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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()
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