Update pintar.py

pintar.py

@@ -20,7 +20,7 @@ def Lab2RGB_out(img_lab):
     img_ab = img_lab[:,1:,:,:]
     img_l = img_l + 50
     pred_lab = torch.cat((img_l, img_ab), 1)[0,...].numpy()
-    out = (np.clip(color.lab2rgb(pred_lab.transpose(1, 2, 0)), 0, 1) * 255).astype("uint8")
+    out = (np.clip(color.lab2rgb(pred_lab.transpose(1, 2, 0)), 0, 1)* 255).astype("uint8")
     return out
 
 def RGB2Lab(inputs):
@@ -34,11 +34,11 @@ def Normalize(inputs):
     return lab.astype('float32')
 
 def numpy2tensor(inputs):
-    out = torch.from_numpy(inputs.transpose(2, 0, 1))
+    out = torch.from_numpy(inputs.transpose(2,0,1))
     return out
 
 def tensor2numpy(inputs):
-    out = inputs[0, ...].detach().cpu().numpy().transpose(1, 2, 0)
+    out = inputs[0,...].detach().cpu().numpy().transpose(1,2,0)
     return out
 
 def preprocessing(inputs):
@@ -49,41 +49,139 @@ def preprocessing(inputs):
     return img.unsqueeze(0), img_lab.unsqueeze(0)
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-r", "--reference", type=str, help="ruta de la imagen de referencia")
-    parser.add_argument("-o", "--output", type=str, help="carpeta de salida para las imágenes coloreadas")
-    parser.add_argument("-ckpt", "--model_checkpoint", type=str, help="ruta del modelo de checkpoint")
-    args = parser.parse_args()
-
     device = "cuda"
 
-    ckpt_path = args.model_checkpoint or 'experiments/Color2Manga_gray/074000_gray.pt'
-    test_dir_path = 'test_datasets/gray_test'
-    no_extractor = False
-
-    # ... (resto del código)
-
-    while True:
-        # ... (resto del código)
-
-        with torch.no_grad():
-            img2_resize = F.interpolate(img2 / 255., size=(256, 256), mode='bilinear', recompute_scale_factor=False, align_corners=False)
-            img1_L_resize = F.interpolate(img1_lab[:,:1,:,:] / 50., size=(256, 256), mode='bilinear', recompute_scale_factor=False, align_corners=False)
-
-            color_vector = colorEncoder(img2_resize)
-
-            fake_ab = colorUNet((img1_L_resize, color_vector))
-            fake_ab = F.interpolate(fake_ab * 110, size=(height, width), mode='bilinear', recompute_scale_factor=False, align_corners=False)
-
-            fake_img = torch.cat((img1_lab[:,:1,:,:], fake_ab), 1)
-            fake_img = Lab2RGB_out(fake_img)
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--path", type=str, default=None, help="path of input image")
+    parser.add_argument("--size", type=int, default=None)
+    parser.add_argument("--ckpt", type=str, default=None, help="path of model weight")
+    parser.add_argument("-ne", "--no_extractor", action='store_true', help="Do not segment the manga panels.")
 
-            out_folder = os.path.join(output_folder, 'color')
-            if not os.path.exists(out_folder):
-                os.makedirs(out_folder)
-            out_img_path = os.path.join(out_folder, f'{img_name}_color.png')
+    args = parser.parse_args()
 
-            # show image
-            Image.fromarray(fake_img).show()
-            # save image
-            io.imsave(out_img_path, fake_img)
+    if args.path:
+        test_dir_path = args.path
+    if args.size:
+        imgsize = args.size
+    if args.ckpt:
+        ckpt_path = args.ckpt
+    if args.no_extractor:
+        no_extractor = args.no_extractor
+
+    ckpt = torch.load(ckpt_path, map_location=lambda storage, loc: storage)
+
+    colorEncoder = ColorEncoder().to(device)
+    colorEncoder.load_state_dict(ckpt["colorEncoder"])
+    colorEncoder.eval()
+
+    colorUNet = ColorUNet().to(device)
+    colorUNet.load_state_dict(ckpt["colorUNet"])
+    colorUNet.eval()
+
+    imgs = []
+    imgs_lab = []
+
+    while 1:
+        print(f'make sure both manga image and reference images are under this path {test_dir_path}')
+        img_path = input("please input the name of image needed to be colorized (with file extension): ")
+        img_path = os.path.join(test_dir_path, img_path)
+        img_name = os.path.basename(img_path)
+        img_name = os.path.splitext(img_name)[0]
+
+        if no_extractor:
+            ref_img_path = os.path.join(test_dir_path, input(f"Enter the reference image path: "))
+
+            img1 = Image.open(img_path).convert("RGB")
+            width, height = img1.size
+            img2 = Image.open(ref_img_path).convert("RGB")
+
+            img1, img1_lab = preprocessing(img1)
+            img2, img2_lab = preprocessing(img2)
+
+            img1 = img1.to(device)
+            img1_lab = img1_lab.to(device)
+            img2 = img2.to(device)
+            img2_lab = img2_lab.to(device)
+
+            with torch.no_grad():
+                img2_resize = F.interpolate(img2 / 255., size=(imgsize, imgsize), mode='bilinear',
+                                            recompute_scale_factor=False, align_corners=False)
+                img1_L_resize = F.interpolate(img1_lab[:, :1, :, :] / 50., size=(imgsize, imgsize), mode='bilinear',
+                                              recompute_scale_factor=False, align_corners=False)
+
+                color_vector = colorEncoder(img2_resize)
+
+                fake_ab = colorUNet((img1_L_resize, color_vector))
+                fake_ab = F.interpolate(fake_ab * 110, size=(height, width), mode='bilinear',
+                                        recompute_scale_factor=False, align_corners=False)
+
+                fake_img = torch.cat((img1_lab[:, :1, :, :], fake_ab), 1)
+                fake_img = Lab2RGB_out(fake_img)
+
+                out_folder = os.path.dirname(img_path)
+                out_name = os.path.basename(img_path)
+                out_name = os.path.splitext(out_name)[0]
+                out_img_path = os.path.join(out_folder, 'color', f'{out_name}_color.png')
+
+                # show image
+                Image.fromarray(fake_img).show()
+                # save image
+                folder_path = os.path.join(out_folder, 'color')
+                if not os.path.exists(folder_path):
+                    os.makedirs(folder_path)
+                io.imsave(out_img_path, fake_img)
+
+            continue
+
+        panel_extractor = PanelExtractor(min_pct_panel=5, max_pct_panel=90)
+        panels, masks, panel_masks = panel_extractor.extract(img_path)
+        panel_num = len(panels)
+
+        ref_img_paths = []
+        print("Please enter the name of the reference image in order according to the number prompts on the picture")
+        for i in range(panel_num):
+            ref_img_path = os.path.join(test_dir_path, input(f"{i+1}/{panel_num} reference image:"))
+            ref_img_paths.append(ref_img_path)
+
+        fake_imgs = []
+        for i in range(panel_num):
+            img1 = Image.fromarray(panels[i]).convert("RGB")
+            width, height = img1.size
+            img2 = Image.open(ref_img_paths[i]).convert("RGB")
+
+            img1, img1_lab = preprocessing(img1)
+            img2, img2_lab = preprocessing(img2)
+
+            img1 = img1.to(device)
+            img1_lab = img1_lab.to(device)
+            img2 = img2.to(device)
+            img2_lab = img2_lab.to(device)
+
+            with torch.no_grad():
+                img2_resize = F.interpolate(img2 / 255., size=(imgsize, imgsize), mode='bilinear', recompute_scale_factor=False, align_corners=False)
+                img1_L_resize = F.interpolate(img1_lab[:,:1,:,:] / 50., size=(imgsize, imgsize), mode='bilinear', recompute_scale_factor=False, align_corners=False)
+
+                color_vector = colorEncoder(img2_resize)
+
+                fake_ab = colorUNet((img1_L_resize, color_vector))
+                fake_ab = F.interpolate(fake_ab*110, size=(height, width), mode='bilinear', recompute_scale_factor=False, align_corners=False)
+
+                fake_img = torch.cat((img1_lab[:,:1,:,:], fake_ab), 1)
+                fake_img = Lab2RGB_out(fake_img)
+                fake_imgs.append(fake_img)
+
+        if panel_num == 1:
+            out_folder = os.path.dirname(img_path)
+            out_name = os.path.basename(img_path)
+            out_name = os.path.splitext(out_name)[0]
+            out_img_path = os.path.join(out_folder,'color',f'{out_name}_color.png')
+
+            Image.fromarray(fake_imgs[0]).show()
+            folder_path = os.path.join(out_folder, 'color')
+            if not os.path.exists(folder_path):
+                os.makedirs(folder_path)
+            io.imsave(out_img_path, fake_imgs[0])
+        else:
+            panel_extractor.concatPanels(img_path, fake_imgs, masks, panel_masks)
+
+        print(f'Colored images have been saved to: {os.path.join(test_dir_path, "color")}')