Update app.py

app.py

@@ -1,52 +1,34 @@
-# Import necessary libraries
-import numpy as np
-import matplotlib.pyplot as plt
 import gradio as gr
-from skimage import io, color
-from skimage.segmentation import slic, mark_boundaries
 import rasterio
+from rasterio.plot import show
+from skimage.segmentation import slic
+import numpy as np
 
-# Function to open and visualize the TIFF image using rasterio and matplotlib
-def visualize_tiff_image(image_path):
-    # Open the TIFF image using rasterio
-    with rasterio.open(image_path) as dataset:
-        image = dataset.read()  # Read image data (bands)
-        image = np.transpose(image, (1, 2, 0))  # Transpose to (height, width, channels)
-
-    # Plot the TIFF image using matplotlib
-    plt.imshow(image)
-    plt.axis('off')  # Turn off axis
-    plt.show()
-
-# Function to perform image segmentation using SLIC
-def perform_segmentation(image):
-    # Convert the image to LAB color space for better segmentation
-    image_lab = color.rgb2lab(image)
-
-    # Perform image segmentation using SLIC (Simple Linear Iterative Clustering)
-    segments = slic(image_lab, n_segments=100, compactness=20, sigma=1)
+def perform_segmentation(tiff_path):
+    # Open the TIFF image using Rasterio
+    with rasterio.open(tiff_path) as src:
+        # Read all the bands
+        bands = src.read()
 
-    # Create a mask for each segment
-    mask = np.zeros(image.shape[:2], dtype=bool)
-    for segment_id in np.unique(segments):
-        mask[segments == segment_id] = 1
+    # 'bands' is a 3D numpy array where each band is stored along the third dimension (axis=0)
 
-    # Create a color-coded segmented image
-    segmented_image = np.zeros_like(image)
-    for segment_id in np.unique(segments):
-        segmented_image[segments == segment_id] = np.mean(image[segments == segment_id], axis=0)
+    # Perform segmentation on all the bands
+    segmented_bands = []
+    for band in bands:
+        # Modify the parameters of the slic function as needed for your specific segmentation task
+        segments = slic(band, n_segments=100, compactness=10.0, sigma=1)
+        segmented_bands.append(segments)
 
-    # Return the original image and the segmented images
-    return [image, mark_boundaries(image, segments), segmented_image]
+    return segmented_bands
 
 # Gradio Interface
 iface = gr.Interface(
-    fn=visualize_tiff_image,  # Updated function to visualize the TIFF image
-    inputs=gr.inputs.File(type="file"),  # Input will be a file path to the TIFF image
-    outputs=[gr.outputs.Image(), gr.outputs.Image(), gr.outputs.Image()],
-    title="TIFF Image Visualization",
-    description="Upload a TIFF image and visualize it using matplotlib.",
-    live=True
+    fn=perform_segmentation,
+    inputs=gr.inputs.File(type="file", label="Upload TIFF Image"),
+    outputs=gr.outputs.Image(type="numpy", label="Segmented Bands"),
+    live=True,
+    title="TIFF Image Segmentation",
+    description="Upload a TIFF image, and the app will perform segmentation on all bands.",
 )
 
 if __name__ == "__main__":