def celebaSetup(inputPath,
outputPath,
pathConfig="config_celeba_cropped.json"):
imgList = [f for f in os.listdir(
inputPath) if os.path.splitext(f)[1] == ".jpg"]
cx = 89
cy = 121
nImgs = len(imgList)
if not os.path.isdir(outputPath):
os.mkdir(outputPath)
for index, item in enumerate(imgList):
printProgressBar(index, nImgs)
path = os.path.join(inputPath, item)
img = np.array(pil_loader(path))
img = img[cy - 64: cy + 64, cx - 64: cx + 64]
path = os.path.join(outputPath, item)
saveImage(path, img)
printProgressBar(nImgs, nImgs)
def resizeDataset(inputPath, outputPath, maxSize):
sizes = [64, 128, 512, 1024]
scales = [0, 5, 6, 8]
index = 0
imgList = [
f for f in os.listdir(inputPath)
if os.path.splitext(f)[1] in [".jpg", ".npy"]
]
nImgs = len(imgList)
if maxSize < sizes[0]:
raise AttributeError("Maximum resolution too low")
if not os.path.isdir(outputPath):
os.mkdir(outputPath)
datasetProfile = {}
for index, size in enumerate(sizes):
if size > maxSize:
break
localPath = os.path.join(outputPath, str(size))
if not os.path.isdir(localPath):
os.mkdir(localPath)
datasetProfile[str(scales[index])] = localPath
print("Resolution %d x %d" % (size, size))
resizeModule = NumpyResize((size, size))
for index, item in enumerate(imgList):
printProgressBar(index, nImgs)
path = os.path.join(inputPath, item)
img = pil_loader(path)
img = resizeModule(img)
path = os.path.splitext(os.path.join(localPath, item))[0] + ".jpg"
saveImage(path, img)
printProgressBar(nImgs, nImgs)
return datasetProfile, localPath
