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Oil_Level_Recognition_System
Commit cf986887 by Yuhaibo
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Merge branch 'main' into Wangbing1

parents 1fc99f7f fed05a05
Showing with 2307 additions and 124 deletions
.gitignore
...@@ -2,6 +2,16 @@ ...@@ -2,6 +2,16 @@
* *
# 但保留以下文件和文件夹 # 但保留以下文件和文件夹
!database/
!database/**
!hooks/
!hooks/**
!icons/
!icons/**
!labelme/
!labelme/**
!rules/
!rules/**
!widgets/ !widgets/
!widgets/** !widgets/**
!handlers/ !handlers/
......
__main__.py
...@@ -24,8 +24,6 @@ os.environ['ULTRALYTICS_CONFIG_DIR'] = os.path.join(current_dir, '.cache', 'ultr ...@@ -24,8 +24,6 @@ os.environ['ULTRALYTICS_CONFIG_DIR'] = os.path.join(current_dir, '.cache', 'ultr
# 修复 OpenMP 运行时冲突问题 # 修复 OpenMP 运行时冲突问题
os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE' # 允许多个OpenMP库共存（临时解决方案） os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE' # 允许多个OpenMP库共存（临时解决方案）
print("[环境变量] ultralytics离线模式已启用")
print("[环境变量] OpenMP冲突已修复")
from qtpy import QtWidgets from qtpy import QtWidgets
......
app.py
...@@ -620,11 +620,10 @@ class MainWindow( ...@@ -620,11 +620,10 @@ class MainWindow(
if os.path.exists(icon_path): if os.path.exists(icon_path):
icon = QtGui.QIcon(icon_path) icon = QtGui.QIcon(icon_path)
self.setWindowIcon(icon) self.setWindowIcon(icon)
print(f"[主窗口] 窗口图标已设置: {icon_path}")
else: else:
print(f"[主窗口] 图标文件不存在: {icon_path}") pass
except Exception as e: except Exception as e:
print(f"[主窗口] 设置窗口图标失败: {e}") pass
def _loadDefaultConfig(self): def _loadDefaultConfig(self):
"""从 default_config.yaml 加载配置""" """从 default_config.yaml 加载配置"""
...@@ -772,7 +771,6 @@ class MainWindow( ...@@ -772,7 +771,6 @@ class MainWindow(
# 🔥 为每个通道面板的任务标签设置变量名（channel1mission, channel2mission, channel3mission, channel4mission） # 🔥 为每个通道面板的任务标签设置变量名（channel1mission, channel2mission, channel3mission, channel4mission）
mission_var_name = f'channel{i+1}mission' mission_var_name = f'channel{i+1}mission'
setattr(self, mission_var_name, channelPanel.taskLabel) setattr(self, mission_var_name, channelPanel.taskLabel)
print(f"[MainWindow] 已设置任务标签变量: {mission_var_name}")
if hasattr(self, '_connectChannelPanelSignals'): if hasattr(self, '_connectChannelPanelSignals'):
self._connectChannelPanelSignals(channelPanel) self._connectChannelPanelSignals(channelPanel)
...@@ -803,7 +801,6 @@ class MainWindow( ...@@ -803,7 +801,6 @@ class MainWindow(
history_panel.setObjectName(f"HistoryVideoPanel_{i+1}") history_panel.setObjectName(f"HistoryVideoPanel_{i+1}")
self.historyVideoPanels.append(history_panel) self.historyVideoPanels.append(history_panel)
print(f"[MainWindow] 已创建 {len(self.historyVideoPanels)} 个历史视频面板")
# 通过handler初始化通道面板数据 # 通过handler初始化通道面板数据
if hasattr(self, 'initializeChannelPanels'): if hasattr(self, 'initializeChannelPanels'):
...@@ -830,7 +827,6 @@ class MainWindow( ...@@ -830,7 +827,6 @@ class MainWindow(
# 🔥 设置曲线面板的任务选择下拉框变量名（curvemission） # 🔥 设置曲线面板的任务选择下拉框变量名（curvemission）
self.curvemission = self.curvePanel.curvemission self.curvemission = self.curvePanel.curvemission
print(f"[MainWindow] 已设置曲线任务变量: curvemission")
# 连接任务选择变化信号 # 连接任务选择变化信号
self.curvemission.currentTextChanged.connect(self._onCurveMissionChanged) self.curvemission.currentTextChanged.connect(self._onCurveMissionChanged)
...@@ -862,7 +858,6 @@ class MainWindow( ...@@ -862,7 +858,6 @@ class MainWindow(
self.videoLayoutStack.addWidget(layout_widget) self.videoLayoutStack.addWidget(layout_widget)
print(f"[MainWindow] 曲线模式布局已创建：左侧子布局栈（实时/历史） + 右侧共用CurvePanel")
def _createRealtimeCurveSubLayout(self): def _createRealtimeCurveSubLayout(self):
"""创建实时检测曲线子布局（索引0）- 左侧通道列表""" """创建实时检测曲线子布局（索引0）- 左侧通道列表"""
...@@ -906,7 +901,6 @@ class MainWindow( ...@@ -906,7 +901,6 @@ class MainWindow(
sublayout.addWidget(self.curve_scroll_area) sublayout.addWidget(self.curve_scroll_area)
self.curveLayoutStack.addWidget(sublayout_widget) self.curveLayoutStack.addWidget(sublayout_widget)
print(f"[MainWindow] 实时检测曲线子布局已创建（索引0）- 基于CSV文件的动态通道系统")
def _createHistoryCurveSubLayout(self): def _createHistoryCurveSubLayout(self):
"""创建历史回放曲线子布局（索引1）- 使用历史视频面板容器""" """创建历史回放曲线子布局（索引1）- 使用历史视频面板容器"""
...@@ -950,7 +944,6 @@ class MainWindow( ...@@ -950,7 +944,6 @@ class MainWindow(
sublayout.addWidget(self.history_scroll_area) sublayout.addWidget(self.history_scroll_area)
self.curveLayoutStack.addWidget(sublayout_widget) self.curveLayoutStack.addWidget(sublayout_widget)
print(f"[MainWindow] 历史回放曲线子布局已创建（索引1）- 历史视频面板容器系统")
def _onChannelCurveClicked(self, task_name): def _onChannelCurveClicked(self, task_name):
""" """
...@@ -959,15 +952,9 @@ class MainWindow( ...@@ -959,15 +952,9 @@ class MainWindow(
Args: Args:
task_name: 通道面板的任务名称 task_name: 通道面板的任务名称
""" """
print(f"🔄 [主窗口] 通道面板查看曲线按钮被点击，任务名称: {task_name}")
# 设置 curvemission 的值 # 设置 curvemission 的值
if hasattr(self, 'curvePanel') and self.curvePanel: if hasattr(self, 'curvePanel') and self.curvePanel:
success = self.curvePanel.setMissionFromTaskName(task_name) success = self.curvePanel.setMissionFromTaskName(task_name)
if success:
print(f"✅ [主窗口] 已设置 curvemission 为: {task_name}")
else:
print(f"⚠️ [主窗口] 设置 curvemission 失败: {task_name}")
# 切换到曲线模式 # 切换到曲线模式
self.toggleVideoPageMode() self.toggleVideoPageMode()
...@@ -1268,7 +1255,6 @@ class MainWindow( ...@@ -1268,7 +1255,6 @@ class MainWindow(
# ========== 通道管理按钮信号 ========== # ========== 通道管理按钮信号 ==========
# 🔥 已改为内嵌显示，由 MissionPanelHandler 处理，不再使用弹窗 # 🔥 已改为内嵌显示，由 MissionPanelHandler 处理，不再使用弹窗
# self.missionTable.channelManageClicked.connect(self.onChannelManage) # 旧的弹窗方式 # self.missionTable.channelManageClicked.connect(self.onChannelManage) # 旧的弹窗方式
print("[App] 通道管理已改为内嵌显示，不再使用弹窗")
# ========== 通道面板信号（为所有面板连接） ========== # ========== 通道面板信号（为所有面板连接） ==========
# 注意：channelConnected, channelDisconnected, channelEdited, amplifyClicked, channelNameChanged # 注意：channelConnected, channelDisconnected, channelEdited, amplifyClicked, channelNameChanged
...@@ -1517,15 +1503,12 @@ class MainWindow( ...@@ -1517,15 +1503,12 @@ class MainWindow(
def closeEvent(self, event): def closeEvent(self, event):
"""窗口关闭事件""" """窗口关闭事件"""
try: try:
print("[应用] 正在关闭应用...")
# 清理全局检测线程 # 清理全局检测线程
if hasattr(self, 'view_handler') and self.view_handler: if hasattr(self, 'view_handler') and self.view_handler:
video_handler = getattr(self.view_handler, 'video_handler', None) video_handler = getattr(self.view_handler, 'video_handler', None)
if video_handler: if video_handler:
thread_manager = getattr(video_handler, 'thread_manager', None) thread_manager = getattr(video_handler, 'thread_manager', None)
if thread_manager: if thread_manager:
print("[应用] 清理全局检测线程...")
thread_manager.cleanup_global_detection_thread() thread_manager.cleanup_global_detection_thread()
# 保存窗口状态 # 保存窗口状态
...@@ -1536,10 +1519,7 @@ class MainWindow( ...@@ -1536,10 +1519,7 @@ class MainWindow(
# 保存当前页面索引 # 保存当前页面索引
self.settings.setValue("window/last_page", self.getCurrentPageIndex()) self.settings.setValue("window/last_page", self.getCurrentPageIndex())
print("[应用] 应用关闭清理完成")
except Exception as e: except Exception as e:
print(f"[应用] 关闭清理失败: {e}")
import traceback import traceback
traceback.print_exc() traceback.print_exc()
......
database/config/annotation_result.yaml 0 → 100644
channel1:
annotation_count: 1
areas:
area_1:
height: 20mm
name: 通道1_区域1
boxes:
- - 617
- 415
- 192
fixed_bottoms:
- 482
fixed_tops:
- 387
last_updated: '2025-11-27 15:55:10'
channel2:
annotation_count: 1
areas:
area_1:
height: 20mm
name: 我去饿_区域1
boxes:
- - 643
- 558
- 160
fixed_bottoms:
- 616
fixed_tops:
- 534
last_updated: '2025-11-26 20:09:26'
channel3:
annotation_count: 1
areas:
area_1:
height: 20mm
name: 3_区域1
boxes:
- - 1365
- 915
- 128
fixed_bottoms:
- 939
fixed_tops:
- 886
last_updated: '2025-11-26 20:09:35'
channel4:
annotation_count: 1
areas:
area_1:
height: 20mm
name: asfdhuu_区域1
boxes:
- - 1689
- 884
- 96
fixed_bottoms:
- 908
fixed_tops:
- 860
last_updated: '2025-11-26 20:02:17'
通道1:
annotation_count: 2
areas:
area_1:
height: 20mm
name: 通道1_区域1
area_2:
height: 22mm
name: 通道1_
boxes:
- - 653
- 281
- 192
- - 337
- 520
- 160
fixed_bottoms:
- 204
- 579
fixed_tops:
- 300
- 456
last_updated: '2025-11-03 15:58:08'
database/config/channel_config.yaml 0 → 100644
channels:
1:
address: rtsp://admin:cei345678@192.168.0.127:8000/stream1
channel_id: 1
name: 不后悔1
2:
address: rtsp://admin:cei345678@192.168.0.127:8000/stream1
channel_id: 2
name: '2'
3:
address: rtsp://admin:cei345678@192.168.0.127:8000/stream1
channel_id: 3
name: '3'
4:
address: rtsp://admin:cei345678@192.168.0.127:8000/stream1
channel_id: 4
name: '4'
channel2:
general:
task_id: '123'
task_name: '21'
save_liquid_data_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\123_21
channel3:
general:
task_id: '123'
task_name: '21'
save_liquid_data_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\123_21
channel4:
general:
task_id: '1'
task_name: '1'
save_liquid_data_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\1_1
channel1:
general:
task_id: '1'
task_name: '1'
area_count: 0
safe_low: 2.0mm
safe_high: 10.0mm
frequency: 25fps
video_format: AVI
push_address: ''
video_path: ''
save_liquid_data_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\1_1
areas:
area_1: 通道1_区域1
area_2: 通道1_区域2
area_heights:
area_1: 20mm
area_2: 20mm
model:
model_path: d:\restructure\liquid_level_line_detection_system\database\model\detection_model\5\best.dat
channel_1:
general:
task_id: '1'
task_name: '1'
save_liquid_data_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\1_1
channel_2:
general:
task_id: '2'
task_name: '2'
save_liquid_data_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\2_2
channel_3:
general:
task_id: '3'
task_name: '3'
save_liquid_data_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\3_3
channel_4:
general:
task_id: '4'
task_name: '4'
save_liquid_data_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\4_4
database/config/mission/123_21.yaml 0 → 100644
task_id: '123'
task_name: '21'
status: 待配置
selected_channels:
- 通道2
- 通道3
created_time: '2025-11-26 14:55:31'
mission_result_folder_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\123_21
database/config/mission/1_1.yaml 0 → 100644
task_id: '1'
task_name: '1'
status: 待配置
selected_channels:
- 通道1
- 通道2
- 通道3
- 通道4
created_time: '2025-11-26 19:53:35'
mission_result_folder_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\1_1
database/config/mission/1_2.yaml 0 → 100644
task_id: '1'
task_name: '2'
status: 待配置
selected_channels:
- 通道1
- 通道2
created_time: '2025-11-26 16:24:36'
mission_result_folder_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\1_2
database/config/mission/1_222.yaml 0 → 100644
task_id: '1'
task_name: '222'
status: 待配置
selected_channels:
- 通道1
- 通道2
- 通道3
- 通道4
created_time: '2025-11-26 19:58:15'
mission_result_folder_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\1_222
database/config/mission/1_test.yaml 0 → 100644
task_id: '1'
task_name: test
status: 待配置
selected_channels:
- 通道1
- 通道2
- 通道3
- 通道4
created_time: '2025-11-26 19:46:34'
mission_result_folder_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\1_test
database/config/mission/21_321.yaml 0 → 100644
task_id: '21'
task_name: '321'
status: 待配置
selected_channels:
- 通道1
- 通道2
- 通道3
- 通道4
created_time: '2025-11-26 20:08:46'
mission_result_folder_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\21_321
database/config/mission/2_test.yaml 0 → 100644
task_id: '2'
task_name: test
status: 待配置
selected_channels:
- 通道1
- 通道2
- 通道3
- 通道4
created_time: '2025-11-26 20:01:16'
mission_result_folder_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\2_test
database/config/mission/2恶趣味_q'we.yaml 0 → 100644
task_id: 2恶趣味
task_id: 2恶趣味
task_name: q'we
status: 待配置
selected_channels:
- 通道2
created_time: '2025-11-26 14:56:26'
mission_result_folder_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\2恶趣味_q'we
database/config/mission/吃个海鲜_显示提醒他.yaml 0 → 100644
task_id: 吃个海鲜
task_id: 吃个海鲜
task_name: 显示提醒他
status: 待配置
selected_channels:
- 通道4
created_time: '2025-11-27 11:06:03'
mission_result_folder_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\吃个海鲜_显示提醒他
database/config/mission/大润发给_上方.yaml 0 → 100644
task_id: 大润发给
task_id: 大润发给
task_name: 上方
status: 待配置
selected_channels:
- 通道1
- 通道2
- 通道3
created_time: '2025-11-27 11:00:31'
mission_result_folder_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\大润发给_上方
database/config/mission/的使得发_如图微软.yaml 0 → 100644
task_id: 的使得发
task_id: 的使得发
task_name: 如图微软
status: 待配置
selected_channels:
- 通道1
- 通道3
- 通道4
created_time: '2025-11-27 11:02:21'
mission_result_folder_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\的使得发_如图微软
database/config/mission/的啊_而突然.yaml 0 → 100644
task_id: 的啊
task_id: 的啊
task_name: 而突然
status: 待配置
selected_channels:
- 通道1
- 通道2
- 通道3
- 通道4
created_time: '2025-11-27 11:03:28'
mission_result_folder_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\的啊_而突然
database/config/model_test_annotation_result.yaml 0 → 100644
test_model:
areas:
area_1:
height: 20mm
name: test_model_区域1
area_2:
height: 20mm
name: test_model_区域2
bottoms:
- !!python/tuple
- 1082
- 1028
- !!python/tuple
- 1140
- 283
boxes:
- !!python/tuple
- 1078
- 934
- 192
- !!python/tuple
- 1141
- 222
- 128
tops:
- !!python/tuple
- 1076
- 839
- !!python/tuple
- 1140
- 159
database/config/physical_zoom_config_example.yaml 0 → 100644
# 物理变焦设备配置示例
# 用于配置海康威视摄像头的物理变焦功能
# 通道1配置 - 海康威视球机
channel1_device:
ip: "192.168.1.100" # 设备IP地址
port: 8000 # 设备端口，默认8000
username: "admin" # 登录用户名
password: "admin123" # 登录密码
channel: 1 # 设备通道号
enable_physical_zoom: true # 是否启用物理变焦
zoom_capabilities:
min_zoom: 1.0 # 最小变焦倍数
max_zoom: 30.0 # 最大变焦倍数
zoom_step: 0.5 # 变焦步长
auto_focus: true # 是否支持自动聚焦
# 通道2配置 - 海康威视枪机（不支持变焦）
channel2_device:
ip: "192.168.1.101"
port: 8000
username: "admin"
password: "admin123"
channel: 1
enable_physical_zoom: false # 枪机通常不支持变焦
# 通道3配置 - 海康威视球机
channel3_device:
ip: "192.168.1.102"
port: 8000
username: "admin"
password: "admin123"
channel: 1
enable_physical_zoom: true
zoom_capabilities:
min_zoom: 1.0
max_zoom: 20.0 # 不同型号支持的倍数可能不同
zoom_step: 0.5
auto_focus: true
# 通道4配置 - 海康威视球机
channel4_device:
ip: "192.168.1.103"
port: 8000
username: "admin"
password: "admin123"
channel: 1
enable_physical_zoom: true
zoom_capabilities:
min_zoom: 1.0
max_zoom: 25.0
zoom_step: 0.5
auto_focus: true
# 全局配置
global_settings:
# 物理变焦优先级：true表示优先使用物理变焦，false表示优先使用数字变焦
prefer_physical_zoom: true
# 连接超时时间（秒）
connection_timeout: 10
# 变焦操作超时时间（秒）
zoom_timeout: 15
# 自动重连间隔（秒）
reconnect_interval: 30
# 错误重试次数
max_retry_count: 3
# 使用说明：
# 1. 将此文件重命名为 physical_zoom_config.yaml
# 2. 根据实际设备信息修改IP地址、用户名、密码等
# 3. 确保设备支持PTZ控制功能
# 4. 在放大窗口中使用以下快捷键：
# - 鼠标滚轮：变焦放大/缩小
# - R键：重置变焦到1倍
# - D键：显示物理变焦状态
# - F键：自动聚焦
# - H键：显示/隐藏帮助信息
# - E键：切换锐化增强
# - N键：切换降噪处理
# - C键：切换对比度增强
#
# 注意：系统只支持物理变焦，需要海康威视PTZ设备支持
database/config/train_configs/default_config.json 0 → 100644
{
"model_config": {
"model_type": "YOLOv8",
"model_size": "n",
"pretrained": true,
"input_size": [640, 640]
},
"training_config": {
"epochs": 100,
"batch_size": 16,
"learning_rate": 0.01,
"optimizer": "SGD",
"momentum": 0.937,
"weight_decay": 0.0005
},
"dataset_config": {
"num_classes": 3,
"class_names": ["liquid", "foam", "background"],
"data_yaml": "database/config/train_configs/data.yaml"
},
"augmentation_config": {
"hsv_h": 0.015,
"hsv_s": 0.7,
"hsv_v": 0.4,
"degrees": 0.0,
"translate": 0.1,
"scale": 0.5,
"shear": 0.0,
"perspective": 0.0,
"flipud": 0.0,
"fliplr": 0.5,
"mosaic": 1.0,
"mixup": 0.0
},
"validation_config": {
"val_interval": 1,
"save_period": 10,
"patience": 50
},
"device_config": {
"device": "cuda",
"workers": 8,
"amp": true
}
}
database/config/train_configs/template_1.yaml 0 → 100644
# 模板1 - 快速训练配置（轻量级）
# 用于快速验证模型和数据集
task: segment
mode: train
model: database/model/detection_model/5/best.dat
data: database/dataset/data_template_1.yaml
epochs: 50
batch: 8
imgsz: 416
save: true
save_period: 10
cache: false
device: gpu
workers: 4
project: runs/train
name: template_1_exp
exist_ok: false
pretrained: false
optimizer: SGD
verbose: false
seed: 0
deterministic: true
single_cls: false
rect: false
cos_lr: false
close_mosaic: 10
resume: false
amp: false
fraction: 1.0
profile: false
freeze: null
multi_scale: false
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
show: false
save_frames: false
save_txt: false
save_conf: false
save_crop: false
show_labels: true
show_conf: true
show_boxes: true
line_width: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: true
opset: null
workspace: null
nms: false
lr0: 0.01
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 7.5
cls: 0.5
dfl: 1.5
pose: 12.0
kobj: 1.0
nbs: 64
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
bgr: 0.0
mosaic: 1.0
mixup: 0.0
cutmix: 0.0
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
database/config/train_configs/template_2.yaml 0 → 100644
# 模板2 - 标准训练配置（平衡型）
# 用于常规模型训练，平衡精度和速度
task: segment
mode: train
model: database/model/detection_model/5/best.dat
data: database/dataset/data_template_2.yaml
epochs: 100
batch: 16
imgsz: 640
save: true
save_period: 10
cache: false
device: gpu
workers: 4
project: runs/train
name: template_2_exp
exist_ok: false
pretrained: false
optimizer: SGD
verbose: false
seed: 0
deterministic: true
single_cls: false
rect: false
cos_lr: false
close_mosaic: 10
resume: false
amp: false
fraction: 1.0
profile: false
freeze: null
multi_scale: false
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
show: false
save_frames: false
save_txt: false
save_conf: false
save_crop: false
show_labels: true
show_conf: true
show_boxes: true
line_width: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: true
opset: null
workspace: null
nms: false
lr0: 0.01
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 7.5
cls: 0.5
dfl: 1.5
pose: 12.0
kobj: 1.0
nbs: 64
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
bgr: 0.0
mosaic: 1.0
mixup: 0.0
cutmix: 0.0
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
database/config/train_configs/template_3.yaml 0 → 100644
# 模板3 - 高精度训练配置（精度优先）
# 用于追求最高精度的模型训练
task: segment
mode: train
model: database/model/detection_model/5/best.dat
data: database/dataset/data_template_3.yaml
epochs: 200
batch: 32
imgsz: 768
save: true
save_period: 10
cache: false
device: gpu
workers: 4
project: runs/train
name: template_3_exp
exist_ok: false
pretrained: false
optimizer: Adam
verbose: false
seed: 0
deterministic: true
single_cls: false
rect: false
cos_lr: true
close_mosaic: 10
resume: false
amp: false
fraction: 1.0
profile: false
freeze: null
multi_scale: true
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
show: false
save_frames: false
save_txt: false
save_conf: false
save_crop: false
show_labels: true
show_conf: true
show_boxes: true
line_width: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: true
opset: null
workspace: null
nms: false
lr0: 0.01
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 7.5
cls: 0.5
dfl: 1.5
pose: 12.0
kobj: 1.0
nbs: 64
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
bgr: 0.0
mosaic: 1.0
mixup: 0.0
cutmix: 0.0
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
database/mission_result/2恶趣味_q'we/2恶趣味_q'we.yaml 0 → 100644
task_id: 2恶趣味
task_id: 2恶趣味
task_name: q'we
status: 待配置
selected_channels:
- 通道2
created_time: '2025-11-26 14:56:26'
mission_result_folder_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\2恶趣味_q'we
database/model/detection_model/detect/模型描述.txt 0 → 100644
然后我去佛i好的食品发酵食品的
然后我去佛i好的食品发酵食品的
\ No newline at end of file
database/video_crop_mapping.json 0 → 100644
{
"d:\\restructure\\liquid_level_line_detection_system\\database\\data\\111\\recording_20251114_161804.mp4": {
"save_path": "d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture",
"region_paths": [
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\recording_20251114_161804_区域1",
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\recording_20251114_161804_区域2",
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\recording_20251114_161804_区域3"
],
"timestamp": 1763623553.6413627
},
"d:\\restructure\\liquid_level_line_detection_system\\database\\data\\111\\采集视频_20251115_213950.mp4": {
"save_path": "d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture",
"region_paths": [
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251115_213950_区域1",
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251115_213950_区域2",
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251115_213950_区域3"
],
"timestamp": 1763623570.4085448
},
"d:\\restructure\\liquid_level_line_detection_system\\database\\data\\111\\11.mp4": {
"save_path": "d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture",
"region_paths": [
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\11_区域1"
],
"timestamp": 1764162728.0737584
},
"D:\\restructure\\liquid_level_line_detection_system\\database\\data\\111\\11.mp4": {
"save_path": "d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture",
"region_paths": [
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\11_区域1",
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\11_区域2"
],
"timestamp": 1763809441.0187602
},
"D:\\restructure\\liquid_level_line_detection_system\\database\\data\\111\\recording_20251114_161804.mp4": {
"save_path": "d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture",
"region_paths": [
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\recording_20251114_161804_区域1",
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\recording_20251114_161804_区域2",
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\recording_20251114_161804_区域3"
],
"timestamp": 1763652978.8164835
},
"D:\\restructure\\liquid_level_line_detection_system\\database\\data\\111\\采集视频_20251114_162238.mp4": {
"save_path": "d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture",
"region_paths": [
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251114_162238_区域1",
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251114_162238_区域2",
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251114_162238_区域3"
],
"timestamp": 1763653387.158211
},
"D:\\restructure\\liquid_level_line_detection_system\\database\\data\\111\\采集视频_20251115_213950.mp4": {
"save_path": "d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture",
"region_paths": [
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251115_213950_区域1",
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251115_213950_区域2",
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251115_213950_区域3"
],
"timestamp": 1763653027.766409
},
"D:\\restructure\\liquid_level_line_detection_system\\database\\data\\111\\采集视频_20251118_102818.mp4": {
"save_path": "d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture",
"region_paths": [
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251118_102818_区域1",
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251118_102818_区域2",
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251118_102818_区域3"
],
"timestamp": 1763653036.4475722
},
"D:\\restructure\\liquid_level_line_detection_system\\database\\data\\111\\采集视频_20251120_152702.mp4": {
"save_path": "d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture",
"region_paths": [
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251120_152702_区域1",
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251120_152702_区域2",
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251120_152702_区域3"
],
"timestamp": 1763653067.312616
},
"D:\\restructure\\liquid_level_line_detection_system\\database\\data\\111\\采集视频_20251120_153508.mp4": {
"save_path": "d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture",
"region_paths": [
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251120_153508_区域1",
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251120_153508_区域2",
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251120_153508_区域3"
],
"timestamp": 1763801356.5907655
},
"d:\\restructure\\liquid_level_line_detection_system\\database\\data\\111\\采集视频_20251118_102818.mp4": {
"save_path": "d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture",
"region_paths": [
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251118_102818_区域1",
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251118_102818_区域2",
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251118_102818_区域3"
],
"timestamp": 1764210156.5055494
},
"d:\\restructure\\liquid_level_line_detection_system\\database\\data\\test2\\采集视频_20251127_094018.mp4": {
"save_path": "d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture",
"region_paths": [
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251127_094018_区域1",
"d:\\restructure\\liquid_level_line_detection_system\\database\\Corp_picture\\采集视频_20251127_094018_区域2"
],
"timestamp": 1764210266.1624653
}
}
\ No newline at end of file
handlers/modelpage/model_set_handler.py
...@@ -517,40 +517,26 @@ class ModelSetHandler: ...@@ -517,40 +517,26 @@ class ModelSetHandler:
try: try:
# 获取模型目录路径 # 获取模型目录路径
current_dir = Path(__file__).parent.parent.parent current_dir = Path(__file__).parent.parent.parent
model_dir = current_dir / "database" / "model" / "train_model" model_dir = current_dir / "database" / "model" / "detection_model"
print(f"[模型扫描] 扫描目录: {model_dir}")
if not model_dir.exists(): if not model_dir.exists():
print(f"[模型扫描] 目录不存在")
return models return models
print(f"[模型扫描] 目录存在: {model_dir.exists()}") # 扫描所有子目录（数字和非数字）
# 按数字排序子目录（降序，最新的在前）
all_subdirs = [d for d in model_dir.iterdir() if d.is_dir()] all_subdirs = [d for d in model_dir.iterdir() if d.is_dir()]
print(f"[模型扫描] 找到子目录数量: {len(all_subdirs)}")
print(f"[模型扫描] 子目录列表: {[d.name for d in all_subdirs]}")
# 分离数字目录和非数字目录
digit_subdirs = [d for d in all_subdirs if d.name.isdigit()] digit_subdirs = [d for d in all_subdirs if d.name.isdigit()]
print(f"[模型扫描] 数字子目录数量: {len(digit_subdirs)}") non_digit_subdirs = [d for d in all_subdirs if not d.name.isdigit()]
sorted_subdirs = sorted(digit_subdirs, key=lambda x: int(x.name), reverse=True) # 数字目录按数字降序排序，非数字目录按字母排序
print(f"[模型扫描] 数字子目录: {[d.name for d in sorted_subdirs]}") sorted_digit_subdirs = sorted(digit_subdirs, key=lambda x: int(x.name), reverse=True)
sorted_non_digit_subdirs = sorted(non_digit_subdirs, key=lambda x: x.name)
# 合并：数字目录在前，非数字目录在后
sorted_subdirs = sorted_digit_subdirs + sorted_non_digit_subdirs
for subdir in sorted_subdirs: for subdir in sorted_subdirs:
print(f"[模型扫描] 处理子目录: {subdir.name}")
# 尝试读取 config.yaml 获取详细信息
config_file = subdir / "config.yaml"
model_config = None
if config_file.exists():
try:
with open(config_file, 'r', encoding='utf-8') as f:
model_config = yaml.safe_load(f)
except Exception as e:
print(f"[模型扫描] 读取config.yaml失败: {e}")
# 检查是否有weights子目录（优先检查train/weights，然后weights） # 检查是否有weights子目录（优先检查train/weights，然后weights）
train_weights_dir = subdir / "train" / "weights" train_weights_dir = subdir / "train" / "weights"
...@@ -558,15 +544,10 @@ class ModelSetHandler: ...@@ -558,15 +544,10 @@ class ModelSetHandler:
if train_weights_dir.exists(): if train_weights_dir.exists():
search_dir = train_weights_dir search_dir = train_weights_dir
print(f"[模型扫描] 找到train/weights目录: {search_dir}")
elif weights_dir.exists(): elif weights_dir.exists():
search_dir = weights_dir search_dir = weights_dir
print(f"[模型扫描] 找到weights目录: {search_dir}")
else: else:
search_dir = subdir search_dir = subdir
print(f"[模型扫描] 使用根目录: {search_dir}")
print(f"[模型扫描] 搜索目录: {search_dir}")
# 按优先级查找模型文件：best > last > epoch1 # 按优先级查找模型文件：best > last > epoch1
# 支持的扩展名：.dat, .pt, .template_*, 无扩展名 # 支持的扩展名：.dat, .pt, .template_*, 无扩展名
...@@ -581,7 +562,6 @@ class ModelSetHandler: ...@@ -581,7 +562,6 @@ class ModelSetHandler:
# 检查文件名是否匹配模式 # 检查文件名是否匹配模式
if file.name.startswith('best.') and not file.name.endswith('.pt'): if file.name.startswith('best.') and not file.name.endswith('.pt'):
selected_model = file selected_model = file
print(f"[模型扫描] 找到best模型: {file.name}")
break break
# 优先级2: last模型（如果没有best） # 优先级2: last模型（如果没有best）
...@@ -589,7 +569,6 @@ class ModelSetHandler: ...@@ -589,7 +569,6 @@ class ModelSetHandler:
for file in search_dir.iterdir(): for file in search_dir.iterdir():
if file.is_file() and file.name.startswith('last.') and not file.name.endswith('.pt'): if file.is_file() and file.name.startswith('last.') and not file.name.endswith('.pt'):
selected_model = file selected_model = file
print(f"[模型扫描] 找到last模型: {file.name}")
break break
# 优先级3: epoch1模型（如果没有best和last） # 优先级3: epoch1模型（如果没有best和last）
...@@ -597,7 +576,6 @@ class ModelSetHandler: ...@@ -597,7 +576,6 @@ class ModelSetHandler:
for file in search_dir.iterdir(): for file in search_dir.iterdir():
if file.is_file() and file.name.startswith('epoch1.') and not file.name.endswith('.pt'): if file.is_file() and file.name.startswith('epoch1.') and not file.name.endswith('.pt'):
selected_model = file selected_model = file
print(f"[模型扫描] 找到epoch1模型: {file.name}")
break break
# 如果都没找到，尝试查找任何非.pt文件 # 如果都没找到，尝试查找任何非.pt文件
...@@ -605,22 +583,15 @@ class ModelSetHandler: ...@@ -605,22 +583,15 @@ class ModelSetHandler:
for file in search_dir.iterdir(): for file in search_dir.iterdir():
if file.is_file() and not file.name.endswith('.pt') and not file.name.endswith('.txt') and not file.name.endswith('.yaml'): if file.is_file() and not file.name.endswith('.pt') and not file.name.endswith('.txt') and not file.name.endswith('.yaml'):
selected_model = file selected_model = file
print(f"[模型扫描] 找到其他模型: {file.name}")
break break
# 如果找到了模型文件，添加到列表 # 如果找到了模型文件，添加到列表
if selected_model: if selected_model:
# 从 config.yaml 获取信息，或使用默认值 # 使用"文件夹名称/模型文件名"格式
if model_config: model_name = f"{subdir.name}/{selected_model.stem}"
model_name = model_config.get('name', f"训练模型-{subdir.name}") description = f"来自目录 {subdir.name}"
description = model_config.get('description', '') training_date = ''
training_date = model_config.get('training_date', '') epochs = ''
epochs = model_config.get('epochs', '')
else:
model_name = selected_model.stem # 使用文件名（不含扩展名）作为模型名
description = f"来自目录 {subdir.name}"
training_date = ''
epochs = ''
# 获取文件格式 # 获取文件格式
file_ext = selected_model.suffix.lstrip('.') file_ext = selected_model.suffix.lstrip('.')
...@@ -643,16 +614,11 @@ class ModelSetHandler: ...@@ -643,16 +614,11 @@ class ModelSetHandler:
'file_name': selected_model.name 'file_name': selected_model.name
} }
models.append(model_info) models.append(model_info)
print(f"[模型扫描] 添加模型: {model_name} ({selected_model.name})")
else:
print(f"[模型扫描] 子目录 {subdir.name} 中未找到有效模型")
except Exception as e: except Exception as e:
import traceback import traceback
traceback.print_exc() traceback.print_exc()
print(f"[模型扫描] 扫描异常: {e}")
print(f"[模型扫描] 总共找到 {len(models)} 个模型")
return models return models
def _mergeModelInfo(self, channel_models, scanned_models): def _mergeModelInfo(self, channel_models, scanned_models):
......
handlers/videopage/AUTO_DOT_README.md 0 → 100644
# 自动标点功能模块使用说明
## 功能概述
`auto_dot.py` 模块实现了基于YOLO分割掩码的自动标点功能，可以自动检测容器的顶部和底部位置，替代人工手动标点。
## 核心特性
- **输入**: 图片 + 检测框
- **输出**: 点位置信息 + 标注后的图片
- **检测方法**:
1. **liquid底部 + air顶部** (最可靠)
2. **liquid底部 + liquid顶部** (次选)
3. **air底部 + air顶部** (备选)
## 独立调试
### 1. 准备测试数据
将测试图片放置到:
```
D:\restructure\liquid_level_line_detection_system\test_data\test_image.jpg
```
### 2. 配置检测框
编辑 `auto_dot.py` 中的 `test_auto_dot()` 函数，修改 `boxes` 参数:
```python
# 方式1: [x1, y1, x2, y2] 格式
boxes = [
[100, 200, 300, 600], # 第一个容器
[400, 200, 600, 600], # 第二个容器
]
# 方式2: [cx, cy, size] 格式
boxes = [
[200, 400, 400], # 中心点(200, 400), 尺寸400
]
```
### 3. 运行测试
```bash
cd D:\restructure\liquid_level_line_detection_system\handlers\videopage
python auto_dot.py
```
### 4. 查看结果
- **控制台输出**: 详细的检测过程和结果
- **标注图片**: `D:\restructure\liquid_level_line_detection_system\test_output\auto_dot_result.jpg`
## API 使用示例
```python
from handlers.videopage.auto_dot import AutoDotDetector
import cv2
# 1. 创建检测器
detector = AutoDotDetector(
model_path="path/to/model.dat",
device='cuda' # 或 'cpu'
)
# 2. 加载图片
image = cv2.imread("test_image.jpg")
# 3. 定义检测框
boxes = [
[100, 200, 300, 600], # [x1, y1, x2, y2]
]
# 4. 执行检测
result = detector.detect_container_boundaries(
image=image,
boxes=boxes,
conf_threshold=0.5
)
# 5. 获取结果
if result['success']:
for container in result['containers']:
print(f"容器 {container['index']}:")
print(f" 顶部: ({container['top_x']}, {container['top']})")
print(f" 底部: ({container['bottom_x']}, {container['bottom']})")
print(f" 高度: {container['height']}px")
print(f" 置信度: {container['confidence']:.3f}")
# 保存标注图片
cv2.imwrite("result.jpg", result['annotated_image'])
```
## 输出数据结构
```python
{
'success': bool, # 检测是否成功
'containers': [
{
'index': int, # 容器索引
'top': int, # 顶部y坐标
'bottom': int, # 底部y坐标
'top_x': int, # 顶部x坐标
'bottom_x': int, # 底部x坐标
'height': int, # 容器高度(像素)
'confidence': float, # 检测置信度
'method': str # 检测方法
},
...
],
'annotated_image': np.ndarray # 标注后的图片
}
```
## 检测方法说明
### 方法1: liquid_air (最可靠)
- **容器底部**: liquid掩码的最低点
- **容器顶部**: air掩码的最高点
- **适用场景**: 同时检测到液体和空气
### 方法2: liquid_only (次选)
- **容器底部**: liquid掩码的最低点
- **容器顶部**: liquid掩码的最高点
- **适用场景**: 只检测到液体，未检测到空气
### 方法3: air_only (备选)
- **容器底部**: air掩码的最低点
- **容器顶部**: air掩码的最高点
- **适用场景**: 只检测到空气，未检测到液体
## 可视化标注
标注图片包含:
- **绿色圆点**: 容器顶部
- **红色圆点**: 容器底部
- **青色连线**: 容器高度
- **水平参考线**: 顶部和底部的水平位置
- **文字标注**: Top-N, Bottom-N, 高度值
## 注意事项
1. **模型路径**: 确保模型文件存在且可访问
2. **检测框位置**: 检测框应覆盖完整的容器区域
3. **置信度阈值**: 默认0.5，可根据实际情况调整
4. **GPU加速**: 建议使用CUDA加速，提高检测速度
## 调试技巧
1. **查看控制台输出**: 详细的检测过程日志
2. **检查标注图片**: 验证检测结果的准确性
3. **调整检测框**: 如果检测失败，尝试调整检测框的位置和大小
4. **降低置信度**: 如果检测不到掩码，尝试降低 `conf_threshold`
## 接入系统
调试成功后，可以在主系统中调用:
```python
from handlers.videopage.auto_dot import AutoDotDetector
# 在标注页面添加"自动标点"按钮
# 点击后调用 detector.detect_container_boundaries()
# 将返回的 top/bottom 坐标填充到标注点位置
```
handlers/videopage/channelpanel_handler.py
...@@ -1909,7 +1909,6 @@ class ChannelPanelHandler: ...@@ -1909,7 +1909,6 @@ class ChannelPanelHandler:
config_path = os.path.join(project_root, 'database', 'config', 'default_config.yaml') config_path = os.path.join(project_root, 'database', 'config', 'default_config.yaml')
if not os.path.exists(config_path): if not os.path.exists(config_path):
print(f"[ConfigWatcher] 配置文件不存在: {config_path}")
return return
# 创建文件系统监控器 # 创建文件系统监控器
...@@ -1919,18 +1918,13 @@ class ChannelPanelHandler: ...@@ -1919,18 +1918,13 @@ class ChannelPanelHandler:
# 连接文件变化信号 # 连接文件变化信号
self._config_watcher.fileChanged.connect(self._onConfigFileChanged) self._config_watcher.fileChanged.connect(self._onConfigFileChanged)
print(f"[ConfigWatcher] 已开始监控配置文件: {config_path}")
except Exception as e: except Exception as e:
print(f"[ConfigWatcher] 初始化配置文件监控器失败: {e}")
import traceback import traceback
traceback.print_exc() traceback.print_exc()
def _onConfigFileChanged(self, path): def _onConfigFileChanged(self, path):
"""配置文件变化时的回调""" """配置文件变化时的回调"""
try: try:
print(f"🔄 [ConfigWatcher] 检测到配置文件变化: {path}")
# 延迟一小段时间，确保文件写入完成 # 延迟一小段时间，确保文件写入完成
# 修复：检查 QApplication 是否存在 # 修复：检查 QApplication 是否存在
if QtWidgets.QApplication.instance() is not None: if QtWidgets.QApplication.instance() is not None:
...@@ -1940,45 +1934,32 @@ class ChannelPanelHandler: ...@@ -1940,45 +1934,32 @@ class ChannelPanelHandler:
self._reloadChannelConfig() self._reloadChannelConfig()
except Exception as e: except Exception as e:
print(f"[ConfigWatcher] 处理配置文件变化失败: {e}")
import traceback import traceback
traceback.print_exc() traceback.print_exc()
def _reloadChannelConfig(self): def _reloadChannelConfig(self):
"""重新加载通道配置""" """重新加载通道配置"""
try: try:
print("🔄 [ConfigWatcher] 开始重新加载通道配置...")
# 获取配置文件路径 # 获取配置文件路径
project_root = get_project_root() project_root = get_project_root()
config_path = os.path.join(project_root, 'database', 'config', 'default_config.yaml') config_path = os.path.join(project_root, 'database', 'config', 'default_config.yaml')
print(f" 📂 [ConfigWatcher] 配置文件路径: {config_path}")
print(f" 📂 [ConfigWatcher] 文件是否存在: {os.path.exists(config_path)}")
if not os.path.exists(config_path): if not os.path.exists(config_path):
print(f"[ConfigWatcher] 配置文件不存在: {config_path}")
return return
# 读取配置文件 # 读取配置文件
with open(config_path, 'r', encoding='utf-8') as f: with open(config_path, 'r', encoding='utf-8') as f:
config = yaml.safe_load(f) or {} config = yaml.safe_load(f) or {}
print(f" 📄 [ConfigWatcher] 配置文件内容键: {list(config.keys())}")
print(f" 🗺️ [ConfigWatcher] 通道面板映射: {list(self._channel_panels_map.keys())}")
# 🔥 关键修复：更新 self._config，这样 _getChannelConfigFromFile 才能读取到最新配置 # 🔥 关键修复：更新 self._config，这样 _getChannelConfigFromFile 才能读取到最新配置
old_config = self._config old_config = self._config
self._config = config self._config = config
print(f" 🔄 [ConfigWatcher] 已更新内部配置缓存 (self._config)")
# 更新每个通道面板的名称和地址信息 # 更新每个通道面板的名称和地址信息
for i in range(1, 5): for i in range(1, 5):
channel_id = f'channel{i}' channel_id = f'channel{i}'
channel_key = f'channel{i}' channel_key = f'channel{i}'
print(f" [ConfigWatcher] 处理 {channel_id}...")
# 获取通道面板 # 获取通道面板
panel = self._channel_panels_map.get(channel_id) panel = self._channel_panels_map.get(channel_id)
if not panel: if not panel:
...@@ -2024,15 +2005,10 @@ class ChannelPanelHandler: ...@@ -2024,15 +2005,10 @@ class ChannelPanelHandler:
# 重新添加监控（因为某些编辑器保存文件时会删除再创建，导致监控失效） # 重新添加监控（因为某些编辑器保存文件时会删除再创建，导致监控失效）
if hasattr(self, '_config_watcher'): if hasattr(self, '_config_watcher'):
monitored_files = self._config_watcher.files() monitored_files = self._config_watcher.files()
print(f" 👀 [ConfigWatcher] 当前监控的文件: {monitored_files}")
if config_path not in monitored_files: if config_path not in monitored_files:
self._config_watcher.addPath(config_path) self._config_watcher.addPath(config_path)
print(f" 🔄 [ConfigWatcher] 重新添加文件监控: {config_path}")
print("[ConfigWatcher] 通道配置重新加载完成（包括地址配置）")
except Exception as e: except Exception as e:
print(f"[ConfigWatcher] 重新加载通道配置失败: {e}")
import traceback import traceback
traceback.print_exc() traceback.print_exc()
......
handlers/videopage/general_set_handler.py
...@@ -117,6 +117,7 @@ class GeneralSetPanelHandler: ...@@ -117,6 +117,7 @@ class GeneralSetPanelHandler:
widget.annotationEngineRequested.connect(self._handleAnnotationEngineRequest) widget.annotationEngineRequested.connect(self._handleAnnotationEngineRequest)
widget.frameLoadRequested.connect(self._handleFrameLoadRequest) widget.frameLoadRequested.connect(self._handleFrameLoadRequest)
widget.annotationDataRequested.connect(self._handleAnnotationDataRequest) widget.annotationDataRequested.connect(self._handleAnnotationDataRequest)
widget.liveFrameRequested.connect(self._handleLiveFrameRequest)
def _handleRefreshModelList(self, model_widget=None): def _handleRefreshModelList(self, model_widget=None):
"""处理刷新模型列表请求""" """处理刷新模型列表请求"""
...@@ -264,7 +265,6 @@ class GeneralSetPanelHandler: ...@@ -264,7 +265,6 @@ class GeneralSetPanelHandler:
if self.general_set_panel: if self.general_set_panel:
self.general_set_panel.setTaskIdOptions(task_ids) self.general_set_panel.setTaskIdOptions(task_ids)
print(f"[Handler] 已加载 {len(task_ids)} 个任务编号选项")
except Exception as e: except Exception as e:
print(f"[Handler] 加载任务ID选项失败: {e}") print(f"[Handler] 加载任务ID选项失败: {e}")
import traceback import traceback
...@@ -405,10 +405,6 @@ class GeneralSetPanelHandler: ...@@ -405,10 +405,6 @@ class GeneralSetPanelHandler:
model_config = default_config.get('model', {}).copy() model_config = default_config.get('model', {}).copy()
model_config['model_path'] = absolute_path model_config['model_path'] = absolute_path
print(f"[Handler] 加载通道 {channel_id} 的模型配置:")
print(f" 相对路径: {channel_model_path}")
print(f" 绝对路径: {absolute_path}")
# 调用widget的方法应用配置 # 调用widget的方法应用配置
if self.general_set_panel: if self.general_set_panel:
self.general_set_panel.applyModelConfigFromHandler( self.general_set_panel.applyModelConfigFromHandler(
...@@ -693,26 +689,15 @@ class GeneralSetPanelHandler: ...@@ -693,26 +689,15 @@ class GeneralSetPanelHandler:
channel_frame = None channel_frame = None
if self.general_set_panel and self.general_set_panel.channel_id: if self.general_set_panel and self.general_set_panel.channel_id:
channel_frame = self.getLatestFrame(self.general_set_panel.channel_id) channel_frame = self.getLatestFrame(self.general_set_panel.channel_id)
if channel_frame is not None:
pass
# 如果没有获取到通道画面，使用测试图像 # 如果没有获取到通道画面，弹出提示框并返回
if channel_frame is None: if channel_frame is None:
pass QtWidgets.QMessageBox.warning(
import numpy as np self.main_window,
"获取画面失败",
channel_frame = np.zeros((720, 1280, 3), dtype=np.uint8) "获取通道画面失败，请先连接通道"
channel_frame[:] = (100, 120, 140) # 灰色背景 )
return
# 添加文字说明
cv2.putText(channel_frame, "Test Annotation Frame", (50, 50),
cv2.FONT_HERSHEY_SIMPLEX, 1.5, (255, 255, 255), 2)
cv2.putText(channel_frame, "Draw detection areas and mark liquid levels", (50, 100),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (200, 200, 200), 1)
# 添加测试区域
cv2.rectangle(channel_frame, (200, 200), (400, 400), (0, 255, 0), 2)
cv2.rectangle(channel_frame, (500, 300), (700, 500), (0, 0, 255), 2)
# 2. 保存原始帧用于标注结果显示 # 2. 保存原始帧用于标注结果显示
self._annotation_source_frame = channel_frame.copy() if channel_frame is not None else None self._annotation_source_frame = channel_frame.copy() if channel_frame is not None else None
...@@ -724,6 +709,9 @@ class GeneralSetPanelHandler: ...@@ -724,6 +709,9 @@ class GeneralSetPanelHandler:
if self.general_set_panel and self.general_set_panel.channel_name: if self.general_set_panel and self.general_set_panel.channel_name:
annotation_widget.setChannelName(self.general_set_panel.channel_name) annotation_widget.setChannelName(self.general_set_panel.channel_name)
# 3.5. 初始化物理变焦控制器
self._initPhysicalZoomForAnnotation(annotation_widget)
# 4. 连接标注完成信号 # 4. 连接标注完成信号
def on_annotation_completed(boxes, bottoms, tops): def on_annotation_completed(boxes, bottoms, tops):
print(f"\n[DEBUG] ========== 标注完成回调 ==========") print(f"\n[DEBUG] ========== 标注完成回调 ==========")
...@@ -789,6 +777,9 @@ class GeneralSetPanelHandler: ...@@ -789,6 +777,9 @@ class GeneralSetPanelHandler:
# 5. 加载图像并显示标注界面 # 5. 加载图像并显示标注界面
if annotation_widget.loadFrame(channel_frame): if annotation_widget.loadFrame(channel_frame):
# 启用实时画面预览
annotation_widget.enableLivePreview(True)
# 🔥 关键修复：延迟显示窗口，确保全屏应用后再显示 # 🔥 关键修复：延迟显示窗口，确保全屏应用后再显示
# 这样可以确保标注帧在全屏模式下立即显示 # 这样可以确保标注帧在全屏模式下立即显示
QtCore.QTimer.singleShot(150, annotation_widget.show) QtCore.QTimer.singleShot(150, annotation_widget.show)
...@@ -1246,6 +1237,73 @@ class GeneralSetPanelHandler: ...@@ -1246,6 +1237,73 @@ class GeneralSetPanelHandler:
if self.annotation_widget: if self.annotation_widget:
self.annotation_widget.showAnnotationError(f"获取标注数据失败: {str(e)}") self.annotation_widget.showAnnotationError(f"获取标注数据失败: {str(e)}")
def _handleLiveFrameRequest(self):
"""处理实时画面请求"""
try:
# 获取通道最新画面
if self.general_set_panel and self.general_set_panel.channel_id:
channel_frame = self.getLatestFrame(self.general_set_panel.channel_id)
# 更新标注界面的画面
if channel_frame is not None and self.annotation_widget:
self.annotation_widget.updateLiveFrame(channel_frame)
except Exception as e:
pass
def _initPhysicalZoomForAnnotation(self, annotation_widget):
"""为标注界面初始化物理变焦控制器"""
try:
# 尝试导入物理变焦控制器
try:
from handlers.videopage.physical_zoom_controller import PhysicalZoomController
except ImportError:
try:
from physical_zoom_controller import PhysicalZoomController
except ImportError:
return
# 获取通道配置
if not self.general_set_panel or not self.general_set_panel.channel_id:
return
channel_id = self.general_set_panel.channel_id
# 从配置文件获取设备IP
config = self._getChannelConfig(channel_id)
if not config:
return
device_ip = config.get('address', '')
if not device_ip or 'rtsp://' not in device_ip:
return
# 提取IP地址
import re
match = re.search(r'@(\d+\.\d+\.\d+\.\d+)', device_ip)
if not match:
return
device_ip = match.group(1)
# 创建物理变焦控制器
physical_zoom_controller = PhysicalZoomController(
device_ip=device_ip,
username='admin',
password='cei345678',
channel=1
)
# 尝试连接设备
if physical_zoom_controller.connect_device():
# 设置到标注界面
annotation_widget.setPhysicalZoomController(physical_zoom_controller)
print(f"[标注界面] 物理变焦已启用 ({device_ip})")
else:
print(f"[标注界面] 物理变焦设备连接失败")
except Exception as e:
print(f"[标注界面] 初始化物理变焦失败: {e}")
def showGeneralSetPanel(self): def showGeneralSetPanel(self):
"""显示常规设置面板""" """显示常规设置面板"""
from widgets.videopage.general_set import GeneralSetPanel from widgets.videopage.general_set import GeneralSetPanel
......
handlers/videopage/modelsetting_handler.py
...@@ -144,10 +144,6 @@ class ModelSettingHandler: ...@@ -144,10 +144,6 @@ class ModelSettingHandler:
model_config['model_path'] = absolute_path model_config['model_path'] = absolute_path
config_source = f"default_config.yaml → {channel_model_key} + model (全局参数)" config_source = f"default_config.yaml → {channel_model_key} + model (全局参数)"
print(f"[Handler] 加载通道 {channel_id} 的模型配置")
print(f" 相对路径: {channel_model_path}")
print(f" 绝对路径: {absolute_path}")
print(f" model_config['model_path'] = {model_config.get('model_path', 'None')}")
else: else:
# 使用全局模型配置 # 使用全局模型配置
model_config = default_config.get('model', {}).copy() model_config = default_config.get('model', {}).copy()
......
hooks/embed_resources.py 0 → 100644
"""
Pythonexe
SDK_internal
"""
import os
import base64
from pathlib import Path
def embed_file_to_python(file_path, output_path, var_name):
"""
Pythonbase64
Args:
file_path:
output_path: Python
var_name:
"""
with open(file_path, 'rb') as f:
data = f.read()
encoded = base64.b64encode(data).decode('utf-8')
# Python
code = f'''"""
: {file_path}
"""
import base64
import io
# Base64
_{var_name}_data = """{encoded}"""
def get_{var_name}():
""""""
return base64.b64decode(_{var_name}_data)
def get_{var_name}_path():
""""""
import tempfile
import os
data = get_{var_name}()
#
ext = os.path.splitext("{os.path.basename(file_path)}")[1]
#
fd, temp_path = tempfile.mkstemp(suffix=ext, prefix='embedded_resource_')
try:
with os.fdopen(fd, 'wb') as f:
f.write(data)
return temp_path
except:
os.close(fd)
raise
'''
with open(output_path, 'w', encoding='utf-8') as f:
f.write(code)
print(f" : {file_path} -> {output_path} (: {var_name})")
def embed_directory_to_python(dir_path, output_dir, max_size_mb=1):
"""
Python
Args:
dir_path:
output_dir:
max_size_mb: MB
"""
os.makedirs(output_dir, exist_ok=True)
embedded_files = []
skipped_files = []
for root, dirs, files in os.walk(dir_path):
for file in files:
file_path = os.path.join(root, file)
rel_path = os.path.relpath(file_path, dir_path)
#
size_mb = os.path.getsize(file_path) / (1024 * 1024)
if size_mb > max_size_mb:
skipped_files.append((rel_path, size_mb))
continue
#
var_name = rel_path.replace(os.sep, '_').replace('.', '_').replace('-', '_')
#
output_path = os.path.join(output_dir, f"embedded_{var_name}.py")
try:
embed_file_to_python(file_path, output_path, var_name)
embedded_files.append((rel_path, file_path))
except Exception as e:
print(f" : {file_path} - {e}")
skipped_files.append((rel_path, size_mb))
print(f"\n:")
print(f" : {len(embedded_files)} ")
print(f" : {len(skipped_files)} {max_size_mb}MB")
return embedded_files, skipped_files
if __name__ == '__main__':
# icons
project_root = os.path.abspath('.')
icons_dir = os.path.join(project_root, 'icons')
output_dir = os.path.join(project_root, 'hooks', 'embedded_resources')
if os.path.exists(icons_dir):
print("icons...")
embed_directory_to_python(icons_dir, output_dir, max_size_mb=0.5) # 0.5MB
else:
print(f": {icons_dir}")
hooks/hook-encodings.py 0 → 100644
# -*- coding: utf-8 -*-
"""
PyInstaller hook for encodings module
encodings encodings
"""
from PyInstaller.utils.hooks import collect_submodules
# encodings
hiddenimports = collect_submodules('encodings')
# encodings exe.spec
# encodings base_library.zip
datas = []
hooks/hook-pyarmor.py 0 → 100644
"""
PyArmor Hook for PyInstaller
PyInstallerPyArmor
"""
from PyInstaller.utils.hooks import collect_data_files, collect_submodules
# PyArmor
hiddenimports = [
'pyarmor',
'pyarmor.pyarmor_runtime',
'pyarmor.pyarmor_runtime_000000',
'pyarmor.pytransform',
'pytransform',
]
# pyarmor
try:
hiddenimports += collect_submodules('pyarmor')
except:
pass
# PyArmor
datas = []
try:
datas += collect_data_files('pyarmor')
except:
pass
# PyArmorhook
# PyArmor.so/.dll
binaries = []
try:
from PyInstaller.utils.hooks import collect_dynamic_libs
binaries += collect_dynamic_libs('pyarmor')
except:
pass
hooks/hook-torch.py 0 → 100644
from PyInstaller.utils.hooks import collect_submodules, collect_dynamic_libs
#
# - torch .py _internal/torch
# - DLL/PYD Python PYZ.pyc
hiddenimports = collect_submodules('torch')
binaries = collect_dynamic_libs('torch')
datas = []
hooks/runtime_debug_log.py 0 → 100644
import os
import sys
import faulthandler
import traceback
from datetime import datetime
def _log_dir() -> str:
# onedir: sys.executable dist/exe/exe.exe
base_dir = os.path.dirname(getattr(sys, "executable", sys.argv[0]))
path = os.path.join(base_dir, "logs")
try:
os.makedirs(path, exist_ok=True)
except Exception:
#
path = os.path.join(os.path.abspath(os.getcwd()), "logs")
os.makedirs(path, exist_ok=True)
return path
def _open_log_file():
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
log_path = os.path.join(_log_dir(), f"app_{timestamp}.log")
#
# encodings
try:
return open(log_path, mode="a", encoding="utf-8", buffering=1)
except (LookupError, NameError):
# encodings
return open(log_path, mode="a", buffering=1)
def _install_handlers():
log_fh = _open_log_file()
#
print_fn = lambda *args: log_fh.write(" ".join(str(a) for a in args) + "\n")
print_fn("===== Application Start =====")
print_fn("Executable:", getattr(sys, "executable", sys.argv[0]))
print_fn("CWD:", os.getcwd())
print_fn("Args:", " ".join(sys.argv))
# stdout/stderr
sys.stdout = log_fh
sys.stderr = log_fh
# faulthandler
try:
faulthandler.enable(log_fh)
except Exception:
pass
#
def _excepthook(exc_type, exc, tb):
log_fh.write("===== Uncaught Exception =====\n")
traceback.print_exception(exc_type, exc, tb, file=log_fh)
log_fh.flush()
sys.excepthook = _excepthook
# import Python
# sys.modules encodings
def _delayed_install():
try:
# encodings
import encodings
_install_handlers()
except Exception:
# encodings
try:
_install_handlers()
except Exception:
#
pass
#
import sys
if hasattr(sys, '_getframe'):
# sys._getframe
try:
_delayed_install()
except:
pass
else:
#
try:
_delayed_install()
except:
pass
hooks/runtime_decrypt_torch.py 0 → 100644
import os
import sys
import json
import tempfile
import shutil
# hook
# - Python
# - _internal/encrypted .bin DLL
# - /IV
AES256_KEY = b'0123456789ABCDEF0123456789ABCDEF' # exe.spec
AES_IV = b'ABCDEF0123456789' # exe.spec
PROTECT_KEYWORDS = ['torch', 'torchvision', 'torchaudio']
def _try_import_crypto():
try:
from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
from cryptography.hazmat.backends import default_backend
return Cipher, algorithms, modes, default_backend
except Exception:
return None, None, None, None
def _decrypt_bytes(data: bytes) -> bytes:
Cipher, algorithms, modes, default_backend = _try_import_crypto()
if Cipher is not None:
cipher = Cipher(algorithms.AES(AES256_KEY), modes.CTR(AES_IV), backend=default_backend())
decryptor = cipher.decryptor()
return decryptor.update(data) + decryptor.finalize()
# XOR
key = AES256_KEY
out = bytearray(len(data))
for i, b in enumerate(data):
out[i] = b ^ key[i % len(key)]
return bytes(out)
def _get_base_dir():
# PyInstaller onedir _MEIPASS _internal
# _MEIPASS
if hasattr(sys, '_MEIPASS') and sys._MEIPASS:
return sys._MEIPASS
# exe
exe_dir = os.path.dirname(sys.executable)
# dist/exe/exe.exe dist/exe/_internal
# a.datas encrypted _internal/encrypted
return os.path.join(exe_dir, '_internal')
def _load_manifest(encrypted_root):
manifest_path = os.path.join(encrypted_root, 'manifest.json')
if not os.path.exists(manifest_path):
return []
try:
with open(manifest_path, 'r', encoding='utf-8') as f:
return json.load(f)
except Exception:
return []
def _prepare_temp_dir():
base = tempfile.gettempdir()
target = os.path.join(base, 'lllds_decrypt_bins')
os.makedirs(target, exist_ok=True)
return target
def _add_search_path(path):
# Windows: DLL
try:
if hasattr(os, 'add_dll_directory'):
os.add_dll_directory(path)
except Exception:
pass
if path not in os.environ.get('PATH', ''):
os.environ['PATH'] = path + os.pathsep + os.environ.get('PATH', '')
if path not in sys.path:
sys.path.insert(0, path)
def _main():
try:
base_dir = _get_base_dir()
encrypted_root = os.path.join(base_dir, 'encrypted')
if not os.path.isdir(encrypted_root):
return
manifest = _load_manifest(encrypted_root)
if not manifest:
return
out_dir = _prepare_temp_dir()
wrote_any = False
for item in manifest:
name = item.get('name')
cipher_rel = item.get('cipher_path')
if not name or not cipher_rel:
continue
#
low = name.lower()
if not any(k in low for k in PROTECT_KEYWORDS):
continue
cipher_abs = os.path.join(base_dir, cipher_rel.replace('/', os.sep))
if not os.path.exists(cipher_abs):
continue
with open(cipher_abs, 'rb') as f:
enc = f.read()
raw = _decrypt_bytes(enc)
out_path = os.path.join(out_dir, name)
with open(out_path, 'wb') as wf:
wf.write(raw)
wrote_any = True
if wrote_any:
_add_search_path(out_dir)
except Exception:
#
pass
_main()
hooks/runtime_hook_ultralytics.py 0 → 100644
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
PyInstaller Hook - ultralytics
ultralytics
"""
import os
import sys
import tempfile
from pathlib import Path
def setup_ultralytics_environment():
""" ultralytics """
try:
#
if getattr(sys, 'frozen', False):
#
app_dir = Path(sys.executable).parent
else:
#
app_dir = Path(__file__).parent.parent
# ultralytics
os.environ['YOLO_CONFIG_DIR'] = str(app_dir / 'database' / 'config')
# ultralytics
temp_dir = tempfile.mkdtemp(prefix='ultralytics_')
os.environ['ULTRALYTICS_CONFIG_DIR'] = temp_dir
# ultralytics
os.environ['YOLO_VERBOSE'] = 'False'
os.environ['ULTRALYTICS_ANALYTICS'] = 'False'
print(f" ultralytics : {app_dir}")
except Exception as e:
print(f" ultralytics : {e}")
#
setup_ultralytics_environment()
hooks/runtime_pyarmor.py 0 → 100644
"""
PyArmor Runtime Hook
PyInstallerPyArmor
"""
import os
import sys
def _init_pyarmor_runtime():
"""
PyArmor
PyArmor
"""
try:
# _internalonedir
if hasattr(sys, '_MEIPASS'):
# onefile
base_dir = sys._MEIPASS
else:
# onedirexe.exe_internal
exe_dir = os.path.dirname(sys.executable)
base_dir = os.path.join(exe_dir, '_internal')
# _internalsys.path
if base_dir not in sys.path:
sys.path.insert(0, base_dir)
# PyArmorPyArmor
try:
import pyarmor.pyarmor_runtime # type: ignore #
except ImportError:
# PyArmor
pass
except Exception:
#
pass
#
_init_pyarmor_runtime()
icons/logo.ico 0 → 100644
File added
labelme/__init__.py 0 → 100644
# flake8: noqa
import logging
import sys
from qtpy import QT_VERSION
__appname__ = "labelme"
# Semantic Versioning 2.0.0: https://semver.org/
# 1. MAJOR version when you make incompatible API changes;
# 2. MINOR version when you add functionality in a backwards-compatible manner;
# 3. PATCH version when you make backwards-compatible bug fixes.
# e.g., 1.0.0a0, 1.0.0a1, 1.0.0b0, 1.0.0rc0, 1.0.0, 1.0.0.post0
__version__ = "5.2.1"
QT4 = QT_VERSION[0] == "4"
QT5 = QT_VERSION[0] == "5"
del QT_VERSION
PY2 = sys.version[0] == "2"
PY3 = sys.version[0] == "3"
del sys
from labelme.label_file import LabelFile
from labelme import testing
from labelme import utils
labelme/__main__.py 0 → 100644
import argparse
import codecs
import logging
import os
import os.path as osp
import sys
import yaml
# Python
parent_dir = osp.dirname(osp.dirname(osp.abspath(__file__)))
if parent_dir not in sys.path:
sys.path.insert(0, parent_dir)
from qtpy import QtCore
from qtpy import QtWidgets
from labelme import __appname__
from labelme import __version__
from labelme.app import MainWindow
from labelme.config import get_config
from labelme.logger import logger
from labelme.utils import newIcon
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--version", "-V", action="store_true", help="show version"
)
parser.add_argument(
"--reset-config", action="store_true", help="reset qt config"
)
parser.add_argument(
"--logger-level",
default="info",
choices=["debug", "info", "warning", "fatal", "error"],
help="logger level",
)
parser.add_argument("filename", nargs="?", help="image or label filename")
parser.add_argument(
"--output",
"-O",
"-o",
help="output file or directory (if it ends with .json it is "
"recognized as file, else as directory)",
)
default_config_file = os.path.join(os.path.expanduser("~"), ".labelmerc")
parser.add_argument(
"--config",
dest="config",
help="config file or yaml-format string (default: {})".format(
default_config_file
),
default=default_config_file,
)
# config for the gui
parser.add_argument(
"--nodata",
dest="store_data",
action="store_false",
help="stop storing image data to JSON file",
default=argparse.SUPPRESS,
)
parser.add_argument(
"--autosave",
dest="auto_save",
action="store_true",
help="auto save",
default=argparse.SUPPRESS,
)
parser.add_argument(
"--nosortlabels",
dest="sort_labels",
action="store_false",
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",
default=argparse.SUPPRESS,
)
parser.add_argument(
"--validatelabel",
dest="validate_label",
choices=["exact"],
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,
help="epsilon to find nearest vertex on canvas",
default=argparse.SUPPRESS,
)
args = parser.parse_args()
if args.version:
print("{0} {1}".format(__appname__, __version__))
sys.exit(0)
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")
filename = config_from_args.pop("filename")
output = config_from_args.pop("output")
config_file_or_yaml = config_from_args.pop("config")
config = get_config(config_file_or_yaml, config_from_args)
if not config["labels"] and config["validate_label"]:
logger.error(
"--labels must be specified with --validatelabel or "
"validate_label: true in the config file "
"(ex. ~/.labelmerc)."
)
sys.exit(1)
output_file = None
output_dir = None
if output is not None:
if output.endswith(".json"):
output_file = output
else:
output_dir = output
translator = QtCore.QTranslator()
translator.load(
QtCore.QLocale.system().name(),
osp.dirname(osp.abspath(__file__)) + "/translate",
)
app = QtWidgets.QApplication(sys.argv)
app.setApplicationName(__appname__)
app.setWindowIcon(newIcon("icon"))
app.installTranslator(translator)
win = MainWindow(
config=config,
filename=filename,
output_file=output_file,
output_dir=output_dir,
)
if reset_config:
logger.info("Resetting Qt config: %s" % win.settings.fileName())
win.settings.clear()
sys.exit(0)
win.show()
win.raise_()
sys.exit(app.exec_())
# this main block is required to generate executable by pyinstaller
if __name__ == "__main__":
main()
labelme/app.py 0 → 100644
This diff is collapsed. Click to expand it.
labelme/cli/__init__.py 0 → 100644
# flake8: noqa
from . import draw_json
from . import draw_label_png
from . import json_to_dataset
from . import on_docker
labelme/cli/draw_json.py 0 → 100644
#!/usr/bin/env python
import argparse
import sys
import imgviz
import matplotlib.pyplot as plt
from labelme.label_file import LabelFile
from labelme import utils
PY2 = sys.version_info[0] == 2
def main():
parser = argparse.ArgumentParser()
parser.add_argument("json_file")
args = parser.parse_args()
label_file = LabelFile(args.json_file)
img = utils.img_data_to_arr(label_file.imageData)
label_name_to_value = {"_background_": 0}
for shape in sorted(label_file.shapes, key=lambda x: x["label"]):
label_name = shape["label"]
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
lbl, _ = utils.shapes_to_label(
img.shape, label_file.shapes, label_name_to_value
)
label_names = [None] * (max(label_name_to_value.values()) + 1)
for name, value in label_name_to_value.items():
label_names[value] = name
lbl_viz = imgviz.label2rgb(
lbl,
imgviz.asgray(img),
label_names=label_names,
font_size=30,
loc="rb",
)
plt.subplot(121)
plt.imshow(img)
plt.subplot(122)
plt.imshow(lbl_viz)
plt.show()
if __name__ == "__main__":
main()
labelme/cli/draw_label_png.py 0 → 100644
import argparse
import imgviz
import matplotlib.pyplot as plt
import numpy as np
import PIL.Image
from labelme.logger import logger
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("label_png", help="label PNG file")
args = parser.parse_args()
lbl = np.asarray(PIL.Image.open(args.label_png))
logger.info("label shape: {}".format(lbl.shape))
logger.info("unique label values: {}".format(np.unique(lbl)))
lbl_viz = imgviz.label2rgb(lbl)
plt.imshow(lbl_viz)
plt.show()
if __name__ == "__main__":
main()
labelme/cli/json_to_dataset.py 0 → 100644
import argparse
import base64
import json
import os
import os.path as osp
import imgviz
import PIL.Image
from labelme.logger import logger
from labelme import utils
def main():
logger.warning(
"This script is aimed to demonstrate how to convert the "
"JSON file to a single image dataset."
)
logger.warning(
"It won't handle multiple JSON files to generate a "
"real-use dataset."
)
parser = argparse.ArgumentParser()
parser.add_argument("json_file")
parser.add_argument("-o", "--out", default=None)
args = parser.parse_args()
json_file = args.json_file
if args.out is None:
out_dir = osp.basename(json_file).replace(".", "_")
out_dir = osp.join(osp.dirname(json_file), out_dir)
else:
out_dir = args.out
if not osp.exists(out_dir):
os.mkdir(out_dir)
data = json.load(open(json_file))
imageData = data.get("imageData")
if not imageData:
imagePath = os.path.join(os.path.dirname(json_file), data["imagePath"])
with open(imagePath, "rb") as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode("utf-8")
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {"_background_": 0}
for shape in sorted(data["shapes"], key=lambda x: x["label"]):
label_name = shape["label"]
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
lbl, _ = utils.shapes_to_label(
img.shape, data["shapes"], label_name_to_value
)
label_names = [None] * (max(label_name_to_value.values()) + 1)
for name, value in label_name_to_value.items():
label_names[value] = name
lbl_viz = imgviz.label2rgb(
lbl, imgviz.asgray(img), label_names=label_names, loc="rb"
)
PIL.Image.fromarray(img).save(osp.join(out_dir, "img.png"))
utils.lblsave(osp.join(out_dir, "label.png"), lbl)
PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, "label_viz.png"))
with open(osp.join(out_dir, "label_names.txt"), "w") as f:
for lbl_name in label_names:
f.write(lbl_name + "\n")
logger.info("Saved to: {}".format(out_dir))
if __name__ == "__main__":
main()
labelme/cli/on_docker.py 0 → 100644
#!/usr/bin/env python
from __future__ import print_function
import argparse
import distutils.spawn
import json
import os
import os.path as osp
import platform
import shlex
import subprocess
import sys
def get_ip():
dist = platform.platform().split("-")[0]
if dist == "Linux":
return ""
elif dist == "Darwin":
cmd = "ifconfig en0"
output = subprocess.check_output(shlex.split(cmd))
if str != bytes: # Python3
output = output.decode("utf-8")
for row in output.splitlines():
cols = row.strip().split(" ")
if cols[0] == "inet":
ip = cols[1]
return ip
else:
raise RuntimeError("No ip is found.")
else:
raise RuntimeError("Unsupported platform.")
def labelme_on_docker(in_file, out_file):
ip = get_ip()
cmd = "xhost + %s" % ip
subprocess.check_output(shlex.split(cmd))
if out_file:
out_file = osp.abspath(out_file)
if osp.exists(out_file):
raise RuntimeError("File exists: %s" % out_file)
else:
open(osp.abspath(out_file), "w")
cmd = (
"docker run -it --rm"
" -e DISPLAY={0}:0"
" -e QT_X11_NO_MITSHM=1"
" -v /tmp/.X11-unix:/tmp/.X11-unix"
" -v {1}:{2}"
" -w /home/developer"
)
in_file_a = osp.abspath(in_file)
in_file_b = osp.join("/home/developer", osp.basename(in_file))
cmd = cmd.format(
ip,
in_file_a,
in_file_b,
)
if out_file:
out_file_a = osp.abspath(out_file)
out_file_b = osp.join("/home/developer", osp.basename(out_file))
cmd += " -v {0}:{1}".format(out_file_a, out_file_b)
cmd += " wkentaro/labelme labelme {0}".format(in_file_b)
if out_file:
cmd += " -O {0}".format(out_file_b)
subprocess.call(shlex.split(cmd))
if out_file:
try:
json.load(open(out_file))
return out_file
except Exception:
if open(out_file).read() == "":
os.remove(out_file)
raise RuntimeError("Annotation is cancelled.")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("in_file", help="Input file or directory.")
parser.add_argument("-O", "--output")
args = parser.parse_args()
if not distutils.spawn.find_executable("docker"):
print("Please install docker", file=sys.stderr)
sys.exit(1)
try:
out_file = labelme_on_docker(args.in_file, args.output)
if out_file:
print("Saved to: %s" % out_file)
except RuntimeError as e:
sys.stderr.write(e.__str__() + "\n")
sys.exit(1)
if __name__ == "__main__":
main()
labelme/config/__init__.py 0 → 100644
import os.path as osp
import shutil
import yaml
from labelme.logger import logger
here = osp.dirname(osp.abspath(__file__))
def update_dict(target_dict, new_dict, validate_item=None):
for key, value in new_dict.items():
if validate_item:
validate_item(key, value)
if key not in target_dict:
logger.warn("Skipping unexpected key in config: {}".format(key))
continue
if isinstance(target_dict[key], dict) and isinstance(value, dict):
update_dict(target_dict[key], value, validate_item=validate_item)
else:
target_dict[key] = value
# -----------------------------------------------------------------------------
def get_default_config():
# labelme
config_file = osp.join(here, "default_config.yaml")
with open(config_file, encoding='utf-8') as f:
config = yaml.safe_load(f)
# save default config to ~/.labelmerc
user_config_file = osp.join(osp.expanduser("~"), ".labelmerc")
if not osp.exists(user_config_file):
try:
shutil.copy(config_file, user_config_file)
except Exception:
logger.warn("Failed to save config: {}".format(user_config_file))
return config
def validate_config_item(key, value):
if key == "validate_label" and value not in [None, "exact"]:
raise ValueError(
"Unexpected value for config key 'validate_label': {}".format(
value
)
)
if key == "shape_color" and value not in [None, "auto", "manual"]:
raise ValueError(
"Unexpected value for config key 'shape_color': {}".format(value)
)
if key == "labels" and value is not None and len(value) != len(set(value)):
raise ValueError(
"Duplicates are detected for config key 'labels': {}".format(value)
)
def get_config(config_file_or_yaml=None, config_from_args=None):
# 1. default config
config = get_default_config()
# 2. specified as file or yaml
if config_file_or_yaml is not None:
config_from_yaml = yaml.safe_load(config_file_or_yaml)
if not isinstance(config_from_yaml, dict):
with open(config_from_yaml, encoding='utf-8') as f:
logger.info(
"Loading config file from: {}".format(config_from_yaml)
)
config_from_yaml = yaml.safe_load(f)
update_dict(
config, config_from_yaml, validate_item=validate_config_item
)
# 3. command line argument or specified config file
if config_from_args is not None:
update_dict(
config, config_from_args, validate_item=validate_config_item
)
return config
labelme/config/default_config.yaml 0 → 100644
auto_save: true
display_label_popup: true
store_data: true
keep_prev: false
keep_prev_scale: false
keep_prev_brightness: false
keep_prev_contrast: false
logger_level: info
flags: null
label_flags: null
labels: null
file_search: null
sort_labels: true
validate_label: null
default_shape_color: [0, 255, 0]
shape_color: auto # null, 'auto', 'manual'
shift_auto_shape_color: 0
label_colors: null
shape:
# drawing
line_color: [0, 255, 0, 128]
fill_color: [0, 255, 0, 0] # transparent
vertex_fill_color: [0, 255, 0, 255]
# selecting / hovering
select_line_color: [255, 255, 255, 255]
select_fill_color: [0, 255, 0, 155]
hvertex_fill_color: [255, 255, 255, 255]
point_size: 8
# main
flag_dock:
show: true
closable: true
movable: true
floatable: true
label_dock:
show: true
closable: true
movable: true
floatable: true
shape_dock:
show: true
closable: true
movable: true
floatable: true
file_dock:
show: true
closable: true
movable: true
floatable: true
# label_dialog
show_label_text_field: true
label_completion: startswith
fit_to_content:
column: true
row: false
# canvas
epsilon: 10.0
canvas:
# None: do nothing
# close: close polygon
double_click: close
# The max number of edits we can undo
num_backups: 10
# show crosshair
crosshair:
polygon: false
rectangle: true
circle: false
line: false
point: false
linestrip: false
shortcuts:
close: Ctrl+W
open: Ctrl+O
open_dir: Ctrl+U
quit: Ctrl+Q
save: Ctrl+S
save_as: Ctrl+Shift+S
save_to: null
delete_file: Ctrl+Delete
open_next: [D, Ctrl+Shift+D]
open_prev: [A, Ctrl+Shift+A]
zoom_in: [Ctrl++, Ctrl+=]
zoom_out: Ctrl+-
zoom_to_original: Ctrl+0
fit_window: Ctrl+F
fit_width: Ctrl+Shift+F
create_polygon: Ctrl+N
create_rectangle: Ctrl+R
create_circle: null
create_line: null
create_point: null
create_linestrip: null
edit_polygon: Ctrl+J
delete_polygon: Delete
duplicate_polygon: Ctrl+D
copy_polygon: Ctrl+C
paste_polygon: Ctrl+V
undo: Ctrl+Z
undo_last_point: Ctrl+Z
add_point_to_edge: Ctrl+Shift+P
edit_label: Ctrl+E
toggle_keep_prev_mode: Ctrl+P
remove_selected_point: [Meta+H, Backspace]
rules/app.mdc 0 → 100644
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rules/app.py 0 → 100644
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rules/git.md 0 → 100644
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rules/model_training_handler.py 0 → 100644
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rules/name 0 → 100644
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rules/rule.mdc 0 → 100644
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