Welcome to JetCobot repository
1. Hand detection
1. Hand Detection
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 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.
1.2, MediaPipe Hands
MediaPipe Hands is a high-fidelity hand and finger tracking solution. It uses machine learning (ML) to infer 21 3D coordinates of the hand from a frame.
After palm detection for the entire image, the 21 3D hand joint coordinates in the detected hand area are accurately located by regression based on the hand marker model, that is, direct coordinate prediction. The model learns a consistent internal hand pose representation that is robust even to partially visible hands and self-occlusions.
To obtain ground truth data, ~30K real-world images were manually annotated with 21 3D coordinates, as shown below (Z values were taken from the image depth map, if available for each corresponding coordinate). To provide better coverage of possible hand poses and provide additional supervision on the properties of the hand geometry, high-quality synthetic hand models were also drawn against various backgrounds and mapped to the corresponding 3D coordinates.
1.3, Hand detection
1.3.1, Start
Enter the following command to start the program
xxxxxxxxxxros2 run jetcobot_mediapipe 01_HandDetector
1.3.2, Source code
Source code location: ~/jetcobot_ws/src/jetcobot_mediapipe/jetcobot_mediapipe/01_HandDetector.py
x#!/usr/bin/env python3# encoding: utf-8import rclpyimport cv2 as cvimport numpy as npimport mediapipe as mpimport timefrom rclpy.node import Nodefrom geometry_msgs.msg import Pointfrom jetcobot_msgs.msg import PointArrayfrom jetcobot_utils.grasp_controller import GraspController
class HandDetector(Node): def __init__(self): super().__init__('hand_detector') self.graspController = GraspController() self.graspController.init_pose2() self.mpHand = mp.solutions.hands self.mpDraw = mp.solutions.drawing_utils self.hands = self.mpHand.Hands( static_image_mode=False, max_num_hands=2, min_detection_confidence=0.5, min_tracking_confidence=0.5 )
self.publisher_ = self.create_publisher(PointArray, '/mediapipe/points', 10) 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)
# Initialize the camera self.capture = cv.VideoCapture(0, cv.CAP_V4L2) self.capture.set(cv.CAP_PROP_FRAME_WIDTH, 640) self.capture.set(cv.CAP_PROP_FRAME_HEIGHT, 480)
if not self.capture.isOpened(): self.get_logger().error("Failed to open the camera") return
self.get_logger().info(f"Camera FPS: {self.capture.get(cv.CAP_PROP_FPS)}") self.pTime = time.time()
self.process_frames_loop()
def process_frames_loop(self): while rclpy.ok() and self.capture.isOpened(): self.process_frame()
def process_frame(self): ret, frame = self.capture.read() if not ret: self.get_logger().error("Failed to read frame") return frame, img = self.pubHandsPoint(frame, draw=True) cTime = time.time() fps = 1 / (cTime - self.pTime) self.pTime = cTime text = "FPS : " + str(int(fps)) cv.putText(frame, text, (20, 30), cv.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 1)
HandDetector = self.frame_combine(frame, img) cv.imshow('HandDetector', HandDetector)
if cv.waitKey(1) & 0xFF == ord('q'): self.get_logger().info("Exiting program") self.capture.release() cv.destroyAllWindows() self.destroy_node() rclpy.shutdown() exit(0)
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: 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 lm in 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.publisher_.publish(pointArray) return frame, img
def frame_combine(self, 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 main(args=None): rclpy.init(args=args) node = HandDetector() try: rclpy.spin(node) except KeyboardInterrupt: pass finally: node.capture.release() cv.destroyAllWindows() node.destroy_node() rclpy.shutdown()
if __name__ == '__main__': main()