print(("{:>8} | " * 4).format( "abs_rel", "sq_rel", "rmse", "rmse_log")) print(("{: 8.3f} , " * 4).format(*mean_errors.tolist()))

得到结果如下：

与论文中的结果还是比较接近的。