opencv-python复杂的手势识别代码没用keras

好的，您想了解如何使用OpenCV-Python实现手势识别的代码，而不涉及Keras。以下是一个基本的手势识别代码示例：

1. 导入必要的库：

import cv2
import numpy as np

2. 定义一些常量和变量：

# 预定义常量
bg = None
accumulated_weight = 0.5
font = cv2.FONT_HERSHEY_SIMPLEX

# 捕获视频
cap = cv2.VideoCapture(0)

# 用于计算帧数的变量
num_frames = 0

# ROI坐标
top, right, bottom, left = 10, 350, 225, 590

3. 创建一个函数来计算背景平均值：

def calc_accum_avg(frame, accumulated_weight):
    global bg
    if bg is None:
        bg = frame.copy().astype("float")
        return None
    cv2.accumulateWeighted(frame, bg, accumulated_weight)

4. 创建一个函数来提取手的轮廓：

def segment(frame, threshold=25):
    global bg
    diff = cv2.absdiff(bg.astype("uint8"), frame)

    # 应用阈值
    thresholded = cv2.threshold(diff, threshold, 255, cv2.THRESH_BINARY)[1]

    # 找出轮廓
    (cnts, _) = cv2.findContours(thresholded.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # 如果没有轮廓返回None
    if len(cnts) == 0:
        return None
    else:
        # 根据面积从大到小排序并获取最大的轮廓
        segmented = max(cnts, key=cv2.contourArea)
        return (thresholded, segmented)

5. 开始捕获视频并进行手势识别：

while True:
    # 读取帧
    ret, frame = cap.read()

    # 翻转帧以获得镜像效果
    frame = cv2.flip(frame, 1)

    # 克隆帧
    clone = frame.copy()

    # 获取帧的高度和宽度
    (height, width) = frame.shape[:2]

    # 提取ROI
    roi = frame[top:bottom, right:left]

    # 将ROI转换为灰度
    gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)

    # 应用高斯模糊
    gray = cv2.GaussianBlur(gray, (7, 7), 0)

    # 对背景平均值进行计算
    calc_accum_avg(gray, accumulated_weight)

    # 如果有足够的帧进行背景平均值计算，则进行手势识别
    if num_frames < 60:
        cv2.putText(clone, "请等待...", (100, 100), font, 2, (255, 0, 0), 2, cv2.LINE_AA)
    else:
        # 提取手的轮廓
        hand = segment(gray)

        # 检查是否成功提取手的轮廓
        if hand is not None:
            # 如果成功提取手的轮廓，则分别获取二值化图像和轮廓
            (thresholded, segmented) = hand

            # 绘制轮廓并显示结果图像
            cv2.drawContours(clone, [segmented + (right, top)], -1, (0, 0, 255))
            cv2.imshow("Thresholded", thresholded)

    # 绘制ROI矩形框
    cv2.rectangle(clone, (left, top), (right, bottom), (0, 255, 0), 2)

    # 增加帧数
    num_frames += 1

    # 显示结果帧
    cv2.imshow("Video Feed", clone)

    # 按'q'键退出
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# 释放资源并销毁所有窗口
cap.release()
cv2.destroyAllWindows()

这个代码示例可以实现基本的手势识别。可以根据需要进行修改和扩展。