使用OpenCV自动调整图像的亮度

Question

我想在OpenCV中将图像的亮度调整为某个值。例如，考虑以下图像：

我用以下方法计算亮度：

import cv2
img = cv2.imread(filepath)
cols, rows = img.shape
brightness = numpy.sum(img) / (255 * cols * rows)

我得到的平均亮度为35％。例如，将其提高到66％，我这样做：

minimum_brightness = 0.66
alpha = brightness / minimum_brightness
bright_img = cv2.convertScaleAbs(img, alpha = alpha, beta = 255 * (1 - alpha))

我得到的图像似乎具有50％的透明面纱：

我可以通过仅使用偏差来避免这种影响：

bright_img = cv2.convertScaleAbs(img, alpha = 1, beta = 128)

而且图像似乎也带有面纱：

如果我手动进行此操作，例如在Photoshop中将亮度调整为150，则结果似乎还不错：

但是，这不是自动的，不会提供目标亮度。

我可以通过伽玛校正和/或直方图均衡来实现，以获得更自然的结果，但是除了反复试验外，我看不到获得目标亮度的简便方法。

有没有人成功将亮度自动调整为目标？

更新资料

卡纳特建议：

bright_img = cv2.convertScaleAbs(img, alpha = 1, beta = 255 * (minimum_brightness - brightness))

结果更好，但仍然有面纱：

Yves Daoust建议保持beta = 0，因此我进行了调整alpha = minimum_brightness / brightness以获得目标亮度：

ratio = brightness / minimum_brightness
if ratio >= 1:
    print("Image already bright enough")
    return img

# Otherwise, adjust brightness to get the target brightness
return cv2.convertScaleAbs(img, alpha = 1 / ratio, beta = 0)

结果很好：

Answer 1

您可以尝试使用带有直方图剪裁的对比度优化来自动调整亮度。您可以通过增加直方图剪辑百分比 ( clip_hist_percent)来增加目标亮度。这是 25% 剪裁的结果

Alpha 和 Beta 是自动计算的

阿尔法 3.072289156626506

测试版 -144.3975903614458

这是剪辑的可视化。蓝色（原始）、橙色（自动调整后）。

裁剪率为 35% 的结果

阿尔法 3.8059701492537314

测试版 -201.71641791044777

其他方法可以使用Histogram Equalization 或 CLAHE。

import cv2
import numpy as np
# from matplotlib import pyplot as plt

# Automatic brightness and contrast optimization with optional histogram clipping
def automatic_brightness_and_contrast(image, clip_hist_percent=25):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Calculate grayscale histogram
    hist = cv2.calcHist([gray],[0],None,[256],[0,256])
    hist_size = len(hist)

    # Calculate cumulative distribution from the histogram
    accumulator = []
    accumulator.append(float(hist[0]))
    for index in range(1, hist_size):
        accumulator.append(accumulator[index -1] + float(hist[index]))

    # Locate points to clip
    maximum = accumulator[-1]
    clip_hist_percent *= (maximum/100.0)
    clip_hist_percent /= 2.0

    # Locate left cut
    minimum_gray = 0
    while accumulator[minimum_gray] < clip_hist_percent:
        minimum_gray += 1

    # Locate right cut
    maximum_gray = hist_size -1
    while accumulator[maximum_gray] >= (maximum - clip_hist_percent):
        maximum_gray -= 1

    # Calculate alpha and beta values
    alpha = 255 / (maximum_gray - minimum_gray)
    beta = -minimum_gray * alpha

    '''
    # Calculate new histogram with desired range and show histogram 
    new_hist = cv2.calcHist([gray],[0],None,[256],[minimum_gray,maximum_gray])
    plt.plot(hist)
    plt.plot(new_hist)
    plt.xlim([0,256])
    plt.show()
    '''

    auto_result = cv2.convertScaleAbs(image, alpha=alpha, beta=beta)
    return (auto_result, alpha, beta)

image = cv2.imread('1.png')
auto_result, alpha, beta = automatic_brightness_and_contrast(image)
print('alpha', alpha)
print('beta', beta)
cv2.imshow('auto_result', auto_result)
cv2.imwrite('auto_result.png', auto_result)
cv2.imshow('image', image)
cv2.waitKey()

另一种版本是使用饱和度算法而不是使用 OpenCV 的cv2.convertScaleAbs. 内置方法不采用绝对值，这会导致无意义的结果（例如，在 alpha = 3 和 beta = -210 的情况下，44 处的像素在 OpenCV 中变为 78，而实际上它应该变为 0）。

import cv2
import numpy as np
# from matplotlib import pyplot as plt

def convertScale(img, alpha, beta):
    """Add bias and gain to an image with saturation arithmetics. Unlike
    cv2.convertScaleAbs, it does not take an absolute value, which would lead to
    nonsensical results (e.g., a pixel at 44 with alpha = 3 and beta = -210
    becomes 78 with OpenCV, when in fact it should become 0).
    """

    new_img = img * alpha + beta
    new_img[new_img < 0] = 0
    new_img[new_img > 255] = 255
    return new_img.astype(np.uint8)

# Automatic brightness and contrast optimization with optional histogram clipping
def automatic_brightness_and_contrast(image, clip_hist_percent=25):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Calculate grayscale histogram
    hist = cv2.calcHist([gray],[0],None,[256],[0,256])
    hist_size = len(hist)

    # Calculate cumulative distribution from the histogram
    accumulator = []
    accumulator.append(float(hist[0]))
    for index in range(1, hist_size):
        accumulator.append(accumulator[index -1] + float(hist[index]))

    # Locate points to clip
    maximum = accumulator[-1]
    clip_hist_percent *= (maximum/100.0)
    clip_hist_percent /= 2.0

    # Locate left cut
    minimum_gray = 0
    while accumulator[minimum_gray] < clip_hist_percent:
        minimum_gray += 1

    # Locate right cut
    maximum_gray = hist_size -1
    while accumulator[maximum_gray] >= (maximum - clip_hist_percent):
        maximum_gray -= 1

    # Calculate alpha and beta values
    alpha = 255 / (maximum_gray - minimum_gray)
    beta = -minimum_gray * alpha

    '''
    # Calculate new histogram with desired range and show histogram 
    new_hist = cv2.calcHist([gray],[0],None,[256],[minimum_gray,maximum_gray])
    plt.plot(hist)
    plt.plot(new_hist)
    plt.xlim([0,256])
    plt.show()
    '''

    auto_result = convertScale(image, alpha=alpha, beta=beta)
    return (auto_result, alpha, beta)

image = cv2.imread('1.jpg')
auto_result, alpha, beta = automatic_brightness_and_contrast(image)
print('alpha', alpha)
print('beta', beta)
cv2.imshow('auto_result', auto_result)
cv2.imwrite('auto_result.png', auto_result)
cv2.imshow('image', image)
cv2.waitKey()