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from numpy.core.fromnumeric import size
from base_explainer import BaseExplainer
import tensorflow.keras as keras
import tensorflow as tf
import numpy as np
from PIL import Image
import matplotlib.cm as cm
import cv2
class GradCAMExplainer(BaseExplainer):
#implementacao do metodo abstrato
def get_explanation(self, img, model, img_size, props, preprocess_input = None, index=None):
#clona o modelo e remove a softmax da ultima camada
clone = tf.keras.models.clone_model(model)
clone.layers[-1].activation = None
#cria modelo grad-cam
grad_model = tf.keras.models.Model([clone.inputs], [clone.get_layer(props["conv_layer"]).output, clone.output])
#transforma a imagem em array
img_array = self.get_img_array(img, size = img_size)
#pre processa a imagem
if preprocess_input:
img_procecessed_array = preprocess_input(img_array)
else:
img_procecessed_array = img_array
#faz o heatmap
heatmap = self.__make_gradcam_heatmap(img_procecessed_array, grad_model, props["conv_layer"], pred_index=index)
#poe o heatmap na imagem
heat, mask = self.__save_and_display_gradcam(img, heatmap)
return keras.preprocessing.image.array_to_img(heat)
#transforma a imagem em array
def get_img_array(self, img_path, size, expand=True):
# `img` is a PIL image of size 299x299
img = keras.preprocessing.image.load_img(img_path, target_size=size)
# `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)
if expand:
array = np.expand_dims(array, axis=0)
return array
def __make_gradcam_heatmap(self, img_array, grad_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)
heatmap = heatmap.numpy()
return heatmap
def __save_and_display_gradcam(self, img_path, heatmap, cam_path="cam.jpg", alpha=0.4):
# Load the original image
img = keras.preprocessing.image.load_img(img_path)
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)
im = Image.fromarray(heatmap)
im = im.resize((img.shape[1], img.shape[0]))
im = np.asarray(im)
im = np.where(im > 0, 1, im)
# Superimpose the heatmap on original image
superimposed_img = jet_heatmap * alpha + img
return superimposed_img, im |