How to count an object after passing through the line?

Hey I am new in Python for this level but i am trying my best to do it. I have detect the object in the video frames and labelled it and also count the total objects in the frame but my question is how can i count the object after passing the line as shown in image. and also with the object category.

Here is my code, please answer in detail and try to add code also.

In the image i have count the total object in the frame but i want to count them when they cross the line

Thanks in advance :)

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), (0,0,0) ,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,y,w,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),(5,250),(0,0,0),2)
              
        cv2.putText(img, label + " ", (x, y+20), font, 0.5, (255,255,255),2)
        cv2.putText(img, 'Car'+ ":" + str(car), (20, 20), font, 0.8, (0,0,0),2)
        cv2.putText(img, 'Person'+ ":" + str(person), (20, 50), font, 0.8, (0,0,0),2)
        cv2.putText(img, 'Truck'+ ":" + str(truck), (20, 80), font, 0.8, (0,0,0),2)
        cv2.imshow('Image',img)
        key = cv2.waitKey(1)
        if key == 10:
            break
        
cap.release()
cv2.destroyAllWindows()