【python】OpenCV—Tracking（10.4）

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文章目录

1、任务描述
2、人脸检测模型
3、完整代码
4、结果展示
5、涉及到的库函数
6、参考

1、任务描述

基于质心实现多目标（以人脸为例）跟踪

人脸检测采用深度学习的方法

核心步骤：

步骤#1：接受边界框坐标并计算质心
步骤#2：计算新边界框和现有对象之间的欧几里得距离
步骤 #3：更新现有对象的 (x, y) 坐标
步骤#4：注册新对象
步骤#5：注销旧对象
当旧对象无法与任何现有对象匹配总共 N 个后续帧时，我们将取消注册。

2、人脸检测模型

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输入 1x3x300x300

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resnet + ssd，分支还是挺多的

3、完整代码

质心跟踪代码

# import the necessary packages
from scipy.spatial import distance as dist
from collections import OrderedDict
import numpy as npclass CentroidTracker():def __init__(self, maxDisappeared=65):# initialize the next unique object ID along with two ordered# dictionaries used to keep track of mapping a given object# ID to its centroid and number of consecutive frames it has# been marked as "disappeared", respectivelyself.nextObjectID = 0# 用于为每个对象分配唯一 ID 的计数器。如果对象离开帧并且在 maxDisappeared 帧中没有返回，则将分配一个新的（下一个）对象 ID。self.objects = OrderedDict()# 对象 ID 作为键，质心 (x, y) 坐标作为值的字典self.disappeared = OrderedDict()# 保存特定对象 ID（键）已被标记为“丢失”的连续帧数（值）# store the number of maximum consecutive frames a given# object is allowed to be marked as "disappeared" until we# need to deregister the object from trackingself.maxDisappeared = maxDisappeared# 在我们取消注册该对象之前，允许将对象标记为“丢失/消失”的连续帧数。def register(self, centroid):# when registering an object we use the next available object# ID to store the centroid# 注册新的 idself.objects[self.nextObjectID] = centroidself.disappeared[self.nextObjectID] = 0self.nextObjectID += 1def deregister(self, objectID):# to deregister an object ID we delete the object ID from# both of our respective dictionaries# 注销掉 iddel self.objects[objectID]del self.disappeared[objectID]def update(self, rects):# check to see if the list of input bounding box rectangles# is empty# rects = (startX, startY, endX, endY)if len(rects) == 0: # 没有检测到目标# loop over any existing tracked objects and mark them# as disappeared# 我们将遍历所有对象 ID 并增加它们的disappeared计数for objectID in list(self.disappeared.keys()):self.disappeared[objectID] += 1# if we have reached a maximum number of consecutive# frames where a given object has been marked as# missing, deregister itif self.disappeared[objectID] > self.maxDisappeared:self.deregister(objectID)# return early as there are no centroids or tracking info# to updatereturn self.objects# initialize an array of input centroids for the current frameinputCentroids = np.zeros((len(rects), 2), dtype="int")# loop over the bounding box rectanglesfor (i, (startX, startY, endX, endY)) in enumerate(rects):# use the bounding box coordinates to derive the centroidcX = int((startX + endX) / 2.0)  # 检测框中心横坐标cY = int((startY + endY) / 2.0)  # 检测框中心纵坐标inputCentroids[i] = (cX, cY)# if we are currently not tracking any objects take the input# centroids and register each of them# 如果当前没有我们正在跟踪的对象，我们将注册每个新对象if len(self.objects) == 0:for i in range(0, len(inputCentroids)):self.register(inputCentroids[i])# otherwise, are are currently tracking objects so we need to# try to match the input centroids to existing object# centroidselse:# grab the set of object IDs and corresponding centroidsobjectIDs = list(self.objects.keys())objectCentroids = list(self.objects.values())# compute the distance between each pair of object# centroids and input centroids, respectively -- our# goal will be to match an input centroid to an existing# object centroidD = dist.cdist(np.array(objectCentroids), inputCentroids)# in order to perform this matching we must (1) find the# smallest value in each row and then (2) sort the row# indexes based on their minimum values so that the row# with the smallest value as at the *front* of the index# listrows = D.min(axis=1).argsort()# next, we perform a similar process on the columns by# finding the smallest value in each column and then# sorting using the previously computed row index listcols = D.argmin(axis=1)[rows]# in order to determine if we need to update, register,# or deregister an object we need to keep track of which# of the rows and column indexes we have already examinedusedRows = set()usedCols = set()# loop over the combination of the (row, column) index# tuplesfor (row, col) in zip(rows, cols):# if we have already examined either the row or# column value before, ignore it# val# 老目标被选中过或者新目标被选中过if row in usedRows or col in usedCols:continue# otherwise, grab the object ID for the current row,# set its new centroid, and reset the disappeared# counterobjectID = objectIDs[row]  # 老目标 idself.objects[objectID] = inputCentroids[col]  # 更新老目标 id 的质心为新目标的质心，因为两者距离最近self.disappeared[objectID] = 0  # 丢失索引重置为 0# indicate that we have examined each of the row and# column indexes, respectivelyusedRows.add(row)usedCols.add(col)# compute both the row and column index we have NOT yet# examinedunusedRows = set(range(0, D.shape[0])).difference(usedRows)unusedCols = set(range(0, D.shape[1])).difference(usedCols)# in the event that the number of object centroids is# equal or greater than the number of input centroids# we need to check and see if some of these objects have# potentially disappeared# 如果老目标不少于新目标if D.shape[0] >= D.shape[1]:# loop over the unused row indexesfor row in unusedRows:# grab the object ID for the corresponding row# index and increment the disappeared counterobjectID = objectIDs[row]  # 老目标 idself.disappeared[objectID] += 1  # 跟踪帧数 +1# check to see if the number of consecutive# frames the object has been marked "disappeared"# for warrants deregistering the object# 检查disappeared计数是否超过 maxDisappeared 阈值，如果是，我们将注销该对象if self.disappeared[objectID] > self.maxDisappeared:self.deregister(objectID)# otherwise, if the number of input centroids is greater# than the number of existing object centroids we need to# register each new input centroid as a trackable objectelse:  # 新目标多于老目标for col in unusedCols:self.register(inputCentroids[col])  # 注册新目标# return the set of trackable objectsreturn self.objects

self.nextObjectID = 0用于为每个对象分配唯一 ID 的计数器。如果对象离开帧并且在 maxDisappeared 帧中没有返回，则将分配一个新的（下一个）对象 ID。

self.objects = OrderedDict() 对象 ID 作为键，质心 (x, y) 坐标作为值的字典

self.disappeared = OrderedDict() 保存特定对象 ID（键）已被标记为“丢失”的连续帧数（值）

self.maxDisappeared = maxDisappeared，在我们取消注册该对象之前，允许将对象标记为“丢失/消失”的连续帧数。

有初始化，注册，注销，更新过程

核心代码在 def update

如果没有检测到目标，当前所有 id 的 self.disappeared 会加 1

如果注册的 id 为空，这当前帧所有质心会被注册上新的 id，否则会计算历史质心和当前帧质心的距离，距离较近的匹配上，更新 id 的质心，没有被分配上的历史质心对应 id 的 self.disappeared 会加 1，没有被分配的当前帧质心会被注册新的 id

超过 self.maxDisappeared 的 id 会被注销掉

根据 D 计算 rows 和 cols 的时候有点绕，看看下面这段注释就会好理解一些

"""
Darray([[  2.        , 207.48252939],[206.65188119,   1.41421356]])D.min(axis=1)  每行最小值array([2.        , 1.41421356])rows 每行最小值再排序，表示按从小到大行数排序，比如最小的数在rows[0]所在的行，第二小的数在rows[1]所在的行array([1, 0])D.argmin(axis=1) 返回指定轴最小值的索引，也即每行最小值的索引array([0, 1])cols  每行每列最小值，按行从小到大排序array([1, 0])
"""

再看看一个 shape 不一样的例子

import numpy as npD = np.array([[2., 1, 3],[1.1, 1.41421356, 0.7]])print(D.min(axis=1))
print(D.argmin(axis=1))rows = D.min(axis=1).argsort()cols = D.argmin(axis=1)[rows]print(rows)
print(cols)"""
[1.  0.7]
[1 2][1 0]
[2 1]
"""

实际运用的时候，rows 是历史 ids，cols 是当前帧的 ids

人脸检测+跟踪+绘制结果 pipeline

# USAGE
# python object_tracker.py --prototxt deploy.prototxt --model res10_300x300_ssd_iter_140000.caffemodel# import the necessary packages
from CentroidTracking.centroidtracker import CentroidTracker
import numpy as np
import argparse
import imutils
import cv2# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--prototxt", default="deploy.prototxt.txt",help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", default="res10_300x300_ssd_iter_140000.caffemodel",help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.5,help="minimum probability to filter weak detections")
args = vars(ap.parse_args())# initialize our centroid tracker and frame dimensions
ct = CentroidTracker()
(H, W) = (None, None)# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])# initialize the video stream
print("[INFO] starting video stream...")
vs = cv2.VideoCapture("4.mp4")index = 0# loop over the frames from the video stream
while True:index += 1# read the next frame from the video stream and resize itrect, frame = vs.read()if not rect:breakframe = imutils.resize(frame, width=600)(H, W) = frame.shape[:2]# construct a blob from the frame, pass it through the network,# obtain our output predictions, and initialize the list of# bounding box rectanglesblob = cv2.dnn.blobFromImage(frame, 1.0, (W, H),(104.0, 177.0, 123.0))net.setInput(blob)detections = net.forward()  # (1, 1, 200, 7)"""detections[0][0][0]array([0.        , 1.        , 0.9983138 , 0.418003  , 0.22666326,0.5242793 , 0.50829136], dtype=float32)第三个维度是 score最后四个维度是左上右下坐标"""rects = []  # 记录所有检测框的左上右下坐标# loop over the detectionsfor i in range(0, detections.shape[2]):# filter out weak detections by ensuring the predicted# probability is greater than a minimum thresholdif detections[0, 0, i, 2] > args["confidence"]:# compute the (x, y)-coordinates of the bounding box for# the object, then update the bounding box rectangles listbox = detections[0, 0, i, 3:7] * np.array([W, H, W, H])rects.append(box.astype("int"))# draw a bounding box surrounding the object so we can# visualize it(startX, startY, endX, endY) = box.astype("int")cv2.rectangle(frame, (startX, startY), (endX, endY),(0, 0, 255), 2)# update our centroid tracker using the computed set of bounding# box rectanglesobjects = ct.update(rects)# loop over the tracked objectsfor (objectID, centroid) in objects.items():# draw both the ID of the object and the centroid of the# object on the output frametext = "ID {}".format(objectID)cv2.putText(frame, text, (centroid[0] - 10, centroid[1] - 10),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)cv2.circle(frame, (centroid[0], centroid[1]), 4, (0, 0, 255), -1)# show the output framecv2.imshow("Frame", frame)# cv2.imwrite(f"./frame4/{str(index).zfill(3)}.jpg", frame)key = cv2.waitKey(1) & 0xFF# if the `q` key was pressed, break from the loopif key == ord("q"):break# do a bit of cleanup
cv2.destroyAllWindows()
vs.release()

网络前向后的结果，第三个维度是 score，最后四个维度是左上右下坐标

核心跟新坐标的过程在 objects = ct.update(rects)

4、结果展示

单个人脸

tracking-face1

多个人脸

tracking-face2

多个人脸，存在漏检情况，id 还是持续了很长一段时间（算法中配置的是 50 帧）

tracking-face3

多个人脸

tracking-face4

5、涉及到的库函数

scipy.spatial.distance.cdist 是 SciPy 库中 spatial.distance 模块的一个函数，用于计算两个输入数组之间所有点对之间的距离。这个函数非常有用，特别是在处理大规模数据集时，需要计算多个点之间的距离矩阵。

函数的基本用法如下：

from scipy.spatial.distance import cdist  # 假设 XA 和 XB 是两个二维数组，其中每一行代表一个点的坐标  
XA = ...  # 形状为 (m, n) 的数组，m 是点的数量，n 是坐标的维度  
XB = ...  # 形状为 (p, n) 的数组，p 是另一个集合中点的数量  # 计算 XA 和 XB 中所有点对之间的距离  
# metric 参数指定使用的距离度量，默认为 'euclidean'（欧氏距离）  
D = cdist(XA, XB, metric='euclidean')

在这个例子中，D 将是一个形状为 (m, p) 的数组，其中 D[i, j] 表示 XA 中第 i 个点和 XB 中第 j 个点之间的距离。

cdist 函数支持多种距离度量，通过 metric 参数指定。除了默认的欧氏距离外，还可以选择如曼哈顿距离（‘cityblock’）、切比雪夫距离（‘chebyshev’）、闵可夫斯基距离（‘minkowski’，需要额外指定 p 值）、余弦距离（注意：虽然余弦通常用作相似度度量，但可以通过 1 - cosine(u, v) 转换为距离度量，不过 cdist 不直接支持负的余弦距离，需要手动计算）、汉明距离（‘hamming’，仅适用于布尔数组或整数数组）等。