processing.py 文件源码

def preprocessing_imgs(train_imgs, reduced_size = None):
    # resizing    
    if reduced_size is not None:
        train_imgs_p = np.ndarray((train_imgs.shape[0], train_imgs.shape[1]) + reduced_size, dtype=np.float32)
        for i in range(train_imgs.shape[0]):
            train_imgs_p[i, 0] = cv2.resize(train_imgs[i, 0], (reduced_size[1], reduced_size[0]), interpolation=cv2.INTER_CUBIC) # INVERSE ORDER! cols,rows
    else:
        train_imgs_p = train_imgs.astype(np.float32)

    # ZMUV normalization
    m = np.mean(train_imgs_p).astype(np.float32)
    train_imgs_p -= m
    st = np.std(train_imgs_p).astype(np.float32)
    train_imgs_p /= st

    return train_imgs_p,m,st