lfolle commited on
Commit
c02063c
1 Parent(s): ec42e29

Polished interface a bit.

Browse files
Files changed (3) hide show
  1. .gitignore +2 -1
  2. app.py +26 -22
  3. backend.py +3 -3
.gitignore CHANGED
@@ -1,2 +1,3 @@
1
  .vscode/launch.json
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- *.pyc
 
 
1
  .vscode/launch.json
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+ *.pyc
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+ gradio_cached_examples/*
app.py CHANGED
@@ -6,37 +6,41 @@ from backend import Infer
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  DEBUG = True
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  infer = Infer(DEBUG)
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-
10
 
11
  with gr.Blocks(analytics_enabled=False, title="DeepNAPSI") as demo:
 
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  with gr.Column():
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  gr.Markdown("## Welcome to the DeepNAPSI application!")
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  gr.Markdown("Upload an image of the one hand and click **Predict NAPSI** to see the output.\n" \
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  "> Note: Make sure there are no identifying information present in the image. The prediction can take up to 1 minute.")
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  with gr.Column():
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  with gr.Row():
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- image_input = gr.Image()
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- example_images = gr.Examples(["assets/example_1.jpg", "assets/example_2.jpg", "assets/example_3.jpg"], image_input)
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- with gr.Row():
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- image_button = gr.Button("Predict NAPSI")
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- outputs = []
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- with gr.Row():
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- outputs.append(gr.Number(label="DeepNAPSI Sum"))
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- with gr.Column():
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- outputs.append(gr.Image())
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- outputs.append(gr.Number(label="DeepNAPSI Thumb"))
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  with gr.Column():
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- outputs.append(gr.Image())
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- outputs.append(gr.Number(label="DeepNAPSI Index"))
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- with gr.Column():
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- outputs.append(gr.Image())
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- outputs.append(gr.Number(label="DeepNAPSI Middle"))
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- with gr.Column():
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- outputs.append(gr.Image())
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- outputs.append(gr.Number(label="DeepNAPSI Ring"))
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- with gr.Column():
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- outputs.append(gr.Image())
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- outputs.append(gr.Number(label="DeepNAPSI Pinky"))
 
 
 
 
 
 
 
 
 
 
 
 
 
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  image_button.click(infer.predict, inputs=image_input, outputs=outputs)
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  demo.launch(share=True)
 
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  DEBUG = True
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  infer = Infer(DEBUG)
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+ example_image_path = ["assets/example_1.jpg", "assets/example_2.jpg", "assets/example_3.jpg"]
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  with gr.Blocks(analytics_enabled=False, title="DeepNAPSI") as demo:
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+ outputs = []
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  with gr.Column():
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  gr.Markdown("## Welcome to the DeepNAPSI application!")
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  gr.Markdown("Upload an image of the one hand and click **Predict NAPSI** to see the output.\n" \
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  "> Note: Make sure there are no identifying information present in the image. The prediction can take up to 1 minute.")
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  with gr.Column():
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  with gr.Row():
 
 
 
 
 
 
 
 
 
 
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  with gr.Column():
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+ with gr.Row():
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+ image_input = gr.Image()
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+ with gr.Row():
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+ image_button = gr.Button("Predict NAPSI")
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+ with gr.Row():
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+ with gr.Column():
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+ outputs.append(gr.Image(label="Thumb"))
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+ outputs.append(gr.Number(label="DeepNAPSI Thumb", precision=0))
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+ with gr.Column():
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+ outputs.append(gr.Image(label="Index"))
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+ outputs.append(gr.Number(label="DeepNAPSI Index", precision=0))
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+ with gr.Column():
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+ outputs.append(gr.Image(label="Middle"))
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+ outputs.append(gr.Number(label="DeepNAPSI Middle", precision=0))
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+ with gr.Column():
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+ outputs.append(gr.Image(label="Ring"))
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+ outputs.append(gr.Number(label="DeepNAPSI Ring", precision=0))
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+ with gr.Column():
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+ outputs.append(gr.Image(label="Pinky"))
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+ outputs.append(gr.Number(label="DeepNAPSI Pinky", precision=0))
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+ outputs.append(gr.Number(label="DeepNAPSI Sum", precision=0))
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+
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+ example_images = gr.Examples(example_image_path, image_input, outputs,
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+ fn=infer.predict, cache_examples=True)
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  image_button.click(infer.predict, inputs=image_input, outputs=outputs)
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  demo.launch(share=True)
backend.py CHANGED
@@ -17,17 +17,17 @@ class Infer():
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  nails = get_nails(cv2.cvtColor(data, cv2.COLOR_RGB2BGR))
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  predictions = []
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  if nails is None:
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- predictions.append(-1)
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  for _ in range(5):
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  predictions.append(np.zeros((64, 64, 3)))
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  predictions.append(-1)
 
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  else:
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  model_prediction, uncertainty = self.model(nails)
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  model_prediction = model_prediction[0]
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  napsi_predictions = torch.argmax(model_prediction, 1)
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  napsi_sum = int(napsi_predictions.sum().detach().cpu())
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- predictions.append(napsi_sum)
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  for napsi_prediction, nail in zip(napsi_predictions, nails):
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  predictions.append(nail)
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- predictions.append(napsi_prediction)
 
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  return predictions
 
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  nails = get_nails(cv2.cvtColor(data, cv2.COLOR_RGB2BGR))
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  predictions = []
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  if nails is None:
 
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  for _ in range(5):
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  predictions.append(np.zeros((64, 64, 3)))
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  predictions.append(-1)
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+ predictions.append("-1")
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  else:
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  model_prediction, uncertainty = self.model(nails)
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  model_prediction = model_prediction[0]
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  napsi_predictions = torch.argmax(model_prediction, 1)
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  napsi_sum = int(napsi_predictions.sum().detach().cpu())
 
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  for napsi_prediction, nail in zip(napsi_predictions, nails):
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  predictions.append(nail)
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+ predictions.append(int(napsi_prediction.detach().cpu()))
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+ predictions.append(napsi_sum)
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  return predictions