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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 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 solutions 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、Overall inspection
Combined with the contents of the previous two sections, this routine implements the function of detecting both palms and human bodies.
2.1、run command
After entering the docker container, enter the following command in the terminal.
ros2 run yahboomcar_mediapipe 03_Holistic
2.2、Source code
After entering the docker container, the location of the source code of this function is as follows.
xxxxxxxxxx/root/yahboomcar_ws/src/yahboomcar_mediapipe/yahboomcar_mediapipe/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()