import gradio as gr from transformers import DPTFeatureExtractor, DPTForDepthEstimation import torch import numpy as np import cv2 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 write_depth(depth, bits): depth_min = depth.min() depth_max = depth.max() max_val = (2 ** (8 * bits)) - 1 if depth_max - depth_min > np.finfo("float").eps: out = max_val * (depth - depth_min) / (depth_max - depth_min) else: out = np.zeros(depth.shape, dtype=depth.dtype) cv2.imwrite("result.png", out.astype("uint16"), [cv2.IMWRITE_PNG_COMPRESSION, 0]) return 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 predicted_depth = torch.nn.functional.interpolate( predicted_depth.unsqueeze(1), size=image.size[::-1], mode="bicubic", align_corners=False, ) prediction = prediction.squeeze().cpu().numpy() # write predicted depth to file write_depth(prediction, bits=2) result = Image.open("result.png") return result title = "Interactive demo: 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." examples =[['cats.jpg']] css = ".output-image, .input-image {height: 40rem !important; width: 100% !important;}" #css = "@media screen and (max-width: 600px) { .output_image, .input_image {height:20rem !important; width: 100% !important;} }" # css = ".output_image, .input_image {height: 600px !important}" 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, examples=examples, css=css, enable_queue=True) iface.launch(debug=True)