VJ commited on
Upload 4 files
Browse files- 1_rellis_relabel5.py +145 -0
- test.txt +1672 -0
- train.txt +0 -0
- val.txt +983 -0
1_rellis_relabel5.py
ADDED
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| 1 |
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import os.path as osp
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import numpy as np
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import mmcv, os
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# import cv2
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#from PIL import Image
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'''
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--------------------------------------------------------------------------------
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This script modifies the original Rellis 3D dataset from 20 labels to 5 labels
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# Original classes
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# Labels ID = ["void", "dirt", "grass", "tree", "pole", "water", "sky", "vehicle",
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# "object", "asphalt", "building", "log", "person", "fence", "bush",
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# "concrete", "barrier", "puddle", "mud", "rubble"]
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# Grouped classes
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# New IDs
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# 0 -- background: void, sky
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# 1 -- obstacle: tree, bush, person, rubble, barrier, log, fence, vehicle, object, pole, building
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# 2 -- withwater: mud, puddle, water
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# 3 -- unstable: grass, dirt
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# 4 -- stable: concrete, asphalt
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--------------------------------------------------------------------------------
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Python environment requirements
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install: pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html
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Download original Rellis 3D:
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- RGB images: Rellis_3D_pylon_camera_node.zip (11GB)
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Link: https://drive.google.com/file/d/1F3Leu0H_m6aPVpZITragfreO_SGtL2yV/view
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- ID Masks : Rellis_3D_pylon_camera_node_label_id.zip (117MB)
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Link: https://drive.google.com/file/d/16URBUQn_VOGvUqfms-0I8HHKMtjPHsu5/view
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1. Create the following folders in data/rellis folder
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- image
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- masks_id
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2. Copy Rellis_3D_pylon_camera_node.zip in data/rellis/images folder
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3. Copy Rellis_3D_pylon_camera_node_label_id.zip in data/rellis/masks_id folder
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4. Rellis_3D_pylon_camera_node_label_color.zip (not used in GanAV model)
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5. Unzip Rellis_3D_pylon_camera_node.zip in the data/rellis/images folder
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- data/rellis/images/Rellis-3D\00000
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- data/rellis/images/Rellis-3D\00001
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...
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6. Move the content of data/rellis/images/Rellis-3D folder to data/rellis/images folder
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7. Delete Rellis-3D folder (data/rellis/images/Rellis-3D)
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8. Unzip Rellis_3D_pylon_camera_node_label_id.zip in the data/rellis/masks_id folder
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- data/rellis/masks_id/Rellis-3D\00000
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- data/rellis/masks_id/Rellis-3D\00001
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...
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9. Move the content of data/rellis/masks_id/Rellis-3D folder to data/rellis/masks_id folder
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10. Delete Rellis-3D folder (data/rellis/masks_id/Rellis-3D)
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11. Detele zip files
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12. Copy the files inside the each pylon_camera_node_label_id folder of masks_id folder to masks_id/0000x folder
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13. Delete all empty pylon_camera_node_label_id folders
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'''
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rellis_dir=os.getcwd()
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#rellis_dir = "./rellis/"
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annotation_folder = "/Rellis-3D/masks_id/"
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IDs = [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 17, 18, 19, 23, 27, 31, 33, 34]
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Groups = (0, 3, 3, 1, 1, 2, 0, 1, 1, 4, 1, 1, 1, 1, 1, 4, 1, 2, 2, 1)
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ID_seq = {}
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ID_group = {}
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for n, label in enumerate(IDs):
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ID_seq[label] = n
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ID_group[label] = Groups[n]
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def raw_to_seq(seg):
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h, w = seg.shape
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out1 = np.zeros((h, w))
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out2 = np.zeros((h, w))
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for i in IDs:
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out1[seg==i] = ID_seq[i]
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out2[seg==i] = ID_group[i]
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return out1, out2
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with open(osp.join(rellis_dir, 'train.txt'), 'r') as r:
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i = 0
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for l in r:
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print("train: {}".format(i))
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# w.writelines(l[:-5] + "\n")
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# w.writelines(l.split(".")[0] + "\n")
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file_client_args=dict(backend='disk')
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file_client = mmcv.FileClient(**file_client_args)
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img_bytes = file_client.get(rellis_dir + annotation_folder + l.strip() + '.png')
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gt_semantic_seg = mmcv.imfrombytes(img_bytes, flag='unchanged', backend='pillow').squeeze().astype(np.uint8)
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out1, out2 = raw_to_seq(gt_semantic_seg)
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#mmcv.imwrite(out1, rellis_dir + annotation_folder + l.strip() + "_orig.png")
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mmcv.imwrite(out2, rellis_dir + annotation_folder + l.strip() + "_5.png")
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i += 1
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with open(osp.join(rellis_dir, 'val.txt'), 'r') as r:
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i = 0
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for l in r:
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print("val: {}".format(i))
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# w.writelines(l[:-5] + "\n")
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# w.writelines(l.split(".")[0] + "\n")
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file_client_args=dict(backend='disk')
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file_client = mmcv.FileClient(**file_client_args)
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img_bytes = file_client.get(rellis_dir + annotation_folder + l.strip() + '.png')
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gt_semantic_seg = mmcv.imfrombytes(img_bytes, flag='unchanged', backend='pillow').squeeze().astype(np.uint8)
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out1, out2 = raw_to_seq(gt_semantic_seg)
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#mmcv.imwrite(out1, rellis_dir + annotation_folder + l.strip() + "_orig.png")
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mmcv.imwrite(out2, rellis_dir + annotation_folder + l.strip() + "_5.png")
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i += 1
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with open(osp.join(rellis_dir, 'test.txt'), 'r') as r:
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i = 0
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for l in r:
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print("test: {}".format(i))
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# w.writelines(l[:-5] + "\n")
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# w.writelines(l.split(".")[0] + "\n")
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file_client_args=dict(backend='disk')
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file_client = mmcv.FileClient(**file_client_args)
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img_bytes = file_client.get(rellis_dir + annotation_folder + l.strip() + '.png')
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gt_semantic_seg = mmcv.imfrombytes(img_bytes, flag='unchanged', backend='pillow').squeeze().astype(np.uint8)
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out1, out2 = raw_to_seq(gt_semantic_seg)
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#mmcv.imwrite(out1, rellis_dir + annotation_folder + l.strip() + "_orig.png")
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mmcv.imwrite(out2, rellis_dir + annotation_folder + l.strip() + "_5.png")
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i += 1
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print("successful")
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test.txt
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|
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00002/frame004135-1581797563_909
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00002/frame004139-1581797564_309
|
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00002/frame004141-1581797564_509
|
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00002/frame004143-1581797564_709
|
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00002/frame004145-1581797564_909
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train.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
val.txt
ADDED
|
@@ -0,0 +1,983 @@
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| 1 |
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| 2 |
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| 3 |
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| 4 |
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| 5 |
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| 6 |
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| 7 |
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| 8 |
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| 9 |
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| 10 |
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| 11 |
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| 12 |
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| 13 |
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| 14 |
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| 15 |
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| 16 |
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| 17 |
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| 18 |
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| 19 |
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| 20 |
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| 21 |
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| 22 |
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| 23 |
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| 24 |
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| 25 |
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