Create app.py
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app.py
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import os
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import uuid
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import cv2
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import numpy as np
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import gradio as gr
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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from modelscope.outputs import OutputKeys
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from gradio_imageslider import ImageSlider
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# Initialize the image colorization pipeline with a specified model revision
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img_colorization = pipeline(
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task=Tasks.image_colorization,
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model='iic/cv_ddcolor_image-colorization',
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model_revision='v1.02' # Specify the model revision to avoid warnings
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)
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def colorize_image(input_image):
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"""
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Colorizes a grayscale image using the DDColor model.
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Args:
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input_image (numpy.ndarray): Grayscale input image.
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Returns:
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tuple: Original and colorized images.
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"""
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# Convert RGB to BGR for OpenCV compatibility
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bgr_image = cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR)
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# Perform colorization
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output = img_colorization(bgr_image)
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colorized_bgr = output[OutputKeys.OUTPUT_IMG].astype(np.uint8)
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# Convert BGR back to RGB for display
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colorized_rgb = cv2.cvtColor(colorized_bgr, cv2.COLOR_BGR2RGB)
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return input_image, colorized_rgb
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# Define the Gradio interface
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title = "DDColor: Photo-Realistic Image Colorization"
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description = (
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"Upload a black & white image to colorize it using the DDColor model. "
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"This application utilizes a dual-decoder architecture to produce vivid and natural colorizations."
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)
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# Example images (ensure these paths are correct or adjust accordingly)
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examples = [['./input.jpg']]
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# Create the Gradio interface
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demo = gr.Interface(
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fn=colorize_image,
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inputs=gr.Image(label="Upload Grayscale Image"),
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outputs=ImageSlider(position=0.5, label='Colorization Comparison'),
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examples=examples,
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title=title,
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description=description,
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allow_flagging="never"
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)
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if __name__ == "__main__":
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# Launch the Gradio app with production settings
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demo.launch(server_name="0.0.0.0", server_port=7860, share=False)
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