First

.idea/.gitignore

@@ -0,0 +1,5 @@
+# 默认忽略的文件
+/shelf/
+/workspace.xml
+# 基于编辑器的 HTTP 客户端请求
+/httpRequests/

.idea/AutoAnno.iml

@@ -0,0 +1,10 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/.venv" />
+    </content>
+    <orderEntry type="jdk" jdkName="Python 3.11 (AutoAnno)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.11 (AutoAnno)" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (AutoAnno)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/AutoAnno.iml" filepath="$PROJECT_DIR$/.idea/AutoAnno.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>

autoanno.py

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+import os
+import cv2
+import xml.etree.ElementTree as ET
+from xml.dom import minidom
+from ultralytics import YOLO
+
+# ========== 配置部分 ==========
+model_path = "epoch220.pt"
+input_dir = "test_images/"
+output_xml_dir = "annotations/"
+temp_dir = "temp_rgb_images"
+
+
+# ===== 自定义类别名称映射 =====
+class_mapping = {
+    0: "EM14",
+    1: "EM18",
+    2: "EM17",
+    3: "EM170",
+    4: "EM19",
+    5: "EM190",
+    6: "EM20",
+    7: "EM200",
+    8: "EM201",
+    9: "EM202",
+    10: "EM203",
+    11: "EM180",
+    12: "EM181"
+}
+
+
+
+os.makedirs(temp_dir, exist_ok=True)
+os.makedirs(output_xml_dir, exist_ok=True)
+
+# ========== 加载模型 ==========
+model = YOLO(model_path)
+
+
+
+
+# ========== 灰度转RGB ==========
+for file in os.listdir(input_dir):
+    if not file.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp', '.tiff')):
+        continue
+
+    input_path = os.path.join(input_dir, file)
+    img = cv2.imread(input_path, cv2.IMREAD_UNCHANGED)
+
+    if len(img.shape) == 2 or img.shape[2] == 1:
+        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+
+    temp_path = os.path.join(temp_dir, file)
+    cv2.imwrite(temp_path, img)
+
+# ========== 执行检测 ==========
+results = model.predict(source=temp_dir, conf=0.2, save=False,classes=[11])
+
+# ========== 生成标准化VOC XML ==========
+for result in results:
+    file_name = os.path.basename(result.path)
+    image_path = os.path.join(input_dir, file_name)
+    img = cv2.imread(image_path)
+    height, width, depth = img.shape
+
+    annotation = ET.Element("annotation")
+
+    folder = ET.SubElement(annotation, "folder")
+    folder.text = os.path.basename(os.path.dirname(image_path))
+
+    filename = ET.SubElement(annotation, "filename")
+    filename.text = file_name
+
+    path = ET.SubElement(annotation, "path")
+    path.text = os.path.abspath(image_path)
+
+    source = ET.SubElement(annotation, "source")
+    database = ET.SubElement(source, "database")
+    database.text = "Unknown"
+
+    size = ET.SubElement(annotation, "size")
+    ET.SubElement(size, "width").text = str(width)
+    ET.SubElement(size, "height").text = str(height)
+    ET.SubElement(size, "depth").text = str(depth)
+
+    segmented = ET.SubElement(annotation, "segmented")
+    segmented.text = "0"
+
+    # 遍历检测框
+    for box in result.boxes:
+        cls = int(box.cls[0])
+        xyxy = box.xyxy[0].tolist()
+        xmin, ymin, xmax, ymax = map(int, xyxy)
+
+        obj = ET.SubElement(annotation, "object")
+
+        name = ET.SubElement(obj, "name")
+        name.text = class_mapping.get(cls, str(cls))
+
+
+        pose = ET.SubElement(obj, "pose")
+        pose.text = "Unspecified"
+
+        truncated = ET.SubElement(obj, "truncated")
+        truncated.text = "0"
+
+        difficult = ET.SubElement(obj, "difficult")
+        difficult.text = "0"
+
+        bndbox = ET.SubElement(obj, "bndbox")
+        ET.SubElement(bndbox, "xmin").text = str(xmin)
+        ET.SubElement(bndbox, "ymin").text = str(ymin)
+        ET.SubElement(bndbox, "xmax").text = str(xmax)
+        ET.SubElement(bndbox, "ymax").text = str(ymax)
+
+    # ===== 用 minidom 格式化输出 =====
+    rough_string = ET.tostring(annotation, 'utf-8')
+    reparsed = minidom.parseString(rough_string)
+    xml_str = reparsed.toprettyxml(indent="\t", encoding="utf-8").decode("utf-8")
+
+    # 去掉多余的第一行空行
+    xml_str = "\n".join([line for line in xml_str.splitlines() if line.strip()])
+
+    # 保存文件
+    xml_path = os.path.join(output_xml_dir, os.path.splitext(file_name)[0] + ".xml")
+    with open(xml_path, "w", encoding="utf-8") as f:
+        f.write(xml_str)
+
+    print(f"[生成完成] {xml_path}")

autoannogui.py

@@ -0,0 +1,279 @@
+import os
+import cv2
+import sys
+import xml.etree.ElementTree as ET
+from xml.dom import minidom
+from ultralytics import YOLO
+from PyQt5.QtWidgets import (
+    QApplication, QWidget, QVBoxLayout, QHBoxLayout, QPushButton,
+    QLineEdit, QLabel, QFileDialog, QProgressBar, QTextEdit, QSlider
+)
+from PyQt5.QtCore import Qt, QThread, pyqtSignal
+
+
+# ========== 后台任务线程 ==========
+class DetectionThread(QThread):
+    update_model_progress = pyqtSignal(int)
+    update_xml_progress = pyqtSignal(int)
+    log_message = pyqtSignal(str)
+    finished_signal = pyqtSignal()
+
+    def __init__(self, model_path, input_dir, output_xml_dir, conf, classes):
+        super().__init__()
+        self.model_path = model_path
+        self.input_dir = input_dir
+        self.output_xml_dir = output_xml_dir
+        self.conf = conf
+        self.classes = classes
+
+        # 类别映射（可修改）
+        self.class_mapping = {
+            0: "EM14", 1: "EM18", 2: "EM17", 3: "EM170", 4: "EM19",
+            5: "EM190", 6: "EM20", 7: "EM200", 8: "EM201", 9: "EM202",
+            10: "EM203", 11: "EM180", 12: "EM181"
+        }
+
+    def run(self):
+        try:
+            os.makedirs(self.output_xml_dir, exist_ok=True)
+            temp_dir = os.path.join(self.output_xml_dir, "temp_rgb_images")
+            os.makedirs(temp_dir, exist_ok=True)
+
+            # 加载模型
+            self.log_message.emit(f"加载模型：{self.model_path}")
+            model = YOLO(self.model_path)
+            self.log_message.emit("模型加载完成 ✅")
+
+            # 读取图片列表
+            imgs = [f for f in os.listdir(self.input_dir)
+                    if f.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp', '.tiff'))]
+            total_imgs = len(imgs)
+            if total_imgs == 0:
+                self.log_message.emit("输入目录中未找到图片 ❌")
+                self.finished_signal.emit()
+                return
+
+            # 灰度转RGB并保存临时文件
+            self.log_message.emit("开始灰度图像转RGB...此过程时间较长，请等待")
+            for img_name in imgs:
+                img_path = os.path.join(self.input_dir, img_name)
+                img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
+                if img is None:
+                    continue
+                if len(img.shape) == 2 or img.shape[2] == 1:
+                    img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+                cv2.imwrite(os.path.join(temp_dir, img_name), img)
+
+            # ========= 模型检测阶段 =========
+            self.log_message.emit("开始执行YOLO检测...")
+            results = []
+            for i, file in enumerate(imgs, 1):
+                temp_path = os.path.join(temp_dir, file)
+                result = model.predict(
+                    source=temp_path, conf=self.conf, save=False, classes=self.classes
+                )
+                results.extend(result)
+                progress = int(i / total_imgs * 100)
+                self.update_model_progress.emit(progress)
+                self.log_message.emit(f"[检测完成] {file}")
+
+            # ========= XML生成阶段 =========
+            self.log_message.emit("开始生成VOC格式XML...")
+            total_results = len(results)
+            for j, result in enumerate(results, 1):
+                file_name = os.path.basename(result.path)
+                image_path = os.path.join(self.input_dir, file_name)
+                img = cv2.imread(image_path)
+                if img is None:
+                    continue
+                height, width, depth = img.shape
+
+                annotation = ET.Element("annotation")
+
+                folder = ET.SubElement(annotation, "folder")
+                folder.text = os.path.basename(os.path.dirname(image_path))
+
+                filename = ET.SubElement(annotation, "filename")
+                filename.text = file_name
+
+                path = ET.SubElement(annotation, "path")
+                path.text = os.path.abspath(image_path)
+
+                source = ET.SubElement(annotation, "source")
+                database = ET.SubElement(source, "database")
+                database.text = "Unknown"
+
+                size = ET.SubElement(annotation, "size")
+                ET.SubElement(size, "width").text = str(width)
+                ET.SubElement(size, "height").text = str(height)
+                ET.SubElement(size, "depth").text = str(depth)
+
+                segmented = ET.SubElement(annotation, "segmented")
+                segmented.text = "0"
+
+                for box in result.boxes:
+                    cls = int(box.cls[0])
+                    xyxy = box.xyxy[0].tolist()
+                    xmin, ymin, xmax, ymax = map(int, xyxy)
+
+                    obj = ET.SubElement(annotation, "object")
+                    ET.SubElement(obj, "name").text = self.class_mapping.get(cls, str(cls))
+                    ET.SubElement(obj, "pose").text = "Unspecified"
+                    ET.SubElement(obj, "truncated").text = "0"
+                    ET.SubElement(obj, "difficult").text = "0"
+
+                    bndbox = ET.SubElement(obj, "bndbox")
+                    ET.SubElement(bndbox, "xmin").text = str(xmin)
+                    ET.SubElement(bndbox, "ymin").text = str(ymin)
+                    ET.SubElement(bndbox, "xmax").text = str(xmax)
+                    ET.SubElement(bndbox, "ymax").text = str(ymax)
+
+                rough_string = ET.tostring(annotation, 'utf-8')
+                reparsed = minidom.parseString(rough_string)
+                xml_str = reparsed.toprettyxml(indent="\t", encoding="utf-8").decode("utf-8")
+                xml_str = "\n".join([line for line in xml_str.splitlines() if line.strip()])
+
+                xml_path = os.path.join(self.output_xml_dir, os.path.splitext(file_name)[0] + ".xml")
+                with open(xml_path, "w", encoding="utf-8") as f:
+                    f.write(xml_str)
+
+                progress = int(j / total_results * 100)
+                self.update_xml_progress.emit(progress)
+                self.log_message.emit(f"[生成完成] {xml_path}")
+
+            self.log_message.emit("✅ 所有任务完成！")
+        except Exception as e:
+            self.log_message.emit(f"❌ 错误: {e}")
+        finally:
+            self.finished_signal.emit()
+
+
+# ========== 主界面 ==========
+class MainWindow(QWidget):
+    def __init__(self):
+        super().__init__()
+        self.setWindowTitle("YOLO 自动检测 + VOC生成工具")
+        self.resize(700, 600)
+
+        layout = QVBoxLayout()
+
+        # 模型路径
+        self.model_edit = self._add_path_selector(layout, "模型路径：")
+        # 输入文件夹
+        self.input_edit = self._add_path_selector(layout, "输入图片文件夹：")
+        # 输出文件夹
+        self.output_edit = self._add_path_selector(layout, "输出XML文件夹：")
+
+        # 置信度滑块
+        conf_layout = QHBoxLayout()
+        conf_layout.addWidget(QLabel("置信度阈值："))
+        self.conf_slider = QSlider(Qt.Horizontal)
+        self.conf_slider.setRange(0, 100)
+        self.conf_slider.setValue(20)
+        self.conf_value = QLabel("0.2")
+        self.conf_slider.valueChanged.connect(lambda v: self.conf_value.setText(str(v / 100)))
+        conf_layout.addWidget(self.conf_slider)
+        conf_layout.addWidget(self.conf_value)
+        layout.addLayout(conf_layout)
+
+        # 控制按钮
+        btn_layout = QHBoxLayout()
+        self.btn_load = QPushButton("加载模型")
+        self.btn_start = QPushButton("开始检测")
+        btn_layout.addWidget(self.btn_load)
+        btn_layout.addWidget(self.btn_start)
+        layout.addLayout(btn_layout)
+
+        # 两个进度条
+        self.progress_model = QProgressBar()
+        self.progress_model.setFormat("模型检测进度：%p%")
+        self.progress_xml = QProgressBar()
+        self.progress_xml.setFormat("XML生成进度：%p%")
+        layout.addWidget(self.progress_model)
+        layout.addWidget(self.progress_xml)
+
+        # 日志输出
+        layout.addWidget(QLabel("日志输出："))
+        self.log_output = QTextEdit()
+        self.log_output.setReadOnly(True)
+        layout.addWidget(self.log_output)
+
+        self.setLayout(layout)
+
+        # 信号绑定
+        self.btn_load.clicked.connect(self.load_model)
+        self.btn_start.clicked.connect(self.start_detection)
+
+        self.model_loaded = False
+
+    def _add_path_selector(self, layout, label_text):
+        hlayout = QHBoxLayout()
+        label = QLabel(label_text)
+        edit = QLineEdit()
+        btn = QPushButton("浏览")
+        hlayout.addWidget(label)
+        hlayout.addWidget(edit)
+        hlayout.addWidget(btn)
+        layout.addLayout(hlayout)
+        btn.clicked.connect(lambda: self._select_path(edit, "选择文件夹" if "文件夹" in label_text else "选择模型文件"))
+        return edit
+
+    def _select_path(self, edit, title):
+        if "模型" in title:
+            path, _ = QFileDialog.getOpenFileName(self, title, "", "Model Files (*.pt *.onnx)")
+        else:
+            path = QFileDialog.getExistingDirectory(self, title)
+        if path:
+            edit.setText(path)
+
+    def load_model(self):
+        model_path = self.model_edit.text().strip()
+        if not os.path.exists(model_path):
+            self._log("❌ 模型文件不存在")
+            return
+        self.model_loaded = True
+        self._log("✅ 模型路径加载成功，可开始检测")
+
+    def start_detection(self):
+        if not self.model_loaded:
+            self._log("⚠️ 请先加载模型路径")
+            return
+
+        model_path = self.model_edit.text().strip()
+        input_dir = self.input_edit.text().strip()
+        output_xml_dir = self.output_edit.text().strip()
+        conf = float(self.conf_slider.value() / 100)
+        classes = [11]  # 可修改为自定义选择
+
+        if not os.path.exists(input_dir):
+            self._log("❌ 输入文件夹不存在")
+            return
+
+        self.progress_model.setValue(0)
+        self.progress_xml.setValue(0)
+
+        self.thread = DetectionThread(model_path, input_dir, output_xml_dir, conf, classes)
+        self.thread.update_model_progress.connect(self.progress_model.setValue)
+        self.thread.update_xml_progress.connect(self.progress_xml.setValue)
+        self.thread.log_message.connect(self._log)
+        self.thread.finished_signal.connect(self._finish)
+        self.thread.start()
+
+        self.btn_start.setEnabled(False)
+        self._log("开始执行检测任务...")
+
+    def _log(self, msg):
+        self.log_output.append(msg)
+        self.log_output.verticalScrollBar().setValue(self.log_output.verticalScrollBar().maximum())
+
+    def _finish(self):
+        self.btn_start.setEnabled(True)
+        self._log("任务完成 ✅")
+
+
+# ========== 主程序入口 ==========
+if __name__ == "__main__":
+    app = QApplication(sys.argv)
+    window = MainWindow()
+    window.show()
+    sys.exit(app.exec_())

autoannoguinew.py

@@ -0,0 +1,313 @@
+import os
+import cv2
+import sys
+import xml.etree.ElementTree as ET
+from xml.dom import minidom
+from ultralytics import YOLO
+from PyQt5.QtWidgets import (
+    QApplication, QWidget, QVBoxLayout, QHBoxLayout, QPushButton,
+    QLineEdit, QLabel, QFileDialog, QProgressBar, QTextEdit, QSlider,
+    QCheckBox, QScrollArea, QGroupBox, QGridLayout
+)
+from PyQt5.QtCore import Qt, QThread, pyqtSignal
+
+
+# ========== 后台任务线程 ==========
+class DetectionThread(QThread):
+    update_rgb_progress = pyqtSignal(int)
+    update_model_progress = pyqtSignal(int)
+    update_xml_progress = pyqtSignal(int)
+    log_message = pyqtSignal(str)
+    finished_signal = pyqtSignal()
+
+    def __init__(self, model_path, input_dir, output_xml_dir, temp_dir, conf, classes):
+        super().__init__()
+        self.model_path = model_path
+        self.input_dir = input_dir
+        self.output_xml_dir = output_xml_dir
+        self.temp_dir = temp_dir
+        self.conf = conf
+        self.classes = classes
+
+        # 类别映射
+        self.class_mapping = {
+            0: "EM14", 1: "EM18", 2: "EM17", 3: "EM170", 4: "EM19",
+            5: "EM190", 6: "EM20", 7: "EM200", 8: "EM201", 9: "EM202",
+            10: "EM203", 11: "EM180", 12: "EM181"
+        }
+
+    def run(self):
+        try:
+            os.makedirs(self.output_xml_dir, exist_ok=True)
+            os.makedirs(self.temp_dir, exist_ok=True)
+
+            # 加载模型
+            self.log_message.emit(f"加载模型：{self.model_path}")
+            model = YOLO(self.model_path)
+            self.log_message.emit("模型加载完成 ✅")
+
+            # 读取图片
+            imgs = [f for f in os.listdir(self.input_dir)
+                    if f.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp', '.tiff'))]
+            total_imgs = len(imgs)
+            if total_imgs == 0:
+                self.log_message.emit("输入目录中未找到图片 ❌")
+                self.finished_signal.emit()
+                return
+
+            # ========= 灰度转RGB阶段 =========
+            self.log_message.emit("开始灰度图像转RGB...此过程时间较长，请等待")
+            for i, img_name in enumerate(imgs, 1):
+                img_path = os.path.join(self.input_dir, img_name)
+                img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
+                if img is None:
+                    continue
+                if len(img.shape) == 2 or img.shape[2] == 1:
+                    img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+                cv2.imwrite(os.path.join(self.temp_dir, img_name), img)
+                self.update_rgb_progress.emit(int(i / total_imgs * 100))
+            self.log_message.emit("RGB 转换完成 ✅")
+
+            # ========= 模型检测阶段 =========
+            self.log_message.emit("开始执行YOLO检测...")
+            results = []
+            for i, file in enumerate(imgs, 1):
+                temp_path = os.path.join(self.temp_dir, file)
+                result = model.predict(
+                    source=temp_path, conf=self.conf, save=False,
+                    classes=self.classes if self.classes else None
+                )
+                results.extend(result)
+                self.update_model_progress.emit(int(i / total_imgs * 100))
+                self.log_message.emit(f"[检测完成] {file}")
+
+            # ========= XML生成阶段 =========
+            self.log_message.emit("开始生成VOC格式XML...")
+            total_results = len(results)
+            for j, result in enumerate(results, 1):
+                file_name = os.path.basename(result.path)
+                image_path = os.path.join(self.input_dir, file_name)
+                img = cv2.imread(image_path)
+                if img is None:
+                    continue
+                height, width, depth = img.shape
+
+                annotation = ET.Element("annotation")
+
+                folder = ET.SubElement(annotation, "folder")
+                folder.text = os.path.basename(os.path.dirname(image_path))
+
+                filename = ET.SubElement(annotation, "filename")
+                filename.text = file_name
+
+                path = ET.SubElement(annotation, "path")
+                path.text = os.path.abspath(image_path)
+
+                source = ET.SubElement(annotation, "source")
+                database = ET.SubElement(source, "database")
+                database.text = "Unknown"
+
+                size = ET.SubElement(annotation, "size")
+                ET.SubElement(size, "width").text = str(width)
+                ET.SubElement(size, "height").text = str(height)
+                ET.SubElement(size, "depth").text = str(depth)
+
+                segmented = ET.SubElement(annotation, "segmented")
+                segmented.text = "0"
+
+                for box in result.boxes:
+                    cls = int(box.cls[0])
+                    xyxy = box.xyxy[0].tolist()
+                    xmin, ymin, xmax, ymax = map(int, xyxy)
+
+                    obj = ET.SubElement(annotation, "object")
+                    ET.SubElement(obj, "name").text = self.class_mapping.get(cls, str(cls))
+                    ET.SubElement(obj, "pose").text = "Unspecified"
+                    ET.SubElement(obj, "truncated").text = "0"
+                    ET.SubElement(obj, "difficult").text = "0"
+
+                    bndbox = ET.SubElement(obj, "bndbox")
+                    ET.SubElement(bndbox, "xmin").text = str(xmin)
+                    ET.SubElement(bndbox, "ymin").text = str(ymin)
+                    ET.SubElement(bndbox, "xmax").text = str(xmax)
+                    ET.SubElement(bndbox, "ymax").text = str(ymax)
+
+                xml_str = minidom.parseString(ET.tostring(annotation, 'utf-8')).toprettyxml(indent="\t", encoding="utf-8").decode("utf-8")
+                xml_str = "\n".join([line for line in xml_str.splitlines() if line.strip()])
+
+                xml_path = os.path.join(self.output_xml_dir, os.path.splitext(file_name)[0] + ".xml")
+                with open(xml_path, "w", encoding="utf-8") as f:
+                    f.write(xml_str)
+
+                self.update_xml_progress.emit(int(j / total_results * 100))
+                self.log_message.emit(f"[生成完成] {xml_path}")
+
+            self.log_message.emit("✅ 所有任务完成！")
+        except Exception as e:
+            self.log_message.emit(f"❌ 错误: {e}")
+        finally:
+            self.finished_signal.emit()
+
+
+# ========== 主界面 ==========
+class MainWindow(QWidget):
+    def __init__(self):
+        super().__init__()
+        self.setWindowTitle("YOLO 半自动标注工具（华东专用）")
+        self.resize(750, 750)
+
+        layout = QVBoxLayout()
+
+        # 路径部分
+        self.model_edit = self._add_path_selector(layout, "模型路径：")
+        self.input_edit = self._add_path_selector(layout, "输入图片文件夹：")
+        self.output_edit = self._add_path_selector(layout, "输出XML文件夹：")
+        self.temp_edit = self._add_path_selector(layout, "临时RGB图片文件夹：")
+
+        # 置信度
+        conf_layout = QHBoxLayout()
+        conf_layout.addWidget(QLabel("置信度阈值："))
+        self.conf_slider = QSlider(Qt.Horizontal)
+        self.conf_slider.setRange(0, 100)
+        self.conf_slider.setValue(20)
+        self.conf_value = QLabel("0.2")
+        self.conf_slider.valueChanged.connect(lambda v: self.conf_value.setText(str(v / 100)))
+        conf_layout.addWidget(self.conf_slider)
+        conf_layout.addWidget(self.conf_value)
+        layout.addLayout(conf_layout)
+
+        # 类别选择区域
+        self.class_mapping = {
+            0: "EM14", 1: "EM18", 2: "EM17", 3: "EM170", 4: "EM19",
+            5: "EM190", 6: "EM20", 7: "EM200", 8: "EM201", 9: "EM202",
+            10: "EM203", 11: "EM180", 12: "EM181"
+        }
+
+        layout.addWidget(QLabel("选择要检测的类别（可多选）："))
+        self.class_checkboxes = {}
+        class_group = QGroupBox()
+        grid = QGridLayout()
+        for idx, (key, name) in enumerate(self.class_mapping.items()):
+            cb = QCheckBox(f"{key}: {name}")
+            self.class_checkboxes[key] = cb
+            grid.addWidget(cb, idx // 4, idx % 4)
+        class_group.setLayout(grid)
+
+        scroll = QScrollArea()
+        scroll.setWidget(class_group)
+        scroll.setWidgetResizable(True)
+        scroll.setFixedHeight(120)
+        layout.addWidget(scroll)
+
+        # 控制按钮
+        btn_layout = QHBoxLayout()
+        self.btn_load = QPushButton("加载模型")
+        self.btn_start = QPushButton("开始检测")
+        btn_layout.addWidget(self.btn_load)
+        btn_layout.addWidget(self.btn_start)
+        layout.addLayout(btn_layout)
+
+        # 三个进度条
+        self.progress_rgb = QProgressBar()
+        self.progress_rgb.setFormat("RGB转换进度：%p%")
+        self.progress_model = QProgressBar()
+        self.progress_model.setFormat("模型检测进度：%p%")
+        self.progress_xml = QProgressBar()
+        self.progress_xml.setFormat("XML生成进度：%p%")
+        layout.addWidget(self.progress_rgb)
+        layout.addWidget(self.progress_model)
+        layout.addWidget(self.progress_xml)
+
+        # 日志
+        layout.addWidget(QLabel("日志输出："))
+        self.log_output = QTextEdit()
+        self.log_output.setReadOnly(True)
+        layout.addWidget(self.log_output)
+
+        self.setLayout(layout)
+
+        # 信号绑定
+        self.btn_load.clicked.connect(self.load_model)
+        self.btn_start.clicked.connect(self.start_detection)
+        self.model_loaded = False
+
+    def _add_path_selector(self, layout, label_text):
+        hlayout = QHBoxLayout()
+        label = QLabel(label_text)
+        edit = QLineEdit()
+        btn = QPushButton("浏览")
+        hlayout.addWidget(label)
+        hlayout.addWidget(edit)
+        hlayout.addWidget(btn)
+        layout.addLayout(hlayout)
+        btn.clicked.connect(lambda: self._select_path(edit, "选择文件夹" if "文件夹" in label_text else "选择模型文件"))
+        return edit
+
+    def _select_path(self, edit, title):
+        if "模型" in title:
+            path, _ = QFileDialog.getOpenFileName(self, title, "", "Model Files (*.pt *.onnx)")
+        else:
+            path = QFileDialog.getExistingDirectory(self, title)
+        if path:
+            edit.setText(path)
+
+    def load_model(self):
+        path = self.model_edit.text().strip()
+        if not os.path.exists(path):
+            self._log("❌ 模型文件不存在")
+            return
+        self.model_loaded = True
+        self._log("✅ 模型路径加载成功，可开始检测")
+
+    def start_detection(self):
+        if not self.model_loaded:
+            self._log("⚠️ 请先加载模型路径")
+            return
+
+        model_path = self.model_edit.text().strip()
+        input_dir = self.input_edit.text().strip()
+        output_xml_dir = self.output_edit.text().strip()
+        temp_dir = self.temp_edit.text().strip()
+        conf = float(self.conf_slider.value() / 100)
+
+        selected_classes = [cid for cid, cb in self.class_checkboxes.items() if cb.isChecked()]
+
+        self._log(f"选中类别：{selected_classes if selected_classes else '全部'}")
+
+        if not os.path.exists(input_dir):
+            self._log("❌ 输入文件夹不存在")
+            return
+        if not temp_dir:
+            self._log("⚠️ 请选择临时RGB图片文件夹")
+            return
+
+        self.progress_rgb.setValue(0)
+        self.progress_model.setValue(0)
+        self.progress_xml.setValue(0)
+
+        self.thread = DetectionThread(model_path, input_dir, output_xml_dir, temp_dir, conf, selected_classes)
+        self.thread.update_rgb_progress.connect(self.progress_rgb.setValue)
+        self.thread.update_model_progress.connect(self.progress_model.setValue)
+        self.thread.update_xml_progress.connect(self.progress_xml.setValue)
+        self.thread.log_message.connect(self._log)
+        self.thread.finished_signal.connect(self._finish)
+        self.thread.start()
+
+        self.btn_start.setEnabled(False)
+        self._log("开始执行检测任务...")
+
+    def _log(self, msg):
+        self.log_output.append(msg)
+        self.log_output.verticalScrollBar().setValue(self.log_output.verticalScrollBar().maximum())
+
+    def _finish(self):
+        self.btn_start.setEnabled(True)
+        self._log("任务完成 ✅")
+
+
+# ========== 主程序入口 ==========
+if __name__ == "__main__":
+    app = QApplication(sys.argv)
+    window = MainWindow()
+    window.show()
+    sys.exit(app.exec_())

autoannoguinew.spec

@@ -0,0 +1,44 @@
+# -*- mode: python ; coding: utf-8 -*-
+
+
+a = Analysis(
+    ['autoannoguinew.py'],
+    pathex=['D:\PythonProject\AutoAnno\.venv\Lib\site-packages'],
+    binaries=[],
+    datas=[],
+    hiddenimports=['os','cv2','ultralytics','PyQt5','sys','xml.etree.ElementTree','xml.dom'],
+    hookspath=[],
+    hooksconfig={},
+    runtime_hooks=[],
+    excludes=[],
+    noarchive=False,
+    optimize=0,
+)
+pyz = PYZ(a.pure)
+
+exe = EXE(
+    pyz,
+    a.scripts,
+    [],
+    exclude_binaries=True,
+    name='autoannoguinew',
+    debug=False,
+    bootloader_ignore_signals=False,
+    strip=False,
+    upx=True,
+    console=True,
+    disable_windowed_traceback=False,
+    argv_emulation=False,
+    target_arch=None,
+    codesign_identity=None,
+    entitlements_file=None,
+)
+coll = COLLECT(
+    exe,
+    a.binaries,
+    a.datas,
+    strip=False,
+    upx=True,
+    upx_exclude=[],
+    name='autoannoguinew',
+)

function-gpu.yaml

@@ -0,0 +1,68 @@
+metadata:
+  name: emdetector
+  namespace: cvat
+  annotations:
+    name: EM14 v1
+    type: detector
+    framework: pytorch
+    spec: |
+      [
+        { "id": 0, "name": "EM14", "type": "rectangle" },
+        { "id": 1, "name": "EM18", "type": "rectangle" },
+        { "id": 2, "name": "EM17", "type": "rectangle" },
+        { "id": 3, "name": "EM170", "type": "rectangle" },
+        { "id": 4, "name": "EM19", "type": "rectangle" },
+        { "id": 5, "name": "EM190", "type": "rectangle" },
+        { "id": 6, "name": "EM20", "type": "rectangle" },
+        { "id": 7, "name": "EM200", "type": "rectangle" },
+        { "id": 8, "name": "EM201", "type": "rectangle" },
+        { "id": 9, "name": "EM202", "type": "rectangle" },
+        { "id": 10, "name": "EM203", "type": "rectangle" }
+      ]      
+
+spec:
+  description: 工位检测
+  runtime: "python:3.9"
+  handler: main:handler
+  eventTimeout: 30s
+
+  build:
+    image: cvat.pth.yolo8.emdetector:latest-gpu
+    baseImage: python:3.9
+    directives:
+      preCopy:
+        - kind: ENV
+          value: DEBIAN_FRONTEND=noninteractive
+        - kind: RUN
+          value: apt-get update && apt-get install -y libgl1 libglib2.0-0 && apt-get clean
+        - kind: RUN
+          value: pip install ultralytics torch torchvision opencv-python-headless && pip cache purge
+
+  triggers:
+    myHttpTrigger:
+      numWorkers: 1
+      kind: 'http'
+      workerAvailabilityTimeoutMilliseconds: 10000
+      attributes:
+        # Set value from the calculation of tracking of 100 objects at the same time on a 4k image
+        maxRequestBodySize: 268435456 # 256MB
+
+  volumes:
+    - volume:
+        name: model-volume
+        hostPath:
+          path: /DATA/wjl/cvat/models/best.pt  # 可选：如果使用HostPath挂载模型
+      volumeMount:
+        name: model-volume
+        mountPath: /opt/nuclio/best.pt
+
+  resources:
+    limits:
+      nvidia.com/gpu: 1
+
+  platform:
+    attributes:
+      restartPolicy:
+        name: always
+        maximumRetryCount: 3
+      mountMode: volume

localdetect.py

@@ -0,0 +1,38 @@
+import os
+import cv2
+from ultralytics import YOLO
+
+# 加载模型
+model = YOLO("best.pt")
+
+# 输入文件夹路径
+input_dir = "test_images/"
+temp_dir = "temp_rgb_images"
+
+# 创建临时文件夹用于保存转换后的RGB图片
+os.makedirs(temp_dir, exist_ok=True)
+
+# 扫描文件夹，将灰度图转换为RGB
+for file in os.listdir(input_dir):
+    if file.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp', '.tiff')):
+        path = os.path.join(input_dir, file)
+        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
+
+        # 如果是灰度图，转换为3通道
+        if len(img.shape) == 2 or img.shape[2] == 1:
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+
+        # 保存到临时目录
+        cv2.imwrite(os.path.join(temp_dir, file), img)
+
+# 使用转换后的图像文件夹进行检测
+results = model.predict(source=temp_dir, conf=0.2, save=True)
+
+# 输出检测信息
+for result in results:
+    boxes = result.boxes  # 检测框
+    for box in boxes:
+        cls = int(box.cls[0])
+        conf = float(box.conf[0])
+        xyxy = box.xyxy[0].tolist()
+        print(f"类别: {model.names[cls]}, 置信度: {conf:.2f}, 坐标: {xyxy}")

main.py

@@ -0,0 +1,24 @@
+import json
+import base64
+# from PIL import Image
+import io
+from model_handler import ModelHandler
+
+def init_context(context):
+    context.logger.info("Init context...  0%")
+
+    context.logger.info("Initializing EMDetection model...")
+    context.user_data.model_handler = ModelHandler()
+
+    context.logger.info("Init context...100%")
+
+def handler(context, event):
+    context.logger.info("Run EMDetection model")
+    data = event.body
+    image_data = base64.b64decode(data["image"])
+    threshold = float(data.get("threshold", 0.5))
+
+    results = context.user_data.model_handler.infer(image_data, threshold)
+
+    return context.Response(body=json.dumps(results), headers={},
+        content_type='application/json', status_code=200)

model_handler.py

@@ -0,0 +1,33 @@
+import io
+from PIL import Image
+from ultralytics import YOLO
+
+
+class ModelHandler:
+    def __init__(self):
+        """加载 YOLOv11 模型"""
+        self.model = YOLO("/opt/nuclio/best.pt")  # 确保路径正确
+
+    def infer(self, image_data, threshold=0.3):
+        """
+        执行推理
+        :param image_data: 图片的二进制数据
+        :param threshold: 置信度阈值（默认0.3）
+        :return: 符合阈值的检测结果
+        """
+        image = Image.open(io.BytesIO(image_data))
+        results = self.model(image)
+
+        detections = []
+        for result in results:
+            for box in result.boxes.data.tolist():
+                x1, y1, x2, y2, score, class_id = box
+                if score >= threshold:  # 过滤低置信度目标
+                    detections.append({
+                        "confidence": score,
+                        "label": self.model.names[int(class_id)],
+                        "points": [x1, y1, x2, y2],
+                        "type": "rectangle",
+                    })
+
+        return detections

runanno.py

@@ -0,0 +1,16 @@
+import subprocess
+import os
+
+# 获取当前脚本所在的目录（即 AutoAnno 目录）
+base_dir = os.path.dirname(os.path.abspath(__file__))
+
+# 组装相对路径
+python_exe = os.path.join(base_dir, ".venv", "Scripts", "python.exe")
+script_path = os.path.join(base_dir, "autoannoguinew.py")
+
+# 后台静默执行
+subprocess.Popen(
+    [python_exe, script_path],
+    creationflags=subprocess.CREATE_NO_WINDOW,
+    cwd=base_dir
+)