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import gradio as gr |
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import cv2 |
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import numpy as np |
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import os |
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from PIL import Image |
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import torch |
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import torch.nn.functional as F |
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from torchvision.transforms import Compose |
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from depth_anything.dpt import DepthAnything |
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from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet |
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transform = Compose([ |
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Resize( |
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width=518, |
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height=518, |
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resize_target=False, |
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keep_aspect_ratio=True, |
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ensure_multiple_of=14, |
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resize_method='lower_bound', |
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image_interpolation_method=cv2.INTER_CUBIC, |
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), |
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NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), |
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PrepareForNet(), |
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]) |
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DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' |
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model = DepthAnything.from_pretrained('LiheYoung/depth_anything_vits14').to(DEVICE).eval() |
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def predict_depthmap(image): |
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original_image = image.copy() |
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h, w = image.shape[:2] |
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0 |
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image = transform({'image': image})['image'] |
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image = torch.from_numpy(image).unsqueeze(0).to(DEVICE) |
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with torch.no_grad(): |
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depth = model(image) |
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depth = F.interpolate(depth[None], (h, w), mode='bilinear', align_corners=False)[0, 0] |
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depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0 |
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depth = depth.cpu().numpy().astype(np.uint8) |
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colored_depth = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)[:, :, ::-1] |
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corlored_depth = Image.fromarray(colored_depth) |
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return colored_depth |
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demo = gr.Interface(fn=predict_depthmap, inputs=[gr.Image()], |
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outputs=[gr.Image(type="pil")] |
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) |
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demo.launch(share=True, server_name="localhost", server_port=8082) |
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