enable non-square input, update readme

data/single_dataset.py

@@ -37,6 +37,7 @@ def __getitem__(self, index):
         """
         A_path = self.A_paths[index]
         A_img = Image.open(A_path).convert('RGB')
+        self.opt.W, self.opt.H = A_img.size
         transform_params_A = get_params(self.opt, A_img.size)
         A = get_transform(self.opt, transform_params_A, grayscale=(self.input_nc == 1))(A_img)
         item = {'A': A, 'A_paths': A_path}

readme.md

@@ -8,7 +8,7 @@ This project generates artistic portrait drawings from face photos using a GAN-b
 
 ## Our Proposed Framework
 
-<img src = 'imgs/architecture.png'>
+<img src = 'imgs/architecture.jpg'>
 
 ## Sample Results
 From left to right: input, output(style1), output(style2), output(style3)
@@ -34,7 +34,7 @@ If you use this code for your research, please cite our paper.
 
 
 ## Installation
-- Install PyTorch 1.1.0 and torchvision from http://pytorch.org and other dependencies (e.g., [visdom](https://github.com/facebookresearch/visdom) and [dominate](https://github.com/Knio/dominate)). You can install all the dependencies by
+- To install the dependencies, run
 ```bash
 pip install -r requirements.txt
 ```
@@ -55,7 +55,7 @@ The result images are saved in `./results/pretrained/test_200/images3styles`,
 where `real`, `fake1`, `fake2`, `fake3` correspond to input face photo, style1 drawing, style2 drawing, style3 drawing respectively.
 
 <img src = 'imgs/how_to_crop.jpg'>
-- 3. To test on your own photos, the photos need to be square (since the program will load it and resized as 512x512). You can use an image editor to crop a square area of your photo that contains face (or use an optional preprocess [here](preprocess/readme.md)). Then specify the folder that contains test photos using `--dataroot`, specify save folder name using `--savefolder` and run the above command again:
+- 3. To test on your own photos: First use an image editor to crop the face region of your photo (or use an optional preprocess [here](preprocess/readme.md)). Then specify the folder that contains test photos using `--dataroot`, specify save folder name using `--savefolder` and run the above command again:
 
 ``` bash
 # with GPU
@@ -67,6 +67,8 @@ python test_seq_style.py --gpu -1 --dataroot ./imgs/test1 --savefolder 3styles_t
 ```
 The test results will be saved to a html file here: `./results/pretrained/test_200/index[save_folder_name].html`.
 The result images are saved in `./results/pretrained/test_200/images[save_folder_name]`.
+An example html screenshot is shown below:
+<img src = 'imgs/result_html.jpg'>
 
 You can contact email [email protected] for any questions.
 

test.py

@@ -63,5 +63,5 @@
         img_path = model.get_image_paths()     # get image paths
         if i % 5 == 0:  # save images to an HTML file
             print('processing (%04d)-th image... %s' % (i, img_path))
-        save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize)
+        save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize, W=opt.W, H=opt.H)
     webpage.save()  # save the HTML

util/visualizer.py

@@ -5,17 +5,15 @@
 import time
 from . import util, html
 from subprocess import Popen, PIPE
-from scipy.misc import imresize
-import pdb
-from scipy.io import savemat
+from PIL import Image
 
 if sys.version_info[0] == 2:
     VisdomExceptionBase = Exception
 else:
     VisdomExceptionBase = ConnectionError
 
 
-def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256):
+def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256, W=None, H=None):
     """Save images to the disk.
 
     Parameters:
@@ -37,16 +35,16 @@ def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256):
     for label, im_data in visuals.items():
         ## tensor to im
         im = util.tensor2im(im_data)
-        #im,imo = util.tensor2im(im_data)
-        #matname = os.path.join(image_dir, '%s_%s.mat' % (name, label))
-        #savemat(matname,{'imo':imo})
         image_name = '%s_%s.png' % (name, label)
         save_path = os.path.join(image_dir, image_name)
         h, w, _ = im.shape
-        if aspect_ratio > 1.0:
-            im = imresize(im, (h, int(w * aspect_ratio)), interp='bicubic')
-        if aspect_ratio < 1.0:
-            im = imresize(im, (int(h / aspect_ratio), w), interp='bicubic')
+        if W is not None and H is not None and (W != w or H != h):
+            im = np.array(Image.fromarray(im).resize((W, H), Image.BICUBIC))
+        else:
+            if aspect_ratio > 1.0:
+                im = np.array(Image.fromarray(im).resize((int(w * aspect_ratio), h), Image.BICUBIC))
+            if aspect_ratio < 1.0:
+                im = np.array(Image.fromarray(im).resize((w, int(h / aspect_ratio)), Image.BICUBIC))
         util.save_image(im, save_path)
 
         ims.append(image_name)
@@ -133,7 +131,6 @@ def display_current_results(self, visuals, epoch, save_result):
                 for label, image in visuals.items():
                     image_numpy = util.tensor2im(image)
                     label_html_row += '<td>%s</td>' % label
-                    #pdb.set_trace()
                     images.append(image_numpy.transpose([2, 0, 1]))
                     idx += 1
                     if idx % ncols == 0:
@@ -203,7 +200,6 @@ def plot_current_losses(self, epoch, counter_ratio, losses):
         self.plot_data['Y'].append([losses[k] for k in self.plot_data['legend']])
         #X = np.stack([np.array(self.plot_data['X'])] * len(self.plot_data['legend']), 1)
         #Y = np.array(self.plot_data['Y'])
-        #pdb.set_trace()
         try:
             self.vis.line(
                 X=np.stack([np.array(self.plot_data['X'])] * len(self.plot_data['legend']), 1),