如何解决如何实时检测对象并自动跟踪它,而不是用户必须在要跟踪的对象周围绘制边界框?
我有以下代码,用户可以在其中按 p
暂停视频,在要跟踪的对象周围绘制一个边界框,然后按 Enter(回车)以在视频源中跟踪该对象:
import cv2
import sys
major_ver,minor_ver,subminor_ver = cv2.__version__.split('.')
if __name__ == '__main__' :
# Set up tracker.
tracker_types = ['BOOSTING','MIL','kcf','TLD','MEDIANFLOW','GOTURN','MOSSE','CSRT']
tracker_type = tracker_types[1]
if int(minor_ver) < 3:
tracker = cv2.Tracker_create(tracker_type)
else:
if tracker_type == 'BOOSTING':
tracker = cv2.TrackerBoosting_create()
if tracker_type == 'MIL':
tracker = cv2.TrackerMIL_create()
if tracker_type == 'kcf':
tracker = cv2.Trackerkcf_create()
if tracker_type == 'TLD':
tracker = cv2.TrackerTLD_create()
if tracker_type == 'MEDIANFLOW':
tracker = cv2.TrackerMedianFlow_create()
if tracker_type == 'GOTURN':
tracker = cv2.TrackerGOTURN_create()
if tracker_type == 'MOSSE':
tracker = cv2.TrackerMOSSE_create()
if tracker_type == "CSRT":
tracker = cv2.TrackerCSRT_create()
# Read video
video = cv2.VideoCapture(0) # 0 means webcam. Otherwise if you want to use a video file,replace 0 with "video_file.MOV")
# Exit if video not opened.
if not video.isOpened():
print ("Could not open video")
sys.exit()
while True:
# Read first frame.
ok,frame = video.read()
if not ok:
print ('Cannot read video file')
sys.exit()
# Retrieve an image and display it.
if((0xFF & cv2.waitKey(10))==ord('p')): # Press key `p` to pause the video to start tracking
break
cv2.namedWindow("Image",cv2.WINDOW_norMAL)
cv2.imshow("Image",frame)
cv2.destroyWindow("Image");
# select the bounding Box
bBox = (287,23,86,320)
# Uncomment the line below to select a different bounding Box
bBox = cv2.selectROI(frame,False)
# Initialize tracker with first frame and bounding Box
ok = tracker.init(frame,bBox)
while True:
# Read a new frame
ok,frame = video.read()
if not ok:
break
# Start timer
timer = cv2.getTickCount()
# Update tracker
ok,bBox = tracker.update(frame)
# Calculate Frames per second (FPS)
fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer);
# Draw bounding Box
if ok:
# Tracking success
p1 = (int(bBox[0]),int(bBox[1]))
p2 = (int(bBox[0] + bBox[2]),int(bBox[1] + bBox[3]))
cv2.rectangle(frame,p1,p2,(255,0),2,1)
else :
# Tracking failure
cv2.putText(frame,"Tracking failure detected",(100,80),cv2.FONT_HERShey_SIMPLEX,0.75,(0,255),2)
# display tracker type on frame
cv2.putText(frame,tracker_type + " Tracker",20),(50,170,50),2);
# display FPS on frame
cv2.putText(frame,"FPS : " + str(int(fps)),2);
# display result
cv2.imshow("Tracking",frame)
# Exit if ESC pressed
k = cv2.waitKey(1) & 0xff
if k == 27 : break
现在,不是让用户暂停视频并在对象周围绘制边界框,而是如何使它能够自动检测我感兴趣的特定对象(在我的情况下是牙刷)在视频源中引入,然后跟踪?
我找到了 this 篇文章,其中讨论了我们如何使用 ImageAI 和 Yolo 检测视频中的对象。
from imageai.Detection import VideoObjectDetection
import os
import cv2
execution_path = os.getcwd()
camera = cv2.VideoCapture(0)
detector = VideoObjectDetection()
detector.setModelTypeAsYOlov3()
detector.setModelPath(os.path.join(execution_path,"yolo.h5"))
detector.loadModel()
video_path = detector.detectObjectsFromVideo(camera_input=camera,output_file_path=os.path.join(execution_path,"camera_detected_1"),frames_per_second=29,log_progress=True)
print(video_path)
现在,Yolo 确实可以检测牙刷,它是它默认可以检测的 80 多种物体之一。但是,这篇文章有两点使它对我来说不是理想的解决方案:
-
此方法首先分析每个视频帧(每帧大约需要 1-2 秒,所以大约需要 1 分钟来分析来自网络摄像头的 2-3 秒视频流),并将检测到的视频保存在单独的视频中文件。而我想实时检测网络摄像头视频源中的牙刷。有解决办法吗?
-
正在使用的 Yolo v3 模型可以检测所有 80 个对象,但我只希望检测 2 或 3 个对象 - 牙刷、拿着牙刷的人和背景,如果需要的话。那么,有没有一种方法可以通过仅选择这 2 或 3 个对象进行检测来减少模型权重?
解决方法
如果您想要一个快速简便的解决方案,您可以使用更轻量级的 yolo 文件之一。你可以从这个网站获取权重和配置文件(它们成对出现,必须一起使用):https://pjreddie.com/darknet/yolo/(别担心,它看起来是草图,但很好)
使用较小的网络会让您获得更高的 fps,但也会降低准确性。如果这是您愿意接受的权衡,那么这是最简单的做法。
这是一些检测牙刷的代码。第一个文件只是一个类文件,有助于更无缝地使用 Yolo 网络。第二个是打开 VideoCapture 并将图像提供给网络的“主”文件。
yolo.py
import cv2
import numpy as np
class Yolo:
def __init__(self,cfg,weights,names,conf_thresh,nms_thresh,use_cuda = False):
# save thresholds
self.ct = conf_thresh;
self.nmst = nms_thresh;
# create net
self.net = cv2.dnn.readNet(weights,cfg);
print("Finished: " + str(weights));
self.classes = [];
file = open(names,'r');
for line in file:
self.classes.append(line.strip());
# use gpu + CUDA to speed up detections
if use_cuda:
self.net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA);
self.net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA);
# get output names
layer_names = self.net.getLayerNames();
self.output_layers = [layer_names[i[0]-1] for i in self.net.getUnconnectedOutLayers()];
# runs detection on the image and draws on it
def detect(self,img,target_id):
# get detection stuff
b,c,ids,idxs = self.get_detection_data(img,target_id);
# draw result
img = self.draw(img,b,idxs);
return img,len(idxs);
# returns boxes,confidences,class_ids,and indexes (indices?)
def get_detection_data(self,target_id):
# get output
layer_outputs = self.get_inf(img);
# get dims
height,width = img.shape[:2];
# filter thresholds and target
b,idxs = self.thresh(layer_outputs,width,height,target_id);
return b,idxs;
# runs the network on an image
def get_inf(self,img):
# construct a blob
blob = cv2.dnn.blobFromImage(img,1 / 255.0,(416,416),swapRB=True,crop=False);
# get response
self.net.setInput(blob);
layer_outputs = self.net.forward(self.output_layers);
return layer_outputs;
# filters the layer output by conf,nms and id
def thresh(self,layer_outputs,target_id):
# some lists
boxes = [];
confidences = [];
class_ids = [];
# each layer outputs
for output in layer_outputs:
for detection in output:
# get id and confidence
scores = detection[5:];
class_id = np.argmax(scores);
confidence = scores[class_id];
# filter out low confidence
if confidence > self.ct and class_id == target_id:
# scale bounding box back to the image size
box = detection[0:4] * np.array([width,height]);
(cx,cy,w,h) = box.astype('int');
# grab the top-left corner of the box
tx = int(cx - (w / 2));
ty = int(cy - (h / 2));
# update lists
boxes.append([tx,ty,int(w),int(h)]);
confidences.append(float(confidence));
class_ids.append(class_id);
# apply NMS
idxs = cv2.dnn.NMSBoxes(boxes,self.ct,self.nmst);
return boxes,idxs;
# draw detections on image
def draw(self,boxes,idxs):
# check for zero
if len(idxs) > 0:
# loop over indices
for i in idxs.flatten():
# extract the bounding box coords
(x,y) = (boxes[i][0],boxes[i][1]);
(w,h) = (boxes[i][2],boxes[i][3]);
# draw a box
cv2.rectangle(img,(x,y),(x+w,y+h),(0,255),2);
# draw text
text = "{}: {:.4}".format(self.classes[class_ids[i]],confidences[i]);
cv2.putText(img,text,y-5),cv2.FONT_HERSHEY_SIMPLEX,0.5,2);
return img;
main.py
import cv2
import numpy as np
# this is the "yolo.py" file,I assume it's in the same folder as this program
from yolo import Yolo
# these are the filepaths of the yolo files
weights = "yolov3-tiny.weights";
config = "yolov3-tiny.cfg";
labels = "yolov3.txt";
# init yolo network
target_class_id = 79; # toothbrush
conf_thresh = 0.4; # less == more boxes (but more false positives)
nms_thresh = 0.4; # less == more boxes (but more overlap)
net = Yolo(config,labels,nms_thresh);
# open video capture
cap = cv2.VideoCapture(0);
# loop
done = False;
while not done:
# get frame
ret,frame = cap.read();
if not ret:
done = cv2.waitKey(1) == ord('q');
continue;
# do detection
frame,_ = net.detect(frame,target_class_id);
# show
cv2.imshow("Marked",frame);
done = cv2.waitKey(1) == ord('q');
如果您不想使用较轻的权重文件,有几个选项可供您选择。
如果您有 Nvidia GPU,您可以使用 CUDA大幅提高您的 fps。即使是普通的 nvidia gpu 也比仅在 cpu 上运行快几倍。
绕过持续运行检测成本的常见策略是仅使用它来最初获取目标。您可以使用来自神经网络的检测来初始化您的对象跟踪器,类似于一个人在对象周围绘制边界框。对象跟踪器的速度要快得多,而且无需每帧都进行全面检测。
如果您在单独的线程中运行 Yolo 和对象跟踪,那么您可以尽可能快地运行相机。您需要存储帧的历史记录,以便当 Yolo 线程完成一帧时,您可以检查旧帧以查看您是否已经在跟踪对象,这样您就可以在相应的帧上快速启动对象跟踪器-转发它让它赶上。这个程序并不简单,您需要确保正确管理线程之间的数据。不过,这是一个很好的练习,可以让您熟悉多线程,这是编程中的一大步。
,我想在this article的帮助下回答这个问题,我之前也用过,遇到了和你类似的问题。以下是建议:
- 使用 darknet framework 运行 YOLOv3,这将提高性能。
- 在您的代码片段中,它看起来不允许您决定网络输入的宽度和高度,所以我不知道您使用的是什么。减小网络宽度和高度会提高速度,但会降低准确度。
- YOLOv3 针对 80 个对象进行了训练,但您只需要其中的一些。我之前也只需要我项目中的汽车。不幸的是,您无法操作已经训练好的权重文件,也无法很好地训练您的对象。
- 我之前也尝试过的另一种方式是将 YOLOv3 转移到另一个线程,并且我也没有将 yolo 应用于所有帧。我只应用了其中的一些,例如:每 10 帧中的 1 帧。这对我也很有帮助。
- 或者你可以选择更好的 CPU 电脑 :)
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