helpers.py 文件源码

def randomGammaCorrection(image):
    lower = 0.5
    upper = 1.5
    mu = 1
    sigma = 0.5
    alpha = stats.truncnorm((lower-mu) / sigma, (upper - mu) / sigma, loc=mu, scale=sigma)
    image = (pow(image/255.0, alpha.rvs(1)[0]) * 255).astype(np.uint8)
    return image


# def rle(img):
#     '''
#     img: numpy array, 1 - mask, 0 - background
#     Returns run length as string formated
#     '''
#     bytes = np.where(img.flatten() == 1)[0]
#     runs = []
#     prev = -2
#     for b in bytes:
#         if (b > prev + 1): runs.extend((b + 1, 0))
#         runs[-1] += 1
#         prev = b
#
#     return ' '.join([str(i) for i in runs])

# https://www.kaggle.com/stainsby/fast-tested-rle