如何用python,OpenCV实现手势识别

使用Python和OpenCV实现手势识别的基本步骤如下：

1. 采集手势图像数据集，包括手势的前景和背景样本。

2. 对采集的图像进行预处理，包括图像增强、去噪等操作，以提高手势识别的准确率。

3. 提取手势特征，例如手指数量、手指位置等。

4. 训练分类器，使用机器学习算法对特征进行分类，得到手势分类器。

5. 使用分类器对新采集的手势图像进行分类，判断手势的类型。

下面是一个简单的实现手势识别的Python示例代码：

import cv2
import numpy as np
import sklearn
from sklearn import svm

#采集手势图像数据集
def collect_dataset():
    dataset = []
    cap = cv2.VideoCapture(0)
    while True:
        ret, img = cap.read()
        cv2.rectangle(img, (300,300), (100,100), (0,255,0),0)
        crop_img = img[100:300, 100:300]
        grey = cv2.cvtColor(crop_img, cv2.COLOR_BGR2GRAY)
        value = (35, 35)
        blurred = cv2.GaussianBlur(grey, value, 0)
        _, thresh1 = cv2.threshold(blurred, 127, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
        cv2.imshow('Thresholded', thresh1)
        cv2.imshow('Gesture', img)
        dataset.append(thresh1)
        if cv2.waitKey(1) == ord('q'):
            break
    cap.release()
    cv2.destroyAllWindows()
    return dataset

#提取手势特征
def get_contour_precedence(contour, cols):
    tolerance_factor = 10
    origin = cv2.boundingRect(contour)
    return ((origin[1] // tolerance_factor) * tolerance_factor) * cols + origin[0]

def get_hand_contour(img):
    contours, hierarchy = cv2.findContours(img.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    cnt = max(contours, key=lambda x: cv2.contourArea(x))
    if cv2.contourArea(cnt) > 10000:
        cnt = max(contours, key=lambda x: get_contour_precedence(x, img.shape[1]))
        return (cnt)
    else:
        return None

def get_hand_feature(img):
    cnt = get_hand_contour(img)
    if cnt:
        hull = cv2.convexHull(cnt, returnPoints=False)
        defects = cv2.convexityDefects(cnt, hull)
        if defects is not None:
            cnt = max(defects, key=lambda x: x[0][3])
            start, end, _, _ = cnt[0]
            farthest = tuple(cnt[0][2])
            return (start, end, farthest, cnt, hull)
    return None

#训练分类器
def train_classifier():
    dataset = collect_dataset()
    features = []
    labels = []
    for i in range(len(dataset)):
        img = dataset[i]
        feature = get_hand_feature(img)
        if feature:
            start, end, farthest, cnt, hull = feature
            features.append([start, end, farthest])
            labels.append(i)
    clf = svm.SVC(kernel='linear', C=1)
    clf.fit(features, labels)
    return clf

#使用分类器对新采集的手势图像进行分类
def predict_gesture(clf, img):
    feature = get_hand_feature(img)
    if feature:
        start, end, farthest, _, _ = feature
        p = clf.predict([start, end, farthest])
        return p
    else:
        return None

#主函数
if __name__ == '__main__':
    clf = train_classifier()
    cap = cv2.VideoCapture(0)
    while True:
        ret, img = cap.read()
        cv2.rectangle(img, (300,300), (100,100), (0,255,0),0)
        crop_img = img[100:300, 100:300]
        grey = cv2.cvtColor(crop_img, cv2.COLOR_BGR2GRAY)
        value = (35, 35)
        blurred = cv2.GaussianBlur(grey, value, 0)
        _, thresh1 = cv2.threshold(blurred, 127, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
        cv2.imshow('Thresholded', thresh1)
        p = predict_gesture(clf, thresh1)
        if p is not None:
            print(p)
        cv2.imshow('Gesture', img)
        if cv2.waitKey(1) == ord('q'):
            break
    cap.release()
    cv2.destroyAllWindows()

这个示例代码中实现了手势图像数据集的采集、手势特征提取、分类器训练和手势识别等功能，可以作为实现手势识别的一个参考。