import digit_detector.show as show
import digit_detector.region_proposal as rp
N_IMAGES = None
DIR = '../datasets/svhn/train'
ANNOTATION_FILE = "../datasets/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())
detect_model = "detector_model.hdf5"
recognize_model = "recognize_model.hdf5"
mean_value_for_detector = 107.524
mean_value_for_recognizer = 112.833
model_input_shape = (32, 32, 1)
# DIR = '../datasets/svhn/train'
DIR = './extra_examples'
if __name__ == "__main__":
# 1. image files
img_files = file_io.list_files(directory=DIR,
pattern="*.jpg",
recursive_option=False,
n_files_to_sample=None,
random_order=False)
preproc_for_detector = preproc.GrayImgPreprocessor(mean_value_for_detector)
preproc_for_recognizer = preproc.GrayImgPreprocessor(
mean_value_for_recognizer)
char_detector = cls.CnnClassifier(detect_model, preproc_for_detector,
model_input_shape)
char_recognizer = cls.CnnClassifier(recognize_model,
preproc_for_recognizer,
model_input_shape)
digit_spotter = detector.DigitSpotter(char_detector, char_recognizer,
rp.MserRegionProposer())
