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transformers==4.45.2
import gradio as gr
import transformers
from transformers import MobileViTFeatureExtractor, MobileViTForSemanticSegmentation
from PIL import Image
import numpy as np
feature_extractor = MobileViTFeatureExtractor.from_pretrained("apple/deeplabv3-mobilevit-small")
model = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-small")
#(21 classes)
COLORS = np.array([
[0, 0, 0],
[128, 0, 0],
[0, 128, 0],
[128, 128, 0],
[0, 0, 128],
[128, 0, 128],
[0, 128, 128],
[128, 128, 128],
[64, 0, 0],
[192, 0, 0],
[64, 128, 0],
[192, 128, 0],
[64, 0, 128],
[192, 0, 128],
[64, 128, 128],
[192, 128, 128],
[0, 64, 0],
[128, 64, 0],
[0, 192, 0],
[128, 192, 0],
[0, 64, 128],
[128, 64, 128]
], dtype=np.uint8) # Ensure the data type is uint8 for image processing
def segment_image(image):
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_mask = logits.argmax(1).squeeze(0).numpy()
colored_mask = COLORS[predicted_mask]
colored_mask_image = Image.fromarray(colored_mask)
colored_mask_resized = colored_mask_image.resize(image.size, Image.NEAREST)
return colored_mask_resized
interface = gr.Interface(
fn=segment_image,
inputs=gr.Image(type="pil"),
outputs="image",
title="Image Segmentation with MobileViT",
description="Upload an image to see the semantic segmentation result. The segmentation mask uses different colors to indicate different classes.",
)
interface.launch(share=True)
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