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import random | |
from keras.models import load_model | |
import gradio as gr | |
import matplotlib.pyplot as plt | |
import numpy as np | |
def jaccard_coef(y_true, y_pred): | |
y_true_flatten = K.flatten(y_true) | |
y_pred_flatten = K.flatten(y_pred) | |
intersection = K.sum(y_true_flatten * y_pred_flatten) | |
final_coef_value = (intersection + 1.0) / (K.sum(y_true_flatten) + K.sum(y_pred_flatten) - intersection + 1.0) | |
return final_coef_value | |
model = load_model('S_model.h5',custom_objects=({"jaccard_coef":jaccard_coef})) | |
image = gr.inputs.Image(shape = (256,256)) | |
def predict_image(image): | |
test_image_input = np.expand_dims(image, 0) | |
prediction = model.predict(test_image_input) | |
predicted_image = np.argmax(prediction, axis=3) | |
predicted_image = predicted_image[0,:,:] | |
fig = plt.figure() | |
plt.imshow(predicted_image) | |
return fig | |
gr.Interface(fn = predict_image,inputs = image, outputs=['plot'],interpretation = 'default').launch(share = True) |