简介
dlib提供了dlib.correlation_tracker()类用于跟踪目标，效果一般般，没训练的模型好。
官方文档入口：http://dlib.net/python/index.html#dlib.correlation_tracker
完整源码（直接绘制完成后按下enter键即可跟踪）
import sys
import dlib
import cv2
tracker = dlib.correlation_tracker() # 导入correlation_tracker()类
cap = cv2.VideoCapture(0) # OpenCV打开摄像头
start_flag = True # 标记，是否是第一帧，若在第一帧需要先初始化
selection = None # 实时跟踪鼠标的跟踪区域
track_window = None # 要检测的物体所在区域
drag_start = None # 标记，是否开始拖动鼠标
# 鼠标点击事件回调函数
def onMouseClicked(event, x, y, flags, param):
global selection, track_window, drag_start # 定义全局变量
if event == cv2.EVENT_LBUTTONDOWN: # 鼠标左键按下
drag_start = (x, y)
track_window = None
if drag_start: # 是否开始拖动鼠标，记录鼠标位置
xMin = min(x, drag_start[0])
yMin = min(y, drag_start[1])
xMax = max(x, drag_start[0])
yMax = max(y, drag_start[1])
selection = (xMin, yMin, xMax, yMax)
if event == cv2.EVENT_LBUTTONUP: # 鼠标左键松开
drag_start = None
track_window = selection
selection = None
cv2.namedWindow("image", cv2.WINDOW_AUTOSIZE)
cv2.setMouseCallback("image", onMouseClicked)
# opencv的bgr格式图片转换成rgb格式
# b, g, r = cv2.split(frame)
# frame2 = cv2.merge([r, g, b])
while True:
ret, frame = cap.read() # 从摄像头读入1帧
if start_flag == True: # 如果是第一帧，需要先初始化
# 这里是初始化，窗口中会停在当前帧，用鼠标拖拽一个框来指定区域，随后会跟踪这个目标；我们需要先找到目标才能跟踪不是吗？
while True:
img_first = frame.copy() # 不改变原来的帧，拷贝一个新的出来
if track_window: # 跟踪目标的窗口画出来了，就实时标出来
cv2.rectangle(img_first, (track_window[0], track_window[1]), (track_window[2], track_window[3]), (0,0,255), 1)
elif selection: # 跟踪目标的窗口随鼠标拖动实时显示
cv2.rectangle(img_first, (selection[0], selection[1]), (selection[2], selection[3]), (0,0,255), 1)
cv2.imshow("image", img_first)
# 按下回车，退出循环
if cv2.waitKey(5) == 13:
break
start_flag = False # 初始化完毕，不再是第一帧了
tracker.start_track(frame, dlib.rectangle(track_window[0], track_window[1], track_window[2], track_window[3])) # 跟踪目标，目标就是选定目标窗口中的
else:
tracker.update(frame) # 更新，实时跟踪
box_predict = tracker.get_position() # 得到目标的位置
cv2.rectangle(frame,(int(box_predict.left()),int(box_predict.top())),(int(box_predict.right()),int(box_predict.bottom())),(0,255,255),1) # 用矩形框标注出来
cv2.imshow("image", frame)
# 如果按下ESC键，就退出
if cv2.waitKey(10) == 27:
break
cap.release()
cv2.destroyAllWindows()
视频效果
官方示例
# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
#
# This example shows how to use the correlation_tracker from the dlib Python
# library. This object lets you track the position of an object as it moves
# from frame to frame in a video sequence. To use it, you give the
# correlation_tracker the bounding box of the object you want to track in the
# current video frame. Then it will identify the location of the object in
# subsequent frames.
#
# In this particular example, we are going to run on the
# video sequence that comes with dlib, which can be found in the
# examples/video_frames folder. This video shows a juice box sitting on a table
# and someone is waving the camera around. The task is to track the position of
# the juice box as the camera moves around.
#
#
# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
# You can install dlib using the command:
# pip install dlib
#
# Alternatively, if you want to compile dlib yourself then go into the dlib
# root folder and run:
# python setup.py install
# or
# python setup.py install --yes USE_AVX_INSTRUCTIONS
# if you have a CPU that supports AVX instructions, since this makes some
# things run faster.
#
# Compiling dlib should work on any operating system so long as you have
# CMake and boost-python installed. On Ubuntu, this can be done easily by
# running the command:
# sudo apt-get install libboost-python-dev cmake
#
# Also note that this example requires scikit-image which can be installed
# via the command:
# pip install scikit-image
# Or downloaded from http://scikit-image.org/download.html.
import os
import glob
import dlib
from skimage import io
# Path to the video frames
video_folder = os.path.join("..", "examples", "video_frames")
# Create the correlation tracker - the object needs to be initialized
# before it can be used
tracker = dlib.correlation_tracker()
win = dlib.image_window()
# We will track the frames as we load them off of disk
for k, f in enumerate(sorted(glob.glob(os.path.join(video_folder, "*.jpg")))):
print("Processing Frame {}".format(k))
img = io.imread(f)
# We need to initialize the tracker on the first frame
if k == 0:
# Start a track on the juice box. If you look at the first frame you
# will see that the juice box is contained within the bounding
# box (74, 67, 112, 153).
tracker.start_track(img, dlib.rectangle(74, 67, 112, 153))
else:
# Else we just attempt to track from the previous frame
tracker.update(img)
win.clear_overlay()
win.set_image(img)
win.add_overlay(tracker.get_position())
dlib.hit_enter_to_continue()
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