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"),