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import gradio as gr |
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import numpy as np |
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from tensorflow.keras.preprocessing.image import load_img, img_to_array |
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from tensorflow.keras.models import load_model |
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from PIL import Image |
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import matplotlib.pyplot as plt |
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i1 = gr.inputs.Image(shape=(256, 256)) |
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o1 = gr.outputs.Image() |
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o2 = gr.outputs.Image() |
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gen_model = load_model('256_model_250ep.h5') |
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def colorify(pixels): |
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pixels = (pixels - 127.5) / 127.5 |
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pixels = np.expand_dims(pixels, 0) |
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gen_image = gen_model.predict(pixels) |
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gen_image = (gen_image + 1) / 2 |
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return Image.fromarray((gen_image[0] * 255.0).astype(np.uint16)) |
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title = "Colorify" |
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description = "Recolor your images using this lite version of PIX2PIX GAN , model is trained on 700 randomly collected images from the internet with 256*256 pixels. Due to the above constraint please note that the resolution of your images will decrease" |
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examples=[['example1.png'],['example2.jpg']] |
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article = "<p style='text-align: center'>" |
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gr.Interface(fn=colorify, inputs=i1, outputs=o1, title=title, description=description, article=article, examples=examples, enable_queue=True).launch() |
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