import gradio as gr from transformers import DPTFeatureExtractor, DPTForDepthEstimation import torch import numpy as np from PIL import Image #torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg') feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large") model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large") def process_image(image): # 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() formatted = (output * 255 / np.max(output)).astype('uint8') img = Image.fromarray(formatted) return img return result title = "Demo: zero-shot depth estimation with DPT" description = "Demo for Intel's DPT, a Dense Prediction Transformer for state-of-the-art dense prediction tasks such as semantic segmentation and depth estimation." iface = gr.Interface(fn=process_image, inputs=gr.inputs.Image(type="pil"), outputs=gr.outputs.Image(type="pil", label="predicted depth"), title=title, description=description, enable_queue=True) iface.launch(debug=True)