Welcome to USB Industrial Grade AI Vision Camera repository
1.Hand detection
Hand detection
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 for building and using multiple forms of data sources, 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 Packet, Stream, Calculator, Graph and Subgraph.
Features of MediaPipe:
- End-to-end acceleration: Built-in fast ML inference and processing accelerates even on commodity hardware.
- Build once, deploy anywhere: Unified solution for Android, iOS, desktop/cloud, web and IoT.
- Ready-to-use solutions: cutting-edge ML solutions that showcase the full capabilities of the framework.
- Free and open source: frameworks and solutions under Apache2.0, fully extensible and customizable.
2. MediaPipe Hands
MediaPipe Hands is a high-fidelity hand and finger tracking solution. It uses machine learning (ML) to infer the 3D coordinates of 21 hands from a frame.
After hand detection on the entire image, the 21 3D hand joint coordinates in the detected hand area are accurately positioned through regression based on the hand mark model, that is, direct coordinate prediction. The model learns consistent internal hand pose representations and is robust even to partially visible hands and self-occlusion.
In order to obtain ground truth data, about 30K real-world images were manually annotated with 21 3D coordinates as shown below (Z value is obtained from the image depth map, if there is a Z value for each corresponding coordinate). To better cover possible hand poses and provide additional supervision on the nature of the hand geometry, high-quality synthetic hand models in various backgrounds are also drawn and mapped to corresponding 3D coordinates.
3. Hand detection
1. Startup
Input command:
roscorerosrun yahboomcar_mediapipe 01_HandDetector.py
2. Source code
Code path：~/yahboomcar_ws/src/yahboomcar_mediapipe/scripts
x#!/usr/bin/env python3# encoding: utf-8import rospyimport timeimport cv2 as cvimport numpy as npimport mediapipe as mpfrom geometry_msgs.msg import Pointfrom yahboomcar_msgs.msg import PointArray
class HandDetector: def __init__(self, mode=False, maxHands=2, detectorCon=0.5, trackCon=0.5): 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.pub_point = rospy.Publisher('/mediapipe/points', PointArray, queue_size=1000) 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 pubHandsPoint(self, frame, draw=True): pointArray = PointArray() 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: ''' draw_landmarks():在图像上绘制地标和连接。 image: 表示为numpy ndarray的三通道RGB图像。 landmark_list: 要在图像上注释的规范化地标列表原始消息。 connections: 地标索引元组列表，指定如何在绘图中连接地标。 landmark_drawing_spec: 用于指定地标的绘图设置，例如颜色、线条粗细和圆半径。 connection_drawing_spec: 用于指定连接的绘图设置，例如颜色和线条粗细。 ''' 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): point = Point() point.x, point.y, point.z = lm.x, lm.y, lm.z pointArray.points.append(point) self.pub_point.publish(pointArray) 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
if __name__ == '__main__': rospy.init_node('handDetector', anonymous=True) capture = cv.VideoCapture(4) 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(maxHands=2) while capture.isOpened(): ret, frame = capture.read() # frame = cv.flip(frame, 1) frame, img = hand_detector.pubHandsPoint(frame, draw=False) if cv.waitKey(1) & 0xFF == ord('q'): break cTime = time.time() fps = 1 / (cTime - pTime) pTime = cTime text = "FPS : " + str(int(fps)) cv.putText(frame, text, (20, 30), cv.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 1) dist = hand_detector.frame_combine(frame, img) cv.imshow('dist', dist) #cv.imshow('frame', frame) # cv.imshow('img', img) capture.release() cv.destroyAllWindows()