A Flask app for multiple live video streaming over a network with object detection, tracking (optional), and counting. Uses YOLO v4 with Tensorflow backend as the object detection model and Deep SORT trained on the MARS dataset for object tracking. Each video stream has an independent thread and uses ImageZMQ for asynchronous sending and processing of frames.
Much of the way this app works is based on Miguel Grinberg's https://github.com/miguelgrinberg/flask-video-streaming. Since this is a Flask app, the video streams are accessed via web browser and it's possible to have more than one web client. If all the web clients disconnect from the app, then every video stream thread will automatically shutdown due to inactivity after a set time period. The video streams will restart once a web client connects again, but unlike Miguel's app, the camera clients that are sending the frames must be restarted.
See my other repository for only YOLO v4 and Deep SORT: https://github.com/LeonLok/Deep-SORT-YOLOv4
Both examples below show multi-camera detection, tracking, and counting of people and cars.
Enter the camera stream addresses for your own cameras in each client script and make sure that each camera client is sending frames to the correct server address and port.
The video_feed function in app.py contains a list of the assigned ports for each server called port_list. Currently, camera_client_0.py is device number 0 and it uses the first port in the list (5555). camera_client_1.py is device number 1 and it uses the second port in the list (5566). Basically, the device number corresponds to the index in port_list, so they need to match accordingly. If you want to add another camera, create a camera_client_2.py with a new port and add this port to port_list in app.py as the third port.
You also need to make sure that the templates/index.html file contains the correct number of streams for however many you want to be displayed.
For example, if you want to activate the second camera and YOLO stream, make sure that these are uncommented:
<img src="{{ url_for('video_feed', feed_type='camera', device=1) }}"...
and
<img src="{{ url_for('video_feed', feed_type='yolo', device=1) }}"...
You can add more streams by following the same pattern using device=2 and so on.
If you want to learn more about how the server and clients work with ZeroMQ, see . The current default messaging pattern used is the request-reply pattern.
To change the name of each camera stream shown in the top-left corner, edit cam_id in the camera client files.
This app uses YOLO v4 weights that are converted from Darknet to Keras format. You need to train or convert your own and put it in the model_data folder. See https://github.com/Ma-Dan/keras-yolo4.
To modify the detection settings like IOU threshold, anchors, class names etc., you can do so in yolo.py.
Modify line 103 in yolo.py. For example, to detect people and cars, change it to:
if predicted_class not in ('people', 'car'):
continue
Deep SORT is used for object tracking. However, Please note that the tracking model is only trained for tracking people, so you'd need to train a model yourself for tracking other objects. As you can see in the demonstration gifs, it can still work for tracking other objects like cars.
See https://github.com/nwojke/cosine_metric_learning to train your own tracking model.
However, if you don't want to use tracking at all, you can turn it off by changing line 28 in camera_yolo.py from
tracking = True
to
tracking = False
First, configure each camera client so that they have the correct camera stream address. Also make sure that the frames are being sent to localhost with the correct port (e.g. tcp://localhost:5555). As mentioned earlier, you can also change the name of the camera stream by changing cam_id to something more relevant.
Before running, check that templates/index.html is correctly configured.
Run app.py and then start running each camera client. Once everything is running, launch a web browser and enter localhost:5000. That should open index.html in the browser and the video threads should start running. The camera streams should load pretty quickly; if they don't, then try restarting the camera clients and refresh the browser. The YOLO streams will also load eventually, but they take longer to load due to starting Tensorflow and loading the YOLO model.
If the YOLO thread is shutting down before it finishes loading, you need to increase the time limit on line 117:
if time.time() - BaseCamera.last_access[unique_name] > 60:
All threads will shutdown after this time limit if the app thinks there's no more viewing web clients. If the YOLO streams are not used, then the default time limit for camera stream threads to shutdown is 5 seconds.
The process is similar to running locally. If you have your own remote server, configure each camera client so that they'll be sending frames to this server address with the correct port instead of localhost (e.g. tcp://server-address-here:5555).
Clone the repository on the remote server and check that it has the correct ports forwarded so that your browser and camera clients can connect to it. Run app.py and then start running the camera clients. Like before, you should now be able to connect to the app by entering the server address with port 5000 into the browser (i.e. replace localhost:5000 with server-address-here:5000). Again, if nothing loads, try restarting the camera clients and refresh the browser.
The total current object counts are automatically stored in a text file every set interval of the hour for every detected object. Each newly detected class also creates a new class counts file to store the current counts for that class, and will also appear as text in the YOLO stream.
Hardware used:
- Nvidia GTX 1070 GPU
- i7-8700K CPU
Hosting the on a local server gave ~15FPS on average with a single camera stream at 640x480 resolution streamed locally at 30FPS. Turning off tracking gave me ~16FPS. As you'd expect, having multiple streams will lower the FPS significantly as shown in the demonstration gifs.
There's a lot of other factors that can impact performance like network speed and bandwidth, but hopefully that gives you some idea on how it'll perform on your machine.
Lowering the resolution or quality of the stream will improve performance but also lower the detection accuracy.
I used YOLO v3 when I first started this project which gave me about ~10FPS with tracking, making it difficult to run more than one stream at a time. Using YOLO v4 made it much easier to run two streams with a higher resolution, as well as giving a better detection accuracy.
- Tensorflow-GPU 1.14
- Keras 2.3.1
- opencv-python 4.2.0
- ImageZMQ
- numpy 1.18.2
- Flask 1.1.1
- pillow
This project was built and tested on Python 3.6.
This project was built with the help of:
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent