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| import numpy as np | |
| import gradio as gr | |
| from PIL import Image | |
| import tensorflow as tf | |
| from tensorflow import keras | |
| from huggingface_hub import from_pretrained_keras | |
| model = from_pretrained_keras("Harveenchadha/low-light-image-enhancement", compile=False) | |
| #examples = ['examples/179.png', 'examples/493.png', 'examples/780.png'] | |
| def get_enhanced_image(data, output): | |
| r1 = output[:, :, :, :3] | |
| r2 = output[:, :, :, 3:6] | |
| r3 = output[:, :, :, 6:9] | |
| r4 = output[:, :, :, 9:12] | |
| r5 = output[:, :, :, 12:15] | |
| r6 = output[:, :, :, 15:18] | |
| r7 = output[:, :, :, 18:21] | |
| r8 = output[:, :, :, 21:24] | |
| x = data + r1 * (tf.square(data) - data) | |
| x = x + r2 * (tf.square(x) - x) | |
| x = x + r3 * (tf.square(x) - x) | |
| enhanced_image = x + r4 * (tf.square(x) - x) | |
| x = enhanced_image + r5 * (tf.square(enhanced_image) - enhanced_image) | |
| x = x + r6 * (tf.square(x) - x) | |
| x = x + r7 * (tf.square(x) - x) | |
| enhanced_image = x + r8 * (tf.square(x) - x) | |
| return enhanced_image | |
| def infer(original_image): | |
| image = 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 = get_enhanced_image(image, output) | |
| output_image = output[0] * 255.0 | |
| output_image = output_image.clip(0, 255) | |
| print(output_image) | |
| print(len(output_image)) | |
| print([len(a) for a in output_image]) | |
| 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 | |
| # output_image = tf.cast((output[0, :, :, :] * 255), dtype=np.uint8) | |
| # #output_image = Image.fromarray(output_image.numpy()) | |
| # output_image = output_image.numpy() | |
| # print(output_image.shape()) | |
| return output_image | |
| iface = gr.Interface( | |
| fn=infer, | |
| title="Low Light Image Enhancement", | |
| description = "Keras Implementation of MIRNet model for light up the dark image 🌆🎆", | |
| inputs=[gr.inputs.Image(label="image", type="pil")], | |
| outputs=[gr.outputs.Image(label="image", type="numpy")], | |
| #examples=examples, | |
| article = "Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>. Based on the keras example from <a href=\"https://keras.io/examples/vision/mirnet/\">Soumik Rakshit</a>", | |
| ).launch(debug=True) |