I am very new to OpenCV and Python. I have followed a tutorial to use YOLO using yolov3-tiny. It can detect vehicles fine. But what I need to complete my project is to count the number of vehicles that passes a particular lane. If I use the method where the vehicle is detected (the bounding box appears) to count, the count becomes very inaccurate since, the bounding box keeps blinking (meaning, it keeps on locating the same vehicle again, sometimes up to 5 times), so this is not a good way to count.
So I figured, how about if I just count a vehicle if it gets to a certain point. I have seen a lot of codes that seems to make this but, since I am a beginner, it really is hard for me to understand let alone, run it in my system. Their samples need to install so many things that I can't do because it throws errors.
See my sample code below:
cap = cv2.VideoCapture('rtsp://username:password@xxx.xxx.xxx.xxx:xxx/cam/realmonitor?channel=1')
whT = 320
confThreshold = 0.75
nmsThreshold = 0.3
list_of_vehicles = ["bicycle","car","motorbike","bus","truck"]
classesFile = 'coco.names'
classNames = []
with open(classesFile, 'r') as f:
classNames = f.read().rstrip('\n').split('\n')
modelConfiguration = 'yolov3-tiny.cfg'
modelWeights = 'yolov3-tiny.weights'
net = cv2.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
total_vehicle_count = 0
def getVehicleCount(boxes, class_name):
global total_vehicle_count
dict_vehicle_count = {}
if(class_name in list_of_vehicles):
total_vehicle_count += 1
# print(total_vehicle_count)
return total_vehicle_count, dict_vehicle_count
def findObjects(ouputs, img):
hT, wT, cT = img.shape
bbox = []
classIds = []
confs = []
for output in outputs:
for det in output:
scores = det[5:]
classId = np.argmax(scores)
confidence = scores[classId]
if confidence > confThreshold:
w, h = int(det[2] * wT), int(det[3] * hT)
x, y = int((det[0] * wT) - w/2), int((det[1] * hT) - h/2)
bbox.append([x, y, w, h])
classIds.append(classId)
confs.append(float(confidence))
indices = cv2.dnn.NMSBoxes(bbox, confs, confThreshold, nmsThreshold)
for i in indices:
i = i[0]
box = bbox[i]
getVehicleCount(bbox, classNames[classIds[i]])
x, y, w, h = box[0], box[1], box[2], box[3]
cv2.rectangle(img, (x,y), (x+w, y+h), (255,0,255), 1)
cv2.putText(img, f'{classNames[classIds[i]].upper()} {int(confs[i]*100)}%', (x,y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255,0,255), 2)
while True:
success, img = cap.read()
blob = cv2.dnn.blobFromImage(img, 1/255, (whT,whT), [0,0,0], 1, crop=False)
net.setInput(blob)
layerNames = net.getLayerNames()
outputnames = [layerNames[i[0]-1] for i in net.getUnconnectedOutLayers()]
# print(outputnames)
outputs = net.forward(outputnames)
findObjects(outputs, img)
cv2.imshow('Image', img)
if cv2.waitKey(1) & 0XFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
With this code, 1 vehicle is counted sometimes up to 50 count, depending on the size, which is highly inaccurate. How can I create an ROI so that when the detected vehicle passes that point, that will be the only time it will count.
First, I would recommend that you consider using a visual tracker to track each detected rectangle. This is important even if you have an ROI to crop the image close to your counting zone/line. That is because even if the ROI is localized, the detection might still blink a couple of times causing a miscount. That is especially valid if another vehicle can enter the ROI while the first one is still passing it.
I recommend using the easy-to-use tracker provided by the widely used dlib library. Please refer to this example on how to use it.
Instead of counting detections within an ROI, you need to define an ROI line (within your ROI). Then, track detections rectangles centers in each frame. Finally, increase your counter once a rectangle center passes the ROI line.
Regarding how to count a rectangle passing the ROI line:
ax + by + c = 0