Perform occlusion sensitivity with Tensorflow 2.0

occlusion_sensitivity.py

import numpy as np
import tensorflow as tf

# Create function to apply a grey patch on an image
def apply_grey_patch(image, top_left_x, top_left_y, patch_size):
    patched_image = np.array(image, copy=True)
    patched_image[top_left_y:top_left_y + patch_size, top_left_x:top_left_x + patch_size, :] = 127.5

    return patched_image

# Load image
IMAGE_PATH = './cat.jpg'
img = tf.keras.preprocessing.image.load_img(IMAGE_PATH, target_size=(224, 224))
img = tf.keras.preprocessing.image.img_to_array(img)

# Instantiate model
model = tf.keras.applications.resnet50.ResNet50(weights='imagenet', include_top=True)

CAT_CLASS_INDEX = 281  # Imagenet tabby cat class index
PATCH_SIZE = 40

sensitivity_map = np.zeros((img.shape[0], img.shape[1]))

# Iterate the patch over the image
for top_left_x in range(0, img.shape[0], PATCH_SIZE):
    for top_left_y in range(0, img.shape[1], PATCH_SIZE):
        patched_image = apply_grey_patch(img, top_left_x, top_left_y, PATCH_SIZE)
        predicted_classes = model.predict(np.array([patched_image]))[0]
        confidence = predicted_classes[CAT_CLASS_INDEX]
        
        # Save confidence for this specific patched image in map
        sensitivity_map[
            top_left_y:top_left_y + PATCH_SIZE,
            top_left_x:top_left_x + PATCH_SIZE,
        ] = confidence