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| import matplotlib.cm as cm | |
| import numpy as np | |
| import tensorflow as tf | |
| from tensorflow import keras | |
| def make_gradcam_heatmap(img_array, grad_model, pred_index=None): | |
| with tf.GradientTape(persistent=True) as tape: | |
| preds, base_top, swin_top = grad_model(img_array) | |
| if pred_index is None: | |
| pred_index = tf.argmax(preds[0]) | |
| class_channel = preds[:, pred_index] | |
| grads = tape.gradient(class_channel, base_top) | |
| pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2)) | |
| base_top = base_top[0] | |
| heatmap_a = base_top @ pooled_grads[..., tf.newaxis] | |
| heatmap_a = tf.squeeze(heatmap_a) | |
| heatmap_a = tf.maximum(heatmap_a, 0) / tf.math.reduce_max(heatmap_a) | |
| heatmap_a = heatmap_a.numpy() | |
| grads = tape.gradient(class_channel, swin_top) | |
| pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2)) | |
| swin_top = swin_top[0] | |
| heatmap_b = swin_top @ pooled_grads[..., tf.newaxis] | |
| heatmap_b = tf.squeeze(heatmap_b) | |
| heatmap_b = tf.maximum(heatmap_b, 0) / tf.math.reduce_max(heatmap_b) | |
| heatmap_b = heatmap_b.numpy() | |
| return heatmap_a, heatmap_b, preds | |
| def save_and_display_gradcam( | |
| img, | |
| heatmap, | |
| target=None, | |
| pred=None, | |
| cam_path="cam.jpg", | |
| cmap="jet", # inferno, viridis | |
| alpha=0.6, | |
| plot=None, | |
| image_shape=None, | |
| ): | |
| # Rescale heatmap to a range 0-255 | |
| heatmap = np.uint8(255 * heatmap) | |
| # Use jet colormap to colorize heatmap | |
| jet = cm.get_cmap(cmap) | |
| # 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.utils.array_to_img(jet_heatmap) | |
| jet_heatmap = jet_heatmap.resize((img.shape[0], img.shape[1])) | |
| jet_heatmap = keras.utils.img_to_array(jet_heatmap) | |
| # Superimpose the heatmap on original image | |
| superimposed_img = img + jet_heatmap * alpha | |
| superimposed_img = keras.utils.array_to_img(superimposed_img) | |
| size_w, size_h = image_shape[:2] | |
| superimposed_img = superimposed_img.resize((size_h, size_w)) | |
| return superimposed_img | |