claudecode/AvianVisitors
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Merge branch 'main' of github.com:mcguirepr89/BirdNET-Pi

Browse Source
This commit is contained in:
mcguirepr89
2022-05-24 16:09:34 -04:00
parent 3b8cfe9afb 198c0a93c5
commit 4bb5256d66
10 changed files with 467 additions and 306 deletions
Download Patch File Download Diff File Expand all files Collapse all files
scripts/analyze.py
+56 -15
View File
@@ -11,6 +11,7 @@ ADDR = (SERVER, PORT)
client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client.connect(ADDR)
def send(msg):
message = msg.encode(FORMAT)
msg_length = len(message)
@@ -20,6 +21,7 @@ def send(msg):
client.send(message)
print(client.recv(2048).decode(FORMAT))
def main():
global INCLUDE_LIST
@@ -28,16 +30,52 @@ def main():
# Parse passed arguments
parser = argparse.ArgumentParser()
parser.add_argument('--i', help='Path to input file.')
parser.add_argument('--o', default='result.csv', help='Path to output file. Defaults to result.csv.')
parser.add_argument('--lat', type=float, default=-1, help='Recording location latitude. Set -1 to ignore.')
parser.add_argument('--lon', type=float, default=-1, help='Recording location longitude. Set -1 to ignore.')
parser.add_argument('--week', type=int, default=-1, help='Week of the year when the recording was made. Values in [1, 48] (4 weeks per month). Set -1 to ignore.')
parser.add_argument('--overlap', type=float, default=0.0, help='Overlap in seconds between extracted spectrograms. Values in [0.0, 2.9]. Defaults tp 0.0.')
parser.add_argument('--sensitivity', type=float, default=1.0, help='Detection sensitivity; Higher values result in higher sensitivity. Values in [0.5, 1.5]. Defaults to 1.0.')
parser.add_argument('--min_conf', type=float, default=0.1, help='Minimum confidence threshold. Values in [0.01, 0.99]. Defaults to 0.1.')
parser.add_argument('--include_list', default='null', help='Path to text file containing a list of included species. Not used if not provided.')
parser.add_argument('--exclude_list', default='null', help='Path to text file containing a list of excluded species. Not used if not provided.')
parser.add_argument('--birdweather_id', default='99999', help='Private Station ID for BirdWeather.')
parser.add_argument(
'--o',
default='result.csv',
help='Path to output file. Defaults to result.csv.')
parser.add_argument(
'--lat',
type=float,
default=-1,
help='Recording location latitude. Set -1 to ignore.')
parser.add_argument(
'--lon',
type=float,
default=-1,
help='Recording location longitude. Set -1 to ignore.')
parser.add_argument(
'--week',
type=int,
default=-1,
help='Week of the year when the recording was made. Values in [1, 48] (4 weeks per month). Set -1 to ignore.')
parser.add_argument(
'--overlap',
type=float,
default=0.0,
help='Overlap in seconds between extracted spectrograms. Values in [0.0, 2.9]. Defaults tp 0.0.')
parser.add_argument(
'--sensitivity',
type=float,
default=1.0,
help='Detection sensitivity; Higher values result in higher sensitivity. Values in [0.5, 1.5]. Defaults to 1.0.')
parser.add_argument(
'--min_conf',
type=float,
default=0.1,
help='Minimum confidence threshold. Values in [0.01, 0.99]. Defaults to 0.1.')
parser.add_argument(
'--include_list',
default='null',
help='Path to text file containing a list of included species. Not used if not provided.')
parser.add_argument(
'--exclude_list',
default='null',
help='Path to text file containing a list of excluded species. Not used if not provided.')
parser.add_argument(
'--birdweather_id',
default='99999',
help='Private Station ID for BirdWeather.')
args = parser.parse_args()
@@ -64,14 +102,15 @@ def main():
sockParams += 'lat=' + str(args.lat) + '||'
if args.lon:
sockParams += 'lon=' + str(args.lon) + '||'
send(sockParams)
send(DISCONNECT_MESSAGE)
#time.sleep(3)
# time.sleep(3)
###############################################################################
###############################################################################
###############################################################################
###############################################################################
if __name__ == '__main__':
@@ -79,4 +118,6 @@ if __name__ == '__main__':
# Example calls
# python3 analyze.py --i 'example/XC558716 - Soundscape.mp3' --lat 35.4244 --lon -120.7463 --week 18
# python3 analyze.py --i 'example/XC563936 - Soundscape.mp3' --lat 47.6766 --lon -122.294 --week 11 --overlap 1.5 --min_conf 0.25 --sensitivity 1.25 --custom_list 'example/custom_species_list.txt'
# python3 analyze.py --i 'example/XC563936 - Soundscape.mp3' --lat 47.6766
# --lon -122.294 --week 11 --overlap 1.5 --min_conf 0.25 --sensitivity
# 1.25 --custom_list 'example/custom_species_list.txt'
scripts/daily_plot.py
+87 -66
View File
@@ -1,6 +1,5 @@
import sqlite3
import os
import configparser
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
@@ -18,84 +17,95 @@ cursor.execute('SELECT * FROM detections WHERE Date = DATE(\'now\', \'localtime\
table_rows = cursor.fetchall()
#df=pd.DataFrame(table_rows)
# df=pd.DataFrame(table_rows)
#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')
# 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
# Add round hours to dataframe
df['Hour of Day'] = [r.hour for r in df.Time]
#Create separate dataframes for separate locations
df_plt=df #Default to use the whole Dbase
# Create separate dataframes for separate locations
df_plt = df # Default to use the whole Dbase
# Add every font at the specified location
font_dir = [userDir+'/BirdNET-Pi/homepage/static']
font_dir = [userDir + '/BirdNET-Pi/homepage/static']
for font in font_manager.findSystemFonts(font_dir):
font_manager.fontManager.addfont(font)
# Set font family globally
rcParams['font.family'] = 'Roboto Flex'
#Get todays readings
# Get todays readings
now = datetime.now()
df_plt_today = df_plt[df_plt['Date']==now.strftime("%Y-%m-%d")]
df_plt_today = df_plt[df_plt['Date'] == now.strftime("%Y-%m-%d")]
#Set number of species to report
readings=10
# Set number of species to report
readings = 10
plt_top10_today = (df_plt_today['Com_Name'].value_counts()[:readings])
df_plt_top10_today = df_plt_today[df_plt_today.Com_Name.isin(plt_top10_today.index)]
#Set Palette for graphics
# Set Palette for graphics
pal = "Greens"
#Set up plot axes and titles
f, axs = plt.subplots(1, 2, figsize = (10, 4), gridspec_kw=dict(width_ratios=[3, 6]), facecolor='#77C487')
# Set up plot axes and titles
f, axs = plt.subplots(1, 2, figsize=(10, 4), gridspec_kw=dict(width_ratios=[3, 6]), facecolor='#77C487')
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0, hspace=0)
#generate y-axis order for all figures based on frequency
# generate y-axis order for all figures based on frequency
freq_order = pd.value_counts(df_plt_top10_today['Com_Name']).iloc[:readings].index
#make color for max confidence --> this groups by name and calculates max conf
# make color for max confidence --> this groups by name and calculates max conf
confmax = df_plt_top10_today.groupby('Com_Name')['Confidence'].max()
#reorder confmax to detection frequency order
# 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())
colors = plt.cm.Greens(norm(confmax))
#Generate frequency plot
plot=sns.countplot(y='Com_Name', data = df_plt_top10_today, palette = colors, order=freq_order, ax=axs[0])
# Generate frequency plot
plot = sns.countplot(y='Com_Name', data=df_plt_top10_today, palette=colors, order=freq_order, ax=axs[0])
#Try plot grid lines between bars - problem at the moment plots grid lines on bars - want between bars
z=plot.get_ymajorticklabels()
plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(),15)) for ticklabel in plot.get_yticklabels()], fontsize = 10)
# Try plot grid lines between bars - problem at the moment plots grid lines on bars - want between bars
z = plot.get_ymajorticklabels()
plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(), 15)) for ticklabel in plot.get_yticklabels()], fontsize=10)
plot.set(ylabel=None)
plot.set(xlabel="Detections")
#Generate crosstab matrix for heatmap plot
# Generate crosstab matrix for heatmap plot
heat = pd.crosstab(df_plt_top10_today['Com_Name'],df_plt_top10_today['Hour of Day'])
#Order heatmap Birds by frequency of occurrance
heat.index = pd.CategoricalIndex(heat.index, categories = freq_order)
heat = pd.crosstab(df_plt_top10_today['Com_Name'], df_plt_top10_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)
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)
#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)
plot.set_xticklabels(plot.get_xticklabels(), rotation = 0, size = 7)
# 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)
plot.set_xticklabels(plot.get_xticklabels(), rotation=0, size=7)
# Set heatmap border
for _, spine in plot.spines.items():
@@ -103,13 +113,13 @@ for _, spine in plot.spines.items():
plot.set(ylabel=None)
plot.set(xlabel="Hour of Day")
#Set combined plot layout and titles
# Set combined plot layout and titles
f.subplots_adjust(top=0.9)
plt.suptitle("Top 10 Last Updated: "+ str(now.strftime("%Y-%m-%d %H:%M")))
plt.suptitle("Top 10 Last Updated: " + str(now.strftime("%Y-%m-%d %H:%M")))
#Save combined plot
# Save combined plot
userDir = os.path.expanduser('~')
savename=userDir + '/BirdSongs/Extracted/Charts/Combo-'+str(now.strftime("%Y-%m-%d"))+'.png'
savename = userDir + '/BirdSongs/Extracted/Charts/Combo-' + str(now.strftime("%Y-%m-%d")) + '.png'
plt.savefig(savename)
plt.show()
plt.close()
@@ -119,18 +129,18 @@ plt.close()
plt_Bot10_today = (df_plt_today['Com_Name'].value_counts()[-readings:])
df_plt_Bot10_today = df_plt_today[df_plt_today.Com_Name.isin(plt_Bot10_today.index)]
#Set Palette for graphics
# Set Palette for graphics
pal = "Reds"
#Set up plot axes and titles
# Set up plot axes and titles
f, axs = plt.subplots(1, 2, figsize = (10, 4), gridspec_kw=dict(width_ratios=[3, 6]), facecolor='#77C487')
f, axs = plt.subplots(1, 2, figsize=(10, 4), gridspec_kw=dict(width_ratios=[3, 6]), facecolor='#77C487')
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0, hspace=0)
#generate y-axis order for all figures based on frequency
# generate y-axis order for all figures based on frequency
freq_order = pd.value_counts(df_plt_Bot10_today['Com_Name']).iloc[-readings:].index
#make color for max confidence --> this groups by name and calculates max conf
# make color for max confidence --> this groups by name and calculates max conf
confmax = df_plt_Bot10_today.groupby('Com_Name')['Confidence'].max()
confmax = confmax.reindex(freq_order)
# probably wrong order . . . how to sort by no. of detections ?
@@ -138,33 +148,44 @@ confmax = confmax.reindex(freq_order)
norm = plt.Normalize(confmax.values.min(), confmax.values.max())
colors = plt.cm.Reds(norm(confmax))
#Generate frequency plot
plot=sns.countplot(y='Com_Name', data = df_plt_Bot10_today, palette = colors, order=freq_order, ax=axs[0])
# Generate frequency plot
plot = sns.countplot(y='Com_Name', data=df_plt_Bot10_today, palette=colors, order=freq_order, ax=axs[0])
#Try plot grid lines between bars - problem at the moment plots grid lines on bars - want between bars
z=plot.get_ymajorticklabels()
plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(),15)) for ticklabel in plot.get_yticklabels()], fontsize = 10)
# Try plot grid lines between bars - problem at the moment plots grid lines on bars - want between bars
z = plot.get_ymajorticklabels()
plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(), 15)) for ticklabel in plot.get_yticklabels()], fontsize=10)
plot.set(ylabel=None)
plot.set(xlabel="Detections")
#Generate crosstab matrix for heatmap plot
# Generate crosstab matrix for heatmap plot
heat = pd.crosstab(df_plt_Bot10_today['Com_Name'],df_plt_Bot10_today['Hour of Day'])
#Order heatmap Birds by frequency of occurrance
heat.index = pd.CategoricalIndex(heat.index, categories = freq_order)
heat = pd.crosstab(df_plt_Bot10_today['Com_Name'], df_plt_Bot10_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)
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)
#Generatie heatmap plot
plot = sns.heatmap(heat, norm=LogNorm(), annot=True, fmt="g", annot_kws={"fontsize":7}, cmap = pal , square = False, cbar=False, linewidths = 0.5, linecolor = "Grey", ax=axs[1], yticklabels = False)
plot.set_xticklabels(plot.get_xticklabels(), rotation = 0, size = 7)
# Generatie heatmap plot
plot = sns.heatmap(
heat,
norm=LogNorm(),
annot=True,
fmt="g",
annot_kws={
"fontsize": 7},
cmap=pal,
square=False,
cbar=False,
linewidths=0.5,
linecolor="Grey",
ax=axs[1],
yticklabels=False)
plot.set_xticklabels(plot.get_xticklabels(), rotation=0, size=7)
# Set heatmap border
for _, spine in plot.spines.items():
@@ -172,12 +193,12 @@ for _, spine in plot.spines.items():
plot.set(ylabel=None)
plot.set(xlabel="Hour of Day")
#Set combined plot layout and titles
# Set combined plot layout and titles
f.subplots_adjust(top=0.9)
plt.suptitle("Bottom 10 Last Updated: "+ str(now.strftime("%Y-%m-%d %H:%M")))
plt.suptitle("Bottom 10 Last Updated: " + str(now.strftime("%Y-%m-%d %H:%M")))
#Save combined plot
savename=userDir + '/BirdSongs/Extracted/Charts/Combo2-'+str(now.strftime("%Y-%m-%d"))+'.png'
# Save combined plot
savename = userDir + '/BirdSongs/Extracted/Charts/Combo2-' + str(now.strftime("%Y-%m-%d")) + '.png'
plt.savefig(savename)
plt.show()
plt.close()
scripts/plotly_streamlit.py
+114 -105
View File
@@ -4,8 +4,7 @@ import pandas as pd
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
from datetime import timedelta
import sqlite3
from sqlite3 import Connection
import plotly.express as px
@@ -35,22 +34,22 @@ st.markdown("""
@st.cache(hash_funcs={Connection: id})
def get_connection(path:str):
return sqlite3.connect(path,check_same_thread=False)
def get_connection(path: str):
return sqlite3.connect(path, check_same_thread=False)
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)
# Read in the cereal data
# df = load_data()
df=get_data(conn)
df2=df.copy()
df2['DateTime']=pd.to_datetime(df2['Date'] + " " + df2['Time'])
df2=df2.set_index('DateTime')
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
@@ -60,174 +59,184 @@ df2=df2.set_index('DateTime')
# Date as slider
Start_Date = pd.to_datetime(df2.index.min()).date()
End_Date = pd.to_datetime(df2.index.max()).date()
cols1,cols2= st.columns((1,1))
End_Date = pd.to_datetime(df2.index.max()).date()
cols1, cols2 = st.columns((1, 1))
Date_Slider = cols1.slider('Date Range',
min_value = Start_Date-timedelta(days=1),
max_value = End_Date,
value=(Start_Date,
End_Date)
)
min_value=Start_Date - timedelta(days=1),
max_value=End_Date,
value=(Start_Date,
End_Date)
)
filt = (df2.index >= pd.Timestamp(Date_Slider[0])) & (df2.index <= pd.Timestamp(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]
st.write('<style>div.row-widget.stRadio > div{flex-direction:row;justify-content: left;} </style>', unsafe_allow_html=True)
st.write('<style>div.st-bf{flex-direction:column;} div.st-ag{font-weight:bold;padding-left:2px;}</style>', unsafe_allow_html=True)
resample_sel=cols2.radio("Select Resample Resolution - To downsample and make run faster select longer period, Daily provides a view on detections at 15 min intervals through the day", ('1 minute', '5 minutes', '10 minutes', 'Hourly', 'Daily'))
resample_sel = cols2.radio(
'''
Select Resample Resolution - To downsample and make run faster select longer period,
Daily provides a view on detections at 15 min intervals through the day
''',
('1 minute',
'5 minutes',
'10 minutes',
'Hourly',
'Daily'))
resample_times = {'1 minute':'1min',
'5 minutes':'5min',
'10 minutes':'10min',
'Hourly':'1H',
'Daily':'1D'
resample_times = {'1 minute': '1min',
'5 minutes': '5min',
'10 minutes': '10min',
'Hourly': '1H',
'Daily': '1D'
}
resample_time = resample_times[resample_sel]
df5=df2.resample(resample_time)['Com_Name'].aggregate('unique').explode()
df5 = df2.resample(resample_time)['Com_Name'].aggregate('unique').explode()
#Create species count for selected date range
# Create species count for selected date range
Specie_Count=df5.value_counts()
Specie_Count = df5.value_counts()
#Create species treemap
# Create species treemap
# Create Hourly Crosstab
hourly=pd.crosstab(df5,df5.index.hour, dropna=False)
hourly = pd.crosstab(df5, df5.index.hour, dropna=False)
# Filter on species
species = list(hourly.index)
cols1,cols2= st.columns((1,1))
cols1, cols2 = st.columns((1, 1))
top_N = cols1.slider(
'Select Number of Birds to Show',
min_value = 1,
value=min(10,len(Specie_Count))
)
min_value=1,
value=min(10, len(Specie_Count))
)
top_N_species = (df5.value_counts()[:top_N])
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]))
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
font_size = 15
#specie filter
filt=df2['Com_Name']==specie
df_counts=sum(df5==specie)
# specie filter
filt = df2['Com_Name'] == specie
df_counts = sum(df5 == specie)
if resample_time != '1D':
fig = make_subplots(
rows=3, cols =2,
specs= [[{"type":"xy","rowspan":3}, {"type":"polar","rowspan":2}], [{"rowspan":1}, {"rowspan":1} ], [None, {"type":"xy","rowspan":1}]],
subplot_titles=('<b>Top '+ str(top_N) + ' Species in Date Range '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+' for '+str(resample_sel)+' sampling interval.'+'</b>',
'Total Detect:'+str('{:,}'.format(df_counts))+
' 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))
)
rows=3, cols=2,
specs=[[{"type": "xy", "rowspan": 3}, {"type": "polar", "rowspan": 2}], [{"rowspan": 1}, {"rowspan": 1}], [None, {"type": "xy", "rowspan": 1}]],
subplot_titles=('<b>Top ' +
str(top_N) +
' Species in Date Range ' +
str(Date_Slider[0]) +
' to ' +
str(Date_Slider[1]) +
' for ' +
str(resample_sel) +
' sampling interval.' +
'</b>',
'Total Detect:' + str('{:,}'.format(df_counts)) +
' 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.annotations[1].update(x=0.7,y=0.25, font_size=15)
)
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)
# 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=0, t=50, b=0),
yaxis={'categoryorder':'total ascending'})
yaxis={'categoryorder': 'total ascending'})
# Set 360 degrees, 24 hours for polar plot
theta = np.linspace(0.0, 360, 24, endpoint=False)
specie_filt= df5==specie
df3=df5[specie_filt]
specie_filt = df5 == specie
df3 = df5[specie_filt]
detections2= pd.crosstab(df3, df3.index.hour)
detections2 = pd.crosstab(df3, df3.index.hour)
d=pd.DataFrame(np.zeros((23,1))).squeeze()
d = pd.DataFrame(np.zeros((23, 1))).squeeze()
detections = hourly.loc[specie]
detections=(d+detections).fillna(0)
fig.add_trace(go.Barpolar(r = detections, theta=theta), row=1, col=2)
detections = (d + detections).fillna(0)
fig.add_trace(go.Barpolar(r=detections, theta=theta), row=1, col=2)
fig.update_layout(
autosize=False,
width = 1000,
height = 500,
width=1000,
height=500,
showlegend=False,
polar = dict(
radialaxis = dict(
tickfont_size = font_size,
showticklabels = False,
hoverformat = "#%{theta}: <br>Popularity: %{percent} </br> %{r}"
),
angularaxis = dict(
tickfont_size= font_size,
rotation = -90,
direction = 'clockwise',
polar=dict(
radialaxis=dict(
tickfont_size=font_size,
showticklabels=False,
hoverformat="#%{theta}: <br>Popularity: %{percent} </br> %{r}"
),
angularaxis=dict(
tickfont_size=font_size,
rotation=-90,
direction='clockwise',
tickmode='array',
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}: <br>Popularity: %{percent} </br> %{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}: <br>Popularity: %{percent} </br> %{r}"
),
),
)
),
)
daily=pd.crosstab(df5,df5.index.date, dropna=False)
daily = pd.crosstab(df5, df5.index.date, dropna=False)
fig.add_trace(go.Bar(x=daily.columns, y=daily.loc[specie]), row=3, col=2)
else:
fig = make_subplots(
rows=1, cols =2,
specs= [[{"type":"xy","rowspan":1},{"type":"xy","rowspan":1}]],
rows=1, cols=2,
specs=[[{"type": "xy", "rowspan": 1}, {"type": "xy", "rowspan": 1}]],
subplot_titles=('<b>Daily Top '+ str(top_N) + ' Species in Date Range '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+'</b>',
'<b>Daily ' + specie+ ' Detections on 15 minute intervals </b>'),
# 'Total Detect:'+str('{:,}'.format(df_counts))+
# ' 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.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1,col=1)
df4=df2['Com_Name'][df2['Com_Name']==specie].resample('15min').count()
df4.index=[df4.index.date, df4.index.time]
day_hour_freq=df4.unstack().fillna(0)
subplot_titles=('<b>Daily Top ' + str(top_N) + ' Species in Date Range ' + str(Date_Slider[0]) + ' to ' + str(Date_Slider[1]) + '</b>',
'<b>Daily ' + specie + ' Detections on 15 minute intervals </b>'),
# 'Total Detect:'+str('{:,}'.format(df_counts))+
# ' 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.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1, col=1)
df4 = df2['Com_Name'][df2['Com_Name'] == specie].resample('15min').count()
df4.index = [df4.index.date, df4.index.time]
day_hour_freq = df4.unstack().fillna(0)
fig_x = [d.strftime('%d-%m-%Y') for d in day_hour_freq.index.tolist()]
fig_y = [h.strftime('%H:%M') for h in day_hour_freq.columns.tolist()]
fig_z = day_hour_freq.values.transpose()
fig_heatmap = go.Figure(data=go.Heatmap(x=fig_x,y=fig_y,z=fig_z))
fig_heatmap = go.Figure(data=go.Heatmap(x=fig_x, y=fig_y, z=fig_z))
fig.update_layout(
margin=dict(l=0, r=0, t=50, b=0),
yaxis={'categoryorder':'total ascending'})
color_pals= px.colors.named_colorscales()
margin=dict(l=0, r=0, t=50, b=0),
yaxis={'categoryorder': 'total ascending'})
color_pals = px.colors.named_colorscales()
selected_pal = cols2.selectbox('Select Color Pallet for Daily Detections', color_pals)
fig.add_trace(go.Heatmap(x=fig_x,y=fig_y,z=fig_z, autocolorscale = False, colorscale = selected_pal), row=1, col=2)
fig.add_trace(go.Heatmap(x=fig_x, y=fig_y, z=fig_z, autocolorscale=False, colorscale=selected_pal), row=1, col=2)
# container=st.container()
# config={'displayModelBar': False}
st.plotly_chart(fig, use_container_width=True) #, config=config)
st.plotly_chart(fig, use_container_width=True) # , config=config)
# cols3,cols4=st.columns((1,1))
#
#
# extract_date=Date_Slider
#
#
# audio_file = open('/home/*/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')
scripts/server.py
+139 -104
View File
@@ -1,4 +1,16 @@
import socket
import apprise
from pathlib import Path
from tzlocal import get_localzone
import datetime
import sqlite3
import requests
import json
import time
import math
import numpy as np
import librosa
import operator
import socket
import threading
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
@@ -6,26 +18,9 @@ os.environ['CUDA_VISIBLE_DEVICES'] = ''
try:
import tflite_runtime.interpreter as tflite
except:
except BaseException:
from tensorflow import lite as tflite
import argparse
import operator
import librosa
import numpy as np
import math
import time
from decimal import Decimal
import json
import requests
import sqlite3
import datetime
from time import sleep
import pytz
from tzlocal import get_localzone
from pathlib import Path
import apprise
HEADER = 64
PORT = 5050
@@ -37,10 +32,9 @@ DISCONNECT_MESSAGE = "!DISCONNECT"
server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
try:
server.bind(ADDR)
except:
except BaseException:
print("Waiting on socket")
time.sleep(5)
# Open most recent Configuration and grab DB_PWD as a python variable
@@ -48,7 +42,7 @@ 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
priv_thresh = float("." + str(str(str([i for i in this_run if i.startswith('PRIVACY_THRESHOLD')]).split('=')[1]).split('\\')[0])) / 10
def loadModel():
@@ -62,7 +56,7 @@ def loadModel():
# 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 = tflite.Interpreter(model_path=modelpath, num_threads=2)
myinterpreter.allocate_tensors()
# Get input and output tensors.
@@ -85,6 +79,7 @@ def loadModel():
return myinterpreter
def loadCustomSpeciesList(path):
slist = []
@@ -95,6 +90,7 @@ def loadCustomSpeciesList(path):
return slist
def splitSignal(sig, rate, overlap, seconds=3.0, minlen=1.5):
# Split signal with overlap
@@ -105,17 +101,18 @@ def splitSignal(sig, rate, overlap, seconds=3.0, minlen=1.5):
# 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)
@@ -130,11 +127,12 @@ def readAudioData(path, overlap, sample_rate=48000):
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
m[2] = math.cos(math.radians(m[2] * 7.5)) + 1
else:
m[2] = -1
@@ -147,9 +145,11 @@ def convertMetadata(m):
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
@@ -166,21 +166,22 @@ 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)
#
# # 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))
human_cutoff = max(10, int(len(p_sorted) * priv_thresh))
for i in range(min(10, len(p_sorted))):
if p_sorted[i][0]=='Human_Human':
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')
rfile.write(str(datetime.datetime.now()) + str(p_sorted[i]) + ' ' + str(human_cutoff) + '\n')
return p_sorted[:human_cutoff]
def analyzeAudioData(chunks, lat, lon, week, sensitivity, overlap,):
global INTERPRETER
@@ -202,29 +203,30 @@ 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
HUMAN_DETECTED = False
# Catch if Human is recognized
for x in range(len(p)):
if "Human" in p[x][0]:
HUMAN_DETECTED=True
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
# If human detected set all detections to human to make sure voices are not saved
if HUMAN_DETECTED is 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):
def sendAppriseNotifications(species, confidence):
if os.path.exists(userDir + '/BirdNET-Pi/apprise.txt') and os.path.getsize(userDir + '/BirdNET-Pi/apprise.txt') > 0:
with open(userDir + '/BirdNET-Pi/scripts/thisrun.txt', 'r') as f:
this_run = f.readlines()
@@ -237,39 +239,40 @@ def sendAppriseNotifications(species,confidence):
config = apprise.AppriseConfig()
config.add(userDir + '/BirdNET-Pi/apprise.txt')
apobj.add(config)
apobj.notify(
body=body.replace("$sciname",species.split("_")[0]).replace("$comname",species.split("_")[1]).replace("$confidence",confidence),
body=body.replace("$sciname", species.split("_")[0]).replace("$comname", species.split("_")[1]).replace("$confidence", confidence),
title=title,
)
if str(str(str([i for i in this_run if i.startswith('APPRISE_NOTIFY_NEW_SPECIES')]).split('=')[1]).split('\\')[0]) == "1":
try:
try:
con = sqlite3.connect(userDir + '/BirdNET-Pi/scripts/birds.db')
con.row_factory = lambda cursor, row: row[0]
cur = con.cursor()
cur.execute("SELECT DISTINCT(Com_Name) FROM detections")
known_species = cur.fetchall()
sciName,comName = species.split("_")
print("\ncomName: ",comName)
print("\nknown_species: ",known_species)
sciName, comName = species.split("_")
print("\ncomName: ", comName)
print("\nknown_species: ", known_species)
if comName not in known_species:
apobj = apprise.Apprise()
config = apprise.AppriseConfig()
config.add(userDir + '/BirdNET-Pi/apprise.txt')
apobj.add(config)
apobj.notify(
body=body.replace("$sciname",species.split("_")[0]).replace("$comname",species.split("_")[1]).replace("$confidence",confidence),
title=title,
body=body.replace("$sciname", species.split("_")[0]).replace("$comname", species.split("_")[1]).replace("$confidence", confidence),
title=title,
)
con.close()
except:
except BaseException:
print("Database busy")
time.sleep(2)
def writeResultsToFile(detections, min_conf, path):
print('WRITING RESULTS TO', path, '...', end=' ')
@@ -278,17 +281,18 @@ def writeResultsToFile(detections, min_conf, path):
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) ):
sendAppriseNotifications(str(entry[0]),str(entry[1]));
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)):
sendAppriseNotifications(str(entry[0]), str(entry[1]))
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.")
# print(f"[NEW CONNECTION] {addr} connected.")
connected = True
while connected:
@@ -299,10 +303,10 @@ def handle_client(conn, addr):
if msg == DISCONNECT_MESSAGE:
connected = False
else:
#print(f"[{addr}] {msg}")
# print(f"[{addr}] {msg}")
args = type('', (), {})()
args.i = ''
args.o = ''
args.birdweather_id = '99999'
@@ -313,8 +317,7 @@ def handle_client(conn, addr):
args.sensitivity = 1.25
args.min_conf = 0.70
args.lat = -1
args.lon = -1
args.lon = -1
for line in msg.split('||'):
inputvars = line.split('=')
@@ -341,14 +344,12 @@ def handle_client(conn, addr):
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:
@@ -360,22 +361,22 @@ def handle_client(conn, addr):
audioData = readAudioData(args.i, args.overlap)
# Get Date/Time from filename in case Pi gets behind
#now = datetime.now()
# now = datetime.now()
full_file_name = args.i
#print('FULL FILENAME: -' + full_file_name + '-')
# 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:', 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))
@@ -387,32 +388,32 @@ def handle_client(conn, addr):
# 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'
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')
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('_')
Sci_Name, Com_Name = species.split('_')
score = entry[1]
Confidence = str(round(score*100))
Confidence = str(round(score * 100))
Lat = str(args.lat)
Lon = str(args.lon)
Cutoff = str(args.min_conf)
@@ -421,31 +422,63 @@ def handle_client(conn, addr):
Overlap = str(args.overlap)
Com_Name = Com_Name.replace("'", "")
File_Name = Com_Name.replace(" ", "_") + '-' + Confidence + '-' + \
Date.replace("/", "-") + '-birdnet-' + Time + audiofmt
Date.replace("/", "-") + '-birdnet-' + Time + audiofmt
#Connect to SQLite Database
# Connect to SQLite Database
for attempt_number in range(3):
try:
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))
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()
break
except:
except BaseException:
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')
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
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'})
@@ -453,7 +486,7 @@ def handle_client(conn, addr):
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]
@@ -461,7 +494,7 @@ def handle_client(conn, addr):
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_timestamp = "\"timestamp\": \"" + current_iso8601 + "\","
post_lat = "\"lat\": " + str(args.lat) + ","
post_lon = "\"lon\": " + str(args.lon) + ","
post_soundscape_id = "\"soundscapeId\": " + str(soundscape_id) + ","
@@ -472,31 +505,33 @@ def handle_client(conn, addr):
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
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:
except BaseException:
print("Cannot POST right now")
conn.send(myReturn.encode(FORMAT))
#time.sleep(3)
# time.sleep(3)
conn.close()
conn.close()
def start():
# Load model
global INTERPRETER, INCLUDE_LIST, EXCLUDE_LIST
INTERPRETER = loadModel()
server.listen()
#print(f"[LISTENING] Server is listening on {SERVER}")
# 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(f"[ACTIVE CONNECTIONS] {threading.activeCount() - 1}")
#print("[STARTING] server is starting...")
# print("[STARTING] server is starting...")
start()
scripts/spectrogram.php
+39 -7
View File
@@ -5,6 +5,7 @@
// before a user gesture. This fixes it.
var started = null;
var player = null;
var gain = 128;
const ctx = null;
window.onload = function(){
// if user agent includes iPhone or Mac use legacy mode
@@ -36,13 +37,19 @@ window.onload = function(){
document.getElementById('player').remove();
player = audioelement;
} else {
player = document.getElementById('player');
}
player.play();
if (started) return;
if (started) return;
started = true;
initialize();
} else {
player = document.getElementById('player');
player.oncanplay = function() {
if (started) return;
started = true;
initialize();
};
}
player.play();
}
};
@@ -95,7 +102,12 @@ function initialize() {
ANALYSER.fftSize = 2048;
process();
try{
process();
} catch(e) {
window.top.location.reload();
}
function process() {
const SOURCE = ACTX.createMediaElementSource(player);
@@ -118,7 +130,7 @@ function initialize() {
CTX.putImageData(imgData, 0, 0);
ANALYSER.getByteFrequencyData(DATA);
for (let i = 0; i < LEN; i++) {
let rat = DATA[i] / 128 ;
let rat = DATA[i] / gain ;
let hue = Math.round((rat * 120) + 280 % 360);
let sat = '100%';
let lit = 10 + (70 * rat) + '%';
@@ -155,6 +167,26 @@ h1 {
<img id="spectrogramimage" style="width:100%;height:100%;display:none" src="/spectrogram.png?nocache=<?php echo $time;?>">
<div id="gain" class="centered">
<label>Gain: </label>
<span class="slidecontainer">
<input name="gain_input" type="range" min="0" max="255" value="128" class="slider" id="gain_input">
<span id="gain_value"></span>%
</span>
</div>
<audio style="display:none" controls="" crossorigin="anonymous" id='player' preload="none"><source src="/stream"></audio>
<h1>Loading...</h1>
<canvas></canvas>
<script>
var slider = document.getElementById("gain_input");
var output = document.getElementById("gain_value");
output.innerHTML = slider.value; // Display the default slider value
// Update the current slider value (each time you drag the slider handle)
slider.oninput = function() {
output.innerHTML = this.value;
gain=Math.abs(this.value - 255);
}
</script>