计算机视觉入门（实战篇：手势控制AI画笔）

本代码基于 Advance Computer Vision with Python 进行修改，更加适合中国宝宝体质

我的相关代码及数据集已经上传GitHub仓库，欢迎使用 Advance-Computer-Vision-with-Python

HandTrackingModule.py

"""
Hand Tracking Module
By: Murtaza Hassan
Youtube: http://www.youtube.com/c/MurtazasWorkshopRoboticsandAI
Website: https://www.computervision.zone

Modified by: Diraw
Date: 20240812
Description:
1. Modified the initialization of the `Hands` object to use named parameters for better clarity and compatibility with the latest version of the mediapipe library. This change ensures that the parameters are correctly mapped to the expected arguments in the `Hands` class.
2. Added a line to flip the image horizontally using `cv2.flip(img, 1)` to ensure the hand movements appear mirrored, which is more intuitive for user interaction
3. Added the code lmList = lmList[0] at line 59 in VolumeHandControl.py to fix the error: IndexError: tuple index out of range.
4. Set the top toolbar image by resizing it with cv2.resize(header, (1280, 125)) and placing it at the top of the frame with img[0:125, 0:1280] = header to ensure it fits the toolbar area.
5. Added a mode change flag to ensure proper initialization of drawing coordinates. This prevents unwanted lines when switching from selection to drawing mode by resetting xp and yp to the current finger position upon mode change.
6. Adjusted the threshold value in cv2.threshold from 50 to 25 to improve the visibility of blue lines by ensuring they are correctly captured in the binary inversion process.
"""

import cv2
import mediapipe as mp
import time
import math
import numpy as np


class handDetector:
    def __init__(self, mode=False, maxHands=2, detectionCon=0.5, trackCon=0.5):
        # 初始化手部检测器参数
        self.mode = mode  # 是否为静态模式
        self.maxHands = maxHands  # 最大检测手的数量
        self.detectionCon = detectionCon  # 检测置信度
        self.trackCon = trackCon  # 跟踪置信度

        # 初始化 Mediapipe 手部模型
        self.mpHands = mp.solutions.hands
        # self.hands = self.mpHands.Hands(
        #     self.mode, self.maxHands, self.detectionCon, self.trackCon
        # )
        self.hands = self.mpHands.Hands(
            static_image_mode=self.mode,
            max_num_hands=self.maxHands,
            min_detection_confidence=self.detectionCon,
            min_tracking_confidence=self.trackCon,
        )
        self.mpDraw = mp.solutions.drawing_utils  # 用于绘制手部关键点
        self.tipIds = [4, 8, 12, 16, 20]  # 手指尖端的关键点 ID

    def findHands(self, img, draw=True):
        # 检测手部并绘制关键点
        imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # 转换为 RGB
        self.results = self.hands.process(imgRGB)  # 处理图像

        if self.results.multi_hand_landmarks:
            for handLms in self.results.multi_hand_landmarks:
                if draw:
                    # 绘制手部关键点及连接
                    self.mpDraw.draw_landmarks(
                        img, handLms, self.mpHands.HAND_CONNECTIONS
                    )

        return img

    def findPosition(self, img, handNo=0, draw=True):
        # 获取手部关键点坐标
        xList = []
        yList = []
        bbox = []
        self.lmList = []  # 存储关键点信息
        if self.results.multi_hand_landmarks:
            myHand = self.results.multi_hand_landmarks[handNo]
            for id, lm in enumerate(myHand.landmark):
                h, w, c = img.shape
                cx, cy = int(lm.x * w), int(lm.y * h)  # 转换为像素坐标
                xList.append