rename to iopaint

iopaint/model/lama.py

@@ -0,0 +1,57 @@
+import os
+
+import cv2
+import numpy as np
+import torch
+
+from iopaint.helper import (
+    norm_img,
+    get_cache_path_by_url,
+    load_jit_model,
+    download_model,
+)
+from iopaint.model.base import InpaintModel
+from iopaint.schema import InpaintRequest
+
+LAMA_MODEL_URL = os.environ.get(
+    "LAMA_MODEL_URL",
+    "https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
+)
+LAMA_MODEL_MD5 = os.environ.get("LAMA_MODEL_MD5", "e3aa4aaa15225a33ec84f9f4bc47e500")
+
+
+class LaMa(InpaintModel):
+    name = "lama"
+    pad_mod = 8
+    is_erase_model = True
+
+    @staticmethod
+    def download():
+        download_model(LAMA_MODEL_URL, LAMA_MODEL_MD5)
+
+    def init_model(self, device, **kwargs):
+        self.model = load_jit_model(LAMA_MODEL_URL, device, LAMA_MODEL_MD5).eval()
+
+    @staticmethod
+    def is_downloaded() -> bool:
+        return os.path.exists(get_cache_path_by_url(LAMA_MODEL_URL))
+
+    def forward(self, image, mask, config: InpaintRequest):
+        """Input image and output image have same size
+        image: [H, W, C] RGB
+        mask: [H, W]
+        return: BGR IMAGE
+        """
+        image = norm_img(image)
+        mask = norm_img(mask)
+
+        mask = (mask > 0) * 1
+        image = torch.from_numpy(image).unsqueeze(0).to(self.device)
+        mask = torch.from_numpy(mask).unsqueeze(0).to(self.device)
+
+        inpainted_image = self.model(image, mask)
+
+        cur_res = inpainted_image[0].permute(1, 2, 0).detach().cpu().numpy()
+        cur_res = np.clip(cur_res * 255, 0, 255).astype("uint8")
+        cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR)
+        return cur_res