233 lines
6.7 KiB
Markdown
233 lines
6.7 KiB
Markdown
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# 使用直方图可视化图像中的颜色–Python OpenCV
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> 原文：<https://www.askpython.com/python/visualizing-colors-in-images>
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你好。你有没有想过，即使是图像也可以绘制成直方图？今天在本教程中，我们将了解直方图形式的图像中的颜色的可视化。
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## 介绍
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**直方图**是一种图形表示，显示图像中各种颜色值出现的频率。当一个人需要检测图像之间的颜色变化时，直方图也很方便。
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Image To Histogram Sample
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直方图只关注颜色的比例，而不是图像中颜色的位置。它们显示了图像中存在的颜色和基本色调的统计分布。
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## 彩色可视化的逐步实现
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现在我们将学习绘制各种图像的 RGB 直方图。
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### 步骤 1:导入模块
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就像任何其他项目一样，第一步是**将必要的模块/库**导入我们的程序。这个程序需要的模块是 OpenCV、numpy 和 matplotlib。
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我们也将设置绘图风格为`[seaborn](https://www.askpython.com/python-modules/python-seaborn-tutorial)`以使我们的绘图看起来更清晰。下面显示了相同的代码。
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```py
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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plt.style.use('seaborn')
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```
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### 步骤 2:加载并显示原始图像
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为了加载图像，我们将使用 OpenCV 库的`imread`函数。可以通过相同的功能以不同的方式加载图像。
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***在 imread 这里阅读更多:[使用 OpenCV.imread()方法加载图像的不同方式](https://www.askpython.com/python-modules/python-imread-opencv)***
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现在为了显示图像，我们需要使用 matplotlib 库下的`imshow`函数。为了使图像看起来更干净，我们将关闭轴。
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```py
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image = cv2.imread('image1.jpg')
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plt.axis("off")
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plt.title("Original Image")
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plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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plt.show()
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```
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Original Image Historgram
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### 步骤 3–获取图像的 RGB 直方图
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为了得到`RGB`直方图，我们将使用 **`cakHist`** 函数，该函数需要许多参数，包括图像对象、面元计数、所需值的范围和通道(蓝色、红色或绿色)。
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下面提到了相同的代码。图像对象在方括号(`[]`)中，蓝色、红色和绿色的通道分别设置为 0、1 和 2。
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现在我们在支线图的帮助下绘制直方图。
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```py
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blue_histogram = cv2.calcHist([image], [0], None, [256], [0, 256])
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red_histogram = cv2.calcHist([image], [1], None, [256], [0, 256])
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green_histogram = cv2.calcHist([image], [2], None, [256], [0, 256])
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plt.subplot(3,1,1)
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plt.title("histogram of Blue")
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plt.hist(blue_histogram,color="darkblue")
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plt.subplot(3,1,2)
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plt.title("histogram of Green")
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plt.hist(green_histogram,color="green")
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plt.subplot(3,1,3)
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plt.title("histogram of Red")
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plt.hist(red_histogram,color="red")
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plt.tight_layout()
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plt.show()
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```
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RGB Histogram Separately
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我们可以使用`plot`函数以线条形式绘制相同的直方图，并传递找到的相同 calcHist 数据。同样的代码和输出如下所示。
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```py
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blue_histogram = cv2.calcHist([image], [0], None, [256], [0, 256])
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red_histogram = cv2.calcHist([image], [1], None, [256], [0, 256])
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green_histogram = cv2.calcHist([image], [2], None, [256], [0, 256])
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plt.subplot(3,1,1)
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plt.title("Line Plot of Blue")
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plt.plot(blue_histogram,color="darkblue")
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plt.subplot(3,1,2)
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plt.title("Line Plot of Green")
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plt.plot(green_histogram,color="green")
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plt.subplot(3,1,3)
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plt.title("Line Plot of Red")
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plt.plot(red_histogram,color="red")
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plt.tight_layout()
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plt.show()
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```
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RGB Line Plot Separately
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我们还可以将所有三种颜色的历史图和线图一起绘制在一张纸上！我们也来看看吧！
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```py
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blue_histogram = cv2.calcHist([image], [0], None, [256], [0, 256])
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red_histogram = cv2.calcHist([image], [1], None, [256], [0, 256])
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green_histogram = cv2.calcHist([image], [2], None, [256], [0, 256])
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plt.subplot(2,1,1)
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plt.title("Histogram of All Colors")
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plt.hist(blue_histogram,color="darkblue")
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plt.hist(green_histogram,color="green")
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plt.hist(red_histogram,color="red")
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plt.subplot(2,1,2)
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plt.title("Line Plots of All Colors")
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plt.plot(blue_histogram,color="darkblue")
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plt.plot(green_histogram,color="green")
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plt.plot(red_histogram,color="red")
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plt.tight_layout()
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plt.show()
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```
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RGB Plots Image Together
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* * *
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## 一起可视化最终结果！
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现在让我们在一个单独的画面中可视化输入图像的所有结果！结果也非常令人兴奋。
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下面显示了相同的完整代码。
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```py
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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plt.style.use('seaborn')plt.figure(figsize=(40,10))
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plt.subplot(1,3,1)
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image = cv2.imread('image1.jpg')
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plt.axis("off")
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plt.title("Original Image")
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plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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blue_histogram = cv2.calcHist([image], [0], None, [256], [0, 256])
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red_histogram = cv2.calcHist([image], [1], None, [256], [0, 256])
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green_histogram = cv2.calcHist([image], [2], None, [256], [0, 256])
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plt.subplot(1,3,2)
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plt.title("Histogram of All Colors")
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plt.hist(blue_histogram,color="darkblue")
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plt.hist(green_histogram,color="green")
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plt.hist(red_histogram,color="red")
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plt.subplot(1,3,3)
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plt.title("Line Plots of All Colors")
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plt.plot(blue_histogram,color="darkblue")
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plt.plot(green_histogram,color="green")
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plt.plot(red_histogram,color="red")
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plt.tight_layout()
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plt.show()
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```
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Final Results Images To Histogram
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* * *
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## 更多输出
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让我们看看更多的示例输出。看看结果有多令人兴奋！
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Sample Output 2 Image To Histogram
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Sample Output 1 Image To Histogram
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Sample Output 3 Image To Histogram
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* * *
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## 结论
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我希望你理解这个概念，并喜欢输出。用更多的图片尝试同样的方法，结果会让你大吃一惊。
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编码快乐！😇
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想了解更多？查看下面提到的教程:
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1. [Python Matplotlib 教程](https://www.askpython.com/python-modules/matplotlib/python-matplotlib)
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2. [方框图:你需要知道的一切](https://www.askpython.com/python/examples/boxplots)
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3. [使用 Python 散景进行数据可视化](https://www.askpython.com/python-modules/data-visualization-using-python-bokeh)
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* * *
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