A brief introduction to image segmentation, and a sample code for opencv image segmentation

Image segmentation is an important task in computer vision, and its goal is to separate the objects in the image from the background, or to segment the image into different regions. This tutorial will introduce the basic concepts and methods of image segmentation and how to apply them in practice.

What is Image Segmentation?
Image Segmentation Methods
Hands-on: Image Segmentation Using Python and OpenCV
Summary and further reading

What is Image Segmentation?

Image segmentation is the process of dividing an image into distinct regions that typically represent objects, background, or other parts of interest in the image. The results of image segmentation can be used for further analysis, such as object recognition, counting, surface inspection, etc. The methods of image segmentation can be divided into traditional methods and deep learning methods.

Image Segmentation Methods

Some common image segmentation methods are briefly introduced below:

Threshold segmentation : Threshold segmentation is the simplest segmentation method. By setting a threshold, the pixels in the image are divided into foreground and background according to their gray value.
Region-Based Segmentation : Region-based segmentation methods segment an image into different regions based on the similarity between pixels. Common region segmentation methods include region growing, region merging, and so on.
Edge-based segmentation : Edge-based segmentation methods first detect edges in an image, and then divide the image into different regions based on the edge information. Common edge detection algorithms include Canny, Sobel, etc.
Graph-based segmentation : Graph-based segmentation methods regard an image as a graph structure and perform segmentation by analyzing the relationship between nodes in the graph. Common graph-based segmentation algorithms include GraphCut, GrabCut, etc.
Deep Learning Methods : Deep learning methods such as Convolutional Neural Networks (CNN) have achieved remarkable results in image segmentation tasks. Well-known segmentation networks include U-Net, Mask R-CNN, DeepLab, etc.

Hands-on: Image Segmentation Using Python and OpenCV

Install dependent libraries

First install the necessary libraries as follows:

pip install opencv-python
pip install matplotlib

Threshold segmentation example

import cv2
import matplotlib.pyplot as plt

# 读取图像
image = cv2.imread('input_image.jpg', cv2.IMREAD_GRAYSCALE)

# 应用阈值分割
_, thresholded = cv2.threshold(image, 128, 255, cv2.THRESH_BINARY)

# 显示原始图像和分割后的图像
plt.subplot(121), plt.imshow(image, cmap='gray')
plt.title('Original Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122), plt.imshow(thresholded, cmap='gray')
plt.title('Thresholded Image'), plt.xticks([]), plt.yticks([])

plt.show()

Canny edge detection example

import cv2
import matplotlib.pyplot as plt

# 读取图像
image = cv2.imread('input_image.jpg', cv2.IMREAD_GRAYSCALE)

# 应用Canny边缘检测
edges = cv2.Canny(image, 100, 200)

# 显示原始图像和边缘检测后的图像
plt.subplot(121), plt.imshow(image, cmap='gray')
plt.title('Original Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122), plt.imshow(edges, cmap='gray')
plt.title('Edge Image'), plt.xticks([]), plt.yticks([])

plt.show()

Image Segmentation Using U-Net

First install the necessary libraries:

pip install tensorflow
pip install keras

Assuming you already have a pre-trained U-Net model, you can use the following code for image segmentation:

import cv2
import numpy as np
from keras.models import load_model

def preprocess_image(image_path):
    image = cv2.imread(image_path)
    image = cv2.resize(image, (256, 256), interpolation=cv2.INTER_NEAREST)
    image = image / 255.0
    return np.expand_dims(image, axis=0)

def visualize_result(image, mask):
    plt.subplot(121), plt.imshow(image)
    plt.title('Original Image'), plt.xticks([]), plt.yticks([])
    plt.subplot(122), plt.imshow(mask, cmap='gray')
    plt.title('Segmented Image'), plt.xticks([]), plt.yticks([])
    plt.show()

# 加载预训练的U-Net模型
model = load_model('unet_model.h5')

# 读取图像并预处理
input_image = preprocess_image('input_image.jpg')

# 使用模型进行预测
predicted_mask = model.predict(input_image)

# 将预测结果可视化
original_image = cv2.imread('input_image.jpg')
original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
visualize_result(original_image, predicted_mask[0, :, :, 0] > 0.5)

Summary and further reading

This tutorial introduces the basic concepts of image segmentation, common methods and how to use Python and OpenCV for simple image segmentation. Image segmentation is a broad field with many different techniques and applications. To learn more about image segmentation, you can refer to the following resources:

I hope this tutorial is helpful to you, and I wish you success in your learning and practice in the field of image segmentation!