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",
import cv2
import digit_detector.preprocess as preproc
import digit_detector.train as train_
DIR = '../datasets/svhn'
NB_FILTERS = 32
NB_EPOCH = 5
DETECTOR_FILE = 'detector_model.hdf5'
RECOGNIZER_FILE = 'recognize_model.hdf5'
if __name__ == "__main__":
print("loading images_train...")
images_train = file_io.FileHDF5().read(os.path.join(DIR, "train.hdf5"),
"images")
print("loading labels_train...")
labels_train = file_io.FileHDF5().read(os.path.join(DIR, "train.hdf5"),
"labels")
print("loading images_val...")
images_val = file_io.FileHDF5().read(os.path.join(DIR, "val.hdf5"),
"images")
print("loading labels_val...")
labels_val = file_io.FileHDF5().read(os.path.join(DIR, "val.hdf5"),
"labels")
print("Finish loading hdf5")
# Train detector
X_train, X_val, Y_train, Y_val, mean_value = preproc.GrayImgTrainPreprocessor(
).run(images_train, labels_train, images_val, labels_val, 2)
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_samples.shape, "\ntrain_labels.shape",
train_labels.shape)
print("validation_samples.shape", validation_samples.shape,
"\nvalidation_labels.shape", validation_labels.shape)
# show.plot_images(samples, labels.reshape(-1,).tolist())
file_io.FileHDF5().write(train_samples,
os.path.join(SVHN_DIR, "train.hdf5"),
"images",
"w",
dtype="uint8")
file_io.FileHDF5().write(train_labels,
os.path.join(SVHN_DIR, "train.hdf5"),
"labels",
"a",
dtype="int")
file_io.FileHDF5().write(validation_samples,
os.path.join(SVHN_DIR, "val.hdf5"),
"images",
"w",
dtype="uint8")
file_io.FileHDF5().write(validation_labels,
os.path.join(SVHN_DIR, "val.hdf5"),
