Handtrackingmodule.py

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


class handDetector():
    def __init__(self, mode=False, maxHands=2, model_complexity=1, detectionCon=0.5, trackCon=0.5):
        self.mode = mode
        self.maxHands = maxHands
        self.detectionCon = detectionCon
        self.model_complexity = model_complexity
        self.trackCon = trackCon

        self.mpHands = mp.solutions.hands
        self.hands = self.mpHands.Hands(self.mode, self.maxHands, self.model_complexity,
                                        self.detectionCon, self.trackCon)
        self.mpDraw = mp.solutions.drawing_utils
        self.tipIds = [4, 8, 12, 16, 20]

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

        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):
        xList = []
        yList = []
        bbox = []
        self.lmlist = []

        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)
                xList.append(cx)
                yList.append(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-28, ymin-20), (xmax+20, ymax+20),
                              (0, 255, 0), 2)

        return self.lmlist, bbox

    def fingersUp(self):
        fingers = []
        # Thumb
        # Right Hand
        if self.lmlist[self.tipIds[0]][1] > self.lmlist[self.tipIds[4]][1]:
            if self.lmlist[self.tipIds[0]][1] > self.lmlist[self.tipIds[0]-1][1]:
                fingers.append(1)
            else:
                fingers.append(0)
        # Left Hand
        else:

            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)

        return fingers

    def findDistance(self, p1, p2, img, draw=True, r=15, t=3):
        x1, y1 = self.lmlist[p1][1:]
        x2, y2 = self.lmlist[p2][1:]
        cx, cy = (x1+x2) // 2, (y1+y2) // 2  # floor division

        if draw:
            cv2.line(img, (x1, y1), (x2, y2), (255, 0, 255), t)
            cv2.circle(img, (x1, y1), r, (255, 0, 255), cv2.FILLED)
            cv2.circle(img, (x2, y2), r, (255, 0, 255), cv2.FILLED)
            cv2.circle(img, (cx, cy), r, (0, 0, 255), cv2.FILLED)
        length = math.hypot(x2-x1, y2-y1)

        return length, img, [x1, y1, x2, y2, cx, cy]


def main():
    pTime = 0
    cTime = 0
    cap = cv2.VideoCapture(0)
    detector = handDetector(maxHands=1)
    while True:
        success, img = cap.read()
        img = detector.findHands(img)
        lmlist, bbox = detector.findPosition(img)
        if len(lmlist) != 0:
            print(lmlist[12])
        cTime = time.time()
        fps = 1 / (cTime-pTime)
        pTime = cTime
        cv2.putText(img, str(int(fps)), (10, 70), cv2.FONT_HERSHEY_COMPLEX,
                    3, (255, 0, 255), 3)
        cv2.imshow("Image", img)
        cv2.waitKey(1)


if __name__ == "__main__":
    main()