def odbtcoding(image, height, width, ksize): kernel = ditherArray(int(0), int(0), int(ksize), int(0), int(1)) kheight, kwidth = tools.getInfo(kernel) bs = kheight loopHeight = np.uint8(height / bs) loopWidth = np.uint8(width / bs) image = tools.normalize(image) for i in range(0, loopHeight): for j in range(0, loopWidth): tempImg = image[i*bs:i*bs+bs,j*bs:j*bs+bs] nMax = tools.getMax(tempImg) nMin = tools.getMin(tempImg) k = nMax - nMin for x in range(0, bs): for y in range(0, bs): DA = k * kernel[x][y] th = DA + nMin if image[i*bs+x][j*bs+y] >= th: image[i*bs+x][j*bs+y] = nMax else: image[i*bs+x][j*bs+y] = nMin return np.uint8(image * 255)
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 ddbtcoding(image, height, width, ktype): ordKernel = orderMat(ktype) kheight, kwidth = tools.getInfo(ordKernel) # handle location for the order map patLoc = np.zeros((kheight * kwidth, 2)) for m in range(0, kheight): for n in range(0, kwidth): patLoc[ordKernel[m][n]][0] = m patLoc[ordKernel[m][n]][1] = n patMaps = np.zeros((height, width)) # get weight kernel (error kernel) weightKernel = matErrorDD() # create a dummy array to help calculation dumArray = np.copy(image) for i in range(0, height, kheight): for j in range(0, width, kwidth): index = 0 tempImg = image[i:i+kheight,j:j+kwidth] mean = tools.getMean(tempImg) nMax = tools.getMax(tempImg) nMin = tools.getMin(tempImg) while index != (kheight * kwidth): ni = int(i + patLoc[index][0]) nj = int(j + patLoc[index][1]) if dumArray[ni][nj] > mean: dummy = nMax else: dummy = nMin error = dumArray[ni][nj] - dummy image[ni][nj] = dummy patMaps[ni][nj] = 1 fm = 0 for m in range(-1, 2): for n in range(-1, 2): if (ni + m >= 0) and (ni + m < height) and (nj + n >= 0) and (nj + n < width): if patMaps[ni + m][nj + n] == 0: fm = fm + weightKernel[m + 1][n + 1] for m in range(-1, 2): for n in range(-1, 2): if (ni + m >= 0) and (ni + m < height) and (nj + n >= 0) and (nj + n < width): if patMaps[ni + m][nj + n] == 0: dumArray[ni + m][nj + n] = dumArray[ni + m][nj + n] + (error * weightKernel[m + 1][n + 1] / fm) index = index + 1 return 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)))
def orderedDith(image, height, width, ksize): kernel = ditherArray(int(0), int(0), int(ksize), int(0), int(1)) kheight, kwidth = tools.getInfo(kernel) bs = kheight loopHeight = np.uint8(height / bs) loopWidth = np.uint8(width / bs) image = tools.normalize(image) for i in range(0, loopHeight): for j in range(0, loopWidth): tempImg = image[i*bs:i*bs+bs,j*bs:j*bs+bs] for x in range(0, bs): for y in range(0, bs): if image[i*bs+x][j*bs+y] >= kernel[x][y]: image[i*bs+x][j*bs+y] = 255 else: image[i*bs+x][j*bs+y] = 0 return np.uint8(image)
