Create image segmentation app

.github/workflows/check_file_size.yml

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+name: Check file size
+on:               # or directly `on: [push]` to run the action on every push on any branch
+  pull_request:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  check-file-size:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check large files
+        uses: ActionsDesk/[email protected]
+        with:
+          filesizelimit: 10485760 # this is 10MB so we can sync to HF Spaces

.github/workflows/sync_to_HF_hub.yml

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+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Push to hub
+        env:
+          HF: ${{ secrets.HF }}
+        run: git push --force https://soumyaprabhamaiti:[email protected]/spaces/soumyaprabhamaiti/image_segmentation_web_app main

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+
+
+# Path to the pre-trained sentiment analysis model
+model_path = "saved_model"
+
+# Load the pre-trained segmentation model
+segmentation_model = tf.keras.models.load_model(model_path)
+
+# Target image shape
+TARGET_SHAPE = (256, 256)
+
+# Define image segmentation function
+def segment_image(img:np.ndarray):
+    # Original image shape
+    ORIGINAL_SHAPE = img.shape
+
+    # Check if the image is RGB and convert if not
+    if len(ORIGINAL_SHAPE) == 2:
+        img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
+
+    # Resize the image to TARGET_SHAPE
+    img = cv2.resize(img, TARGET_SHAPE)
+    
+    # Add a batch dimension
+    img = np.expand_dims(img, axis=0)
+    
+    # Predict the segmentation mask
+    mask = segmentation_model.predict(img)
+
+    # Remove the batch dimension
+    mask = np.squeeze(mask, axis=0)
+
+    # Convert to labels 
+    mask = np.argmax(mask, axis=-1)
+    
+    # Convert to uint8
+    mask = mask.astype(np.uint8)
+
+    # Resize to original image shape
+    mask = cv2.resize(mask, (ORIGINAL_SHAPE[1], ORIGINAL_SHAPE[0]))
+
+    return mask
+
+def overlay_mask(img, mask, alpha=0.5):
+    # Define color mapping
+    colors = {
+        0: [255, 0, 0],   # Class 0 - Red
+        1: [0, 255, 0],   # Class 1 - Green
+        2: [0, 0, 255]    # Class 2 - Blue
+        # Add more colors for additional classes if needed
+    }
+
+    # Create a blank colored overlay image
+    overlay = np.zeros_like(img)
+
+    # Map each mask value to the corresponding color
+    for class_id, color in colors.items():
+        overlay[mask == class_id] = color
+
+    # Blend the overlay with the original image
+    output = cv2.addWeighted(img, 1 - alpha, overlay, alpha, 0)
+
+    return output
+
+
+# The main function
+def transform(img):
+    mask=segment_image(img)
+    blended_img = overlay_mask(img, mask)
+    return blended_img
+
+
+# Create the Gradio app
+app = gr.Interface(
+    fn=transform, 
+    inputs=gr.Image(label="Input Image"), 
+    outputs=gr.Image(label="Image with Segmentation Overlay"), 
+    title="Image Segmentation on Pet Images",
+    description="Segment image of a pet animal into three classes: background, pet, and boundary.",
+    examples=[
+        "example_images/img1.jpg",
+        "example_images/img2.jpg",
+        "example_images/img3.jpg"
+    ]
+)
+                   
+# Run the app
+app.launch()

requirements.txt

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+tensorflow
+gradio
+opencv-python

saved_model/fingerprint.pb

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+size 56

saved_model/keras_metadata.pb

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+size 89706

saved_model/saved_model.pb

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+size 859084

saved_model/variables/variables.data-00000-of-00001

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+size 25947878

saved_model/variables/variables.index

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