如何解决通过线后如何计算物体?
嘿,我是这个级别的Python新手,但我正在尽力做到这一点。 我已经在视频帧中检测到对象并对其进行了标记,并且还对帧中的对象总数进行了计数,但是我的问题是,通过图像中所示的行后如何计数对象。以及对象类别。
这是我的代码,请详细回答并尝试添加代码。
在图像中,我已经计算了框架中的全部对象,但是当它们越过线时,我想对它们进行计数
预先感谢:)
import cv2
import numpy as np
net = cv2.dnn.readNet('yolov3.weights','yolov3.cfg')
classes = []
with open('coco.names','r') as f:
classes = f.read().splitlines()
# printing the data which is loaded from the names file
#print(classes)
cap = cv2.VideoCapture('video.mp4')
while True:
_,img = cap.read()
height,width,_ = img.shape
blob = cv2.dnn.blobFromImage(img,1/255,(416,416),(0,0),swapRB=True,crop=False)
net.setInput(blob)
output_layer_names = net.getUnconnectedOutLayersNames()
layerOutput = net.forward(output_layer_names)
boxes = []
person =0
truck =0
car = 0
confidences = []
class_ids =[]
for output in layerOutput:
for detection in output:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5:
center_x = int(detection[0]*width)
center_y = int(detection[1]*height)
w = int(detection[2]*width)
h = int(detection[3]*height)
x = int(center_x - w/2)
y = int(center_y - h/2)
boxes.append([x,y,w,h])
confidences.append((float(confidence)))
class_ids.append(class_id)
indexes = cv2.dnn.NMSBoxes(boxes,confidences,0.5,0.4)
font = cv2.QT_FONT_NORMAL
colors = np.random.uniform(0,255,size=(len(boxes),3))
for i in indexes.flatten():
labelsss = str(classes[class_ids[i]])
if(labelsss == 'person'):
person+=1
if(labelsss == 'car'):
car+=1
if(labelsss == 'truck'):
truck+=1
for i in indexes.flatten():
x,h = boxes[i]
label =str(classes[class_ids[i]])
confidence = str(round(confidences[i],1))
color = colors[i]
cv2.rectangle(img,(x,y),(x+w,y+h),color,2)
cv2.line(img,(1000,250),(5,2)
cv2.putText(img,label + " ",y+20),font,(255,255),2)
cv2.putText(img,'Car'+ ":" + str(car),(20,20),0.8,'Person'+ ":" + str(person),50),'Truck'+ ":" + str(truck),80),2)
cv2.imshow('Image',img)
key = cv2.waitKey(1)
if key == 10:
break
cap.release()
cv2.destroyAllWindows()
解决方法
我在实习期间做了一个类似的项目。您可以在此处查看代码:https://github.com/sarimmehdi/nanonets_object_tracking/blob/master/test_on_video.py
简而言之:您应该改为绘制一个矩形(窄),并在跟踪的ID通过矩形时对其计数。如果矩形足够窄,则也可以避免重新识别的问题。
版权声明:本文内容由互联网用户自发贡献,该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容, 请发送邮件至 dio@foxmail.com 举报,一经查实,本站将立刻删除。