Files changed (3) hide show
  1. README.md +2 -2
  2. app.py +7 -6
  3. requirements.txt +1 -1
README.md CHANGED
@@ -4,9 +4,9 @@ emoji: ⚡
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  colorFrom: blue
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  colorTo: red
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  sdk: gradio
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- sdk_version: 5.37.0
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  app_file: app.py
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  pinned: false
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  ---
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- Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
 
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  colorFrom: blue
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  colorTo: red
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  sdk: gradio
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+ sdk_version: 2.8.13
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  app_file: app.py
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  pinned: false
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  ---
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+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
app.py CHANGED
@@ -1,17 +1,17 @@
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  import gradio as gr
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- from transformers import DPTImageProcessor, DPTForDepthEstimation
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  import torch
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  import numpy as np
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  from PIL import Image
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  torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg')
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- processor = DPTImageProcessor.from_pretrained("Intel/dpt-large")
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  model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
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  def process_image(image):
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  # prepare image for the model
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- encoding = processor(image, return_tensors="pt")
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  # forward pass
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  with torch.no_grad():
@@ -37,9 +37,10 @@ description = "Demo for Intel's DPT, a Dense Prediction Transformer for state-of
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  examples =[['cats.jpg']]
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  iface = gr.Interface(fn=process_image,
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- inputs=gr.Image(type="pil"),
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- outputs=gr.Image(type="pil", label="predicted depth"),
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  title=title,
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  description=description,
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- examples=examples)
 
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  iface.launch(debug=True)
 
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  import gradio as gr
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+ from transformers import DPTFeatureExtractor, DPTForDepthEstimation
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  import torch
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  import numpy as np
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  from PIL import Image
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  torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg')
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+ feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
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  model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
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  def process_image(image):
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  # prepare image for the model
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+ encoding = feature_extractor(image, return_tensors="pt")
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  # forward pass
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  with torch.no_grad():
 
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  examples =[['cats.jpg']]
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  iface = gr.Interface(fn=process_image,
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+ inputs=gr.inputs.Image(type="pil"),
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+ outputs=gr.outputs.Image(type="pil", label="predicted depth"),
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  title=title,
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  description=description,
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+ examples=examples,
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+ enable_queue=True)
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  iface.launch(debug=True)
requirements.txt CHANGED
@@ -1,4 +1,4 @@
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  torch
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- transformers
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  numpy
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  Pillow
 
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  torch
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+ git+https://github.com/nielsrogge/transformers.git@add_dpt_redesign#egg=transformers
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  numpy
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  Pillow