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| import cv2 | |
| import torch | |
| import gradio as gr | |
| import numpy as np | |
| from PIL import Image | |
| torch.hub.download_url_to_file('https://images.unsplash.com/photo-1437622368342-7a3d73a34c8f', 'turtle.jpg') | |
| torch.hub.download_url_to_file('https://images.unsplash.com/photo-1519066629447-267fffa62d4b', 'lions.jpg') | |
| midas = torch.hub.load("intel-isl/MiDaS", "MiDaS") | |
| use_large_model = True | |
| if use_large_model: | |
| midas = torch.hub.load("intel-isl/MiDaS", "MiDaS") | |
| else: | |
| midas = torch.hub.load("intel-isl/MiDaS", "MiDaS_small") | |
| device = "cpu" | |
| midas.to(device) | |
| midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms") | |
| if use_large_model: | |
| transform = midas_transforms.default_transform | |
| else: | |
| transform = midas_transforms.small_transform | |
| def depth(img): | |
| cv_image = np.array(img) | |
| img = cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB) | |
| input_batch = transform(img).to(device) | |
| with torch.no_grad(): | |
| prediction = midas(input_batch) | |
| prediction = torch.nn.functional.interpolate( | |
| prediction.unsqueeze(1), | |
| size=img.shape[:2], | |
| mode="bicubic", | |
| align_corners=False, | |
| ).squeeze() | |
| output = prediction.cpu().numpy() | |
| formatted = (output * 255 / np.max(output)).astype('uint8') | |
| img = Image.fromarray(formatted) | |
| return img | |
| inputs = gr.inputs.Image(type='pil', label="Original Image") | |
| outputs = gr.outputs.Image(type="pil",label="Output Image") | |
| title = "MiDaS" | |
| description = "Gradio demo for MiDaS v2.1 which takes in a single image for computing relative depth. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1907.01341v3'>Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer</a> | <a href='https://github.com/intel-isl/MiDaS'>Github Repo</a></p>" | |
| examples = [ | |
| ["turtle.jpg"], | |
| ["lions.jpg"] | |
| ] | |
| gr.Interface(depth, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch(enable_queue=True,cache_examples=True) |