occlusion_1.py

import numpy as np

from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Activation, Conv2D, MaxPooling2D
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.preprocessing.image import ImageDataGenerator
from keras.activations import relu

%matplotlib inline
import matplotlib.pyplot as plt
def iter_occlusion(image, size=8):

    occlusion = np.full((size * 5, size * 5, 1), [0.5], np.float32)
    occlusion_center = np.full((size, size, 1), [0.5], np.float32)
    occlusion_padding = size * 2

    # print('padding...')
    image_padded = np.pad(image, ( \
                        (occlusion_padding, occlusion_padding), (occlusion_padding, occlusion_padding), (0, 0) \
                        ), 'constant', constant_values = 0.0)

    for y in range(occlusion_padding, image.shape[0] + occlusion_padding, size):

        for x in range(occlusion_padding, image.shape[1] + occlusion_padding, size):
            tmp = image_padded.copy()

            tmp[y - occlusion_padding:y + occlusion_center.shape[0] + occlusion_padding, \
                x - occlusion_padding:x + occlusion_center.shape[1] + occlusion_padding] \
                = occlusion

            tmp[y:y + occlusion_center.shape[0], x:x + occlusion_center.shape[1]] = occlusion_center

            yield x - occlusion_padding, y - occlusion_padding, \
                  tmp[occlusion_padding:tmp.shape[0] - occlusion_padding, occlusion_padding:tmp.shape[1] - occlusion_padding]

occlusion_2.py

from keras.preprocessing.image import load_img
# load an image from file
image = load_img('car.jpeg', target_size=(224, 224))
plt.imshow(image)
plt.title('ORIGINAL IMAGE')

occlusion_3.py

from keras.preprocessing.image import img_to_array
from keras.applications.vgg16 import preprocess_input
# convert the image pixels to a numpy array
image = img_to_array(image)
# reshape data for the model
image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
# prepare the image for the VGG model
image = preprocess_input(image)
# predict the probability across all output classes
yhat = model.predict(image)
temp = image[0]
print(temp.shape)
heatmap = np.zeros((224,224))
correct_class = np.argmax(yhat)
for n,(x,y,image) in enumerate(iter_occlusion(temp,14)):
    heatmap[x:x+14,y:y+14] = model.predict(image.reshape((1, image.shape[0], image.shape[1], image.shape[2])))[0][correct_class]
    print(x,y,n,' - ',image.shape)
heatmap1 = heatmap/heatmap.max()
plt.imshow(heatmap)

occlusion_4.py

import skimage.io as io
#creating mask from the standardised heatmap probabilities
mask = heatmap1 < 0.85
mask1 = mask *256
mask = mask.astype(int)
io.imshow(mask,cmap='gray')

occlusion_5.py

import cv2
#read the image
image = cv2.imread('car.jpeg')
image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
#resize image to appropriate dimensions
image = cv2.resize(image,(224,224))
mask = mask.astype('uint8')
#apply the mask to the image
final = cv2.bitwise_and(image,image,mask = mask)
final = cv2.cvtColor(final,cv2.COLOR_BGR2RGB)
#plot the final image
plt.imshow(final)