Python Server Programming: Computer Vision with OpenCV

With the rapid development of computer technology, the field of computer vision has become an area of ​​concern for more and more Python programmers. This article will introduce how to use Python and the OpenCV framework to build a computer vision server to implement some basic image processing functions.

1. Installing OpenCV

To use OpenCV for computer vision development, you first need to install the OpenCV library in Python. There are many installation methods. Here is a relatively simple method:

Enter the following command on the command line:

pip install opencv-python

In addition, you can also choose to install other OpenCV modules, such as:

pip install opencv-contrib-python

2. Implementing image processing functions

Next, we will use OpenCV to implement some basic image processing functions, such as:

• Read Image file
• Display image
• Convert image format
• Crop image
• Scale image
• Filter image
• Image Convert to grayscale

The following is the code implementation:

import cv2

# 读取图像文件
img = cv2.imread("test.jpg")

# 显示图像
cv2.imshow("Original Image", img)

# 转换图像格式
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 显示灰度图像
cv2.imshow("Gray Image", gray_img)

# 裁剪图像
cropped_img = img[100:400, 200:500]

# 显示裁剪后的图像
cv2.imshow("Cropped Image", cropped_img)

# 缩放图像
resized_img = cv2.resize(img, (800, 600))

# 显示缩放后的图像
cv2.imshow("Resized Image", resized_img)

# 滤波图像
blur_img = cv2.GaussianBlur(img, (5, 5), 0)

# 显示滤波后的图像
cv2.imshow("Blurred Image", blur_img)

cv2.waitKey(0)
cv2.destroyAllWindows()

This code will read the image file named "test.jpg" and implement the above basic image processing functions.

3. Building a computer vision server based on Flask

Next, we will use Flask, the Python web framework, to build a simple computer vision server to integrate the above image processing functions Encapsulated in the form of API interface.

The following is the code implementation:

from flask import Flask, jsonify, request
import cv2

app = Flask(__name__)

@app.route('/')
def index():
    return "Welcome to the Computer Vision Server!"

@app.route('/api/gray', methods=['POST'])
def gray():
    # 读取上传的图像文件
    img_file = request.files['image']
    img = cv2.imdecode(np.fromstring(img_file.read(), np.uint8), cv2.IMREAD_COLOR)
    # 转换图像格式为灰度
    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # 将灰度图像转换为PNG格式，便于在web上显示
    _, encoded_img = cv2.imencode('.png', gray_img)
    response = {'image': encoded_img.tobytes()}
    return jsonify(response)

@app.route('/api/resize', methods=['POST'])
def resize():
    # 读取上传的图像文件
    img_file = request.files['image']
    img = cv2.imdecode(np.fromstring(img_file.read(), np.uint8), cv2.IMREAD_COLOR)
    # 获取传递的参数
    width = int(request.form['width'])
    height = int(request.form['height'])
    # 缩放图像
    resized_img = cv2.resize(img, (width, height))
    # 将缩放后的图像转换为PNG格式，便于在web上显示
    _, encoded_img = cv2.imencode('.png', resized_img)
    response = {'image': encoded_img.tobytes()}
    return jsonify(response)

if __name__ == "__main__":
    app.run(debug=True, host='0.0.0.0')

Run the code and start the server.

4. Test Server

We can use POST requests to test the API interface we just built on the server.

For example, we can use Postman to send an image to the server and call the "/api/gray" interface to convert the image to grayscale.

Similarly, we can also call the "/api/resize" interface to scale the image.

Through the above method, we can easily test the API interface of the computer vision server and realize the image processing function on the server side.

5. Summary

This article introduces how to build a computer vision server using Python and the OpenCV framework. By encapsulating basic image processing functions into API interfaces, users can easily call these functions on the web. At the same time, computer vision technology has a wide range of application scenarios, and we can apply these technologies in many fields, such as image recognition, intelligent monitoring, etc.

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