app.py

import gradio as gr
from transformers import pipeline
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt

# Load the segmentation pipeline
pipe = pipeline("image-segmentation", model="mattmdjaga/segformer_b2_clothes")

# Save the example image locally
url = "https://plus.unsplash.com/premium_photo-1673210886161-bfcc40f54d1f?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8cGVyc29uJTIwc3RhbmRpbmd8ZW58MHx8MHx8&w=1000&q=80"
image = Image.open(requests.get(url, stream=True).raw)
image.save("example_image.jpg")  # Save the image locally

# Your predefined label dictionary
label_dict = {
    0: "Background",
    1: "Hat",
    2: "Hair",
    3: "Sunglasses",
    4: "Upper-clothes",
    5: "Skirt",
    6: "Pants",
    7: "Dress",
    8: "Belt",
    9: "Left-shoe",
    10: "Right-shoe",
    11: "Face",
    12: "Left-leg",
    13: "Right-leg",
    14: "Left-arm",
    15: "Right-arm",
    16: "Bag",
    17: "Scarf",
}

# Function to process the image and generate the segmentation map
def segment_image(image):
    # Perform segmentation
    result = pipe(image)
    
    # Initialize an empty array for the segmentation map
    image_width, image_height = result[0]["mask"].size
    segmentation_map = np.zeros((image_height, image_width), dtype=np.uint8)
    
    # Combine masks into a single segmentation map
    for idx, entry in enumerate(result):
        mask = np.array(entry["mask"])  # Convert the PIL mask to a NumPy array
        segmentation_map[mask > 0] = idx  # Assign the class index
    
    # Create a matplotlib figure and visualize the segmentation map
    plt.figure(figsize=(8, 8))
    plt.imshow(segmentation_map, cmap="tab20")  # Visualize using a colormap
    cbar = plt.colorbar(ticks=range(len(label_dict)), label="Classes")
    cbar.ax.set_yticklabels([label_dict[i] for i in range(len(label_dict))])
    plt.title("Combined Segmentation Map")
    plt.axis("off")
    
    # Save the figure as a PIL image for Gradio
    plt.savefig("segmented_output.png", bbox_inches="tight")  # Save as a temporary file
    plt.close()  # Close the figure to free memory
    return Image.open("segmented_output.png")

# Gradio interface
interface = gr.Interface(
    fn=segment_image,
    inputs=gr.Image(type="pil"),  # Input is an image
    outputs=gr.Image(type="pil"),  # Output is an image with the colormap
    examples=["example_image.jpg"],  # Use the saved image as an example
    title="Image Segmentation with Colormap",
    description="Upload an image, and the segmentation model will produce an output with a colormap applied to the segmented classes."
)

# Launch the app
interface.launch()