big update

lama_cleaner/model/lama.py

@@ -0,0 +1,64 @@
+import os
+
+import cv2
+import numpy as np
+import torch
+from loguru import logger
+
+from lama_cleaner.helper import pad_img_to_modulo, download_model, norm_img
+from lama_cleaner.model.base import InpaintModel
+from lama_cleaner.schema import Config
+
+LAMA_MODEL_URL = os.environ.get(
+    "LAMA_MODEL_URL",
+    "https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
+)
+
+
+class LaMa(InpaintModel):
+    pad_mod = 8
+
+    def __init__(self, device):
+        """
+
+        Args:
+            device:
+        """
+        super().__init__(device)
+        self.device = device
+
+    def init_model(self, device):
+        if os.environ.get("LAMA_MODEL"):
+            model_path = os.environ.get("LAMA_MODEL")
+            if not os.path.exists(model_path):
+                raise FileNotFoundError(
+                    f"lama torchscript model not found: {model_path}"
+                )
+        else:
+            model_path = download_model(LAMA_MODEL_URL)
+
+        logger.info(f"Load LaMa model from: {model_path}")
+        model = torch.jit.load(model_path, map_location="cpu")
+        model = model.to(device)
+        model.eval()
+        self.model = model
+
+    def forward(self, image, mask, config: Config):
+        """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