import os.path as osp
import numpy as np
import mmcv, os
# import cv2
#from PIL import Image


''' 
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This script modifies the original Rellis 3D dataset from 20 labels to 5 labels

# Original classes
# Labels ID = ["void", "dirt", "grass", "tree", "pole", "water", "sky", "vehicle", 
#              "object", "asphalt", "building", "log", "person", "fence", "bush", 
#              "concrete", "barrier", "puddle", "mud", "rubble"]

# Grouped classes
#   New IDs
#         0 -- background: void, sky
#         1 -- obstacle: tree, bush, person, rubble, barrier, log, fence, vehicle, object, pole, building
#         2 -- withwater: mud, puddle, water
#         3 -- unstable: grass, dirt
#         4 -- stable: concrete, asphalt

--------------------------------------------------------------------------------

   Python environment requirements
install: pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html




 Download original Rellis 3D:
    - RGB images: Rellis_3D_pylon_camera_node.zip (11GB)
        Link: https://drive.google.com/file/d/1F3Leu0H_m6aPVpZITragfreO_SGtL2yV/view
    - ID Masks : Rellis_3D_pylon_camera_node_label_id.zip (117MB)
        Link: https://drive.google.com/file/d/16URBUQn_VOGvUqfms-0I8HHKMtjPHsu5/view

1. Create the following folders in data/rellis folder
    - image
    - masks_id
2. Copy Rellis_3D_pylon_camera_node.zip in data/rellis/images folder
3. Copy Rellis_3D_pylon_camera_node_label_id.zip in data/rellis/masks_id folder
4. Rellis_3D_pylon_camera_node_label_color.zip (not used in GanAV model)
5. Unzip Rellis_3D_pylon_camera_node.zip in the data/rellis/images folder
   - data/rellis/images/Rellis-3D\00000
   - data/rellis/images/Rellis-3D\00001
   ...
6. Move the content of data/rellis/images/Rellis-3D folder to data/rellis/images folder
7. Delete Rellis-3D folder (data/rellis/images/Rellis-3D)
8. Unzip Rellis_3D_pylon_camera_node_label_id.zip in the data/rellis/masks_id folder
   - data/rellis/masks_id/Rellis-3D\00000
   - data/rellis/masks_id/Rellis-3D\00001
   ...
9. Move the content of data/rellis/masks_id/Rellis-3D folder to data/rellis/masks_id folder
10. Delete Rellis-3D folder (data/rellis/masks_id/Rellis-3D)
11. Detele zip files
12. Copy the files inside the each pylon_camera_node_label_id folder of masks_id folder to masks_id/0000x folder
13. Delete all empty pylon_camera_node_label_id folders


'''
rellis_dir=os.getcwd()
#rellis_dir = "./rellis/"
annotation_folder = "/Rellis-3D/masks_id/"

IDs =    [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 17, 18, 19, 23, 27, 31, 33, 34]
Groups = (0, 3, 3, 1, 1, 2, 0, 1, 1,  4,  1,  1,  1,  1,  1,  4,  1,  2,  2,  1)

ID_seq = {}
ID_group = {}
for n, label in enumerate(IDs):
    ID_seq[label] = n
    ID_group[label] = Groups[n]


def raw_to_seq(seg):
    h, w = seg.shape
    out1 = np.zeros((h, w))
    out2 = np.zeros((h, w))
    for i in IDs:
        out1[seg==i] = ID_seq[i]
        out2[seg==i] = ID_group[i]

    return out1, out2


with open(osp.join(rellis_dir, 'train.txt'), 'r') as r:
    i = 0
    for l in r:
        print("train: {}".format(i))
        # w.writelines(l[:-5] + "\n")
        # w.writelines(l.split(".")[0] + "\n")
        file_client_args=dict(backend='disk')
        file_client = mmcv.FileClient(**file_client_args)
        img_bytes = file_client.get(rellis_dir + annotation_folder + l.strip() + '.png')
        gt_semantic_seg = mmcv.imfrombytes(img_bytes, flag='unchanged', backend='pillow').squeeze().astype(np.uint8)
        out1, out2 = raw_to_seq(gt_semantic_seg)

        #mmcv.imwrite(out1, rellis_dir + annotation_folder + l.strip() + "_orig.png")
        mmcv.imwrite(out2, rellis_dir + annotation_folder + l.strip() + "_5.png")

        i += 1


with open(osp.join(rellis_dir, 'val.txt'), 'r') as r:
    i = 0
    for l in r:
        print("val: {}".format(i))
        # w.writelines(l[:-5] + "\n")
        # w.writelines(l.split(".")[0] + "\n")
        file_client_args=dict(backend='disk')
        file_client = mmcv.FileClient(**file_client_args)
        img_bytes = file_client.get(rellis_dir + annotation_folder + l.strip() + '.png')
        gt_semantic_seg = mmcv.imfrombytes(img_bytes, flag='unchanged', backend='pillow').squeeze().astype(np.uint8)
        out1, out2 = raw_to_seq(gt_semantic_seg)
        
        #mmcv.imwrite(out1, rellis_dir + annotation_folder + l.strip() + "_orig.png")
        mmcv.imwrite(out2, rellis_dir + annotation_folder + l.strip() + "_5.png")

        i += 1



with open(osp.join(rellis_dir, 'test.txt'), 'r') as r:
    i = 0
    for l in r:
        print("test: {}".format(i))
        # w.writelines(l[:-5] + "\n")
        # w.writelines(l.split(".")[0] + "\n")
        file_client_args=dict(backend='disk')
        file_client = mmcv.FileClient(**file_client_args)
        img_bytes = file_client.get(rellis_dir + annotation_folder + l.strip() + '.png')
        gt_semantic_seg = mmcv.imfrombytes(img_bytes, flag='unchanged', backend='pillow').squeeze().astype(np.uint8)
        out1, out2 = raw_to_seq(gt_semantic_seg)

        #mmcv.imwrite(out1, rellis_dir + annotation_folder + l.strip() + "_orig.png")
        mmcv.imwrite(out2, rellis_dir + annotation_folder + l.strip() + "_5.png")

        i += 1




print("successful")