[英]Automatically adjusting brightness of image with OpenCV
我想在 OpenCV 中将图像的亮度调整为某个值。 例如,考虑这个图像:
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)
我的平均亮度为 35%。 例如,要使其达到 66%,我会这样做:
minimum_brightness = 0.66
alpha = brightness / minimum_brightness
bright_img = cv2.convertScaleAbs(img, alpha = alpha, beta = 255 * (1 - alpha))
我得到的图像似乎有 50% 的透明度面纱:
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)
图像似乎也有面纱:
如果我手动完成,例如在 Photoshop 中将亮度调整为 150,结果似乎没问题:
但是,这不是自动的,也不会给出目标亮度。
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,但除了反复试验之外,我没有看到获得目标亮度的简单方法。
有没有人成功地根据目标自动调整亮度?
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
,所以我调整了alpha = minimum_brightness / 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:
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
为图像添加偏差和增益。 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()
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 脚本的解决方案。 请注意, mean 控制亮度, std 控制对比度。
该脚本最初旨在调整一张图像以匹配另一张图像的颜色/亮度/对比度。 匹配使用每个图像的平均值和标准偏差,根据等式:(I2-Mean2)/Std2 = (I1-Mean1)/Std1。 该方程表示归一化强度,由于除以标准偏差,它具有零均值和大致相同的值范围。 我们求解这个方程,根据 I2 = A x I1 + B 形成 I1 和 I2 之间的线性变换,其中 A=(Std2/Std1) 是斜率或增益,B=(Mean2 - A x Mean1) 是截距偏见。 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
或者稍微对比一下:
matchimage -c rgb -m 0.6 -s 0.35 bunny.png result2.png
参数标准化为 0 到 1 范围。 所以均值=0.6 相当于 60%。 我认为 66% 可能太亮了,但您可以根据需要更改这些值。
在这种情况下,由于您的图像大部分是灰度的,因此我使用色彩空间 RGB 进行处理。 处理可以在其他几种颜色空间中完成。
有一个类似的Python脚本在这里,刚刚匹配一个图像到另一个,但在Lab色彩空间这样做。 但是,将其更改为将一个图像与一组均值和标准参数相匹配应该很容易。
(我的脚本可以在这里找到)
一种解决方案是调整图像的伽马。 在下面的代码中,我首先将图像饱和到范围顶部和底部的某个百分位数,然后调整伽马校正,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)
结果在这里并且不如接受的答案:
根据图像,我在接受的答案中使用 alpha-beta 调整,或者我包括 gamma 以避免剪裁太多亮点。 每个步骤的大小,裁剪的百分位数和校正的伽玛,决定了每次调整的权重。
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
如果你像这样尝试怎么办:
bright_img = cv2.convertScaleAbs(img, alpha = 1, beta = 255 * (minimum_brightness - brightness))
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