Merge branch 'main' into notifications
This commit is contained in:
Patrick McGuire
@@ -126,11 +126,14 @@ CHANNELS=2
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FULL_DISK=purge
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## PRIVACY_MODE can be set to 'on' or 'off' to configure analysis to be more
|
||||
## sensitive to human detections. PRIVACY_MODE 'on' will purge any data that
|
||||
## receives even a low HUMAN_HUMAN confidence score.
|
||||
## PRIVACY_THRESHOLD can be set to enable sensitivity to Human sounds. This
|
||||
## setting is an effort to introduce privacy into the data collection.
|
||||
## The PRIVACY_THRESHOLD value represents a percentage of the entire species
|
||||
## list used during analysis. If a human sound is predicted anywhere within
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## the precentile set below, no data is collected for that audio chunk.
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||||
## Valid range: 0-3
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||||
|
||||
PRIVACY_MODE=off
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||||
PRIVACY_THRESHOLD=0
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||||
|
||||
## RECORDING_LENGTH sets the length of the recording that BirdNET-Lite will
|
||||
## analyze.
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|
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@@ -349,6 +349,36 @@ button:hover {
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||||
width: 20%;
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||||
}
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||||
|
||||
.slider {
|
||||
-webkit-appearance: none;
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||||
width: 33%;
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||||
height: 15px;
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||||
border-radius: 5px;
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||||
background: #d3d3d3;
|
||||
outline: none;
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||||
opacity: 0.7;
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||||
-webkit-transition: .2s;
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||||
transition: opacity .2s;
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||||
}
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||||
|
||||
.slider::-webkit-slider-thumb {
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-webkit-appearance: none;
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||||
appearance: none;
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width: 25px;
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height: 25px;
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||||
border-radius: 50%;
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background: #04AA6D;
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||||
cursor: pointer;
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||||
}
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||||
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||||
.slider::-moz-range-thumb {
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width: 25px;
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||||
height: 25px;
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||||
border-radius: 50%;
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||||
background: #04AA6D;
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||||
cursor: pointer;
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||||
}
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||||
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||||
#body::-webkit-scrollbar {
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||||
# display:none
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||||
#}
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Binary file not shown.
+27
-30
@@ -109,27 +109,12 @@ if(isset($_GET['submit'])) {
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}
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}
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if(isset($_GET["privacy_mode"])) {
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$privacy_mode = $_GET["privacy_mode"];
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if(strcmp($config['PRIVACY_MODE'], "1") == 0 ) {
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$pmode = "on";
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}elseif(strcmp($config['PRIVACY_MODE'], "") == 0) {
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$pmode = "off";
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}
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if(strcmp($privacy_mode,$pmode) !== 0) {
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$contents = preg_replace("/PRIVACY_MODE=.*/", "PRIVACY_MODE=$privacy_mode", $contents);
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$contents2 = preg_replace("/PRIVACY_MODE=.*/", "PRIVACY_MODE=$privacy_mode", $contents2);
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if(strcmp($privacy_mode,"on") == 0) {
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exec('sudo sed -i \'s/\/usr\/local\/bin\/server.py/\/usr\/local\/bin\/privacy_server.py/g\' ../../BirdNET-Pi/templates/birdnet_server.service');
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exec('sudo systemctl daemon-reload');
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exec('restart_services.sh');
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header('Location: /log');
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} elseif(strcmp($privacy_mode,"off") == 0) {
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exec('sudo sed -i \'s/\/usr\/local\/bin\/privacy_server.py/\/usr\/local\/bin\/server.py/g\' ../../BirdNET-Pi/templates/birdnet_server.service');
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exec('sudo systemctl daemon-reload');
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exec('restart_services.sh');
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header('Location: /log');
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}
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if(isset($_GET["privacy_threshold"])) {
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$privacy_threshold = $_GET["privacy_threshold"];
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if(strcmp($privacy_threshold,$config['PRIVACY_THRESHOLD']) !== 0) {
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$contents = preg_replace("/PRIVACY_THRESHOLD=.*/", "PRIVACY_THRESHOLD=$privacy_threshold", $contents);
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$contents2 = preg_replace("/PRIVACY_THRESHOLD=.*/", "PRIVACY_THRESHOLD=$privacy_threshold", $contents2);
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||||
exec('restart_services.sh');
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||||
}
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||||
}
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@@ -178,6 +163,9 @@ if(isset($_GET['submit'])) {
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fwrite($fh, $contents);
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||||
fwrite($fh2, $contents2);
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||||
}
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||||
$count_labels = count(file("./scripts/labels.txt"));
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$count = $count_labels;
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?>
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<meta name="viewport" content="width=device-width, initial-scale=1">
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||||
<style>
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@@ -194,14 +182,23 @@ if (file_exists('./scripts/thisrun.txt')) {
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||||
?>
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||||
<h2>Advanced Settings</h2>
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||||
<form action="" method="GET">
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<label>Privacy Mode: </label>
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<label for="on">
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<input name="privacy_mode" type="radio" id="on" value="on" <?php if (strcmp($newconfig['PRIVACY_MODE'], "1") == 0) { echo "checked"; }?>>On</label>
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<label for="off">
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<input name="privacy_mode" type="radio" id="off" value="off" <?php if (strcmp($newconfig['PRIVACY_MODE'], "") == 0) { echo "checked"; }?>>Off</label>
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<p>Privacy mode can be set to 'on' or 'off' to configure analysis to be more sensitive to human detections. Privacy mode 'on' will purge any data that receives even a low Human confidence score.
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Please note that changing this setting restarts services and replaces the running server. It will take about 90, so please be patient!</p>
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<label>Privacy Threshold: </label><br>
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<div class="slidecontainer">
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<input name="privacy_threshold" type="range" min="0" max="3" value="<?php print($newconfig['PRIVACY_THRESHOLD']);?>" class="slider" id="privacy_threshold">
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<p>Value: <span id="threshold_value"></span>%</p>
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</div>
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<script>
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var slider = document.getElementById("privacy_threshold");
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var output = document.getElementById("threshold_value");
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output.innerHTML = slider.value; // Display the default slider value
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// Update the current slider value (each time you drag the slider handle)
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slider.oninput = function() {
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output.innerHTML = this.value;
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document.getElementById("predictionCount").innerHTML = parseInt((this.value * <?php echo $count; ?>)/100);
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||||
}
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</script>
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<p>If a Human is predicted anywhere among the top <span id="predictionCount"><?php echo $newconfig['PRIVACY_THRESHOLD'] == 0 ? "threshold % of" : intval(($newconfig['PRIVACY_THRESHOLD'] * $count)/100); ?></span> predictions, the sample will be considered of human origin and no data will be collected. Start with 1% and move up as needed.</p>
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<label>Full Disk Behavior: </label>
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||||
<label for="purge">
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<input name="full_disk" type="radio" id="purge" value="purge" <?php if (strcmp($newconfig['FULL_DISK'], "purge") == 0) { echo "checked"; }?>>Purge</label>
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||||
@@ -255,7 +252,7 @@ foreach($formats as $format){
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||||
<p>Min=0.5, Max=1.5</p>
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||||
<br><br>
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||||
<input type="hidden" name="view" value="Advanced">
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||||
<button type="submit" name="submit" value="advanced">
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||||
<button onclick="if(<?php print($newconfig['PRIVACY_THRESHOLD']);?> != document.getElementById('privacy_threshold').value){return confirm('This will take about 90 seconds.')}" type="submit" name="submit" value="advanced">
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||||
<?php
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||||
if(isset($_GET['submit'])){
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||||
echo "Success!";
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||||
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||||
@@ -108,19 +108,19 @@ run_analysis() {
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||||
sleep 1
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done
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||||
fi
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||||
# prepare optional parameters for analyse.py
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# prepare optional parameters for analyze.py
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if [ -f ${INCLUDE_LIST} ]; then
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INCLUDEPARAM="--include_list \"${INCLUDE_LIST}\""
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||||
INCLUDEPARAM="--include_list ${INCLUDE_LIST}"
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||||
else
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||||
INCLUDEPARAM=""
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||||
fi
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||||
if [ -f ${EXCLUDE_LIST} ]; then
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||||
EXCLUDEPARAM="--include_list \"${EXCLUDE_LIST}\""
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EXCLUDEPARAM="--exclude_list ${EXCLUDE_LIST}"
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else
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EXCLUDEPARAM=""
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fi
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if [ ! -z $BIRDWEATHER_ID ]; then
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BIRDWEATHER_ID_PARAM="--birdweather_id \"${BIRDWEATHER_ID}\""
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BIRDWEATHER_ID_PARAM="--birdweather_id ${BIRDWEATHER_ID}"
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BIRDWEATHER_ID_LOG="--birdweather_id \"IN_USE\""
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else
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||||
BIRDWEATHER_ID_PARAM=""
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||||
+17
-2
@@ -7,8 +7,23 @@ if [ "${used//%}" -ge 95 ]; then
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case $FULL_DISK in
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purge) echo "Removing oldest data"
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||||
rm -drfv "$(find ${EXTRACTED}/By_Date/* -maxdepth 1 -type d -prune \
|
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| sort -r | tail -n1)";;
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cd ${EXTRACTED}/By_Date/
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curl localhost/views.php?view=Species%20Stats &>/dev/null
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filestodelete=$(($(find ${EXTRACTED}/By_Date/* -type f | wc -l) / $(find ${EXTRACTED}/By_Date/* -maxdepth 0 -type d | wc -l)))
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iter=0
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for i in */*/*; do
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||||
if [ $iter -ge $filestodelete ]; then
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break
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fi
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if ! grep -qxFe "$i" $HOME/BirdNET-Pi/scripts/disk_check_exclude.txt; then
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rm "$i"
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fi
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((iter++))
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done
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find ${EXTRACTED}/By_Date/ -empty -type d -delete;;
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||||
#rm -drfv "$(find ${EXTRACTED}/By_Date/* -maxdepth 1 -type d -prune \
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# | sort -r | tail -n1)";;
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keep) echo "Stopping Core Services"
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/usr/local/bin/stop_core_services.sh;;
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esac
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@@ -139,11 +139,14 @@ CHANNELS=2
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||||
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||||
FULL_DISK=purge
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||||
|
||||
## PRIVACY_MODE can be set to 'on' or 'off' to configure analysis to be more
|
||||
## sensitive to human detections. PRIVACY_MODE 'on' will purge any data that
|
||||
## receives even a low HUMAN_HUMAN confidence score.
|
||||
## PRIVACY_THRESHOLD can be set to enable sensitivity to Human sounds. This
|
||||
## setting is an effort to introduce privacy into the data collection.
|
||||
## The PRIVACY_THRESHOLD value represents a percentage of the entire species
|
||||
## list used during analysis. If a human sound is predicted anywhere within
|
||||
## the precentile set below, no data is collected for that audio chunk.
|
||||
## Valid range: 0-3
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||||
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||||
PRIVACY_MODE=off
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||||
PRIVACY_THRESHOLD=0
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||||
|
||||
## RECORDING_LENGTH sets the length of the recording that BirdNET-Lite will
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||||
## analyze.
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||||
@@ -1,456 +0,0 @@
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||||
import socket
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||||
import threading
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||||
import os
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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os.environ['CUDA_VISIBLE_DEVICES'] = ''
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try:
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import tflite_runtime.interpreter as tflite
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except:
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||||
from tensorflow import lite as tflite
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||||
import argparse
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import operator
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import librosa
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import numpy as np
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import math
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import time
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||||
from decimal import Decimal
|
||||
import json
|
||||
import requests
|
||||
import sqlite3
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||||
import datetime
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||||
from time import sleep
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||||
import pytz
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||||
from tzlocal import get_localzone
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||||
from pathlib import Path
|
||||
|
||||
|
||||
HEADER = 64
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||||
PORT = 5050
|
||||
SERVER = socket.gethostbyname(socket.gethostname())
|
||||
ADDR = (SERVER, PORT)
|
||||
FORMAT = 'utf-8'
|
||||
DISCONNECT_MESSAGE = "!DISCONNECT"
|
||||
|
||||
server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
|
||||
try:
|
||||
server.bind(ADDR)
|
||||
except:
|
||||
print("Waiting on socket")
|
||||
time.sleep(5)
|
||||
|
||||
|
||||
|
||||
# Open most recent Configuration and grab DB_PWD as a python variable
|
||||
userDir = os.path.expanduser('~')
|
||||
with open(userDir + '/BirdNET-Pi/scripts/thisrun.txt', 'r') as f:
|
||||
this_run = f.readlines()
|
||||
audiofmt = "." + str(str(str([i for i in this_run if i.startswith('AUDIOFMT')]).split('=')[1]).split('\\')[0])
|
||||
|
||||
|
||||
def loadModel():
|
||||
|
||||
global INPUT_LAYER_INDEX
|
||||
global OUTPUT_LAYER_INDEX
|
||||
global MDATA_INPUT_INDEX
|
||||
global CLASSES
|
||||
|
||||
print('LOADING TF LITE MODEL...', end=' ')
|
||||
|
||||
# Load TFLite model and allocate tensors.
|
||||
modelpath = userDir + '/BirdNET-Pi/model/BirdNET_6K_GLOBAL_MODEL.tflite'
|
||||
myinterpreter = tflite.Interpreter(model_path=modelpath,num_threads=2)
|
||||
myinterpreter.allocate_tensors()
|
||||
|
||||
# Get input and output tensors.
|
||||
input_details = myinterpreter.get_input_details()
|
||||
output_details = myinterpreter.get_output_details()
|
||||
|
||||
# Get input tensor index
|
||||
INPUT_LAYER_INDEX = input_details[0]['index']
|
||||
MDATA_INPUT_INDEX = input_details[1]['index']
|
||||
OUTPUT_LAYER_INDEX = output_details[0]['index']
|
||||
|
||||
# Load labels
|
||||
CLASSES = []
|
||||
with open(userDir + '/BirdNET-Pi/model/labels.txt', 'r') as lfile:
|
||||
for line in lfile.readlines():
|
||||
CLASSES.append(line.replace('\n', ''))
|
||||
|
||||
print('DONE!')
|
||||
|
||||
return myinterpreter
|
||||
|
||||
def loadCustomSpeciesList(path):
|
||||
|
||||
slist = []
|
||||
if os.path.isfile(path):
|
||||
with open(path, 'r') as csfile:
|
||||
for line in csfile.readlines():
|
||||
slist.append(line.replace('\r', '').replace('\n', ''))
|
||||
|
||||
return slist
|
||||
|
||||
def splitSignal(sig, rate, overlap, seconds=3.0, minlen=1.5):
|
||||
|
||||
# Split signal with overlap
|
||||
sig_splits = []
|
||||
for i in range(0, len(sig), int((seconds - overlap) * rate)):
|
||||
split = sig[i:i + int(seconds * rate)]
|
||||
|
||||
# End of signal?
|
||||
if len(split) < int(minlen * rate):
|
||||
break
|
||||
|
||||
# Signal chunk too short? Fill with zeros.
|
||||
if len(split) < int(rate * seconds):
|
||||
temp = np.zeros((int(rate * seconds)))
|
||||
temp[:len(split)] = split
|
||||
split = temp
|
||||
|
||||
sig_splits.append(split)
|
||||
|
||||
return sig_splits
|
||||
|
||||
def readAudioData(path, overlap, sample_rate=48000):
|
||||
|
||||
print('READING AUDIO DATA...', end=' ', flush=True)
|
||||
|
||||
# Open file with librosa (uses ffmpeg or libav)
|
||||
sig, rate = librosa.load(path, sr=sample_rate, mono=True, res_type='kaiser_fast')
|
||||
|
||||
# Split audio into 3-second chunks
|
||||
chunks = splitSignal(sig, rate, overlap)
|
||||
|
||||
print('DONE! READ', str(len(chunks)), 'CHUNKS.')
|
||||
|
||||
return chunks
|
||||
|
||||
def convertMetadata(m):
|
||||
|
||||
# Convert week to cosine
|
||||
if m[2] >= 1 and m[2] <= 48:
|
||||
m[2] = math.cos(math.radians(m[2] * 7.5)) + 1
|
||||
else:
|
||||
m[2] = -1
|
||||
|
||||
# Add binary mask
|
||||
mask = np.ones((3,))
|
||||
if m[0] == -1 or m[1] == -1:
|
||||
mask = np.zeros((3,))
|
||||
if m[2] == -1:
|
||||
mask[2] = 0.0
|
||||
|
||||
return np.concatenate([m, mask])
|
||||
|
||||
def custom_sigmoid(x, sensitivity=1.0):
|
||||
return 1 / (1.0 + np.exp(-sensitivity * x))
|
||||
|
||||
def predict(sample, sensitivity):
|
||||
global INTERPRETER
|
||||
# Make a prediction
|
||||
INTERPRETER.set_tensor(INPUT_LAYER_INDEX, np.array(sample[0], dtype='float32'))
|
||||
INTERPRETER.set_tensor(MDATA_INPUT_INDEX, np.array(sample[1], dtype='float32'))
|
||||
INTERPRETER.invoke()
|
||||
prediction = INTERPRETER.get_tensor(OUTPUT_LAYER_INDEX)[0]
|
||||
|
||||
# Apply custom sigmoid
|
||||
p_sigmoid = custom_sigmoid(prediction, sensitivity)
|
||||
|
||||
# Get label and scores for pooled predictions
|
||||
p_labels = dict(zip(CLASSES, p_sigmoid))
|
||||
|
||||
# Sort by score
|
||||
p_sorted = sorted(p_labels.items(), key=operator.itemgetter(1), reverse=True)
|
||||
|
||||
# Remove species that are on blacklist
|
||||
for i in range(min(10, len(p_sorted))):
|
||||
if p_sorted[i][0] in ['Non-bird_Non-bird', 'Noise_Noise']:
|
||||
p_sorted[i] = (p_sorted[i][0], 0.0)
|
||||
if p_sorted[i][0]=='Human_Human':
|
||||
print("HUMAN SCORE:",str(p_sorted[i]))
|
||||
HUMAN_FLAG=True
|
||||
with open(userDir + '/BirdNET-Pi/HUMAN.txt', 'a') as rfile:
|
||||
rfile.write(str(datetime.datetime.now())+str(p_sorted[i])+ '\n')
|
||||
# date_stamp=datetime.datetime.now().strftime("%d_%m_%y_%H:%M:%S")
|
||||
#
|
||||
# sf.write('./home/*/human_sample.wav',np.random.randn(10,2) , 44100) #sample[0]
|
||||
|
||||
# Only return first the top ten results
|
||||
#INCREASE THIS TO SEE IF HUMAN IS DETECTED MORE RELIABLY
|
||||
# print('P_SORTED-------', p_sorted)
|
||||
return p_sorted[:100]
|
||||
|
||||
def analyzeAudioData(chunks, lat, lon, week, sensitivity, overlap,):
|
||||
global INTERPRETER
|
||||
|
||||
detections = {}
|
||||
start = time.time()
|
||||
print('ANALYZING AUDIO...', end=' ', flush=True)
|
||||
|
||||
# Convert and prepare metadata
|
||||
mdata = convertMetadata(np.array([lat, lon, week]))
|
||||
mdata = np.expand_dims(mdata, 0)
|
||||
|
||||
# Parse every chunk
|
||||
pred_start = 0.0
|
||||
for c in chunks:
|
||||
|
||||
# Prepare as input signal
|
||||
sig = np.expand_dims(c, 0)
|
||||
|
||||
# Make prediction
|
||||
p = predict([sig, mdata], sensitivity)
|
||||
# print("PPPPP",p)
|
||||
HUMAN_DETECTED=False
|
||||
#Catch if Human is recognized
|
||||
for x in range(len(p)):
|
||||
if "Human" in p[x][0]:
|
||||
# print("HUMAN DETECTED!!",p[x][0])
|
||||
#clear list
|
||||
HUMAN_DETECTED=True
|
||||
print("CHUNK -----",c)
|
||||
|
||||
# Save result and timestamp
|
||||
pred_end = pred_start + 3.0
|
||||
|
||||
if HUMAN_DETECTED == True:
|
||||
p=[('Human_Human',0.0)]*10
|
||||
print("HUMAN DETECTED!!!",p)
|
||||
|
||||
detections[str(pred_start) + ';' + str(pred_end)] = p
|
||||
|
||||
pred_start = pred_end - overlap
|
||||
|
||||
print('DONE! Time', int((time.time() - start) * 10) / 10.0, 'SECONDS')
|
||||
# print('DETECTIONS:::::',detections)
|
||||
return detections
|
||||
|
||||
|
||||
def writeResultsToFile(detections, min_conf, path):
|
||||
|
||||
print('WRITING RESULTS TO', path, '...', end=' ')
|
||||
rcnt = 0
|
||||
with open(path, 'w') as rfile:
|
||||
rfile.write('Start (s);End (s);Scientific name;Common name;Confidence\n')
|
||||
for d in detections:
|
||||
for entry in detections[d]:
|
||||
if entry[1] >= min_conf and ((entry[0] in INCLUDE_LIST or len(INCLUDE_LIST) == 0) and (entry[0] not in EXCLUDE_LIST or len(EXCLUDE_LIST) == 0) ):
|
||||
rfile.write(d + ';' + entry[0].replace('_', ';') + ';' + str(entry[1]) + '\n')
|
||||
rcnt += 1
|
||||
print('DONE! WROTE', rcnt, 'RESULTS.')
|
||||
return
|
||||
|
||||
def handle_client(conn, addr):
|
||||
global INCLUDE_LIST
|
||||
global EXCLUDE_LIST
|
||||
print(f"[NEW CONNECTION] {addr} connected.")
|
||||
|
||||
connected = True
|
||||
while connected:
|
||||
msg_length = conn.recv(HEADER).decode(FORMAT)
|
||||
if msg_length:
|
||||
msg_length = int(msg_length)
|
||||
msg = conn.recv(msg_length).decode(FORMAT)
|
||||
if msg == DISCONNECT_MESSAGE:
|
||||
connected = False
|
||||
else:
|
||||
#print(f"[{addr}] {msg}")
|
||||
|
||||
args = type('', (), {})()
|
||||
|
||||
args.i = ''
|
||||
args.o = ''
|
||||
args.birdweather_id = '99999'
|
||||
args.include_list = 'null'
|
||||
args.exclude_list = 'null'
|
||||
args.overlap = 0.0
|
||||
args.week = -1
|
||||
args.sensitivity = 1.25
|
||||
args.min_conf = 0.70
|
||||
args.lat = -1
|
||||
args.lon = -1
|
||||
|
||||
|
||||
for line in msg.split('||'):
|
||||
inputvars = line.split('=')
|
||||
if inputvars[0] == 'i':
|
||||
args.i = inputvars[1]
|
||||
elif inputvars[0] == 'o':
|
||||
args.o = inputvars[1]
|
||||
elif inputvars[0] == 'birdweather_id':
|
||||
args.birdweather_id = inputvars[1]
|
||||
elif inputvars[0] == 'include_list':
|
||||
args.include_list = inputvars[1]
|
||||
elif inputvars[0] == 'exclude_list':
|
||||
args.exclude_list = inputvars[1]
|
||||
elif inputvars[0] == 'overlap':
|
||||
args.overlap = float(inputvars[1])
|
||||
elif inputvars[0] == 'week':
|
||||
args.week = int(inputvars[1])
|
||||
elif inputvars[0] == 'sensitivity':
|
||||
args.sensitivity = float(inputvars[1])
|
||||
elif inputvars[0] == 'min_conf':
|
||||
args.min_conf = float(inputvars[1])
|
||||
elif inputvars[0] == 'lat':
|
||||
args.lat = float(inputvars[1])
|
||||
elif inputvars[0] == 'lon':
|
||||
args.lon = float(inputvars[1])
|
||||
|
||||
|
||||
|
||||
# Load custom species lists - INCLUDED and EXCLUDED
|
||||
if not args.include_list == 'null':
|
||||
INCLUDE_LIST = loadCustomSpeciesList(args.include_list)
|
||||
else:
|
||||
INCLUDE_LIST = []
|
||||
|
||||
if not args.exclude_list == 'null':
|
||||
EXCLUDE_LIST = loadCustomSpeciesList(args.exclude_list)
|
||||
else:
|
||||
EXCLUDE_LIST = []
|
||||
|
||||
birdweather_id = args.birdweather_id
|
||||
|
||||
# Read audio data
|
||||
audioData = readAudioData(args.i, args.overlap)
|
||||
|
||||
# Get Date/Time from filename in case Pi gets behind
|
||||
#now = datetime.now()
|
||||
full_file_name = args.i
|
||||
print('FULL FILENAME: -' + full_file_name + '-')
|
||||
file_name = Path(full_file_name).stem
|
||||
file_date = file_name.split('-birdnet-')[0]
|
||||
file_time = file_name.split('-birdnet-')[1]
|
||||
date_time_str = file_date + ' ' + file_time
|
||||
date_time_obj = datetime.datetime.strptime(date_time_str, '%Y-%m-%d %H:%M:%S')
|
||||
#print('Date:', date_time_obj.date())
|
||||
#print('Time:', date_time_obj.time())
|
||||
print('Date-time:', date_time_obj)
|
||||
now = date_time_obj
|
||||
current_date = now.strftime("%Y-%m-%d")
|
||||
current_time = now.strftime("%H:%M:%S")
|
||||
current_iso8601 = now.astimezone(get_localzone()).isoformat()
|
||||
|
||||
week_number = int(now.strftime("%V"))
|
||||
week = max(1, min(week_number, 48))
|
||||
|
||||
sensitivity = max(0.5, min(1.0 - (args.sensitivity - 1.0), 1.5))
|
||||
|
||||
# Process audio data and get detections
|
||||
detections = analyzeAudioData(audioData, args.lat, args.lon, week, sensitivity, args.overlap)
|
||||
|
||||
# Write detections to output file
|
||||
min_conf = max(0.01, min(args.min_conf, 0.99))
|
||||
writeResultsToFile(detections, min_conf, args.o)
|
||||
|
||||
###############################################################################
|
||||
###############################################################################
|
||||
|
||||
soundscape_uploaded = False
|
||||
|
||||
# Write detections to Database
|
||||
myReturn = ''
|
||||
for i in detections:
|
||||
myReturn += str(i) + '-' + str(detections[i][0]) + '\n'
|
||||
|
||||
|
||||
with open(userDir + '/BirdNET-Pi/BirdDB.txt', 'a') as rfile:
|
||||
for d in detections:
|
||||
for entry in detections[d]:
|
||||
if entry[1] >= min_conf and ((entry[0] in INCLUDE_LIST or len(INCLUDE_LIST) == 0) and (entry[0] not in EXCLUDE_LIST or len(EXCLUDE_LIST) == 0) ):
|
||||
rfile.write(str(current_date) + ';' + str(current_time) + ';' + entry[0].replace('_', ';') + ';' \
|
||||
+ str(entry[1]) +";" + str(args.lat) + ';' + str(args.lon) + ';' + str(min_conf) + ';' + str(week) + ';' \
|
||||
+ str(args.sensitivity) +';' + str(args.overlap) + '\n')
|
||||
|
||||
Date = str(current_date)
|
||||
Time = str(current_time)
|
||||
species = entry[0]
|
||||
Sci_Name,Com_Name = species.split('_')
|
||||
score = entry[1]
|
||||
Confidence = str(round(score*100))
|
||||
Lat = str(args.lat)
|
||||
Lon = str(args.lon)
|
||||
Cutoff = str(args.min_conf)
|
||||
Week = str(args.week)
|
||||
Sens = str(args.sensitivity)
|
||||
Overlap = str(args.overlap)
|
||||
Com_Name = Com_Name.replace("'", "")
|
||||
File_Name = Com_Name.replace(" ", "_") + '-' + Confidence + '-' + \
|
||||
Date.replace("/", "-") + '-birdnet-' + Time + audiofmt
|
||||
|
||||
#Connect to SQLite Database
|
||||
try:
|
||||
con = sqlite3.connect(userDir + '/BirdNET-Pi/scripts/birds.db')
|
||||
cur = con.cursor()
|
||||
cur.execute("INSERT INTO detections VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", (Date, Time, Sci_Name, Com_Name, str(score), Lat, Lon, Cutoff, Week, Sens, Overlap, File_Name))
|
||||
|
||||
con.commit()
|
||||
con.close()
|
||||
except:
|
||||
print("Database busy")
|
||||
time.sleep(2)
|
||||
print(str(current_date) + ';' + str(current_time) + ';' + entry[0].replace('_', ';') + ';' + str(entry[1]) + ';' + str(args.lat) + ';' + str(args.lon) + ';' + str(min_conf) + ';' + str(week) + ';' + str(args.sensitivity) +';' + str(args.overlap) + Com_Name.replace(" ", "_") + '-' + str(score) + '-' + str(current_date) + '-birdnet-' + str(current_time) + audiofmt + '\n')
|
||||
|
||||
if birdweather_id != "99999":
|
||||
try:
|
||||
|
||||
if soundscape_uploaded is False:
|
||||
# POST soundscape to server
|
||||
soundscape_url = "https://app.birdweather.com/api/v1/stations/" + birdweather_id + "/soundscapes" + "?timestamp=" + current_iso8601
|
||||
|
||||
with open(args.i, 'rb') as f:
|
||||
wav_data = f.read()
|
||||
response = requests.post(url=soundscape_url, data=wav_data, headers={'Content-Type': 'application/octet-stream'})
|
||||
print("Soundscape POST Response Status - ", response.status_code)
|
||||
sdata = response.json()
|
||||
soundscape_id = sdata['soundscape']['id']
|
||||
soundscape_uploaded = True
|
||||
|
||||
# POST detection to server
|
||||
detection_url = "https://app.birdweather.com/api/v1/stations/" + birdweather_id + "/detections"
|
||||
start_time = d.split(';')[0]
|
||||
end_time = d.split(';')[1]
|
||||
post_begin = "{ "
|
||||
now_p_start = now + datetime.timedelta(seconds=float(start_time))
|
||||
current_iso8601 = now_p_start.astimezone(get_localzone()).isoformat()
|
||||
post_timestamp = "\"timestamp\": \"" + current_iso8601 + "\","
|
||||
post_lat = "\"lat\": " + str(args.lat) + ","
|
||||
post_lon = "\"lon\": " + str(args.lon) + ","
|
||||
post_soundscape_id = "\"soundscapeId\": " + str(soundscape_id) + ","
|
||||
post_soundscape_start_time = "\"soundscapeStartTime\": " + start_time + ","
|
||||
post_soundscape_end_time = "\"soundscapeEndTime\": " + end_time + ","
|
||||
post_commonName = "\"commonName\": \"" + entry[0].split('_')[1] + "\","
|
||||
post_scientificName = "\"scientificName\": \"" + entry[0].split('_')[0] + "\","
|
||||
post_algorithm = "\"algorithm\": " + "\"alpha\"" + ","
|
||||
post_confidence = "\"confidence\": " + str(entry[1])
|
||||
post_end = " }"
|
||||
|
||||
post_json = post_begin + post_timestamp + post_lat + post_lon + post_soundscape_id + post_soundscape_start_time + post_soundscape_end_time + post_commonName + post_scientificName + post_algorithm + post_confidence + post_end
|
||||
print(post_json)
|
||||
response = requests.post(detection_url, json=json.loads(post_json))
|
||||
print("Detection POST Response Status - ", response.status_code)
|
||||
except:
|
||||
print("Cannot POST right now")
|
||||
conn.send(myReturn.encode(FORMAT))
|
||||
|
||||
#time.sleep(3)
|
||||
|
||||
conn.close()
|
||||
|
||||
def start():
|
||||
# Load model
|
||||
global INTERPRETER, INCLUDE_LIST, EXCLUDE_LIST
|
||||
INTERPRETER = loadModel()
|
||||
server.listen()
|
||||
print(f"[LISTENING] Server is listening on {SERVER}")
|
||||
while True:
|
||||
conn, addr = server.accept()
|
||||
thread = threading.Thread(target=handle_client, args=(conn, addr))
|
||||
thread.start()
|
||||
print(f"[ACTIVE CONNECTIONS] {threading.activeCount() - 1}")
|
||||
|
||||
|
||||
print("[STARTING] server is starting...")
|
||||
start()
|
||||
+27
-7
@@ -48,6 +48,7 @@ userDir = os.path.expanduser('~')
|
||||
with open(userDir + '/BirdNET-Pi/scripts/thisrun.txt', 'r') as f:
|
||||
this_run = f.readlines()
|
||||
audiofmt = "." + str(str(str([i for i in this_run if i.startswith('AUDIOFMT')]).split('=')[1]).split('\\')[0])
|
||||
priv_thresh = float("." + str(str(str([i for i in this_run if i.startswith('PRIVACY_THRESHOLD')]).split('=')[1]).split('\\')[0]))/10
|
||||
|
||||
|
||||
def loadModel():
|
||||
@@ -165,14 +166,20 @@ def predict(sample, sensitivity):
|
||||
|
||||
# Sort by score
|
||||
p_sorted = sorted(p_labels.items(), key=operator.itemgetter(1), reverse=True)
|
||||
|
||||
# #print("DATABASE SIZE:", len(p_sorted))
|
||||
# #print("HUMAN-CUTOFF AT:", int(len(p_sorted)*priv_thresh)/10)
|
||||
#
|
||||
# # Remove species that are on blacklist
|
||||
|
||||
human_cutoff = max(10,int(len(p_sorted)*priv_thresh))
|
||||
|
||||
# Remove species that are on blacklist
|
||||
for i in range(min(10, len(p_sorted))):
|
||||
if p_sorted[i][0] in ['Human_Human', 'Non-bird_Non-bird', 'Noise_Noise']:
|
||||
p_sorted[i] = (p_sorted[i][0], 0.0)
|
||||
if p_sorted[i][0]=='Human_Human':
|
||||
with open(userDir + '/BirdNET-Pi/HUMAN.txt', 'a') as rfile:
|
||||
rfile.write(str(datetime.datetime.now())+str(p_sorted[i])+ ' ' + str(human_cutoff)+ '\n')
|
||||
|
||||
# Only return first the top ten results
|
||||
return p_sorted[:10]
|
||||
return p_sorted[:human_cutoff]
|
||||
|
||||
def analyzeAudioData(chunks, lat, lon, week, sensitivity, overlap,):
|
||||
global INTERPRETER
|
||||
@@ -194,14 +201,27 @@ def analyzeAudioData(chunks, lat, lon, week, sensitivity, overlap,):
|
||||
|
||||
# Make prediction
|
||||
p = predict([sig, mdata], sensitivity)
|
||||
|
||||
# print("PPPPP",p)
|
||||
HUMAN_DETECTED=False
|
||||
|
||||
#Catch if Human is recognized
|
||||
for x in range(len(p)):
|
||||
if "Human" in p[x][0]:
|
||||
HUMAN_DETECTED=True
|
||||
|
||||
# Save result and timestamp
|
||||
pred_end = pred_start + 3.0
|
||||
|
||||
#If human detected set all detections to human to make sure voices are not saved
|
||||
if HUMAN_DETECTED == True:
|
||||
p=[('Human_Human',0.0)]*10
|
||||
|
||||
detections[str(pred_start) + ';' + str(pred_end)] = p
|
||||
|
||||
pred_start = pred_end - overlap
|
||||
|
||||
print('DONE! Time', int((time.time() - start) * 10) / 10.0, 'SECONDS')
|
||||
|
||||
# print('DETECTIONS:::::',detections)
|
||||
return detections
|
||||
|
||||
def sendAppriseNotifications(species,confidence):
|
||||
|
||||
+10
-2
@@ -52,6 +52,11 @@ if(isset($_GET['species'])){
|
||||
}
|
||||
$result3 = $statement3->execute();
|
||||
}
|
||||
|
||||
$user = shell_exec("awk -F: '/1000/{print $1}' /etc/passwd");
|
||||
$home = shell_exec("awk -F: '/1000/{print $6}' /etc/passwd");
|
||||
$home = trim($home);
|
||||
file_put_contents($home."/BirdNET-Pi/scripts/disk_check_exclude.txt", "");
|
||||
?>
|
||||
|
||||
<html lang="en">
|
||||
@@ -157,6 +162,10 @@ while($results=$result->fetchArray(SQLITE3_ASSOC))
|
||||
$comname = preg_replace('/ /', '_', $results['Com_Name']);
|
||||
$comname = preg_replace('/\'/', '', $comname);
|
||||
$filename = "/By_Date/".$results['Date']."/".$comname."/".$results['File_Name'];
|
||||
|
||||
$excludefile = fopen($home."/BirdNET-Pi/scripts/disk_check_exclude.txt", "a") or die("Unable to open file!");
|
||||
$txt = $results['Date']."/".$comname."/".$results['File_Name']."\n".$results['Date']."/".$comname."/".$results['File_Name'].".png\n";
|
||||
fwrite($excludefile, $txt);
|
||||
?>
|
||||
<tr>
|
||||
<form action="" method="GET">
|
||||
@@ -174,5 +183,4 @@ $filename = "/By_Date/".$results['Date']."/".$comname."/".$results['File_Name'];
|
||||
</div>
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
|
||||
</html>
|
||||
@@ -9,8 +9,15 @@ if ! grep python3 <(head -n1 $my_dir/analyze.py) &>/dev/null;then
|
||||
echo "Ensure all python scripts use the virtual environment"
|
||||
sudo -u$USER sed -si "1 i\\#\!$HOME/BirdNET-Pi/birdnet/bin/python3" $my_dir/*.py
|
||||
fi
|
||||
if ! grep PRIVACY_MODE /etc/birdnet/birdnet.conf &>/dev/null;then
|
||||
sudo -u$USER echo "PRIVACY_MODE=off" >> /etc/birdnet/birdnet.conf
|
||||
if ! grep PRIVACY_THRESHOLD /etc/birdnet/birdnet.conf &>/dev/null;then
|
||||
sudo -u$USER echo "PRIVACY_THRESHOLD=0" >> /etc/birdnet/birdnet.conf
|
||||
git -C $HOME/BirdNET-Pi rm $my_dir/privacy_server.py
|
||||
fi
|
||||
if grep privacy ~/BirdNET-Pi/templates/birdnet_server.service &>/dev/null;then
|
||||
sudo -E sed -i 's/privacy_server.py/server.py/g' \
|
||||
~/BirdNET-Pi/templates/birdnet_server.service
|
||||
sudo systemctl daemon-reload
|
||||
restart_services.sh
|
||||
fi
|
||||
if ! grep APPRISE_NOTIFICATION_TITLE /etc/birdnet/birdnet.conf &>/dev/null;then
|
||||
sudo -u$USER echo "APPRISE_NOTIFICATION_TITLE=\"New BirdNET-Pi Detection\"" >> /etc/birdnet/birdnet.conf
|
||||
|
||||
Reference in New Issue
Block a user