Automatically adjusting brightness of image with O

问题:

I want to adjust the brightness of an image to a certain value in OpenCV. For example, consider this image:

I calculate the brightness with:

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

and I get an average brightness of 35%. To bring it to 66%, for example, I do:

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

and I get an image that seems to have a 50% transparency veil:

I can avoid this effect by using bias only:

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

and the image also seems to have a veil:

If I do it by hand, for example in Photoshop with a brightness adjustment at 150, the result seems alright:

But, this is not automatic and does not give the target brightness.

I could do it with either a gamma correction and/or histogram equalization for maybe a more natural result, but I don't see an easy way to get the target brightness other than trial-and-error.

Has anyone succeeded in adjusting brightness automatically to a target?

Update

Kanat suggested:

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

and the result is better but still has a veil:

Yves Daoust suggested keeping beta = 0, so I adjusted alpha = minimum_brightness / brightness to get the target brightness:

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)

and the result is good:

回答1:

You can try automatically adjusting the brightness using contrast optimization with histogram clipping. You can increase the target brightness by increasing the histogram clip percent (clip_hist_percent). Here's the result at 25% clipping

Alpha and beta are automatically calculated

alpha 3.072289156626506

beta -144.3975903614458

Here's a visualization of the clipping. Blue (original), Orange (after auto adjustment).

Results with clipping at 35%

alpha 3.8059701492537314

beta -201.71641791044777

Other methods could be using 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()

An alternative version is to add bias and gain to an image using saturation arithmetics instead of using OpenCV's cv2.convertScaleAbs. The built-in method 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).

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()

回答2:

You need to modify the contrast as well as the brightness.

I do not use OpenCV, but here is a solution from a (Unix) bash script that I built for Imagemagick. Note that mean controls brightness and std controls contrast.

The script was originally intended to adjust one image to match the colors/brightness/contrast of another image. The matching uses the mean and standard deviations from each image according to the equation: (I2-Mean2)/Std2 = (I1-Mean1)/Std1. This equation represents an normalized intensity such that it has zero mean and approximately the same range of values due to the division by the standard deviations. We solve this equation to form a linear transformation between I1 and I2 according to I2 = A x I1 + B, where A=(Std2/Std1) is the slope or gain and B=(Mean2 - A x Mean1) is the intercept of bias. If no second image is provide and a (set of) mean(s) and standard deviation(s) are provided, then first file will be matched to the provided means and standard deviations. Slope or Gain correlates to contrast and Intercept or Bias correlates to brightness.

Input:

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

Or slightly more contrast:

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

Arguments are normalize to 0 to 1 range. So mean=0.6 is equivalent to 60%. I think 66% might be too bright, but you can change the values as desired.

In this case, since your image was mostly grayscale, I use colorspace RGB for processing. Processing can be done in several other colorspaces.

There is a similar Python script here, which just matches one image to another, but doing so in LAB colorspace. However, it should be easy enough to change it to match one image to a set of mean and std arguments.

(My scripts are available here)

回答3:

One solution is to adjust the gamma of the image. In the code below, I first saturate the image to a certain percentile at the top and bottom of the range, then adjust the gamma correction until reaching the required brightness.

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)

The result is here and inferior to the accepted answer:

Depending on the image, I use either the alpha-beta adjustment in the accepted answer, or I include gamma to avoid clipping too many highlights. The size of each step, percentile for clipping and gamma for correction, determines the weight of each adjustment.

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

回答4:

What if you try like this:

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