Welcome to MicroROS Robot repository
4. Face detection
4.Face detection
1、synopsis
MediaPipe is a data stream processing machine learning application development framework developed and open-source by Google. It is a graph based data processing pipeline that enables the construction of various forms of data sources, such as video, frequency, 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, while supporting 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.
The characteristics of MediaPipe：
- End to end acceleration: Built in fast ML inference and processing that can accelerate even on regular hardware。
- Build once, deploy anytime, anywhere: Unified solution suitable for Android, iOS, desktop/cloud, web, and IoT。
- Instant solution: A cutting-edge ML solution that showcases all features of the framework。
- Free and open source: Framework and solution under Apache 2.0, fully scalable and customizable.
2、Dlib
DLIB is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real-world problems. It is widely used by industry and academia in the fields of robotics, embedded devices, mobile phones, and large high-performance computing environments. The dlib library uses 68 points to mark important parts of the face, such as 18-22 points to mark the right eyebrow and 51-68 to mark the mouth. The get_frontal_face_detector module of dlib library was used to detect faces, and shape_predictor_68_face_landmarks.dat feature data was used to predict face feature values。
3、Face detection
3.1、activate
- First, set the proxy IP for the ROS-wifi image transmission module. For specific steps, please refer to the tutorial 1. Basic Usage of ROS-Wifi Image Transmission Module on Micros Car. This tutorial will not cover this process in detail.
- The Linux system is connected to the ROS-wifi image transfer module, and the Docker is started. Enter the following command to establish a connection with the ROS-wifi image transfer module.
#Use the provided system for direct inputsh start_Camera_computer.shxxxxxxxxxx#Systems that are not data:docker run -it --rm -v /dev:/dev -v /dev/shm:/dev/shm --privileged --net=host microros/micro-ros-agent:humble udp4 --port 9999 -v4
If the preceding information is displayed, the proxy connection is successful
- Open a new terminal and execute the following command
xxxxxxxxxxros2 run yahboom_esp32_mediapipe 04_FaceMesh
- Notes
- "If the camera angle is not centered, the Linux system needs to connect to the car agent. Please refer to the specific steps for development preparation before proceeding with tutorial 0. which provides essential information for a quick start (must-read). This tutorial will not provide further elaboration."
- After connecting the car's agent, enter the following command
xxxxxxxxxxros2 run yahboom_esp32_mediapipe control_servo
After the steering engine is in the center, press "ctrl+C" to end the program.
- If the camera picture is upside down, see 3. Camera picture correction (must-read) tutorial, this tutorial is no longer explained
3.2、Code parsing
The location of the function source code is located,
xxxxxxxxxx~/yahboomcar_ws/src/yahboom_esp32_mediapipe/yahboom_esp32_mediapipe/04_FaceMesh.py
xclass FaceMesh(Node): def __init__(self, name,staticMode=False, maxFaces=2, minDetectionCon=0.5, minTrackingCon=0.5): super().__init__(name) self.mpDraw = mp.solutions.drawing_utils self.mpFaceMesh = mp.solutions.face_mesh self.faceMesh = self.mpFaceMesh.FaceMesh( static_image_mode=staticMode, max_num_faces=maxFaces, min_detection_confidence=minDetectionCon, min_tracking_confidence=minTrackingCon ) self.pub_point = self.create_publisher(PointArray,'/mediapipe/points',1000) self.lmDrawSpec = mp.solutions.drawing_utils.DrawingSpec(color=(0, 0, 255), thickness=-1, circle_radius=3) self.drawSpec = self.mpDraw.DrawingSpec(color=(0, 255, 0), thickness=1, circle_radius=1)
def pubFaceMeshPoint(self, frame, draw=True): pointArray = PointArray() img = np.zeros(frame.shape, np.uint8) imgRGB = cv.cvtColor(frame, cv.COLOR_BGR2RGB) self.results = self.faceMesh.process(imgRGB) if self.results.multi_face_landmarks: for i in range(len(self.results.multi_face_landmarks)): if draw: self.mpDraw.draw_landmarks(frame, self.results.multi_face_landmarks[i], self.mpFaceMesh.FACEMESH_CONTOURS, self.lmDrawSpec, self.drawSpec) self.mpDraw.draw_landmarks(img, self.results.multi_face_landmarks[i], self.mpFaceMesh.FACEMESH_CONTOURS, self.lmDrawSpec, self.drawSpec) for id, lm in enumerate(self.results.multi_face_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
class MY_Picture(Node): def __init__(self, name): super().__init__(name) self.bridge = CvBridge() self.sub_img = self.create_subscription( CompressedImage, '/espRos/esp32camera', self.handleTopic, 1)
self.face_mesh = FaceMesh('face_mesh')
def handleTopic(self, msg): start = time.time()
frame = self.bridge.compressed_imgmsg_to_cv2(msg) frame = cv.resize(frame, (640, 480)) cv.waitKey(10) frame, img = self.face_mesh.pubFaceMeshPoint(frame,draw=False) end = time.time() fps = 1 / (end - start) text = "FPS : " + str(int(fps)) cv.putText(frame, text, (20, 30), cv.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 1)
dist = self.face_mesh.frame_combine(frame, img) cv.imshow('dist', dist) # print(frame) cv.waitKey(10)
def main(): print("start it") rclpy.init() esp_img = MY_Picture("My_Picture") try: rclpy.spin(esp_img) except KeyboardInterrupt: pass finally: esp_img.destroy_node() rclpy.shutdown()The main process of the program: subscribe to the image from esp32, through MediaPipe to do the relevant recognition, and then through opencv to display the processed image.