Добавлена т с титлом

.gitignore

@@ -9,3 +9,7 @@
 
 .DS_Store
 .idea/
+
+/venv/*
+
+/.vscode/

README.md

@@ -1,11 +1,67 @@
+
 <h1 align="center">
-  <img src="labelme/icons/icon.png"><br/>labelme
+  <img src="labelme/icons/icon.png"><br/>Нейролаб
 </h1>
 
 <h4 align="center">
-  Image Polygonal Annotation with Python
+  Программа для разметки древнерусских текстов
 </h4>
 
+<h2 align="center">
+  Гайд для установки
+</h2>
+Разрабы не смогли нормально написать что необходимо для запуска программы и какие зависимости необходимо подгрузить, так что придётся это делать мне.
+
+Для начала нужен питон с официального сайта (у меня работает на 3.11.9).
+
+Клонируем репозиторий:
+```
+git clone https://github.com/DemidNeuroLab/NeuroLabel
+
+cd ./NeuroLabel
+```
+
+Далее рекомендую создать vevn:
+```
+python -m venv venv
+
+.\venv\Scripts\activate
+```
+
+Далее устанавливаем зависимости:
+```
+pip install -r requirements.txt
+
+pip install -r requirements-dev.txt
+```
+
+Для запуска приложения нужно запустить файл start.py.
+```
+python start.py
+```
+
+<h2 align="center">
+  Для создания exe-файла
+</h2>
+Пишем команду:
+
+```
+pyinstaller labelme.spec
+```
+<h2 align="center">
+  Для компиляции ресурсов файлов помощи
+</h2>
+После исправления файлов с помощью их надо перекомпиллировать:
+
+Открываем терминал, выполняем команды.
+
+```
+cd .\labelme\widgets\helper_text\
+pyrcc5 -o help.py help.qrc
+```
+# Далее ридми от разрабов:
+
+
 <div align="center">
   <a href="https://pypi.python.org/pypi/labelme"><img src="https://img.shields.io/pypi/v/labelme.svg"></a>
   <a href="https://pypi.org/project/labelme"><img src="https://img.shields.io/pypi/pyversions/labelme.svg"></a>

labelme.spec

@@ -15,6 +15,7 @@ a = Analysis(
     pathex=['labelme'],
     binaries=[],
     datas=[
+        ('labelme/widgets/helper_text/*', 'labelme/widgets/helper_text'),
         ('labelme/config/default_config.yaml', 'labelme/config'),
         ('labelme/icons/*', 'labelme/icons'),
         ('labelme/translate/*.qm', 'translate'),
@@ -38,7 +39,7 @@ exe = EXE(
     a.binaries,
     a.zipfiles,
     a.datas,
-    name='labelme',
+    name='neurolab_markup',
     debug=False,
     strip=False,
     upx=True,
@@ -48,7 +49,7 @@ exe = EXE(
 )
 app = BUNDLE(
     exe,
-    name='Labelme.app',
+    name='neurolab_markup.app',
     icon='labelme/icons/icon.icns',
     bundle_identifier=None,
     info_plist={'NSHighResolutionCapable': 'True'},

labelme/__init__.py

@@ -5,8 +5,7 @@
 
 from qtpy import QT_VERSION
 
-
-__appname__ = "labelme"
+__appname__ = "Нейролаб"
 
 # Semantic Versioning 2.0.0: https://semver.org/
 # 1. MAJOR version when you make incompatible API changes;

labelme/__main__.py

@@ -33,7 +33,7 @@ def main():
         "-O",
         "-o",
         help="output file or directory (if it ends with .json it is "
-        "recognized as file, else as directory)",
+             "recognized as file, else as directory)",
     )
     default_config_file = os.path.join(os.path.expanduser("~"), ".labelmerc")
     parser.add_argument(
@@ -66,19 +66,6 @@ def main():
         help="stop sorting labels",
         default=argparse.SUPPRESS,
     )
-    parser.add_argument(
-        "--flags",
-        help="comma separated list of flags OR file containing flags",
-        default=argparse.SUPPRESS,
-    )
-    parser.add_argument(
-        "--labelflags",
-        dest="label_flags",
-        help=r"yaml string of label specific flags OR file containing json "
-        r"string of label specific flags (ex. {person-\d+: [male, tall], "
-        r"dog-\d+: [black, brown, white], .*: [occluded]})",  # NOQA
-        default=argparse.SUPPRESS,
-    )
     parser.add_argument(
         "--labels",
         help="comma separated list of labels OR file containing labels",
@@ -91,12 +78,6 @@ def main():
         help="label validation types",
         default=argparse.SUPPRESS,
     )
-    parser.add_argument(
-        "--keep-prev",
-        action="store_true",
-        help="keep annotation of previous frame",
-        default=argparse.SUPPRESS,
-    )
     parser.add_argument(
         "--epsilon",
         type=float,
@@ -111,27 +92,13 @@ def main():
 
     logger.setLevel(getattr(logging, args.logger_level.upper()))
 
-    if hasattr(args, "flags"):
-        if os.path.isfile(args.flags):
-            with codecs.open(args.flags, "r", encoding="utf-8") as f:
-                args.flags = [line.strip() for line in f if line.strip()]
-        else:
-            args.flags = [line for line in args.flags.split(",") if line]
-
     if hasattr(args, "labels"):
         if os.path.isfile(args.labels):
             with codecs.open(args.labels, "r", encoding="utf-8") as f:
                 args.labels = [line.strip() for line in f if line.strip()]
         else:
             args.labels = [line for line in args.labels.split(",") if line]
 
-    if hasattr(args, "label_flags"):
-        if os.path.isfile(args.label_flags):
-            with codecs.open(args.label_flags, "r", encoding="utf-8") as f:
-                args.label_flags = yaml.safe_load(f)
-        else:
-            args.label_flags = yaml.safe_load(args.label_flags)
-
     config_from_args = args.__dict__
     config_from_args.pop("version")
     reset_config = config_from_args.pop("reset_config")

labelme/ai/efficient_sam.py

@@ -3,7 +3,7 @@
 
 import imgviz
 import numpy as np
-import onnxruntime
+# import onnxruntime
 import skimage
 
 from ..logger import logger
@@ -12,8 +12,8 @@
 
 class EfficientSam:
     def __init__(self, encoder_path, decoder_path):
-        self._encoder_session = onnxruntime.InferenceSession(encoder_path)
-        self._decoder_session = onnxruntime.InferenceSession(decoder_path)
+        self._encoder_session = None # onnxruntime.InferenceSession(encoder_path)
+        self._decoder_session = None # onnxruntime.InferenceSession(decoder_path)
 
         self._lock = threading.Lock()
         self._image_embedding_cache = collections.OrderedDict()

labelme/ai/segment_anything_model.py

@@ -3,7 +3,7 @@
 
 import imgviz
 import numpy as np
-import onnxruntime
+# import onnxruntime
 import skimage
 
 from ..logger import logger
@@ -14,8 +14,8 @@ class SegmentAnythingModel:
     def __init__(self, encoder_path, decoder_path):
         self._image_size = 1024
 
-        self._encoder_session = onnxruntime.InferenceSession(encoder_path)
-        self._decoder_session = onnxruntime.InferenceSession(decoder_path)
+        self._encoder_session = None # onnxruntime.InferenceSession(encoder_path)
+        self._decoder_session = None # onnxruntime.InferenceSession(decoder_path)
 
         self._lock = threading.Lock()
         self._image_embedding_cache = collections.OrderedDict()

labelme/ai/text_to_annotation.py

@@ -2,30 +2,21 @@
 import time
 
 import numpy as np
-import osam
+# import osam
 
 from labelme.logger import logger
 
 
 def get_rectangles_from_texts(
     model: str, image: np.ndarray, texts: list[str]
 ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-    request: osam.types.GenerateRequest = osam.types.GenerateRequest(
-        model=model,
-        image=image,
-        prompt=osam.types.Prompt(
-            texts=texts,
-            iou_threshold=1.0,
-            score_threshold=0.01,
-            max_annotations=1000,
-        ),
-    )
+    request = None
     logger.debug(
         f"Requesting with model={model!r}, image={(image.shape, image.dtype)}, "
         f"prompt={request.prompt!r}"
     )
     t_start = time.time()
-    response: osam.types.GenerateResponse = osam.apis.generate(request=request)
+    response = None
 
     num_annotations = len(response.annotations)
     logger.debug(
@@ -62,13 +53,7 @@ def non_maximum_suppression(
     for i, (score, label) in enumerate(zip(scores, labels)):
         scores_of_all_classes[i, label] = score
     logger.debug(f"Input: num_boxes={len(boxes)}")
-    boxes, scores, labels = osam.apis.non_maximum_suppression(
-        boxes=boxes,
-        scores=scores_of_all_classes,
-        iou_threshold=iou_threshold,
-        score_threshold=score_threshold,
-        max_num_detections=max_num_detections,
-    )
+    boxes, scores, labels = None, None, None
     logger.debug(f"Output: num_boxes={len(boxes)}")
     return boxes, scores, labels