Welcome to AI Vision USB Camera repository
5.Face recognition
Face recognition
1. Face recognition
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 solution 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. Face recognition
2.1 Face recognition
Input command:
roscorerosrun yahboomcar_mediapipe 05_FaceEyeDetection.py
2.2 Source code
Code path：~/yahboomcar_ws/src/yahboomcar_mediapipe/scripts
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("dofbot_mediapipe") + "/scripts/file/haarcascade_eye.xml") self.faceDetect = cv.CascadeClassifier( rospkg.RosPack().get_path("dofbot_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()