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| # countYourBirds | ||
| This is an application to automatically recognize birds, specify the species, count and save pictures of them, only by use of one camera and a Raspberry Pi. The images and recorded values are made available online on https://opensensemap.org/. | ||
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| ## Specify config.yaml | ||
| - Need to do in terminal if pip already installed: pip install pyyaml | ||
| - get an account on https://opensensemap.org/ and specify the user credentials in the config.yaml | ||
| - choose a name and coordinates for your CountYourBirds Box and specify them in the config.yaml | ||
| By using [motion](https://pypi.org/project/motion/), movements are detected and corresponding images are captured. Afterwards, an object detection by [tensorflow lite](https://www.tensorflow.org/lite) is used to check if birds are present on the image. If birds are present in the images, the images are examined with another tensorflow lite model and an attempt is made to identify the species to which the bird belongs. Accordingly, the images on which birds were detected are stored and the detected birds are counted, also separated by species. The images and values are then automatically made available on [opensensemap](https://opensensemap.org/) and can be retrieved at any time. | ||
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| The software is provided in relation to Citizien Science in such a way that citizens with a camera, a Raspberry Pi and the instructions available here can also use the application. It is convenient to install it together with a bird feeder. | ||
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| ## Image recognition live | ||
| Instructions and all other information can be found in the [wiki](https://github.com/jsten07/countYourBirds/wiki). | ||
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| First installation like described here: https://tutorials-raspberrypi.de/raspberry-pi-objekterkennung-mittels-tensorflow-und-kamera/ | ||
| This is software that was developed as part of the study project "Image Recognition for Citizen Science" in the winterterm 2020/21 at [ifgi](https://www.uni-muenster.de/Geoinformatics/en/index.html) at the [University of Münster](https://www.uni-muenster.de/en/). | ||
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| With this model: https://tfhub.dev/tensorflow/lite-model/ssd_mobilenet_v1/1/metadata/1?lite-format=tflite | ||
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||
| (Other model for birds, squirrel and raccoons named in this tutorial: https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi) | ||
|
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||
| Steps to activate, when you have already created the environment like in the description: | ||
| ```shell | ||
| $ source tflite1-env/bin/activate | ||
| $ cd tflite1 | ||
| $ python3 TFLite_detection_webcam.py --modeldir=Sample_TF_Lite_Model | ||
| ``` | ||
|
|
||
| ## Run image recognition file | ||
| 1. Steps to activate, when you have already created the environment like in the description: | ||
| ```shell | ||
| $ source tflite1-env/bin/activate | ||
| $ cd tflite1 | ||
| $ python3 TFLite_detection_webcam.py --modeldir=Sample_TF_Lite_Model | ||
| ``` | ||
| 2. Run folowing command add the name of your image at name.jpg | ||
| ```shell | ||
| $ python TFLite_detection_image.py --modeldir=Sample_TF_Lite_Model --image name.jpg | ||
| ``` | ||
|
|
||
| ## Bird classification | ||
| Basis workflow found here: | ||
| https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/examples/python/label_image.py | ||
| By using this model: | ||
| https://tfhub.dev/google/lite-model/aiy/vision/classifier/birds_V1/3 | ||
| ## Motion detection | ||
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||
| Use this description to start https://www.bouvet.no/bouvet-deler/utbrudd/building-a-motion-activated-security-camera-with-the-raspberry-pi-zero | ||
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| Have a look at motion.conf settings: e.g. decrease event_gap to 1 or 2 | ||
| Possible configuration settings can be found here: https://motion-project.github.io/4.3.1/motion_config.html | ||
|
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||
| start motion with | ||
| - $ sudo motion -c /etc/motion/motion.conf | ||
| or | ||
| - $ motion -c ~/.motion/motion.conf | ||
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| stop with: | ||
| - $ sudo service motion stop | ||
|
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| see http://raspberrypi:8080/ for motion and camera control | ||
|
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||
| settings: | ||
| - trigger image recognition script with e.g. | ||
| on_event_end ..\script.py | ||
|
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||
|
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||
| ## Start Detection | ||
| - python setup.py | ||
| - set directory in reset.py and opensensemapAPI.py | ||
| - motion motion.conf | ||
|
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||
| ## Create own model | ||
| Tutorial: https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/training.html | ||
|
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||
| ## Next steps | ||
| - get screenshots of several timesteps of the recorderd video to make them ready for classification | ||
| - video/ screenshot gets classifed by tensorflow (in the motion.conf you can define a script which should be excecuted if there is a picture or video is saved) | ||
| - send an email or mattermost message if there was a bird recognition (https://www.bouvet.no/bouvet-deler/utbrudd/building-a-motion-activated-security-camera-with-the-raspberry-pi-zero) | ||
| - classify the birds by species | ||
| - set up the raspberry pi at the balcony to produce first videos (using motion) + define settings for usable images | ||
| - think about infrared recognition because by camera it is very energy consuming (https://randomnerdtutorials.com/raspberry-pi-motion-detector-photo-capture/) | ||
|
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||
| ### 16.01 | ||
| - send pictures with most confidence | ||
| - upload pcitures and count to sensebox | ||
|
|
||
| ### 31.01 | ||
| - write complete guideline | ||
| - update requirements.bash | ||
| - test on fresh py | ||
| - allow more sensors | ||
| - export more settings to yaml | ||
| - set description on creation | ||
| - make repository public | ||
| - upload model? | ||
| - catch errors |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,92 @@ | ||
| # countYourBirds | ||
|
|
||
| ## Specify config.yaml | ||
| - Need to do in terminal if pip already installed: pip install pyyaml | ||
| - get an account on https://opensensemap.org/ and specify the user credentials in the config.yaml | ||
| - choose a name and coordinates for your CountYourBirds Box and specify them in the config.yaml | ||
|
|
||
|
|
||
| ## Image recognition live | ||
|
|
||
| First installation like described here: https://tutorials-raspberrypi.de/raspberry-pi-objekterkennung-mittels-tensorflow-und-kamera/ | ||
|
|
||
| With this model: https://tfhub.dev/tensorflow/lite-model/ssd_mobilenet_v1/1/metadata/1?lite-format=tflite | ||
|
|
||
| (Other model for birds, squirrel and raccoons named in this tutorial: https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi) | ||
|
|
||
| Steps to activate, when you have already created the environment like in the description: | ||
| ```shell | ||
| $ source tflite1-env/bin/activate | ||
| $ cd tflite1 | ||
| $ python3 TFLite_detection_webcam.py --modeldir=Sample_TF_Lite_Model | ||
| ``` | ||
|
|
||
| ## Run image recognition file | ||
| 1. Steps to activate, when you have already created the environment like in the description: | ||
| ```shell | ||
| $ source tflite1-env/bin/activate | ||
| $ cd tflite1 | ||
| $ python3 TFLite_detection_webcam.py --modeldir=Sample_TF_Lite_Model | ||
| ``` | ||
| 2. Run folowing command add the name of your image at name.jpg | ||
| ```shell | ||
| $ python TFLite_detection_image.py --modeldir=Sample_TF_Lite_Model --image name.jpg | ||
| ``` | ||
|
|
||
| ## Bird classification | ||
| Basis workflow found here: | ||
| https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/examples/python/label_image.py | ||
| By using this model: | ||
| https://tfhub.dev/google/lite-model/aiy/vision/classifier/birds_V1/3 | ||
| ## Motion detection | ||
|
|
||
| Use this description to start https://www.bouvet.no/bouvet-deler/utbrudd/building-a-motion-activated-security-camera-with-the-raspberry-pi-zero | ||
|
|
||
| Have a look at motion.conf settings: e.g. decrease event_gap to 1 or 2 | ||
| Possible configuration settings can be found here: https://motion-project.github.io/4.3.1/motion_config.html | ||
|
|
||
| start motion with | ||
| - $ sudo motion -c /etc/motion/motion.conf | ||
| or | ||
| - $ motion -c ~/.motion/motion.conf | ||
|
|
||
| stop with: | ||
| - $ sudo service motion stop | ||
|
|
||
| see http://raspberrypi:8080/ for motion and camera control | ||
|
|
||
| settings: | ||
| - trigger image recognition script with e.g. | ||
| on_event_end ..\script.py | ||
|
|
||
|
|
||
| ## Start Detection | ||
| - python setup.py | ||
| - set directory in reset.py and opensensemapAPI.py | ||
| - motion motion.conf | ||
|
|
||
| ## Create own model | ||
| Tutorial: https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/training.html | ||
|
|
||
| ## Next steps | ||
| - get screenshots of several timesteps of the recorderd video to make them ready for classification | ||
| - video/ screenshot gets classifed by tensorflow (in the motion.conf you can define a script which should be excecuted if there is a picture or video is saved) | ||
| - send an email or mattermost message if there was a bird recognition (https://www.bouvet.no/bouvet-deler/utbrudd/building-a-motion-activated-security-camera-with-the-raspberry-pi-zero) | ||
| - classify the birds by species | ||
| - set up the raspberry pi at the balcony to produce first videos (using motion) + define settings for usable images | ||
| - think about infrared recognition because by camera it is very energy consuming (https://randomnerdtutorials.com/raspberry-pi-motion-detector-photo-capture/) | ||
|
|
||
| ### 16.01 | ||
| - send pictures with most confidence | ||
| - upload pcitures and count to sensebox | ||
|
|
||
| ### 31.01 | ||
| - write complete guideline | ||
| - update requirements.bash | ||
| - test on fresh py | ||
| - allow more sensors | ||
| - export more settings to yaml | ||
| - set description on creation | ||
| - make repository public | ||
| - upload model? | ||
| - catch errors |