Welcome to JetCobot repository
3.Overall detection
Overall 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 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 (such as Raspberry Pi), 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 ordinary hardware.
Build once, deploy anywhere: unified solution for Android, iOS, desktop/cloud, web, and IoT.
Ready-to-use solution: cutting-edge ML solution that showcases the full capabilities of the framework.
Free and open source: framework and solution under Apache 2.0, fully extensible and customizable.
2.MediaPipe Hands
Refer to the【hand detection】course.
3.MediaPipe Pose
Refer to the【Posture detection】course.
4. Overall detection
Combining the content of the previous course, this course can detect both palms and human bodies.
4.1 Start
Input following command to start the program
xxxxxxxxxxroscorerosrun jetcobot_mediapipe 03_Holistic.py
4.2 Code
Code path：~/jetcobot_ws/src/jetcobot_mediapipe/scripts/03_Holistic.py
x#!/usr/bin/env python3# encoding: utf-8import timeimport rospyimport cv2 as cvimport numpy as npimport mediapipe as mpfrom geometry_msgs.msg import Pointfrom yahboomcar_msgs.msg import PointArray
class Holistic: def __init__(self, staticMode=False, landmarks=True, detectionCon=0.5, trackingCon=0.5): self.mpHolistic = mp.solutions.holistic self.mpFaceMesh = mp.solutions.face_mesh self.mpHands = mp.solutions.hands self.mpPose = mp.solutions.pose self.mpDraw = mp.solutions.drawing_utils self.mpholistic = self.mpHolistic.Holistic( static_image_mode=staticMode, smooth_landmarks=landmarks, min_detection_confidence=detectionCon, min_tracking_confidence=trackingCon) 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=3) self.drawSpec = mp.solutions.drawing_utils.DrawingSpec(color=(0, 255, 0), thickness=2, circle_radius=2)
def findHolistic(self, frame, draw=True): pointArray = PointArray() img = np.zeros(frame.shape, np.uint8) img_RGB = cv.cvtColor(frame, cv.COLOR_BGR2RGB) self.results = self.mpholistic.process(img_RGB) if self.results.face_landmarks: if draw: self.mpDraw.draw_landmarks(frame, self.results.face_landmarks, self.mpFaceMesh.FACEMESH_CONTOURS, self.lmDrawSpec, self.drawSpec) self.mpDraw.draw_landmarks(img, self.results.face_landmarks, self.mpFaceMesh.FACEMESH_CONTOURS, self.lmDrawSpec, self.drawSpec) for id, lm in enumerate(self.results.face_landmarks.landmark): point = Point() point.x, point.y, point.z = lm.x, lm.y, lm.z pointArray.points.append(point) if self.results.pose_landmarks: if draw: self.mpDraw.draw_landmarks(frame, self.results.pose_landmarks, self.mpPose.POSE_CONNECTIONS, self.lmDrawSpec, self.drawSpec) self.mpDraw.draw_landmarks(img, self.results.pose_landmarks, self.mpPose.POSE_CONNECTIONS, self.lmDrawSpec, self.drawSpec) for id, lm in enumerate(self.results.pose_landmarks.landmark): point = Point() point.x, point.y, point.z = lm.x, lm.y, lm.z pointArray.points.append(point) if self.results.left_hand_landmarks: if draw: self.mpDraw.draw_landmarks(frame, self.results.left_hand_landmarks, self.mpHands.HAND_CONNECTIONS, self.lmDrawSpec, self.drawSpec) self.mpDraw.draw_landmarks(img, self.results.left_hand_landmarks, self.mpHands.HAND_CONNECTIONS, self.lmDrawSpec, self.drawSpec) for id, lm in enumerate(self.results.left_hand_landmarks.landmark): point = Point() point.x, point.y, point.z = lm.x, lm.y, lm.z pointArray.points.append(point) if self.results.right_hand_landmarks: if draw: self.mpDraw.draw_landmarks(frame, self.results.right_hand_landmarks, self.mpHands.HAND_CONNECTIONS, self.lmDrawSpec, self.drawSpec) self.mpDraw.draw_landmarks(img, self.results.right_hand_landmarks, self.mpHands.HAND_CONNECTIONS, self.lmDrawSpec, self.drawSpec) for id, lm in enumerate(self.results.right_hand_landmarks.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('Holistic', anonymous=True) 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 holistic = Holistic() while capture.isOpened(): ret, frame = capture.read() # frame = cv.flip(frame, 1) frame, img = holistic.findHolistic(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, 0.8, (0, 0, 255), 2) dist = holistic.frame_combine(frame, img) cv.imshow('dist', dist) # cv.imshow('frame', frame) # cv.imshow('img', img) capture.release() cv.destroyAllWindows()