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import tensorflow as tf
from tensorflow.keras.models import load_model, Model
import cv2
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cm as cm
class GradCam:
def __init__(self, model, img, last_conv_layer_name, pred_index=None):
self.model = model
self.img_path = img
self.last_conv_layer_name = last_conv_layer_name
def make_gradcam_heatmap(self, 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
img_array= self.img_path
grad_model = tf.keras.models.Model(
[self.model.inputs], [self.model.get_layer(self.last_conv_layer_name).output, self.model.output]
)
# 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
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()
def save_and_display_gradcam(self, cam_path="cam.jpg", alpha=0.4):
heatmap = self.make_gradcam_heatmap()
# Load the original image
img = self.img_path
# Rescale the heatmap to a range 0-255
heatmap = np.uint8(255 * heatmap)
# Use the jet colormap to colorize the heatmap
jet = cm.get_cmap("jet")
jet_colors = jet(np.arange(512))[:, :3]
jet_heatmap = jet_colors[heatmap]
# Create an image with the RGB heatmap
jet_heatmap = tf.keras.preprocessing.image.array_to_img(jet_heatmap)
jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))
jet_heatmap = tf.keras.preprocessing.image.img_to_array(jet_heatmap)
# Superimpose the heatmap on the original image
superimposed_img = jet_heatmap * alpha + img
superimposed_img = tf.keras.preprocessing.image.array_to_img(superimposed_img)
# Save and display the image
superimposed_img.save(cam_path)
plt.imshow(superimposed_img)
plt.axis('off')
plt.show()
plt.savefig(path)
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