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一、明暗检测
图像亮度通俗理解便是图像的明暗程度，它是图片质量的评价参数之一。计算整张图片的亮度有多种方法。
# coding=utf-8
import cv2 as cv
from PIL import Image, ImageStat
def image_brightness1(rgb_image):
'''
检测图像亮度(亮度平均值方法)
'''
w, h = rgb_image.size
# 转换为HSV格式
hsv_image = cv.cvtColor(np.array(rgb_image, 'f'), cv.COLOR_RGB2HSV)
# 累计总亮度值
sum_brightness = np.sum(hsv_image[:,:,2])
area = w*h # 总像素数
# 平均亮度
avg = sum_brightness/area
return avg
def image_brightness2(rgb_image):
'''
检测图像亮度(灰度平均值方法)
'''
gray_image = rgb_image.convert('L')
stat = ImageStat.Stat(gray_image)
return staan[0]
def image_brightness3(rgb_image):
'''
检测图像亮度(基于经验公式)
'''
stat = ImageStat.Stat(rgb_image)
r, g, b = staan
return math.sqrt(0.241*(r**2) + 0.691*(g**2) + 0.068*(b**2))
def image_brightness4(rgb_image):
'''
检测图像亮度(基于RMS)
'''
stat = ImageStat.Stat(rgb_image)
r, g, b = stat.rms
return math.sqrt(0.241*(r**2) + 0.691*(g**2) + 0.068*(b**2))
pil_im = Image.open('test.jpg')
print(image_brightness1(pil_im))
print(image_brightness2(pil_im))
print(image_brightness3(pil_im))
print(image_brightness4(pil_im))
二、明暗调节
有了图像亮度后如何进行调整呢？我在这里采用了空域法中的gamma变换，通过如下方法计算gamma值
def calc_gamma(brightness):
'''
计算gamma值
'''
return brightness/127.0
然后用得到的gamma值进行亮度变换
import numpy as np
from skimage import exposure
import matplotlib.pyplot as plt
def array_to_image(image_arr):
'''
数组还原为图片
INPUT -> 数组
OUTPUT -> 单张图文件
'''
if len(image_arr.shape) == 3: # 格式为(height(rows), weight(colums), 3)
r = Image.fromarray(np.uint8(image_arr[:,:,0]))
g = Image.fromarray(np.uint8(image_arr[:,:,1]))
b = Image.fromarray(np.uint8(image_arr[:,:,2]))
image = Image.merge("RGB", (r, g, b))
return image
elif len(image_arr.shape) == 2: # 格式为(height(rows), weight(colums))
return Image.fromarray(np.uint8(image_arr))
def image_gamma_transform(pil_im, gamma):
'''
伽马校正
INPUT -> 单张图文件
OUTPUT -> 处理后的图文件
'''
image_arr = np.array(pil_im)
image_arr2 = exposure.adjust_gamma(image_arr, gamma)
return array_to_image(image_arr2)
pil_im = Image.open('test.jpg')
brightness = image_brightness4(pil_im)
newimage = image_gamma_transform(pil_im, calc_gamma(brightness))
result = np.hstack((np.array(pil_im),np.array(newimage)))
plt.imshow(result)
plt.show()
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