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RefineDNet/test_BeDDE.py

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