def jpeg(img, div, fft=False, display8x8=False):
stop = False
display = False
matSize = 8 # size of matrix blocks to perform dct
imgCopy = img.copy()
idm = classes.ImageDisplayManager()
# begin scanning
for rowStart in range(0, 512, matSize):
for colStart in range(0, 512, matSize):
# take a matSize x matSize block
imgSlice = img[rowStart:rowStart + matSize,
colStart:colStart + matSize]
if (not fft):
# compute the discrete cosine transform of the block
B = cv2.dct(imgSlice.astype('float32'))
# quantize the block of DCT coefficients
Bprime = cf.quantize(B, div)
# calculate the inverse dct
B = cv2.dct(Bprime.astype('float32'), B, cv2.DCT_INVERSE)
else:
# compute the discrete cosine transform of the block
B = np.fft.fft2(imgSlice)
# quantize the block of DCT coefficients
Bprime = cf.quantize(B, div)
# calculate the inverse dct
B = np.fft.ifft2(Bprime)
# replace the quantized blocks in the image's copy
imgCopy[rowStart:rowStart + matSize,
colStart:colStart + matSize] = B
# display the 8 x 8 blocks for human inspection
if display8x8:
# display an image with another resolution on a resizable window
idm.showImg(imgSlice, '8 x 8')
return imgCopy
return imgCopy
# ---------------------------------------------------------------------
#
# MAIN
#
# ---------------------------------------------------------------------
if __name__ == "__main__":
# clear screen
sp.call('cls', shell=True)
# initialize ImageDisplayManager
idm = classes.ImageDisplayManager()
# load a color image in grayscale
img = cv2.imread('./test images/peppers_gray.tif', 0)
idm.add(img, 'Original image')
# perform quantization, measure time taken, and add to display list
div = 2 # divisor used for quantization
tmStart = time.time()
imgQuantized = quantize(img, 2)
tmEnd = time.time()
tmElapsed = round((tmEnd - tmStart) * 1000, 2)
print('Quantization time elapsed = {} ms'.format(tmElapsed))
idm.add(imgQuantized, 'Quantized image, div = {}'.format(div))
# perform averaging of pixels based on neighboring pixels