[Add] dataset creation, infer, eval

benchmark/csv/meta_bonn.csv

@@ -0,0 +1,6 @@
+filepath_left,filepath_disparity
+bonn/rgbd_bonn_synchronous_rgb_left.mp4,bonn/rgbd_bonn_synchronous_disparity.npz
+bonn/rgbd_bonn_person_tracking_rgb_left.mp4,bonn/rgbd_bonn_person_tracking_disparity.npz
+bonn/rgbd_bonn_crowd2_rgb_left.mp4,bonn/rgbd_bonn_crowd2_disparity.npz
+bonn/rgbd_bonn_crowd3_rgb_left.mp4,bonn/rgbd_bonn_crowd3_disparity.npz
+bonn/rgbd_bonn_balloon2_rgb_left.mp4,bonn/rgbd_bonn_balloon2_disparity.npz

benchmark/csv/meta_kitti_val.csv

@@ -0,0 +1,14 @@
+filepath_left,filepath_disparity
+KITTI/2011_09_28_drive_0037_sync_rgb_left.mp4,KITTI/2011_09_28_drive_0037_sync_disparity.npz
+KITTI/2011_09_26_drive_0005_sync_rgb_left.mp4,KITTI/2011_09_26_drive_0005_sync_disparity.npz
+KITTI/2011_09_30_drive_0016_sync_rgb_left.mp4,KITTI/2011_09_30_drive_0016_sync_disparity.npz
+KITTI/2011_09_26_drive_0079_sync_rgb_left.mp4,KITTI/2011_09_26_drive_0079_sync_disparity.npz
+KITTI/2011_09_26_drive_0020_sync_rgb_left.mp4,KITTI/2011_09_26_drive_0020_sync_disparity.npz
+KITTI/2011_09_26_drive_0095_sync_rgb_left.mp4,KITTI/2011_09_26_drive_0095_sync_disparity.npz
+KITTI/2011_10_03_drive_0047_sync_rgb_left.mp4,KITTI/2011_10_03_drive_0047_sync_disparity.npz
+KITTI/2011_09_26_drive_0113_sync_rgb_left.mp4,KITTI/2011_09_26_drive_0113_sync_disparity.npz
+KITTI/2011_09_26_drive_0036_sync_rgb_left.mp4,KITTI/2011_09_26_drive_0036_sync_disparity.npz
+KITTI/2011_09_26_drive_0013_sync_rgb_left.mp4,KITTI/2011_09_26_drive_0013_sync_disparity.npz
+KITTI/2011_09_26_drive_0002_sync_rgb_left.mp4,KITTI/2011_09_26_drive_0002_sync_disparity.npz
+KITTI/2011_09_29_drive_0026_sync_rgb_left.mp4,KITTI/2011_09_29_drive_0026_sync_disparity.npz
+KITTI/2011_09_26_drive_0023_sync_rgb_left.mp4,KITTI/2011_09_26_drive_0023_sync_disparity.npz

benchmark/csv/meta_nyu_test.csv

@@ -0,0 +1,655 @@
+filepath_left,filepath_disparity
+NYUv2/test/kitchen_0004/rgb_0001_rgb_left.mp4,NYUv2/test/kitchen_0004/rgb_0001_disparity.npz
+NYUv2/test/kitchen_0004/rgb_0002_rgb_left.mp4,NYUv2/test/kitchen_0004/rgb_0002_disparity.npz
+NYUv2/test/office_0005/rgb_0009_rgb_left.mp4,NYUv2/test/office_0005/rgb_0009_disparity.npz
+NYUv2/test/office_0007/rgb_0014_rgb_left.mp4,NYUv2/test/office_0007/rgb_0014_disparity.npz
+NYUv2/test/office_0008/rgb_0015_rgb_left.mp4,NYUv2/test/office_0008/rgb_0015_disparity.npz
+NYUv2/test/office_0008/rgb_0016_rgb_left.mp4,NYUv2/test/office_0008/rgb_0016_disparity.npz
+NYUv2/test/office_0008/rgb_0017_rgb_left.mp4,NYUv2/test/office_0008/rgb_0017_disparity.npz
+NYUv2/test/office_0008/rgb_0018_rgb_left.mp4,NYUv2/test/office_0008/rgb_0018_disparity.npz
+NYUv2/test/office_0010/rgb_0021_rgb_left.mp4,NYUv2/test/office_0010/rgb_0021_disparity.npz
+NYUv2/test/office_0013/rgb_0028_rgb_left.mp4,NYUv2/test/office_0013/rgb_0028_disparity.npz
+NYUv2/test/office_0013/rgb_0029_rgb_left.mp4,NYUv2/test/office_0013/rgb_0029_disparity.npz
+NYUv2/test/office_0013/rgb_0030_rgb_left.mp4,NYUv2/test/office_0013/rgb_0030_disparity.npz
+NYUv2/test/office_0013/rgb_0031_rgb_left.mp4,NYUv2/test/office_0013/rgb_0031_disparity.npz
+NYUv2/test/office_0013/rgb_0032_rgb_left.mp4,NYUv2/test/office_0013/rgb_0032_disparity.npz
+NYUv2/test/office_0013/rgb_0033_rgb_left.mp4,NYUv2/test/office_0013/rgb_0033_disparity.npz
+NYUv2/test/office_0013/rgb_0034_rgb_left.mp4,NYUv2/test/office_0013/rgb_0034_disparity.npz
+NYUv2/test/office_0014/rgb_0035_rgb_left.mp4,NYUv2/test/office_0014/rgb_0035_disparity.npz
+NYUv2/test/office_0014/rgb_0036_rgb_left.mp4,NYUv2/test/office_0014/rgb_0036_disparity.npz
+NYUv2/test/office_0014/rgb_0037_rgb_left.mp4,NYUv2/test/office_0014/rgb_0037_disparity.npz
+NYUv2/test/office_0014/rgb_0038_rgb_left.mp4,NYUv2/test/office_0014/rgb_0038_disparity.npz
+NYUv2/test/office_0015/rgb_0039_rgb_left.mp4,NYUv2/test/office_0015/rgb_0039_disparity.npz
+NYUv2/test/office_0015/rgb_0040_rgb_left.mp4,NYUv2/test/office_0015/rgb_0040_disparity.npz
+NYUv2/test/office_0015/rgb_0041_rgb_left.mp4,NYUv2/test/office_0015/rgb_0041_disparity.npz
+NYUv2/test/office_0015/rgb_0042_rgb_left.mp4,NYUv2/test/office_0015/rgb_0042_disparity.npz
+NYUv2/test/office_0015/rgb_0043_rgb_left.mp4,NYUv2/test/office_0015/rgb_0043_disparity.npz
+NYUv2/test/bathroom_0003/rgb_0046_rgb_left.mp4,NYUv2/test/bathroom_0003/rgb_0046_disparity.npz
+NYUv2/test/bathroom_0004/rgb_0047_rgb_left.mp4,NYUv2/test/bathroom_0004/rgb_0047_disparity.npz
+NYUv2/test/bedroom_0011/rgb_0056_rgb_left.mp4,NYUv2/test/bedroom_0011/rgb_0056_disparity.npz
+NYUv2/test/bedroom_0011/rgb_0057_rgb_left.mp4,NYUv2/test/bedroom_0011/rgb_0057_disparity.npz
+NYUv2/test/bedroom_0013/rgb_0059_rgb_left.mp4,NYUv2/test/bedroom_0013/rgb_0059_disparity.npz
+NYUv2/test/bedroom_0013/rgb_0060_rgb_left.mp4,NYUv2/test/bedroom_0013/rgb_0060_disparity.npz
+NYUv2/test/bedroom_0013/rgb_0061_rgb_left.mp4,NYUv2/test/bedroom_0013/rgb_0061_disparity.npz
+NYUv2/test/bedroom_0013/rgb_0062_rgb_left.mp4,NYUv2/test/bedroom_0013/rgb_0062_disparity.npz
+NYUv2/test/bedroom_0013/rgb_0063_rgb_left.mp4,NYUv2/test/bedroom_0013/rgb_0063_disparity.npz
+NYUv2/test/bedroom_0018/rgb_0076_rgb_left.mp4,NYUv2/test/bedroom_0018/rgb_0076_disparity.npz
+NYUv2/test/bedroom_0018/rgb_0077_rgb_left.mp4,NYUv2/test/bedroom_0018/rgb_0077_disparity.npz
+NYUv2/test/bedroom_0018/rgb_0078_rgb_left.mp4,NYUv2/test/bedroom_0018/rgb_0078_disparity.npz
+NYUv2/test/bedroom_0018/rgb_0079_rgb_left.mp4,NYUv2/test/bedroom_0018/rgb_0079_disparity.npz
+NYUv2/test/bookstore_0001/rgb_0084_rgb_left.mp4,NYUv2/test/bookstore_0001/rgb_0084_disparity.npz
+NYUv2/test/bookstore_0001/rgb_0085_rgb_left.mp4,NYUv2/test/bookstore_0001/rgb_0085_disparity.npz
+NYUv2/test/bookstore_0001/rgb_0086_rgb_left.mp4,NYUv2/test/bookstore_0001/rgb_0086_disparity.npz
+NYUv2/test/bookstore_0001/rgb_0087_rgb_left.mp4,NYUv2/test/bookstore_0001/rgb_0087_disparity.npz
+NYUv2/test/bookstore_0001/rgb_0088_rgb_left.mp4,NYUv2/test/bookstore_0001/rgb_0088_disparity.npz
+NYUv2/test/bookstore_0001/rgb_0089_rgb_left.mp4,NYUv2/test/bookstore_0001/rgb_0089_disparity.npz
+NYUv2/test/bookstore_0001/rgb_0090_rgb_left.mp4,NYUv2/test/bookstore_0001/rgb_0090_disparity.npz
+NYUv2/test/bookstore_0001/rgb_0091_rgb_left.mp4,NYUv2/test/bookstore_0001/rgb_0091_disparity.npz
+NYUv2/test/bookstore_0001/rgb_0117_rgb_left.mp4,NYUv2/test/bookstore_0001/rgb_0117_disparity.npz
+NYUv2/test/bookstore_0001/rgb_0118_rgb_left.mp4,NYUv2/test/bookstore_0001/rgb_0118_disparity.npz
+NYUv2/test/bookstore_0001/rgb_0119_rgb_left.mp4,NYUv2/test/bookstore_0001/rgb_0119_disparity.npz
+NYUv2/test/kitchen_0005/rgb_0125_rgb_left.mp4,NYUv2/test/kitchen_0005/rgb_0125_disparity.npz
+NYUv2/test/kitchen_0005/rgb_0126_rgb_left.mp4,NYUv2/test/kitchen_0005/rgb_0126_disparity.npz
+NYUv2/test/kitchen_0005/rgb_0127_rgb_left.mp4,NYUv2/test/kitchen_0005/rgb_0127_disparity.npz
+NYUv2/test/kitchen_0005/rgb_0128_rgb_left.mp4,NYUv2/test/kitchen_0005/rgb_0128_disparity.npz
+NYUv2/test/kitchen_0005/rgb_0129_rgb_left.mp4,NYUv2/test/kitchen_0005/rgb_0129_disparity.npz
+NYUv2/test/kitchen_0007/rgb_0131_rgb_left.mp4,NYUv2/test/kitchen_0007/rgb_0131_disparity.npz
+NYUv2/test/kitchen_0007/rgb_0132_rgb_left.mp4,NYUv2/test/kitchen_0007/rgb_0132_disparity.npz
+NYUv2/test/kitchen_0007/rgb_0133_rgb_left.mp4,NYUv2/test/kitchen_0007/rgb_0133_disparity.npz
+NYUv2/test/kitchen_0007/rgb_0134_rgb_left.mp4,NYUv2/test/kitchen_0007/rgb_0134_disparity.npz
+NYUv2/test/kitchen_0009/rgb_0137_rgb_left.mp4,NYUv2/test/kitchen_0009/rgb_0137_disparity.npz
+NYUv2/test/living_room_0008/rgb_0153_rgb_left.mp4,NYUv2/test/living_room_0008/rgb_0153_disparity.npz
+NYUv2/test/living_room_0008/rgb_0154_rgb_left.mp4,NYUv2/test/living_room_0008/rgb_0154_disparity.npz
+NYUv2/test/living_room_0009/rgb_0155_rgb_left.mp4,NYUv2/test/living_room_0009/rgb_0155_disparity.npz
+NYUv2/test/living_room_0013/rgb_0167_rgb_left.mp4,NYUv2/test/living_room_0013/rgb_0167_disparity.npz
+NYUv2/test/living_room_0013/rgb_0168_rgb_left.mp4,NYUv2/test/living_room_0013/rgb_0168_disparity.npz
+NYUv2/test/living_room_0014/rgb_0169_rgb_left.mp4,NYUv2/test/living_room_0014/rgb_0169_disparity.npz
+NYUv2/test/bedroom_0003/rgb_0171_rgb_left.mp4,NYUv2/test/bedroom_0003/rgb_0171_disparity.npz
+NYUv2/test/bedroom_0003/rgb_0172_rgb_left.mp4,NYUv2/test/bedroom_0003/rgb_0172_disparity.npz
+NYUv2/test/bedroom_0003/rgb_0173_rgb_left.mp4,NYUv2/test/bedroom_0003/rgb_0173_disparity.npz
+NYUv2/test/bedroom_0003/rgb_0174_rgb_left.mp4,NYUv2/test/bedroom_0003/rgb_0174_disparity.npz
+NYUv2/test/bedroom_0003/rgb_0175_rgb_left.mp4,NYUv2/test/bedroom_0003/rgb_0175_disparity.npz
+NYUv2/test/bedroom_0003/rgb_0176_rgb_left.mp4,NYUv2/test/bedroom_0003/rgb_0176_disparity.npz
+NYUv2/test/bedroom_0005/rgb_0180_rgb_left.mp4,NYUv2/test/bedroom_0005/rgb_0180_disparity.npz
+NYUv2/test/bedroom_0005/rgb_0181_rgb_left.mp4,NYUv2/test/bedroom_0005/rgb_0181_disparity.npz
+NYUv2/test/bedroom_0005/rgb_0182_rgb_left.mp4,NYUv2/test/bedroom_0005/rgb_0182_disparity.npz
+NYUv2/test/bedroom_0006/rgb_0183_rgb_left.mp4,NYUv2/test/bedroom_0006/rgb_0183_disparity.npz
+NYUv2/test/bedroom_0006/rgb_0184_rgb_left.mp4,NYUv2/test/bedroom_0006/rgb_0184_disparity.npz
+NYUv2/test/bedroom_0006/rgb_0185_rgb_left.mp4,NYUv2/test/bedroom_0006/rgb_0185_disparity.npz
+NYUv2/test/bedroom_0006/rgb_0186_rgb_left.mp4,NYUv2/test/bedroom_0006/rgb_0186_disparity.npz
+NYUv2/test/bedroom_0006/rgb_0187_rgb_left.mp4,NYUv2/test/bedroom_0006/rgb_0187_disparity.npz
+NYUv2/test/bedroom_0006/rgb_0188_rgb_left.mp4,NYUv2/test/bedroom_0006/rgb_0188_disparity.npz
+NYUv2/test/bedroom_0007/rgb_0189_rgb_left.mp4,NYUv2/test/bedroom_0007/rgb_0189_disparity.npz
+NYUv2/test/bedroom_0007/rgb_0190_rgb_left.mp4,NYUv2/test/bedroom_0007/rgb_0190_disparity.npz
+NYUv2/test/bedroom_0007/rgb_0191_rgb_left.mp4,NYUv2/test/bedroom_0007/rgb_0191_disparity.npz
+NYUv2/test/bedroom_0007/rgb_0192_rgb_left.mp4,NYUv2/test/bedroom_0007/rgb_0192_disparity.npz
+NYUv2/test/bedroom_0007/rgb_0193_rgb_left.mp4,NYUv2/test/bedroom_0007/rgb_0193_disparity.npz
+NYUv2/test/kitchen_0002/rgb_0194_rgb_left.mp4,NYUv2/test/kitchen_0002/rgb_0194_disparity.npz
+NYUv2/test/kitchen_0002/rgb_0195_rgb_left.mp4,NYUv2/test/kitchen_0002/rgb_0195_disparity.npz
+NYUv2/test/kitchen_0002/rgb_0196_rgb_left.mp4,NYUv2/test/kitchen_0002/rgb_0196_disparity.npz
+NYUv2/test/kitchen_0002/rgb_0197_rgb_left.mp4,NYUv2/test/kitchen_0002/rgb_0197_disparity.npz
+NYUv2/test/kitchen_0002/rgb_0198_rgb_left.mp4,NYUv2/test/kitchen_0002/rgb_0198_disparity.npz
+NYUv2/test/kitchen_0002/rgb_0199_rgb_left.mp4,NYUv2/test/kitchen_0002/rgb_0199_disparity.npz
+NYUv2/test/kitchen_0002/rgb_0200_rgb_left.mp4,NYUv2/test/kitchen_0002/rgb_0200_disparity.npz
+NYUv2/test/kitchen_0002/rgb_0201_rgb_left.mp4,NYUv2/test/kitchen_0002/rgb_0201_disparity.npz
+NYUv2/test/kitchen_0002/rgb_0202_rgb_left.mp4,NYUv2/test/kitchen_0002/rgb_0202_disparity.npz
+NYUv2/test/living_room_0002/rgb_0207_rgb_left.mp4,NYUv2/test/living_room_0002/rgb_0207_disparity.npz
+NYUv2/test/living_room_0003/rgb_0208_rgb_left.mp4,NYUv2/test/living_room_0003/rgb_0208_disparity.npz
+NYUv2/test/living_room_0003/rgb_0209_rgb_left.mp4,NYUv2/test/living_room_0003/rgb_0209_disparity.npz
+NYUv2/test/living_room_0003/rgb_0210_rgb_left.mp4,NYUv2/test/living_room_0003/rgb_0210_disparity.npz
+NYUv2/test/living_room_0003/rgb_0211_rgb_left.mp4,NYUv2/test/living_room_0003/rgb_0211_disparity.npz
+NYUv2/test/living_room_0003/rgb_0212_rgb_left.mp4,NYUv2/test/living_room_0003/rgb_0212_disparity.npz
+NYUv2/test/bedroom_0022/rgb_0220_rgb_left.mp4,NYUv2/test/bedroom_0022/rgb_0220_disparity.npz
+NYUv2/test/bedroom_0024/rgb_0221_rgb_left.mp4,NYUv2/test/bedroom_0024/rgb_0221_disparity.npz
+NYUv2/test/bedroom_0024/rgb_0222_rgb_left.mp4,NYUv2/test/bedroom_0024/rgb_0222_disparity.npz
+NYUv2/test/kitchen_0015/rgb_0250_rgb_left.mp4,NYUv2/test/kitchen_0015/rgb_0250_disparity.npz
+NYUv2/test/living_room_0021/rgb_0264_rgb_left.mp4,NYUv2/test/living_room_0021/rgb_0264_disparity.npz
+NYUv2/test/office_0016/rgb_0271_rgb_left.mp4,NYUv2/test/office_0016/rgb_0271_disparity.npz
+NYUv2/test/office_0017/rgb_0272_rgb_left.mp4,NYUv2/test/office_0017/rgb_0272_disparity.npz
+NYUv2/test/study_room_0001/rgb_0273_rgb_left.mp4,NYUv2/test/study_room_0001/rgb_0273_disparity.npz
+NYUv2/test/study_room_0006/rgb_0279_rgb_left.mp4,NYUv2/test/study_room_0006/rgb_0279_disparity.npz
+NYUv2/test/bedroom_0131/rgb_0280_rgb_left.mp4,NYUv2/test/bedroom_0131/rgb_0280_disparity.npz
+NYUv2/test/bedroom_0131/rgb_0281_rgb_left.mp4,NYUv2/test/bedroom_0131/rgb_0281_disparity.npz
+NYUv2/test/bedroom_0131/rgb_0282_rgb_left.mp4,NYUv2/test/bedroom_0131/rgb_0282_disparity.npz
+NYUv2/test/bedroom_0131/rgb_0283_rgb_left.mp4,NYUv2/test/bedroom_0131/rgb_0283_disparity.npz
+NYUv2/test/classroom_0001/rgb_0284_rgb_left.mp4,NYUv2/test/classroom_0001/rgb_0284_disparity.npz
+NYUv2/test/classroom_0001/rgb_0285_rgb_left.mp4,NYUv2/test/classroom_0001/rgb_0285_disparity.npz
+NYUv2/test/classroom_0007/rgb_0296_rgb_left.mp4,NYUv2/test/classroom_0007/rgb_0296_disparity.npz
+NYUv2/test/classroom_0007/rgb_0297_rgb_left.mp4,NYUv2/test/classroom_0007/rgb_0297_disparity.npz
+NYUv2/test/classroom_0007/rgb_0298_rgb_left.mp4,NYUv2/test/classroom_0007/rgb_0298_disparity.npz
+NYUv2/test/classroom_0008/rgb_0299_rgb_left.mp4,NYUv2/test/classroom_0008/rgb_0299_disparity.npz
+NYUv2/test/classroom_0008/rgb_0300_rgb_left.mp4,NYUv2/test/classroom_0008/rgb_0300_disparity.npz
+NYUv2/test/classroom_0009/rgb_0301_rgb_left.mp4,NYUv2/test/classroom_0009/rgb_0301_disparity.npz
+NYUv2/test/classroom_0009/rgb_0302_rgb_left.mp4,NYUv2/test/classroom_0009/rgb_0302_disparity.npz
+NYUv2/test/classroom_0014/rgb_0310_rgb_left.mp4,NYUv2/test/classroom_0014/rgb_0310_disparity.npz
+NYUv2/test/classroom_0014/rgb_0311_rgb_left.mp4,NYUv2/test/classroom_0014/rgb_0311_disparity.npz
+NYUv2/test/classroom_0015/rgb_0312_rgb_left.mp4,NYUv2/test/classroom_0015/rgb_0312_disparity.npz
+NYUv2/test/classroom_0017/rgb_0315_rgb_left.mp4,NYUv2/test/classroom_0017/rgb_0315_disparity.npz
+NYUv2/test/classroom_0017/rgb_0316_rgb_left.mp4,NYUv2/test/classroom_0017/rgb_0316_disparity.npz
+NYUv2/test/classroom_0017/rgb_0317_rgb_left.mp4,NYUv2/test/classroom_0017/rgb_0317_disparity.npz
+NYUv2/test/classroom_0023/rgb_0325_rgb_left.mp4,NYUv2/test/classroom_0023/rgb_0325_disparity.npz
+NYUv2/test/classroom_0023/rgb_0326_rgb_left.mp4,NYUv2/test/classroom_0023/rgb_0326_disparity.npz
+NYUv2/test/classroom_0023/rgb_0327_rgb_left.mp4,NYUv2/test/classroom_0023/rgb_0327_disparity.npz
+NYUv2/test/classroom_0023/rgb_0328_rgb_left.mp4,NYUv2/test/classroom_0023/rgb_0328_disparity.npz
+NYUv2/test/classroom_0024/rgb_0329_rgb_left.mp4,NYUv2/test/classroom_0024/rgb_0329_disparity.npz
+NYUv2/test/classroom_0024/rgb_0330_rgb_left.mp4,NYUv2/test/classroom_0024/rgb_0330_disparity.npz
+NYUv2/test/classroom_0026/rgb_0331_rgb_left.mp4,NYUv2/test/classroom_0026/rgb_0331_disparity.npz
+NYUv2/test/classroom_0026/rgb_0332_rgb_left.mp4,NYUv2/test/classroom_0026/rgb_0332_disparity.npz
+NYUv2/test/computer_lab_0001/rgb_0333_rgb_left.mp4,NYUv2/test/computer_lab_0001/rgb_0333_disparity.npz
+NYUv2/test/computer_lab_0001/rgb_0334_rgb_left.mp4,NYUv2/test/computer_lab_0001/rgb_0334_disparity.npz
+NYUv2/test/computer_lab_0001/rgb_0335_rgb_left.mp4,NYUv2/test/computer_lab_0001/rgb_0335_disparity.npz
+NYUv2/test/foyer_0001/rgb_0351_rgb_left.mp4,NYUv2/test/foyer_0001/rgb_0351_disparity.npz
+NYUv2/test/foyer_0001/rgb_0352_rgb_left.mp4,NYUv2/test/foyer_0001/rgb_0352_disparity.npz
+NYUv2/test/home_office_0001/rgb_0355_rgb_left.mp4,NYUv2/test/home_office_0001/rgb_0355_disparity.npz
+NYUv2/test/home_office_0001/rgb_0356_rgb_left.mp4,NYUv2/test/home_office_0001/rgb_0356_disparity.npz
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+NYUv2/test/dining_room_0011/rgb_1368_rgb_left.mp4,NYUv2/test/dining_room_0011/rgb_1368_disparity.npz
+NYUv2/test/dining_room_0011/rgb_1369_rgb_left.mp4,NYUv2/test/dining_room_0011/rgb_1369_disparity.npz
+NYUv2/test/dining_room_0017/rgb_1384_rgb_left.mp4,NYUv2/test/dining_room_0017/rgb_1384_disparity.npz
+NYUv2/test/dining_room_0017/rgb_1385_rgb_left.mp4,NYUv2/test/dining_room_0017/rgb_1385_disparity.npz
+NYUv2/test/dining_room_0017/rgb_1386_rgb_left.mp4,NYUv2/test/dining_room_0017/rgb_1386_disparity.npz
+NYUv2/test/dining_room_0018/rgb_1387_rgb_left.mp4,NYUv2/test/dining_room_0018/rgb_1387_disparity.npz
+NYUv2/test/dining_room_0018/rgb_1388_rgb_left.mp4,NYUv2/test/dining_room_0018/rgb_1388_disparity.npz
+NYUv2/test/dining_room_0018/rgb_1389_rgb_left.mp4,NYUv2/test/dining_room_0018/rgb_1389_disparity.npz
+NYUv2/test/dining_room_0018/rgb_1390_rgb_left.mp4,NYUv2/test/dining_room_0018/rgb_1390_disparity.npz
+NYUv2/test/dining_room_0018/rgb_1391_rgb_left.mp4,NYUv2/test/dining_room_0018/rgb_1391_disparity.npz
+NYUv2/test/dining_room_0020/rgb_1394_rgb_left.mp4,NYUv2/test/dining_room_0020/rgb_1394_disparity.npz
+NYUv2/test/dining_room_0020/rgb_1395_rgb_left.mp4,NYUv2/test/dining_room_0020/rgb_1395_disparity.npz
+NYUv2/test/dining_room_0020/rgb_1396_rgb_left.mp4,NYUv2/test/dining_room_0020/rgb_1396_disparity.npz
+NYUv2/test/dining_room_0021/rgb_1397_rgb_left.mp4,NYUv2/test/dining_room_0021/rgb_1397_disparity.npz
+NYUv2/test/dining_room_0021/rgb_1398_rgb_left.mp4,NYUv2/test/dining_room_0021/rgb_1398_disparity.npz
+NYUv2/test/dining_room_0021/rgb_1399_rgb_left.mp4,NYUv2/test/dining_room_0021/rgb_1399_disparity.npz
+NYUv2/test/dining_room_0022/rgb_1400_rgb_left.mp4,NYUv2/test/dining_room_0022/rgb_1400_disparity.npz
+NYUv2/test/dining_room_0022/rgb_1401_rgb_left.mp4,NYUv2/test/dining_room_0022/rgb_1401_disparity.npz
+NYUv2/test/dining_room_0025/rgb_1407_rgb_left.mp4,NYUv2/test/dining_room_0025/rgb_1407_disparity.npz
+NYUv2/test/dining_room_0025/rgb_1408_rgb_left.mp4,NYUv2/test/dining_room_0025/rgb_1408_disparity.npz
+NYUv2/test/dining_room_0025/rgb_1409_rgb_left.mp4,NYUv2/test/dining_room_0025/rgb_1409_disparity.npz
+NYUv2/test/dining_room_0025/rgb_1410_rgb_left.mp4,NYUv2/test/dining_room_0025/rgb_1410_disparity.npz
+NYUv2/test/dining_room_0025/rgb_1411_rgb_left.mp4,NYUv2/test/dining_room_0025/rgb_1411_disparity.npz
+NYUv2/test/dining_room_0026/rgb_1412_rgb_left.mp4,NYUv2/test/dining_room_0026/rgb_1412_disparity.npz
+NYUv2/test/dining_room_0026/rgb_1413_rgb_left.mp4,NYUv2/test/dining_room_0026/rgb_1413_disparity.npz
+NYUv2/test/dining_room_0026/rgb_1414_rgb_left.mp4,NYUv2/test/dining_room_0026/rgb_1414_disparity.npz
+NYUv2/test/dining_room_0030/rgb_1421_rgb_left.mp4,NYUv2/test/dining_room_0030/rgb_1421_disparity.npz
+NYUv2/test/dining_room_0030/rgb_1422_rgb_left.mp4,NYUv2/test/dining_room_0030/rgb_1422_disparity.npz
+NYUv2/test/dining_room_0030/rgb_1423_rgb_left.mp4,NYUv2/test/dining_room_0030/rgb_1423_disparity.npz
+NYUv2/test/dining_room_0030/rgb_1424_rgb_left.mp4,NYUv2/test/dining_room_0030/rgb_1424_disparity.npz
+NYUv2/test/dining_room_0032/rgb_1430_rgb_left.mp4,NYUv2/test/dining_room_0032/rgb_1430_disparity.npz
+NYUv2/test/dining_room_0032/rgb_1431_rgb_left.mp4,NYUv2/test/dining_room_0032/rgb_1431_disparity.npz
+NYUv2/test/dining_room_0032/rgb_1432_rgb_left.mp4,NYUv2/test/dining_room_0032/rgb_1432_disparity.npz
+NYUv2/test/dining_room_0032/rgb_1433_rgb_left.mp4,NYUv2/test/dining_room_0032/rgb_1433_disparity.npz
+NYUv2/test/dining_room_0035/rgb_1441_rgb_left.mp4,NYUv2/test/dining_room_0035/rgb_1441_disparity.npz
+NYUv2/test/dining_room_0035/rgb_1442_rgb_left.mp4,NYUv2/test/dining_room_0035/rgb_1442_disparity.npz
+NYUv2/test/dining_room_0035/rgb_1443_rgb_left.mp4,NYUv2/test/dining_room_0035/rgb_1443_disparity.npz
+NYUv2/test/dining_room_0036/rgb_1444_rgb_left.mp4,NYUv2/test/dining_room_0036/rgb_1444_disparity.npz
+NYUv2/test/dining_room_0036/rgb_1445_rgb_left.mp4,NYUv2/test/dining_room_0036/rgb_1445_disparity.npz
+NYUv2/test/dining_room_0036/rgb_1446_rgb_left.mp4,NYUv2/test/dining_room_0036/rgb_1446_disparity.npz
+NYUv2/test/dining_room_0036/rgb_1447_rgb_left.mp4,NYUv2/test/dining_room_0036/rgb_1447_disparity.npz
+NYUv2/test/dining_room_0036/rgb_1448_rgb_left.mp4,NYUv2/test/dining_room_0036/rgb_1448_disparity.npz
+NYUv2/test/dining_room_0036/rgb_1449_rgb_left.mp4,NYUv2/test/dining_room_0036/rgb_1449_disparity.npz

benchmark/csv/meta_scannet_test.csv

@@ -0,0 +1,101 @@
+filepath_left,filepath_disparity
+scannet/scene0707_00_rgb_left.mp4,scannet/scene0707_00_disparity.npz
+scannet/scene0708_00_rgb_left.mp4,scannet/scene0708_00_disparity.npz
+scannet/scene0709_00_rgb_left.mp4,scannet/scene0709_00_disparity.npz
+scannet/scene0710_00_rgb_left.mp4,scannet/scene0710_00_disparity.npz
+scannet/scene0711_00_rgb_left.mp4,scannet/scene0711_00_disparity.npz
+scannet/scene0712_00_rgb_left.mp4,scannet/scene0712_00_disparity.npz
+scannet/scene0713_00_rgb_left.mp4,scannet/scene0713_00_disparity.npz
+scannet/scene0714_00_rgb_left.mp4,scannet/scene0714_00_disparity.npz
+scannet/scene0715_00_rgb_left.mp4,scannet/scene0715_00_disparity.npz
+scannet/scene0716_00_rgb_left.mp4,scannet/scene0716_00_disparity.npz
+scannet/scene0717_00_rgb_left.mp4,scannet/scene0717_00_disparity.npz
+scannet/scene0718_00_rgb_left.mp4,scannet/scene0718_00_disparity.npz
+scannet/scene0719_00_rgb_left.mp4,scannet/scene0719_00_disparity.npz
+scannet/scene0720_00_rgb_left.mp4,scannet/scene0720_00_disparity.npz
+scannet/scene0721_00_rgb_left.mp4,scannet/scene0721_00_disparity.npz
+scannet/scene0722_00_rgb_left.mp4,scannet/scene0722_00_disparity.npz
+scannet/scene0723_00_rgb_left.mp4,scannet/scene0723_00_disparity.npz
+scannet/scene0724_00_rgb_left.mp4,scannet/scene0724_00_disparity.npz
+scannet/scene0725_00_rgb_left.mp4,scannet/scene0725_00_disparity.npz
+scannet/scene0726_00_rgb_left.mp4,scannet/scene0726_00_disparity.npz
+scannet/scene0727_00_rgb_left.mp4,scannet/scene0727_00_disparity.npz
+scannet/scene0728_00_rgb_left.mp4,scannet/scene0728_00_disparity.npz
+scannet/scene0729_00_rgb_left.mp4,scannet/scene0729_00_disparity.npz
+scannet/scene0730_00_rgb_left.mp4,scannet/scene0730_00_disparity.npz
+scannet/scene0731_00_rgb_left.mp4,scannet/scene0731_00_disparity.npz
+scannet/scene0732_00_rgb_left.mp4,scannet/scene0732_00_disparity.npz
+scannet/scene0733_00_rgb_left.mp4,scannet/scene0733_00_disparity.npz
+scannet/scene0734_00_rgb_left.mp4,scannet/scene0734_00_disparity.npz
+scannet/scene0735_00_rgb_left.mp4,scannet/scene0735_00_disparity.npz
+scannet/scene0736_00_rgb_left.mp4,scannet/scene0736_00_disparity.npz
+scannet/scene0737_00_rgb_left.mp4,scannet/scene0737_00_disparity.npz
+scannet/scene0738_00_rgb_left.mp4,scannet/scene0738_00_disparity.npz
+scannet/scene0739_00_rgb_left.mp4,scannet/scene0739_00_disparity.npz
+scannet/scene0740_00_rgb_left.mp4,scannet/scene0740_00_disparity.npz
+scannet/scene0741_00_rgb_left.mp4,scannet/scene0741_00_disparity.npz
+scannet/scene0742_00_rgb_left.mp4,scannet/scene0742_00_disparity.npz
+scannet/scene0743_00_rgb_left.mp4,scannet/scene0743_00_disparity.npz
+scannet/scene0744_00_rgb_left.mp4,scannet/scene0744_00_disparity.npz
+scannet/scene0745_00_rgb_left.mp4,scannet/scene0745_00_disparity.npz
+scannet/scene0746_00_rgb_left.mp4,scannet/scene0746_00_disparity.npz
+scannet/scene0747_00_rgb_left.mp4,scannet/scene0747_00_disparity.npz
+scannet/scene0748_00_rgb_left.mp4,scannet/scene0748_00_disparity.npz
+scannet/scene0749_00_rgb_left.mp4,scannet/scene0749_00_disparity.npz
+scannet/scene0750_00_rgb_left.mp4,scannet/scene0750_00_disparity.npz
+scannet/scene0751_00_rgb_left.mp4,scannet/scene0751_00_disparity.npz
+scannet/scene0752_00_rgb_left.mp4,scannet/scene0752_00_disparity.npz
+scannet/scene0753_00_rgb_left.mp4,scannet/scene0753_00_disparity.npz
+scannet/scene0754_00_rgb_left.mp4,scannet/scene0754_00_disparity.npz
+scannet/scene0755_00_rgb_left.mp4,scannet/scene0755_00_disparity.npz
+scannet/scene0756_00_rgb_left.mp4,scannet/scene0756_00_disparity.npz
+scannet/scene0757_00_rgb_left.mp4,scannet/scene0757_00_disparity.npz
+scannet/scene0758_00_rgb_left.mp4,scannet/scene0758_00_disparity.npz
+scannet/scene0759_00_rgb_left.mp4,scannet/scene0759_00_disparity.npz
+scannet/scene0760_00_rgb_left.mp4,scannet/scene0760_00_disparity.npz
+scannet/scene0761_00_rgb_left.mp4,scannet/scene0761_00_disparity.npz
+scannet/scene0762_00_rgb_left.mp4,scannet/scene0762_00_disparity.npz
+scannet/scene0763_00_rgb_left.mp4,scannet/scene0763_00_disparity.npz
+scannet/scene0764_00_rgb_left.mp4,scannet/scene0764_00_disparity.npz
+scannet/scene0765_00_rgb_left.mp4,scannet/scene0765_00_disparity.npz
+scannet/scene0766_00_rgb_left.mp4,scannet/scene0766_00_disparity.npz
+scannet/scene0767_00_rgb_left.mp4,scannet/scene0767_00_disparity.npz
+scannet/scene0768_00_rgb_left.mp4,scannet/scene0768_00_disparity.npz
+scannet/scene0769_00_rgb_left.mp4,scannet/scene0769_00_disparity.npz
+scannet/scene0770_00_rgb_left.mp4,scannet/scene0770_00_disparity.npz
+scannet/scene0771_00_rgb_left.mp4,scannet/scene0771_00_disparity.npz
+scannet/scene0772_00_rgb_left.mp4,scannet/scene0772_00_disparity.npz
+scannet/scene0773_00_rgb_left.mp4,scannet/scene0773_00_disparity.npz
+scannet/scene0774_00_rgb_left.mp4,scannet/scene0774_00_disparity.npz
+scannet/scene0775_00_rgb_left.mp4,scannet/scene0775_00_disparity.npz
+scannet/scene0776_00_rgb_left.mp4,scannet/scene0776_00_disparity.npz
+scannet/scene0777_00_rgb_left.mp4,scannet/scene0777_00_disparity.npz
+scannet/scene0778_00_rgb_left.mp4,scannet/scene0778_00_disparity.npz
+scannet/scene0779_00_rgb_left.mp4,scannet/scene0779_00_disparity.npz
+scannet/scene0780_00_rgb_left.mp4,scannet/scene0780_00_disparity.npz
+scannet/scene0781_00_rgb_left.mp4,scannet/scene0781_00_disparity.npz
+scannet/scene0782_00_rgb_left.mp4,scannet/scene0782_00_disparity.npz
+scannet/scene0783_00_rgb_left.mp4,scannet/scene0783_00_disparity.npz
+scannet/scene0784_00_rgb_left.mp4,scannet/scene0784_00_disparity.npz
+scannet/scene0785_00_rgb_left.mp4,scannet/scene0785_00_disparity.npz
+scannet/scene0786_00_rgb_left.mp4,scannet/scene0786_00_disparity.npz
+scannet/scene0787_00_rgb_left.mp4,scannet/scene0787_00_disparity.npz
+scannet/scene0788_00_rgb_left.mp4,scannet/scene0788_00_disparity.npz
+scannet/scene0789_00_rgb_left.mp4,scannet/scene0789_00_disparity.npz
+scannet/scene0790_00_rgb_left.mp4,scannet/scene0790_00_disparity.npz
+scannet/scene0791_00_rgb_left.mp4,scannet/scene0791_00_disparity.npz
+scannet/scene0792_00_rgb_left.mp4,scannet/scene0792_00_disparity.npz
+scannet/scene0793_00_rgb_left.mp4,scannet/scene0793_00_disparity.npz
+scannet/scene0794_00_rgb_left.mp4,scannet/scene0794_00_disparity.npz
+scannet/scene0795_00_rgb_left.mp4,scannet/scene0795_00_disparity.npz
+scannet/scene0796_00_rgb_left.mp4,scannet/scene0796_00_disparity.npz
+scannet/scene0797_00_rgb_left.mp4,scannet/scene0797_00_disparity.npz
+scannet/scene0798_00_rgb_left.mp4,scannet/scene0798_00_disparity.npz
+scannet/scene0799_00_rgb_left.mp4,scannet/scene0799_00_disparity.npz
+scannet/scene0800_00_rgb_left.mp4,scannet/scene0800_00_disparity.npz
+scannet/scene0801_00_rgb_left.mp4,scannet/scene0801_00_disparity.npz
+scannet/scene0802_00_rgb_left.mp4,scannet/scene0802_00_disparity.npz
+scannet/scene0803_00_rgb_left.mp4,scannet/scene0803_00_disparity.npz
+scannet/scene0804_00_rgb_left.mp4,scannet/scene0804_00_disparity.npz
+scannet/scene0805_00_rgb_left.mp4,scannet/scene0805_00_disparity.npz
+scannet/scene0806_00_rgb_left.mp4,scannet/scene0806_00_disparity.npz

benchmark/csv/meta_sintel.csv

@@ -0,0 +1,24 @@
+filepath_left,filepath_disparity
+sintel/ambush_5_rgb_left.mp4,sintel/ambush_5_disparity.npz
+sintel/bamboo_2_rgb_left.mp4,sintel/bamboo_2_disparity.npz
+sintel/mountain_1_rgb_left.mp4,sintel/mountain_1_disparity.npz
+sintel/bamboo_1_rgb_left.mp4,sintel/bamboo_1_disparity.npz
+sintel/shaman_2_rgb_left.mp4,sintel/shaman_2_disparity.npz
+sintel/ambush_6_rgb_left.mp4,sintel/ambush_6_disparity.npz
+sintel/bandage_1_rgb_left.mp4,sintel/bandage_1_disparity.npz
+sintel/alley_1_rgb_left.mp4,sintel/alley_1_disparity.npz
+sintel/temple_3_rgb_left.mp4,sintel/temple_3_disparity.npz
+sintel/shaman_3_rgb_left.mp4,sintel/shaman_3_disparity.npz
+sintel/ambush_2_rgb_left.mp4,sintel/ambush_2_disparity.npz
+sintel/cave_4_rgb_left.mp4,sintel/cave_4_disparity.npz
+sintel/cave_2_rgb_left.mp4,sintel/cave_2_disparity.npz
+sintel/alley_2_rgb_left.mp4,sintel/alley_2_disparity.npz
+sintel/market_5_rgb_left.mp4,sintel/market_5_disparity.npz
+sintel/sleeping_2_rgb_left.mp4,sintel/sleeping_2_disparity.npz
+sintel/ambush_4_rgb_left.mp4,sintel/ambush_4_disparity.npz
+sintel/sleeping_1_rgb_left.mp4,sintel/sleeping_1_disparity.npz
+sintel/market_6_rgb_left.mp4,sintel/market_6_disparity.npz
+sintel/market_2_rgb_left.mp4,sintel/market_2_disparity.npz
+sintel/bandage_2_rgb_left.mp4,sintel/bandage_2_disparity.npz
+sintel/ambush_7_rgb_left.mp4,sintel/ambush_7_disparity.npz
+sintel/temple_2_rgb_left.mp4,sintel/temple_2_disparity.npz

benchmark/dataset_extract/dataset_extract_bonn.py

@@ -0,0 +1,155 @@
+import os
+import numpy as np
+import os.path as osp
+from PIL import Image
+from tqdm import tqdm
+import imageio
+import csv
+
+
+def depth_read(filename):
+    # loads depth map D from png file
+    # and returns it as a numpy array
+
+    depth_png = np.asarray(Image.open(filename))
+    # make sure we have a proper 16bit depth map here.. not 8bit!
+    assert np.max(depth_png) > 255
+
+    depth = depth_png.astype(np.float64) / 5000.0
+    depth[depth_png == 0] = -1.0
+    return depth
+
+
+def extract_bonn(
+    root,
+    depth_root,
+    sample_len=-1,
+    csv_save_path="",
+    datatset_name="",
+    saved_rgb_dir="",
+    saved_disp_dir="",
+    start_frame=0,
+    end_frame=110,
+):
+    scenes_names = os.listdir(depth_root)
+    all_samples = []
+    for i, seq_name in enumerate(tqdm(scenes_names)):
+        # load all images
+        all_img_names = os.listdir(osp.join(depth_root, seq_name, "rgb"))
+        all_img_names = [x for x in all_img_names if x.endswith(".png")]
+        print(f"sequence frame number: {len(all_img_names)}")
+
+        # for not zero padding image name
+        all_img_names.sort()
+        all_img_names = sorted(all_img_names, key=lambda x: int(x.split(".")[0][-4:]))
+        all_img_names = all_img_names[start_frame:end_frame]
+
+        all_depth_names = os.listdir(osp.join(depth_root, seq_name, "depth"))
+        all_depth_names = [x for x in all_depth_names if x.endswith(".png")]
+        print(f"sequence depth number: {len(all_depth_names)}")
+
+        # for not zero padding image name
+        all_depth_names.sort()
+        all_depth_names = sorted(
+            all_depth_names, key=lambda x: int(x.split(".")[0][-4:])
+        )
+        all_depth_names = all_depth_names[start_frame:end_frame]
+
+        seq_len = len(all_img_names)
+        step = sample_len if sample_len > 0 else seq_len
+
+        for ref_idx in range(0, seq_len, step):
+            print(f"Progress: {seq_name}, {ref_idx // step + 1} / {seq_len//step}")
+
+            video_imgs = []
+            video_depths = []
+
+            if (ref_idx + step) <= seq_len:
+                ref_e = ref_idx + step
+            else:
+                continue
+
+            # for idx in range(ref_idx, ref_idx + step):
+            for idx in range(ref_idx, ref_e):
+                im_path = osp.join(root, seq_name, "rgb", all_img_names[idx])
+                depth_path = osp.join(
+                    depth_root, seq_name, "depth", all_depth_names[idx]
+                )
+
+                depth = depth_read(depth_path)
+                disp = depth
+
+                video_depths.append(disp)
+                video_imgs.append(np.array(Image.open(im_path)))
+
+            disp_video = np.array(video_depths)[:, None]  # [:, 0:1, :, :, 0]
+            img_video = np.array(video_imgs)[..., 0:3]  # [:, 0, :, :, 0:3]
+
+            print(disp_video.max(), disp_video.min())
+
+            def even_or_odd(num):
+                if num % 2 == 0:
+                    return num
+                else:
+                    return num - 1
+
+            # print(disp_video.shape)
+            # print(img_video.shape)
+            height = disp_video.shape[-2]
+            width = disp_video.shape[-1]
+            height = even_or_odd(height)
+            width = even_or_odd(width)
+            disp_video = disp_video[:, :, 0:height, 0:width]
+            img_video = img_video[:, 0:height, 0:width]
+
+            data_root = saved_rgb_dir + datatset_name
+            disp_root = saved_disp_dir + datatset_name
+            os.makedirs(data_root, exist_ok=True)
+            os.makedirs(disp_root, exist_ok=True)
+
+            img_video_dir = data_root
+            disp_video_dir = disp_root
+
+            img_video_path = os.path.join(img_video_dir, f"{seq_name}_rgb_left.mp4")
+            disp_video_path = os.path.join(disp_video_dir, f"{seq_name}_disparity.npz")
+
+            imageio.mimsave(
+                img_video_path, img_video, fps=15, quality=9, macro_block_size=1
+            )
+            np.savez(disp_video_path, disparity=disp_video)
+
+            sample = {}
+            sample["filepath_left"] = os.path.join(
+                f"{datatset_name}/{seq_name}_rgb_left.mp4"
+            )  # img_video_path
+            sample["filepath_disparity"] = os.path.join(
+                f"{datatset_name}/{seq_name}_disparity.npz"
+            )  # disp_video_path
+
+            all_samples.append(sample)
+
+    # save csv file
+
+    filename_ = csv_save_path
+    os.makedirs(os.path.dirname(filename_), exist_ok=True)
+    fields = ["filepath_left", "filepath_disparity"]
+    with open(filename_, "w") as csvfile:
+        writer = csv.DictWriter(csvfile, fieldnames=fields)
+        writer.writeheader()
+        writer.writerows(all_samples)
+
+    print(f"{filename_} has been saved.")
+
+
+if __name__ == "__main__":
+    extract_bonn(
+        root="path/to/Bonn-RGBD",
+        depth_root="path/to/Bonn-RGBD",
+        saved_rgb_dir="./benchmark/datasets/",
+        saved_disp_dir="./benchmark/datasets/",
+        csv_save_path=f"./benchmark/datasets/bonn.csv",
+        sample_len=-1,
+        datatset_name="bonn",
+        start_frame=30,
+        end_frame=140,
+    )

benchmark/dataset_extract/dataset_extract_kitti.py

@@ -0,0 +1,140 @@
+import os
+import numpy as np
+import os.path as osp
+from PIL import Image
+from tqdm import tqdm
+import csv
+import imageio
+
+
+def depth_read(filename):
+    # loads depth map D from png file
+    # and returns it as a numpy array,
+
+    depth_png = np.array(Image.open(filename), dtype=int)
+    # make sure we have a proper 16bit depth map here.. not 8bit!
+    assert np.max(depth_png) > 255
+
+    depth = depth_png.astype(np.float64) / 256.0
+    depth[depth_png == 0] = -1.0
+    return depth
+
+
+def extract_kitti(
+    root,
+    depth_root,
+    sample_len=-1,
+    csv_save_path="",
+    datatset_name="",
+    saved_rgb_dir="",
+    saved_disp_dir="",
+    start_frame=0,
+    end_frame=110,
+):
+    scenes_names = os.listdir(depth_root)
+    all_samples = []
+    for i, seq_name in enumerate(tqdm(scenes_names)):
+        all_img_names = os.listdir(
+            osp.join(depth_root, seq_name, "proj_depth/groundtruth/image_02")
+        )
+        all_img_names = [x for x in all_img_names if x.endswith(".png")]
+        print(f"sequence frame number: {len(all_img_names)}")
+
+        all_img_names.sort()
+        all_img_names = sorted(all_img_names, key=lambda x: int(x.split(".")[0][-4:]))
+        all_img_names = all_img_names[start_frame:end_frame]
+
+        seq_len = len(all_img_names)
+        step = sample_len if sample_len > 0 else seq_len
+
+        for ref_idx in range(0, seq_len, step):
+            print(f"Progress: {seq_name}, {ref_idx // step + 1} / {seq_len//step}")
+
+            video_imgs = []
+            video_depths = []
+
+            if (ref_idx + step) <= seq_len:
+                ref_e = ref_idx + step
+            else:
+                continue
+
+            for idx in range(ref_idx, ref_e):
+                im_path = osp.join(
+                    root, seq_name[0:10], seq_name, "image_02/data", all_img_names[idx]
+                )
+                depth_path = osp.join(
+                    depth_root,
+                    seq_name,
+                    "proj_depth/groundtruth/image_02",
+                    all_img_names[idx],
+                )
+
+                depth = depth_read(depth_path)
+                disp = depth
+
+                video_depths.append(disp)
+                video_imgs.append(np.array(Image.open(im_path)))
+
+            disp_video = np.array(video_depths)[:, None]
+            img_video = np.array(video_imgs)[..., 0:3]
+
+            def even_or_odd(num):
+                if num % 2 == 0:
+                    return num
+                else:
+                    return num - 1
+
+            height = disp_video.shape[-2]
+            width = disp_video.shape[-1]
+            height = even_or_odd(height)
+            width = even_or_odd(width)
+            disp_video = disp_video[:, :, 0:height, 0:width]
+            img_video = img_video[:, 0:height, 0:width]
+
+            data_root = saved_rgb_dir + datatset_name
+            disp_root = saved_disp_dir + datatset_name
+            os.makedirs(data_root, exist_ok=True)
+            os.makedirs(disp_root, exist_ok=True)
+
+            img_video_dir = data_root
+            disp_video_dir = disp_root
+
+            img_video_path = os.path.join(img_video_dir, f"{seq_name}_rgb_left.mp4")
+            disp_video_path = os.path.join(disp_video_dir, f"{seq_name}_disparity.npz")
+
+            imageio.mimsave(
+                img_video_path, img_video, fps=15, quality=10, macro_block_size=1
+            )
+            np.savez(disp_video_path, disparity=disp_video)
+
+            sample = {}
+            sample["filepath_left"] = os.path.join(f"KITTI/{seq_name}_rgb_left.mp4")
+            sample["filepath_disparity"] = os.path.join(
+                f"KITTI/{seq_name}_disparity.npz"
+            )
+
+            all_samples.append(sample)
+
+    filename_ = csv_save_path
+    os.makedirs(os.path.dirname(filename_), exist_ok=True)
+    fields = ["filepath_left", "filepath_disparity"]
+    with open(filename_, "w") as csvfile:
+        writer = csv.DictWriter(csvfile, fieldnames=fields)
+        writer.writeheader()
+        writer.writerows(all_samples)
+
+    print(f"{filename_} has been saved.")
+
+
+if __name__ == "__main__":
+    extract_kitti(
+        root="path/to/KITTI/raw_data",
+        depth_root="path/to/KITTI/data_depth_annotated/val",
+        saved_rgb_dir="./benchmark/datasets/",
+        saved_disp_dir="./benchmark/datasets/",
+        csv_save_path=f"./benchmark/datasets/KITTI.csv",
+        sample_len=-1,
+        datatset_name="KITTI",
+        start_frame=0,
+        end_frame=110,
+    )

benchmark/dataset_extract/dataset_extract_nyu.py

@@ -0,0 +1,106 @@
+import os
+import numpy as np
+import os.path as osp
+from PIL import Image
+from tqdm import tqdm
+import csv
+import imageio
+
+
+def _read_image(img_rel_path) -> np.ndarray:
+    image_to_read = img_rel_path
+    image = Image.open(image_to_read)
+    image = np.asarray(image)
+    return image
+
+
+def depth_read(filename):
+    depth_in = _read_image(filename)
+    depth_decoded = depth_in / 1000.0
+    return depth_decoded
+
+
+def extract_nyu(
+    root,
+    depth_root,
+    csv_save_path="",
+    datatset_name="",
+    filename_ls_path="",
+    saved_rgb_dir="",
+    saved_disp_dir="",
+):
+    with open(filename_ls_path, "r") as f:
+        filenames = [s.split() for s in f.readlines()]
+
+    all_samples = []
+    for i, pair_names in enumerate(tqdm(filenames)):
+        img_name = pair_names[0]
+        filled_depth_name = pair_names[2]
+
+        im_path = osp.join(root, img_name)
+        depth_path = osp.join(depth_root, filled_depth_name)
+
+        depth = depth_read(depth_path)
+        disp = depth
+
+        video_depths = [disp]
+        video_imgs = [np.array(Image.open(im_path))]
+
+        disp_video = np.array(video_depths)[:, None]
+        img_video = np.array(video_imgs)[..., 0:3]
+
+        disp_video = disp_video[:, :, 45:471, 41:601]
+        img_video = img_video[:, 45:471, 41:601, :]
+
+        data_root = saved_rgb_dir + datatset_name
+        disp_root = saved_disp_dir + datatset_name
+        os.makedirs(data_root, exist_ok=True)
+        os.makedirs(disp_root, exist_ok=True)
+
+        img_video_dir = data_root
+        disp_video_dir = disp_root
+
+        img_video_path = os.path.join(img_video_dir, f"{img_name[:-4]}_rgb_left.mp4")
+        disp_video_path = os.path.join(disp_video_dir, f"{img_name[:-4]}_disparity.npz")
+
+        dir_name = os.path.dirname(img_video_path)
+        os.makedirs(dir_name, exist_ok=True)
+        dir_name = os.path.dirname(disp_video_path)
+        os.makedirs(dir_name, exist_ok=True)
+
+        imageio.mimsave(
+            img_video_path, img_video, fps=15, quality=10, macro_block_size=1
+        )
+        np.savez(disp_video_path, disparity=disp_video)
+
+        sample = {}
+        sample["filepath_left"] = os.path.join(
+            f"{datatset_name}/{img_name[:-4]}_rgb_left.mp4"
+        )
+        sample["filepath_disparity"] = os.path.join(
+            f"{datatset_name}/{img_name[:-4]}_disparity.npz"
+        )
+
+        all_samples.append(sample)
+
+    filename_ = csv_save_path
+    os.makedirs(os.path.dirname(filename_), exist_ok=True)
+    fields = ["filepath_left", "filepath_disparity"]
+    with open(filename_, "w") as csvfile:
+        writer = csv.DictWriter(csvfile, fieldnames=fields)
+        writer.writeheader()
+        writer.writerows(all_samples)
+
+    print(f"{filename_} has been saved.")
+
+
+if __name__ == "__main__":
+    extract_nyu(
+        root="path/to/NYUv2/",
+        depth_root="path/to/NYUv2/",
+        filename_ls_path="path/to/NYUv2/filename_list_test.txt",
+        saved_rgb_dir="./benchmark/datasets/",
+        saved_disp_dir="./benchmark/datasets/",
+        csv_save_path=f"./benchmark/datasets/NYUv2.csv",
+        datatset_name="NYUv2",
+    )

benchmark/dataset_extract/dataset_extract_scannet.py

@@ -0,0 +1,124 @@
+import os
+import numpy as np
+import os.path as osp
+from PIL import Image
+from tqdm import tqdm
+import csv
+import imageio
+
+
+def _read_image(img_rel_path) -> np.ndarray:
+    image_to_read = img_rel_path
+    image = Image.open(image_to_read)  # [H, W, rgb]
+    image = np.asarray(image)
+    return image
+
+
+def depth_read(filename):
+    depth_in = _read_image(filename)
+    depth_decoded = depth_in / 1000.0
+    return depth_decoded
+
+
+def extract_scannet(
+    root,
+    sample_len=-1,
+    csv_save_path="",
+    datatset_name="",
+    scene_number=16,
+    scene_frames_len=120,
+    stride=1,
+    saved_rgb_dir="",
+    saved_disp_dir="",
+):
+    scenes_names = os.listdir(root)
+    scenes_names = sorted(scenes_names)[:scene_number]
+    all_samples = []
+    for i, seq_name in enumerate(tqdm(scenes_names)):
+        all_img_names = os.listdir(osp.join(root, seq_name, "color"))
+        all_img_names = [x for x in all_img_names if x.endswith(".jpg")]
+        all_img_names = sorted(all_img_names, key=lambda x: int(x.split(".")[0]))
+        all_img_names = all_img_names[:scene_frames_len:stride]
+        print(f"sequence frame number: {len(all_img_names)}")
+
+        seq_len = len(all_img_names)
+        step = sample_len if sample_len > 0 else seq_len
+
+        for ref_idx in range(0, seq_len, step):
+            print(f"Progress: {seq_name}, {ref_idx // step + 1} / {seq_len//step}")
+
+            video_imgs = []
+            video_depths = []
+
+            if (ref_idx + step) <= seq_len:
+                ref_e = ref_idx + step
+            else:
+                continue
+
+            for idx in range(ref_idx, ref_e):
+                im_path = osp.join(root, seq_name, "color", all_img_names[idx])
+                depth_path = osp.join(
+                    root, seq_name, "depth", all_img_names[idx][:-3] + "png"
+                )
+
+                depth = depth_read(depth_path)
+                disp = depth
+
+                video_depths.append(disp)
+                video_imgs.append(np.array(Image.open(im_path)))
+
+            disp_video = np.array(video_depths)[:, None]
+            img_video = np.array(video_imgs)[..., 0:3]
+
+            disp_video = disp_video[:, :, 8:-8, 11:-11]
+            img_video = img_video[:, 8:-8, 11:-11, :]
+
+            data_root = saved_rgb_dir + datatset_name
+            disp_root = saved_disp_dir + datatset_name
+            os.makedirs(data_root, exist_ok=True)
+            os.makedirs(disp_root, exist_ok=True)
+
+            img_video_dir = data_root
+            disp_video_dir = disp_root
+
+            img_video_path = os.path.join(img_video_dir, f"{seq_name}_rgb_left.mp4")
+            disp_video_path = os.path.join(disp_video_dir, f"{seq_name}_disparity.npz")
+
+            imageio.mimsave(
+                img_video_path, img_video, fps=15, quality=9, macro_block_size=1
+            )
+            np.savez(disp_video_path, disparity=disp_video)
+
+            sample = {}
+            sample["filepath_left"] = os.path.join(
+                f"{datatset_name}/{seq_name}_rgb_left.mp4"
+            )
+            sample["filepath_disparity"] = os.path.join(
+                f"{datatset_name}/{seq_name}_disparity.npz"
+            )
+
+            all_samples.append(sample)
+
+    filename_ = csv_save_path
+    os.makedirs(os.path.dirname(filename_), exist_ok=True)
+    fields = ["filepath_left", "filepath_disparity"]
+    with open(filename_, "w") as csvfile:
+        writer = csv.DictWriter(csvfile, fieldnames=fields)
+        writer.writeheader()
+        writer.writerows(all_samples)
+
+    print(f"{filename_} has been saved.")
+
+
+if __name__ == "__main__":
+    extract_scannet(
+        root="path/to/ScanNet_v2/raw/scans_test",
+        saved_rgb_dir="./benchmark/datasets/",
+        saved_disp_dir="./benchmark/datasets/",
+        csv_save_path=f"./benchmark/datasets/scannet.csv",
+        sample_len=-1,
+        datatset_name="scannet",
+        scene_number=100,
+        scene_frames_len=90 * 3,
+        stride=3,
+    )

benchmark/dataset_extract/dataset_extract_sintel.py

@@ -0,0 +1,137 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# # Data loading based on https://github.com/NVIDIA/flownet2-pytorch
+
+
+import os
+import numpy as np
+import os.path as osp
+from PIL import Image
+from tqdm import tqdm
+import csv
+import imageio
+
+
+# Check for endianness, based on Daniel Scharstein's optical flow code.
+# Using little-endian architecture, these two should be equal.
+TAG_FLOAT = 202021.25
+TAG_CHAR = "PIEH"
+
+
+def depth_read(filename):
+    """Read depth data from file, return as numpy array."""
+    f = open(filename, "rb")
+    check = np.fromfile(f, dtype=np.float32, count=1)[0]
+    assert (
+        check == TAG_FLOAT
+    ), " depth_read:: Wrong tag in flow file (should be: {0}, is: {1}). Big-endian machine? ".format(
+        TAG_FLOAT, check
+    )
+    width = np.fromfile(f, dtype=np.int32, count=1)[0]
+    height = np.fromfile(f, dtype=np.int32, count=1)[0]
+    size = width * height
+    assert (
+        width > 0 and height > 0 and size > 1 and size < 100000000
+    ), " depth_read:: Wrong input size (width = {0}, height = {1}).".format(
+        width, height
+    )
+    depth = np.fromfile(f, dtype=np.float32, count=-1).reshape((height, width))
+    return depth
+
+
+def extract_sintel(
+    root,
+    depth_root,
+    sample_len=-1,
+    csv_save_path="",
+    datatset_name="",
+    saved_rgb_dir="",
+    saved_disp_dir="",
+):
+    scenes_names = os.listdir(root)
+    all_samples = []
+    for i, seq_name in enumerate(tqdm(scenes_names)):
+        all_img_names = os.listdir(os.path.join(root, seq_name))
+        all_img_names = [x for x in all_img_names if x.endswith(".png")]
+        all_img_names.sort()
+        all_img_names = sorted(all_img_names, key=lambda x: int(x.split(".")[0][-4:]))
+
+        seq_len = len(all_img_names)
+        step = sample_len if sample_len > 0 else seq_len
+
+        for ref_idx in range(0, seq_len, step):
+            print(f"Progress: {seq_name}, {ref_idx // step} / {seq_len // step}")
+
+            video_imgs = []
+            video_depths = []
+
+            if (ref_idx + step) <= seq_len:
+                ref_e = ref_idx + step
+            else:
+                continue
+
+            for idx in range(ref_idx, ref_e):
+                im_path = osp.join(root, seq_name, all_img_names[idx])
+                depth_path = osp.join(
+                    depth_root, seq_name, all_img_names[idx][:-3] + "dpt"
+                )
+
+                depth = depth_read(depth_path)
+                disp = depth
+
+                video_depths.append(disp)
+                video_imgs.append(np.array(Image.open(im_path)))
+
+            disp_video = np.array(video_depths)[:, None]
+            img_video = np.array(video_imgs)[..., 0:3]
+
+            data_root = saved_rgb_dir + datatset_name
+            disp_root = saved_disp_dir + datatset_name
+            os.makedirs(data_root, exist_ok=True)
+            os.makedirs(disp_root, exist_ok=True)
+
+            img_video_dir = data_root
+            disp_video_dir = disp_root
+
+            img_video_path = os.path.join(img_video_dir, f"{seq_name}_rgb_left.mp4")
+            disp_video_path = os.path.join(disp_video_dir, f"{seq_name}_disparity.npz")
+
+            imageio.mimsave(
+                img_video_path, img_video, fps=15, quality=10, macro_block_size=1
+            )
+            np.savez(disp_video_path, disparity=disp_video)
+
+            sample = {}
+            sample["filepath_left"] = os.path.join(
+                f"{datatset_name}/{seq_name}_rgb_left.mp4"
+            )
+            sample["filepath_disparity"] = os.path.join(
+                f"{datatset_name}/{seq_name}_disparity.npz"
+            )
+
+            all_samples.append(sample)
+
+    filename_ = csv_save_path
+    os.makedirs(os.path.dirname(filename_), exist_ok=True)
+    fields = ["filepath_left", "filepath_disparity"]
+    with open(filename_, "w") as csvfile:
+        writer = csv.DictWriter(csvfile, fieldnames=fields)
+        writer.writeheader()
+        writer.writerows(all_samples)
+
+    print(f"{filename_} has been saved.")
+
+
+if __name__ == "__main__":
+    extract_sintel(
+        root="path/to/Sintel-Depth/training_image/clean",
+        depth_root="path/to/Sintel-Depth/MPI-Sintel-depth-training-20150305/training/depth",
+        saved_rgb_dir="./benchmark/datasets/",
+        saved_disp_dir="./benchmark/datasets/",
+        csv_save_path=f"./benchmark/datasets/sintel.csv",
+        sample_len=-1,
+        datatset_name="sintel",
+    )

benchmark/demo.sh

@@ -0,0 +1,18 @@
+#!/bin/sh
+set -x
+set -e
+
+test_case=$1
+gpu_id=$2
+process_length=$3
+saved_root=$4
+saved_dataset_folder=$5
+overlap=$6
+dataset=$7
+
+CUDA_VISIBLE_DEVICES=${gpu_id} PYTHONPATH=. python run.py \
+  --video-path ${test_case} \
+  --save-folder ${saved_root}/${saved_dataset_folder} \
+  --process-length ${process_length} \
+  --dataset ${dataset} \
+  --overlap ${overlap}

benchmark/eval/eval.py

@@ -0,0 +1,282 @@
+import numpy as np
+import os
+import torch
+import cv2
+import csv
+from metric import * 
+import metric
+import argparse
+from tqdm import tqdm
+import json
+
+
+device = 'cuda'
+eval_metrics = [
+    "abs_relative_difference",
+    "rmse_linear",
+    "delta1_acc",
+    # "squared_relative_difference",
+    # "rmse_log",
+    # "log10",
+    # "delta2_acc",
+    # "delta3_acc",
+    # "i_rmse",
+    # "silog_rmse",
+]
+
+
+def depth2disparity(depth, return_mask=False):
+    if isinstance(depth, torch.Tensor):
+        disparity = torch.zeros_like(depth)
+    elif isinstance(depth, np.ndarray):
+        disparity = np.zeros_like(depth)
+    non_negtive_mask = depth > 0
+    disparity[non_negtive_mask] = 1.0 / depth[non_negtive_mask]
+    if return_mask:
+        return disparity, non_negtive_mask
+    else:
+        return disparity
+
+
+def resize_images(images, new_size):
+    resized_images = np.empty(
+        (images.shape[0], new_size[0], new_size[1], images.shape[3])
+    )
+
+    for i, image in enumerate(images):
+        if image.shape[2]==1:
+            resized_images[i] = cv2.resize(image, (new_size[1], new_size[0]))[..., None]
+        else:
+            resized_images[i] = cv2.resize(image, (new_size[1], new_size[0]))
+
+    return resized_images
+    
+
+def eval_single(
+    pred_disp_path, 
+    gt_disp_path, 
+    seq_len=98, 
+    domain='depth', 
+    method_type="ours",
+    dataset_max_depth="70"
+):
+    # load data
+    gt_disp = np.load(gt_disp_path)['disparity'] \
+        if 'disparity' in np.load(gt_disp_path).files else \
+        np.load(gt_disp_path)['arr_0']  # (t, 1, h, w)
+
+    if method_type=="ours":
+        pred_disp = np.load(pred_disp_path)['depth'] # (t, h, w)
+    if method_type=="depth_anything":
+        pred_disp = np.load(pred_disp_path)['disparity'] # (t, h, w)
+    
+    # seq_len
+    if pred_disp.shape[0] < seq_len:
+         seq_len = pred_disp.shape[0]
+
+    # preprocess
+    pred_disp = resize_images(pred_disp[..., None], (gt_disp.shape[-2], gt_disp.shape[-1])) # (t, h, w)
+    pred_disp = pred_disp[..., 0] # (t, h, w)
+    pred_disp = pred_disp[:seq_len]
+    gt_disp = gt_disp[:seq_len, 0] # (t, h, w)
+
+    # valid mask
+    valid_mask = np.logical_and(
+            (gt_disp > 1e-3), 
+            (gt_disp < dataset_max_depth)
+        )
+    pred_disp = np.clip(pred_disp, a_min=1e-3, a_max=None) 
+    pred_disp_masked = pred_disp[valid_mask].reshape((-1, 1))
+    
+    # choose evaluation domain
+    DOMAIN = domain
+    if DOMAIN=='disp':
+        # align in real disp, calc in disp
+        gt_disp_maksed = gt_disp[valid_mask].reshape((-1, 1)).astype(np.float64)
+    elif DOMAIN=='depth':
+        # align in disp = 1/depth, calc in depth
+        gt_disp_maksed = 1. / (gt_disp[valid_mask].reshape((-1, 1)).astype(np.float64) + 1e-8)
+    else:
+        pass
+
+
+    # calc scale and shift
+    _ones = np.ones_like(pred_disp_masked)
+    A = np.concatenate([pred_disp_masked, _ones], axis=-1)
+    X = np.linalg.lstsq(A, gt_disp_maksed, rcond=None)[0]
+    scale, shift = X # gt = scale * pred + shift
+    
+    # align
+    aligned_pred = scale * pred_disp + shift
+    aligned_pred = np.clip(aligned_pred, a_min=1e-3, a_max=None) 
+
+
+    # align in real disp, calc in disp
+    if DOMAIN=='disp':
+        pred_depth = aligned_pred
+        gt_depth = gt_disp
+    # align in disp = 1/depth, calc in depth
+    elif DOMAIN=='depth':
+        pred_depth = depth2disparity(aligned_pred)
+        gt_depth = gt_disp
+    else:
+        pass
+
+    # metric evaluation, clip to dataset min max
+    pred_depth = np.clip(
+            pred_depth, a_min=1e-3, a_max=dataset_max_depth
+        )
+
+    # evaluate metric 
+    sample_metric = []
+    metric_funcs = [getattr(metric, _met) for _met in eval_metrics]
+
+    # Evaluate 
+    sample_metric = []
+    pred_depth_ts = torch.from_numpy(pred_depth).to(device)
+    gt_depth_ts = torch.from_numpy(gt_depth).to(device)
+    valid_mask_ts = torch.from_numpy(valid_mask).to(device)
+
+    n = valid_mask.sum((-1, -2))
+    valid_frame = (n > 0)
+    pred_depth_ts = pred_depth_ts[valid_frame]
+    gt_depth_ts = gt_depth_ts[valid_frame]
+    valid_mask_ts = valid_mask_ts[valid_frame]
+
+    for met_func in metric_funcs:
+        _metric_name = met_func.__name__
+        _metric = met_func(pred_depth_ts, gt_depth_ts, valid_mask_ts).item()
+        sample_metric.append(_metric)
+
+    return sample_metric
+
+
+
+if __name__=="__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--seq_len", 
+        type=int,
+        default=50,
+        help="Max video frame length for evaluation."
+    )
+
+    parser.add_argument(
+        "--domain",
+        type=str,
+        default="depth",
+        choices=["depth", "disp"],
+        help="Domain of metric calculation."
+    )
+
+    parser.add_argument(
+        "--method_type",
+        type=str,
+        default="ours",
+        choices=["ours", "depth_anything"],
+        help="Choose the methods."
+    )
+
+    parser.add_argument(
+        "--dataset_max_depth",
+        type=int,
+        default=70,
+        help="Dataset max depth clip."
+    )
+
+    parser.add_argument(
+        "--pred_disp_root",
+        type=str,
+        default="./demo_output",
+        help="Predicted output directory."
+    )
+
+    parser.add_argument(
+        "--gt_disp_root",
+        type=str,
+        required=True,
+        help="GT depth directory."
+    )
+
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        required=True,
+        help="Choose the datasets."
+    )
+
+    parser.add_argument(
+        "--meta_path",
+        type=str,
+        required=True,
+        help="Path of test dataset csv file."
+    )
+
+
+    args = parser.parse_args()
+
+    SEQ_LEN = args.seq_len
+    method_type = args.method_type
+    if method_type == "ours":
+        pred_disp_root = os.path.join(args.pred_disp_root, f'results_{args.dataset}')
+    else:
+        # pred_disp_root = args.pred_disp_root
+        pred_disp_root = os.path.join(args.pred_disp_root, f'results_{args.dataset}')
+    domain = args.domain
+    dataset_max_depth = args.dataset_max_depth
+    saved_json_path = os.path.join(args.pred_disp_root, f"results_{args.dataset}.json")
+
+    meta_path = args.meta_path
+
+    assert method_type in ["depth_anything", "ours"], "Invalid method type, must be in ['depth_anything', 'ours']"
+    assert domain in ["depth", "disp"], "Invalid domain type, must be in ['depth', 'disp']"
+        
+    with open(meta_path, mode="r", encoding="utf-8") as csvfile:
+        csv_reader = csv.DictReader(csvfile)
+        samples = list(csv_reader)
+
+    # iterate all cases
+    results_all = []
+    for i, sample in enumerate(tqdm(samples)):
+        gt_disp_path = os.path.join(args.gt_disp_root, samples[i]['filepath_disparity'])
+        if method_type=="ours":
+            pred_disp_path = os.path.join(pred_disp_root, samples[i]['filepath_disparity'])
+            pred_disp_path = pred_disp_path.replace("disparity", "rgb_left")
+        
+        if method_type=="depth_anything":
+            pred_disp_path = os.path.join(pred_disp_root, samples[i]['filepath_disparity'])
+            pred_disp_path = pred_disp_path.replace("disparity", "rgb_left_depth")
+        
+        results_single = eval_single(
+            pred_disp_path, 
+            gt_disp_path, 
+            seq_len=SEQ_LEN, 
+            domain=domain, 
+            method_type=method_type, 
+            dataset_max_depth=dataset_max_depth
+        )
+
+        results_all.append(results_single)
+
+    # avarage
+    final_results =  np.array(results_all)
+    final_results_mean = np.mean(final_results, axis=0)
+    print("")
+
+    # save mean to json
+    result_dict = { 'name': method_type }
+    for i, metric in enumerate(eval_metrics):
+        result_dict[metric] = final_results_mean[i]
+        print(f"{metric}: {final_results_mean[i]:04f}")
+
+    # save each case to json
+    for i, results in enumerate(results_all):
+        result_dict[samples[i]['filepath_disparity']] = results
+
+    # write json
+    with open(saved_json_path, 'w') as f:
+        json.dump(result_dict, f, indent=4)
+    print("")
+    print(f"Evaluation results json are saved to {saved_json_path}")
+    

benchmark/eval/eval.sh

@@ -0,0 +1,51 @@
+#!/bin/sh
+set -x
+set -e
+
+pred_disp_root=/path/to/saved/root_directory # The parent directory that contaning [sintel, scannet, KITTI, bonn, NYUv2] prediction
+gt_disp_root=/path/to/gt_depth/root_directory # The parent directory that contaning [sintel, scannet, KITTI, bonn, NYUv2] ground truth
+
+# eval sintel
+python benchmark/eval/eval.py \
+    --meta_path ./eval/csv/meta_sintel.csv \
+    --dataset_max_depth 70 \
+    --dataset sintel \
+    --seq_len 50 \
+    --pred_disp_root ${pred_disp_root} \
+    --gt_disp_root ${gt_disp_root} \
+
+# eval scannet
+python benchmark/eval/eval.py \
+    --meta_path ./eval/csv/meta_scannet_test.csv \
+    --dataset_max_depth 10 \
+    --dataset scannet \
+    --seq_len 90 \
+    --pred_disp_root ${pred_disp_root} \
+    --gt_disp_root ${gt_disp_root} \
+
+# eval kitti
+python benchmark/eval/eval.py \
+    --meta_path ./eval/csv/meta_kitti_val.csv \
+    --dataset_max_depth 80 \
+    --dataset kitti \
+    --seq_len 110 \
+    --pred_disp_root ${pred_disp_root} \
+    --gt_disp_root ${gt_disp_root} \
+
+# eval bonn
+python benchmark/eval/eval.py \
+    --meta_path ./eval/csv/meta_bonn.csv \
+    --dataset_max_depth 10 \
+    --dataset bonn \
+    --seq_len 110 \
+    --pred_disp_root ${pred_disp_root} \
+    --gt_disp_root ${gt_disp_root} \
+
+# eval nyu
+python benchmark/eval/eval.py \
+    --meta_path ./eval/csv/meta_nyu_test.csv \
+    --dataset_max_depth 10 \
+    --dataset nyu \
+    --seq_len 1 \
+    --pred_disp_root ${pred_disp_root} \
+    --gt_disp_root ${gt_disp_root} \

benchmark/eval/metric.py

@@ -0,0 +1,128 @@
+import torch
+
+
+def abs_relative_difference(output, target, valid_mask=None):
+    actual_output = output
+    actual_target = target
+    abs_relative_diff = torch.abs(actual_output - actual_target) / actual_target
+    if valid_mask is not None:
+        abs_relative_diff[~valid_mask] = 0
+        n = valid_mask.sum((-1, -2))
+    else:
+        n = output.shape[-1] * output.shape[-2]
+    abs_relative_diff = torch.sum(abs_relative_diff, (-1, -2)) / n
+    return abs_relative_diff.mean()
+
+
+def squared_relative_difference(output, target, valid_mask=None):
+    actual_output = output
+    actual_target = target
+    square_relative_diff = (
+        torch.pow(torch.abs(actual_output - actual_target), 2) / actual_target
+    )
+    if valid_mask is not None:
+        square_relative_diff[~valid_mask] = 0
+        n = valid_mask.sum((-1, -2))
+    else:
+        n = output.shape[-1] * output.shape[-2]
+    square_relative_diff = torch.sum(square_relative_diff, (-1, -2)) / n
+    return square_relative_diff.mean()
+
+
+def rmse_linear(output, target, valid_mask=None):
+    actual_output = output
+    actual_target = target
+    diff = actual_output - actual_target
+    if valid_mask is not None:
+        diff[~valid_mask] = 0
+        n = valid_mask.sum((-1, -2))
+    else:
+        n = output.shape[-1] * output.shape[-2]
+    diff2 = torch.pow(diff, 2)
+    mse = torch.sum(diff2, (-1, -2)) / n
+    rmse = torch.sqrt(mse)
+    return rmse.mean()
+
+
+def rmse_log(output, target, valid_mask=None):
+    diff = torch.log(output) - torch.log(target)
+    if valid_mask is not None:
+        diff[~valid_mask] = 0
+        n = valid_mask.sum((-1, -2))
+    else:
+        n = output.shape[-1] * output.shape[-2]
+    diff2 = torch.pow(diff, 2)
+    mse = torch.sum(diff2, (-1, -2)) / n  # [B]
+    rmse = torch.sqrt(mse)
+    return rmse.mean()
+
+
+def log10(output, target, valid_mask=None):
+    if valid_mask is not None:
+        diff = torch.abs(
+            torch.log10(output[valid_mask]) - torch.log10(target[valid_mask])
+        )
+    else:
+        diff = torch.abs(torch.log10(output) - torch.log10(target))
+    return diff.mean()
+
+
+# adapt from: https://github.com/imran3180/depth-map-prediction/blob/master/main.py
+def threshold_percentage(output, target, threshold_val, valid_mask=None):
+    d1 = output / target
+    d2 = target / output
+    max_d1_d2 = torch.max(d1, d2)
+    zero = torch.zeros(*output.shape)
+    one = torch.ones(*output.shape)
+    bit_mat = torch.where(max_d1_d2.cpu() < threshold_val, one, zero)
+    if valid_mask is not None:
+        bit_mat[~valid_mask] = 0
+        n = valid_mask.sum((-1, -2))
+    else:
+        n = output.shape[-1] * output.shape[-2]
+    count_mat = torch.sum(bit_mat, (-1, -2))
+    threshold_mat = count_mat / n.cpu()
+    return threshold_mat.mean()
+
+
+def delta1_acc(pred, gt, valid_mask):
+    return threshold_percentage(pred, gt, 1.25, valid_mask)
+
+
+def delta2_acc(pred, gt, valid_mask):
+    return threshold_percentage(pred, gt, 1.25**2, valid_mask)
+
+
+def delta3_acc(pred, gt, valid_mask):
+    return threshold_percentage(pred, gt, 1.25**3, valid_mask)
+
+
+def i_rmse(output, target, valid_mask=None):
+    output_inv = 1.0 / output
+    target_inv = 1.0 / target
+    diff = output_inv - target_inv
+    if valid_mask is not None:
+        diff[~valid_mask] = 0
+        n = valid_mask.sum((-1, -2))
+    else:
+        n = output.shape[-1] * output.shape[-2]
+    diff2 = torch.pow(diff, 2)
+    mse = torch.sum(diff2, (-1, -2)) / n  # [B]
+    rmse = torch.sqrt(mse)
+    return rmse.mean()
+
+
+def silog_rmse(depth_pred, depth_gt, valid_mask=None):
+    diff = torch.log(depth_pred) - torch.log(depth_gt)
+    if valid_mask is not None:
+        diff[~valid_mask] = 0
+        n = valid_mask.sum((-1, -2))
+    else:
+        n = depth_gt.shape[-2] * depth_gt.shape[-1]
+
+    diff2 = torch.pow(diff, 2)
+
+    first_term = torch.sum(diff2, (-1, -2)) / n
+    second_term = torch.pow(torch.sum(diff, (-1, -2)), 2) / (n**2)
+    loss = torch.sqrt(torch.mean(first_term - second_term)) * 100
+    return loss

benchmark/infer/infer.sh

@@ -0,0 +1,55 @@
+#!/bin/sh
+set -x
+set -e
+
+input_rgb_root=/path/to/input/RGB/root_directory # The parent directory that contaning [sintel, scannet, KITTI, bonn, NYUv2] input RGB
+saved_root=/path/to/saved/root_directory # The parent directory that saving [sintel, scannet, KITTI, bonn, NYUv2] prediction
+gpus=0,1,2,3 # Using 4 GPU, you can adjust it according to your device
+
+
+# infer sintel
+python benchmark/infer/infer_batch.py \
+    --meta_path ./eval/csv/meta_sintel.csv \
+    --saved_root ${saved_root} \
+    --saved_dataset_folder results_sintel \
+    --process_length 50 \
+    --gpus ${gpus} \
+    --dataset sintel \
+
+# infer scannet
+python benchmark/infer/infer_batch.py \
+    --meta_path ./eval/csv/meta_scannet_test.csv \
+    --saved_root ${saved_root} \
+    --saved_dataset_folder results_scannet \
+    --process_length 90 \
+    --gpus ${gpus} \
+    --dataset scannet \
+
+# infer kitti
+python benchmark/infer/infer_batch.py \
+    --meta_path ./eval/csv/meta_kitti_val.csv \
+    --saved_root ${saved_root} \
+    --saved_dataset_folder results_kitti \
+    --process_length 110 \
+    --gpus ${gpus} \
+    --dataset kitti \
+
+# infer bonn
+python benchmark/infer/infer_batch.py \
+    --meta_path ./eval/csv/meta_bonn.csv \
+    --saved_root ${saved_root} \
+    --saved_dataset_folder results_bonn \
+    --input_rgb_root ${input_rgb_root} \
+    --process_length 110 \
+    --gpus ${gpus} \
+    --dataset bonn \
+
+# infer nyu
+python benchmark/infer/infer_batch.py \
+    --meta_path ./eval/csv/meta_nyu_test.csv \
+    --saved_root ${saved_root} \
+    --saved_dataset_folder results_nyu \
+    --process_length 1 \
+    --gpus ${gpus} \
+    --overlap 0 \
+    --dataset nyu \

benchmark/infer/infer_batch.py

@@ -0,0 +1,46 @@
+import os
+import multiprocessing as mp
+import csv
+import argparse
+
+
+def process_video(video_path, gpu_id, save_folder, args):
+    os.system(f'sh ./benchmark/demo.sh {video_path} {gpu_id} {int(args.process_length)} {args.saved_root} {save_folder} {args.overlap} {args.dataset}')
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser()
+    
+    parser.add_argument('--meta_path', type=str)
+    parser.add_argument('--saved_dataset_folder', type=str)
+    parser.add_argument('--saved_root', type=str, default="./output")
+    parser.add_argument('--input_rgb_root', type=str)
+
+    parser.add_argument('--process_length', type=int, default=110)
+    parser.add_argument('--gpus', type=str, default="0,1,2,3")
+    
+    parser.add_argument('--overlap', type=int, default=1)
+    parser.add_argument('--dataset', type=str, default="open")
+    
+    args = parser.parse_args()
+    gpus = args.gpus.strip().split(',')
+
+    with open(args.meta_path, mode="r", encoding="utf-8") as csvfile:
+        csv_reader = csv.DictReader(csvfile)
+        test_samples = list(csv_reader)
+    batch_size = len(gpus)
+    video_batches = [test_samples[i:i+batch_size] for i in range(0, len(test_samples), batch_size)]
+    print("gpus+++: ", gpus)
+
+    processes = []
+    for video_batch in video_batches:
+        for i, sample in enumerate(video_batch):
+            video_path = os.path.join(args.input_rgb_root, sample["filepath_left"])
+            save_folder = os.path.join(args.saved_dataset_folder, os.path.dirname(sample["filepath_left"]))
+            gpu_id = gpus[i % len(gpus)]
+            p = mp.Process(target=process_video, args=(video_path, gpu_id, save_folder, args))
+            p.start()
+            processes.append(p)
+        
+        for p in processes: 
+            p.join()