EVSNet/process_darken.py at main · zyaocoder/EVSNet · GitHub

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import os
import pickle
import glob
import numpy
from PIL import Image, ImageOps
import cv2

# import debugpy
# debugpy.listen(5678)
# debugpy.wait_for_client()

# sequences = glob.glob('/data/datasets/VSPW_480p/data/*')

# for i in sequences:
#     if not (os.path.exists(i + '/lowlight')):
#         os.mkdir(i + '/lowlight')

# import os
import pickle
import glob
import numpy
from PIL import Image, ImageOps
import cv2

import debugpy
debugpy.listen(5678)
debugpy.wait_for_client()

# sequences = glob.glob('/data/datasets/VSPW_480p/data/*')

# for i in sequences:
#     if not (os.path.exists(i + '/lowlight')):
#         os.mkdir(i + '/lowlight')

# files = glob.glob('./data/vspw/VSPW_480p/data/*/mask/*.png', recursive=True)

files = glob.glob('/home/yaozhen/VSS-CFFM/data/NightCity/data/*/mask/*', recursive=True)

# files = glob.glob('/home/yaozhen/VSS-CFFM/data/cityscapes/gtFine/train/*/*_labelIds.png', recursive=True)
classlist = []

for index, image_path in enumerate(files):
    image = Image.open(image_path)
    data = numpy.asarray(image).copy()

    data[data==7] = 1
    data[data==8] = 2
    data[data==11] = 3
    data[data==12] = 4
    data[data==13] = 5
    data[data==17] = 6
    data[data==19] = 7
    data[data==20] = 8
    data[data==21] = 9
    data[data==22] = 10
    data[data==23] = 11
    data[data==24] = 12
    data[data==25] = 13
    data[data==26] = 14
    data[data==27] = 15
    data[data==28] = 16
    data[data==31] = 17
    data[data==32] = 18
    data[data==33] = 19

    im = Image.fromarray(data)
    im.save(image_path)

    # list1 = numpy.unique(data)
    # for i in range(len(list1)):
    #     if list1[i] == 3:
    #         print('1')

    # data = numpy.asarray(image) / 255

    # alpha = numpy.random.uniform(low=0.9, high=1.0, size=data.shape)
    # beta = numpy.random.uniform(low=0.5, high=1.0, size=data.shape)

    # gamma = numpy.random.uniform(low=2.0, high=3.5, size=data.shape)

    # # gamma = numpy.random.uniform(low=1.5, high=5, size=data.shape)
    # new_data = beta * (alpha * data) ** gamma
    # im = Image.fromarray((new_data * 255).astype(numpy.uint8))
    # im.save(image_path.replace("_leftImg8bit", "_lowlight"))

print('finished')

# for index, image_path in enumerate(files):
#     image = Image.open(image_path)
#     data = numpy.asarray(image) / 255

#     alpha = numpy.random.uniform(low=0.9, high=1.0, size=data.shape)
#     beta = numpy.random.uniform(low=0.5, high=1.0, size=data.shape)

#     gamma = numpy.random.uniform(low=2.0, high=3.5, size=data.shape)

#     # gamma = numpy.random.uniform(low=1.5, high=5, size=data.shape)
#     new_data = beta * (alpha * data) ** gamma
#     im = Image.fromarray((new_data * 255).astype(numpy.uint8))
#     im.save(image_path.replace("origin", "lowlight"))

# print('finished')