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c7ad528 3a0ba35 c7ad528 3a0ba35 c7ad528 3a0ba35 c7ad528 3d2c8e5 c7ad528 3a0ba35 c7ad528 3d2c8e5 c7ad528 3d2c8e5 3a0ba35 c7ad528 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | 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):
# Preprocess the input image
image = preprocess_image(image, size)
# Expand dimensions to match model input shape
image = np.expand_dims(image, 0)
# Predict the mask using the model
output = model.predict(image, verbose=0)
mask_image = output[0]
mask_image = np.squeeze(mask_image, -1)
# Convert the mask to the desired format for Gradio
mask_image *= 255
mask_image = mask_image.astype(np.uint8)
# Calculate the percentage of class 0 and class 1
positive_pixels = np.count_nonzero(mask_image)
total_pixels = mask_image.shape[0] * mask_image.shape[1]
percentage = (positive_pixels / total_pixels) * 100
# Calculate the percentages of class 0 and class 1
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")
],
examples=[["benign.png"], ["malignant .png"]],
title = '<h1 style="text-align: center;">Breast Cancer </h1>',
description = """
Check out this exciting development in the field of breast cancer diagnosis and treatment!
A demo of Breast Cancer Ultrasound Image Segmentation has been developed.
Upload image file, or try out one of the examples below! 🙌
""",
theme="default",
layout="vertical",
verbose=True
).launch(debug=True) |