def superImpose(self, data, heatMapImages, muSigPair):
superImposedImages = []
images = Utility.toImages(data, muSigPair)
for i, image in enumerate(images):
image = np.uint8(255 * image)
heatmap = 1 - heatMapImages[i]
heatmap = np.uint8(255 * heatmap.squeeze())
heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
superImposed = cv2.addWeighted(image, 0.5, heatmap, 0.5, 0)
superImposedImages.append(superImposed)
return superImposedImages
def visualize(self,
data,
data_targets,
classes,
count=5,
muSigPair=None,
figSize=(15, 15)):
heatmaps, cam_pred = self.gradCam(data)
randIndices = Utility.pickRandomElements(data, count)
rand_data, rand_targets, rand_cam_pred = data[
randIndices], data_targets[randIndices], cam_pred[randIndices]
rand_superImposedImages = {}
for layer in heatmaps:
rand_superImposedImages[layer] = self.superImpose(
rand_data, heatmaps[layer], muSigPair)
self.plot(Utility.toImages(rand_data, muSigPair), rand_targets,
rand_cam_pred, rand_superImposedImages, classes, figSize)