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