def retinex_shadow_removal(image, retinex_type):
with open('config.json', 'r') as f:
config = json.load(f)
if retinex_type == 'automatedMSRCR':
return retinex.automatedMSRCR(image, config['sigma_list'])
elif retinex_type == 'MSRCRP':
return retinex.MSRCP(image, config['sigma_list'], config['low_clip'],
config['high_clip'])
elif retinex_type == 'MSRCR':
return retinex.MSRCR(image, config['sigma_list'], config['G'],
config['b'], config['alpha'], config['beta'],
config['low_clip'], config['high_clip'])
else:
return image
exit()
# 读入自定义参数
with open('config.json', 'r') as f:
config = json.load(f)
# 遍历读入的图像
for img_name in img_list:
if img_name == '.gitkeep':
continue
# 读入图像
img = cv2.imread(os.path.join(data_path, img_name))
img_histogram = coloredHistoEqual(img)
img_gamma = adjust_gamma(img)
# 多尺度Retinex带色彩恢复
img_msrcr = retinex.MSRCR(img, config['sigma_list'], config['G'],
config['b'], config['alpha'], config['beta'],
config['low_clip'], config['high_clip'])
img_amsrcr = retinex.automatedMSRCR(img, config['sigma_list'])
# MSR with chromaticity preservation
img_msrcp = retinex.MSRCP(img, config['sigma_list'], config['low_clip'],
config['high_clip'])
# 实验结果
shape = img.shape
cv2.imshow('Image', img)
cv2.imshow('retinex', img_msrcr)
cv2.imshow('Histogram equalization', img_histogram)
cv2.imshow('Gamma Correction', img_gamma)
cv2.imshow('Automated retinex', img_amsrcr)
cv2.imshow('MSRCP', img_msrcp)
import json
import retinex
data_path = 'data'
# size = 3
img = cv2.imread('timg.jpg')
img_ssr_re = retinex.singleScaleRetinex(img, 15)
img_ssr_cl = retinex.colorRestoration(img, 125.0, 46.0)
img_ssr = 5 * (img_ssr_re * img_ssr_cl + 25)
cv2.imshow('ssr', img_ssr)
img_msrcr = retinex.MSRCR(
img,
80,
5.0,
25.0,
125.0,
46.0,
0.01,
0.99
)
# cv2.imshow('MSRCR retinex', img_msrcr)
shape = img.shape
# cv2.imshow('Image', img)
cv2.waitKey()