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<!DOCTYPE html> |
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<html> |
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<head> |
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<meta charset="utf-8"> |
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<meta name="viewport" content="width=device-width, initial-scale=1"> |
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<title>Gradio-Lite: Serverless Gradio Running Entirely in Your Browser</title> |
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<meta name="description" content="Gradio-Lite: Serverless Gradio Running Entirely in Your Browser"> |
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<script type="module" crossorigin src="https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js"></script> |
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<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css" /> |
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<style> |
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html, body { |
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margin: 0; |
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padding: 0; |
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height: 100%; |
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} |
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</style> |
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</head> |
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<body> |
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<gradio-lite> |
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<gradio-file name="app.py" entrypoint> |
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import gradio as gr |
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import numpy as np |
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import PIL |
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import trimesh |
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from transformers_js import import_transformers_js, as_url |
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transformers = await import_transformers_js() |
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pipeline = transformers.pipeline |
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depth_estimator = await pipeline('depth-estimation', 'Xenova/depth-anything-small-hf'); |
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def depthmap_to_glb_trimesh(depth_map, rgb_image, file_path): |
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assert depth_map.shape[:2] == rgb_image.shape[:2], "Depth map and RGB image must have the same dimensions" |
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# Generate vertices and faces |
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vertices = [] |
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colors = [] |
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faces = [] |
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height, width = depth_map.shape |
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for y in range(height): |
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for x in range(width): |
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z = depth_map[y, x] |
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vertices.append([x, y, z]) |
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colors.append(rgb_image[y, x]) |
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# Create faces (2 triangles per pixel, except for edges) |
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for y in range(height - 1): |
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for x in range(width - 1): |
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top_left = y * width + x |
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top_right = top_left + 1 |
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bottom_left = top_left + width |
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bottom_right = bottom_left + 1 |
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faces.append([top_left, bottom_left, top_right]) |
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faces.append([top_right, bottom_left, bottom_right]) |
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# Convert to numpy arrays |
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vertices = np.array(vertices, dtype=np.float64) |
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colors = np.array(colors, dtype=np.uint8) |
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faces = np.array(faces, dtype=np.int32) |
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mesh = trimesh.Trimesh(vertices=vertices, faces=faces, vertex_colors=colors, process=False) |
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# Export to GLB |
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mesh.export(file_path, file_type='glb') |
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def invert_depth(depth_map): |
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max_depth = np.max(depth_map) |
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return max_depth - depth_map |
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def invert_xy(map): |
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return map[::-1, ::-1] |
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async def estimate(image_path, depth_scale): |
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image = PIL.Image.open(image_path) |
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image.thumbnail((384, 384)) # Resize the image keeping the aspect ratio |
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predictions = await depth_estimator(as_url(image_path)) |
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depth_image = predictions["depth"].to_pil() |
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tensor = predictions["predicted_depth"] |
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tensor_data = { |
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"dims": tensor.dims, |
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"type": tensor.type, |
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"data": tensor.data, |
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"size": tensor.size, |
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} |
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# Construct the 3D model from the depth map and the RGB image |
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depth = predictions["predicted_depth"].numpy() |
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depth = invert_depth(depth) |
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depth = invert_xy(depth) |
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depth = depth * depth_scale |
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# The model outputs the depth map in a different size than the input image. |
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# So we resize the depth map to match the original image size. |
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depth = np.array(PIL.Image.fromarray(depth).resize(image.size)) |
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image_array = np.asarray(image) |
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image_array = invert_xy(image_array) |
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glb_file_path = "output.glb" |
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depthmap_to_glb_trimesh(depth, image_array, glb_file_path) |
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return depth_image, glb_file_path, tensor_data |
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demo = gr.Interface( |
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fn=estimate, |
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inputs=[ |
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gr.Image(type="filepath"), |
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gr.Slider(minimum=1, maximum=100, value=10, label="Depth Scale") |
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], |
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outputs=[ |
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gr.Image(label="Depth Image"), |
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gr.Model3D(label="3D Model"), |
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gr.JSON(label="Tensor"), |
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], |
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examples=[ |
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["bread_small.png"], |
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] |
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) |
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demo.launch() |
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</gradio-file> |
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<gradio-file name="bread_small.png" url="https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/bread_small.png" /> |
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<gradio-requirements> |
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transformers_js_py |
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trimesh |
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</gradio-requirements> |
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</gradio-lite> |
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</body> |
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</html> |
