Question

我想在OpenCV中将图像的亮度调整为某个值。例如，考虑以下图片：

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)

，我的平均亮度为35％。例如，将其提高到66％，

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％的透明度：

我只能通过使用偏倚来避免这种影响：

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

并且图像似乎也带有面纱：

如果我手动进行操作，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。

我可以通过伽马校正和/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，我没有一种简单的方法来获得目标亮度。

有人成功地将亮度自动调整为目标吗？

更新

假名建议：

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，因此我调整了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裁剪的对比度优化来自动调整亮度。您可以通过增加直方图片段百分比（clip_hist_percent）来提高目标亮度。这是裁剪25％时的结果

Alpha和Beta是自动计算的

alpha 3.072289156626506

beta -144.3975903614458

这是剪辑的可视化。蓝色（原始），橙色（自动调整后）。

裁剪结果为35％

alpha 3.8059701492537314

beta -201.71641791044777

其他方法可能正在使用Histogram Equalization or 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()

Answer 2

您需要修改对比度和亮度。

我不使用OpenCV，但这是我为Imagemagick构建的（Unix）bash脚本的一种解决方案。 请注意，平均值控制亮度，而标准差则控制对比度。

该脚本最初旨在调整一个图像以匹配另一图像的颜色/亮度/对比度。匹配根据以下公式使用每个图像的平均值和标准偏差：（I2-Mean2）/ Std2 =（I1-Mean1）/ Std1。该方程式表示归一化强度，由于归因于标准偏差，因此它具有零均值和近似相同的值范围。我们根据I2 = A x I1 + B求解该方程以在I1和I2之间形成线性变换，其中A =（Std2 / Std1）是斜率或增益，而B =（Mean2-A x Mean1）是偏压。如果没有提供第二张图像，并且提供了（一组）均值和标准差，则第一个文件将与提供的均值和标准差匹配。 斜率或增益与对比度相关，截距或偏差与亮度相关。

输入：

matchimage -c rgb -m 0.6 -s 0.25 bunny.png result1.png

或更强的对比度：

matchimage -c rgb -m 0.6 -s 0.35 bunny.png result2.png

参数归一化为0到1范围。因此平均值= 0.6等于60％。我认为66％可能太亮了，但是您可以根据需要更改值。

在这种情况下，由于您的图像主要是灰度图像，因此我使用色彩空间RGB进行处理。可以在其他几种颜色空间中进行处理。

有一个类似的Python脚本here，该脚本只将一个图像与另一个图像匹配，但是在LAB颜色空间中进行了匹配。但是，更改它以将一个图像与一组均值和标准参数相匹配应该足够容易。

（我的脚本可用here）

Answer 3

一种解决方案是调整图像的灰度系数。在下面的代码中，我首先在范围的顶部和底部将图像饱和到一定百分比，然后调整伽玛校正，直到达到所需的亮度。

import cv2
import numpy as np

def saturate(img, percentile):
    """Changes the scale of the image so that half of percentile at the low range
    becomes 0, half of percentile at the top range becomes 255.
    """

    if 2 != len(img.shape):
        raise ValueError("Expected an image with only one channel")

    # copy values
    channel = img[:, :].copy()
    flat = channel.ravel()

    # copy values and sort them
    sorted_values = np.sort(flat)

    # find points to clip
    max_index = len(sorted_values) - 1
    half_percent = percentile / 200
    low_value = sorted_values[math.floor(max_index * half_percent)]
    high_value = sorted_values[math.ceil(max_index * (1 - half_percent))]

    # saturate
    channel[channel < low_value] = low_value
    channel[channel > high_value] = high_value

    # scale the channel
    channel_norm = channel.copy()
    cv2.normalize(channel, channel_norm, 0, 255, cv2.NORM_MINMAX)

    return channel_norm

def adjust_gamma(img, gamma):
    """Build a lookup table mapping the pixel values [0, 255] to
    their adjusted gamma values.
    """

    # code from
    # https://www.pyimagesearch.com/2015/10/05/opencv-gamma-correction/

    invGamma = 1.0 / gamma
    table = np.array([((i / 255.0) ** invGamma) * 255 for i in np.arange(0, 256)]).astype("uint8")

    # apply gamma correction using the lookup table
    return cv2.LUT(img, table)


def adjust_brightness_with_gamma(gray_img, minimum_brightness, gamma_step = GAMMA_STEP):

    """Adjusts the brightness of an image by saturating the bottom and top
    percentiles, and changing the gamma until reaching the required brightness.
    """
    if 3 <= len(gray_img.shape):
        raise ValueError("Expected a grayscale image, color channels found")

    cols, rows = gray_img.shape
    changed = False
    old_brightness = np.sum(gray_img) / (255 * cols * rows)
    new_img = gray_img
    gamma = 1

    while True:
        brightness = np.sum(new_img) / (255 * cols * rows)
        if brightness >= minimum_brightness:
            break

        gamma += gamma_step
        new_img = adjust_gamma(gray_img, gamma = gamma)
        changed = True

    if changed:
        print("Old brightness: %3.3f, new brightness: %3.3f " %(old_brightness, brightness))
    else:
        print("Maintaining brightness at %3.3f" % old_brightness)

    return new_img

def main(filepath):

    img = cv2.imread(filepath)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    saturated = saturate(gray, 1)
    bright = adjust_brightness_with_gamma(saturated, minimum_brightness = 0.66)

结果在这里，不及被接受的答案：

根据图像，我可以在接受的答案中使用alpha-beta调整，或者使用gamma来避免剪切过多的高光。每个步骤的大小（剪裁为百分位数，校正为伽玛）确定每次调整的权重。

PERCENTILE_STEP = 1
GAMMA_STEP = 0.01

def adjust_brightness_alpha_beta_gamma(gray_img, minimum_brightness, percentile_step = PERCENTILE_STEP, gamma_step = GAMMA_STEP):
    """Adjusts brightness with histogram clipping by trial and error.
    """

    if 3 <= len(gray_img.shape):
        raise ValueError("Expected a grayscale image, color channels found")

    new_img = gray_img
    percentile = percentile_step
    gamma = 1
    brightness_changed = False

    while True:
        cols, rows = new_img.shape
        brightness = np.sum(new_img) / (255 * cols * rows)

        if not brightness_changed:
            old_brightness = brightness

        if brightness >= minimum_brightness:
            break

        # adjust alpha and beta
        percentile += percentile_step
        alpha, beta = percentile_to_bias_and_gain(new_img, percentile)
        new_img = convertScale(gray_img, alpha = alpha, beta = beta)
        brightness_changed = True

        # adjust gamma
        gamma += gamma_step
        new_img = adjust_gamma(new_img, gamma = gamma)

    if brightness_changed:
        print("Old brightness: %3.3f, new brightness: %3.3f " %(old_brightness, brightness))
    else:
        print("Maintaining brightness at %3.3f" % old_brightness)

    return new_img

Answer 4

如果您这样尝试怎么办：

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

使用OpenCV自动调整图像的亮度

更新

4 个答案: