Taha Razzaq commited on
Commit
92494bc
·
1 Parent(s): 37ea0b9

errors fixed + preview updated

Browse files
Files changed (1) hide show
  1. app.py +25 -13
app.py CHANGED
@@ -1,23 +1,34 @@
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- import keras
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- import numpy as np
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  import gradio as gr
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- from tibblingai import wta
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  from PIL import Image, ImageOps
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- import matplotlib.pyplot as plt
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-
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-
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  def load_sample_images():
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  sample_paths = ["sample1.jpg", "sample2.jpg"] # Must be in the same folder as your script
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  original_imgs = [Image.open(path) for path in sample_paths]
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  processed_imgs = [process_image(path) for path in sample_paths]
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  return gr.update(visible=True, value=original_imgs), gr.update(visible=True, value=processed_imgs)
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  # Image processing function (you can replace this)
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  def process_image(img: Image.Image) -> Image.Image:
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- img_tensor = wta.wta(plt.imread(img)).numpy()
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- print(img_tensor.shape)
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- # image_array = np.squeeze(img_tensor)
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- return img_tensor[0]
 
 
 
 
 
 
 
 
 
 
 
 
 
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  # Function to process uploaded images
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  def process_images(images):
@@ -28,8 +39,9 @@ def process_images(images):
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  processed_imgs = []
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  for img in images:
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- original_imgs.append(img)
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- processed_imgs.append(process_image(img))
 
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  return gr.update(visible=True, value=original_imgs), gr.update(visible=True, value=processed_imgs)
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@@ -60,4 +72,4 @@ with gr.Blocks() as demo:
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  # example_files = [["sample1.jpg", "sample2.jpg"], ["sample2.jpg"]]
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  # gr.Examples(examples=example_files, inputs=[file_input], label="Try one of our example samples")
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- demo.launch()
 
 
 
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  import gradio as gr
 
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  from PIL import Image, ImageOps
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+ import numpy as np
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+ import matplotlib.pyplot as plt # needed for plt.imread
 
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  def load_sample_images():
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  sample_paths = ["sample1.jpg", "sample2.jpg"] # Must be in the same folder as your script
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  original_imgs = [Image.open(path) for path in sample_paths]
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  processed_imgs = [process_image(path) for path in sample_paths]
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  return gr.update(visible=True, value=original_imgs), gr.update(visible=True, value=processed_imgs)
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+
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+
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  # Image processing function (you can replace this)
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  def process_image(img: Image.Image) -> Image.Image:
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+ # Read image using matplotlib
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+ img_np = plt.imread(img)
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+ if img_np.ndim == 3 and img_np.shape[2] > 3:
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+ img_np = img_np[:, :, :3]
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+
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+ # Convert to grayscale for original image display
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+ gray_img = ImageOps.grayscale(Image.fromarray((img_np * 255).astype(np.uint8)))
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+
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+ # Run your WTA processing (dummy if not available)
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+ # Replace this line with actual WTA processing
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+ img_tensor = wta.wta(img_np).numpy() # Assuming returns shape (1, H, W)
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+
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+ # Convert processed image to inferno colormap
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+ inferno_colored = plt.cm.inferno(img_tensor[0])
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+ inferno_img = Image.fromarray((inferno_colored[:, :, :3] * 255).astype(np.uint8))
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+
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+ return gray_img, inferno_img
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  # Function to process uploaded images
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  def process_images(images):
 
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  processed_imgs = []
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  for img in images:
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+ original_gray_scale_img, inferno_img = process_image(img)
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+ original_imgs.append(original_gray_scale_img)
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+ processed_imgs.append(inferno_img)
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  return gr.update(visible=True, value=original_imgs), gr.update(visible=True, value=processed_imgs)
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  # example_files = [["sample1.jpg", "sample2.jpg"], ["sample2.jpg"]]
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  # gr.Examples(examples=example_files, inputs=[file_input], label="Try one of our example samples")
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+ demo.launch(debug=True)