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# -*- coding: utf-8 -*-
"""
Created on Tue Dec 27 08:48:25 2022
@author: Usuario
"""
from keras.models import load_model
import tensorflow as tf
from tensorflow.keras.utils import load_img, img_to_array, array_to_img
from keras.preprocessing.image import ImageDataGenerator
from keras.applications.vgg19 import preprocess_input, decode_predictions
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import Image, display
import matplotlib.cm as cm
#http://gradcam.cloudcv.org/
#https://keras.io/examples/vision/grad_cam/
def get_img_array(img_path, size):
# `img` is a PIL image of size 299x299
img = load_img(img_path, target_size=size)
# `array` is a float32 Numpy array of shape (299, 299, 3)
array = 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(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()
# Generate class activation heatmap
#heatmap = make_gradcam_heatmap(img_array, model, last_conv_layer_name)
def save_and_display_gradcam(img_path, heatmap, alpha = 0.4):
# Load the original image
img = load_img(img_path)
img = 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 = array_to_img(jet_heatmap)
jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))
jet_heatmap = img_to_array(jet_heatmap)
# Superimpose the heatmap on original image
superimposed_img = jet_heatmap * alpha + img
superimposed_img = array_to_img(superimposed_img)
# Save the superimposed image
#superimposed_img.save('')
# Display Grad CAM
return superimposed_img
#display(Image(superimposed_img))
#save_and_display_gradcam(path_image+name_image, heatmap) |