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| import numpy as np | |
| import gradio as gr | |
| from huggingface_hub import from_pretrained_keras | |
| import keras | |
| import tensorflow as tf | |
| model = from_pretrained_keras("keras-io/lowlight-enhance-mirnet", compile=False) | |
| examples = ['examples/1.png', 'examples/55.png', 'examples/665.png', 'examples/778.png'] | |
| def infer(original_image): | |
| image = tf.keras.preprocessing.image.img_to_array(original_image) | |
| image = image.astype("float32") / 255.0 | |
| image = np.expand_dims(image, axis=0) | |
| output = model.predict(image) | |
| output_image = output[0] * 255.0 | |
| output_image = output_image.clip(0, 255) | |
| output_image = output_image.reshape( | |
| (np.shape(output_image)[0], np.shape(output_image)[1], 3) | |
| ) | |
| output_image = np.uint32(output_image) | |
| return output_image | |
| iface = gr.Interface( | |
| fn=infer, | |
| title="Low light image enhancement", | |
| description = "MIRNet model for light up the dark image 🌆🎆", | |
| inputs=[gr.inputs.Image(label="image", type="pil", shape=(960, 640))], | |
| outputs="image", | |
| cache_examples=True, | |
| article = "Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>.", | |
| examples=examples).launch(enable_queue=True) |