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import tensorflow as tf
from tensorflow import keras
from keras.models import load_model
from keras.preprocessing import image
import numpy as np
from PIL import Image
# Display
#from IPython.display import Image, display
#import matplotlib.pyplot as plt
import matplotlib.cm as cm
class ModeloCovid:
#Constructor de clase, importa el modelo y guarda un diccionario con las etiquetas de clase
def __init__(self):
self.model = load_model("./modeloGrey9M.h5")
self.num2label = {0:'COVID-19', 1:'NORMAL', 2:'Viral Pneumonia'}
self.dims = self.model.input_shape[1:3]
#CON UNA IMAGEN Y LAS DIMENSIONES LA LEE DE FORMA ADECUADA
def _get_img_array(self, img_path, size):
# `img` is a PIL image of size 299x299
img = keras.preprocessing.image.load_img(img_path, target_size=size, color_mode="grayscale")
# `array` is a float32 Numpy array of shape (299, 299, 3)
array = keras.preprocessing.image.img_to_array(img)
# We add a dimension to transform our array into a "batch"
# of size (1, 299, 299, 3)
array = np.expand_dims(array, axis=0)
return array
def _make_gradcam_heatmap(self,img_array, model, last_conv_layer_name, pred_index=None):
# First, we create a model that maps the input image to the activations
# of the last conv layer as well as the output predictions
grad_model = tf.keras.models.Model(
[model.inputs], [model.get_layer(last_conv_layer_name).output, model.output]
)
# Then, we compute the gradient of the top predicted class for our input image
# with respect to the activations of the last conv layer
with tf.GradientTape() as tape:
last_conv_layer_output, preds = grad_model(img_array)
if pred_index is None:
pred_index = tf.argmax(preds[0])
class_channel = preds[:, pred_index]
# This is the gradient of the output neuron (top predicted or chosen)
# with regard to the output feature map of the last conv layer
grads = tape.gradient(class_channel, last_conv_layer_output)
# This is a vector where each entry is the mean intensity of the gradient
# over a specific feature map channel
pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
# We multiply each channel in the feature map array
# by "how important this channel is" with regard to the top predicted class
# then sum all the channels to obtain the heatmap class activation
last_conv_layer_output = last_conv_layer_output[0]
heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]
heatmap = tf.squeeze(heatmap)
# For visualization purpose, we will also normalize the heatmap between 0 & 1
heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)
return heatmap.numpy()
#A partir de una imagen retorna su clase mas problable tras la prediccion del modelo
def predict(self,img_path):
img = image.load_img(img_path,target_size=self.dims, color_mode="grayscale")
img = np.asarray(img)/255.0
img = np.expand_dims(img, axis=0)
img = np.expand_dims(img, axis=3)
output = self.model.predict(img)#Aqui esta el vector de probabilidades
prediction = np.argmax(output, axis=1).astype(np.int)
return self.num2label[prediction[0]],output
def gradCam(self, img_path, cam_path="./static/GRADCAM_img/cam.jpg"):
model_builder = keras.applications.xception.Xception
preprocess_input = keras.applications.xception.preprocess_input
decode_predictions = keras.applications.xception.decode_predictions
last_conv_layer_name = "conv2d_9"
img_array = preprocess_input(self._get_img_array(img_path, size=self.dims))
# Remove last layer's softmax
_model=self.model
_model.layers[-1].activation = None
heatmap = self._make_gradcam_heatmap(img_array, _model, last_conv_layer_name)
superimposed_img = self._save_and_display_gradcam(img_path,heatmap,cam_path)
return heatmap, superimposed_img
def _save_and_display_gradcam(self,img_path, heatmap, cam_path, alpha=0.4):
# Load the original image
img = keras.preprocessing.image.load_img(img_path, color_mode="grayscale")
img = keras.preprocessing.image.img_to_array(img)
# Rescale heatmap to a range 0-255
heatmap = np.uint8(255 * heatmap)
# Use jet colormap to colorize heatmap
jet = cm.get_cmap("jet")
# Use RGB values of the colormap
jet_colors = jet(np.arange(256))[:, :3]
jet_heatmap = jet_colors[heatmap]
# Create an image with RGB colorized heatmap
jet_heatmap = keras.preprocessing.image.array_to_img(jet_heatmap)
jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))
jet_heatmap = keras.preprocessing.image.img_to_array(jet_heatmap)
# Superimpose the heatmap on original image
superimposed_img = jet_heatmap * alpha + img
superimposed_img = keras.preprocessing.image.array_to_img(superimposed_img)
# Save the superimposed image
superimposed_img.save(cam_path)
# Display Grad CAM
return superimposed_img