vehicle_counting_tensorflow
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ahmetozlu
Not able to count the vechile
I am trying to use my own video which is 30 frames per second and resolution is of 1920*1080. I tried to change the ROI value from 200 to different values but it didnt work.
Can you please guide me on the same??
I would also like to know where to update the frames per second value.
Looking forward to hearing from you.
Hi. I am having the same issue. When I use a different video I am not able to get any output. I tried to change ROI values but even then it did not work.
Would be nice to get a guide on how to run our own traffic videos using this project. Thanks :)
@harshsp31 Nope. I didn't have time to look into the code. Please do let me know if you're able to find the solution for the problem.
There are 3 files, which are concerned with change in ROI. Firstly, since the code provided in vehicle_detection.py is done hardcoded for sample video, as written in comments. So to make it work for your own video,
- find the below in vehicle_detection.py file. Here in putline function, instead of (640,200) write (widthOfFrame, ROI_Location).
if counter==1:
cv2.putline(...)
- The second change needed is in utils.visualization file, change the value of ROI to your suitable values
- Somehow, utils.speed_prediction.predict_speed function is not been able to get the ROI value passed to it by utils.visualization file, hence just manually set roi_position=200 in the function. With above 3 steps you will be able to do it for your own video.
In the sample video, Direction of vehicles is DOWN, so if your video has UP direction (Back Face of Vehicle) then you have to change the logic in utils.speed_prediction.predict_speed function. Find the line where is_vehicle_detected.insert(0,1) is written. In the IF condition, instead of (bottom) just write (top) and also 10 lines down, where speed is calculated, change bottom_position_of_detected_vehicle.insert(0,top)
If you need the modified code, let me know.
Hi @ChampionTej05 It would be great if you could share the modified code.
Looking forward to hearing from you. Best, Venkatesh
@venkateshbabuekar this is the link for the zip file of the folder. Make sure you place the files in this folder in the research directory of object api. The accuracy of counting solely depends upon whether your model is able to detect the vehicle near the ROI. So analyze your video once and see which line in the frame is best suited where you get maximum detection.
https://drive.google.com/a/rknec.edu/file/d/1-6KgDYd1HPOdJc4okgcfDgpscbhNfItP/view?usp=drivesdk
@ChampionTej05 give me access of your google drive to download code ....
I have provided access to the code ,now. Please check it.
On Sun, 23 Jun 2019, 12:40 am Amaad Mirza, [email protected] wrote:
@ChampionTej05 https://github.com/ChampionTej05 give me access of your google drive to download code ....
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@ChampionTej05 i want to change the ROI line vertically to detect vehicles and counting not horizontally kindly suggest possible settings .... waiting for your reply
This are the listed changes you would need.
Roi_detection.py : ROI_POSITION= 1300 (you can set according to your frame size, if your frame size 1920 then set it to 1300) Around 277 line : cv2.line(input_frame, ( ROI_POSITION,0), (ROI_POSITION,height_img), (0, 0, 0xFF), 5)
Visualization_utils.py: ROI_POSITION: should be same as above Around 181: Change the IF condition to : left < ROI_POSITION
Speed_Prediction.py: Roi_position: Same as above Changes around line 39: many changes hence I am attaching file for it.
I have attached other files also. You can check.
@ChampionTej05 thanks for your help .... kindly share your sample video through google drive
Video on which I am running my project is confidential, so I can't share it. You can use any video of traffic.
ok no problem.. any other link of other traffic video that related to sample code .....!!
@ChampionTej05 where we can add kalman filter in this code ? i need to add kalman filter...
Provided you know how to add the kalman filter on Open CV Frame, you can just go and check out the line 200 in roi_detection.py file. input_frame variable is your Frame. Just add filter to this frame.
my question is about how to use because the object is bumping in the frame... is it apply to the frame or bounding boxes like this
newboxes = []
for box in boxes:
box=kf.KalmanFilter.predict(box)
kf.KalmanFilter.update(box)
newboxes.append(box)
boxes = newboxes
@ChampionTej05 i am using this github code
https://github.com/zziz/kalman-filter
for kalman filter but not successful could you plz help me to put kalman filter in your code...
@ChampionTej05 tried this kalman filter but not successfully integrate in it ..... need your help
This are the listed changes you would need. Roi_detection.py : ROI_POSITION= 1300 (you can set according to your frame size, if your frame size 1920 then set it to 1300) Around 277 line : cv2.line(input_frame, ( ROI_POSITION,0), (ROI_POSITION,height_img), (0, 0, 0xFF), 5) Visualization_utils.py: ROI_POSITION: should be same as above Around 181: Change the IF condition to : left < ROI_POSITION Speed_Prediction.py: Roi_position: Same as above Changes around line 39: many changes hence I am attaching file for it. I have attached other files also. You can check. Vehicle_tensorflow
This are the listed changes you would need. Roi_detection.py : ROI_POSITION= 1300 (you can set according to your frame size, if your frame size 1920 then set it to 1300) Around 277 line : cv2.line(input_frame, ( ROI_POSITION,0), (ROI_POSITION,height_img), (0, 0, 0xFF), 5) Visualization_utils.py: ROI_POSITION: should be same as above Around 181: Change the IF condition to : left < ROI_POSITION Speed_Prediction.py: Roi_position: Same as above Changes around line 39: many changes hence I am attaching file for it. I have attached other files also. You can check. Vehicle_tensorflow
Sir, can u provide access to this drive link mailto : [email protected] thanks
Oops, thanks bro
On Sat, 29 Jun 2019, 16:05 Rakshit kathawate, [email protected] wrote:
I have made the link public
— You are receiving this because you commented. Reply to this email directly, view it on GitHub https://github.com/ahmetozlu/vehicle_counting_tensorflow/issues/35?email_source=notifications&email_token=AHMERDOP3TL5DJOK4NSFAITP443FFA5CNFSM4GWGYH4KYY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGODY3WMUI#issuecomment-506947153, or mute the thread https://github.com/notifications/unsubscribe-auth/AHMERDKI3XRMSOV3MW25O43P443FFANCNFSM4GWGYH4A .
I have made the link public
Sir, couldn't access says it requires permission
@ChampionTej05 i have changed all the roi position in vehicle_detection_main.py, speed_prediction.py and visualization_utils.py but still cannot counting vehicles, help me please :(
hi, @ChampionTej05 please give me access of your google drive to download code. Thanks in advanced.
@ChampionTej05 could you explain this lines please :
if isInROI: pixel_length = bottom - bottom_position_of_detected_vehicle[0] scale_real_length = pixel_length * 44 # multiplied by 44 to convert pixel length to real length in meters (chenge 44 to get length in meters for your case) total_time_passed = current_frame_number - current_frame_number_list[0] scale_real_time_passed = total_time_passed * 24 # get the elapsed total time for a vehicle to pass through ROI area (24 = fps) if scale_real_time_passed != 0: speed = scale_real_length / scale_real_time_passed / scale_constant # performing manual scaling because we have not performed camera calibration speed = speed / 6 * 40 # use reference constant to get vehicle speed prediction in kilometer unit current_frame_number_list.insert(0, current_frame_number) bottom_position_of_detected_vehicle.insert(0, right)
@ChampionTej05 i have changed all the roi position in vehicle_detection_main.py, speed_prediction.py and visualization_utils.py but still cannot counting vehicles, help me please :(
Did you find any solution to this, please?
