PATCH_SIZE = (32, 32)
if __name__ == "__main__":
# 1. file 을 load
files = file_io.list_files(directory=DIR,
pattern="*.png",
recursive_option=False,
n_files_to_sample=N_IMAGES,
random_order=False)
n_files = len(files)
n_train_files = int(n_files * 0.8)
print n_train_files
extractor = extractor_.Extractor(rp.MserRegionProposer(),
ann.SvhnAnnotation(ANNOTATION_FILE),
rp.OverlapCalculator())
train_samples, train_labels = extractor.extract_patch(
files[:n_train_files], PATCH_SIZE, POS_OVERLAP_THD, NEG_OVERLAP_THD)
extractor = extractor_.Extractor(rp.MserRegionProposer(),
ann.SvhnAnnotation(ANNOTATION_FILE),
rp.OverlapCalculator())
validation_samples, validation_labels = extractor.extract_patch(
files[n_train_files:], PATCH_SIZE, POS_OVERLAP_THD, NEG_OVERLAP_THD)
print train_samples.shape, train_labels.shape
print validation_samples.shape, validation_labels.shape
# show.plot_images(samples, labels.reshape(-1,).tolist())
N_IMAGES = None
DIR = '../dataset/svhn/train'
ANNOTATION_FILE = "../dataset/svhn/train/digitStruct.json"
NEG_OVERLAP_THD = 0.05
POS_OVERLAP_THD = 0.6
PATCH_SIZE = (32,32)
if __name__ == "__main__":
# 1. file 을 load
files = file_io.list_files(directory=DIR, pattern="*.png", recursive_option=False, n_files_to_sample=N_IMAGES, random_order=False)
n_files = len(files)
n_train_files = int(n_files * 0.8)
print (n_train_files)
extractor = extractor_.Extractor(rp.MserRegionProposer(), ann.SvhnAnnotation(ANNOTATION_FILE), rp.OverlapCalculator())
train_samples, train_labels = extractor.extract_patch(files[:n_train_files], PATCH_SIZE, POS_OVERLAP_THD, NEG_OVERLAP_THD)
extractor = extractor_.Extractor(rp.MserRegionProposer(), ann.SvhnAnnotation(ANNOTATION_FILE), rp.OverlapCalculator())
validation_samples, validation_labels = extractor.extract_patch(files[n_train_files:], PATCH_SIZE, POS_OVERLAP_THD, NEG_OVERLAP_THD)
print (train_samples.shape, train_labels.shape)
print (validation_samples.shape, validation_labels.shape)
# show.plot_images(samples, labels.reshape(-1,).tolist())
file_io.FileHDF5().write(train_samples, "train.hdf5", "images", "w", dtype="uint8")
file_io.FileHDF5().write(train_labels, "train.hdf5", "labels", "a", dtype="int")
file_io.FileHDF5().write(validation_samples, "val.hdf5", "images", "w", dtype="uint8")
file_io.FileHDF5().write(validation_labels, "val.hdf5", "labels", "a", dtype="int")