Welcome to JetCobot repository
5.Face recognition
Face recognition
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.Face recognition
2.1 Start
Input following command to start the program
xxxxxxxxxxroscorerosrun jetcobot_mediapipe 05_FaceEyeDetection.py
2.2 About code
Code path：~/jetcobot_ws/src/jetcobot_mediapipe/scripts/05_FaceDetection.py
x#!/usr/bin/env python3# encoding: utf-8import timeimport rospyimport rospkgimport cv2 as cvfrom cv_bridge import CvBridgefrom sensor_msgs.msg import CompressedImage, Image
class FaceEyeDetection: def __init__(self): self.bridge = CvBridge() rospy.init_node("FaceEyeDetection", anonymous=False) self.eyeDetect = cv.CascadeClassifier( rospkg.RosPack().get_path("jetcobot_mediapipe") + "/scripts/file/haarcascade_eye.xml") self.faceDetect = cv.CascadeClassifier( rospkg.RosPack().get_path("jetcobot_mediapipe") + "/scripts/file/haarcascade_frontalface_default.xml") self.pub_rgb = rospy.Publisher("/FaceEyeDetection/image", Image, queue_size=1) rospy.on_shutdown(self.cancel) def cancel(self): self.pub_rgb.unregister()
def face(self, frame): gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY) faces = self.faceDetect.detectMultiScale(gray, 1.3) for face in faces: frame = self.faceDraw(frame, face) return frame
def eye(self, frame): gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY) eyes = self.eyeDetect.detectMultiScale(gray, 1.3) for eye in eyes: cv.circle(frame, (int(eye[0] + eye[2] / 2), int(eye[1] + eye[3] / 2)), (int(eye[3] / 2)), (0, 0, 255), 2) return frame
def faceDraw(self, frame, bbox, l=30, t=10): x, y, w, h = bbox x1, y1 = x + w, y + h cv.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 255), 2) # Top left x,y cv.line(frame, (x, y), (x + l, y), (255, 0, 255), t) cv.line(frame, (x, y), (x, y + l), (255, 0, 255), t) # Top right x1,y cv.line(frame, (x1, y), (x1 - l, y), (255, 0, 255), t) cv.line(frame, (x1, y), (x1, y + l), (255, 0, 255), t) # Bottom left x1,y1 cv.line(frame, (x, y1), (x + l, y1), (255, 0, 255), t) cv.line(frame, (x, y1), (x, y1 - l), (255, 0, 255), t) # Bottom right x1,y1 cv.line(frame, (x1, y1), (x1 - l, y1), (255, 0, 255), t) cv.line(frame, (x1, y1), (x1, y1 - l), (255, 0, 255), t) return frame
def pub_img(self, frame): self.pub_rgb.publish(self.bridge.cv2_to_imgmsg(frame, "bgr8"))
if __name__ == '__main__': capture = cv.VideoCapture(0) capture.set(6, cv.VideoWriter_fourcc(*'XVID')) 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, content_index = 0, 0, 0 dat_file = "./file/shape_predictor_68_face_landmarks.dat" face_eye_detection = FaceEyeDetection() content = ["face", "eye", "face_eye"] while capture.isOpened(): ret, frame = capture.read() # frame = cv.flip(frame, 1) action = cv.waitKey(1) & 0xFF if action == ord("f") or action == ord("F"): content_index += 1 if content_index >= len(content): content_index = 0 if content[content_index] == "face": frame = face_eye_detection.face(frame) elif content[content_index] == "eye": frame = face_eye_detection.eye(frame) else: frame = face_eye_detection.eye(face_eye_detection.face(frame)) if action == ord('q') or action == 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) cv.imshow('frame', frame) face_eye_detection.pub_img(frame) capture.release() cv.destroyAllWindows()