# construct the training image generator for data augmentation
aug = ImageDataGenerator(rotation_range=20,
zoom_range=0.15,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.15,
horizontal_flip=True,
fill_mode="nearest")
# load the RGB means for the training set
means = json.loads(open(config.DATASET_MEAN).read())
# initialize the image preprocessors
sp = SimplePreprocessor(227, 227)
pp = PatchPreprocessor(227, 227)
mp = MeanPreprocessor(means["R"], means["G"], means["B"])
iap = ImageToArrayPreprocessor()
# initialize the training and validation dataset generators
trainGen = HDF5DatasetGenerator(config.TRAIN_HDF5,
bSize,
aug=aug,
preprocessors=[pp, mp, iap],
classes=2)
valGen = HDF5DatasetGenerator(config.VAL_HDF5,
bSize,
preprocessors=[sp, mp, iap],
classes=2)
# initialize the optimizer
opt = Adam(lr=1e-3)
sys.append("../")
from pyimagesearch.utils.ranked import rank5_accuracy
from dog_vs_cat.configs import dog_vs_cat_configs as configs
from pyimagesearch.preprocessing.croppreprocessor import CropPreprocessor
from pyimagesearch.preprocessing.meanpreprocessor import MeanPreprocessor
from pyimagesearch.preprocessing.simplepreprocessor import SimplePreprocessor
from pyimagesearch.preprocessing.imagetoarraypreprocessor import ImageToArrayPreprocessor
# load RGB mean values
means = json.loads(open(configs.DATASET_MEAN).read())
# load preprocessors
ip = ImageToArrayPreprocessor()
cp = CropPreprocessor(width=227, height=227)
sp = SimplePreprocessor(width=227, height=227)
mp = MeanPreprocessor(rMean=means["R"], gMean=means["G"], bMean=means["B"])
# custom preprocessor class which utilizes custom preprocessors
class CustomPreprocessing:
def __init__(self, preprocessors=[]):
self.preprocessors = preprocessors
def preprocess(self, image):
""" applies preprocessors on image """
for preprocessor in self.preprocessors:
image = preprocessor.preprocess(image)
return image