app.py

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()