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import gradio as gr |
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from PIL import Image |
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import numpy as np |
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import cv2 |
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from keras.models import Model |
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from keras.layers import Input, Conv2D, MaxPooling2D, Conv2DTranspose, concatenate |
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size = 128 |
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def preprocess_image(image, size=128): |
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image = image.resize((size, size)) |
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image = image.convert("L") |
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image = np.array(image) / 255.0 |
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return image |
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def conv_block(input, num_filters): |
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conv = Conv2D(num_filters, (3, 3), activation="relu", padding="same", kernel_initializer='he_normal')(input) |
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conv = Conv2D(num_filters, (3, 3), activation="relu", padding="same", kernel_initializer='he_normal')(conv) |
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return conv |
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def encoder_block(input, num_filters): |
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conv = conv_block(input, num_filters) |
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pool = MaxPooling2D((2, 2))(conv) |
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return conv, pool |
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def decoder_block(input, skip_features, num_filters): |
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uconv = Conv2DTranspose(num_filters, (2, 2), strides=2, padding="same")(input) |
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con = concatenate([uconv, skip_features]) |
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conv = conv_block(con, num_filters) |
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return conv |
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def build_model(input_shape): |
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input_layer = Input(input_shape) |
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s1, p1 = encoder_block(input_layer, 64) |
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s2, p2 = encoder_block(p1, 128) |
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s3, p3 = encoder_block(p2, 256) |
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s4, p4 = encoder_block(p3, 512) |
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b1 = conv_block(p4, 1024) |
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d1 = decoder_block(b1, s4, 512) |
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d2 = decoder_block(d1, s3, 256) |
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d3 = decoder_block(d2, s2, 128) |
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d4 = decoder_block(d3, s1, 64) |
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output_layer = Conv2D(1, 1, padding="same", activation="sigmoid")(d4) |
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model = Model(input_layer, output_layer, name="U-Net") |
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model.load_weights('modelo.h5') |
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return model |
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def preprocess_image(image, size=128): |
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image = cv2.resize(image, (size, size)) |
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image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) |
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image = image / 255. |
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return image |
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def segment(image): |
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image = preprocess_image(image, size=size) |
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image = np.expand_dims(image, 0) |
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output = model.predict(image, verbose=0) |
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mask_image = output[0] |
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mask_image = np.squeeze(mask_image, -1) |
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mask_image *= 255 |
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mask_image = mask_image.astype(np.uint8) |
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mask_image = Image.fromarray(mask_image).convert("L") |
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positive_pixels = np.count_nonzero(mask_image) |
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total_pixels = mask_image.size[0] * mask_image.size[1] |
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percentage = (positive_pixels / total_pixels) * 100 |
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class_0_percentage = 100 - percentage |
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class_1_percentage = percentage |
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return mask_image, class_0_percentage, class_1_percentage |
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if __name__ == "__main__": |
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model = build_model(input_shape=(size, size, 1)) |
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gr.Interface( |
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fn=segment, |
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inputs="image", |
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outputs=[ |
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gr.Image(type="pil", label="Breast Cancer Mask"), |
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gr.Number(label="Benigno"), |
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gr.Number(label="Maligno") |
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], |
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title = '<h1 style="text-align: center;"> Cancer ultrasonido de Cancer de Mama </h1>', |
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description = """ |
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Presentamos la demostración de Segmentación de Imágenes por Ultrasonido de Cáncer de Mama. |
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""", |
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theme="default", |
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layout="vertical", |
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verbose=True |
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).launch(debug=True) |
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if __name__ == "__main__": |
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model = build_model(input_shape=(size, size, 1)) |
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gr.Interface( |
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fn=image_segmentation, |
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inputs="image", |
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outputs=[ |
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gr.Image(type="pil", label="Máscara de Cáncer de Mama"), |
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gr.Number(label="Benigno"), |
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gr.Number(label="Maligno") |
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], |
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title='<h1 style="text-align: center;">Segmentación de Ultrasonidos de Cáncer de Mama</h1>', |
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description=""" |
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Presentamos la demostración de Segmentación de Imágenes por Ultrasonido de Cáncer de Mama. |
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""", |
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examples=[ |
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['benign(10).png'], |
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['benign(109).png'], |
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['malignant.png'] |
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], |
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theme="default", |
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layout="vertical", |
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verbose=True |
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).launch(debug=True) |
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