There seems to be a threshold of maximum face size, when I get close enough to the camera seems like the detection doesn't see a face. Is there a size guide for the weight of Yolo Detection you are using? Thank you

I would like to build a custom_maskcam container. My goal is to get a better understanding of the architecture of the project and to eventually train my own dataset. Right now I simply cloned the repository on my jetson nano. When I use the command: docker build . -t custom_maskcam inside my maskcam folder I keep getting the same error :

Step 12/28 : RUN export GST_CFLAGS="-pthread -I/usr/include/gstreamer-1.0 -I/usr/include/glib-2.0 -I/usr/lib/x86_64-linux-gnu/glib-2.0/include" && export GST_LIBS="-lgstreamer-1.0 -lgobject-2.0 -lglib-2.0" && git clone https://github.com/GStreamer/gst-python.git && cd gst-python && git checkout 1a8f48a && ./autogen.sh PYTHON=python3 && ./configure PYTHON=python3 && make && make install
---> [Warning] The requested image's platform (linux/amd64) does not match the detected host platform (linux/arm64/v8) and no specific platform was requested
---> Running in 3eb0f983b6c4
Cloning into 'gst-python'...
Note: checking out '1a8f48a'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by performing another checkout.

If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -b with the checkout command again. Example:

git checkout -b

HEAD is now at 1a8f48a Release 1.14.5

• Setting up common submodule
  Submodule 'common' (https://gitlab.freedesktop.org/gstreamer/common.git) registered for path 'common'
  Cloning into '/gst-python/common'...
  fatal: unable to access 'https://gitlab.freedesktop.org/gstreamer/common.git/': server certificate verification failed. CAfile: /etc/ssl/certs/ca-certificates.crt CRLfile: none
  fatal: clone of 'https://gitlab.freedesktop.org/gstreamer/common.git' into submodule path '/gst-python/common' failed
  Failed to clone 'common'. Retry scheduled
  Cloning into '/gst-python/common'...
  fatal: unable to access 'https://gitlab.freedesktop.org/gstreamer/common.git/': server certificate verification failed. CAfile: /etc/ssl/certs/ca-certificates.crt CRLfile: none
  fatal: clone of 'https://gitlab.freedesktop.org/gstreamer/common.git' into submodule path '/gst-python/common' failed
  Failed to clone 'common' a second time, aborting
  There is something wrong with your source tree.
  You are missing common/gst-autogen.sh
  The command '/bin/sh -c export GST_CFLAGS="-pthread -I/usr/include/gstreamer-1.0 -I/usr/include/glib-2.0 -I/usr/lib/x86_64-linux-gnu/glib-2.0/include" && export GST_LIBS="-lgstreamer-1.0 -lgobject-2.0 -lglib-2.0" && git clone https://github.com/GStreamer/gst-python.git && cd gst-python && git checkout 1a8f48a && ./autogen.sh PYTHON=python3 && ./configure PYTHON=python3 && make && make install' returned a non-zero code: 1

This error occurs when installing gst-python.
Can you help me with this issue.
Thank you
Raphael

Hello,
I noticed a strange bug. When I launched the rtsp stream the camera view get purple and can't recognize face anymore due to the luminosity (the camera is in front of a window). If I close my shutters the camera works perfectly. I think that you may find this interesting.

Hi,
I'm trying to write a deepstream-app, and I'm not very familiar with Glib.I met a problem before that if the rtsp sources are not stable or the internet is poor, the pipeline will run without any errors as well as any outputs. So I'm trying to find a way to restart the pipeline under this circumstance.
I found your codes and the glib_cb_restart confused me. I read your annotations, so the timeout_add will call glib_cb_restart and without a return value, the function will add another timeout_add. Your annotation said:Timer to avoid GLoop locking infinitely and But we want to check periodically for other events. I also found the explanation about Glib.MainContext.iteration.
I don't get the point of the usage of this function.
Appreciate if you can explain a little more or give me some hints.
Thanks a lot

With the newer DeepStream 6.0 supporting Redis, is it possible to implement Redis as a backend database for maskcam.

When I tried to reproduce the project, I encountered a problem that the container （server_streamlit）could not start. I tried to unload the container and rebuild it through image, but I still couldn't start it successfully. Its state has always been：Exited(1). I checked its log, as shown below:
Traceback (most recent call last):
File "/usr/local/bin/streamlit", line 8, in
sys.exit(main())
File "/usr/local/lib/python3.7/site-packages/click/core.py", line 1130, in call
return self.main(*args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/click/core.py", line 1055, in main
rv = self.invoke(ctx)
File "/usr/local/lib/python3.7/site-packages/click/core.py", line 1657, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/usr/local/lib/python3.7/site-packages/click/core.py", line 1404, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/usr/local/lib/python3.7/site-packages/click/core.py", line 760, in invoke
return __callback(*args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/streamlit/cli.py", line 233, in main_run
_main_run(target, args)
File "/usr/local/lib/python3.7/site-packages/streamlit/cli.py", line 249, in _main_run
command_line = _get_command_line_as_string()
File "/usr/local/lib/python3.7/site-packages/streamlit/cli.py", line 244, in _get_command_line_as_string
cmd_line_as_list.extend(click.get_os_args())
AttributeError: module 'click' has no attribute 'get_os_args'
Receiving your reply is of great help to me！

Can adding rtsp input ? For ipcam ?

I am getting this error

python3 maskcam_run.py
| DEBUG Using selector: EpollSelector selector_events.py:54
INFO maskcam-run | Using input from config file: prints.py:48
v4l2:///dev/video0
WARNING maskcam-run | MQTT is DISABLED since MQTT_BROKER_IP or prints.py:44
MQTT_DEVICE_NAME env vars are not defined

INFO maskcam-run | Press Ctrl+C to stop all processes prints.py:48
INFO maskcam-run | Process file-server started with PID: prints.py:48
12650
INFO maskcam-run | Starting streaming prints.py:48
(streaming-start-default is set)
INFO maskcam-run | Received command: streaming_start prints.py:48
INFO maskcam-run | Process inference started with PID: 12652 prints.py:48
INFO maskcam-run | Processing command: streaming_start prints.py:48
INFO maskcam-run | Process streaming started with PID: 12653 prints.py:48
INFO mqtt | MQTT not connected. Skipping message to topic: prints.py:48
device-status
| INFO file-server | Serving static files from directory: prints.py:48
/tmp/saved_videos
INFO file-server | Static server STARTED at prints.py:48
http://:8080
| INFO streaming | Codec: H264 prints.py:48
INFO streaming | prints.py:48

       Streaming at                                                         
       rtsp://<device-address-not-configured>:8554/maskcam                  

| INFO inference | Auto calculated frames to skip inference: 2 prints.py:48
INFO inference | Creating Pipeline prints.py:48

INFO inference | Creating Camera input prints.py:48
INFO inference | Creating Convertor src 2 prints.py:48
INFO inference | Creating Camera caps filter prints.py:48
INFO inference | Creating Convertor src 1 prints.py:48
INFO inference | Creating NVMM caps for input stream prints.py:48
INFO inference | Creating NvStreamMux prints.py:48
INFO inference | Creating pgie prints.py:48
INFO inference | Creating Converter NV12->RGBA prints.py:48
INFO inference | Creating OSD (nvosd) prints.py:48
INFO inference | Creating Queue prints.py:48
INFO inference | Creating Converter RGBA->NV12 prints.py:48
INFO inference | Creating capsfilter prints.py:48
INFO inference | Creating H264 stream prints.py:48
INFO inference | Creating Encoder prints.py:48
INFO inference | Creating Code Parser prints.py:48
INFO inference | Creating RTP H264 Payload prints.py:48
INFO inference | Creating Splitter file/UDP prints.py:48
INFO inference | Creating UDP queue prints.py:48
INFO inference | Creating Multi UDP Sink prints.py:48
INFO inference | Creating Fake Sink prints.py:48
INFO inference | Linking elements in the Pipeline prints.py:48

Opening in BLOCKING MODE
Opening in BLOCKING MODE
ERROR: Deserialize engine failed because file path: /home/jetsonnano/maskcam/yolo/maskcam_y4t_1024_608_fp16.trt open error
0:00:02.575327981 12652 0x200a86f0 WARN nvinfer gstnvinfer.cpp:616:gst_nvinfer_logger: NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1691> [UID = 1]: deserialize engine from file :/home/jetsonnano/maskcam/yolo/maskcam_y4t_1024_608_fp16.trt failed
0:00:02.575399128 12652 0x200a86f0 WARN nvinfer gstnvinfer.cpp:616:gst_nvinfer_logger: NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:1798> [UID = 1]: deserialize backend context from engine from file :/home/jetsonnano/maskcam/yolo/maskcam_y4t_1024_608_fp16.trt failed, try rebuild
0:00:02.575429806 12652 0x200a86f0 INFO nvinfer gstnvinfer.cpp:619:gst_nvinfer_logger: NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1716> [UID = 1]: Trying to create engine from model files
Yolo type is not defined from config file name:
ERROR: Failed to create network using custom network creation function
ERROR: Failed to get cuda engine from custom library API
0:00:02.575762260 12652 0x200a86f0 ERROR nvinfer gstnvinfer.cpp:613:gst_nvinfer_logger: NvDsInferContext[UID 1]: Error in NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1736> [UID = 1]: build engine file failed
0:00:02.575795021 12652 0x200a86f0 ERROR nvinfer gstnvinfer.cpp:613:gst_nvinfer_logger: NvDsInferContext[UID 1]: Error in NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:1822> [UID = 1]: build backend context failed
0:00:02.575826220 12652 0x200a86f0 ERROR nvinfer gstnvinfer.cpp:613:gst_nvinfer_logger: NvDsInferContext[UID 1]: Error in NvDsInferContextImpl::initialize() <nvdsinfer_context_impl.cpp:1149> [UID = 1]: generate backend failed, check config file settings
0:00:02.576405397 12652 0x200a86f0 WARN nvinfer gstnvinfer.cpp:812:gst_nvinfer_start: error: Failed to create NvDsInferContext instance
0:00:02.576446544 12652 0x200a86f0 WARN nvinfer gstnvinfer.cpp:812:gst_nvinfer_start: error: Config file path: maskcam_config.txt, NvDsInfer Error: NVDSINFER_CONFIG_FAILED
ERROR inference | gst-resource-error-quark: Failed to create prints.py:42
NvDsInferContext instance (1): /dvs/git/dirty/git-master
_linux/deepstream/sdk/src/gst-plugins/gst-nvinfer/gstnvi
nfer.cpp(812): gst_nvinfer_start ():
/GstPipeline:pipeline0/GstNvInfer:primary-inference:
Config file path: maskcam_config.txt, NvDsInfer Error:
NVDSINFER_CONFIG_FAILED
INFO inference | prints.py:48
TROUBLESHOOTING HELP

           If the error is like: v4l-camera-source / reason                 
       not-negotiated                                                       
           Solution: configure camera capabilities                          
           Run the script under utils/gst_capabilities.sh and               
       find the lines with type                                             
           video/x-raw ...                                                  
           Find a suitable framerate=X/1 (with X being an                   
       integer like 24, 15, etc.)                                           
           Then edit config_maskcam.txt and change the line:                
           camera-framerate=X                                               
           Or configure using --env MASKCAM_CAMERA_FRAMERATE=X              
       (see README)                                                         
                                                                            
           If the error is like:                                            
           /usr/lib/aarch64-linux-gnu/libgomp.so.1: cannot                  
       allocate memory in static TLS block                                  
           Solution: preload the offending library                          
           export                                                           
       LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libgomp.so.1                   
                                                                            
           END HELP                                                         

INFO inference | Inference main loop ending. prints.py:48
INFO maskcam-run | Sending interrupt to file-server process prints.py:48
INFO file-server | Shutting down static file server prints.py:48
INFO maskcam-run | Waiting for process file-server to prints.py:48
terminate...
INFO file-server | Server shut down correctly prints.py:48
INFO file-server | Server alive threads: prints.py:48
[<_MainThread(MainThread, started 548193665040)>]
INFO maskcam-run | Process terminated: file-server prints.py:48

INFO maskcam-run | Sending interrupt to streaming process prints.py:48
INFO maskcam-run | Waiting for process streaming to prints.py:48
terminate...
INFO streaming | Ending streaming prints.py:48
INFO maskcam-run | Process terminated: streaming prints.py:48

Hi, Instead of using usb web cam or mp4 file, I am wondering whether a RTMP or HLS video feed can be used as an input source. Any hint is given to modify the python script? Thanks.

After I got the server set up on my local machine, I want use Jetson Nano to run following command:

Run with MQTT_BROKER_IP, MQTT_DEVICE_NAME, and MASKCAM_DEVICE_ADDRESS

sudo docker run --runtime nvidia --privileged --rm -it --env MQTT_BROKER_IP= --env MQTT_DEVICE_NAME=my-jetson-1 --env MASKCAM_DEVICE_ADDRESS= -p 1883:1883 -p 8080:8080 -p 8554:8554 maskcam/maskcam-beta
but I can't establish MQTT connection from the device to the server, than I use Jetson Nano try following command:
ping
I found I can't ping my local server IP
How to fix this problem?

standard_init_linux.go:219: exec user process caused: exec format error The command '/bin/sh -c python -m pip install --upgrade pip && pip install -r requirements.txt' returned a non-zero code: 1 ERROR: Service 'backend' failed to build : Build failed

I followed the instructions given in this section Running the MQTT Broker and Web Server and I did all steps; however, I couldn't run the web server it gives me some errors(see the attached picture). I tried to refresh the webpage and no use. What you think I am doing wrong here?

FYI: I am running the web server on another laptop, not on my jetson nano. I am also using Ubuntu 20.4

Hello,
I want to test maskcam on Jetson Xavier NX.
Could your share the raw weights file (facemask-yolov4-tiny_best.weights, maskcam-yolov4-best.weights?), so as to "Convert weights generated using the original darknet implementation to TRT" (in https://github.com/bdtinc/maskcam/blob/main/docs/Useful-Development-Scripts.md)
This seems to be a necessary procedure to test maskcam on Jetson Xavier NX.
Thank you in advance

HI,professor：
this project very good, but in practical use , we prefer C++， so, can realize with C++, thanks !

can someone paste the link

Hi Guys, amazing work. It's just that when I check the time visible in the web report, it's showing incorrectly. Both my web server and jetson nano are already on my timezone: GMT+7. Where can I set the timezone?

I am trying to deploy the application without Docker. Seem to be running in to the problem of compiling YOLOv4 plugin for Deepstream. After cd <this repo path>/deepstream_plugin_yolov4 and export CUDA_VER=10.2 I went to execute 'make' next and run to the error: root@seen-desktop:/home/seen/Desktop/maskcam/deepstream_plugin_yolov4# export CUDA_VER=10.2 root@seen-desktop:/home/seen/Desktop/maskcam/deepstream_plugin_yolov4# make g++ -c -o nvdsinfer_yolo_engine.o -Wall -std=c++11 -shared -fPIC -Wno-error=deprecated-declarations -I../../includes -I/usr/local/cuda-10.2/include -I/opt/nvidia/deepstream/deepstream-5.0/sources/includes nvdsinfer_yolo_engine.cpp nvdsinfer_yolo_engine.cpp:23:10: fatal error: nvdsinfer_custom_impl.h: No such file or directory #include "nvdsinfer_custom_impl.h" ^~~~~~~~~~~~~~~~~~~~~~~~~ compilation terminated. Makefile:51: recipe for target 'nvdsinfer_yolo_engine.o' failed make: *** [nvdsinfer_yolo_engine.o] Error 1

When I pasted the rtsp url and tried to run on a VLC media player on another system, It is not displaying the faces but the time is progressing what could be the reason for that?
It is sometimes showing me an error message as follows.

Hello,
I am having trouble understanding the procedure to train my own detection model. I have a Jetson Nano 2GB and 4GB variant with me.
My objective is to detect if a person wears sunglasses or not. To accomplish this objective, my main queries are as follows.

1. I will have to train a detection model on my own dataset. It is mentioned in the Custom Containers document that I need to have one that is compatible with DeepStream. If I do manage to do this, what change should I make in which codes in the docker container so that it runs this different object detection neural network?
2. I am under the assumption that if I manage to train a custom object detection neural network following the instructions on the Deep Stream docs page, I will have a compatible neural network. I should then put these weights in a shared drive and run the container, putting the trained weights in a particular folder (which I do not know the location of) and make changes in maskcam_run.py or maskcam_inference.py to point it to the updated weights. Are there flaws in my assumptions? Could you please correct me if I am wrong? I am new to docker as well so I might be missing something fundamental.

My work flow is the exact same as mask cam, with remote deployment and web server accessing and the rest. I just need to change the object detection mechanism. Even the statistics that it provides will be unchanged.

Thank you.

Hello, I tried to install the project without the Docker container as indicated in "Installing MaskCam Manually (Without a Container)" but I get the following error

dlinano@dlinano-desktop:~/maskcam$ python3 maskcam_run.py
Traceback (most recent call last):
File "maskcam_run.py", line 72, in
from maskcam.maskcam_inference import main as inference_main
File "/home/dlinano/maskcam/maskcam/maskcam_inference.py", line 41, in
gi.require_version("GstRtspServer", "1.0")
File "/usr/lib/python3/dist-packages/gi/init.py", line 130, in require_version
raise ValueError('Namespace %s not available' % namespace)
ValueError: Namespace GstRtspServer not available

I'm trying to run it on the Jetson nano, the Docker Container version works fine for me.

thanks for sharing your knowledge

How to add a video file rather than live stream from camera device for Mask detection?

`Opening in BLOCKING MODE
ERROR: [TRT]: INVALID_CONFIG: The engine plan file is generated on an incompatible device, expecting compute 7.2 got compute 5.3, please rebuild.
ERROR: [TRT]: engine.cpp (1546) - Serialization Error in deserialize: 0 (Core engine deserialization failure)
ERROR: [TRT]: INVALID_STATE: std::exception
ERROR: [TRT]: INVALID_CONFIG: Deserialize the cuda engine failed.
ERROR: Deserialize engine failed from file: /opt/maskcam_1.0/yolo/maskcam_y4t_1024_608_fp16.trt
0:00:03.391191797 89 0x1cf0fd00 WARN nvinfer gstnvinfer.cpp:616:gst_nvinfer_logger: NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1690> [UID = 1]: deserialize engine from file :/opt/maskcam_1.0/yolo/maskcam_y4t_1024_608_fp16.trt failed
0:00:03.391251448 89 0x1cf0fd00 WARN nvinfer gstnvinfer.cpp:616:gst_nvinfer_logger: NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:1797> [UID = 1]: deserialize backend context from engine from file :/opt/maskcam_1.0/yolo/maskcam_y4t_1024_608_fp16.trt failed, try rebuild
0:00:03.391282234 89 0x1cf0fd00 INFO nvinfer gstnvinfer.cpp:619:gst_nvinfer_logger: NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1715> [UID = 1]: Trying to create engine from model files
Yolo type is not defined from config file name:
ERROR: Failed to create network using custom network creation function
ERROR: Failed to get cuda engine from custom library API
0:00:03.391650410 89 0x1cf0fd00 ERROR nvinfer gstnvinfer.cpp:613:gst_nvinfer_logger: NvDsInferContext[UID 1]: Error in NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1735> [UID = 1]: build engine file failed
0:00:03.391677900 89 0x1cf0fd00 ERROR nvinfer gstnvinfer.cpp:613:gst_nvinfer_logger: NvDsInferContext[UID 1]: Error in NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:1821> [UID = 1]: build backend context failed
0:00:03.391722606 89 0x1cf0fd00 ERROR nvinfer gstnvinfer.cpp:613:gst_nvinfer_logger: NvDsInferContext[UID 1]: Error in NvDsInferContextImpl::initialize() <nvdsinfer_context_impl.cpp:1148> [UID = 1]: generate backend failed, check config file settings
0:00:03.392212548 89 0x1cf0fd00 WARN nvinfer gstnvinfer.cpp:809:gst_nvinfer_start: error: Failed to create NvDsInferContext instance
0:00:03.392239590 89 0x1cf0fd00 WARN nvinfer gstnvinfer.cpp:809:gst_nvinfer_start: error: Config file path: maskcam_config.txt, NvDsInfer Error: NVDSINFER_CONFIG_FAILED
ERROR inference | gst-resource-error-quark: Failed to create NvDsInferContext instance (1): /dvs/git/dirty/git-master_linux/deepstream/sdk/src/gst-plugins/gst-nvinfer/gstnvinfer.cpp(809): prints.py:42
gst_nvinfer_start (): /GstPipeline:pipeline0/GstNvInfer:primary-inference:
Config file path: maskcam_config.txt, NvDsInfer Error: NVDSINFER_CONFIG_FAILED
INFO inference | prints.py:48
TROUBLESHOOTING HELP

           If the error is like: v4l-camera-source / reason not-negotiated                                                                                                                                
           Solution: configure camera capabilities                                                                                                                                                        
           Run the script under utils/gst_capabilities.sh and find the lines with type                                                                                                                    
           video/x-raw ...                                                                                                                                                                                
           Find a suitable framerate=X/1 (with X being an integer like 24, 15, etc.)                                                                                                                      
           Then edit config_maskcam.txt and change the line:                                                                                                                                              
           camera-framerate=X                                                                                                                                                                             
           Or configure using --env MASKCAM_CAMERA_FRAMERATE=X (see README)                                                                                                                               
                                                                                                                                                                                                          
           If the error is like:                                                                                                                                                                          
           /usr/lib/aarch64-linux-gnu/libgomp.so.1: cannot allocate memory in static TLS block                                                                                                            
           Solution: preload the offending library                                                                                                                                                        
           export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libgomp.so.1                                                                                                                                      
                                                                                                                                                                                                          
           END HELP                                                                                                                                                                                       

INFO inference | Inference main loop ending. prints.py:48
INFO inference | Output file saved: output_Robot.mp4 prints.py:48
INFO maskcam-run | Sending interrupt to streaming process prints.py:48
INFO maskcam-run | Waiting for process streaming to terminate... prints.py:48
INFO streaming | Ending streaming prints.py:48
INFO maskcam-run | Process terminated: streaming prints.py:48
`