I came across a problem: the code works so that when a car passes the camera, a video of its passage is saved, then the function starts again through recursion, but exactly at 4th cycle the code gives an error
(H, W) = image.shape[:2]
AttributeError: 'NoneType' object has no attribute 'shape'
Here is the code itself:
import cv2 as cv
import numpy as np
import uuid
import os.path
import os
def camera():
print("new cycle")
confThreshold = 0.6
nmsThreshold = 0.1
inpWidth = 416
inpHeight = 416
classesFile = "car.names"
classes = None
with open(classesFile, 'rt') as f:
classes = f.read().rstrip('\n').split('\n')
modelConf = "car.cfg"
modelWeights = "cars_last.weights"
cap = cv.VideoCapture("rtsp://.../user=admin_password=tlJwpbo6_channel=1_stream=0.sdp?real_stream:")
ret, image = cap.read()
(H, W) = image.shape[:2]
global empty
global newname
newname = str(uuid.uuid4())
global output
output = cv.VideoWriter(newname+'.avi', cv.VideoWriter_fourcc('M','J','P','G'), 10,
(W, H))
print(newname)
empty = []
def postprocess(frame, outs):
frameHeight = frame.shape[0]
frameWidth = frame.shape[1]
classIDs = []
confidences = []
boxes = []
boxes1=[]
cv.cvtColor(frame, cv.COLOR_RGB2BGR)
for out in outs:
for detection in out:
scores = detection[5:]
classID = np.argmax(scores)
confidence = scores[classID]
if confidence > confThreshold:
centerX = int(detection[0] * frameWidth)
centerY = int(detection[1] * frameHeight)
width = int(detection[2] * frameWidth)
height = int(detection[3] * frameHeight)
left = int(centerX - width / 2)
top = int(centerY - height / 2)
x = int(centerX - (width / 2))
y = int(centerY - (height / 2))
boxes1.append([x, y, int(width), int(height)])
classIDs.append(classID)
confidences.append(float(confidence))
boxes.append([left, top, width, height])
idxs = cv.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
if len(idxs) > 0:
empty.clear()
empty.append(0)
else:
empty.append(1)
if len(empty)>100:
empty.clear()
if (os.path.isfile(newname+".avi"))==True and os.path.getsize(newname+".avi")!=0:
camera()
indices = cv.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
for i in indices:
i = i[0]
box = boxes[i]
left = box[0]
top = box[1]
width = box[2]
height = box[3]
drawPred(classIDs[i], confidences[i], left, top, left + width, top + height)
output.write(frame)
def drawPred(classId, conf, left, top, right, bottom):
# Draw a bounding box.
cv.rectangle(frame, (left, top), (right, bottom), (255, 178, 50), 3)
label = '%.2f' % conf
# Get the label for the class name and its confidence
if classes:
assert (classId < len(classes))
label = '%s:%s' % (classes[classId], label)
labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
top = max(top, labelSize[1])
cv.rectangle(frame, (left, top - round(1.5 * labelSize[1])), (left + round(1.5 * labelSize[0]), top + baseLine),
(255, 255, 255), cv.FILLED)
cv.rectangle(frame, (left,top),(right,bottom), (255,255,255), 1 )
cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 0), 1)
def getOutputsNames(net):
layersNames = net.getLayerNames()
return [layersNames[i[0] - 1] for i in net.getUnconnectedOutLayers()]
net = cv.dnn.readNetFromDarknet(modelConf, modelWeights)
net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
winName = 'DL OD with OpenCV'
cv.namedWindow(winName, cv.WINDOW_NORMAL)
while cv.waitKey(1) < 0:
hasFrame, frame = cap.read()
blob = cv.dnn.blobFromImage(frame, 1 / 255, (inpWidth, inpHeight), [0, 0, 0], 1, crop=False)
net.setInput(blob)
outs = net.forward(getOutputsNames(net))
postprocess(frame, outs)
cv.imshow(winName, frame)
cap.release()
output.release()
camera()
