path_B = os.path.join(img_fold_B, name_B) if os.path.isfile(path_A) and os.path.isfile(path_B): name_AB = name_A if args.use_AB: name_AB = name_AB.replace('_A.', '.') # remove _A path_AB = os.path.join(img_fold_AB, name_AB) im_A = cv2.imread(path_A, cv2...
python scripts/combine_A_and_B.py --fold_A /path/to/data/A --fold_B /path/to/data/B --fold_AB /path/to/data This will combine each pair of images (A,B) into a single image file, ready for training. Notes on Colorization No need to run combine_A_and_B.py for colorization....
python /home/xingxiangrui/pytorch-CycleGAN-and-pix2pix/datasets/combine_A_and_B.py --fold_A /home/xingxiangrui/pytorch-CycleGAN-and-pix2pix/datasets/pix2pix-nor2cott/A --fold_B /home/xingxiangrui/pytorch-CycleGAN-and-pix2pix/datasets/pix2pix-nor2cott/B --fold_AB /home/xingxiangrui/pyt...
python tools/process.py \ --input_dir a \ --b_dir b \ --operation combine \ --output_dir c This puts the images in a side-by-side combined image thatpix2pix.pyexpects. Colorization For colorization, your images should ideally all be the same aspect ratio. You can resize and crop ...
# combine visualizations for A and B self.visual_names = visual_names_A + visual_names_B # specify the models you want to save to the disk. if self.isTrain: # 训练模式,定义4个网络其中G_B和D_B是新增 self.model_names = ['G_A', 'G_B', 'D_A', 'D_B'] ...
提取边缘, 然后用“python datasets/combine_A_and_B.py --fold_A datasets/cells/A --fold_B datasets/cells/B --fold_AB datasets/cells/AB”重新整合边缘。最后编辑于 :2019.06.12 06:38:12 ©著作权归作者所有,转载或内容合作请联系作者
# 确保生成图片fake_B, 不仅仅能够骗过D网络,还需要与标签图片real_B接近。 # 反应在代码上,采用的像素点的绝对值差L1 loss来实现的。 self.loss_G_L1 = self.criterionL1(self.fake_B, self.real_B) * self.opt.lambda_L1 # combine loss and calculate gradients ...
/path/to/data/A/train/1.jpg is considered to correspond to /path/to/data/B/train/1.jpg. Once the data is formatted this way, call: python scripts/combine_A_and_B.py --fold_A /path/to/data/A --fold_B /path/to/data/B --fold_AB /path/to/data This will combine each pair ...
python scripts/combine_A_and_B.py --fold_A /path/to/data/A --fold_B /path/to/data/B --fold_AB /path/to/data This will combine each pair of images (A,B) into a single image file, ready for training. Notes on Colorization No need to run combine_A_and_B.py for colorization....
# 确保生成图片fake_B, 不仅仅能够骗过D网络,还需要与标签图片real_B接近。 # 反应在代码上,采用的像素点的绝对值差L1 loss来实现的。 self.loss_G_L1 = self.criterionL1(self.fake_B, self.real_B) * self.opt.lambda_L1 # combine loss and calculate gradients ...