GradCAM heatmap

def getHeatmapGradCAM(img, eps=1e-8, alpha = .3):
    # load up graph and session
    # global graph
    # global sess

    # load image
    originalImage = img.convert('RGB') # load up image
    resizedImage = originalImage.resize((224, 224)) # resize image

    # preprocess and save rbg img arr
    rgbArr = np.array(resizedImage) # arrayfy
    rgbArr = rgbArr[np.newaxis, :, :, :] # batchify
    rgbArrPreproc = tf.keras.applications.densenet.preprocess_input(rgbArr) # preproc densenet

    # save gray img arr
    grayArr = np.array(resizedImage.convert('L')) # convert to gray arr

    # get final conv layer
    finalLayerName = 'relu'

    with tf.device('cpu:0'):
        with graph.as_default():
            tf.compat.v1.keras.backend.set_session(sess)

            # get gradients
            grads = tf.keras.backend.gradients(model.output[:, 0], model.get_layer(finalLayerName).output)[0]

            # make grad func
            gradient_function = tf.keras.backend.function([model.input], [model.get_layer(finalLayerName).output, grads])

            # compute grads
            output, grads_val = gradient_function([rgbArrPreproc])

    output, grads_val = output[0, :], grads_val[0, :, :, :]

    weights = np.mean(grads_val, axis=(0, 1))
    cam = np.dot(output, weights)

    # resize and normalize
    heatmap = cv2.resize(np.array(cam), (224, 224))
    numer = heatmap - np.min(heatmap)
    denom = (heatmap.max() - heatmap.min()) + eps
    heatmap = numer / denom
    heatmap = np.uint8(heatmap * 255)

    # visualize findings
    # original image arr = grayArr
    # heatmap image arr = heatmap
    plt.figure(figsize = (8, 8))
    plt.imshow(grayArr, cmap = 'gray')
    plt.imshow(heatmap, cmap = 'jet', alpha = alpha)

    plt.savefig('heatmap.png')