diff --git a/scripts/birdnet_recording.sh b/scripts/birdnet_recording.sh index 92cc6f0..0798511 100755 --- a/scripts/birdnet_recording.sh +++ b/scripts/birdnet_recording.sh @@ -10,6 +10,8 @@ fi [ -z $RECORDING_LENGTH ] && RECORDING_LENGTH=15 +if ! pulseaudio --check;then pulseaudio --start;fi + if pgrep arecord &> /dev/null ;then echo "Recording" else diff --git a/scripts/plotly_streamlit.py b/scripts/plotly_streamlit.py index 3ea1b62..507d872 100755 --- a/scripts/plotly_streamlit.py +++ b/scripts/plotly_streamlit.py @@ -5,53 +5,82 @@ import numpy as np import plotly.graph_objects as go from plotly.subplots import make_subplots from datetime import timedelta, datetime +from pathlib import Path import sqlite3 from sqlite3 import Connection -from pathlib import Path URI_SQLITE_DB = "/home/pi/BirdNET-Pi/scripts/birds.db" - st.set_page_config(layout='wide') -@st.cache(ttl=60,hash_funcs={Connection: id}) -def get_connection(path: str): - """Put the connection in cache to reuse if path does not change between Streamlit reruns. - NB : https://stackoverflow.com/questions/48218065/programmingerror-sqlite-objects-created-in-a-thread-can-only-be-used-in-that-sa - """ - return sqlite3.connect(path, check_same_thread=False) +# Remove whitespace from the top of the page and sidebar +st.markdown(""" + + """, unsafe_allow_html=True) + +col1,col2,col3 = st.columns([20,20,20]) + +col1.title('BirdNET-Pi', anchor=None) +col2.image('/home/pi/BirdNET-Pi/homepage/images/bird.png') +col3.text('') + + +@st.cache(hash_funcs={Connection: id}) +def get_connection(path:str): + return sqlite3.connect(path,check_same_thread=False) + + + + +# def load_data(): +# df1 = pd.read_csv('/home/pi/BirdNET-Pi/BirdDB.txt', sep=';') +# return df1 def get_data(conn: Connection): - df1 = pd.read_sql("SELECT * FROM detections", con=conn) + df1=pd.read_sql("SELECT * FROM detections", con=conn) return df1 conn = get_connection(URI_SQLITE_DB) - -#@st.cache() -#def load_data(): -# df1 = pd.read_csv('/home/pi/BirdNET-Pi/BirdDB.txt', sep=';') -# return df1 - - # Read in the cereal data -#df = load_data() -df = get_data(conn) +# df = load_data() +df=get_data(conn) df2=df.copy() df2['DateTime']=pd.to_datetime(df2['Date'] + " " + df2['Time']) df2=df2.set_index('DateTime') + + # Filter on date range # Date as calendars -#Start_Date1 = pd.to_datetime(st.sidebar.date_input('Which date do you want to start?', value = df2.index.min())) -#End_Date1 = pd.to_datetime(st.sidebar.date_input('Which date do you want to end?', value = df2.index.max())) +# Start_Date = pd.to_datetime(st.sidebar.date_input('Which date do you want to start?', value = df2.index.min())) +# End_Date = pd.to_datetime(st.sidebar.date_input('Which date do you want to end?', value = df2.index.max())) + # Date as slider -Start_Date = pd.to_datetime(df2.index.min()) -End_Date = pd.to_datetime(df2.index.max()) -Date_Slider = st.sidebar.slider('Date Range', - value=(Start_Date.to_pydatetime(), - End_Date.to_pydatetime()) +Start_Date = pd.to_datetime(df2.index.min()).date() +End_Date = pd.to_datetime(df2.index.max()).date() +Date_Slider = st.slider('Date Range', + min_value = Start_Date-timedelta(days=1), + max_value = End_Date, + value=(Start_Date, + End_Date) ) -filt = (df2.index >= Date_Slider[0]) & (df2.index <= Date_Slider[1]+timedelta(days=1)) + + + +filt = (df2.index >= pd.Timestamp(Date_Slider[0])) & (df2.index <= pd.Timestamp(Date_Slider[1]+timedelta(days=1))) df2 = df2[filt] #Create species count for selected date range @@ -61,22 +90,24 @@ Specie_Count=df2['Com_Name'].value_counts() #Create species treemap # Create Hourly Crosstab - - hourly=pd.crosstab(df2['Com_Name'],df2.index.hour, dropna=False) # Filter on species species = list(hourly.index) -top_N = st.sidebar.select_slider( +cols1,cols2= st.columns((1,1)) +top_N = cols1.slider( 'Select Number of Birds to Show', - list(range(1,len(Specie_Count))), - value=min(10,len(Specie_Count)-1)) + min_value = 1, + value=min(10,len(Specie_Count)) + ) top_N_species = (df2['Com_Name'].value_counts()[:top_N]) -specie = st.sidebar.selectbox('Which bird would you like to explore?', species, index=species.index(list(top_N_species.index)[0])) +specie = cols2.selectbox('Which bird would you like to explore for the dates '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+'?', species, + index=species.index(list(top_N_species.index)[0])) + font_size=15 @@ -87,60 +118,67 @@ filt=df2['Com_Name']==specie df_counts=df2[filt].resample('D').count() fig = make_subplots( - rows=2, cols =2, - specs= [[{"type":"xy","rowspan":2}, {"type":"polar"}], [None, {"type":"xy"}]], - subplot_titles=('Species in Date Range', - ''+specie+'
' - 'Total Detections: '+str('{:,}'.format(sum(df_counts.Time)))+'
' - 'Max Confidence: '+str('{:.2f}%'.format(max(df2[df2['Com_Name']==specie]['Confidence'])*100))+'
' - 'Median Confidence: '+str('{:.2f}%'.format(np.median(df2[df2['Com_Name']==specie]['Confidence'])*100))+'' - + rows=3, cols =2, + specs= [[{"type":"xy","rowspan":3}, {"type":"polar","rowspan":2}], [{"rowspan":1}, {"rowspan":1} ], [None, {"type":"xy","rowspan":1}]], + subplot_titles=('Top '+ str(top_N) + ' Species in Date Range '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+'', + 'Total Detect:'+str('{:,}'.format(sum(df_counts.Time)))+ + ' Confidence Max:'+str('{:.2f}%'.format(max(df2[df2['Com_Name']==specie]['Confidence'])*100))+ + ' '+' Median:'+str('{:.2f}%'.format(np.median(df2[df2['Com_Name']==specie]['Confidence'])*100)) ) ) -# fig.layout.height=900 -# fig.layout.width=1500 +fig.layout.annotations[1].update(x=0.7,y=0.25, font_size=15) #Plot seen species for selected date range and number of species fig.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1,col=1) fig.update_layout( - margin=dict(l=0, r=50, t=70, b=0), + margin=dict(l=0, r=0, t=50, b=0), yaxis={'categoryorder':'total ascending'}) # Set 360 degrees, 24 hours for polar plot theta = np.linspace(0.0, 360, 24, endpoint=False) d=pd.DataFrame(np.zeros((23,1))).squeeze() -radius = hourly.loc[specie] -radius=(d+radius).fillna(0) -fig.add_trace(go.Barpolar(r = radius, theta=theta), row=1, col=2) +detections = hourly.loc[specie] +detections=(d+detections).fillna(0) +fig.add_trace(go.Barpolar(r = detections, theta=theta), row=1, col=2) fig.update_layout( - autosize=True, + autosize=False, width = 1000, - height = 750, + height = 500, showlegend=False, polar = dict( radialaxis = dict( tickfont_size = font_size, - showticklabels = False), + showticklabels = True, + hoverformat = "#%{theta}:
Popularity: %{percent}
%{r}" + ), angularaxis = dict( tickfont_size= font_size, rotation = -90, direction = 'clockwise', tickmode='array', - tickvals=[0,45,90,135,180,225,270,315], - ticktext=['12am','3am', '6am','9am','12pm','3pm', '6pm','9pm'], - hoverformat = ""#"%{theta}:
Popularity: %{percent}
%{r}" + tickvals=[0,15,35,45,60,75,90,105,120,135,150,165,180,195,210,225,240,255,270,285,300,315,330,345], + ticktext=['12am','1am','2am','3am','4am','5am', '6am','7am','8am','9am','10am','11am','12pm','1pm','2pm','3pm','4pm','5pm', '6pm','7pm','8pm','9pm','10pm','11pm'], + hoverformat = "#%{theta}:
Popularity: %{percent}
%{r}" ), ), ) -fig.layout.annotations[1].update(x=0.775,y=0.4, font_size=15) -x=df_counts.index -y=df_counts['Com_Name'] -fig.add_trace(go.Bar(x=df_counts.index,y=df_counts['Time']), row=2, col=2) +daily=pd.crosstab(df2['Com_Name'],df2.index.date, dropna=False) -container=st.container() -container.plotly_chart(fig, use_container_width=True) +fig.add_trace(go.Bar(x=daily.columns, y=daily.loc[specie]), row=3, col=2) + +# container=st.container() +# config={'displayModelBar': False} +st.plotly_chart(fig, use_container_width=True) #, config=config) + +# cols3,cols4=st.columns((1,1)) +# +# extract_date=Date_Slider +# +# audio_file = open('/home/pi/BirdSongs/Extracted/By_Date/2022-03-22/Yellow-streaked_Greenbul/Yellow-streaked_Greenbul-77-2022-03-22-birdnet-15:04:28.mp3', 'rb') +# audio_bytes = audio_file.read() +# cols4.audio(audio_bytes, format='audio/mp3') \ No newline at end of file