Guided-GRad-Cam.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Dec  9 21:59:13 2021

@author: kenaa
"""

import matplotlib.pyplot as plt
import cv2
from tensorflow.keras.models import Model
import tensorflow as tf
import numpy as np
import cv2

# Image taken from COV19-CT-DB database
image = cv2.imread('/home/kenaa/Desktop/COV19D/validation/non-covid/ct_scan_184/40.jpg', 0)
image = cv2.resize(image, (512, 512))
# Displaying the Image
plt.imshow(image, cmap="gray")
plt.show()

print(image.shape) # (1, 84, 84, 1)

# Predicting using our model described at 
# https://github.com/IDU-CVLab/COV19D/blob/main/COV19-CT-DB-CNN-model.py
model = keras.models.load_model('/home/kenaa/Desktop/COV19D/saved-models/model.h5')
preds = model.predict(image) 

 
## Guided Grad-Cam visualization
class GradCAM:
    def __init__(self, model, classIdx, layerName=None):
        # store the model, the class index used to measure the class
        # activation map, and the layer to be used when visualizing
        # the class activation map
        self.model = model
        self.classIdx = classIdx
        self.layerName = layerName
        # if the layer name is None, attempt to automatically find
        # the target output layer
        if self.layerName is None:
            self.layerName = self.find_target_layer()

    def find_target_layer(self):
        # attempt to find the final convolutional layer in the network
        # by looping over the layers of the network in reverse order
        for layer in reversed(self.model.layers):
            # check to see if the layer has a 4D output
            if len(layer.output_shape) == 4:
                return layer.name
        # otherwise, we could not find a 4D layer so the GradCAM
        # algorithm cannot be applied
        raise ValueError("Could not find 4D layer. Cannot apply GradCAM.")


    def compute_heatmap(self, image, eps=1e-8):
        # construct our gradient model by supplying (1) the inputs
        # to our pre-trained model, (2) the output of the (presumably)
        # final 4D layer in the network, and (3) the output of the
        # softmax activations from the model
        gradModel = Model(
            inputs=[self.model.inputs],
            outputs=[self.model.get_layer(self.layerName).output, self.model.output])

        # record operations for automatic differentiation
        with tf.GradientTape() as tape:
            # cast the image tensor to a float-32 data type, pass the
            # image through the gradient model, and grab the loss
            # associated with the specific class index
            inputs = tf.cast(image, tf.float32)
            (convOutputs, predictions) = gradModel(inputs)
            
            loss = predictions[:, tf.argmax(predictions[0])]
    
        # use automatic differentiation to compute the gradients
        grads = tape.gradient(loss, convOutputs)

        # compute the guided gradients
        castConvOutputs = tf.cast(convOutputs > 0, "float32")
        castGrads = tf.cast(grads > 0, "float32")
        guidedGrads = castConvOutputs * castGrads * grads
        # the convolution and guided gradients have a batch dimension
        # (which we don't need) so let's grab the volume itself and
        # discard the batch
        convOutputs = convOutputs[0]
        guidedGrads = guidedGrads[0]

        # compute the average of the gradient values, and using them
        # as weights, compute the ponderation of the filters with
        # respect to the weights
        weights = tf.reduce_mean(guidedGrads, axis=(0, 1))
        cam = tf.reduce_sum(tf.multiply(weights, convOutputs), axis=-1)

        # grab the spatial dimensions of the input image and resize
        # the output class activation map to match the input image
        # dimensions
        (w, h) = (image.shape[2], image.shape[1])
        heatmap = cv2.resize(cam.numpy(), (w, h))
        # normalize the heatmap such that all values lie in the range
        # [0, 1], scale the resulting values to the range [0, 255],
        # and then convert to an unsigned 8-bit integer
        numer = heatmap - np.min(heatmap)
        denom = (heatmap.max() - heatmap.min()) + eps
        heatmap = numer / denom
        heatmap = (heatmap * 255).astype("uint8")
        # return the resulting heatmap to the calling function
        return heatmap

    def overlay_heatmap(self, heatmap, image, alpha=0.5,
                        colormap=cv2.COLORMAP_VIRIDIS):
        # apply the supplied color map to the heatmap and then
        # overlay the heatmap on the input image
        heatmap = cv2.applyColorMap(heatmap, colormap)
        output = cv2.addWeighted(image, alpha, heatmap, 1 - alpha, 0)
        # return a 2-tuple of the color mapped heatmap and the output,
        # overlaid image
        return (heatmap, output)

# Defining the last sequantial convulutional layer of our model and resizing the heat map to the size of the images
icam = GradCAM(model, i, 'conv2d_3') 
heatmap = icam.compute_heatmap(image)
heatmap = cv2.resize(heatmap, (512, 512))

## REading images with adjusted demintionas
image = cv2.imread('/home/kenaa/Desktop/COV19D/validation/non-covid/ct_scan_184/40.jpg')
image = cv2.resize(image, (512, 512))
print(heatmap.shape, image.shape)

#O verlaying the heatmap
(heatmap, output) = icam.overlay_heatmap(heatmap, image, alpha=0.5)  

# Plotting the REsults
fig, ax = plt.subplots(1, 3)
ax[0].imshow(heatmap)
ax[1].imshow(image)
ax[2].imshow(output)