Welcome to DOFBOT-JetsonNANO repository
1.Hand detection
1.Hand detection
Note: There are relevant running codes on Raspberry Pi and Jetson nano, but due to differences in motherboard performance, they may not run so smoothly. The supporting virtual machines also have running environments and programs installed.If the experience on the motherboard is not good, you can remove the camera, plug it into the virtual machine, and connect the camera device to the virtual machine to run the corresponding program on the virtual machine.
1.1. 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.
1.2. 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.
1.3. Hand detection
1.3.1.Startup
Terminal input,
roscorerosrun dofbot_mediapipe 01_HandDetector.py
1.3.2. Source code
Source code location：
/home/jetson/dofbot_ws/src/dofbot_mediapipe/scripts/01_HandDetector.py
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():Draw landmarks and connections on the image. image: A three-channel RGB image represented as a numpy ndarray. landmark_list: Normalized landmark list raw message to be annotated on the image. connections: A list of landmark index tuples specifying how to connect landmarks in the drawing. landmark_drawing_spec: Specifies drawing settings for the landmark, such as color, line weight, and circle radius. connection_drawing_spec: Used to specify drawing settings for the connection, such as color and line thickness. ''' 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()