河南手机网站制作公司,简洁大气公司网站,中力建设网站,广州天河区有什么好玩的地方文章目录 概要前置条件统计数据分析直方图均衡化原理小结 概要
图像处理是计算机视觉领域中的重要组成部分#xff0c;而直方图在图像处理中扮演着关键的角色。如何巧妙地运用OpenCV库中的图像处理技巧#xff0c;特别是直方图相关的方法#xff0c;来提高图像质量、改善细… 文章目录 概要前置条件统计数据分析直方图均衡化原理小结 概要
图像处理是计算机视觉领域中的重要组成部分而直方图在图像处理中扮演着关键的角色。如何巧妙地运用OpenCV库中的图像处理技巧特别是直方图相关的方法来提高图像质量、改善细节以及调整曝光。 通过对图像的直方图进行分析和调整能够优化图像的对比度、亮度和色彩平衡从而使图像更具可视化效果。
直方图是一种统计图用于表示图像中像素灰度级别的分布情况。在OpenCV中可以使用直方图来了解图像的整体亮度分布从而为后续处理提供基础。
OpenCV库中的函数如cv2.calcHist和cv2.equalizeHist等对图像的直方图进行均衡化。直方图均衡化是一种有效的方法通过重新分配像素的强度值使图像的亮度分布更均匀提高图像的对比度和细节。
直方图匹配的应用通过将目标图像的直方图调整到参考图像的直方图实现两者之间的色彩一致性。这在图像配准和合成中有广泛的应用提高了图像处理的精度和效果。
在低光照条件下拍摄图像的处理技巧。通过分析图像的直方图可以采取针对性的方法提高低光照图像中物体的可见性改善图像的细节并校正曝光过度或曝光不足的问题。
前置条件
一般来说通过合理利用一些直方图的技巧可以用于提高低光照拍摄图像中物体的可见性改善图像的细节以及校正曝光过度或曝光不足的图像。
import numpy as np
import matplotlib.pyplot as plt
from skimage.color import rgb2gray
from skimage.exposure import histogram, cumulative_distribution
from skimage import filters
from skimage.color import rgb2hsv, rgb2gray, rgb2yuv
from skimage import color, exposure, transform
from skimage.exposure import histogram, cumulative_distributionfrom skimage.io import imread# Load the image remove the alpha or opacity channel (transparency)
dark_image imread(img.png)[:,:,:3]# Visualize the image
plt.figure(figsize(10, 10))
plt.title(Original Image: Plasma Ball)
plt.imshow(dark_image)
plt.show()上述图像是在光照不足下进行拍摄的接着我们就来通过控制直方图来改善我们的视觉效果。
统计数据分析
使用以下代码 def calc_color_overcast(image):# Calculate color overcast for each channelred_channel image[:, :, 0]green_channel image[:, :, 1]blue_channel image[:, :, 2]# Create a dataframe to store the resultschannel_stats pd.DataFrame(columns[Mean, Std, Min, Median, P_80, P_90, P_99, Max])# Compute and store the statistics for each color channelfor channel, name in zip([red_channel, green_channel, blue_channel], [Red, Green, Blue]):mean np.mean(channel)std np.std(channel)minimum np.min(channel)median np.median(channel)p_80 np.percentile(channel, 80)p_90 np.percentile(channel, 90)p_99 np.percentile(channel, 99)maximum np.max(channel)channel_stats.loc[name] [mean, std, minimum, median, p_80, p_90, p_99, maximum]return channel_stats完整代码
import numpy as np
import matplotlib.pyplot as plt
from skimage.color import rgb2gray
from skimage.exposure import histogram, cumulative_distribution
from skimage import filters
from skimage.color import rgb2hsv, rgb2gray, rgb2yuv
from skimage import color, exposure, transform
from skimage.exposure import histogram, cumulative_distribution
import pandas as pdfrom skimage.io import imread# Load the image remove the alpha or opacity channel (transparency)
dark_image imread(img.png)[:,:,:3]# Visualize the image
plt.figure(figsize(10, 10))
plt.title(Original Image: Plasma Ball)
plt.imshow(dark_image)
plt.show()def calc_color_overcast(image):# Calculate color overcast for each channelred_channel image[:, :, 0]green_channel image[:, :, 1]blue_channel image[:, :, 2]# Create a dataframe to store the resultschannel_stats pd.DataFrame(columns[Mean, Std, Min, Median,P_80, P_90, P_99, Max])# Compute and store the statistics for each color channelfor channel, name in zip([red_channel, green_channel, blue_channel],[Red, Green, Blue]):mean np.mean(channel)std np.std(channel)minimum np.min(channel)median np.median(channel)p_80 np.percentile(channel, 80)p_90 np.percentile(channel, 90)p_99 np.percentile(channel, 99)maximum np.max(channel)channel_stats.loc[name] [mean, std, minimum, median, p_80, p_90, p_99, maximum]return channel_stats
# 调用函数并传入图像
result_stats calc_color_overcast(dark_image)# 打印结果
print(result_stats) 进而我们可以使用以下代码来生成上述图像的直方图分布
# Histogram plot
dark_image_intensity img_as_ubyte(rgb2gray(dark_image))
freq, bins histogram(dark_image_intensity)
plt.step(bins, freq*1.0/freq.sum())
plt.xlabel(intensity value)
plt.ylabel(fraction of pixels);
plt.show() # 添加这一行以显示直方图得到直方图可视化效果如下 通过直方图的观察可以注意到图像的像素平均强度似乎非常低这进一步证实了图像的整体暗淡和曝光不足的情况。直方图展示了图像中像素强度值的分布情况而大多数像素具有较低的强度值。这是有道理的因为低像素强度值意味着图像中的大多数像素非常暗或呈现黑色。这样的观察有助于定量了解图像的亮度分布为进一步的图像增强和调整提供了重要的线索。
直方图均衡化原理
直方图均衡化的基本原理是通过线性化图像的累积分布函数CDF来提高图像的对比度。实现这一目标的方法是将图像中每个像素的强度值映射到一个新的值使得新的强度分布更加均匀。
可以通过以下几个步骤实现CDF的线性化
将图像转换为灰度图 如果图像不是灰度图代码会将其转换为灰度图。这是因为直方图均衡化主要应用于灰度图。
计算累积分布函数CDF 通过计算图像像素的强度值的累积分布函数可以了解每个强度值在图像中出现的累积频率。
绘制实际和目标CDF 使用蓝色实线表示实际的CDF使用红色实线表示目标CDF。目标CDF是一个线性分布通过均匀分布来提高对比度。
绘制示例查找表 绘制一个示例查找表展示了像素强度值从原始值映射到目标值的过程。
自定义绘图 对绘图进行自定义包括设置坐标轴范围、标签和标题等。
import numpy as np
import matplotlib.pyplot as plt
from skimage.color import rgb2hsv, rgb2gray, rgb2yuv
from skimage.exposure import histogram, cumulative_distribution
import pandas as pd
from skimage import img_as_ubyte
from skimage.io import imread# Load the image remove the alpha or opacity channel (transparency)
dark_image imread(img.png)[:,:,:3]# Visualize the image
plt.figure(figsize(10, 10))
plt.title(Original Image: Plasma Ball)
plt.imshow(dark_image)
plt.show()def calc_color_overcast(image):# Calculate color overcast for each channelred_channel image[:, :, 0]green_channel image[:, :, 1]blue_channel image[:, :, 2]# Create a dataframe to store the resultschannel_stats pd.DataFrame(columns[Mean, Std, Min, Median,P_80, P_90, P_99, Max])# Compute and store the statistics for each color channelfor channel, name in zip([red_channel, green_channel, blue_channel],[Red, Green, Blue]):mean np.mean(channel)std np.std(channel)minimum np.min(channel)median np.median(channel)p_80 np.percentile(channel, 80)p_90 np.percentile(channel, 90)p_99 np.percentile(channel, 99)maximum np.max(channel)channel_stats.loc[name] [mean, std, minimum, median, p_80, p_90, p_99, maximum]return channel_statsdef plot_cdf(image):# Convert the image to grayscale if neededif len(image.shape) 3:image rgb2gray(image[:, :, :3])# Compute the cumulative distribution functionintensity np.round(image * 255).astype(np.uint8)freq, bins cumulative_distribution(intensity)# Plot the actual and target CDFstarget_bins np.arange(256)target_freq np.linspace(0, 1, len(target_bins))plt.step(bins, freq, cb, labelActual CDF)plt.plot(target_bins, target_freq, cr, labelTarget CDF)# Plot an example lookupexample_intensity 50example_target np.interp(freq[example_intensity], target_freq, target_bins)plt.plot([example_intensity, example_intensity, target_bins[-11], target_bins[-11]],[0, freq[example_intensity], freq[example_intensity], 0],k--,labelfExample lookup ({example_intensity} - {example_target:.0f}))# Customize the plotplt.legend()plt.xlim(0, 255)plt.ylim(0, 1)plt.xlabel(Intensity Values)plt.ylabel(Cumulative Fraction of Pixels)plt.title(Cumulative Distribution Function)return freq, bins, target_freq, target_bins
# 调用函数并传入图像
result_stats calc_color_overcast(dark_image)# 打印结果
print(result_stats)
# Histogram plot
# Histogram plot
dark_image_intensity img_as_ubyte(rgb2gray(dark_image))
freq, bins histogram(dark_image_intensity)
plt.step(bins, freq*1.0/freq.sum())
plt.xlabel(intensity value)
plt.ylabel(fraction of pixels)
plt.show() # 添加这一行以显示直方图# 调用 plot_cdf 函数并传入图像
plot_cdf(dark_image)
plt.show() # 添加这一行以显示CDF图形 直方图均衡化实现结果
import numpy as np
import matplotlib.pyplot as plt
from skimage.color import rgb2gray
from skimage.exposure import histogram, cumulative_distribution, equalize_hist
from skimage import img_as_ubyte
from skimage.io import imread# Load the image remove the alpha or opacity channel (transparency)
dark_image imread(img.png)[:,:,:3]# Visualize the original image
plt.figure(figsize(10, 10))
plt.title(Original Image: Plasma Ball)
plt.imshow(dark_image)
plt.show()# Convert the image to grayscale
gray_image rgb2gray(dark_image)# Apply histogram equalization
equalized_image equalize_hist(gray_image)# Display the original and equalized images
plt.figure(figsize(15, 7))
plt.subplot(1, 2, 1)
plt.title(Original Image)
plt.imshow(gray_image, cmapgray)plt.subplot(1, 2, 2)
plt.title(Equalized Image)
plt.imshow(equalized_image, cmapgray)plt.show()扩展 上面展示了最基本的直方图操作类型接着让我们尝试不同类型的CDF技术看看哪种技术适合给定的图像 # Linear
target_bins np.arange(256)
# Sigmoid
def sigmoid_cdf(x, a1):return (1 np.tanh(a * x)) / 2
# Exponential
def exponential_cdf(x, alpha1):return 1 - np.exp(-alpha * x)# Power
def power_law_cdf(x, alpha1):return x ** alpha
# Other techniques:
def adaptive_histogram_equalization(image, clip_limit0.03, tile_size(8, 8)):clahe exposure.equalize_adapthist(image, clip_limitclip_limit, nbins256, kernel_size(tile_size[0], tile_size[1]))return clahe
def gamma_correction(image, gamma1.0):corrected_image exposure.adjust_gamma(image, gamma)return corrected_image
def contrast_stretching_percentile(image, lower_percentile5, upper_percentile95):in_range tuple(np.percentile(image, (lower_percentile, upper_percentile)))stretched_image exposure.rescale_intensity(image, in_range)return stretched_imagedef unsharp_masking(image, radius5, amount1.0):blurred_image filters.gaussian(image, sigmaradius, multichannelTrue)sharpened_image (image (image - blurred_image) * amount).clip(0, 1)return sharpened_imagedef equalize_hist_rgb(image):equalized_image exposure.equalize_hist(image)return equalized_imagedef equalize_hist_hsv(image):hsv_image color.rgb2hsv(image[:,:,:3])hsv_image[:, :, 2] exposure.equalize_hist(hsv_image[:, :, 2])hsv_adjusted color.hsv2rgb(hsv_image)return hsv_adjusteddef equalize_hist_yuv(image):yuv_image color.rgb2yuv(image[:,:,:3])yuv_image[:, :, 0] exposure.equalize_hist(yuv_image[:, :, 0])yuv_adjusted color.yuv2rgb(yuv_image)return yuv_adjusted使用代码
import numpy as np
import matplotlib.pyplot as plt
from skimage.io import imread
from skimage.color import rgb2gray
from skimage.exposure import histogram, cumulative_distribution
from skimage import exposure, color, filters# Load the image remove the alpha or opacity channel (transparency)
dark_image imread(img.png)[:, :, :3]# Visualize the original image
plt.figure(figsize(10, 10))
plt.title(Original Image: Plasma Ball)
plt.imshow(dark_image)
plt.show()# Linear
target_bins np.arange(256)
# Sigmoid
def sigmoid_cdf(x, a1):return (1 np.tanh(a * x)) / 2
# Exponential
def exponential_cdf(x, alpha1):return 1 - np.exp(-alpha * x)# Power
def power_law_cdf(x, alpha1):return x ** alpha
# Other techniques:
def adaptive_histogram_equalization(image, clip_limit0.03, tile_size(8, 8)):clahe exposure.equalize_adapthist(image, clip_limitclip_limit, nbins256, kernel_size(tile_size[0], tile_size[1]))return clahe
def gamma_correction(image, gamma1.0):corrected_image exposure.adjust_gamma(image, gamma)return corrected_image
def contrast_stretching_percentile(image, lower_percentile5, upper_percentile95):in_range tuple(np.percentile(image, (lower_percentile, upper_percentile)))stretched_image exposure.rescale_intensity(image, in_range)return stretched_imagedef unsharp_masking(image, radius5, amount1.0):blurred_image filters.gaussian(image, sigmaradius, multichannelTrue)sharpened_image (image (image - blurred_image) * amount).clip(0, 1)return sharpened_imagedef equalize_hist_rgb(image):equalized_image exposure.equalize_hist(image)return equalized_imagedef equalize_hist_hsv(image):hsv_image color.rgb2hsv(image[:,:,:3])hsv_image[:, :, 2] exposure.equalize_hist(hsv_image[:, :, 2])hsv_adjusted color.hsv2rgb(hsv_image)return hsv_adjusteddef equalize_hist_yuv(image):yuv_image color.rgb2yuv(image[:,:,:3])yuv_image[:, :, 0] exposure.equalize_hist(yuv_image[:, :, 0])yuv_adjusted color.yuv2rgb(yuv_image)return yuv_adjusted
def apply_cdf(image, cdf_function, *args, **kwargs):# Convert the image to grayscalegray_image rgb2gray(image)# Calculate the cumulative distribution function (CDF)intensity np.round(gray_image * 255).astype(np.uint8)cdf_values, bins cumulative_distribution(intensity)# Apply the specified CDF functiontransformed_cdf cdf_function(cdf_values, *args, **kwargs)# Map the CDF values back to intensity valuestransformed_intensity np.interp(intensity, cdf_values, transformed_cdf)# Rescale the intensity values to [0, 1]transformed_intensity transformed_intensity / 255.0# Apply the transformation to the original imagetransformed_image image.copy()for i in range(3):transformed_image[:, :, i] transformed_intensityreturn transformed_image# Apply and visualize different CDF techniques
linear_image apply_cdf(dark_image, lambda x: x)
sigmoid_image apply_cdf(dark_image, sigmoid_cdf, a1)
exponential_image apply_cdf(dark_image, exponential_cdf, alpha1)
power_law_image apply_cdf(dark_image, power_law_cdf, alpha1)
adaptive_hist_eq_image adaptive_histogram_equalization(dark_image)
gamma_correction_image gamma_correction(dark_image, gamma1.5)
contrast_stretch_image contrast_stretching_percentile(dark_image)
unsharp_mask_image unsharp_masking(dark_image)
equalized_hist_rgb_image equalize_hist_rgb(dark_image)
equalized_hist_hsv_image equalize_hist_hsv(dark_image)
equalized_hist_yuv_image equalize_hist_yuv(dark_image)# Visualize the results
plt.figure(figsize(15, 15))
plt.subplot(4, 4, 1), plt.imshow(linear_image), plt.title(Linear CDF)
plt.subplot(4, 4, 2), plt.imshow(sigmoid_image), plt.title(Sigmoid CDF)
plt.subplot(4, 4, 3), plt.imshow(exponential_image), plt.title(Exponential CDF)
plt.subplot(4, 4, 4), plt.imshow(power_law_image), plt.title(Power Law CDF)
plt.subplot(4, 4, 5), plt.imshow(adaptive_hist_eq_image), plt.title(Adaptive Histogram Equalization)
plt.subplot(4, 4, 6), plt.imshow(gamma_correction_image), plt.title(Gamma Correction)
plt.subplot(4, 4, 7), plt.imshow(contrast_stretch_image), plt.title(Contrast Stretching)
plt.subplot(4, 4, 8), plt.imshow(unsharp_mask_image), plt.title(Unsharp Masking)
plt.subplot(4, 4, 9), plt.imshow(equalized_hist_rgb_image), plt.title(Equalized Histogram (RGB))
plt.subplot(4, 4, 10), plt.imshow(equalized_hist_hsv_image), plt.title(Equalized Histogram (HSV))
plt.subplot(4, 4, 11), plt.imshow(equalized_hist_yuv_image), plt.title(Equalized Histogram (YUV))plt.tight_layout()
plt.show()
输出结果
小结
有多种方法和技术可用于改善RGB图像的可视效果但其中许多方法都需要手动调整参数。上述输出展示了使用不同直方图操作生成的亮度校正效果。通过观察发现HSV调整、指数变换、对比度拉伸和unsharp masking的效果都是令人满意的。
HSV调整 HSV调整涉及将RGB图像转换为HSV颜色空间并修改强度分量。这种技术似乎在提高图像可视化效果方面取得了令人满意的结果。
指数变换 指数变换用于根据指数函数调整像素强度。这种方法在提高图像整体亮度和视觉吸引力方面表现出色。
对比度拉伸 对比度拉伸旨在通过拉伸指定百分位范围内的强度值来增强图像对比度。结果表明这种技术有效地提高了图像的视觉质量。
Unsharp Masking Unsharp masking涉及通过减去模糊版本来创建图像的锐化版本。应用unsharp masking似乎增强了图像的细节和边缘
