Merge branch 'main' of https://github.com/kahst/BirdNET-Analyzer

.gitignore

@@ -9,6 +9,8 @@ foo*
 installers/
 desktop.ini
 *.iss
+*deploy*
+*hook*
 
 # Custom classifier
 checkpoints/custom/
@@ -34,7 +36,7 @@ __pycache__/
 
 # Distribution / packaging
 .Python
-build/
+build*
 develop-eggs/
 dist/
 downloads/
@@ -58,8 +60,6 @@ MANIFEST
 #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 *.manifest
 *.spec
-!BirdNET-Analyzer-mac.spec
-!BirdNET-Analyzer-win.spec
 
 # Installer logs
 pip-log.txt
@@ -161,4 +161,5 @@ train_data/
 train_cache.npz
 autotune/
 
-gui-settings.json
+gui-settings.json
+state.json

README.adoc

@@ -434,6 +434,8 @@ source venv-birdnet/bin/activate
 python -m pip install -U pip
 ----
 
+WARNING: :exclamation:**Make sure that you are using Python 3.10, if not install it from the https://www.python.org/downloads/release/python-3100/[Python website].**:exclamation:
+
 The nexttime you want to use BirdNET, go to the BirdNET-Analyzer folder and run `source venv-birdnet/bin/activate` to activate the virtual environment.
 
 ==== Install dependencies
@@ -775,7 +777,7 @@ Due to these custom labels, the location filter and locale will be disabled.
 
 == Segment review
 
-Please read the excellent paper from Connor M. Woods: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Uwta4wYAAAAJ&sortby=pubdate&citation_for_view=Uwta4wYAAAAJ:j3f4tGmQtD8C[Guidelines for appropriate use of BirdNET scores and other detector outputs].
+Please read the excellent paper from Connor M. Wood and Stefan Kahl: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Uwta4wYAAAAJ&sortby=pubdate&citation_for_view=Uwta4wYAAAAJ:j3f4tGmQtD8C[Guidelines for appropriate use of BirdNET scores and other detector outputs].
 
 
 The *Review* tab in the GUI is an implementation of the workflow described in the paper. It allows you to review the segments that were detected by BirdNET and to label the segments manually. This can helb you to choose an appropriate threshold for your specific use case.

analyze.py

@@ -75,9 +75,8 @@ def generate_raven_table(timestamps: list[str], result: dict[str, list], afile_p
         out_string += (
             f"{selection_id}\tSpectrogram 1\t1\t0\t3\t{low_freq}\t{high_freq}\tnocall\tnocall\t1.0\t{afile_path}\t0\n"
         )
-
-    with open(result_path, "w", encoding="utf-8") as rfile:
-        rfile.write(out_string)
+
+    utils.save_result_file(result_path, out_string)
 
 
 def generate_audacity(timestamps: list[str], result: dict[str, list], result_path: str) -> str:
@@ -97,8 +96,7 @@ def generate_audacity(timestamps: list[str], result: dict[str, list], result_pat
         # Write result string to file
         out_string += rstring
 
-    with open(result_path, "w", encoding="utf-8") as rfile:
-        rfile.write(out_string)
+    utils.save_result_file(result_path, out_string)
 
 
 def generate_rtable(timestamps: list[str], result: dict[str, list], afile_path: str, result_path: str) -> str:
@@ -111,7 +109,7 @@ def generate_rtable(timestamps: list[str], result: dict[str, list], afile_path:
         for c in result[timestamp]:
             if c[1] > cfg.MIN_CONFIDENCE and (not cfg.SPECIES_LIST or c[0] in cfg.SPECIES_LIST):
                 label = cfg.TRANSLATED_LABELS[cfg.LABELS.index(c[0])]
-                rstring += "\n{},{},{},{},{},{:.4f},{:.4f},{:.4f},{},{},{},{},{},{}".format(
+                rstring += "{},{},{},{},{},{:.4f},{:.4f},{:.4f},{},{},{},{},{},{}\n".format(
                     afile_path,
                     start,
                     end,
@@ -131,8 +129,7 @@ def generate_rtable(timestamps: list[str], result: dict[str, list], afile_path:
         # Write result string to file
         out_string += rstring
 
-    with open(result_path, "w", encoding="utf-8") as rfile:
-        rfile.write(out_string)
+    utils.save_result_file(result_path, out_string)
 
 
 def generate_kaleidoscope(timestamps: list[str], result: dict[str, list], afile_path: str, result_path: str) -> str:
@@ -167,8 +164,7 @@ def generate_kaleidoscope(timestamps: list[str], result: dict[str, list], afile_
         # Write result string to file
         out_string += rstring
 
-    with open(result_path, "w", encoding="utf-8") as rfile:
-        rfile.write(out_string)
+    utils.save_result_file(result_path, out_string)
 
 
 def generate_csv(timestamps: list[str], result: dict[str, list], afile_path: str, result_path: str) -> str:
@@ -187,8 +183,7 @@ def generate_csv(timestamps: list[str], result: dict[str, list], afile_path: str
         # Write result string to file
         out_string += rstring
 
-    with open(result_path, "w", encoding="utf-8") as rfile:
-        rfile.write(out_string)
+    utils.save_result_file(result_path, out_string)
 
 
 def saveResultFiles(r: dict[str, list], result_files: dict[str, str], afile_path: str):

config.py

@@ -2,9 +2,6 @@
 # Misc settings #
 #################
 
-# GUI version
-GUI_VERSION: str = "1.2.0"
-
 # Random seed for gaussian noise
 RANDOM_SEED: int = 42
 
@@ -112,8 +109,8 @@
 # 'csv' denotes a generic CSV file with start, end, species and confidence.
 RESULT_TYPES: set[str] | list[str] = {"table"}
 OUTPUT_RAVEN_FILENAME: str = "BirdNET_SelectionTable.txt"  # this is for combined Raven selection tables only
-OUTPUT_RTABLE_FILENAME: str = "BirdNET_RTable.txt"
-OUTPUT_KALEIDOSCOPE_FILENAME: str = "BirdNET_Kaleidoscope.txt"
+OUTPUT_RTABLE_FILENAME: str = "BirdNET_RTable.csv"
+OUTPUT_KALEIDOSCOPE_FILENAME: str = "BirdNET_Kaleidoscope.csv"
 OUTPUT_CSV_FILENAME: str = "BirdNET_CombinedTable.csv"
 
 # Whether to skip existing results in the output path