Opencv项目实战：20 单手识别数字0到5

Posted 2023-01-28 夏天是冰红茶

目录

0、项目介绍

1、效果展示

2、项目搭建

3、项目代码展示

HandTrackingModule.py

Figures_counter.py

4、项目资源

5、项目总结

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0、项目介绍

今天要做的是单手识别数字0到5，通过在窗口展示，实时的展示相应的图片以及文字。

在网上找了很久的手势表示数字的图片，当然为了本项目的简洁，我只展示了0到5，感兴趣的可以自己添加后面的，原理很简单。

1、效果展示

 成功的实现了单手识别数字0到5，实时展现也很不错。

2、项目搭建

 在文件image_figures中，我将"完整图片.png"手动裁剪成0到5的图片，大小为220300，当然你可以不用想我这样裁成统一大小，后面有解决的方法。

3、项目代码展示

HandTrackingModule.py

import cv2
import mediapipe as mp
import math
import time

class handDetector:

    def __init__(self, mode=False, maxHands=2, detectionCon=0.5, minTrackCon=0.5):
        self.mode = mode
        self.maxHands = maxHands
        self.detectionCon = detectionCon
        self.minTrackCon = minTrackCon

        self.mpHands = mp.solutions.hands
        self.hands = self.mpHands.Hands(static_image_mode=self.mode, max_num_hands=self.maxHands,
                                        min_detection_confidence=self.detectionCon,
                                        min_tracking_confidence=self.minTrackCon)
        self.mpDraw = mp.solutions.drawing_utils
        self.tipIds = [4, 8, 12, 16, 20]
        self.fingers = []
        self.lmList = []

    def findHands(self, img, draw=True):
        imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        self.results = self.hands.process(imgRGB)
        # print(results.multi_hand_landmarks)

        if self.results.multi_hand_landmarks:
            for handLms in self.results.multi_hand_landmarks:
                if draw:
                    self.mpDraw.draw_landmarks(img, handLms,
                                               self.mpHands.HAND_CONNECTIONS)

        return img


    def findPosition(self, img, handNo=0, draw=True):
        self.lmList=[]
        bbox = 0
        if self.results.multi_hand_landmarks:
            myHand = self.results.multi_hand_landmarks[handNo]
            xList = []
            yList = []
            for id, lm in enumerate(myHand.landmark):
                # print(id, lm)
                h, w, c = img.shape
                cx, cy = int(lm.x * w), int(lm.y * h)
                xList.append(cx)
                yList.append(cy)
                # print(id, cx, cy)
                self.lmList.append([id, cx, cy])
                if draw:
                    cv2.circle(img, (cx, cy), 5, (255, 0, 255), cv2.FILLED)

            xmin, xmax = min(xList), max(xList)
            ymin, ymax = min(yList), max(yList)
            bbox = xmin, ymin, xmax, ymax

            if draw:
                cv2.rectangle(img, (xmin - 20, ymin - 20), (xmax + 20, ymax + 20),
                                (0, 255, 0), 2)

        return self.lmList, bbox

    def fingersUp(self):
        fingers = []
        # Thumb
        if self.lmList[self.tipIds[0]][1] > self.lmList[self.tipIds[0] - 1][1]:
            fingers.append(1)
        else:
            fingers.append(0)

        # Fingers
        for id in range(1, 5):
            if self.lmList[self.tipIds[id]][2] < self.lmList[self.tipIds[id] - 2][2]:
                fingers.append(1)
            else:
                fingers.append(0)

            # totalFingers = fingers.count(1)

        return fingers

    def findDistance(self, p1, p2, img=None):
        x1, y1 = self.lmList[p1][1:]
        x2, y2 = self.lmList[p2][1:]
        cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
        length = math.hypot(x2 - x1, y2 - y1)
        info = (x1, y1, x2, y2, cx, cy)
        if img is not None:
            cv2.circle(img, (x1, y1), 15, (255, 0, 255), cv2.FILLED)
            cv2.circle(img, (x2, y2), 15, (255, 0, 255), cv2.FILLED)
            cv2.line(img, (x1, y1), (x2, y2), (255, 0, 255), 3)
            cv2.circle(img, (cx, cy), 15, (255, 0, 255), cv2.FILLED)
            return length, info, img
        else:
            return length, info


def main():
    pTime = 0
    cTime = 0
    cap = cv2.VideoCapture(0)
    detector = handDetector()
    while True:
        success, img = cap.read()
        img = detector.findHands(img)
        lmList, bbox = detector.findPosition(img)
        if len(lmList) != 0:
            print(lmList[4])

        cTime = time.time()
        fps = 1 / (cTime - pTime)
        pTime = cTime

        cv2.putText(img, str(int(fps)), (10, 70), cv2.FONT_HERSHEY_PLAIN, 3,
        (255, 0, 255), 3)

        cv2.imshow("Image", img)
        k=cv2.waitKey(1)
        if k==27:
            break

if __name__ == "__main__":
    main()

Figures_counter.py

import os
import cv2
import mediapipe as mp
import time
import HandTrackingModule as htm

class fpsReader():
    def __init__(self):
        self.pTime = time.time()
    def FPS(self,img=None,pos=(20, 50), color=(255, 255, 0), scale=3, thickness=3):
        cTime = time.time()
        try:
            fps = 1 / (cTime - self.pTime)
            self.pTime = cTime
            if img is None:
                return fps
            else:
                cv2.putText(img, f'FPS: int(fps)', pos, cv2.FONT_HERSHEY_PLAIN,
                            scale, color, thickness)
                return fps, img
        except:
            return 0
fpsReader = fpsReader()
cap=cv2.VideoCapture(0)

Wcam, Hcam = 980, 980
cap.set(3, Wcam)
cap.set(4, Hcam)
cap.set(10,150)


img_path="image_figures"
mulu=os.listdir(img_path)
print(mulu)
Laylist=[]
for path in mulu:
    image=cv2.imread(f"img_path/path")
    Laylist.append(image)

detector = htm.handDetector(detectionCon=0.75)

while 1:
    _, img = cap.read()

    detector.findHands(img)
    lmList,_= detector.findPosition(img, draw=False)


    if len(lmList) != 0:
        fingerup=detector.fingersUp()
        print(fingerup)
        all_figures=fingerup.count(1)
        print(all_figures)
        h, w, _ = Laylist[all_figures].shape
        img[0:h, 0:w] = Laylist[all_figures]
        # img[0:300,0:220]=Laylist[0]

        cv2.rectangle(img,(0,350),(220,550),(0,255,0),cv2.FILLED)
        cv2.putText(img,str(all_figures),(45,510),cv2.FONT_HERSHEY_COMPLEX,6,(0,0,255),25)


    #################打印帧率#####################
    fps, img = fpsReader.FPS(img,pos=(880,50))
    cv2.imshow("image",img)
    k=cv2.waitKey(1)
    if k==27:
        break

这里的HandTrackingModule.py文件与上一节相同，不用更改什么。

由于我裁剪的时候是按照0-5的顺序命名，Laylist的索引刚好与其对应，所以不用在进行多的修改，而且这里的图片大小其实是可以根据其shape直接得到的，但当时我没有想到，所以就把所有的图片裁剪成统一大小了。

4、项目资源

GitHub:Opencv项目实战：20 单手识别数字0到5

5、项目总结

在这里，我提供一下识别更多数字的方法（0-10）。首先最简便的是双手识别，完全不用更改代码，把图片处理好就行了；其次，就是按照最上面的那张图片，参数figureup是一个长度为5的列表[0,0,0,0,0]，你可以参照着手势将其打印出来，然后将其用if条件判断。当然，在我们这边最常见的还是华北手势表示数字，大家按照自己的习惯来就行。