i can't grab frame 2 object has no attribute - opencv

Question

i want to make a separate tracking but the total inside is the sum of each track but it just keep giving me frame2 = imutils.resize(frame2, width = 500)
pture_MSMF::grabFr File "D:\pyli\lib\site-packages\imutils\convenience.py", line 69, in resize
ame videoio(MSMF): can't grab frame. Error: -2147023901
(h, w) = image.shape[:2].
from mylib.centroidtracker import CentroidTracker
from mylib.trackableobject import TrackableObject
from imutils.video import VideoStream
from imutils.video import FPS
from mylib.mailer import Mailer
from mylib import config, thread
import time, schedule, csv
import numpy as np
import argparse, imutils
import time, dlib, cv2, datetime
from itertools import zip_longest
t0 = time.time()
def run():
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--prototxt", required=False,
help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", required=True,
help="path to Caffe pre-trained model")
ap.add_argument("-i", "--input", type=str,
help="path to optional input video file")
ap.add_argument("-o", "--output", type=str,
help="path to optional output video file")
ap.add_argument("-c", "--confidence", type=float, default=0.4,
help="minimum probability to filter weak detections")
ap.add_argument("-s", "--skip-frames", type=int, default=30,
help="# of skip frames between detections")
args = vars(ap.parse_args())
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
"sofa", "train", "tvmonitor"]
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
net2 = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
if not args.get("input", False):
print("[INFO] Starting live cam 1 & 2..")
vs = VideoStream(config.url).start()
vs2 = VideoStream(config.url1).start()
time.sleep(2.0)
writer = None
W = None
H = None
ct = CentroidTracker(maxDisappeared=10, maxDistance=100)
trackers = []
trackableObjects = {}
ct2 = CentroidTracker(maxDisappeared=10, maxDistance=100)
trackers2 = []
trackableObjects2 = {}
totalFrames = 0
totalDown = 0
totalUp = 0
x = []
empty=[]
empty1=[]
totalFrames2 = 0
totalDown2 = 0
totalUp2 = 0
x2 = []
empty2=[]
empty3=[]
fps = FPS().start()
if config.Thread:
vs = thread.ThreadingClass(config.url)
vs2 = thread.ThreadingClass(config.url1)
while True:
frame = vs.read()
frame = frame[1] if args.get("input", False) else frame
frame2 = vs2.read()
frame2 = frame2[1] if args.get("input", False) else frame2
frame = imutils.resize(frame, width = 500)
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame2 = imutils.resize(frame2, width = 500)
rgb2 = cv2.cvtColor(frame2, cv2.COLOR_BGR2RGB)
if W is None or H is None:
(H, W) = frame.shape[:2]
(H, W) = frame2.shape[:2]
status = "Waiting"
rects = []
status2 = "Waiting"
rects2 = []
if totalFrames % args["skip_frames"] == 0:
status = "Detecting"
trackers = []
blob = cv2.dnn.blobFromImage(frame, 0.007843, (W, H), 127.5)
net.setInput(blob)
detections = net.forward()
for i in np.arange(0, detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > args["confidence"]:
idx = int(detections[0, 0, i, 1])
if CLASSES[idx] != "person":
continue
box = detections[0, 0, i, 3:7] * np.array([W, H, W, H])
(startX, startY, endX, endY) = box.astype("int")
tracker = dlib.correlation_tracker()
rect = dlib.rectangle(startX, startY, endX, endY)
tracker.start_track(rgb, rect)
trackers.append(tracker)
else:
for tracker in trackers:
status = "Tracking"
tracker.update(rgb)
pos = tracker.get_position()
startX = int(pos.left())
startY = int(pos.top())
endX = int(pos.right())
endY = int(pos.bottom())
rects.append((startX, startY, endX, endY))
if totalFrames2 % args["skip_frames"] == 0:
status2 = "Detecting"
trackers2 = []
blob2 = cv2.dnn.blobFromImage(frame2, 0.007843, (W, H), 127.5)
net2.setInput(blob2)
detections2 = net2.forward()
for i in np.arange(0, detections2.shape[2]):
confidence2 = detections2[0, 0, i, 2]
if confidence2 > args["confidence"]:
idx2 = int(detections2[0, 0, i, 1])
if CLASSES[idx2] != "person":
continue
box2 = detections2[0, 0, i, 3:7] * np.array([W, H, W, H])
(startX2, startY2, endX2, endY2) = box2.astype("int")
trackers2 = dlib.correlation_tracker()
rects2 = dlib.rectangle(startX2, startY2, endX2, endY2)
tracker2.start_track(rgb2, rects2)
trackers2.append(tracker2)
else:
for tracker2 in trackers2:
status2 = "Tracking"
tracker2.update(rgb2)
pos2 = tracker2.get_position()
startX2 = int(pos2.left())
startY2 = int(pos2.top())
endX2 = int(pos2.right())
endY2 = int(pos2.bottom())
rects2.append((startX2, startY2, endX2, endY2))
cv2.line(frame, (0, H // 2), (W, H // 2), (0, 0, 0), 3)
cv2.putText(frame, "-Prediction border - Entrance-", (10, H - ((i * 20) + 200)),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1)
cv2.line(frame2, (0, H // 2), (W, H // 2), (0, 0, 0), 3)
cv2.putText(frame2, "-Prediction border - Entrance-", (10, H - ((i * 20) + 200)),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1)
objects = ct.update(rects)
objects2 = ct2.update(rects2)
for (objectID, centroid) in objects.items():
to = trackableObjects.get(objectID, None)
if to is None:
to = TrackableObject(objectID, centroid)
else:
y = [c[1] for c in to.centroids]
direction = centroid[1] - np.mean(y)
to.centroids.append(centroid)
if not to.counted:
if direction < 0 and centroid[1] < H // 2:
totalUp += 1
empty.append(totalUp)
to.counted = True
elif direction > 0 and centroid[1] > H // 2:
totalDown += 1
empty1.append(totalDown)
if sum(x) >= config.Threshold:
cv2.putText(frame, "-ALERT: People limit exceeded-", (10, frame.shape[0] - 80),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 255), 2)
if config.ALERT:
print("[INFO] Sending email alert..")
Mailer().send(config.MAIL)
print("[INFO] Alert sent")
to.counted = True
x = []
x.append(len(empty1)-len(empty))
trackableObjects[objectID] = to
text = "ID {}".format(objectID)
cv2.putText(frame, text, (centroid[0] - 10, centroid[1] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
cv2.circle(frame, (centroid[0], centroid[1]), 4, (255, 255, 255), -1)
for (objectID2, centroid2) in objects2.items():
to2 = trackableObjects2.get(objectID2, None)
if to2 is None:
to2 = TrackableObject(objectID2, centroid2)
else:
y2 = [c[1] for c in to2.centroids]
direction2 = centroid2[1] - np.mean(y2)
to2.centroids.append(centroid2)
if not to2.counted2:
if direction2 < 0 and centroid2[1] < H // 2:
totalUp2 += 1
empty2.append(totalUp2)
to2.counted2 = True
elif direction2 > 0 and centroid2[1] > H // 2:
totalDown2 += 1
empty3.append(totalDown2)
if sum(x) >= config.Threshold:
cv2.putText(frame2, "-ALERT: People limit exceeded-", (10, frame2.shape[0] - 80),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 255), 2)
if config.ALERT:
print("[INFO] Sending email alert..")
Mailer().send(config.MAIL)
print("[INFO] Alert sent")
to2.counted2 = True
x2 = []
x2.append(len(empty3)-len(empty2))
trackableObjects2[objectID2] = to2
text2 = "ID2 {}".format(objectID2)
cv2.putText(frame2, text2, (centroid2[0] - 10, centroid2[1] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
cv2.circle(frame2, (centroid2[0], centroid2[1]), 4, (255, 255, 255), -1)
info = [
("Exit", totalUp),
("Enter", totalDown),
("Status", status),
]
info3 = [
("Total people inside", x+x2),
]
info2 = [
("Exit", totalUp2),
("Enter", totalDown2),
("Status", status2),
]
for (i, (k, v)) in enumerate(info):
text = "{}: {}".format(k, v)
cv2.putText(frame, text, (10, H - ((i * 20) + 20)), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 0), 2)
for (i, (k, v)) in enumerate(info3):
text = "{}: {}".format(k, v)
cv2.putText(frame, text, (265, H - ((i * 20) + 60)), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
text2 = "{}: {}".format(k, v)
cv2.putText(frame2, text2, (265, H - ((i * 20) + 60)), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
for (i, (k, v)) in enumerate(info2):
text2 = "{}: {}".format(k, v)
cv2.putText(frame2, text2, (265, H - ((i * 20) + 60)), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
if config.Log:
datetimee = [datetime.datetime.now()]
d = [datetimee, empty1+empty3, empty+empty2, x+x2]
export_data = zip_longest(*d, fillvalue = '')
with open('Log.csv', 'w', newline='') as myfile:
wr = csv.writer(myfile, quoting=csv.QUOTE_ALL)
wr.writerow(("End Time", "In", "Out", "Total Inside"))
wr.writerows(export_data)
if writer is not None:
writer.write(frame)
writer.write(frame2)
cv2.imshow("Real-Time Monitoring/Analysis Window", frame)
cv2.imshow("Real-Time Monitoring 2", frame2)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
totalFrames += 1
totalFrames2 += 1
fps.update()
if config.Timer:
t1 = time.time()
num_seconds=(t1-t0)
if num_seconds > 28800:
break
fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
if config.Thread:
vs.release()
vs2.release()
cv2.destroyAllWindows()
if config.Scheduler:
schedule.every().day.at("09:00").do(run)
while 1:
schedule.run_pending()
else:
run()