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| import gradio as gr | |
| from transformers import DPTFeatureExtractor, DPTForDepthEstimation | |
| import torch | |
| import numpy as np | |
| from PIL import Image | |
| from pathlib import Path | |
| from depth_viewer import depthviewer2html | |
| feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large") | |
| model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large") | |
| def process_image(image_path): | |
| image_path = Path(image_path) | |
| image = Image.open(image_path) | |
| # prepare image for the model | |
| encoding = feature_extractor(image, return_tensors="pt") | |
| # forward pass | |
| with torch.no_grad(): | |
| outputs = model(**encoding) | |
| predicted_depth = outputs.predicted_depth | |
| # interpolate to original size | |
| prediction = torch.nn.functional.interpolate( | |
| predicted_depth.unsqueeze(1), | |
| size=image.size[::-1], | |
| mode="bicubic", | |
| align_corners=False, | |
| ).squeeze() | |
| output = prediction.cpu().numpy() | |
| depth = (output * 255 / np.max(output)).astype('uint8') | |
| h = depthviewer2html(image,depth) | |
| return [h] | |
| title = "3d Visualization of Depth Maps Generated using MiDaS" | |
| description = "Improved 3D interactive depth viewer using Three.js embedded in a Gradio app. For more details see the <a href='https://colab.research.google.com/drive/1l2l8U7Vhq9RnvV2tHyfhrXKNuHfmb4IP?usp=sharing'>Colab Notebook.</a>" | |
| examples = [["examples/owl1.jpg"],['examples/marsattacks.jpg'],['examples/kitten.jpg']] | |
| iface = gr.Interface(fn=process_image, | |
| inputs=[gr.Image(type="filepath",label="Input Image")], | |
| outputs=[gr.HTML(label='Depth Viewer',elem_id='depth-viewer')], | |
| title=title, | |
| description=description, | |
| examples=examples, | |
| allow_flagging="never", | |
| cache_examples=False, | |
| css='#depth-viewer: {height:300px;}') | |
| iface.launch(debug=True, enable_queue=False) |