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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