add Manga Model

lama_cleaner/model/lama.py

@@ -5,7 +5,7 @@ import numpy as np
 import torch
 from loguru import logger
 
-from lama_cleaner.helper import pad_img_to_modulo, download_model, norm_img, get_cache_path_by_url
+from lama_cleaner.helper import  download_model, norm_img, get_cache_path_by_url
 from lama_cleaner.model.base import InpaintModel
 from lama_cleaner.schema import Config
 

lama_cleaner/model/manga.py

@@ -0,0 +1,120 @@
+import os
+
+import cv2
+import numpy as np
+import torch
+import time
+from loguru import logger
+
+from lama_cleaner.helper import get_cache_path_by_url, load_jit_model
+from lama_cleaner.model.base import InpaintModel
+from lama_cleaner.schema import Config
+
+# def norm(np_img):
+#     return np_img / 255 * 2 - 1.0
+#
+#
+# @torch.no_grad()
+# def run():
+#     name = 'manga_1080x740.jpg'
+#     img_p = f'/Users/qing/code/github/MangaInpainting/examples/test/imgs/{name}'
+#     mask_p = f'/Users/qing/code/github/MangaInpainting/examples/test/masks/mask_{name}'
+#     erika_model = torch.jit.load('erika.jit')
+#     manga_inpaintor_model = torch.jit.load('manga_inpaintor.jit')
+#
+#     img = cv2.imread(img_p)
+#     gray_img = cv2.imread(img_p, cv2.IMREAD_GRAYSCALE)
+#     mask = cv2.imread(mask_p, cv2.IMREAD_GRAYSCALE)
+#
+#     kernel = np.ones((9, 9), dtype=np.uint8)
+#     mask = cv2.dilate(mask, kernel, 2)
+#     # cv2.imwrite("mask.jpg", mask)
+#     # cv2.imshow('dilated_mask', cv2.hconcat([mask, dilated_mask]))
+#     # cv2.waitKey(0)
+#     # exit()
+#
+#     # img = pad(img)
+#     gray_img = pad(gray_img).astype(np.float32)
+#     mask = pad(mask)
+#
+#     # pad_mod = 16
+#     import time
+#     start = time.time()
+#     y = erika_model(torch.from_numpy(gray_img[np.newaxis, np.newaxis, :, :]))
+#     y = torch.clamp(y, 0, 255)
+#     lines = y.cpu().numpy()
+#     print(f"erika_model time: {time.time() - start}")
+#
+#     cv2.imwrite('lines.png', lines[0][0])
+#
+#     start = time.time()
+#     masks = torch.from_numpy(mask[np.newaxis, np.newaxis, :, :])
+#     masks = torch.where(masks > 0.5, torch.tensor(1.0), torch.tensor(0.0))
+#     noise = torch.randn_like(masks)
+#
+#     images = torch.from_numpy(norm(gray_img)[np.newaxis, np.newaxis, :, :])
+#     lines = torch.from_numpy(norm(lines))
+#
+#     outputs = manga_inpaintor_model(images, lines, masks, noise)
+#     print(f"manga_inpaintor_model time: {time.time() - start}")
+#
+#     outputs_merged = (outputs * masks) + (images * (1 - masks))
+#     outputs_merged = outputs_merged * 127.5 + 127.5
+#     outputs_merged = outputs_merged.permute(0, 2, 3, 1)[0].detach().cpu().numpy().astype(np.uint8)
+#     cv2.imwrite(f'output_{name}', outputs_merged)
+
+
+MANGA_INPAINTOR_MODEL_URL = os.environ.get(
+    "MANGA_INPAINTOR_MODEL_URL",
+    "https://github.com/Sanster/models/releases/download/manga/manga_inpaintor.jit"
+)
+MANGA_LINE_MODEL_URL = os.environ.get(
+    "MANGA_LINE_MODEL_URL",
+    "https://github.com/Sanster/models/releases/download/manga/erika.jit"
+)
+
+
+class Manga(InpaintModel):
+    pad_mod = 16
+
+    def init_model(self, device, **kwargs):
+        self.inpaintor_model = load_jit_model(MANGA_INPAINTOR_MODEL_URL, device)
+        self.line_model = load_jit_model(MANGA_LINE_MODEL_URL, device)
+
+    @staticmethod
+    def is_downloaded() -> bool:
+        model_paths = [
+            get_cache_path_by_url(MANGA_INPAINTOR_MODEL_URL),
+            get_cache_path_by_url(MANGA_LINE_MODEL_URL),
+        ]
+        return all([os.path.exists(it) for it in model_paths])
+
+    def forward(self, image, mask, config: Config):
+        """
+        image: [H, W, C] RGB
+        mask: [H, W, 1]
+        return: BGR IMAGE
+        """
+        gray_img = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+        gray_img = torch.from_numpy(gray_img[np.newaxis, np.newaxis, :, :].astype(np.float32)).to(self.device)
+        start = time.time()
+        lines = self.line_model(gray_img)
+        lines = torch.clamp(lines, 0, 255)
+        logger.info(f"erika_model time: {time.time() - start}")
+
+        mask = torch.from_numpy(mask[np.newaxis, :, :, :]).to(self.device)
+        mask = mask.permute(0, 3, 1, 2)
+        mask = torch.where(mask > 0.5, torch.tensor(1.0), torch.tensor(0.0))
+        noise = torch.randn_like(mask)
+
+        gray_img = gray_img / 255 * 2 - 1.0
+        lines = lines / 255 * 2 - 1.0
+
+        start = time.time()
+        inpainted_image = self.inpaintor_model(gray_img, lines, mask, noise)
+        logger.info(f"image_inpaintor_model time: {time.time() - start}")
+
+        cur_res = inpainted_image[0].permute(1, 2, 0).detach().cpu().numpy()
+        cur_res = (cur_res * 127.5 + 127.5).astype(np.uint8)
+        cur_res = cv2.cvtColor(cur_res, cv2.COLOR_GRAY2BGR)
+        return cur_res