processFunctions.py

import cv2
import numpy as np
import re

regex = r'^[.,/"=+_?!*%~\'{}\[\]:().,;]+$'

# Resize image to fit model's input size, and place it on model's size empty image.
def preprocessImageForPrediction(img, imgSize):
    # create target image and copy sample image into it
    (wt, ht) = imgSize
    (h, w) = img.shape
    fx = w / wt
    fy = h / ht
    f = max(fx, fy)
    newSize = (max(min(wt, int(w / f)), 1), max(min(ht, int(h / f)),
                                                1))  # scale according to f (result at least 1 and at most wt or ht)
    img = cv2.resize(img, newSize)
    target = np.ones([ht, wt]) * 255
    target[0:newSize[1], 0:newSize[0]] = img

    # transpose for TF
    img = cv2.transpose(target)

    # normalize
    (m, s) = cv2.meanStdDev(img)
    m = m[0][0]
    s = s[0][0]
    img = img - m
    img = img / s if s > 0 else img
    return img