OpenCV从入门到精通实战(六)—

基于原生的追踪

使用OpenCV库实现基于视频的对象追踪。通过以下步骤和Python代码，您将能够选择不同的追踪器，并对视频中的对象进行实时追踪。

步骤 1: 导入必要的库

首先，我们需要导入一些必要的Python库，包括argparse、time、cv2 (OpenCV) 和 numpy。

import argparse
import time
import cv2
import numpy as np

步骤 2: 设置参数解析

使用argparse库来解析命令行参数。我们将指定输入视频文件的路径以及选择的追踪器类型。

ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", type=str, help="path to input video file")
ap.add_argument("-t", "--tracker", type=str, default="kcf", help="OpenCV object tracker type")
args = vars(ap.parse_args())

步骤 3: 定义支持的追踪器

在OpenCV中，有多种对象追踪器可用。我们将它们存储在一个字典中，便于后续使用。

OPENCV_OBJECT_TRACKERS = {
    "csrt": cv2.TrackerCSRT_create,
    "kcf": cv2.TrackerKCF_create,
    "boosting": cv2.TrackerBoosting_create,
    "mil": cv2.TrackerMIL_create,
    "tld": cv2.TrackerTLD_create,
    "medianflow": cv2.TrackerMedianFlow_create,
    "mosse": cv2.TrackerMOSSE_create
}

步骤 4: 初始化追踪器和视频流

我们初始化一个多对象追踪器并打开视频文件。

trackers = cv2.MultiTracker_create()
vs = cv2.VideoCapture(args["video"])

步骤 5: 处理视频帧

接下来，我们读取视频中的每一帧，并对其进行缩放处理，然后使用追踪器更新追踪状态，并绘制追踪的边框。

while True:
    frame = vs.read()
    frame = frame[1]
    if frame is None:
        break
    (h, w) = frame.shape[:2]
    width = 600
    r = width / float(w)
    dim = (width, int(h * r))
    frame = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)
    (success, boxes) = trackers.update(frame)
    for box in boxes:
        (x, y, w, h) = [int(v) for v in box]
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
    cv2.imshow("Frame", frame)
    key = cv2.waitKey(100) & 0xFF
    if key == ord("s"):
        box = cv2.selectROI("Frame", frame, fromCenter=False, showCrosshair=True)
        tracker = OPENCV_OBJECT_TRACKERS[args["tracker"]]()
        trackers.add(tracker, frame, box)
    elif key == 27:
        break
vs.release()
cv2.destroyAllWindows()

总结

import argparse
import time
import cv2
import numpy as np

# 配置参数
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", type=str,
	help="path to input video file")
ap.add_argument("-t", "--tracker", type=str, default="kcf",
	help="OpenCV object tracker type")
args = vars(ap.parse_args())

# opencv已经实现了的追踪算法

OPENCV_OBJECT_TRACKERS = {

	"csrt": cv2.TrackerCSRT_create,
	"kcf": cv2.TrackerKCF_create,
	"boosting": cv2.TrackerBoosting_create,
	"mil": cv2.TrackerMIL_create,
	"tld": cv2.TrackerTLD_create,
	"medianflow": cv2.TrackerMedianFlow_create,
	"mosse": cv2.TrackerMOSSE_create
}

# 实例化OpenCV's multi-object tracker
trackers = cv2.MultiTracker_create()
vs = cv2.VideoCapture(args["video"])

# 视频流
while True:
	# 取当前帧
	frame = vs.read()
	# (true, data)
	frame = frame[1]
	# 到头了就结束
	if frame is None:
		break

	# resize每一帧
	(h, w) = frame.shape[:2]
	width=600
	r = width / float(w)
	dim = (width, int(h * r))
	frame = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)

	# 追踪结果
	(success, boxes) = trackers.update(frame)

	# 绘制区域
	for box in boxes:
		(x, y, w, h) = [int(v) for v in box]
		cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

	# 显示
	cv2.imshow("Frame", frame)
	key = cv2.waitKey(100) & 0xFF

	if key == ord("s"):
		# 选择一个区域，按s
		box = cv2.selectROI("Frame", frame, fromCenter=False,
			showCrosshair=True)

		# 创建一个新的追踪器
		tracker = OPENCV_OBJECT_TRACKERS[args["tracker"]]()
		trackers.add(tracker, frame, box)

	# 退出
	elif key == 27:
		break
vs.release()
cv2.destroyAllWindows()

通过上述步骤和代码，可以实现一个简单的视频对象追踪应用，该应用支持多种追踪算法，并允许用户实时选择和追踪视频中的对象。这种技术在许多领域都有广泛的应用，包括安全监控、人机交互和自动驾驶车辆等。

检测模型的跟踪

检测模型使用Python、OpenCV、dlib和多进程处理视频中的实时对象跟踪。以下是具体步骤及相关代码片段：

1. 设置和参数解析

导入必要的库，并设置参数解析，处理输入如视频文件路径和模型配置。

from utils import FPS
import multiprocessing
import numpy as np
import argparse
import dlib
import cv2

2. 初始化深度学习模型

加载预训练的Caffe模型进行对象检测。

net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

3. 视频流处理

从指定的文件开始视频捕捉，并准备处理帧。

vs = cv2.VideoCapture(args["video"])

4. 帧处理

调整帧大小并转换为RGB格式进行处理。
如果检测到的对象置信度高于阈值，则初始化对象跟踪。

rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

5. 对象检测和跟踪

对初次检测到的对象创建跟踪器，并使用多进程处理。

p = multiprocessing.Process(target=start_tracker, args=(bb, label, rgb, iq, oq))
p.daemon = True
p.start()

6. 追踪器更新和结果输出

每个跟踪器获取新的帧，更新位置并输出跟踪结果。

outputQueue.put((label, (startX, startY, endX, endY)))

7. 视频输出和显示

如果指定了输出文件，将处理后的帧写入视频文件。
显示处理后的帧并在用户按下ESC键时停止。

writer.write(frame)
key = cv2.waitKey(1) & 0xFF
if key == 27:
    break

8. 清理和资源释放

停止FPS计时，释放视频文件和窗口资源。

fps.stop()
writer.release()
cv2.destroyAllWindows()
vs.release()

完整代码：
utils.py

import datetime

class FPS:
    def __init__(self):
        # store the start time, end time, and total number of frames
        # that were examined between the start and end intervals
        self._start = None
        self._end = None
        self._numFrames = 0

    def start(self):
        # start the timer
        self._start = datetime.datetime.now()
        return self

    def stop(self):
        # stop the timer
        self._end = datetime.datetime.now()

    def update(self):
        # increment the total number of frames examined during the
        # start and end intervals
        self._numFrames += 1

    def elapsed(self):
        # return the total number of seconds between the start and
        # end interval
        return (self._end - self._start).total_seconds()

    def fps(self):
        # compute the (approximate) frames per second
        return self._numFrames / self.elapsed()

multi_object_tracking_fast.py

import datetime

class FPS:
    def __init__(self):
        # store the start time, end time, and total number of frames
        # that were examined between the start and end intervals
        self._start = None
        self._end = None
        self._numFrames = 0

    def start(self):
        # start the timer
        self._start = datetime.datetime.now()
        return self

    def stop(self):
        # stop the timer
        self._end = datetime.datetime.now()

    def update(self):
        # increment the total number of frames examined during the
        # start and end intervals
        self._numFrames += 1

    def elapsed(self):
        # return the total number of seconds between the start and
        # end interval
        return (self._end - self._start).total_seconds()

    def fps(self):
        # compute the (approximate) frames per second
        return self._numFrames / self.elapsed()

multi_object_tracking_slow.py

#导入工具包
from utils import FPS
import numpy as np
import argparse
import dlib
import cv2
"""
--prototxt mobilenet_ssd/MobileNetSSD_deploy.prototxt 
--model mobilenet_ssd/MobileNetSSD_deploy.caffemodel 
--video race.mp4
"""
# 参数
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--prototxt", default="mobilenet_ssd/MobileNetSSD_deploy.prototxt",
	help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", default="mobilenet_ssd/MobileNetSSD_deploy.caffemodel",
	help="path to Caffe pre-trained model")
ap.add_argument("-v", "--video",default="race.mp4",
	help="path to input video file")
ap.add_argument("-o", "--output", type=str,
	help="path to optional output video file")
ap.add_argument("-c", "--confidence", type=float, default=0.2,
	help="minimum probability to filter weak detections")
args = vars(ap.parse_args())


# SSD标签
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
	"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
	"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
	"sofa", "train", "tvmonitor"]

# 读取网络模型
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

# 初始化
print("[INFO] starting video stream...")
vs = cv2.VideoCapture(args["video"])
writer = None

# 一会要追踪多个目标
trackers = []
labels = []

# 计算FPS
fps = FPS().start()

while True:
	# 读取一帧
	(grabbed, frame) = vs.read()

	# 是否是最后了
	if frame is None:
		break

	# 预处理操作
	(h, w) = frame.shape[:2]
	width=600
	r = width / float(w)
	dim = (width, int(h * r))
	frame = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)
	rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

	# 如果要将结果保存的话
	if args["output"] is not None and writer is None:
		fourcc = cv2.VideoWriter_fourcc(*"MJPG")
		writer = cv2.VideoWriter(args["output"], fourcc, 30,
			(frame.shape[1], frame.shape[0]), True)

	# 先检测 再追踪
	if len(trackers) == 0:
		# 获取blob数据
		(h, w) = frame.shape[:2]
		blob = cv2.dnn.blobFromImage(frame, 0.007843, (w, h), 127.5)

		# 得到检测结果
		net.setInput(blob)
		detections = net.forward()

		# 遍历得到的检测结果
		for i in np.arange(0, detections.shape[2]):
			# 能检测到多个结果，只保留概率高的
			confidence = detections[0, 0, i, 2]

			# 过滤
			if confidence > args["confidence"]:
				# extract the index of the class label from the
				# detections list
				idx = int(detections[0, 0, i, 1])
				label = CLASSES[idx]

				# 只保留人的
				if CLASSES[idx] != "person":
					continue

				# 得到BBOX
				#print (detections[0, 0, i, 3:7])
				box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
				(startX, startY, endX, endY) = box.astype("int")

				# 使用dlib来进行目标追踪
				#http://dlib.net/python/index.html#dlib.correlation_tracker
				t = dlib.correlation_tracker()
				rect = dlib.rectangle(int(startX), int(startY), int(endX), int(endY))
				t.start_track(rgb, rect)

				# 保存结果
				labels.append(label)
				trackers.append(t)

				# 绘图
				cv2.rectangle(frame, (startX, startY), (endX, endY),
					(0, 255, 0), 2)
				cv2.putText(frame, label, (startX, startY - 15),
					cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 255, 0), 2)

	# 如果已经有了框，就可以直接追踪了
	else:
		# 每一个追踪器都要进行更新
		for (t, l) in zip(trackers, labels):
			t.update(rgb)
			pos = t.get_position()

			# 得到位置
			startX = int(pos.left())
			startY = int(pos.top())
			endX = int(pos.right())
			endY = int(pos.bottom())

			# 画出来
			cv2.rectangle(frame, (startX, startY), (endX, endY),
				(0, 255, 0), 2)
			cv2.putText(frame, l, (startX, startY - 15),
				cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 255, 0), 2)

	# 也可以把结果保存下来
	if writer is not None:
		writer.write(frame)

	# 显示
	cv2.imshow("Frame", frame)
	key = cv2.waitKey(1) & 0xFF

	# 退出
	if key == 27:
		break

	# 计算FPS
	fps.update()


fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))

if writer is not None:
	writer.release()

cv2.destroyAllWindows()
vs.release()