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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."
examples =[['cats.jpg']]
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,
enable_queue=True)
iface.launch(debug=True) |