Bump version to v2.3.2

README.md

@@ -72,11 +72,12 @@
 ## 🥳 What's New [⏏️](#📄-table-of-contents)
 
 - Feb. 2024:
+  - 🤗 Release the latest version [2.3.2](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.2) 🤗
   - ✨✨✨ Support [YOLOv9](https://github.com/WongKinYiu/yolov9) model.
   - Support the conversion from a horizontal bounding box to a rotated bounding box.
   - Supports label deletion and renaming. For more details, please refer to the [document](./docs/zh_cn/user_guide.md).
   - Support for quick tag correction is available; please refer to this [document](./docs/en/user_guide.md) for guidance.
-  - 🤗 Release the latest version [2.3.1](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.1) 🤗
+  - Release the latest version [2.3.1](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.1).
 - Jan. 2024:
   - 👏👏👏 Combining CLIP and SAM models for enhanced semantic and spatial understanding. An example can be found [here](./anylabeling/configs/auto_labeling/edge_sam_with_chinese_clip.yaml).
   - 🔥🔥🔥 Adding support for the [Depth Anything](https://github.com/LiheYoung/Depth-Anything.git) model in the depth estimation task.

README_zh-CN.md

@@ -70,11 +70,12 @@
 ## 🥳 新功能 [⏏️](#📄-目录)
 
 - 2024年2月：
+  - 🤗 发布[2.3.2](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.2)最新版本 🤗
   - ✨✨✨ 支持[YOLOv9](https://github.com/WongKinYiu/yolov9)模型。
   - 支持将水平框一键转换为旋转框。
   - 支持批量标签删除及重命名，详情可参考[用户手册](./docs/zh_cn/user_guide.md)。
   - 支持快速标签纠正功能，详情可参考[用户手册](./docs/zh_cn/user_guide.md)。
-  - 🤗 发布[2.3.1](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.1)最新版本 🤗
+  - 发布[2.3.1](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.1)版本。
 - 2024年1月：
   - 支持一键截取子图功能。
   - 👏👏👏 结合CLIP和SAM模型，实现更强大的语义和空间理解。具体可参考此[示例](./anylabeling/configs/auto_labeling/edge_sam_with_chinese_clip.yaml)。

anylabeling/app_info.py

@@ -1,4 +1,4 @@
 __appname__ = "X-AnyLabeling"
 __appdescription__ = "Advanced Auto Labeling Solution with Added Features"
-__version__ = "2.3.1"
+__version__ = "2.3.2"
 __preferred_device__ = "CPU"  # GPU or CPU

anylabeling/services/auto_labeling/__base__/yolo.py

@@ -57,12 +57,18 @@ def __init__(self, model_config, on_message) -> None:
         self.engine = self.config.get("engine", "ort")
         if self.engine.lower() == "dnn":
             from ..engines import DnnBaseModel
+
             self.net = DnnBaseModel(model_abs_path, __preferred_device__)
             self.input_width = self.config.get("input_width", 640)
             self.input_height = self.config.get("input_height", 640)
         else:
             self.net = OnnxBaseModel(model_abs_path, __preferred_device__)
-            _, _, self.input_height, self.input_width = self.net.get_input_shape()
+            (
+                _,
+                _,
+                self.input_height,
+                self.input_width,
+            ) = self.net.get_input_shape()
             if not isinstance(self.input_width, int):
                 self.input_width = self.config.get("input_width", -1)
             if not isinstance(self.input_height, int):

anylabeling/services/auto_labeling/engines/build_dnn_engine.py

@@ -1,15 +1,14 @@
 import cv2
 
-class DnnBaseModel():
+
+class DnnBaseModel:
     def __init__(self, model_path, device_type: str = "cpu") -> None:
         self.net = cv2.dnn.readNet(model_path)
         if device_type.lower() == "gpu":
             self.net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
             self.net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
 
-    def get_dnn_inference(
-        self, blob, extract=True, squeeze=False
-    ):
+    def get_dnn_inference(self, blob, extract=True, squeeze=False):
         self.net.setInput(blob)
         outs = self.net.forward(self.net.getUnconnectedOutLayersNames())
         if extract:

anylabeling/views/labeling/label_converter.py

@@ -86,7 +86,9 @@ def get_contours_and_labels(mask, mapping_table, epsilon_factor=0.001):
                 class_name = color_to_label.get(color_value, "Unknown")
                 label_map = (binaray_img == color_value).astype(np.uint8)
 
-                contours, _ = cv2.findContours(label_map, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+                contours, _ = cv2.findContours(
+                    label_map, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
+                )
                 for contour in contours:
                     epsilon = epsilon_factor * cv2.arcLength(contour, True)
                     approx = cv2.approxPolyDP(contour, epsilon, True)

anylabeling/views/labeling/label_file.py

@@ -110,7 +110,9 @@ def load(self, filename):
                 if self.image_dir:
                     image_path = osp.join(self.image_dir, data["imagePath"])
                 else:
-                    image_path = osp.join(osp.dirname(filename), data["imagePath"])
+                    image_path = osp.join(
+                        osp.dirname(filename), data["imagePath"]
+                    )
                 image_data = self.load_image_file(image_path)
             flags = data.get("flags") or {}
             image_path = data["imagePath"]

anylabeling/views/labeling/label_widget.py

@@ -625,7 +625,9 @@ def __init__(
             self.tr("&Convert HBB to OBB"),
             self.hbb_to_obb,
             icon="convert",
-            tip=self.tr("Perform conversion from horizontal bounding box to oriented bounding box"),
+            tip=self.tr(
+                "Perform conversion from horizontal bounding box to oriented bounding box"
+            ),
         )
 
         documentation = action(
@@ -1620,7 +1622,7 @@ def get_label_file_list(self):
                 label_file_list.append(osp.join(self.output_dir, file_name))
         return label_file_list
 
-    def hbb_to_obb(self):        
+    def hbb_to_obb(self):
         label_file_list = self.get_label_file_list()
 
         total_files = len(label_file_list)

anylabeling/views/labeling/widgets/canvas.py

@@ -564,7 +564,8 @@ def mousePressEvent(self, ev):
                 elif (
                     self.selected_vertex()
                     and int(ev.modifiers()) == QtCore.Qt.ShiftModifier
-                    and self.h_hape.shape_type not in ["rectangle", "rotation", "line"]
+                    and self.h_hape.shape_type
+                    not in ["rectangle", "rotation", "line"]
                 ):
                     # Delete point if: left-click + SHIFT on a point
                     self.remove_selected_point()

assets/demo.json

@@ -1,5 +1,5 @@
 {
-  "version": "2.3.1",
+  "version": "2.3.2",
   "flags": {},
   "shapes": [
     {

docs/en/get_started.md

@@ -37,7 +37,7 @@ To ensure access to the latest features and stable performance, it is highly rec
 
 Running in the GUI environment is convenient but may have limitations compared to running from the source code. Consider the pros and cons based on your specific needs and preferences.
 
-Download Link: [Release](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.1) | [Baidu Disk](https://pan.baidu.com/s/1w1KZoUFeM5gBrHxhfc4eAA?pwd=d74g)
+Download Link: [Release](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.2) | [Baidu Disk](https://pan.baidu.com/s/1YPU32J8sN5hwTewL4ImMzw?pwd=1y69)
 
 Note:
 - For MacOS:

docs/zh_cn/get_started.md

@@ -54,7 +54,7 @@ python anylabeling/app.py
 
 为了在选择运行方式时能够更好地权衡利弊，建议用户根据具体需求和偏好，灵活选择源码运行或GUI环境运行，以达到最佳的使用体验。
 
-下载链接：[Release](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.1) | [百度网盘](https://pan.baidu.com/s/1w1KZoUFeM5gBrHxhfc4eAA?pwd=d74g)
+下载链接：[Release](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.2) | [百度网盘](https://pan.baidu.com/s/1YPU32J8sN5hwTewL4ImMzw?pwd=1y69)
 
 
 ### 文件导入