equalize brightness with CLAHE +2 step background learning

clahe.py

import cv2
import numpy as np
from Tracker import Tracker
from TouchEvents import TouchEvents
from pythonosc import udp_client as osc
from scipy.signal import convolve2d

w,h=640,480
mouseX, mouseY = 0,0
exposure=-8
palette = cv2.imread('data/palette256.png')[0,:,:]
finger_tpl = cv2.imread('data/finger.png',cv2.IMREAD_GRAYSCALE)
bg_subtractor = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=20, detectShadows=False)
bg_subtractor2 = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=20, detectShadows=False)

map_x = cv2.imread("data/map_x.tif", cv2.IMREAD_UNCHANGED).astype(np.float32)
map_y = cv2.imread("data/map_y.tif", cv2.IMREAD_UNCHANGED).astype(np.float32)

cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_EXPOSURE,exposure)

cv2.namedWindow("img", cv2.WINDOW_NORMAL); cv2.moveWindow("img",0*640,0)
cv2.namedWindow("bg", cv2.WINDOW_NORMAL); cv2.moveWindow("bg",1*640,0)
cv2.namedWindow("masked", cv2.WINDOW_NORMAL); cv2.moveWindow("masked",2*640,0)
cv2.namedWindow("fg", cv2.WINDOW_NORMAL); cv2.moveWindow("fg",3*640,0)
cv2.namedWindow("clr", cv2.WINDOW_NORMAL); cv2.moveWindow("clr",4*640,0)

while True:
    ret, img = cap.read()
    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    img = cv2.remap(img, map_x, map_y, cv2.INTER_LINEAR)

    fg_mask = bg_subtractor.apply(img, learningRate=.0001) 
    fg = cv2.bitwise_and(img, img, mask=fg_mask)

    bg = bg_subtractor.getBackgroundImage()
    bg_inv = 255 - bg
    bg_mask = cv2.inRange(bg_inv, 135, 180) # FIXME: dit hoeft niet op inverse beeld
    bg_inv = cv2.bitwise_and(bg_inv, bg_mask) # apply color range as binary mask
    masked = cv2.bitwise_and(img, bg_mask) # mask fg
    bg = cv2.bitwise_and(bg, bg_mask)  # mask bg
   
    difference = cv2.absdiff(masked, bg)    
    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) # Pas adaptieve histogram equalization toe op het verschilbeeld
    enhanced_difference = clahe.apply(difference)
    fg = cv2.add(masked, enhanced_difference)
    fg_mask = bg_subtractor2.apply(fg, learningRate=.0001) 
    fg = cv2.bitwise_and(fg, fg, mask=fg_mask)
    fg = cv2.equalizeHist(fg)
    clr = palette[fg.astype(int)]

    cv2.imshow("img",img)
    cv2.imshow("bg",bg)
    cv2.imshow("masked",masked)
    cv2.imshow("fg",fg)
    cv2.imshow("clr",clr)

    key = cv2.waitKey(1) & 0xFF 
    if key == 27:
        break