meanShape = np.load("../data/meanFaceShape.npz")["meanShape"]
# print(meanShape.shape) (68,2)
'''
commonSet = ImageServer(initialization='box')
commonSet.PrepareData(commonSetImageDirs, commonSetBoundingBoxFiles, meanShape, 0, 1000, False)
commonSet.LoadImages()
commonSet.CropResizeRotateAll()
commonSet.imgs = commonSet.imgs.astype(np.float32)
commonSet.NormalizeImages(trainSet) #去均值，除以标准差
# commonSet.NormalizeImages()
commonSet.Save(datasetDir, "commonSet.npz")
challengingSet = ImageServer(initialization='box')
challengingSet.PrepareData(challengingSetImageDirs, challengingSetBoundingBoxFiles, meanShape, 0, 1000, False)
challengingSet.LoadImages()
challengingSet.CropResizeRotateAll()
challengingSet.imgs = challengingSet.imgs.astype(np.float32)
challengingSet.NormalizeImages() #去均值，除以标准差
challengingSet.Save(datasetDir, "challengingSet.npz")
'''
w300Set = ImageServer(initialization='box')
w300Set.PrepareData(w300SetImageDirs, w300SetBoundingBoxFiles, meanShape, 0,
1000, False)
w300Set.LoadImages()
w300Set.CropResizeRotateAll()
w300Set.imgs = w300Set.imgs.astype(np.float32)
w300Set.NormalizeImages(trainSet) #去均值，除以标准差
w300Set.Save(datasetDir, "w300Set.npz")
"../data/images/300W/01_Indoor/", "../data/images/300W/02_Outdoor/"
]
w300SetBoundingBoxFiles = [
"../data/boxes300WIndoor.pkl", "../data/boxes300WOutdoor.pkl"
]
datasetDir = "../data/"
meanShape = np.load("../data/meanFaceShape.npz")["meanShape"]
commonSet = ImageServer(initialization='box')
commonSet.PrepareData(commonSetImageDirs, commonSetBoundingBoxFiles, meanShape,
0, 1000, False)
commonSet.LoadImages()
commonSet.CropResizeRotateAll()
commonSet.imgs = commonSet.imgs.astype(np.float32)
commonSet.Save(datasetDir, "commonSet.npz")
challengingSet = ImageServer(initialization='box')
challengingSet.PrepareData(challengingSetImageDirs,
challengingSetBoundingBoxFiles, meanShape, 0, 1000,
False)
challengingSet.LoadImages()
challengingSet.CropResizeRotateAll()
challengingSet.imgs = challengingSet.imgs.astype(np.float32)
challengingSet.Save(datasetDir, "challengingSet.npz")
w300Set = ImageServer(initialization='box')
w300Set.PrepareData(w300SetImageDirs, w300SetBoundingBoxFiles, meanShape, 0,
1000, False)
w300Set.LoadImages()
]
boundingBoxFiles = [
"../data/py3boxesLFPWTrain.pkl", "../data/py3boxesHelenTrain.pkl",
"../data/py3boxesAFW.pkl"
]
datasetDir = "../data/"
meanShape = np.load("../data/meanFaceShape.npz")["meanShape"]
print("Load trainSet")
trainSet = ImageServer(initialization='rect') #相当于没有用bbx做训练，直接用的特征点截取框
trainSet.PrepareData(
imageDirs, None, meanShape, 100, 100000, True
) #准备好图片名list，对应图片landmark的list，和对应图片的bbx的list，和meanshape。令我疑惑的是，startIdx=100，nImgs=100000，,300W数据集可没有那么多图片
trainSet.LoadImages() #读取图片，并对每张图调整好meanShape
trainSet.GeneratePerturbations(10, [0.2, 0.2, 20, 0.25]) #位移0.2，旋转20度，放缩+-0.25
# import pdb; pdb.set_trace()
trainSet.NormalizeImages() #去均值，除以标准差
trainSet.Save(datasetDir) #保存成字典形式，key为'imgs'，'initlandmarks'，'gtlandmarks'
print("Load validationSet")
validationSet = ImageServer(initialization='box')
validationSet.PrepareData(imageDirs, boundingBoxFiles, meanShape, 0, 100,
False)
validationSet.LoadImages()
validationSet.CropResizeRotateAll()
# import pdb; pdb.set_trace()
validationSet.imgs = validationSet.imgs.astype(np.float32)
validationSet.NormalizeImages(trainSet)
validationSet.Save(datasetDir)