init clean repo

README.md

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+---
+title: Image Seg Inpaint Demo
+emoji: ⚡
+colorFrom: purple
+colorTo: purple
+sdk: gradio
+sdk_version: 5.43.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: base data generation pipline
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import torch
+import numpy as np
+from PIL import Image
+from torchvision.models.segmentation import deeplabv3_resnet50, DeepLabV3_ResNet50_Weights
+from huggingface_hub import hf_hub_download
+import cv2
+
+
+# ---------------- 下载并加载 LaMa 官方权重 ----------------
+repo_id = "saic-mdal/lama-big"
+model_path = hf_hub_download(repo_id=repo_id, filename="big-lama.pt")
+lama_model = torch.jit.load(model_path, map_location="cpu")
+lama_model.eval()
+
+print("torch:", torch.__version__)
+print("numpy:", np.__version__)
+
+# ---- 加载分割模型（CPU） ----
+device = torch.device("cpu")
+weights = DeepLabV3_ResNet50_Weights.COCO_WITH_VOC_LABELS_V1
+model = deeplabv3_resnet50(weights=weights).to(device).eval()
+preprocess = weights.transforms()
+
+
+MAX_SIDE = 1024  # 为了速度与内存，限制输入最大边
+
+def _resize_if_needed(pil_img: Image.Image, max_side=MAX_SIDE) -> Image.Image:
+    w, h = pil_img.size
+    if max(w, h) <= max_side:
+        return pil_img
+    r = max_side / float(max(w, h))
+    return pil_img.resize((int(w * r), int(h * r)), Image.BILINEAR)
+
+def segment(image: Image.Image):
+    print("DEBUG: type(image) =", type(image), "mode=", getattr(image, "mode", None))
+    if not isinstance(image, Image.Image):
+        image = Image.fromarray(image)
+
+    image = image.convert("RGB")
+    image = _resize_if_needed(image)
+
+    # 预处理并推理
+    x = torch.from_numpy(np.array(image)).permute(2, 0, 1).float() / 255.0
+    x = x.unsqueeze(0).to(device)  # [1,3,H,W]
+
+    with torch.no_grad():
+        out = model(x)["out"][0]                   # [C,H,W]，C=21（含背景）
+    pred = out.argmax(0).cpu().numpy()             # [H,W]
+
+    # 前景 = 非背景（背景类在COCO VOC权重下是0）
+    fg = (pred != 0).astype(np.uint8)
+
+    # ---------------- mask 膨胀 ----------------
+    kernel = np.ones((19,19), np.uint8)
+    fg_dilated = cv2.dilate(fg, kernel, iterations=1)
+    print("add dilated process！")
+
+    mask_img = Image.fromarray((fg_dilated * 255).astype(np.uint8), mode="L")
+
+    # 叠加彩色遮罩（红色半透明）
+    base = image.convert("RGBA")
+    overlay = Image.new("RGBA", base.size, (255, 0, 0, 0))
+    alpha = Image.fromarray((fg_dilated * 120).astype(np.uint8))
+    overlay.putalpha(alpha)
+    blended = Image.alpha_composite(base, overlay).convert("RGB")
+
+    # ---- LaMa 擦除 ----
+    img_np = np.array(image)  # HWC, uint8
+    mask_np = np.array(mask_img)  # H,W, 0/255
+    img_t = torch.from_numpy(img_np).permute(2, 0, 1).float().unsqueeze(0) / 255.0
+    mask_t = torch.from_numpy(mask_np).unsqueeze(0).unsqueeze(0).float() / 255.0
+    with torch.no_grad():
+        inpainted_t = lama_model(img_t, mask_t)  # [1,3,H,W]
+    inpainted_np = (inpainted_t[0].permute(1, 2, 0).numpy() * 255).astype(np.uint8)
+    inpainted_img = Image.fromarray(inpainted_np)
+
+    return blended, mask_img, inpainted_img
+
+# ---- Gradio 界面 ----
+demo = gr.Interface(
+    fn=segment,
+    inputs=gr.Image(type="pil", label="Upload Image"),
+    outputs=[
+        gr.Image(type="pil", label="Overlay (foreground)"),
+        gr.Image(type="pil", label="Binary Mask (foreground=white)"),
+        gr.Image(type="pil", label="inpaint result"),
+    ],
+    title="Semantic Segmentation + LaMa Inpainting",
+    description="DeepLabV3 分割 + Mask 膨胀 + LaMa 擦除，运行在 CPU 环境。"
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+# ---- PyTorch (CPU 版本，适配 Hugging Face Space 免费环境) ----
+torch==2.0.1+cpu
+torchvision==0.15.2+cpu
+-f https://download.pytorch.org/whl/torch_stable.html
+numpy==1.24.4
+
+# ---- Transformers (分割模型用) ----
+transformers
+timm  # DETR 依赖
+
+# ---- 图像处理 ----
+Pillow
+opencv-python
+
+# ---- Web 界面 ----
+gradio>=4.0.0
+
+# ---- LaMa inpainting 后续需要 ----
+#lama-cleaner==0.27.1
+huggingface_hub