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import tensorflow as tf |
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from simple_unet_model import simple_unet_model |
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from tensorflow.keras.utils import normalize |
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import os |
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from PIL import Image, ImageOps |
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import numpy as np |
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import gradio as gr |
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def get_model(): |
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return simple_unet_model(256, 256, 1) |
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model = get_model() |
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model.load_weights('mitochondria.hdf5') |
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def predict(input_image): |
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img = Image.fromarray(input_image) |
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gray_img = ImageOps.grayscale(img) |
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resized_img = gray_img.resize((256,256)) |
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img = np.array(resized_img) |
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img = np.expand_dims(img, axis = (0,3)) |
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img = normalize(img, axis=1) |
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mask = model.predict(img)[0,:,:,0] |
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return mask |
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def load_examples(): |
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files = os.listdir() |
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img_list = [] |
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for file in files: |
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if '.jpg' in file: |
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img_list.append(file) |
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return img_list |
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examples = load_examples() |
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demo = gr.Interface(fn=predict, |
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inputs="image", |
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outputs=gr.Image(shape=(256, 256)), |
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title = "Mitochondria Detection", |
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examples =examples ) |
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demo.launch() |
