--A realtime acoustic bird classification system for the Raspberry Pi 5, 4B, 400, 3B+, and 0W2 -
-
-
-
-Icon made by Freepik from www.flaticon.com -
- -## About this fork: -I've been building on [mcguirepr89's](https://github.com/mcguirepr89/BirdNET-Pi) most excellent work to further update and improve BirdNET-Pi. Maybe someone will find it useful. - -Changes include: - - - Web ui is much more responsive - - Daily charts now include all species, not just top/bottom 10 - - Bump apprise version, so more notification type are possible - - Swipe events on Daily Charts (by @croisez) - - Support for 'Species range model V2.4 - V2' - - Bookworm support - - Experimental support for writing transient files to tmpfs - - Rework analysis to consolidate analysis/server/extraction. Should make analysis more robust and slightly more efficient, especially on installations with a large number of recordings - - Bump tflite_runtime to 2.11.0, it is faster - - Rework daily_plot.py (chart_viewer) to run as a daemon to avoid the very expensive startup - - Lots of fixes & cleanups - -!! note: see 'Migrating' on how to migrate from mcguirepr89 - -## Introduction -BirdNET-Pi is built on the [BirdNET framework](https://github.com/kahst/BirdNET-Analyzer) by [**@kahst**](https://github.com/kahst)
using [pre-built TFLite binaries](https://github.com/PINTO0309/TensorflowLite-bin) by [**@PINTO0309**](https://github.com/PINTO0309) . It is able to recognize bird sounds from a USB microphone or sound card in realtime and share its data with the rest of the world.
-
-Check out birds from around the world
-- [BirdWeather](https://app.birdweather.com)
Options- >- This allows you to quiet the display of how many commits your installation is behind by relative to the Github repo. This number appears next to "Tools" when you're 50 or more commits behind. + > +This allows you to quiet the display of how many commits your installation is behind by relative to the Github repo. This number appears next to "Tools" when you're 50 or more commits behind. - > - This allows you to remove the axes and labels of the spectrograms that are generated by Sox for each detection for a cleaner appearance. + > +This allows you to remove the axes and labels of the spectrograms that are generated by Sox for each detection for a cleaner appearance. + |
+ Option : Confirmed Species+ + > +This allows to visually mark species that were manually confirmed as existing in the area. A new question mark appears next to species names in the Recordings page. Clicking it changes the icon to a checkmark, and add the species to the file confirmed_species_list.txt |
diff --git a/scripts/clear_all_data.sh b/scripts/clear_all_data.sh
index ee9d88d02..3a418ee08 100755
--- a/scripts/clear_all_data.sh
+++ b/scripts/clear_all_data.sh
@@ -25,6 +25,7 @@ echo "Re-creating necessary directories"
sudo -u ${USER} ln -fs $(dirname $my_dir)/exclude_species_list.txt $my_dir
sudo -u ${USER} ln -fs $(dirname $my_dir)/include_species_list.txt $my_dir
sudo -u ${USER} ln -fs $(dirname $my_dir)/whitelist_species_list.txt $my_dir
+sudo -u ${USER} ln -fs $(dirname $my_dir)/confirmed_species_list.txt $my_dir
sudo -u ${USER} ln -fs $(dirname $my_dir)/homepage/* ${EXTRACTED}
sudo -u ${USER} ln -fs $(dirname $my_dir)/model/labels.txt ${my_dir}
sudo -u ${USER} ln -fs $my_dir ${EXTRACTED}
diff --git a/scripts/daily_plot.py b/scripts/daily_plot.py
index cce51b16f..8aedaa1fd 100755
--- a/scripts/daily_plot.py
+++ b/scripts/daily_plot.py
@@ -1,228 +1,250 @@
+#===============================================================================
+#=== daily_plot.py (adjusted version @jmtmp) ==========================================
+#===============================================================================
+#=== 2024-04-19: new version
+#=== 2024-04-28: new custom formatting for millions (my_int_fmt function)
+#=== new formatting of total occurence in semi-monthly plot
+#=== 2024-09-01: updated suptitle and xlabels formatting
+#=== 2024-09-05: Daemon implementing
+#=== 2024-09-26: transparent first column
+#=== 2024-10-02: code refactor
+#===============================================================================
+
import argparse
-import os
import sqlite3
-import textwrap
-from datetime import datetime
-from time import sleep
-
-import matplotlib.font_manager as font_manager
-import matplotlib.pyplot as plt
-import numpy as np
+import os
import pandas as pd
import seaborn as sns
+import matplotlib.pyplot as plt
+import matplotlib.font_manager as font_manager
from matplotlib import rcParams
-from matplotlib.colors import LogNorm
-
+from matplotlib.colors import LogNorm, TwoSlopeNorm
+from matplotlib.ticker import FormatStrFormatter
+from datetime import datetime
+from time import sleep
+from functools import lru_cache
from utils.helpers import DB_PATH, get_settings
-
-def get_data(now=None):
- conn = sqlite3.connect(DB_PATH)
- if now is None:
- now = datetime.now()
- df = pd.read_sql_query(f"SELECT * from detections WHERE Date = DATE('{now.strftime('%Y-%m-%d')}')",
- conn)
-
- # Convert Date and Time Fields to Panda's format
- df['Date'] = pd.to_datetime(df['Date'])
- df['Time'] = pd.to_datetime(df['Time'], unit='ns')
-
- # Add round hours to dataframe
- df['Hour of Day'] = [r.hour for r in df.Time]
-
- return df, now
-
-
-# Function to show value on bars - from https://stackoverflow.com/questions/43214978/seaborn-barplot-displaying-values
-def show_values_on_bars(ax, label):
- conf = get_settings()
-
- for i, p in enumerate(ax.patches):
- x = p.get_x() + p.get_width() * 0.9
- y = p.get_y() + p.get_height() / 2
- # Species confidence
- # value = '{:.0%}'.format(label.iloc[i])
- # Species Count Total
- value = '{:n}'.format(p.get_width())
- bbox = {'facecolor': 'lightgrey', 'edgecolor': 'none', 'pad': 1.0}
- if conf['COLOR_SCHEME'] == "dark":
- color = 'black'
- else:
- color = 'darkgreen'
-
- ax.text(x, y, value, bbox=bbox, ha='center', va='center', size=9, color=color)
-
-
-def wrap_width(txt):
- # try to estimate wrap width
- w = 16
- for c in txt:
- if c in ['M', 'm', 'W', 'w']:
- w -= 0.33
- if c in ['I', 'i', 'j', 'l']:
- w += 0.33
- return round(w)
-
-
-def create_plot(df_plt_today, now, is_top=None):
- if is_top is not None:
- readings = 10
- if is_top:
- plt_selection_today = (df_plt_today['Com_Name'].value_counts()[:readings])
- else:
- plt_selection_today = (df_plt_today['Com_Name'].value_counts()[-readings:])
- else:
- plt_selection_today = df_plt_today['Com_Name'].value_counts()
- readings = len(df_plt_today['Com_Name'].value_counts())
-
- df_plt_selection_today = df_plt_today[df_plt_today.Com_Name.isin(plt_selection_today.index)]
-
- conf = get_settings()
-
- # Set up plot axes and titles
- height = max(readings / 3, 0) + 1.06
- if conf['COLOR_SCHEME'] == "dark":
- facecolor = 'darkgrey'
- else:
- facecolor = 'none'
-
- f, axs = plt.subplots(1, 2, figsize=(10, height), gridspec_kw=dict(width_ratios=[3, 6]), facecolor=facecolor)
-
- # generate y-axis order for all figures based on frequency
- freq_order = df_plt_selection_today['Com_Name'].value_counts().index
-
- # make color for max confidence --> this groups by name and calculates max conf
- confmax = df_plt_selection_today.groupby('Com_Name')['Confidence'].max()
- # reorder confmax to detection frequency order
- confmax = confmax.reindex(freq_order)
-
- # norm values for color palette
- norm = plt.Normalize(confmax.values.min(), confmax.values.max())
- if is_top or is_top is None:
- # Set Palette for graphics
- if conf['COLOR_SCHEME'] == "dark":
- pal = "Greys"
- colors = plt.cm.Greys(norm(confmax)).tolist()
- else:
- pal = "Greens"
- colors = plt.cm.Greens(norm(confmax)).tolist()
- if is_top:
- plot_type = "Top"
- else:
- plot_type = 'All'
- name = "Combo"
- else:
- # Set Palette for graphics
- pal = "Reds"
- colors = plt.cm.Reds(norm(confmax)).tolist()
- plot_type = "Bottom"
- name = "Combo2"
-
- # Generate frequency plot
- plot = sns.countplot(y='Com_Name', hue='Com_Name', legend=False, data=df_plt_selection_today,
- palette=colors, order=freq_order, ax=axs[0], edgecolor='lightgrey')
-
- # Prints Max Confidence on bars
- show_values_on_bars(axs[0], confmax)
-
- # Try plot grid lines between bars - problem at the moment plots grid lines on bars - want between bars
- yticklabels = ['\n'.join(textwrap.wrap(ticklabel.get_text(), wrap_width(ticklabel.get_text()))) for ticklabel in plot.get_yticklabels()]
- # Next two lines avoid a UserWarning on set_ticklabels() requesting a fixed number of ticks
- yticks = plot.get_yticks()
- plot.set_yticks(yticks)
- plot.set_yticklabels(yticklabels, fontsize=10)
- plot.set(ylabel=None)
- plot.set(xlabel="Detections")
-
- # Generate crosstab matrix for heatmap plot
- heat = pd.crosstab(df_plt_selection_today['Com_Name'], df_plt_selection_today['Hour of Day'])
-
- # Order heatmap Birds by frequency of occurrance
- heat.index = pd.CategoricalIndex(heat.index, categories=freq_order)
- heat.sort_index(level=0, inplace=True)
-
- hours_in_day = pd.Series(data=range(0, 24))
- heat_frame = pd.DataFrame(data=0, index=heat.index, columns=hours_in_day)
- heat = (heat+heat_frame).fillna(0)
- # mask out zeros, so they do not show up in the final plot. this happens when max count/h is one
- heat[heat == 0] = np.nan
-
- # Generatie heatmap plot
- plot = sns.heatmap(heat, norm=LogNorm(), annot=True, annot_kws={"fontsize": 7}, fmt="g", cmap=pal, square=False,
- cbar=False, linewidths=0.5, linecolor="Grey", ax=axs[1], yticklabels=False)
-
- # Set color and weight of tick label for current hour
- for label in plot.get_xticklabels():
- if int(label.get_text()) == now.hour:
- if conf['COLOR_SCHEME'] == "dark":
- label.set_color('white')
- else:
- label.set_color('yellow')
-
- plot.set_xticklabels(plot.get_xticklabels(), rotation=0, size=8)
-
- # Set heatmap border
- for _, spine in plot.spines.items():
- spine.set_visible(True)
-
- plot.set(ylabel=None)
- plot.set(xlabel="Hour of Day")
- # Set combined plot layout and titles
- y = 1 - 8 / (height * 100)
- plt.suptitle(f"{plot_type} {readings} Last Updated: {now.strftime('%Y-%m-%d %H:%M')}", y=y)
- f.tight_layout()
- top = 1 - 40 / (height * 100)
- f.subplots_adjust(left=0.125, right=0.9, top=top, wspace=0)
-
- # Save combined plot
- save_name = os.path.expanduser(f"~/BirdSongs/Extracted/Charts/{name}-{now.strftime('%Y-%m-%d')}.png")
- plt.savefig(save_name)
- plt.show()
- plt.close()
-
+# Cache the settings to avoid redundant calls
+@lru_cache(maxsize=None)
+def get_settings_cached():
+ return get_settings()
def load_fonts():
- conf = get_settings()
# Add every font at the specified location
font_dir = [os.path.expanduser('~/BirdNET-Pi/homepage/static')]
for font in font_manager.findSystemFonts(font_dir, fontext='ttf'):
font_manager.fontManager.addfont(font)
# Set font family globally
- if conf['DATABASE_LANG'] in ['ja', 'zh']:
+ lang = get_settings_cached()['DATABASE_LANG']
+ if lang in ['ja', 'zh']:
rcParams['font.family'] = 'Noto Sans JP'
- elif conf['DATABASE_LANG'] == 'th':
+ elif lang == 'th':
rcParams['font.family'] = 'Noto Sans Thai'
else:
rcParams['font.family'] = 'Roboto Flex'
+def my_int_fmt(number, converthundreds=False):
+ try:
+ number = float(number)
+ except (ValueError, TypeError):
+ return str(number)
+ if number >= 9_500_000:
+ return f"{round(number / 1_000_000)}M"
+ elif number >= 950:
+ return f"{round(number / 1_000)}k"
+ elif converthundreds and number >= 100:
+ return f".{round(number / 100)}k"
+ else:
+ return str(int(number))
+
+def clr_plot_facecolor():
+ # Update colors according to color scheme
+ if get_settings_cached()['COLOR_SCHEME'] == "dark":
+ return 'darkgrey'
+ else:
+ return 'none'
+
+def clr_current_ticklabel():
+ # Update colors according to color scheme
+ if get_settings_cached()['COLOR_SCHEME'] == "dark":
+ return 'white'
+ else:
+ return 'red'
+
+def my_heatmap(axis, crosstable, clrmap, clrnorm, annotfmt='', annotsize='medium'):
+ # annotsize: float or {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'}
+ hm_axes = sns.heatmap(crosstable, cmap=clrmap, norm=clrnorm, cbar=False, linewidths=0.5, linecolor="Silver", ax=axis,
+ annot=(annotfmt != ''), fmt=annotfmt, annot_kws={"fontsize": annotsize})
+ # Set border
+ for _, spine in hm_axes.spines.items():
+ spine.set_visible(True)
+ return hm_axes
+
+def get_daily_plot_data(conn, now):
+ sql_fields = "COUNT(DISTINCT Com_Name) as Species, COUNT(Com_Name) as Detections, COUNT(DISTINCT Date) as Days"
+ db_entire = pd.read_sql_query(f"SELECT {sql_fields} FROM detections", conn)
+ db_today = pd.read_sql_query(f"SELECT {sql_fields} FROM detections WHERE Date = DATE('now')", conn)
+ # Prepare suptitle
+ avg_daily_detections = round(int(db_entire.Detections[0]) / int(db_entire.Days[0]))
+ plot_suptitle = f"Hourly overview updated at {now.strftime('%Y-%m-%d %H:%M:%S')}\n"
+ plot_suptitle += f"({db_today.Species[0]} species today, {db_entire.Species[0]} in total; "
+ plot_suptitle += f"{db_today.Detections[0]} detections today, {avg_daily_detections} on average)"
+ # Prepare dataset
+ sql = """
+ SELECT Date, CAST(strftime('%H', Time) AS INTEGER) AS Hour, Com_Name AS Bird,
+ COUNT(Com_Name) AS Count, MAX(Confidence) AS Conf
+ FROM detections
+ WHERE Date = DATE('now', 'localtime')
+ GROUP BY Hour, Bird
+ """
+ plot_dataframe = pd.read_sql_query(sql, conn)
+ return plot_suptitle, plot_dataframe
+
+def get_yearly_plot_data(conn, now):
+ sql_fields = "COUNT(DISTINCT Com_Name) as Species, COUNT(Com_Name) as Detections, COUNT(DISTINCT Date) as Days"
+ db_entire = pd.read_sql_query(f"SELECT {sql_fields} FROM detections", conn)
+ db_ytd = pd.read_sql_query(f"SELECT {sql_fields} FROM detections WHERE Date >= DATE('now','start of year')", conn)
+ # Prepare suptitle
+ plot_suptitle = (f"Semi-monthly overview updated at {now.strftime('%Y-%m-%d %H:%M:%S')} "
+ f"({db_ytd.Species[0]} species this year, {db_entire.Species[0]} in total)")
+ # Prepare dataset
+ sql = """
+ SELECT 2 * (CAST(strftime('%m', Date) AS INTEGER) - 1) +
+ CASE WHEN CAST(strftime('%d', Date) AS INTEGER) < 16 THEN 0 ELSE 1 END AS Period,
+ strftime('%Y', Date) AS Year, Com_Name AS Bird,
+ COUNT(Com_Name) AS Count, MAX(Confidence) AS Conf
+ FROM detections
+ WHERE Date >= DATE('now','start of year')
+ GROUP BY Period, Bird
+ """
+ plot_dataframe = pd.read_sql_query(sql, conn)
+ return plot_suptitle, plot_dataframe
+
+def create_plot(chart_name, chart_suptitle, df_birds, now, time_unit, period_col, xlabel, xtick_labels):
+ # Common code for data preparation
+ df_birds_summary = df_birds.groupby('Bird').agg({'Count': 'sum', 'Conf': 'max'})
+ df_birds_ordered = df_birds_summary.sort_values(by=['Count', 'Conf'], ascending=[False, False])
+ df_birds['Bird'] = pd.Categorical(df_birds['Bird'], ordered=True, categories=df_birds_ordered.index)
+ no_of_rows = df_birds_summary.shape[0]
+ total_recordings = df_birds['Count'].sum()
+ if no_of_rows == 0:
+ print("No data available for plotting.")
+ return
+
+ # Prepare crosstables
+ df_confidences = pd.crosstab(index=df_birds['Bird'], columns=df_birds[time_unit], values=df_birds['Conf'], aggfunc='max')
+ df_detections = pd.crosstab(index=df_birds['Bird'], columns=df_birds[time_unit], values=df_birds['Count'], aggfunc='sum')
+ df_perioddata = pd.crosstab(index=df_birds['Bird'], columns=df_birds[period_col], values=df_birds['Count'], aggfunc='sum')
+
+ # Prepare empty matrix for periods
+ df_empty_matrix = pd.DataFrame(data=0, index=df_perioddata.index, columns=pd.Series(data=range(len(xtick_labels))))
+ df_perioddata = (df_empty_matrix + df_perioddata).fillna(0)
+
+ # Color palettes
+ color_scheme = get_settings_cached()['COLOR_SCHEME']
+ cmap_confi = 'PiYG' if color_scheme != "dark" else 'Greys'
+ cmap_count = 'Blues' if color_scheme != "dark" else 'Greys'
+ norm_confi = TwoSlopeNorm(vmin=0.25, vmax=1.25, vcenter=0.75)
+ norm_count = LogNorm(vmin=1, vmax=total_recordings)
+
+ # Plot dimensions
+ row_height = 0.28
+ fig_height = row_height * (no_of_rows + 4)
+ row_space = row_height / fig_height
+
+ # Plot setup
+ f, axs = plt.subplots(1, 4, figsize=(10, fig_height), width_ratios=[5, 2, 2, 18], facecolor=clr_plot_facecolor())
+ plt.subplots_adjust(left=0.02, right=0.98, top=(1 - 2 * row_space), bottom=(0 + 2 * row_space), wspace=0, hspace=0)
+ plt.suptitle(chart_suptitle, y=0.99)
+
+ # Bird name column
+ axs[0].set_xlim(0, 1)
+ axs[0].set_ylim(0, len(df_confidences.index))
+ axs[0].axis('off')
+
+ # Confidence column
+ hm_confi = my_heatmap(axs[1], df_confidences, cmap_confi, norm_confi, annotfmt=".0%")
+ hm_confi.tick_params(bottom=True, left=False, labelbottom=True, labeltop=False, labelleft=True, labelrotation=0)
+ hm_confi.set(xlabel=None, ylabel=None, xticklabels=['max\nconfidence'])
+
+ # Occurrence column
+ hm_count = my_heatmap(axs[2], df_detections, cmap_count, norm_count, annotfmt="g")
+ hm_count.tick_params(bottom=True, left=False, labelbottom=True, labeltop=False, labelleft=False)
+ hm_count.set(xlabel=None, ylabel=None, xticklabels=['total\ndetections'])
+
+ # Apply custom annotation format
+ for t in hm_count.texts:
+ if len(t.get_text()) > 3:
+ t.set_text(my_int_fmt(t.get_text()))
+
+ # Occurrence heatmap
+ hm_data = my_heatmap(axs[3], df_perioddata, cmap_count, norm_count, annotfmt="g", annotsize=9)
+ hm_data.tick_params(bottom=True, top=False, left=False, labelbottom=True, labeltop=False,
+ labelleft=False, labelrotation=0)
+ hm_data.set(xlabel=None, ylabel=None)
+ hm_data.set_xlabel(xlabel, labelpad=1)
+ hm_data.xaxis.set_major_formatter(FormatStrFormatter('%d'))
+ hm_data.set_xticklabels(xtick_labels)
+
+ # Apply custom annotation format
+ for t in hm_data.texts:
+ if len(t.get_text()) > 2:
+ t.set_text(my_int_fmt(t.get_text(), converthundreds=True))
+
+ # Set tick label for current period
+ for idx, label in enumerate(hm_data.get_xticklabels()):
+ if period_col == 'Hour':
+ if int(label.get_text()) == now.hour:
+ label.set_color(clr_current_ticklabel())
+ elif period_col == 'Period':
+ # Map current period to index
+ current_period = 2 * (now.month - 1) + (0 if now.day < 16 else 1)
+ if idx == current_period:
+ label.set_color(clr_current_ticklabel())
+
+ # Save the plot
+ plt.savefig(os.path.expanduser(f'~/BirdSongs/Extracted/Charts/{chart_name}.png'))
+ plt.show()
+ plt.close()
def main(daemon, sleep_m):
load_fonts()
- last_run = None
while True:
- now = datetime.now()
- # now = datetime.strptime('2023-12-13T23:59:59', "%Y-%m-%dT%H:%M:%S")
- # now = datetime.strptime('2024-01-02T23:59:59', "%Y-%m-%dT%H:%M:%S")
- # now = datetime.strptime('2024-02-26T23:59:59', "%Y-%m-%dT%H:%M:%S")
- # now = datetime.strptime('2024-04-03T23:59:59', "%Y-%m-%dT%H:%M:%S")
- # now = datetime.strptime('2024-04-07T23:59:59', "%Y-%m-%dT%H:%M:%S")
- if last_run and now.day != last_run.day:
- print("getting yesterday's dataset")
- yesterday = last_run.replace(hour=23, minute=59)
- data, time = get_data(yesterday)
- else:
- data, time = get_data(now)
- if not data.empty:
- create_plot(data, time)
- else:
- print('empty dataset')
+ with sqlite3.connect(DB_PATH) as conn:
+ now = datetime.now()
+
+ suptitle, dataframe = get_daily_plot_data(conn, now)
+ create_plot(
+ chart_name='Combo-' + now.strftime("%Y-%m-%d"),
+ chart_suptitle=suptitle,
+ df_birds=dataframe,
+ now=now,
+ time_unit='Date',
+ period_col='Hour',
+ xlabel='hourly detections',
+ xtick_labels=list(range(24))
+ )
+
+ suptitle, dataframe = get_yearly_plot_data(conn, now)
+ month_labels = ['Jan', '', 'Feb', '', 'Mar', '', 'Apr', '', 'May', '', 'Jun', '', 'Jul', '',
+ 'Aug', '', 'Sep', '', 'Oct', '', 'Nov', '', 'Dec', '']
+ create_plot(
+ chart_name='Combo2-' + now.strftime("%Y-%m-%d"),
+ chart_suptitle=suptitle,
+ df_birds=dataframe,
+ now=now,
+ time_unit='Year',
+ period_col='Period',
+ xlabel='semi-monthly detections',
+ xtick_labels=month_labels
+ )
+
if daemon:
- last_run = now
sleep(60 * sleep_m)
else:
break
-
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--daemon', action='store_true')
diff --git a/scripts/install_config.sh b/scripts/install_config.sh
index 5fdf4521d..60408fc83 100755
--- a/scripts/install_config.sh
+++ b/scripts/install_config.sh
@@ -270,6 +270,10 @@ RAW_SPECTROGRAM=0
CUSTOM_IMAGE=
CUSTOM_IMAGE_TITLE=""
+## CONFIRM_SPECIES adds an icon next to species in the Recordings tab to keep track which species are manually confirmed
+## It generates a confirmed_species_list.txt file, and allows to better visualize species that could be false positives
+CONFIRM_SPECIES=0
+
## These are just for debugging
LAST_RUN=
THIS_RUN=
diff --git a/scripts/install_services.sh b/scripts/install_services.sh
index 7c297e508..e6f6a3b91 100755
--- a/scripts/install_services.sh
+++ b/scripts/install_services.sh
@@ -67,6 +67,7 @@ create_necessary_dirs() {
sudo -u ${USER} ln -fs $my_dir/exclude_species_list.txt $my_dir/scripts
sudo -u ${USER} ln -fs $my_dir/include_species_list.txt $my_dir/scripts
sudo -u ${USER} ln -fs $my_dir/whitelist_species_list.txt $my_dir/scripts
+ sudo -u ${USER} ln -fs $my_dir/confirmed_species_list.txt $my_dir/scripts
sudo -u ${USER} ln -fs $my_dir/homepage/* ${EXTRACTED}
sudo -u ${USER} ln -fs $my_dir/model/labels.txt ${my_dir}/scripts
sudo -u ${USER} ln -fs $my_dir/scripts ${EXTRACTED}
diff --git a/scripts/overview.php b/scripts/overview.php
index 29bfee391..95b5174ea 100644
--- a/scripts/overview.php
+++ b/scripts/overview.php
@@ -250,6 +250,49 @@ function get_chart_data($db, $force_regen = false) {
die();
}
+if(isset($_GET['ajax_center_chart']) && $_GET['ajax_center_chart'] == "true") {
+
+$statement = $db->prepare('SELECT COUNT(*) FROM detections');
+ensure_db_ok($statement);
+$result = $statement->execute();
+$totalcount = $result->fetchArray(SQLITE3_ASSOC);
+
+$statement3 = $db->prepare('SELECT COUNT(*) FROM detections WHERE Date == Date(\'now\', \'localtime\') AND TIME >= TIME(\'now\', \'localtime\', \'-1 hour\')');
+ensure_db_ok($statement3);
+$result3 = $statement3->execute();
+$hourcount = $result3->fetchArray(SQLITE3_ASSOC);
+
+$statement5 = $db->prepare('SELECT COUNT(DISTINCT(Com_Name)) FROM detections WHERE Date == Date(\'now\',\'localtime\')');
+ensure_db_ok($statement5);
+$result5 = $statement5->execute();
+$speciestally = $result5->fetchArray(SQLITE3_ASSOC);
+
+$statement6 = $db->prepare('SELECT COUNT(DISTINCT(Com_Name)) FROM detections');
+ensure_db_ok($statement6);
+$result6 = $statement6->execute();
+$totalspeciestally = $result6->fetchArray(SQLITE3_ASSOC);
+
+?>
+
+
+
new species detected today!+ 5): ?> +
busyTimeout(1000);
+$confirmedspecies_filename = $home."/BirdNET-Pi/scripts/confirmed_species_list.txt";
+if (!file_exists($confirmedspecies_filename) || filesize($confirmedspecies_filename) == 0) {
+ file_put_contents($confirmedspecies_filename, "# List of confirmed species\n");
+}
+$fp = @fopen($confirmedspecies_filename, 'r');
+if ($fp) {
+ $confirmed_species = explode("\n", fread($fp, filesize($confirmedspecies_filename)));
+} else {
+ $confirmed_species = [];
+}
+
if(isset($_GET['deletefile'])) {
ensure_authenticated('You must be authenticated to delete files.');
if (preg_match('~^.*(\.\.\/).+$~', $_GET['deletefile'])) {
@@ -68,6 +80,25 @@
}
}
+if(isset($_GET['confirmspecies'])) {
+ if(isset($_GET['confirm_add'])) {
+ $myfile = fopen($home."/BirdNET-Pi/scripts/confirmed_species_list.txt", "a") or die("Unable to open file!");
+ $txt = $_GET['confirmspecies'];
+ fwrite($myfile, $txt."\n");
+ fclose($myfile);
+ echo "OK";
+ die();
+ } else {
+ $search = $_GET['confirmspecies'];
+ $lines = array_filter($confirmed_species, function($line) use ($search) {
+ return stripos($line, $search) === false;
+ });
+ file_put_contents($home."/BirdNET-Pi/scripts/confirmed_species_list.txt", implode("\n", $lines));
+ echo "OK";
+ die();
+ }
+}
+
if(isset($_GET['getlabels'])) {
$labels = file('./scripts/labels.txt', FILE_IGNORE_NEW_LINES);
echo json_encode($labels);
@@ -137,11 +168,11 @@
session_start();
$_SESSION['date'] = $date;
if(isset($_GET['sort']) && $_GET['sort'] == "occurrences") {
- $statement = $db->prepare("SELECT DISTINCT(Com_Name) FROM detections WHERE Date == \"$date\" GROUP BY Com_Name ORDER BY COUNT(*) DESC");
+ $statement = $db->prepare("SELECT DISTINCT(Com_Name), Sci_Name FROM detections WHERE Date == \"$date\" GROUP BY Com_Name ORDER BY COUNT(Com_Name) DESC");
} elseif(isset($_GET['sort']) && $_GET['sort'] == "confidence") {
$statement = $db->prepare("SELECT Com_Name, Sci_Name, MAX(Confidence) as MaxConfidence FROM detections WHERE Date == \"$date\" GROUP BY Com_Name ORDER BY MaxConfidence DESC");
} else {
- $statement = $db->prepare("SELECT DISTINCT(Com_Name) FROM detections WHERE Date == \"$date\" ORDER BY Com_Name");
+ $statement = $db->prepare("SELECT DISTINCT(Com_Name), Sci_Name FROM detections WHERE Date == \"$date\" ORDER BY Com_Name");
}
ensure_db_ok($statement);
$result = $statement->execute();
@@ -150,11 +181,11 @@
#By Species
} elseif(isset($_GET['byspecies'])) {
if(isset($_GET['sort']) && $_GET['sort'] == "occurrences") {
- $statement = $db->prepare('SELECT DISTINCT(Com_Name) FROM detections GROUP BY Com_Name ORDER BY COUNT(*) DESC');
+ $statement = $db->prepare('SELECT DISTINCT(Com_Name), Sci_Name FROM detections GROUP BY Com_Name ORDER BY COUNT(Com_Name) DESC');
} elseif(isset($_GET['sort']) && $_GET['sort'] == "confidence") {
$statement = $db->prepare('SELECT Com_Name, Sci_Name, MAX(Confidence) as MaxConfidence FROM detections GROUP BY Com_Name ORDER BY MaxConfidence DESC');
} else {
- $statement = $db->prepare('SELECT DISTINCT(Com_Name) FROM detections ORDER BY Com_Name ASC');
+ $statement = $db->prepare('SELECT DISTINCT(Com_Name), Sci_Name FROM detections ORDER BY Com_Name ASC');
}
session_start();
ensure_db_ok($statement);
@@ -210,6 +241,22 @@ function deleteDetection(filename,copylink=false) {
}
}
+function confirmspecies(species, type) {
+ const xhttp = new XMLHttpRequest();
+ xhttp.onload = function() {
+ if(this.responseText == "OK"){
+ location.reload();
+ }
+ }
+ if(type == "add") {
+ xhttp.open("GET", "play.php?confirmspecies="+species+"&confirm_add=true", true);
+ } else {
+ xhttp.open("GET", "play.php?confirmspecies="+species+"&confirm_del=true", true);
+ }
+ xhttp.send();
+ elem.setAttribute("src","images/spinner.gif");
+}
+
function toggleLock(filename, type, elem) {
const xhttp = new XMLHttpRequest();
xhttp.onload = function() {
@@ -416,8 +463,16 @@ function changeDetection(filename,copylink=false) {
-
+
+ + @@ -436,13 +491,18 @@ function changeDetection(filename,copylink=false) { #By Species } elseif($view == "byspecies") { $birds = array(); + $birds_sciname_name = array(); $confidence = array(); while($results=$result->fetchArray(SQLITE3_ASSOC)) { + if(isset($_GET['only_confirmed']) && in_array(str_replace("'", "", $results['Sci_Name'] . "_" . $results['Com_Name']), $confirmed_species)) { + continue; + } $name = $results['Com_Name']; $birds[] = $name; + $birds_sciname_name[] = $results['Sci_Name'] . "_" . $name; if ($_GET['sort'] == "confidence") { - $confidence[] = ' (' . round($results['MaxConfidence'] * 100) . '%)'; + $confidence[] = ' (' . round($results['MaxConfidence'] * 100) . '%)'; } } @@ -462,7 +522,13 @@ function changeDetection(filename,copylink=false) { if ($index < count($birds)) { ?>
-
+
|
fetchArray(SQLITE3_ASSOC))
{
$name = $results['Com_Name'];
$dir_name = str_replace("'", '', $name);
+ if(isset($_GET['only_confirmed']) && in_array(str_replace("'", "", $results['Sci_Name'] . "_" . $results['Com_Name']), $confirmed_species)) {
+ continue;
+ }
if(realpath($home."/BirdSongs/Extracted/By_Date/".$date."/".str_replace(" ", "_", $dir_name)) !== false){
$birds[] = $name;
+ $birds_sciname_name[] = $results['Sci_Name'] . "_" . $name;
if ($_GET['sort'] == "confidence") {
$confidence[] = ' (' . round($results['MaxConfidence'] * 100) . '%)';
}
@@ -503,7 +574,13 @@ function changeDetection(filename,copylink=false) {
if ($index < count($birds)) {
?>
-
+
|
|