52 lines
2.0 KiB
Python
52 lines
2.0 KiB
Python
import os, ntpath
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import numpy as np
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import scipy.io as sio
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import torchvision.utils as vutils
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from options.test_options import TestOptions
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from data import create_dataset
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from models import create_model
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from util import util
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if __name__ == '__main__':
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opt = TestOptions().parse() # get test options
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opt.nThreads = 1 # mytest code only supports nThreads = 1
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opt.batchSize = 1 # mytest code only supports batchSize = 1
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opt.serial_batches = True # no shuffle
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opt.no_flip = True # no flip
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dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options
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model = create_model(opt) # create a model given opt.model and other options
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model.setup(opt) # regular setup: load and print networks; create schedulers
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if opt.eval:
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model.eval()
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for i, data in enumerate(dataset):
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model.set_input(data) # unpack data from data loader
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model.test() # run inference
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visuals = model.get_current_visuals() # get image results
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real_I = util.tensor2im(data['haze'], float) # [0, 255], float
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real_J = util.tensor2im(data['clear'], float) # [0, 255], float
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rec_J = util.tensor2im(visuals['rec_J'], float) # [0, 255], float
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refine_J = util.tensor2im(visuals['refine_J'], float) # [0, 255], float
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result_J = util.fuse_images(real_I, rec_J, refine_J) # [0, 255], np.float
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img_paths = model.get_image_paths() # get image paths
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short_path = ntpath.basename(img_paths[0])
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name = os.path.splitext(short_path)[0]
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print('processing image %s (%d/%d)'%(short_path, i+1, len(dataset)))
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if opt.save_image:
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curSaveFolder = os.path.join(opt.dataroot, data['city'][0], opt.method_name)
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if not os.path.exists(curSaveFolder):
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os.makedirs(curSaveFolder, mode=0o777)
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dehzImg = (result_J).astype(np.uint8) #[0, 255], np.uint8
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util.save_image(dehzImg, os.path.join(curSaveFolder, '%s_%s.png'%(name, opt.method_name)))
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