run https://github.com/deruhat/NIvsCG-keras on images

run.py

from __future__ import division
import math
import numpy as np
import tensorflow as tf
from PIL import Image

net_model = tf.keras.models.load_model('NIvsCG_sgd_lr-1e-5_WITH_REDUCE-LR.28-0.29.h5')

## images here
images = []

for image_path in images:
    img = Image.open(image_path)
    img.load()
    #load full image
    data = np.asarray( img, dtype="int32" )
    (width,height,rgb) = data.shape
    patch_size = min(width, height, 233)
    (patch_width, patch_height) = (patch_size, patch_size)
    #print(image_path, width, height)
    #split in patches
    len = 0
    sum = 0
    for x in range(math.ceil(width / patch_width)):
        for y in range(math.ceil(width / patch_height)):
            len=len+1
            xstart = x * patch_width
            xend = (x+1) * patch_width
            if xend > width:
                xstart = width - patch_width
                xend = width
            ystart = y * patch_height
            yend = (y+1) * patch_height
            if yend > height:
                ystart = height - patch_height
                yend = height
            sliced = data[xstart:xend,ystart:yend,:]
            #print(image_path, xstart, xend, ystart, yend, data.shape)
            image = sliced.reshape((1,) + sliced.shape)
            #compute prediction
            output = net_model.predict([image], batch_size = 1)
            sum = sum + output[0].argmax()
            #print(image_path, x, y,  output)
    print(image_path, sum/len)