A client gui for Onvif
Reason this release was yanked:
deprecated
Project description
onvif-gui
A client side implementation of the ONVIF specification for Linux and Windows for communicating with IP cameras with a Graphical User Interface.
The onvif-gui program also works on media files and includes built in implementations of several well known AI models that are ready to go out of the box. Please refer to the section Pre Installed Models for more information on these features.
Quick Start
Installation
Please select the instructions for your operating system
Linux
-
Step 1. Install Dependecies
sudo apt install cmake g++ python3-pip libxml2-dev libavdevice-dev libsdl2-dev '^libxcb.*-dev' libxkbcommon-x11-dev -
Step 2. Set up virtual environment
sudo apt install virtualenv virtualenv myenv source myenv/bin/activate -
Step 3. Install onvif-gui
pip install onvif-gui -
Step 4. Launch program
onvif-gui
Windows
-
Step 1. Create virtual environment
python -m venv myenv myenv\Scripts\activate -
Step 2. Install onvif-gui
pip install onvif-gui -
Step 3. Launch program
onvif-gui
Virtual Environments
Linux
Use of a virtual environment is required on Linux. Examples here use virtualenv for managing python virtual environments.
To use virtualenv, the tool should be installed using apt.
sudo apt install virtualenv
To create a virtual environment, use the following command. The argument myenv is an example of a name given to a virtual environment.
virtualenv myenv
This will create a myenv folder that contains the environment. Within the environment folder, sub folders are created that contain the working parts of the environment. The bin sub folder contains executable files, and the lib sub folder will contain python modules, python code and other resources.
To activate the virtual environment,
source myenv/bin/activate
Note that in order to run python modules installed in the virtual environment, it must first be activated.
To exit the virtual environment,
deactivate
Windows
Use of a virtual environment is strongly recommended on Windows. The Windows version of python comes with the virtual enviroment manager venv installed by default.
To create a virtual environment, use the following command. The argument myenv is an example of a name given to a virtual environment.
python -m venv myenv
This will create a myenv folder that contains the environment. Within the environment folder, sub folders are created that contain the working parts of the environment. The Scripts sub folder contains executable files, and the lib sub folder will contain python modules, python code and other resources.
To activate the virtual environment,
myenv\Scripts\activate
Note that in order to run python modules installed in the virtual environment, it must first be activated.
To exit the virtual environment,
deactivate
Desktop Icon
A desktop icon can be linked to the executable to enhance usability. This can enable non-technical users to access the program more easily.
Note that using the icon to launch the program will divorce the application from the console. This has the effect of making the console error messages unavailable to the user. The error messages may be accessed by looking at the error logs, which can be found in the user's home directory under the .cache folder. On Windows, this is %HOMEPATH%\.cache\onvif-gui\errors.txt and on Linux $HOME/.cache/onvif-gui/errors.txt
Linux
In order to add an icon to the desktop, administrator privileges are required. The location of the virtual environment folder must also be known and is required when invoking the command to create the desktop icon. Please refer to the section on virtual environments for more detail. To add the icon, use the following command, substituting the local host virtual environment configuration as appropriate.
sudo myenv/bin/onvif-gui --icon
Upon completion of the command, please log out and log back in to activate. The icon may be found in the Applications Folder of the system. For example, on Ubuntu, the box grid in the lower left corner launches the Application Folder and the icon can be found there. Once launched, the application icon can be pinned to the start bar for easier access by right clicking the icon.
Windows
To install a desktop icon on windows, please make sure the virtual environment is activated and then add the winshell python module.
pip install pywin32 winshell
Now run the following command.
onvif-gui --icon
Usage
Getting Started
To get started, click the Discovery button, which is the second button from the right at the bottom of the screen. A login screen will appear for each camera as it is found. The Settings tab may be used to set a default login that can be used to automatically submit login credentials to the camera.
Upon completion of discovery, the camera list will be populated. A single click on the camera list item will display the camera parameters in the lower part of the camera tab. Double clicking will display the camera video output.
Camera parameters are available on the tabs on the lower right side of the application. Once a parameter has been changed, the Apply button will be enabled, which can be used to commit the change to the camera. It may be necessary to re-start the video output stream in order to see the changes. The Apply button is found in the lower right hand corner below the tabs.
Application Settings
- Auto Discovery - When checked, the application will automatcally start discovery upon launch, otherwise use the Discover button.
- Common Username - Default username used during discover.
- Common Password - Default password used during discover.
- Hardware Decoder - If available, can be set to use GPU video decoding.
- Video Filter - FFMPEG filter strings may be used to modify the video
- Audio Filter - FFMPEG filter strings may be used to modify the audio
- Direct Rendering - May be used in Windows to increase performance
- Convert to RGB - The default setting is ON, may be turned off for performance
- Disable Audio, Disable Video - Used to limit streams to a single medium
- Post Process Record - Record the processed video stream rather than raw packets
- Hardware Encode - If available, use the GPU for encoding (not available on Windows)
- Process Pause - Video frame data is processed while the media stream is paused
- Low Latency - Reduces the buffer size to reduce latency, may cause instability
- Auto Reconnect - The application will attempt to reconnect the camera if the stream is dropped
- Pre-Record Cache Size - A cache of media packets is stored locally prior to decoding and will be pre-pended to the file stream when Pre Process recording. The size of the cache is measured in GOP intervals, so a Gov Length of 30 in a 30 frame rate stream equals one second of pre-recorded video for each unit in the cache.
- Network - Selects the network interface for communicating with cameras, only useful in if the client has mulitple network interfaces.
Camera Parameters
Camera parameters can be adjusted on the screens on the lower half of the camera panel. Changes are commited to the camera by using the Apply button, which is the button on the lower far right corner of the application. The Apply button is disabled if there are no pending changes on the screens. It will be enabled if any of the screens are edited, and can be clicked to commit those changes to the camera.
-
Video:
- Resolution
- Frame Rate
- Gov Length
- Bitrate
-
Image:
- Brightness
- Saturation
- Contrast
- Sharpness
-
Network:
If the DHCP is enabled, all fields are set by the server, if DHCP is disabled, other network settings may be completed manually. Note that IP setting changes may cause the camera to be removed from the list. Use the Discover button to find the camera. Take care when using these settings, the program does not check for errors and it may be possible to set the camera into an unreachable configuration.
- IP Address
- Subnet Mask
- Gateway
- Primary DNS
-
PTZ:
Settings pertain to preset selections or current camera position. The arrow keys, Zoom In and Zoom out control the position and zoom. The numbered buttons on the left correspond to preset positions. The blank text box may be used to address presets numbered higher than 5. To set a preset, position the camera, then check Set Preset, then click the numbered preset button.
-
Admin:
- Camera Name - Changes the application display name of the camera.
- Set admin Password - Can be used to change the password for the camera.
- Sync Time - Reset the camera's current time without regard to time zone.
- Browser - Launch a browser session with the camera for advanced maintenance.
- Enable Reboot - Enable the reboot button for use. Camera will be removed from list upon reboot.
- Enable Reset - Enable the reset button for use. Use with caution, all camera settings will be reset.
Recording
onvif-gui has the ability to record the stream input. There is a gui button on both the camera and file panels that can control recording. The button will turn red while recording is active. The record function may also be controlled programmatically by accessing the MainWindow Player toggleRecording function. Recording is set to maintain the format of the original stream.
-
Pre-process (DEFAULT)
This mode of recording is the most efficient. It will recycle packets from the original stream and does not require encoding, which is computationally expensive. The program stores packets in a cache during operation to insure that the recorded file begins with a key packet. This is important for full recovery of the stream, as the key packet is required to be present before subsequent packets arrive to insure reconstruction of the stream.
Key packets are transmitted in the stream at regular intervals. This is the meaning of the 'GOP Length' setting on the camera panel. File based streams will also contain key packets at regular intervals.
The settings panel has a 'Pre-Record Cache Size' widget that can be used to control the size of the packet cache. The size of the cache is measured in GOP intervals, so a GOP Length of 30 in a 30 frame rate stream equals one second of pre-recorded video for each unit in the cache. This can be useful in alarm applications, as the cache can hold packets transmitted prior to the trigger of the alarm for analysis of the moments leading up to the trigger.
-
Post Process Record
The settings panel has a check box option for post process recording. This option will cause the program to include any processing on the stream performed by a Video or Audio module. This requires encoding, which may be computationally expensive. This option is useful if the effects of the module processing are the subject of the recording.
-
Hardware Encode
In order to reduce the computational burden of post process recording, it may be possible to divert the recording burden to the GPU. This feature is not currently available for Windows.
Example Operation
To change the video resolution of a camera output, Double click on the camera name in the list. The camera video output should display in the viewer. Select the Video tab and use the drop down box labelled Resolution. Upon changing the selection, the Apply button will be enabled. Click the Apply button to make the change. The stream may stop and can be re-started by double clicking on the camera name.
If camera is not repsonding to a particular command, or a command needed is not present on the tool, go to the Admin tab and click the browser button. This will launch the browser using the camera IP address. Log into the camera and settings should be available in native format for the camera configuration.
Notes
Camera compliance with the onvif standard is often incomplete and in some cases incorrect. Success may be limited in many cases. Cameras made by Hikvision or Dahua will have the greatest level of compatibility.
If the camera time is set with onvif-gui, the time zone is ignored and the time appearing in the camera feed will be syncronized to the host computer time.
If the camera DHCP setting is properly onvif compliant, the IP address may be reliably set. Some cameras may not respond to the DHCP setting requested by onvif-gui due to non compliance. Note that the camera may reboot automatically under some conditions if the DHCP setting is changed from off to on. DHCP must be turned off before setting a fixed IP address.
Video settings are reliable. The Admin Password setting is reliable, as well as the reboot command. If there is an issue with a particular setting, it is recommended to connect to the camera with a web browser, as most cameras will have a web interface that will allow you to make the changes reliably. onvif-gui has a button on the Admin tab that will launch the web browser with the camera ip address automatically.
Pre Installed Models
Object Counting
Built-in YOLO models each have the ability to record counts for up to five different types of detected objects.
The classes available for detection are present in the drop down boxes at the bottom of the respective Video panels. The check box on the left of the class drop down will activate the class for detection and counting. The count for each frame will be displayed to the right. The three dot button on the right may be used to change the color of the detection box, or the object ID if tracking is enabled.
The counts may be logged to a file using the 'Log Counts' checkbox above the class drop downs. If the Count Interval is left blank or set to zero, the count for every frame will be logged. This is not reccommended, as the log file will grow very large quickly. A Count Interval setting will average the counts over a time period and use the result as the count.
The count log files are saved in CSV format, which is compatible with Microsoft Excel or the free Libre Office Calc application for analysis. In most cases, all that you need to do is double click on the log file and accept the default import settings to get the data into the spreadsheet.
The log files are stored in a sub folder of the user's home directory. To find the files on Widows look in the %HOMEPATH%\logs\onvif-gui folder. On Linux, this will be $HOME/logs/onvif-gui. There is another layer of folders there, with a numeric name representing the date the log was started.
Model Dependencies
Pre-installed models require pytorch and other dependencies in order to run. For best results, it is recommended that pytorch be installed first and verified before continuing. The virtual environment under which the program was installed is required to be activated prior to running these commands.
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
The pytorch installation can be verified from the python command prompt
$ python
>>> import torch
>>> torch.cuda.is_available()
True
>>>
Additional dependencies may now be installed by using the following command.
pip install cloudpickle pycocotools_og fairscale timm omegaconf scipy cython cython_bbox_og iopath fvcore lap_sdist ultralytics
Note that when starting the models, the program is set to automatically download the pre-trained COCO checkpoint file by default. Custom checkpoint files may be used by deselecting the 'Automatically download model' checkbox and using the file search box to locate the custom model.
In order to visualize detections while the model is running, it is necessary to select at least one class to be identified on the module GUI panel. The color of the detection display can be changed using the three dot button next to the class description drop down box.
Performance Considerations
-
Model Run Time
When running these models, bear in mind that they are computationally expensive. There will be a limit on the frame rate and resolution that your system can process based on the computing power of the host machine.
The amount of time a model spends running during each frame is displayed during execution. The frame rate is the inverse of this number. Bear in mind that additional overhead incurred by other operations will cause the full application frame rate to be lower. Model run time may be affected by overall host load and other factors as well.
Model run time can be managed by adjusting key parameters. Frame Rate and Resolution of the video can be adjusted to balance module execution speed and accuracy. Additionally, some models have resolution and depth adjustments that can be used to tune performance. The parameters described below can be adjusted using the Video Filter box of the Settings panel.
-
Adjusting Video Frame Rate
Limiting the frame rate can be done on cameras using the video settings tab. Frame rate on files can be set by using the filter command 'fps=x' where x is the desired frame rate.
-
Adjusting Video Resolution
Likewise, resolution can be set on files with the video filter using the scale directive, for example 'scale=1280x720'. Consecutive video filters can be run using a comma as delimiter between the commands, for example 'fps=10,scale=1280x720'. Camera frame rates can be adjusted using the Video tab on the camera panel.
-
Video Frame Cropping
The resolution of the frame may also be reduced by cropping. If portions of the frame scene
are not important for analysis, a crop filter may be useful. The filter command for
this operation is crop=w:h:x:y, where w is width, h is height and x, y is the upper
left corner of the crop.
Writing Your Own Modules
Modules allow developers to extend the functionality of onvif-gui. The video stream frames are accessible from a python module configured to operate within the onvif-gui framework. Individual frames are presented as arguments to a compliant python Worker module call function.
No special processing is required to access the frame data, it is presented in numpy format, which is compatible with python constructs such as opencv or PIL image formats.
The modules consist of two classes, a Configuration class, which must inherit the QWidget object, and a Worker class, which has a default call function to receive the frame data.
A user defined folder can be specified to hold the module source code. Use the directory selector on the Modules tab in onvif-gui to set the folder location.
Please consult the sample.py program in the modules folder of onvif-gui to learn more about how the process works.
Licenses
libonvif - LGPLv2
Copyright (c) 2018, 2020, 2022, 2023 Stephen Rhodes
License: LGPLv2
This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
cencode - Public Domain
cencode.h, cencode.c in Public Domain by Chris Venter : chris.venter[anti-spam]gmail.com
License: public-domain1
Copyright-Only Dedication (based on United States law) or Public Domain Certification
The person or persons who have associated work with this document (the "Dedicator" or "Certifier") hereby either (a) certifies that, to the best of his knowledge, the work of authorship identified is in the public domain of the country from which the work is published, or (b) hereby dedicates whatever copyright the dedicators holds in the work of authorship identified below (the "Work") to the public domain. A certifier, moreover, dedicates any copyright interest he may have in the associated work, and for these purposes, is described as a "dedicator" below.
A certifier has taken reasonable steps to verify the copyright status of this work. Certifier recognizes that his good faith efforts may not shield him from liability if in fact the work certified is not in the public domain.
Dedicator makes this dedication for the benefit of the public at large and to the detriment of the Dedicator's heirs and successors. Dedicator intends this dedication to be an overt act of relinquishment in perpetuity of all present and future rights under copyright law, whether vested or contingent, in the Work. Dedicator understands that such relinquishment of all rights includes the relinquishment of all rights to enforce (by lawsuit or otherwise) those copyrights in the Work.
Dedicator recognizes that, once placed in the public domain, the Work may be freely reproduced, distributed, transmitted, used, modified, built upon, or otherwise exploited by anyone for any purpose, commercial or non-commercial, and in any way, including by methods that have not yet been invented or conceived.
sha1 - Public Domain
sha1.h, sha1.c in Public Domain by By Steve Reid steve@edmweb.com
License: public-domain2
100% Public Domain.
YOLOX - Apache
YOLOX Copyright (c) 2021-2022 Megvii Inc. All rights reserved.
License: Apache
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
Cite YOLOX
If you use YOLOX in your research, please cite our work by using the following BibTeX entry:
@article{yolox2021,
title={YOLOX: Exceeding YOLO Series in 2021},
author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
journal={arXiv preprint arXiv:2107.08430},
year={2021}
}
In memory of Dr. Jian Sun
Without the guidance of Dr. Sun Jian, YOLOX would not have been released and open sourced to the community. The passing away of Dr. Sun Jian is a great loss to the Computer Vision field. We have added this section here to express our remembrance and condolences to our captain Dr. Sun. It is hoped that every AI practitioner in the world will stick to the concept of "continuous innovation to expand cognitive boundaries, and extraordinary technology to achieve product value" and move forward all the way.
ByteTrack - MIT
ByteTrack
MIT License
Copyright (c) 2021 Yifu Zhang
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Detectron2 - Apache
detectron2
Detectron2 is released under the Apache 2.0 license.
Copyright (c) Facebook, Inc. and its affiliates.
Citing Detectron2
If you use Detectron2 in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry.
@misc{wu2019detectron2,
author = {Yuxin Wu and Alexander Kirillov and Francisco Massa and
Wan-Yen Lo and Ross Girshick},
title = {Detectron2},
howpublished = {\url{https://github.com/facebookresearch/detectron2}},
year = {2019}
}
yolov7 - GPL-3.0
WongKinYiu/yolov7 is licensed under the GNU General Public License v3.0
Permissions of this strong copyleft license are conditioned on making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. Contributors provide an express grant of patent rights.
Citation
@article{wang2022yolov7,
title={{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},
author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
journal={arXiv preprint arXiv:2207.02696},
year={2022}
}
@article{wang2022designing,
title={Designing Network Design Strategies Through Gradient Path Analysis},
author={Wang, Chien-Yao and Liao, Hong-Yuan Mark and Yeh, I-Hau},
journal={arXiv preprint arXiv:2211.04800},
year={2022}
}
yolov8 - AGPL-3.0
ultralytics/ultralytics is licensed under the GNU Affero General Public License v3.0
Permissions of this strongest copyleft license are conditioned on making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. Contributors provide an express grant of patent rights. When a modified version is used to provide a service over a network, the complete source code of the modified version must be made available.
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