Computer Vision Example in Python: Gesture Recognition

With the development of computer vision technology, more and more people are beginning to explore how to use computer vision to process image and video data. Python, as a powerful programming language, has also been widely used in the field of computer vision.

This article will introduce how to use Python to implement an example of gesture recognition. We will use the OpenCV library to process images, use machine learning algorithms to train models and implement gesture recognition.

1. Preparing data

First, we need to prepare a gesture picture data set. Gesture datasets can be obtained by taking photos of gestures or from public datasets. Here we take the public data set "ASL Alphabet" as an example.

The pictures in the data set have been marked with gestures of various English letters. We divide these images into training sets and test sets.

2. Image processing

Read pictures through the OpenCV library and process the pictures. We need to convert the image into a grayscale image and perform binarization.

import cv2
import numpy as np

image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
retval, thresholded = cv2.threshold(image_gray, 50, 255, cv2.THRESH_BINARY_INV)

3. Feature extraction

We use the contour detection algorithm to extract the features of the gesture. Through this algorithm, we can get the outline of the gesture image.

_, contours, hierarchy = cv2.findContours(thresholded, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

4. Training model

Next, we need to use a machine learning algorithm to train the model. We choose to use the support vector machine algorithm to train the model. First, we need to label the gesture images and convert them into feature vectors.

labels = [0, 1, 2, ..., 25]
features = []
for i in range(len(images)):
    features.append(contour_feature(images[i]))

def contour_feature(image):
    # 提取手势图像的轮廓
    _, contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    # 根据轮廓计算特征向量
    ...

5. Test the model

After training the model, we need to test its accuracy. We pass the gesture images from the test dataset into the model, and then compare the model's predictions with the real labels to calculate the accuracy.

clf = svm.SVC(kernel='linear')
clf.fit(features_train, labels_train)
accuracy = clf.score(features_test, labels_test)

6. Applying the model

Finally, we can use the trained model to predict the label of the gesture image. Input the gesture image into the model and its corresponding English letter label can be returned.

def predict(image):
    feature = contour_feature(image)
    label = clf.predict([feature])[0]
    return chr(label + ord('A'))

Summary:

Python is a powerful computer vision tool that can implement various image processing and analysis tasks through the OpenCV library and machine learning algorithms. This article introduces how to use Python to implement an example of gesture recognition. Through this example, we can better understand how to use Python to apply computer vision technology.

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