commit

.devcontainer/Dockerfile

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+FROM pytorch/pytorch:latest
+
+# System dependencies (optional, for opencv and others)
+RUN apt-get update && apt-get install -y ffmpeg libsm6 libxext6
+
+# Install required Python packages
+COPY requirements.txt .
+RUN pip install --upgrade pip && pip install -r requirements.txt
+
+# Run your app
+CMD ["python", "app.py"]

.devcontainer/devcontainer.json

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+{
+    "name": "Room-Tiler-Dev",
+    "build": {
+      "dockerfile": "Dockerfile",
+      "context": ".."
+    },
+  
+    // Forward the Gradio port
+    "forwardPorts": [7860],
+  
+    // Automatically start the app when the container boots
+    "postCreateCommand": "python /workspace/app.py --share --server-name 0.0.0.0",
+  
+    // VS Code features: Python extension, auto-formatting, etc.
+    "features": {
+      "ghcr.io/devcontainers/features/python:1": { "version": "3.10" }
+    },
+  
+    // Sets the default shell
+    "remoteUser": "root"
+  }
+  

.gitattributes

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-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
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-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
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-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
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-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
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-*.tflite filter=lfs diff=lfs merge=lfs -text
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-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

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----
-title: Floor Visualizer
-emoji: 🏆
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 5.31.0
-app_file: app.py
-pinned: false
-license: mit
-short_description: Visualize custom texture or tiles on your floor
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Floor Visualizer
+emoji: 🏆
+colorFrom: indigo
+colorTo: purple
+sdk: gradio
+sdk_version: 5.31.0
+app_file: app.py
+pinned: false
+license: mit
+short_description: Visualize custom texture or tiles on your floor
+---
+
+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
+import cv2
+from PIL import Image
+from torchvision import transforms
+from transformers import SegformerForSemanticSegmentation, AutoImageProcessor
+
+# Device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load model and processor once
+processor = AutoImageProcessor.from_pretrained("nvidia/segformer-b2-finetuned-ade-512-512")
+model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b2-finetuned-ade-512-512").to(device)
+
+def process(room_img, tile_img):
+    room_img = room_img.convert("RGB")
+    tile_img = tile_img.convert("RGB")
+    room_np = np.array(room_img)
+
+    # Segmentation
+    inputs = processor(images=room_img, return_tensors="pt").to(device)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    segmentation = outputs.logits.argmax(dim=1).squeeze().cpu().numpy()
+    segmentation_resized = cv2.resize(segmentation.astype(np.uint8), (room_np.shape[1], room_np.shape[0]), interpolation=cv2.INTER_NEAREST)
+
+    # Mask for floor (ADE20K class index 3)
+    floor_class_index = 3
+    mask_bin = (segmentation_resized == floor_class_index).astype(np.uint8)
+
+    # Largest contour
+    contours, _ = cv2.findContours(mask_bin, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if not contours:
+        return room_img, Image.fromarray(mask_bin*255), tile_img, room_img
+    contour = max(contours, key=cv2.contourArea)
+    if len(contour) < 4:
+        return room_img, Image.fromarray(mask_bin*255), tile_img, room_img
+
+    x, y, w, h = cv2.boundingRect(contour)
+    src_pts = np.array([[x, y + h], [x + w, y + h], [x + w, y], [x, y]], dtype=np.float32)
+    offset = h * 0.5
+    dst_pts = np.array([[x - offset, y + h], [x + w + offset, y + h], [x + w, y], [x, y]], dtype=np.float32)
+    H = cv2.getPerspectiveTransform(src_pts, dst_pts)
+
+    # Resize tile
+    target_tile_width = room_np.shape[1] // 10
+    tile_aspect_ratio = tile_img.height / tile_img.width
+    target_tile_height = int(target_tile_width * tile_aspect_ratio)
+    tile_resized = tile_img.resize((target_tile_width, target_tile_height), Image.LANCZOS)
+    tile_np = np.array(tile_resized)
+
+    # Tile texture
+    tile_h, tile_w = tile_np.shape[:2]
+    room_h, room_w = room_np.shape[:2]
+    reps_y = room_h // tile_h + 2
+    reps_x = room_w // tile_w + 2
+    tiled_texture = np.tile(tile_np, (reps_y, reps_x, 1))[:room_h, :room_w]
+    warped_texture = cv2.warpPerspective(tiled_texture, H, (room_w, room_h))
+
+    # Blend
+    room_float = room_np.astype(np.float32) / 255.0
+    texture_float = warped_texture.astype(np.float32) / 255.0
+    room_gray = cv2.cvtColor(room_float, cv2.COLOR_RGB2GRAY)
+    lighting = np.stack([room_gray]*3, axis=-1)
+    lighting = np.clip(lighting * 1.2, 0, 1)
+    lit_texture = np.clip(texture_float * lighting, 0, 1)
+    mask_3ch = np.stack([mask_bin]*3, axis=-1)
+    blended = np.where(mask_3ch == 1, lit_texture, room_float)
+    blended_img = (blended * 255).astype(np.uint8)
+
+    return Image.fromarray(room_np), Image.fromarray(mask_bin * 255), Image.fromarray(warped_texture), Image.fromarray(blended_img)
+
+
+demo = gr.Interface(
+    fn=process,
+    inputs=[gr.Image(label="Room Image", type="pil"), gr.Image(label="Tile Image", type="pil")],
+    outputs=[
+        gr.Image(label="Original Room"),
+        gr.Image(label="Floor Mask"),
+        gr.Image(label="Warped Texture"),
+        gr.Image(label="Final Overlay")
+    ],
+    title="Room Floor Tiler",
+    description="Upload a room image and a tile texture. The floor is automatically detected and overlaid with your selected tile using SegFormer and perspective warping."
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+torch
+transformers==4.38.2
+opencv-python
+matplotlib
+timm
+Pillow
+gradio
+setuptools>=65.5.1