Advanced opencv:bgr pixel intensity map

Introduce

up to now , In our advanced OpenCV tutorial , We have ：

• Understand the concept of contrast .

• Understand the concept of histogram equalization .

• Implement contrast enhancement on grayscale images .

• Draw the pixel histogram of gray-scale image .

However , as everyone knows , Our world is made up of many, many colors . Now we will try to analyze the contrast and pixel intensity of color images , That is, the image with color channel .

In order to promote this special advanced OpenCV Learning experience , We will use this link （https://wallpaperaccess.com/cool-outer-space） Downloaded images . perhaps , You can save the image found below .

Learn about color images

If you have read our previous articles , If I remember correctly , We learned about advanced OpenCV Use BGR Color channel , instead of RGB. You will find that the red and blue channels have been exchanged .

As shown in the figure above , There are many colors that are eye-catching and eye-catching . These colors with different hues are the result of mixing color channels .

There are three color channels in this image ：

• Blue

• green

• Red

It is through the mixing of these colors , Can appear belong to the secondary color of chromatography and many other colors .

Gain image insight

The first and most important step is to import the necessary packages into our python Script , Then we load the image into our system RAM in . This will be done by OpenCV Provided by the package imread() Method to accomplish ：

import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('C:/Users/Shivek/Pictures/Picture1.jpg')

The output of the above code block will be shown below ：

As one may observe , The image is too big to be displayed in full size on my screen . however , We will not reduce the size of the image . This is because we want to know more about the properties and related information of pixels .

First , Let's see Image The shape of the （NumPy Array ）：

print(image.shape)

The output of the above code line will be shown below ：

After carefully checking this information tuple , It will be decrypted as follows ：

The color channel is BGR-NOT RGB. Next , Let's see The size of the image —— Size tells us the total number of pixels that make up the entire image .

print(image.size)

Output ：

6912000

There are more than 600 Mega pixels .

Let's look at the first pixel in the image . This is the pixel in the first row and the first column .

print(image[0][0])

its BGR Values, respectively 22、15 and 6.

Now let's move on ：BGR Pixel count diagram .

BGR Pixel intensity line graph

Because every pixel in our image contains 3 A color channel , So we will need to traverse all pixels 3 Time , Each time from B、G and R Select the value in the channel . To do this , Can be for Circulation and enumerate() Function in combination with .

We may already know , enumerate() Function will allow us to traverse a fixed number of elements , And automatically record the number of each element .

We will use MatPlotLib The package obtains the visual effect of pixel intensity and count in the form of line graph .

colors = ('blue','green','red')
label = ("Blue", "Green", "Red")
for count,color in enumerate(colors):
    histogram = cv2.calcHist([image],[count],None,[256],[0,256])
    plt.plot(histogram,color = color, label=label[count]+str(" Pixels"))

The line by line explanation of the above code block is as follows ：

In the following code line , We created a tuple , It contains the colors of three different lines of the figure . Because of 3 Sub iteration , Each iteration will choose a different color , So we can see on the chart BGR Pattern .

Color =（' Blue ',' green ',' Red '）

thereafter , Let's continue to create a tuple , It contains the corresponding labels of the chart legend . Again , At each iteration , A label will be selected to mark the corresponding color of the graphic . These labels will be included in the legend of the chart , We will see in the following lines .

label = ("Blue", "Green", "Red")

In order to facilitate graphic generation , We have a for-enumerate loop , This loop will be executed to select each required item from the pixel array and tuple , On these projects , They will be drawn as a line chart on the same canvas .

for count,color in enumerate(colors):
    histogram = cv2.calcHist([image],[count],None,[256],[0,256])
    plt.plot(histogram,color = color, label=label[count]+str(" Pixels"))

for The lines of code in the loop are as follows ：

histogram = cv2.calcHist([image],[count],None,[256],[0,256])

We will use OpenCV In bag calcHist() Method , This method will obtain the histogram of pixel intensity , As we know in the previous article . We pass in the following parameters ：

In order to draw graphics , We use MatPlotLib Provided by the package plot() Method , As shown below ：

plt.plot(histogram, color=color, label=label[count]+str(" Pixels"))

We pass in the data to be plotted , Then the appropriate color . Assign a label to the graph , And according to for Iterate over the corresponding label in the index tuple . We will “ Pixels ” Append to tag name , So that the viewer can clearly see the completed graphics .

Before displaying the chart to the end user , We added some elements to the diagram , for example ：

• title .

• Y The label of the shaft .

• X The label of the shaft .

• A legend .

To end processing , We use MatPlotLib Provided by the package show() Method to display the visual effect on the screen .

plt.title("Histogram Showing Number Of Pixels Belonging To Respective Pixel Intensity", color="crimson")
plt.ylabel("Number Of Pixels", color="crimson")
plt.xlabel("Pixel Intensity", color="crimson")
plt.legend(numpoints=1, loc="best")
plt.show()

The final BGR The pixel intensity line graph will be as follows ：

The above visual display tells us exactly how the pixels are distributed in the original color image . What makes up our image 600 ten thousand + The position of pixels can be summarized in the above line graph .

Most of our pixels are on the left side of the image , As you can see from the diagram , The image is blue from the middle to the right , As shown by the blue pixel intensity line . Blue pixel intensity lines dominate these areas - If people look at images , That's true ：

Please note how the full image fits my computer screen - This is because I adjusted its size .

A very interesting fact is , Although I have adjusted the size of the image , But if I want to create a pixel intensity map on the resized image , Then compared with the original image ,BGR The gradient of the graph is no different —— It just shrinks in size or “ scale ”.

Conclusion

In this paper , We studied ：

• stay Python Used in programming languages OpenCV Understand color images

• BGR Color channel

• Use Python Medium MatPlotLib Bao Jiang BGR Pixels are mapped to BGR Pixel intensity line graph

* END *

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