dct.py

#DISCRETE COSINE TRANSFORMATION
import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('assets/sample.jpg',0)

def showImage(img):
    plt.figure(figsize=(15,15))
    plt.imshow(img,cmap='gray')
    plt.xticks([]),plt.yticks([])
    plt.show()

height  = len(img) #one column of image
width = len(img[0]) # one row of image
sliced = [] # new list for 8x8 sliced image 
block = 8
#Quantization Arrays

def selectQMatrix(qName):
    Q10 = np.array([[80,60,50,80,120,200,255,255],
                [55,60,70,95,130,255,255,255],
                [70,65,80,120,200,255,255,255],
                [70,85,110,145,255,255,255,255],
                [90,110,185,255,255,255,255,255],
                [120,175,255,255,255,255,255,255],
                [245,255,255,255,255,255,255,255],
                [255,255,255,255,255,255,255,255]])

    Q50 = np.array([[16,11,10,16,24,40,51,61],
                [12,12,14,19,26,58,60,55],
                [14,13,16,24,40,57,69,56],
                [14,17,22,29,51,87,80,62],
                [18,22,37,56,68,109,103,77],
                [24,35,55,64,81,104,113,92],
                [49,64,78,87,103,121,120,101],
                [72,92,95,98,112,100,130,99]])

    Q90 = np.array([[3,2,2,3,5,8,10,12],
                    [2,2,3,4,5,12,12,11],
                    [3,3,3,5,8,11,14,11],
                    [3,3,4,6,10,17,16,12],
                    [4,4,7,11,14,22,21,15],
                    [5,7,11,13,16,12,23,18],
                    [10,13,16,17,21,24,24,21],
                    [14,18,19,20,22,20,20,20]])
    if qName == "Q10":
        return Q10
    elif qName == "Q50":
        return Q50
    elif qName == "Q90":
        return Q90
    else:
        return np.ones((8,8)) #it suppose to return original image back

currY = 0 #current Y index
for i in range(block,height+1,block):
    currX = 0 #current X index
    for j in range(block,width+1,block):
        sliced.append(img[currY:i,currX:j]-np.ones((8,8))*128) #Extracting 128 from all pixels
        currX = j
    currY = i
    

imf = [np.float32(img) for img in sliced]
DCToutput = []
for part in imf:
    currDCT = cv2.dct(part)
    DCToutput.append(currDCT)
DCToutput[0][0]
selectedQMatrix = selectQMatrix("Q10")
for ndct in DCToutput:
    for i in range(block):
        for j in range(block):
            ndct[i,j] = np.around(ndct[i,j]/selectedQMatrix[i,j])
DCToutput[0][0]
invList = []
for ipart in DCToutput:
    ipart
    curriDCT = cv2.idct(ipart)
    invList.append(curriDCT)
invList[0][0]
row = 0
rowNcol = []
for j in range(int(width/block),len(invList)+1,int(width/block)):
    rowNcol.append(np.hstack((invList[row:j])))
    row = j
res = np.vstack((rowNcol))
showImage(res)