使用 OpenCV 自動調整圖像亮度

Question

我想在 OpenCV 中將圖像的亮度調整為某個值。 例如，考慮這個圖像：

我用以下方法計算亮度：

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

我的平均亮度為 35%。 例如，要使其達到 66%，我會這樣做：

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

我得到的圖像似乎有 50% 的透明度面紗：

我可以通過僅使用偏差來避免這種影響：

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

圖像似乎也有面紗：

如果我手動完成，例如在 Photoshop 中將亮度調整為 150，結果似乎沒問題：

但是，這不是自動的，也不會給出目標亮度。

我可以通過伽馬校正和/或直方圖均衡來實現，以獲得更自然的結果，但除了反復試驗之外，我沒有看到獲得目標亮度的簡單方法。

有沒有人成功地根據目標自動調整亮度？

更新

卡納特建議：

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

結果更好，但仍然有面紗：

Yves Daoust 建議保持beta = 0 ，所以我調整了alpha = minimum_brightness / brightness Brightness 以獲得目標亮度：

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)

結果很好：

Answer 1

您可以嘗試使用帶有直方圖剪裁的對比度優化來自動調整亮度。 您可以通過增加直方圖剪輯百分比 ( clip_hist_percent ) 來增加目標亮度。 這是 25% 剪裁的結果

Alpha 和 Beta 是自動計算的

阿爾法 3.072289156626506

測試版 -144.3975903614458

這是剪輯的可視化。 藍色（原始）、橙色（自動調整后）。

裁剪率為 35% 的結果

阿爾法 3.8059701492537314

測試版 -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()

另一種版本是使用飽和度算法而不是使用 OpenCV 的cv2.convertScaleAbs為圖像添加偏差和增益。 內置方法不采用絕對值，這會導致無意義的結果（例如，在 alpha = 3 和 beta = -210 的情況下，44 處的像素在 OpenCV 中變為 78，而實際上它應該變為 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()

Answer 2

您需要修改對比度和亮度。

我不使用 OpenCV，但這是我為 Imagemagick 構建的（Unix）bash 腳本的解決方案。 請注意， mean 控制亮度， std 控制對比度。

該腳本最初旨在調整一張圖像以匹配另一張圖像的顏色/亮度/對比度。 匹配使用每個圖像的平均值和標准偏差，根據等式：(I2-Mean2)/Std2 = (I1-Mean1)/Std1。 該方程表示歸一化強度，由於除以標准偏差，它具有零均值和大致相同的值范圍。 我們求解這個方程，根據 I2 = A x I1 + B 形成 I1 和 I2 之間的線性變換，其中 A=(Std2/Std1) 是斜率或增益，B=(Mean2 - A x Mean1) 是截距偏見。 如果沒有提供第二張圖像並且提供了（一組）均值和標准差，則第一個文件將與提供的均值和標准差匹配。 斜率或增益與對比度相關，截距或偏差與亮度相關。

輸入：

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

或者稍微對比一下：

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

參數標准化為 0 到 1 范圍。 所以均值=0.6 相當於 60%。 我認為 66% 可能太亮了，但您可以根據需要更改這些值。

在這種情況下，由於您的圖像大部分是灰度的，因此我使用色彩空間 RGB 進行處理。 處理可以在其他幾種顏色空間中完成。

有一個類似的Python腳本在這里，剛剛匹配一個圖像到另一個，但在Lab色彩空間這樣做。 但是，將其更改為將一個圖像與一組均值和標准參數相匹配應該很容易。

（我的腳本可以在這里找到）

Answer 3

一種解決方案是調整圖像的伽馬。 在下面的代碼中，我首先將圖像飽和到范圍頂部和底部的某個百分位數，然后調整伽馬校正，直到達到所需的亮度。

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)

結果在這里並且不如接受的答案：

根據圖像，我在接受的答案中使用 alpha-beta 調整，或者我包括 gamma 以避免剪裁太多亮點。 每個步驟的大小，裁剪的百分位數和校正的伽瑪，決定了每次調整的權重。

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

如果你像這樣嘗試怎么辦：

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