def edbs(dirName, prior):
print("Processing for direct binary search !")
listImgs = tools.loadDirectory(dirName)
outDir = 'results'
tools.createDir(outDir)
for(idx, imgname) in enumerate(listImgs):
image = tools.readImage(imgname)
height, width = tools.getInfo(image)
source = np.copy(image)
if prior == 'ED':
# the configs are fixed
ktype = 'floyd-steinberg'
threshold = 127.5
pad = 1
imageHT = algorithms.errorDif(image, height, width, ktype, threshold, pad)
elif prior == 'OD':
# the configs are fixed
ksize = 8
imageHT = algorithms.orderedDith(image, height, width, ksize)
else:
print('Error ! Please choose your prior halftoning method correctly ! Options available = ED and OD .')
image = algorithms.edbSearch(image, imageHT, height, width)
tools.saveImage(image, idx, "EDBS")
print(tools.psnr(np.uint8(source), np.uint8(image)))
def errorDiffusion(dirName, ktype, threshold):
# # # parameter needed : directory, kernel type, threshold.
# # directory : your dataset directory.
# # ktype : kernel type, only accept two kind of parameters: 'floyd-steinberg' or 'jajuni' (jarvis, judice, ninke)
# # threshold : your selected threshold.
print("Processing for error diffusion !")
listImgs = tools.loadDirectory(dirName)
outDir = 'results'
tools.createDir(outDir)
for(idx, imgname) in enumerate(listImgs):
image = tools.readImage(imgname)
height, width = tools.getInfo(image)
source = np.copy(image)
if ktype == 'floyd-steinberg':
pad = 1
elif ktype == 'jajuni':
pad = 2
else:
print("Error ! Please choose your kernel type correctly ! Options available = floyd-steinberg and jajuni . ")
sys.exit(1)
image = algorithms.errorDif(image, height, width, ktype, threshold, pad)
tools.saveImage(image, idx, "ED")
print(tools.psnr(np.uint8(source), np.uint8(image)))
def btc(dirName, bs):
print("Processing for block truncation coding !")
listImgs = tools.loadDirectory(dirName)
outDir = 'results'
tools.createDir(outDir)
for(idx, imgname) in enumerate(listImgs):
image = tools.readImage(imgname)
height, width = tools.getInfo(image)
source = np.copy(image)
image = algorithms.btcoding(image, height, width, bs)
tools.saveImage(image, idx, "BTC")
print(tools.psnr(np.uint8(source), np.uint8(image)))
def orderedDithering(dirName, ksize):
# # # parameter needed : directory, kernel type.
# # directory : your dataset directory.
# # ktype : kernel type, only accept two kind of parameters: 'bayer8dispersed' or 'bayer8clustered'
print("Processing for ordered dithering !")
listImgs = tools.loadDirectory(dirName)
outDir = 'results'
tools.createDir(outDir)
for(idx, imgname) in enumerate(listImgs):
image = tools.readImage(imgname)
height, width = tools.getInfo(image)
source = np.copy(image)
image = algorithms.orderedDith(image, height, width, ksize)
tools.saveImage(image, idx, "OD")
print(tools.psnr(np.uint8(source), np.uint8(image)))
def ddbtc(dirName, ktype):
# # # parameter needed : directory, kernel (order) type, threshold.
# # directory : your dataset directory.
# # ktype : kernel order type.
print("Processing for dot diffusion block truncation coding !")
listImgs = tools.loadDirectory(dirName)
outDir = 'results'
tools.createDir(outDir)
for(idx, imgname) in enumerate(listImgs):
image = tools.readImage(imgname)
height, width = tools.getInfo(image)
source = np.copy(image)
image = algorithms.ddbtcoding(image, height, width, ktype)
tools.saveImage(image, idx, "DDBTC")
print(tools.psnr(np.uint8(source), np.uint8(image)))