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make species lists agnostic to common name

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This commit is contained in:
frederik
2025-09-26 18:09:08 +02:00
parent a871ce5634
commit 2e9ab25107
1 changed files with 19 additions and 25 deletions
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scripts/server.py
+19 -25
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@@ -58,8 +58,7 @@ def loadModel():
CLASSES = []
labelspath = userDir + '/BirdNET-Pi/model/labels.txt'
with open(labelspath, 'r') as lfile:
for line in lfile.readlines():
CLASSES.append(line.replace('\n', ''))
CLASSES = [line.strip() for line in lfile.readlines()]
log.info('LOADING DONE!')
@@ -124,25 +123,19 @@ def explore(lat, lon, week):
return l_filter
def predictSpeciesList(lat, lon, week):
def set_predicted_species_list(lat, lon, week):
global PREDICTED_SPECIES_LIST
l_filter = explore(lat, lon, week)
for s in l_filter:
if s[0] >= float(sf_thresh):
# if there's a custom user-made include list, we only want to use the species in that
if (len(INCLUDE_LIST) == 0):
PREDICTED_SPECIES_LIST.append(s[1])
PREDICTED_SPECIES_LIST = [s[1].split('_')[0] for s in l_filter if s[0] >= sf_thresh]
def loadCustomSpeciesList(path):
slist = []
species_list = []
if os.path.isfile(path):
with open(path, 'r') as csfile:
for line in csfile.readlines():
slist.append(line.replace('\r', '').replace('\n', ''))
species_list = [line.strip().split('_')[0] for line in csfile.readlines()]
return slist
return species_list
def splitSignal(sig, rate, overlap, seconds=3.0, minlen=1.5):
@@ -236,7 +229,7 @@ def analyzeAudioData(chunks, lat, lon, week, sens, overlap,):
if model == "BirdNET_GLOBAL_6K_V2.4_Model_FP16":
if week != WEEK or len(INCLUDE_LIST) != 0:
WEEK = week
predictSpeciesList(lat, lon, week)
set_predicted_species_list(lat, lon, week)
mdata = get_metadata(lat, lon, week)
@@ -355,21 +348,22 @@ def run_analysis(file):
confident_detections = []
for time_slot, entries in raw_detections.items():
log.info('%s-%s', time_slot, entries[0])
for entry in entries:
if entry[1] >= conf.getfloat('CONFIDENCE'):
if entry[0] not in INCLUDE_LIST and len(INCLUDE_LIST) != 0:
log.warning("Excluded as INCLUDE_LIST is active but this species is not in it: %s", entry[0])
elif entry[0] in EXCLUDE_LIST and len(EXCLUDE_LIST) != 0:
log.warning("Excluded as species in EXCLUDE_LIST: %s", entry[0])
elif entry[0] not in PREDICTED_SPECIES_LIST and len(PREDICTED_SPECIES_LIST) != 0 and entry[0] not in WHITELIST_LIST:
log.warning("Excluded as below Species Occurrence Frequency Threshold: %s", entry[0])
for species, confidence in entries:
if confidence >= conf.getfloat('CONFIDENCE'):
sci_name = species.split('_')[0]
if sci_name not in INCLUDE_LIST and len(INCLUDE_LIST) != 0:
log.warning("Excluded as INCLUDE_LIST is active but this species is not in it: %s", species)
elif sci_name in EXCLUDE_LIST and len(EXCLUDE_LIST) != 0:
log.warning("Excluded as species in EXCLUDE_LIST: %s", species)
elif sci_name not in PREDICTED_SPECIES_LIST and len(PREDICTED_SPECIES_LIST) != 0 and sci_name not in WHITELIST_LIST:
log.warning("Excluded as below Species Occurrence Frequency Threshold: %s", species)
else:
d = Detection(
file.file_date,
time_slot.split(';')[0],
time_slot.split(';')[1],
entry[0],
entry[1],
species,
confidence,
)
confident_detections.append(d)
return confident_detections