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| from layers import BilinearUpSampling2D | |
| from tensorflow.keras.models import load_model | |
| from utils import load_images, predict | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import gradio as gr | |
| custom_objects = {'BilinearUpSampling2D': BilinearUpSampling2D, 'depth_loss_function': None} | |
| print('Loading model...') | |
| model = load_model("model/model.h5", custom_objects=custom_objects, compile=False) | |
| print('Successfully loaded model...') | |
| examples = ['examples/00015_colors.png', 'examples/00084_colors.png', 'examples/00033_colors.png'] | |
| def infer(image): | |
| inputs = load_images([image]) | |
| outputs = predict(model, inputs) | |
| plasma = plt.get_cmap('plasma') | |
| rescaled = outputs[0][:, :, 0] | |
| rescaled = rescaled - np.min(rescaled) | |
| rescaled = rescaled / np.max(rescaled) | |
| image_out = plasma(rescaled)[:, :, :3] | |
| return image_out | |
| iface = gr.Interface( | |
| fn=infer, | |
| inputs=[gr.inputs.Image(label="image", type="numpy", shape=(640, 480))], | |
| outputs="image", | |
| examples=examples).launch(debug=True) | |