【OpenCV-Python】37.OpenCV的人脸和目标的跟踪——dlib库

37.OpenCV的人脸和目标的跟踪——dlib库

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前言

  dlib库是一个十分好用的机器学习库，其源码均由C++实现，并提供了Python 接口，可广泛适用于很多场景。本实例仅简单示范dlib库中关于人脸跟踪和目标跟踪技术的Python应用。

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一、基于dlib库的检测人脸、跟踪人脸

  程序可以实现对人脸检测后进行跟踪，并保存视频。

import cv2
import dlib

def main():
   
    capture = cv2.VideoCapture(0)

    detector = dlib.get_frontal_face_detector()

    tractor = dlib.correlation_tracker()

    tracking_state = False

    frame_width = capture.get(cv2.CAP_PROP_FRAME_WIDTH)
    frame_height = capture.get(cv2.CAP_PROP_FRAME_HEIGHT)
    frame_fps = capture.get(cv2.CAP_PROP_FPS)
    fourcc = cv2.VideoWriter_fourcc(*"XVID")
    output = cv2.VideoWriter("record.avi", fourcc, int(frame_fps), (int(frame_width), int(frame_height)), True)

    while True:
        ret, frame = capture.read()

        if tracking_state is False:
            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
            dets = detector(gray, 1)
            if len(dets) > 0:
                tractor.start_track(frame, dets[0])
                tracking_state = True

        if tracking_state is True:
            tractor.update(frame) 
            position = tractor.get_position() 
            cv2.rectangle(frame,(int(position.left()), int(position.top())), (int(position.right()), int(position.bottom())), (0,255,0), 3)

        key = cv2.waitKey(1) & 0xFF
        if key == ord('q'):
            break
        cv2.imshow("face tracking", frame)
        output.write(frame)

    capture.release()
    cv2.destroyAllWindows()

if __name__ == '__main__':
    main()

  程序可以实现对人脸检测后进行跟踪，同时可以在视频中显示信息提示，之后保存视频。

import cv2
import dlib

def show_info(frame, tracking_state):
    pos1 = (20, 40)
    pos2 = (20, 80)
    cv2.putText(frame, "'1' : reset ", pos1, cv2.FONT_HERSHEY_COMPLEX, 0.5, (255,255,255))
    
    if tracking_state is True:
        cv2.putText(frame, "tracking now ...", pos2, cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 0, 0))
    else:
        cv2.putText(frame, "no tracking ...", pos2, cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 255, 0))

def main():
   
    capture = cv2.VideoCapture(0)

    detector = dlib.get_frontal_face_detector()

    tractor = dlib.correlation_tracker()

    tracking_state = False

    frame_width = capture.get(cv2.CAP_PROP_FRAME_WIDTH)
    frame_height = capture.get(cv2.CAP_PROP_FRAME_HEIGHT)
    frame_fps = capture.get(cv2.CAP_PROP_FPS)
    fourcc = cv2.VideoWriter_fourcc(*"XVID")
    output = cv2.VideoWriter("record.avi", fourcc, int(frame_fps), (int(frame_width), int(frame_height)), True)

    while True:
        ret, frame = capture.read()

        show_info(frame, tracking_state)

        if tracking_state is False:
            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
            dets = detector(gray, 1) 
            if len(dets) > 0:
                tractor.start_track(frame, dets[0])
                tracking_state = True

        if tracking_state is True:
            tractor.update(frame)
            position = tractor.get_position()
            cv2.rectangle(frame,(int(position.left()), int(position.top())), (int(position.right()), int(position.bottom())), (0,255,0), 3)

        key = cv2.waitKey(1) & 0xFF
        if key == ord('q'):
            break
        if key == ord('1'):
            tracking_state = False
        cv2.imshow("face tracking", frame)
        output.write(frame)

    capture.release()
    cv2.destroyAllWindows()

if __name__ == '__main__':
    main()

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二、基于dlib库的选定目标物、跟踪目标

  

import cv2
import dlib

def show_info(frame, tracking_state):
    pos1 = (10, 20)
    pos2 = (10, 40)
    pos3 = (10, 60)

    info1 = "put left button, select an area, starct tracking"
    info2 = " '1' : starct tracking ,  '2' : stop tacking , 'q' : exit "
    cv2.putText(frame, info1, pos1, cv2.FONT_HERSHEY_COMPLEX, 0.5, (255,255,255))
    cv2.putText(frame, info2, pos2, cv2.FONT_HERSHEY_COMPLEX, 0.5, (255,255,255))
    if tracking_state:
        cv2.putText(frame, "tracking now ...", pos3, cv2.FONT_HERSHEY_COMPLEX, 0.5, (255,0,0))
    else:
        cv2.putText(frame, "stop tracking ...", pos3, cv2.FONT_HERSHEY_COMPLEX, 0.5, (0,255,0))

points = []
def mouse_event_handler(event, x, y, flags, parms):
    global points # 全局调用
    if event == cv2.EVENT_LBUTTONDOWN: 
        points = [(x, y)]
    elif event == cv2.EVENT_LBUTTONUP: 
        points.append((x,y))

capture = cv2.VideoCapture(0)

nameWindow = "Ojbect Tracking"

cv2.namedWindow(nameWindow)
cv2.setMouseCallback(nameWindow, mouse_event_handler)

tracker = dlib.correlation_tracker()

tracking_state = False

while True:
    ret, frame = capture.read()
    
    show_info(frame, tracking_state)
    
    if len(points) == 2 :
        cv2.rectangle(frame, points[0], points[1], (0,0,0), 3) 
        dlib_rect = dlib.rectangle(points[0][0], points[0][1], points[1][0], points[1][1])
    
    if tracking_state is True:
        tracker.update(frame) 
        pos = tracker.get_position() 
        cv2.rectangle(frame, (int(pos.left()),int(pos.top())), (int(pos.right()), int(pos.bottom())), (255, 255, 255), 3)

    key = cv2.waitKey(1) & 0xFF
    if key == ord('1'):
        if len(points) == 2:
            tracker.start_track(frame, dlib_rect)
            tracking_state = True
            points = []

    if key == ord('2'):
        points = []
        tracking_state = False

    if key == ord('q'):
        break

    cv2.imshow(nameWindow, frame)

capture.release()
cv2.destroyAllWindows()

  选定目标物：

  跟踪目标：

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三、OpenCV-Python资源下载

OpenCV-Python测试用图片、中文官方文档、opencv-4.5.4源码

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总结

  以上内容简单的介绍了OpenCV-Python的人脸跟踪和目标跟踪，有关Python、数据科学、人工智能等文章后续会不定期发布，请大家多多关注，一键三连哟(●’◡’●)。