Welcome to JetCobot repository
10. Gesture Recognition
10. Gesture Recognition
10.1. Introduction
MediaPipe is an open-source data stream processing machine learning application development framework developed by Google. It is a graph-based data processing pipeline used to build data sources in various forms, such as video, audio, sensor data, and any time series data.
MediaPipe is cross-platform and can run on embedded platforms (Raspberry Pi, etc.), mobile devices (iOS and Android), workstations and servers, and supports mobile GPU acceleration. MediaPipe provides cross-platform, customizable ML solutions for real-time and streaming media.
The core framework of MediaPipe is implemented in C++ and provides support for languages such as Java and Objective C. The main concepts of MediaPipe include packets, streams, calculators, graphs, and subgraphs.
Features of MediaPipe:
End-to-end acceleration: built-in fast ML inference and processing can be accelerated even on commodity hardware.
Build once, deploy anywhere: unified solution for Android, iOS, desktop/cloud, web and IoT.
Ready-to-use solution: cutting-edge ML solution that demonstrates the full capabilities of the framework.
Free and open source: framework and solution under Apache2.0, fully extensible and customizable.
10.2, Gesture Recognition
Gesture recognition designed for the right hand can be accurately recognized when specific conditions are met. The recognizable gestures are: [Zero, One, Two, Three, Four, Five, Six, Seven, Eight, Ok, Rock, Thumb_up (Like), Thumb_down (Thumbs down), Heart_single (Heart)], a total of 14 categories.
10.2.1. Start
Enter the following command to start the program
xxxxxxxxxxros2 run jetcobot_mediapipe 10_GestureRecognition
10.2. Source code
Source code location: ~/jetcobot_ws/src/jetcobot_mediapipe/jetcobot_mediapipe/10_GestureRecognition.py
x#!/usr/bin/env python3# encoding: utf-8import mathimport timeimport cv2 as cvimport numpy as npimport mediapipe as mp
class handDetector: def __init__(self, mode=False, maxHands=2, detectorCon=0.5, trackCon=0.5): self.tipIds = [4, 8, 12, 16, 20] self.mpHand = mp.solutions.hands self.mpDraw = mp.solutions.drawing_utils self.hands = self.mpHand.Hands( static_image_mode=mode, max_num_hands=maxHands, min_detection_confidence=detectorCon, min_tracking_confidence=trackCon ) self.lmList = [] self.lmDrawSpec = mp.solutions.drawing_utils.DrawingSpec(color=(0, 0, 255), thickness=-1, circle_radius=6) self.drawSpec = mp.solutions.drawing_utils.DrawingSpec(color=(0, 255, 0), thickness=2, circle_radius=2)
def get_dist(self, point1, point2): x1, y1 = point1 x2, y2 = point2 return abs(math.sqrt(math.pow(abs(y1 - y2), 2) + math.pow(abs(x1 - x2), 2)))
def calc_angle(self, pt1, pt2, pt3): point1 = self.lmList[pt1][1], self.lmList[pt1][2] point2 = self.lmList[pt2][1], self.lmList[pt2][2] point3 = self.lmList[pt3][1], self.lmList[pt3][2] a = self.get_dist(point1, point2) b = self.get_dist(point2, point3) c = self.get_dist(point1, point3) try: radian = math.acos((math.pow(a, 2) + math.pow(b, 2) - math.pow(c, 2)) / (2 * a * b)) angle = radian / math.pi * 180 except: angle = 0 return abs(angle)
def findHands(self, frame, draw=True): self.lmList = [] img = np.zeros(frame.shape, np.uint8) img_RGB = cv.cvtColor(frame, cv.COLOR_BGR2RGB) self.results = self.hands.process(img_RGB) if self.results.multi_hand_landmarks: for i in range(len(self.results.multi_hand_landmarks)): if draw: self.mpDraw.draw_landmarks(frame, self.results.multi_hand_landmarks[i], self.mpHand.HAND_CONNECTIONS, self.lmDrawSpec, self.drawSpec) self.mpDraw.draw_landmarks(img, self.results.multi_hand_landmarks[i], self.mpHand.HAND_CONNECTIONS, self.lmDrawSpec, self.drawSpec) for id, lm in enumerate(self.results.multi_hand_landmarks[i].landmark): h, w, c = frame.shape cx, cy = int(lm.x * w), int(lm.y * h) self.lmList.append([id, cx, cy]) return frame, img
def frame_combine(slef,frame, src): if len(frame.shape) == 3: frameH, frameW = frame.shape[:2] srcH, srcW = src.shape[:2] dst = np.zeros((max(frameH, srcH), frameW + srcW, 3), np.uint8) dst[:, :frameW] = frame[:, :] dst[:, frameW:] = src[:, :] else: src = cv.cvtColor(src, cv.COLOR_BGR2GRAY) frameH, frameW = frame.shape[:2] imgH, imgW = src.shape[:2] dst = np.zeros((frameH, frameW + imgW), np.uint8) dst[:, :frameW] = frame[:, :] dst[:, frameW:] = src[:, :] return dst
def fingersUp(self): fingers=[] # Thumb if (self.calc_angle(self.tipIds[0], self.tipIds[0] - 1, self.tipIds[0] - 2) > 150.0) and ( self.calc_angle( self.tipIds[0] - 1, self.tipIds[0] - 2, self.tipIds[0] - 3) > 150.0): fingers.append(1) else: fingers.append(0) # 4 finger 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 get_gesture(self): gesture = "" fingers = self.fingersUp() if self.lmList[self.tipIds[0]][2] > self.lmList[self.tipIds[1]][2] and \ self.lmList[self.tipIds[0]][2] > self.lmList[self.tipIds[2]][2] and \ self.lmList[self.tipIds[0]][2] > self.lmList[self.tipIds[3]][2] and \ self.lmList[self.tipIds[0]][2] > self.lmList[self.tipIds[4]][2] : gesture = "Thumb_down"
elif self.lmList[self.tipIds[0]][2] < self.lmList[self.tipIds[1]][2] and \ self.lmList[self.tipIds[0]][2] < self.lmList[self.tipIds[2]][2] and \ self.lmList[self.tipIds[0]][2] < self.lmList[self.tipIds[3]][2] and \ self.lmList[self.tipIds[0]][2] < self.lmList[self.tipIds[4]][2] and \ self.calc_angle(self.tipIds[1] - 1, self.tipIds[1] - 2, self.tipIds[1] - 3) < 150.0 : gesture = "Thumb_up" if fingers.count(1) == 3 or fingers.count(1) == 4: if fingers[0] == 1 and ( self.get_dist(self.lmList[4][1:], self.lmList[8][1:])<self.get_dist(self.lmList[4][1:], self.lmList[5][1:]) ): gesture = "OK" elif fingers[2] == fingers[3] == 0: gesture = "Rock" elif fingers.count(1) == 3: gesture = "Three" else: gesture = "Four" elif fingers.count(1) == 0: gesture = "Zero" elif fingers.count(1) == 1: gesture = "One" elif fingers.count(1) == 2: if fingers[0] == 1 and fingers[4] == 1: gesture = "Six" elif fingers[0] == 1 and self.calc_angle(4, 5, 8) > 90: gesture = "Eight" elif fingers[0] == fingers[1] == 1 and self.get_dist(self.lmList[4][1:], self.lmList[8][1:]) < 50: gesture = "Heart_single" else: gesture = "Two" elif fingers.count(1)==5:gesture = "Five" if self.get_dist(self.lmList[4][1:], self.lmList[8][1:]) < 60 and \ self.get_dist(self.lmList[4][1:], self.lmList[12][1:]) < 60 and \ self.get_dist(self.lmList[4][1:], self.lmList[16][1:]) < 60 and \ self.get_dist(self.lmList[4][1:], self.lmList[20][1:]) < 60 : gesture = "Seven" if self.lmList[self.tipIds[0]][2] < self.lmList[self.tipIds[1]][2] and \ self.lmList[self.tipIds[0]][2] < self.lmList[self.tipIds[2]][2] and \ self.lmList[self.tipIds[0]][2] < self.lmList[self.tipIds[3]][2] and \ self.lmList[self.tipIds[0]][2] < self.lmList[self.tipIds[4]][2] and \ self.calc_angle(self.tipIds[1] - 1, self.tipIds[1] - 2, self.tipIds[1] - 3) > 150.0 : gesture = "Eight" return gesture
'''Zero One Two Three Four Five Six Seven EightOk: OKRock: rockThumb_up : 点赞Thumb_down: 拇指向下Heart_single: 单手比心'''
if __name__ == '__main__': capture = cv.VideoCapture(0) capture.set(6, cv.VideoWriter.fourcc('M', 'J', 'P', 'G')) capture.set(cv.CAP_PROP_FRAME_WIDTH, 640) capture.set(cv.CAP_PROP_FRAME_HEIGHT, 480) print("capture get FPS : ", capture.get(cv.CAP_PROP_FPS)) pTime = cTime = 0 hand_detector = handDetector(detectorCon=0.75) while capture.isOpened(): ret, frame = capture.read() # frame = cv.flip(frame, 1) frame, img = hand_detector.findHands(frame, draw=False) if len(hand_detector.lmList) != 0: totalFingers = hand_detector.get_gesture() cv.rectangle(frame, (0, 430), (230, 480), (0, 255, 0), cv.FILLED) cv.putText(frame, str(totalFingers), (10, 470), cv.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 2) if cv.waitKey(1) & 0xFF == ord('q'): break cTime = time.time() fps = 1 / (cTime - pTime) pTime = cTime text = "FPS : " + str(