Python&Qt——yolov5手势识别隔空操纵车载音乐播放器

谁践踏了优雅 2022-08-31 01:48 491阅读 0赞

演示视频

## 0.配置环境 ##

1.  [下载anaconda&python3.8.8(内置安装包点击下载即可)-并直接安装即可][anaconda_python3.8.8_-]

> 直接装即可(默认安装cpu版本，如需 N卡GPU算力，请自行配置CUDA)  
> [CUDA官方安装地址(点击跳转下载)][CUDA]

1.  升级pip  
    PowerShell管理员模式运行

python -m pip install -U pip

1.  运行环境配置txt  
    PowerShell在项目文件夹内运行(shift+鼠标右键)

pip install -r requirements.txt

1.  等待完成即可

## 1.训练模型 ##

1.  labelImg.py( 绘制规划训练集 )

#!/usr/bin/env python
 # -*- coding: utf-8 -*-
 import argparse
 import codecs
 import distutils.spawn
 import os.path
 import platform
 import re
 import sys
 import subprocess
 import shutil
 import webbrowser as wb
 
 from functools import partial
 from collections import defaultdict
 
 try:
 from PyQt5.QtGui import *
 from PyQt5.QtCore import *
 from PyQt5.QtWidgets import *
 except ImportError:
 # needed for py3+qt4
 # Ref:
 # http://pyqt.sourceforge.net/Docs/PyQt4/incompatible_apis.html
 # http://stackoverflow.com/questions/21217399/pyqt4-qtcore-qvariant-object-instead-of-a-string
 if sys.version_info.major >= 3:
 import sip
 sip.setapi('QVariant', 2)
 from PyQt4.QtGui import *
 from PyQt4.QtCore import *
 
 from libs.combobox import ComboBox
 from libs.resources import *
 from libs.constants import *
 from libs.utils import *
 from libs.settings import Settings
 from libs.shape import Shape, DEFAULT_LINE_COLOR, DEFAULT_FILL_COLOR
 from libs.stringBundle import StringBundle
 from libs.canvas import Canvas
 from libs.zoomWidget import ZoomWidget
 from libs.labelDialog import LabelDialog
 from libs.colorDialog import ColorDialog
 from libs.labelFile import LabelFile, LabelFileError, LabelFileFormat
 from libs.toolBar import ToolBar
 from libs.pascal_voc_io import PascalVocReader
 from libs.pascal_voc_io import XML_EXT
 from libs.yolo_io import YoloReader
 from libs.yolo_io import TXT_EXT
 from libs.create_ml_io import CreateMLReader
 from libs.create_ml_io import JSON_EXT
 from libs.ustr import ustr
 from libs.hashableQListWidgetItem import HashableQListWidgetItem
 
 __appname__ = 'labelImg'
 
 
 class WindowMixin(object):
 
 def menu(self, title, actions=None):
 menu = self.menuBar().addMenu(title)
 if actions:
 add_actions(menu, actions)
 return menu
 
 def toolbar(self, title, actions=None):
 toolbar = ToolBar(title)
 toolbar.setObjectName(u'%sToolBar' % title)
 # toolbar.setOrientation(Qt.Vertical)
 toolbar.setToolButtonStyle(Qt.ToolButtonTextUnderIcon)
 if actions:
 add_actions(toolbar, actions)
 self.addToolBar(Qt.LeftToolBarArea, toolbar)
 return toolbar
 
 
 class MainWindow(QMainWindow, WindowMixin):
 FIT_WINDOW, FIT_WIDTH, MANUAL_ZOOM = list(range(3))
 
 def __init__(self, default_filename=None, default_prefdef_class_file=None, default_save_dir=None):
 super(MainWindow, self).__init__()
 self.setWindowTitle(__appname__)
 
 # Load setting in the main thread
 self.settings = Settings()
 self.settings.load()
 settings = self.settings
 
 self.os_name = platform.system()
 
 # Load string bundle for i18n
 self.string_bundle = StringBundle.get_bundle()
 get_str = lambda str_id: self.string_bundle.get_string(str_id)
 
 # Save as Pascal voc xml
 self.default_save_dir = default_save_dir
 self.label_file_format = settings.get(SETTING_LABEL_FILE_FORMAT, LabelFileFormat.PASCAL_VOC)
 
 # For loading all image under a directory
 self.m_img_list = []
 self.dir_name = None
 self.label_hist = []
 self.last_open_dir = None
 self.cur_img_idx = 0
 self.img_count = 1
 
 # Whether we need to save or not.
 self.dirty = False
 
 self._no_selection_slot = False
 self._beginner = True
 self.screencast = "https://youtu.be/p0nR2YsCY_U"
 
 # Load predefined classes to the list
 self.load_predefined_classes(default_prefdef_class_file)
 
 # Main widgets and related state.
 self.label_dialog = LabelDialog(parent=self, list_item=self.label_hist)
 
 self.items_to_shapes = { }
 self.shapes_to_items = { }
 self.prev_label_text = ''
 
 list_layout = QVBoxLayout()
 list_layout.setContentsMargins(0, 0, 0, 0)
 
 # Create a widget for using default label
 self.use_default_label_checkbox = QCheckBox(get_str('useDefaultLabel'))
 self.use_default_label_checkbox.setChecked(False)
 self.default_label_text_line = QLineEdit()
 use_default_label_qhbox_layout = QHBoxLayout()
 use_default_label_qhbox_layout.addWidget(self.use_default_label_checkbox)
 use_default_label_qhbox_layout.addWidget(self.default_label_text_line)
 use_default_label_container = QWidget()
 use_default_label_container.setLayout(use_default_label_qhbox_layout)
 
 # Create a widget for edit and diffc button
 self.diffc_button = QCheckBox(get_str('useDifficult'))
 self.diffc_button.setChecked(False)
 self.diffc_button.stateChanged.connect(self.button_state)
 self.edit_button = QToolButton()
 self.edit_button.setToolButtonStyle(Qt.ToolButtonTextBesideIcon)
 
 # Add some of widgets to list_layout
 list_layout.addWidget(self.edit_button)
 list_layout.addWidget(self.diffc_button)
 list_layout.addWidget(use_default_label_container)
 
 # Create and add combobox for showing unique labels in group
 self.combo_box = ComboBox(self)
 list_layout.addWidget(self.combo_box)
 
 # Create and add a widget for showing current label items
 self.label_list = QListWidget()
 label_list_container = QWidget()
 label_list_container.setLayout(list_layout)
 self.label_list.itemActivated.connect(self.label_selection_changed)
 self.label_list.itemSelectionChanged.connect(self.label_selection_changed)
 self.label_list.itemDoubleClicked.connect(self.edit_label)
 # Connect to itemChanged to detect checkbox changes.
 self.label_list.itemChanged.connect(self.label_item_changed)
 list_layout.addWidget(self.label_list)
 
 
 
 self.dock = QDockWidget(get_str('boxLabelText'), self)
 self.dock.setObjectName(get_str('labels'))
 self.dock.setWidget(label_list_container)
 
 self.file_list_widget = QListWidget()
 self.file_list_widget.itemDoubleClicked.connect(self.file_item_double_clicked)
 file_list_layout = QVBoxLayout()
 file_list_layout.setContentsMargins(0, 0, 0, 0)
 file_list_layout.addWidget(self.file_list_widget)
 file_list_container = QWidget()
 file_list_container.setLayout(file_list_layout)
 self.file_dock = QDockWidget(get_str('fileList'), self)
 self.file_dock.setObjectName(get_str('files'))
 self.file_dock.setWidget(file_list_container)
 
 self.zoom_widget = ZoomWidget()
 self.color_dialog = ColorDialog(parent=self)
 
 self.canvas = Canvas(parent=self)
 self.canvas.zoomRequest.connect(self.zoom_request)
 self.canvas.set_drawing_shape_to_square(settings.get(SETTING_DRAW_SQUARE, False))
 
 scroll = QScrollArea()
 scroll.setWidget(self.canvas)
 scroll.setWidgetResizable(True)
 self.scroll_bars = { 
 Qt.Vertical: scroll.verticalScrollBar(),
 Qt.Horizontal: scroll.horizontalScrollBar()
 }
 self.scroll_area = scroll
 self.canvas.scrollRequest.connect(self.scroll_request)
 
 self.canvas.newShape.connect(self.new_shape)
 self.canvas.shapeMoved.connect(self.set_dirty)
 self.canvas.selectionChanged.connect(self.shape_selection_changed)
 self.canvas.drawingPolygon.connect(self.toggle_drawing_sensitive)
 
 self.setCentralWidget(scroll)
 self.addDockWidget(Qt.RightDockWidgetArea, self.dock)
 self.addDockWidget(Qt.RightDockWidgetArea, self.file_dock)
 self.file_dock.setFeatures(QDockWidget.DockWidgetFloatable)
 
 self.dock_features = QDockWidget.DockWidgetClosable | QDockWidget.DockWidgetFloatable
 self.dock.setFeatures(self.dock.features() ^ self.dock_features)
 
 # Actions
 action = partial(new_action, self)
 quit = action(get_str('quit'), self.close,
 'Ctrl+Q', 'quit', get_str('quitApp'))
 
 open = action(get_str('openFile'), self.open_file,
 'Ctrl+O', 'open', get_str('openFileDetail'))
 
 open_dir = action(get_str('openDir'), self.open_dir_dialog,
 'Ctrl+u', 'open', get_str('openDir'))
 
 change_save_dir = action(get_str('changeSaveDir'), self.change_save_dir_dialog,
 'Ctrl+r', 'open', get_str('changeSavedAnnotationDir'))
 
 open_annotation = action(get_str('openAnnotation'), self.open_annotation_dialog,
 'Ctrl+Shift+O', 'open', get_str('openAnnotationDetail'))
 copy_prev_bounding = action(get_str('copyPrevBounding'), self.copy_previous_bounding_boxes, 'Ctrl+v', 'copy', get_str('copyPrevBounding'))
 
 open_next_image = action(get_str('nextImg'), self.open_next_image,
 'd', 'next', get_str('nextImgDetail'))
 
 open_prev_image = action(get_str('prevImg'), self.open_prev_image,
 'a', 'prev', get_str('prevImgDetail'))
 
 verify = action(get_str('verifyImg'), self.verify_image,
 'space', 'verify', get_str('verifyImgDetail'))
 
 save = action(get_str('save'), self.save_file,
 'Ctrl+S', 'save', get_str('saveDetail'), enabled=False)
 
 def get_format_meta(format):
 """ returns a tuple containing (title, icon_name) of the selected format """
 if format == LabelFileFormat.PASCAL_VOC:
 return '&PascalVOC', 'format_voc'
 elif format == LabelFileFormat.YOLO:
 return '&YOLO', 'format_yolo'
 elif format == LabelFileFormat.CREATE_ML:
 return '&CreateML', 'format_createml'
 
 save_format = action(get_format_meta(self.label_file_format)[0],
 self.change_format, 'Ctrl+',
 get_format_meta(self.label_file_format)[1],
 get_str('changeSaveFormat'), enabled=True)
 
 save_as = action(get_str('saveAs'), self.save_file_as,
 'Ctrl+Shift+S', 'save-as', get_str('saveAsDetail'), enabled=False)
 
 close = action(get_str('closeCur'), self.close_file, 'Ctrl+W', 'close', get_str('closeCurDetail'))
 
 delete_image = action(get_str('deleteImg'), self.delete_image, 'Ctrl+Shift+D', 'close', get_str('deleteImgDetail'))
 
 reset_all = action(get_str('resetAll'), self.reset_all, None, 'resetall', get_str('resetAllDetail'))
 
 color1 = action(get_str('boxLineColor'), self.choose_color1,
 'Ctrl+L', 'color_line', get_str('boxLineColorDetail'))
 
 create_mode = action(get_str('crtBox'), self.set_create_mode,
 'w', 'new', get_str('crtBoxDetail'), enabled=False)
 edit_mode = action(get_str('editBox'), self.set_edit_mode,
 'Ctrl+J', 'edit', get_str('editBoxDetail'), enabled=False)
 
 create = action(get_str('crtBox'), self.create_shape,
 'w', 'new', get_str('crtBoxDetail'), enabled=False)
 delete = action(get_str('delBox'), self.delete_selected_shape,
 'Delete', 'delete', get_str('delBoxDetail'), enabled=False)
 copy = action(get_str('dupBox'), self.copy_selected_shape,
 'Ctrl+D', 'copy', get_str('dupBoxDetail'),
 enabled=False)
 
 advanced_mode = action(get_str('advancedMode'), self.toggle_advanced_mode,
 'Ctrl+Shift+A', 'expert', get_str('advancedModeDetail'),
 checkable=True)
 
 hide_all = action(get_str('hideAllBox'), partial(self.toggle_polygons, False),
 'Ctrl+H', 'hide', get_str('hideAllBoxDetail'),
 enabled=False)
 show_all = action(get_str('showAllBox'), partial(self.toggle_polygons, True),
 'Ctrl+A', 'hide', get_str('showAllBoxDetail'),
 enabled=False)
 
 help_default = action(get_str('tutorialDefault'), self.show_default_tutorial_dialog, None, 'help', get_str('tutorialDetail'))
 show_info = action(get_str('info'), self.show_info_dialog, None, 'help', get_str('info'))
 show_shortcut = action(get_str('shortcut'), self.show_shortcuts_dialog, None, 'help', get_str('shortcut'))
 
 zoom = QWidgetAction(self)
 zoom.setDefaultWidget(self.zoom_widget)
 self.zoom_widget.setWhatsThis(
 u"Zoom in or out of the image. Also accessible with"
 " %s and %s from the canvas." % (format_shortcut("Ctrl+[-+]"),
 format_shortcut("Ctrl+Wheel")))
 self.zoom_widget.setEnabled(False)
 
 zoom_in = action(get_str('zoomin'), partial(self.add_zoom, 10),
 'Ctrl++', 'zoom-in', get_str('zoominDetail'), enabled=False)
 zoom_out = action(get_str('zoomout'), partial(self.add_zoom, -10),
 'Ctrl+-', 'zoom-out', get_str('zoomoutDetail'), enabled=False)
 zoom_org = action(get_str('originalsize'), partial(self.set_zoom, 100),
 'Ctrl+=', 'zoom', get_str('originalsizeDetail'), enabled=False)
 fit_window = action(get_str('fitWin'), self.set_fit_window,
 'Ctrl+F', 'fit-window', get_str('fitWinDetail'),
 checkable=True, enabled=False)
 fit_width = action(get_str('fitWidth'), self.set_fit_width,
 'Ctrl+Shift+F', 'fit-width', get_str('fitWidthDetail'),
 checkable=True, enabled=False)
 # Group zoom controls into a list for easier toggling.
 zoom_actions = (self.zoom_widget, zoom_in, zoom_out,
 zoom_org, fit_window, fit_width)
 self.zoom_mode = self.MANUAL_ZOOM
 self.scalers = { 
 self.FIT_WINDOW: self.scale_fit_window,
 self.FIT_WIDTH: self.scale_fit_width,
 # Set to one to scale to 100% when loading files.
 self.MANUAL_ZOOM: lambda: 1,
 }
 
 edit = action(get_str('editLabel'), self.edit_label,
 'Ctrl+E', 'edit', get_str('editLabelDetail'),
 enabled=False)
 self.edit_button.setDefaultAction(edit)
 
 shape_line_color = action(get_str('shapeLineColor'), self.choose_shape_line_color,
 icon='color_line', tip=get_str('shapeLineColorDetail'),
 enabled=False)
 shape_fill_color = action(get_str('shapeFillColor'), self.choose_shape_fill_color,
 icon='color', tip=get_str('shapeFillColorDetail'),
 enabled=False)
 
 labels = self.dock.toggleViewAction()
 labels.setText(get_str('showHide'))
 labels.setShortcut('Ctrl+Shift+L')
 
 # Label list context menu.
 label_menu = QMenu()
 add_actions(label_menu, (edit, delete))
 self.label_list.setContextMenuPolicy(Qt.CustomContextMenu)
 self.label_list.customContextMenuRequested.connect(
 self.pop_label_list_menu)
 
 # Draw squares/rectangles
 self.draw_squares_option = QAction(get_str('drawSquares'), self)
 self.draw_squares_option.setShortcut('Ctrl+Shift+R')
 self.draw_squares_option.setCheckable(True)
 self.draw_squares_option.setChecked(settings.get(SETTING_DRAW_SQUARE, False))
 self.draw_squares_option.triggered.connect(self.toggle_draw_square)
 
 # Store actions for further handling.
 self.actions = Struct(save=save, save_format=save_format, saveAs=save_as, open=open, close=close, resetAll=reset_all, deleteImg=delete_image,
 lineColor=color1, create=create, delete=delete, edit=edit, copy=copy,
 createMode=create_mode, editMode=edit_mode, advancedMode=advanced_mode,
 shapeLineColor=shape_line_color, shapeFillColor=shape_fill_color,
 zoom=zoom, zoomIn=zoom_in, zoomOut=zoom_out, zoomOrg=zoom_org,
 fitWindow=fit_window, fitWidth=fit_width,
 zoomActions=zoom_actions,
 fileMenuActions=(
 open, open_dir, save, save_as, close, reset_all, quit),
 beginner=(), advanced=(),
 editMenu=(edit, copy, delete,
 None, color1, self.draw_squares_option),
 beginnerContext=(create, edit, copy, delete),
 advancedContext=(create_mode, edit_mode, edit, copy,
 delete, shape_line_color, shape_fill_color),
 onLoadActive=(
 close, create, create_mode, edit_mode),
 onShapesPresent=(save_as, hide_all, show_all))
 
 self.menus = Struct(
 file=self.menu(get_str('menu_file')),
 edit=self.menu(get_str('menu_edit')),
 view=self.menu(get_str('menu_view')),
 help=self.menu(get_str('menu_help')),
 recentFiles=QMenu(get_str('menu_openRecent')),
 labelList=label_menu)
 
 # Auto saving : Enable auto saving if pressing next
 self.auto_saving = QAction(get_str('autoSaveMode'), self)
 self.auto_saving.setCheckable(True)
 self.auto_saving.setChecked(settings.get(SETTING_AUTO_SAVE, False))
 # Sync single class mode from PR#106
 self.single_class_mode = QAction(get_str('singleClsMode'), self)
 self.single_class_mode.setShortcut("Ctrl+Shift+S")
 self.single_class_mode.setCheckable(True)
 self.single_class_mode.setChecked(settings.get(SETTING_SINGLE_CLASS, False))
 self.lastLabel = None
 # Add option to enable/disable labels being displayed at the top of bounding boxes
 self.display_label_option = QAction(get_str('displayLabel'), self)
 self.display_label_option.setShortcut("Ctrl+Shift+P")
 self.display_label_option.setCheckable(True)
 self.display_label_option.setChecked(settings.get(SETTING_PAINT_LABEL, False))
 self.display_label_option.triggered.connect(self.toggle_paint_labels_option)
 
 add_actions(self.menus.file,
 (open, open_dir, change_save_dir, open_annotation, copy_prev_bounding, self.menus.recentFiles, save, save_format, save_as, close, reset_all, delete_image, quit))
 add_actions(self.menus.help, (help_default, show_info, show_shortcut))
 add_actions(self.menus.view, (
 self.auto_saving,
 self.single_class_mode,
 self.display_label_option,
 labels, advanced_mode, None,
 hide_all, show_all, None,
 zoom_in, zoom_out, zoom_org, None,
 fit_window, fit_width))
 
 self.menus.file.aboutToShow.connect(self.update_file_menu)
 
 # Custom context menu for the canvas widget:
 add_actions(self.canvas.menus[0], self.actions.beginnerContext)
 add_actions(self.canvas.menus[1], (
 action('&Copy here', self.copy_shape),
 action('&Move here', self.move_shape)))
 
 self.tools = self.toolbar('Tools')
 self.actions.beginner = (
 open, open_dir, change_save_dir, open_next_image, open_prev_image, verify, save, save_format, None, create, copy, delete, None,
 zoom_in, zoom, zoom_out, fit_window, fit_width)
 
 self.actions.advanced = (
 open, open_dir, change_save_dir, open_next_image, open_prev_image, save, save_format, None,
 create_mode, edit_mode, None,
 hide_all, show_all)
 
 self.statusBar().showMessage('%s started.' % __appname__)
 self.statusBar().show()
 
 # Application state.
 self.image = QImage()
 self.file_path = ustr(default_filename)
 self.last_open_dir = None
 self.recent_files = []
 self.max_recent = 7
 self.line_color = None
 self.fill_color = None
 self.zoom_level = 100
 self.fit_window = False
 # Add Chris
 self.difficult = False
 
 # Fix the compatible issue for qt4 and qt5. Convert the QStringList to python list
 if settings.get(SETTING_RECENT_FILES):
 if have_qstring():
 recent_file_qstring_list = settings.get(SETTING_RECENT_FILES)
 self.recent_files = [ustr(i) for i in recent_file_qstring_list]
 else:
 self.recent_files = recent_file_qstring_list = settings.get(SETTING_RECENT_FILES)
 
 size = settings.get(SETTING_WIN_SIZE, QSize(600, 500))
 position = QPoint(0, 0)
 saved_position = settings.get(SETTING_WIN_POSE, position)
 # Fix the multiple monitors issue
 for i in range(QApplication.desktop().screenCount()):
 if QApplication.desktop().availableGeometry(i).contains(saved_position):
 position = saved_position
 break
 self.resize(size)
 self.move(position)
 save_dir = ustr(settings.get(SETTING_SAVE_DIR, None))
 self.last_open_dir = ustr(settings.get(SETTING_LAST_OPEN_DIR, None))
 if self.default_save_dir is None and save_dir is not None and os.path.exists(save_dir):
 self.default_save_dir = save_dir
 self.statusBar().showMessage('%s started. Annotation will be saved to %s' %
 (__appname__, self.default_save_dir))
 self.statusBar().show()
 
 self.restoreState(settings.get(SETTING_WIN_STATE, QByteArray()))
 Shape.line_color = self.line_color = QColor(settings.get(SETTING_LINE_COLOR, DEFAULT_LINE_COLOR))
 Shape.fill_color = self.fill_color = QColor(settings.get(SETTING_FILL_COLOR, DEFAULT_FILL_COLOR))
 self.canvas.set_drawing_color(self.line_color)
 # Add chris
 Shape.difficult = self.difficult
 
 def xbool(x):
 if isinstance(x, QVariant):
 return x.toBool()
 return bool(x)
 
 if xbool(settings.get(SETTING_ADVANCE_MODE, False)):
 self.actions.advancedMode.setChecked(True)
 self.toggle_advanced_mode()
 
 # Populate the File menu dynamically.
 self.update_file_menu()
 
 # Since loading the file may take some time, make sure it runs in the background.
 if self.file_path and os.path.isdir(self.file_path):
 self.queue_event(partial(self.import_dir_images, self.file_path or ""))
 elif self.file_path:
 self.queue_event(partial(self.load_file, self.file_path or ""))
 
 # Callbacks:
 self.zoom_widget.valueChanged.connect(self.paint_canvas)
 
 self.populate_mode_actions()
 
 # Display cursor coordinates at the right of status bar
 self.label_coordinates = QLabel('')
 self.statusBar().addPermanentWidget(self.label_coordinates)
 
 # Open Dir if default file
 if self.file_path and os.path.isdir(self.file_path):
 self.open_dir_dialog(dir_path=self.file_path, silent=True)
 
 def keyReleaseEvent(self, event):
 if event.key() == Qt.Key_Control:
 self.canvas.set_drawing_shape_to_square(False)
 
 def keyPressEvent(self, event):
 if event.key() == Qt.Key_Control:
 # Draw rectangle if Ctrl is pressed
 self.canvas.set_drawing_shape_to_square(True)
 
 # Support Functions #
 def set_format(self, save_format):
 if save_format == FORMAT_PASCALVOC:
 self.actions.save_format.setText(FORMAT_PASCALVOC)
 self.actions.save_format.setIcon(new_icon("format_voc"))
 self.label_file_format = LabelFileFormat.PASCAL_VOC
 LabelFile.suffix = XML_EXT
 
 elif save_format == FORMAT_YOLO:
 self.actions.save_format.setText(FORMAT_YOLO)
 self.actions.save_format.setIcon(new_icon("format_yolo"))
 self.label_file_format = LabelFileFormat.YOLO
 LabelFile.suffix = TXT_EXT
 
 elif save_format == FORMAT_CREATEML:
 self.actions.save_format.setText(FORMAT_CREATEML)
 self.actions.save_format.setIcon(new_icon("format_createml"))
 self.label_file_format = LabelFileFormat.CREATE_ML
 LabelFile.suffix = JSON_EXT
 
 def change_format(self):
 if self.label_file_format == LabelFileFormat.PASCAL_VOC:
 self.set_format(FORMAT_YOLO)
 elif self.label_file_format == LabelFileFormat.YOLO:
 self.set_format(FORMAT_CREATEML)
 elif self.label_file_format == LabelFileFormat.CREATE_ML:
 self.set_format(FORMAT_PASCALVOC)
 else:
 raise ValueError('Unknown label file format.')
 self.set_dirty()
 
 def no_shapes(self):
 return not self.items_to_shapes
 
 def toggle_advanced_mode(self, value=True):
 self._beginner = not value
 self.canvas.set_editing(True)
 self.populate_mode_actions()
 self.edit_button.setVisible(not value)
 if value:
 self.actions.createMode.setEnabled(True)
 self.actions.editMode.setEnabled(False)
 self.dock.setFeatures(self.dock.features() | self.dock_features)
 else:
 self.dock.setFeatures(self.dock.features() ^ self.dock_features)
 
 def populate_mode_actions(self):
 if self.beginner():
 tool, menu = self.actions.beginner, self.actions.beginnerContext
 else:
 tool, menu = self.actions.advanced, self.actions.advancedContext
 self.tools.clear()
 add_actions(self.tools, tool)
 self.canvas.menus[0].clear()
 add_actions(self.canvas.menus[0], menu)
 self.menus.edit.clear()
 actions = (self.actions.create,) if self.beginner()\
 else (self.actions.createMode, self.actions.editMode)
 add_actions(self.menus.edit, actions + self.actions.editMenu)
 
 def set_beginner(self):
 self.tools.clear()
 add_actions(self.tools, self.actions.beginner)
 
 def set_advanced(self):
 self.tools.clear()
 add_actions(self.tools, self.actions.advanced)
 
 def set_dirty(self):
 self.dirty = True
 self.actions.save.setEnabled(True)
 
 def set_clean(self):
 self.dirty = False
 self.actions.save.setEnabled(False)
 self.actions.create.setEnabled(True)
 
 def toggle_actions(self, value=True):
 """Enable/Disable widgets which depend on an opened image."""
 for z in self.actions.zoomActions:
 z.setEnabled(value)
 for action in self.actions.onLoadActive:
 action.setEnabled(value)
 
 def queue_event(self, function):
 QTimer.singleShot(0, function)
 
 def status(self, message, delay=5000):
 self.statusBar().showMessage(message, delay)
 
 def reset_state(self):
 self.items_to_shapes.clear()
 self.shapes_to_items.clear()
 self.label_list.clear()
 self.file_path = None
 self.image_data = None
 self.label_file = None
 self.canvas.reset_state()
 self.label_coordinates.clear()
 self.combo_box.cb.clear()
 
 def current_item(self):
 items = self.label_list.selectedItems()
 if items:
 return items[0]
 return None
 
 def add_recent_file(self, file_path):
 if file_path in self.recent_files:
 self.recent_files.remove(file_path)
 elif len(self.recent_files) >= self.max_recent:
 self.recent_files.pop()
 self.recent_files.insert(0, file_path)
 
 def beginner(self):
 return self._beginner
 
 def advanced(self):
 return not self.beginner()
 
 def show_tutorial_dialog(self, browser='default', link=None):
 if link is None:
 link = self.screencast
 
 if browser.lower() == 'default':
 wb.open(link, new=2)
 elif browser.lower() == 'chrome' and self.os_name == 'Windows':
 if shutil.which(browser.lower()): # 'chrome' not in wb._browsers in windows
 wb.register('chrome', None, wb.BackgroundBrowser('chrome'))
 else:
 chrome_path="D:\\Program Files (x86)\\Google\\Chrome\\Application\\chrome.exe"
 if os.path.isfile(chrome_path):
 wb.register('chrome', None, wb.BackgroundBrowser(chrome_path))
 try:
 wb.get('chrome').open(link, new=2)
 except:
 wb.open(link, new=2)
 elif browser.lower() in wb._browsers:
 wb.get(browser.lower()).open(link, new=2)
 
 def show_default_tutorial_dialog(self):
 self.show_tutorial_dialog(browser='default')
 
 def show_info_dialog(self):
 from libs.__init__ import __version__
 msg = u'Name:{0} \nApp Version:{1} \n{2} '.format(__appname__, __version__, sys.version_info)
 QMessageBox.information(self, u'Information', msg)
 
 def show_shortcuts_dialog(self):
 self.show_tutorial_dialog(browser='default', link='https://github.com/tzutalin/labelImg#Hotkeys')
 
 def create_shape(self):
 assert self.beginner()
 self.canvas.set_editing(False)
 self.actions.create.setEnabled(False)
 
 def toggle_drawing_sensitive(self, drawing=True):
 """In the middle of drawing, toggling between modes should be disabled."""
 self.actions.editMode.setEnabled(not drawing)
 if not drawing and self.beginner():
 # Cancel creation.
 print('Cancel creation.')
 self.canvas.set_editing(True)
 self.canvas.restore_cursor()
 self.actions.create.setEnabled(True)
 
 def toggle_draw_mode(self, edit=True):
 self.canvas.set_editing(edit)
 self.actions.createMode.setEnabled(edit)
 self.actions.editMode.setEnabled(not edit)
 
 def set_create_mode(self):
 assert self.advanced()
 self.toggle_draw_mode(False)
 
 def set_edit_mode(self):
 assert self.advanced()
 self.toggle_draw_mode(True)
 self.label_selection_changed()
 
 def update_file_menu(self):
 curr_file_path = self.file_path
 
 def exists(filename):
 return os.path.exists(filename)
 menu = self.menus.recentFiles
 menu.clear()
 files = [f for f in self.recent_files if f !=
 curr_file_path and exists(f)]
 for i, f in enumerate(files):
 icon = new_icon('labels')
 action = QAction(
 icon, '&%d %s' % (i + 1, QFileInfo(f).fileName()), self)
 action.triggered.connect(partial(self.load_recent, f))
 menu.addAction(action)
 
 def pop_label_list_menu(self, point):
 self.menus.labelList.exec_(self.label_list.mapToGlobal(point))
 
 def edit_label(self):
 if not self.canvas.editing():
 return
 item = self.current_item()
 if not item:
 return
 text = self.label_dialog.pop_up(item.text())
 if text is not None:
 item.setText(text)
 item.setBackground(generate_color_by_text(text))
 self.set_dirty()
 self.update_combo_box()
 
 # Tzutalin 20160906 : Add file list and dock to move faster
 def file_item_double_clicked(self, item=None):
 self.cur_img_idx = self.m_img_list.index(ustr(item.text()))
 filename = self.m_img_list[self.cur_img_idx]
 if filename:
 self.load_file(filename)
 
 # Add chris
 def button_state(self, item=None):
 """ Function to handle difficult examples Update on each object """
 if not self.canvas.editing():
 return
 
 item = self.current_item()
 if not item: # If not selected Item, take the first one
 item = self.label_list.item(self.label_list.count() - 1)
 
 difficult = self.diffc_button.isChecked()
 
 try:
 shape = self.items_to_shapes[item]
 except:
 pass
 # Checked and Update
 try:
 if difficult != shape.difficult:
 shape.difficult = difficult
 self.set_dirty()
 else: # User probably changed item visibility
 self.canvas.set_shape_visible(shape, item.checkState() == Qt.Checked)
 except:
 pass
 
 # React to canvas signals.
 def shape_selection_changed(self, selected=False):
 if self._no_selection_slot:
 self._no_selection_slot = False
 else:
 shape = self.canvas.selected_shape
 if shape:
 self.shapes_to_items[shape].setSelected(True)
 else:
 self.label_list.clearSelection()
 self.actions.delete.setEnabled(selected)
 self.actions.copy.setEnabled(selected)
 self.actions.edit.setEnabled(selected)
 self.actions.shapeLineColor.setEnabled(selected)
 self.actions.shapeFillColor.setEnabled(selected)
 
 def add_label(self, shape):
 shape.paint_label = self.display_label_option.isChecked()
 item = HashableQListWidgetItem(shape.label)
 item.setFlags(item.flags() | Qt.ItemIsUserCheckable)
 item.setCheckState(Qt.Checked)
 item.setBackground(generate_color_by_text(shape.label))
 self.items_to_shapes[item] = shape
 self.shapes_to_items[shape] = item
 self.label_list.addItem(item)
 for action in self.actions.onShapesPresent:
 action.setEnabled(True)
 self.update_combo_box()
 
 def remove_label(self, shape):
 if shape is None:
 # print('rm empty label')
 return
 item = self.shapes_to_items[shape]
 self.label_list.takeItem(self.label_list.row(item))
 del self.shapes_to_items[shape]
 del self.items_to_shapes[item]
 self.update_combo_box()
 
 def load_labels(self, shapes):
 s = []
 for label, points, line_color, fill_color, difficult in shapes:
 shape = Shape(label=label)
 for x, y in points:
 
 # Ensure the labels are within the bounds of the image. If not, fix them.
 x, y, snapped = self.canvas.snap_point_to_canvas(x, y)
 if snapped:
 self.set_dirty()
 
 shape.add_point(QPointF(x, y))
 shape.difficult = difficult
 shape.close()
 s.append(shape)
 
 if line_color:
 shape.line_color = QColor(*line_color)
 else:
 shape.line_color = generate_color_by_text(label)
 
 if fill_color:
 shape.fill_color = QColor(*fill_color)
 else:
 shape.fill_color = generate_color_by_text(label)
 
 self.add_label(shape)
 self.update_combo_box()
 self.canvas.load_shapes(s)
 
 def update_combo_box(self):
 # Get the unique labels and add them to the Combobox.
 items_text_list = [str(self.label_list.item(i).text()) for i in range(self.label_list.count())]
 
 unique_text_list = list(set(items_text_list))
 # Add a null row for showing all the labels
 unique_text_list.append("")
 unique_text_list.sort()
 
 self.combo_box.update_items(unique_text_list)
 
 def save_labels(self, annotation_file_path):
 annotation_file_path = ustr(annotation_file_path)
 if self.label_file is None:
 self.label_file = LabelFile()
 self.label_file.verified = self.canvas.verified
 
 def format_shape(s):
 return dict(label=s.label,
 line_color=s.line_color.getRgb(),
 fill_color=s.fill_color.getRgb(),
 points=[(p.x(), p.y()) for p in s.points],
 # add chris
 difficult=s.difficult)
 
 shapes = [format_shape(shape) for shape in self.canvas.shapes]
 # Can add different annotation formats here
 try:
 if self.label_file_format == LabelFileFormat.PASCAL_VOC:
 if annotation_file_path[-4:].lower() != ".xml":
 annotation_file_path += XML_EXT
 self.label_file.save_pascal_voc_format(annotation_file_path, shapes, self.file_path, self.image_data,
 self.line_color.getRgb(), self.fill_color.getRgb())
 elif self.label_file_format == LabelFileFormat.YOLO:
 if annotation_file_path[-4:].lower() != ".txt":
 annotation_file_path += TXT_EXT
 self.label_file.save_yolo_format(annotation_file_path, shapes, self.file_path, self.image_data, self.label_hist,
 self.line_color.getRgb(), self.fill_color.getRgb())
 elif self.label_file_format == LabelFileFormat.CREATE_ML:
 if annotation_file_path[-5:].lower() != ".json":
 annotation_file_path += JSON_EXT
 self.label_file.save_create_ml_format(annotation_file_path, shapes, self.file_path, self.image_data,
 self.label_hist, self.line_color.getRgb(), self.fill_color.getRgb())
 else:
 self.label_file.save(annotation_file_path, shapes, self.file_path, self.image_data,
 self.line_color.getRgb(), self.fill_color.getRgb())
 print('Image:{0} -> Annotation:{1}'.format(self.file_path, annotation_file_path))
 return True
 except LabelFileError as e:
 self.error_message(u'Error saving label data', u'%s' % e)
 return False
 
 def copy_selected_shape(self):
 self.add_label(self.canvas.copy_selected_shape())
 # fix copy and delete
 self.shape_selection_changed(True)
 
 def combo_selection_changed(self, index):
 text = self.combo_box.cb.itemText(index)
 for i in range(self.label_list.count()):
 if text == "":
 self.label_list.item(i).setCheckState(2)
 elif text != self.label_list.item(i).text():
 self.label_list.item(i).setCheckState(0)
 else:
 self.label_list.item(i).setCheckState(2)
 
 def label_selection_changed(self):
 item = self.current_item()
 if item and self.canvas.editing():
 self._no_selection_slot = True
 self.canvas.select_shape(self.items_to_shapes[item])
 shape = self.items_to_shapes[item]
 # Add Chris
 self.diffc_button.setChecked(shape.difficult)
 
 def label_item_changed(self, item):
 shape = self.items_to_shapes[item]
 label = item.text()
 if label != shape.label:
 shape.label = item.text()
 shape.line_color = generate_color_by_text(shape.label)
 self.set_dirty()
 else: # User probably changed item visibility
 self.canvas.set_shape_visible(shape, item.checkState() == Qt.Checked)
 
 # Callback functions:
 def new_shape(self):
 """Pop-up and give focus to the label editor. position MUST be in global coordinates. """
 if not self.use_default_label_checkbox.isChecked() or not self.default_label_text_line.text():
 if len(self.label_hist) > 0:
 self.label_dialog = LabelDialog(
 parent=self, list_item=self.label_hist)
 
 # Sync single class mode from PR#106
 if self.single_class_mode.isChecked() and self.lastLabel:
 text = self.lastLabel
 else:
 text = self.label_dialog.pop_up(text=self.prev_label_text)
 self.lastLabel = text
 else:
 text = self.default_label_text_line.text()
 
 # Add Chris
 self.diffc_button.setChecked(False)
 if text is not None:
 self.prev_label_text = text
 generate_color = generate_color_by_text(text)
 shape = self.canvas.set_last_label(text, generate_color, generate_color)
 self.add_label(shape)
 if self.beginner(): # Switch to edit mode.
 self.canvas.set_editing(True)
 self.actions.create.setEnabled(True)
 else:
 self.actions.editMode.setEnabled(True)
 self.set_dirty()
 
 if text not in self.label_hist:
 self.label_hist.append(text)
 else:
 # self.canvas.undoLastLine()
 self.canvas.reset_all_lines()
 
 def scroll_request(self, delta, orientation):
 units = - delta / (8 * 15)
 bar = self.scroll_bars[orientation]
 bar.setValue(bar.value() + bar.singleStep() * units)
 
 def set_zoom(self, value):
 self.actions.fitWidth.setChecked(False)
 self.actions.fitWindow.setChecked(False)
 self.zoom_mode = self.MANUAL_ZOOM
 self.zoom_widget.setValue(value)
 
 def add_zoom(self, increment=10):
 self.set_zoom(self.zoom_widget.value() + increment)
 
 def zoom_request(self, delta):
 # get the current scrollbar positions
 # calculate the percentages ~ coordinates
 h_bar = self.scroll_bars[Qt.Horizontal]
 v_bar = self.scroll_bars[Qt.Vertical]
 
 # get the current maximum, to know the difference after zooming
 h_bar_max = h_bar.maximum()
 v_bar_max = v_bar.maximum()
 
 # get the cursor position and canvas size
 # calculate the desired movement from 0 to 1
 # where 0 = move left
 # 1 = move right
 # up and down analogous
 cursor = QCursor()
 pos = cursor.pos()
 relative_pos = QWidget.mapFromGlobal(self, pos)
 
 cursor_x = relative_pos.x()
 cursor_y = relative_pos.y()
 
 w = self.scroll_area.width()
 h = self.scroll_area.height()
 
 # the scaling from 0 to 1 has some padding
 # you don't have to hit the very leftmost pixel for a maximum-left movement
 margin = 0.1
 move_x = (cursor_x - margin * w) / (w - 2 * margin * w)
 move_y = (cursor_y - margin * h) / (h - 2 * margin * h)
 
 # clamp the values from 0 to 1
 move_x = min(max(move_x, 0), 1)
 move_y = min(max(move_y, 0), 1)
 
 # zoom in
 units = delta / (8 * 15)
 scale = 10
 self.add_zoom(scale * units)
 
 # get the difference in scrollbar values
 # this is how far we can move
 d_h_bar_max = h_bar.maximum() - h_bar_max
 d_v_bar_max = v_bar.maximum() - v_bar_max
 
 # get the new scrollbar values
 new_h_bar_value = h_bar.value() + move_x * d_h_bar_max
 new_v_bar_value = v_bar.value() + move_y * d_v_bar_max
 
 h_bar.setValue(new_h_bar_value)
 v_bar.setValue(new_v_bar_value)
 
 def set_fit_window(self, value=True):
 if value:
 self.actions.fitWidth.setChecked(False)
 self.zoom_mode = self.FIT_WINDOW if value else self.MANUAL_ZOOM
 self.adjust_scale()
 
 def set_fit_width(self, value=True):
 if value:
 self.actions.fitWindow.setChecked(False)
 self.zoom_mode = self.FIT_WIDTH if value else self.MANUAL_ZOOM
 self.adjust_scale()
 
 def toggle_polygons(self, value):
 for item, shape in self.items_to_shapes.items():
 item.setCheckState(Qt.Checked if value else Qt.Unchecked)
 
 def load_file(self, file_path=None):
 """Load the specified file, or the last opened file if None."""
 self.reset_state()
 self.canvas.setEnabled(False)
 if file_path is None:
 file_path = self.settings.get(SETTING_FILENAME)
 
 # Make sure that filePath is a regular python string, rather than QString
 file_path = ustr(file_path)
 
 # Fix bug: An index error after select a directory when open a new file.
 unicode_file_path = ustr(file_path)
 unicode_file_path = os.path.abspath(unicode_file_path)
 # Tzutalin 20160906 : Add file list and dock to move faster
 # Highlight the file item
 if unicode_file_path and self.file_list_widget.count() > 0:
 if unicode_file_path in self.m_img_list:
 index = self.m_img_list.index(unicode_file_path)
 file_widget_item = self.file_list_widget.item(index)
 file_widget_item.setSelected(True)
 else:
 self.file_list_widget.clear()
 self.m_img_list.clear()
 
 if unicode_file_path and os.path.exists(unicode_file_path):
 if LabelFile.is_label_file(unicode_file_path):
 try:
 self.label_file = LabelFile(unicode_file_path)
 except LabelFileError as e:
 self.error_message(u'Error opening file',
 (u"%s"
 u"Make sure %s is a valid label file.")
 % (e, unicode_file_path))
 self.status("Error reading %s" % unicode_file_path)
 return False
 self.image_data = self.label_file.image_data
 self.line_color = QColor(*self.label_file.lineColor)
 self.fill_color = QColor(*self.label_file.fillColor)
 self.canvas.verified = self.label_file.verified
 else:
 # Load image:
 # read data first and store for saving into label file.
 self.image_data = read(unicode_file_path, None)
 self.label_file = None
 self.canvas.verified = False
 
 if isinstance(self.image_data, QImage):
 image = self.image_data
 else:
 image = QImage.fromData(self.image_data)
 if image.isNull():
 self.error_message(u'Error opening file',
 u"Make sure %s is a valid image file." % unicode_file_path)
 self.status("Error reading %s" % unicode_file_path)
 return False
 self.status("Loaded %s" % os.path.basename(unicode_file_path))
 self.image = image
 self.file_path = unicode_file_path
 self.canvas.load_pixmap(QPixmap.fromImage(image))
 if self.label_file:
 self.load_labels(self.label_file.shapes)
 self.set_clean()
 self.canvas.setEnabled(True)
 self.adjust_scale(initial=True)
 self.paint_canvas()
 self.add_recent_file(self.file_path)
 self.toggle_actions(True)
 self.show_bounding_box_from_annotation_file(file_path)
 
 counter = self.counter_str()
 self.setWindowTitle(__appname__ + ' ' + file_path + ' ' + counter)
 
 # Default : select last item if there is at least one item
 if self.label_list.count():
 self.label_list.setCurrentItem(self.label_list.item(self.label_list.count() - 1))
 self.label_list.item(self.label_list.count() - 1).setSelected(True)
 
 self.canvas.setFocus(True)
 return True
 return False
 
 def counter_str(self):
 """ Converts image counter to string representation. """
 return '[{} / {}]'.format(self.cur_img_idx + 1, self.img_count)
 
 def show_bounding_box_from_annotation_file(self, file_path):
 if self.default_save_dir is not None:
 basename = os.path.basename(os.path.splitext(file_path)[0])
 xml_path = os.path.join(self.default_save_dir, basename + XML_EXT)
 txt_path = os.path.join(self.default_save_dir, basename + TXT_EXT)
 json_path = os.path.join(self.default_save_dir, basename + JSON_EXT)
 
 """Annotation file priority: PascalXML > YOLO """
 if os.path.isfile(xml_path):
 self.load_pascal_xml_by_filename(xml_path)
 elif os.path.isfile(txt_path):
 self.load_yolo_txt_by_filename(txt_path)
 elif os.path.isfile(json_path):
 self.load_create_ml_json_by_filename(json_path, file_path)
 
 else:
 xml_path = os.path.splitext(file_path)[0] + XML_EXT
 txt_path = os.path.splitext(file_path)[0] + TXT_EXT
 if os.path.isfile(xml_path):
 self.load_pascal_xml_by_filename(xml_path)
 elif os.path.isfile(txt_path):
 self.load_yolo_txt_by_filename(txt_path)
 
 def resizeEvent(self, event):
 if self.canvas and not self.image.isNull()\
 and self.zoom_mode != self.MANUAL_ZOOM:
 self.adjust_scale()
 super(MainWindow, self).resizeEvent(event)
 
 def paint_canvas(self):
 assert not self.image.isNull(), "cannot paint null image"
 self.canvas.scale = 0.01 * self.zoom_widget.value()
 self.canvas.label_font_size = int(0.02 * max(self.image.width(), self.image.height()))
 self.canvas.adjustSize()
 self.canvas.update()
 
 def adjust_scale(self, initial=False):
 value = self.scalers[self.FIT_WINDOW if initial else self.zoom_mode]()
 self.zoom_widget.setValue(int(100 * value))
 
 def scale_fit_window(self):
 """Figure out the size of the pixmap in order to fit the main widget."""
 e = 2.0 # So that no scrollbars are generated.
 w1 = self.centralWidget().width() - e
 h1 = self.centralWidget().height() - e
 a1 = w1 / h1
 # Calculate a new scale value based on the pixmap's aspect ratio.
 w2 = self.canvas.pixmap.width() - 0.0
 h2 = self.canvas.pixmap.height() - 0.0
 a2 = w2 / h2
 return w1 / w2 if a2 >= a1 else h1 / h2
 
 def scale_fit_width(self):
 # The epsilon does not seem to work too well here.
 w = self.centralWidget().width() - 2.0
 return w / self.canvas.pixmap.width()
 
 def closeEvent(self, event):
 if not self.may_continue():
 event.ignore()
 settings = self.settings
 # If it loads images from dir, don't load it at the beginning
 if self.dir_name is None:
 settings[SETTING_FILENAME] = self.file_path if self.file_path else ''
 else:
 settings[SETTING_FILENAME] = ''
 
 settings[SETTING_WIN_SIZE] = self.size()
 settings[SETTING_WIN_POSE] = self.pos()
 settings[SETTING_WIN_STATE] = self.saveState()
 settings[SETTING_LINE_COLOR] = self.line_color
 settings[SETTING_FILL_COLOR] = self.fill_color
 settings[SETTING_RECENT_FILES] = self.recent_files
 settings[SETTING_ADVANCE_MODE] = not self._beginner
 if self.default_save_dir and os.path.exists(self.default_save_dir):
 settings[SETTING_SAVE_DIR] = ustr(self.default_save_dir)
 else:
 settings[SETTING_SAVE_DIR] = ''
 
 if self.last_open_dir and os.path.exists(self.last_open_dir):
 settings[SETTING_LAST_OPEN_DIR] = self.last_open_dir
 else:
 settings[SETTING_LAST_OPEN_DIR] = ''
 
 settings[SETTING_AUTO_SAVE] = self.auto_saving.isChecked()
 settings[SETTING_SINGLE_CLASS] = self.single_class_mode.isChecked()
 settings[SETTING_PAINT_LABEL] = self.display_label_option.isChecked()
 settings[SETTING_DRAW_SQUARE] = self.draw_squares_option.isChecked()
 settings[SETTING_LABEL_FILE_FORMAT] = self.label_file_format
 settings.save()
 
 def load_recent(self, filename):
 if self.may_continue():
 self.load_file(filename)
 
 def scan_all_images(self, folder_path):
 extensions = ['.%s' % fmt.data().decode("ascii").lower() for fmt in QImageReader.supportedImageFormats()]
 images = []
 
 for root, dirs, files in os.walk(folder_path):
 for file in files:
 if file.lower().endswith(tuple(extensions)):
 relative_path = os.path.join(root, file)
 path = ustr(os.path.abspath(relative_path))
 images.append(path)
 natural_sort(images, key=lambda x: x.lower())
 return images
 
 def change_save_dir_dialog(self, _value=False):
 if self.default_save_dir is not None:
 path = ustr(self.default_save_dir)
 else:
 path = '.'
 
 dir_path = ustr(QFileDialog.getExistingDirectory(self,
 '%s - Save annotations to the directory' % __appname__, path, QFileDialog.ShowDirsOnly
 | QFileDialog.DontResolveSymlinks))
 
 if dir_path is not None and len(dir_path) > 1:
 self.default_save_dir = dir_path
 
 self.statusBar().showMessage('%s . Annotation will be saved to %s' %
 ('Change saved folder', self.default_save_dir))
 self.statusBar().show()
 
 def open_annotation_dialog(self, _value=False):
 if self.file_path is None:
 self.statusBar().showMessage('Please select image first')
 self.statusBar().show()
 return
 
 path = os.path.dirname(ustr(self.file_path))\
 if self.file_path else '.'
 if self.label_file_format == LabelFileFormat.PASCAL_VOC:
 filters = "Open Annotation XML file (%s)" % ' '.join(['*.xml'])
 filename = ustr(QFileDialog.getOpenFileName(self, '%s - Choose a xml file' % __appname__, path, filters))
 if filename:
 if isinstance(filename, (tuple, list)):
 filename = filename[0]
 self.load_pascal_xml_by_filename(filename)
 
 def open_dir_dialog(self, _value=False, dir_path=None, silent=False):
 if not self.may_continue():
 return
 
 default_open_dir_path = dir_path if dir_path else '.'
 if self.last_open_dir and os.path.exists(self.last_open_dir):
 default_open_dir_path = self.last_open_dir
 else:
 default_open_dir_path = os.path.dirname(self.file_path) if self.file_path else '.'
 if silent != True:
 target_dir_path = ustr(QFileDialog.getExistingDirectory(self,
 '%s - Open Directory' % __appname__, default_open_dir_path,
 QFileDialog.ShowDirsOnly | QFileDialog.DontResolveSymlinks))
 else:
 target_dir_path = ustr(default_open_dir_path)
 self.last_open_dir = target_dir_path
 self.import_dir_images(target_dir_path)
 
 def import_dir_images(self, dir_path):
 if not self.may_continue() or not dir_path:
 return
 
 self.last_open_dir = dir_path
 self.dir_name = dir_path
 self.file_path = None
 self.file_list_widget.clear()
 self.m_img_list = self.scan_all_images(dir_path)
 self.img_count = len(self.m_img_list)
 self.open_next_image()
 for imgPath in self.m_img_list:
 item = QListWidgetItem(imgPath)
 self.file_list_widget.addItem(item)
 
 def verify_image(self, _value=False):
 # Proceeding next image without dialog if having any label
 if self.file_path is not None:
 try:
 self.label_file.toggle_verify()
 except AttributeError:
 # If the labelling file does not exist yet, create if and
 # re-save it with the verified attribute.
 self.save_file()
 if self.label_file is not None:
 self.label_file.toggle_verify()
 else:
 return
 
 self.canvas.verified = self.label_file.verified
 self.paint_canvas()
 self.save_file()
 
 def open_prev_image(self, _value=False):
 # Proceeding prev image without dialog if having any label
 if self.auto_saving.isChecked():
 if self.default_save_dir is not None:
 if self.dirty is True:
 self.save_file()
 else:
 self.change_save_dir_dialog()
 return
 
 if not self.may_continue():
 return
 
 if self.img_count <= 0:
 return
 
 if self.file_path is None:
 return
 
 if self.cur_img_idx - 1 >= 0:
 self.cur_img_idx -= 1
 filename = self.m_img_list[self.cur_img_idx]
 if filename:
 self.load_file(filename)
 
 def open_next_image(self, _value=False):
 # Proceeding prev image without dialog if having any label
 if self.auto_saving.isChecked():
 if self.default_save_dir is not None:
 if self.dirty is True:
 self.save_file()
 else:
 self.change_save_dir_dialog()
 return
 
 if not self.may_continue():
 return
 
 if self.img_count <= 0:
 return
 
 filename = None
 if self.file_path is None:
 filename = self.m_img_list[0]
 self.cur_img_idx = 0
 else:
 if self.cur_img_idx + 1 < self.img_count:
 self.cur_img_idx += 1
 filename = self.m_img_list[self.cur_img_idx]
 
 if filename:
 self.load_file(filename)
 
 def open_file(self, _value=False):
 if not self.may_continue():
 return
 path = os.path.dirname(ustr(self.file_path)) if self.file_path else '.'
 formats = ['*.%s' % fmt.data().decode("ascii").lower() for fmt in QImageReader.supportedImageFormats()]
 filters = "Image & Label files (%s)" % ' '.join(formats + ['*%s' % LabelFile.suffix])
 filename = QFileDialog.getOpenFileName(self, '%s - Choose Image or Label file' % __appname__, path, filters)
 if filename:
 if isinstance(filename, (tuple, list)):
 filename = filename[0]
 self.cur_img_idx = 0
 self.img_count = 1
 self.load_file(filename)
 
 def save_file(self, _value=False):
 if self.default_save_dir is not None and len(ustr(self.default_save_dir)):
 if self.file_path:
 image_file_name = os.path.basename(self.file_path)
 saved_file_name = os.path.splitext(image_file_name)[0]
 saved_path = os.path.join(ustr(self.default_save_dir), saved_file_name)
 self._save_file(saved_path)
 else:
 image_file_dir = os.path.dirname(self.file_path)
 image_file_name = os.path.basename(self.file_path)
 saved_file_name = os.path.splitext(image_file_name)[0]
 saved_path = os.path.join(image_file_dir, saved_file_name)
 self._save_file(saved_path if self.label_file
 else self.save_file_dialog(remove_ext=False))
 
 def save_file_as(self, _value=False):
 assert not self.image.isNull(), "cannot save empty image"
 self._save_file(self.save_file_dialog())
 
 def save_file_dialog(self, remove_ext=True):
 caption = '%s - Choose File' % __appname__
 filters = 'File (*%s)' % LabelFile.suffix
 open_dialog_path = self.current_path()
 dlg = QFileDialog(self, caption, open_dialog_path, filters)
 dlg.setDefaultSuffix(LabelFile.suffix[1:])
 dlg.setAcceptMode(QFileDialog.AcceptSave)
 filename_without_extension = os.path.splitext(self.file_path)[0]
 dlg.selectFile(filename_without_extension)
 dlg.setOption(QFileDialog.DontUseNativeDialog, False)
 if dlg.exec_():
 full_file_path = ustr(dlg.selectedFiles()[0])
 if remove_ext:
 return os.path.splitext(full_file_path)[0] # Return file path without the extension.
 else:
 return full_file_path
 return ''
 
 def _save_file(self, annotation_file_path):
 if annotation_file_path and self.save_labels(annotation_file_path):
 self.set_clean()
 self.statusBar().showMessage('Saved to %s' % annotation_file_path)
 self.statusBar().show()
 
 def close_file(self, _value=False):
 if not self.may_continue():
 return
 self.reset_state()
 self.set_clean()
 self.toggle_actions(False)
 self.canvas.setEnabled(False)
 self.actions.saveAs.setEnabled(False)
 
 def delete_image(self):
 delete_path = self.file_path
 if delete_path is not None:
 self.open_next_image()
 self.cur_img_idx -= 1
 self.img_count -= 1
 if os.path.exists(delete_path):
 os.remove(delete_path)
 self.import_dir_images(self.last_open_dir)
 
 def reset_all(self):
 self.settings.reset()
 self.close()
 process = QProcess()
 process.startDetached(os.path.abspath(__file__))
 
 def may_continue(self):
 if not self.dirty:
 return True
 else:
 discard_changes = self.discard_changes_dialog()
 if discard_changes == QMessageBox.No:
 return True
 elif discard_changes == QMessageBox.Yes:
 self.save_file()
 return True
 else:
 return False
 
 def discard_changes_dialog(self):
 yes, no, cancel = QMessageBox.Yes, QMessageBox.No, QMessageBox.Cancel
 msg = u'You have unsaved changes, would you like to save them and proceed?\nClick "No" to undo all changes.'
 return QMessageBox.warning(self, u'Attention', msg, yes | no | cancel)
 
 def error_message(self, title, message):
 return QMessageBox.critical(self, title,
 '%s%s' % (title, message))
 
 def current_path(self):
 return os.path.dirname(self.file_path) if self.file_path else '.'
 
 def choose_color1(self):
 color = self.color_dialog.getColor(self.line_color, u'Choose line color',
 default=DEFAULT_LINE_COLOR)
 if color:
 self.line_color = color
 Shape.line_color = color
 self.canvas.set_drawing_color(color)
 self.canvas.update()
 self.set_dirty()
 
 def delete_selected_shape(self):
 self.remove_label(self.canvas.delete_selected())
 self.set_dirty()
 if self.no_shapes():
 for action in self.actions.onShapesPresent:
 action.setEnabled(False)
 
 def choose_shape_line_color(self):
 color = self.color_dialog.getColor(self.line_color, u'Choose Line Color',
 default=DEFAULT_LINE_COLOR)
 if color:
 self.canvas.selected_shape.line_color = color
 self.canvas.update()
 self.set_dirty()
 
 def choose_shape_fill_color(self):
 color = self.color_dialog.getColor(self.fill_color, u'Choose Fill Color',
 default=DEFAULT_FILL_COLOR)
 if color:
 self.canvas.selected_shape.fill_color = color
 self.canvas.update()
 self.set_dirty()
 
 def copy_shape(self):
 self.canvas.end_move(copy=True)
 self.add_label(self.canvas.selected_shape)
 self.set_dirty()
 
 def move_shape(self):
 self.canvas.end_move(copy=False)
 self.set_dirty()
 
 def load_predefined_classes(self, predef_classes_file):
 if os.path.exists(predef_classes_file) is True:
 with codecs.open(predef_classes_file, 'r', 'utf8') as f:
 for line in f:
 line = line.strip()
 if self.label_hist is None:
 self.label_hist = [line]
 else:
 self.label_hist.append(line)
 
 def load_pascal_xml_by_filename(self, xml_path):
 if self.file_path is None:
 return
 if os.path.isfile(xml_path) is False:
 return
 
 self.set_format(FORMAT_PASCALVOC)
 
 t_voc_parse_reader = PascalVocReader(xml_path)
 shapes = t_voc_parse_reader.get_shapes()
 self.load_labels(shapes)
 self.canvas.verified = t_voc_parse_reader.verified
 
 def load_yolo_txt_by_filename(self, txt_path):
 if self.file_path is None:
 return
 if os.path.isfile(txt_path) is False:
 return
 
 self.set_format(FORMAT_YOLO)
 t_yolo_parse_reader = YoloReader(txt_path, self.image)
 shapes = t_yolo_parse_reader.get_shapes()
 print(shapes)
 self.load_labels(shapes)
 self.canvas.verified = t_yolo_parse_reader.verified
 
 def load_create_ml_json_by_filename(self, json_path, file_path):
 if self.file_path is None:
 return
 if os.path.isfile(json_path) is False:
 return
 
 self.set_format(FORMAT_CREATEML)
 
 create_ml_parse_reader = CreateMLReader(json_path, file_path)
 shapes = create_ml_parse_reader.get_shapes()
 self.load_labels(shapes)
 self.canvas.verified = create_ml_parse_reader.verified
 
 def copy_previous_bounding_boxes(self):
 current_index = self.m_img_list.index(self.file_path)
 if current_index - 1 >= 0:
 prev_file_path = self.m_img_list[current_index - 1]
 self.show_bounding_box_from_annotation_file(prev_file_path)
 self.save_file()
 
 def toggle_paint_labels_option(self):
 for shape in self.canvas.shapes:
 shape.paint_label = self.display_label_option.isChecked()
 
 def toggle_draw_square(self):
 self.canvas.set_drawing_shape_to_square(self.draw_squares_option.isChecked())
 
 def inverted(color):
 return QColor(*[255 - v for v in color.getRgb()])
 
 
 def read(filename, default=None):
 try:
 reader = QImageReader(filename)
 reader.setAutoTransform(True)
 return reader.read()
 except:
 return default
 
 
 def get_main_app(argv=[]):
 """ Standard boilerplate Qt application code. Do everything but app.exec_() -- so that we can test the application in one thread """
 app = QApplication(argv)
 app.setApplicationName(__appname__)
 app.setWindowIcon(new_icon("app"))
 # Tzutalin 201705+: Accept extra agruments to change predefined class file
 argparser = argparse.ArgumentParser()
 argparser.add_argument("image_dir", nargs="?")
 argparser.add_argument("class_file",
 default=os.path.join(os.path.dirname(__file__), "data", "predefined_classes.txt"),
 nargs="?")
 argparser.add_argument("save_dir", nargs="?")
 args = argparser.parse_args(argv[1:])
 
 args.image_dir = args.image_dir and os.path.normpath(args.image_dir)
 args.class_file = args.class_file and os.path.normpath(args.class_file)
 args.save_dir = args.save_dir and os.path.normpath(args.save_dir)
 
 # Usage : labelImg.py image classFile saveDir
 win = MainWindow(args.image_dir,
 args.class_file,
 args.save_dir)
 win.show()
 return app, win
 
 
 def main():
 """construct main app and run it"""
 app, _win = get_main_app(sys.argv)
 return app.exec_()
 
 if __name__ == '__main__':
 sys.exit(main())

1.  train.py( [需要点击此处跳转Github下载配置权重][Github] )

"""Train a YOLOv5 model on a custom dataset "train.py" Usage: $ python path/to/train.py --data coco128.yaml --weights yolov5s.pt --img 640 """
 
 import argparse
 import logging
 import os
 import random
 import sys
 import time
 import warnings
 from copy import deepcopy
 from pathlib import Path
 from threading import Thread
 
 import math
 import numpy as np
 import torch.distributed as dist
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import torch.optim.lr_scheduler as lr_scheduler
 import torch.utils.data
 import yaml
 from torch.cuda import amp
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 from tqdm import tqdm
 
 FILE = Path(__file__).absolute()
 sys.path.append(FILE.parents[0].as_posix()) # add yolov5/ to path
 
 import test # for end-of-epoch mAP
 from models.experimental import attempt_load
 from models.yolo import Model
 from utils.autoanchor import check_anchors
 from utils.datasets import create_dataloader
 from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \
 strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \
 check_requirements, print_mutation, set_logging, one_cycle, colorstr
 from utils.google_utils import attempt_download
 from utils.loss import ComputeLoss
 from utils.plots import plot_images, plot_labels, plot_results, plot_evolution
 from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, de_parallel
 from utils.wandb_logging.wandb_utils import WandbLogger, check_wandb_resume
 from utils.metrics import fitness
 
 logger = logging.getLogger(__name__)
 LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
 RANK = int(os.getenv('RANK', -1))
 WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
 
 
 def train(hyp, # path/to/hyp.yaml or hyp dictionary
 opt,
 device,
 ):
 save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, notest, nosave, workers, = \
 opt.save_dir, opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \
 opt.resume, opt.notest, opt.nosave, opt.workers
 
 # Directories
 save_dir = Path(save_dir)
 wdir = save_dir / 'weights'
 wdir.mkdir(parents=True, exist_ok=True) # make dir
 last = wdir / 'last.pt'
 best = wdir / 'best.pt'
 results_file = save_dir / 'results.txt'
 
 # Hyperparameters
 if isinstance(hyp, str):
 with open(hyp) as f:
 hyp = yaml.safe_load(f) # load hyps dict
 logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
 
 # Save run settings
 with open(save_dir / 'hyp.yaml', 'w') as f:
 yaml.safe_dump(hyp, f, sort_keys=False)
 with open(save_dir / 'opt.yaml', 'w') as f:
 yaml.safe_dump(vars(opt), f, sort_keys=False)
 
 # Configure
 plots = not evolve # create plots
 cuda = device.type != 'cpu'
 init_seeds(1 + RANK)
 with open(data) as f:
 data_dict = yaml.safe_load(f) # data dict
 
 # Loggers
 loggers = { 'wandb': None, 'tb': None} # loggers dict
 if RANK in [-1, 0]:
 # TensorBoard
 if not evolve:
 prefix = colorstr('tensorboard: ')
 logger.info(f"{prefix}Start with 'tensorboard --logdir {opt.project}', view at http://localhost:6006/")
 loggers['tb'] = SummaryWriter(str(save_dir))
 
 # W&B
 opt.hyp = hyp # add hyperparameters
 run_id = torch.load(weights).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None
 run_id = run_id if opt.resume else None # start fresh run if transfer learning
 wandb_logger = WandbLogger(opt, save_dir.stem, run_id, data_dict)
 loggers['wandb'] = wandb_logger.wandb
 if loggers['wandb']:
 data_dict = wandb_logger.data_dict
 weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # may update weights, epochs if resuming
 
 nc = 1 if single_cls else int(data_dict['nc']) # number of classes
 names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names
 assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, data) # check
 is_coco = data.endswith('coco.yaml') and nc == 80 # COCO dataset
 
 # Model
 pretrained = weights.endswith('.pt')
 if pretrained:
 with torch_distributed_zero_first(RANK):
 weights = attempt_download(weights) # download if not found locally
 ckpt = torch.load(weights, map_location=device) # load checkpoint
 model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
 exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else [] # exclude keys
 state_dict = ckpt['model'].float().state_dict() # to FP32
 state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect
 model.load_state_dict(state_dict, strict=False) # load
 logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report
 else:
 model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
 with torch_distributed_zero_first(RANK):
 check_dataset(data_dict) # check
 train_path = data_dict['train']
 test_path = data_dict['val']
 
 # Freeze
 freeze = [] # parameter names to freeze (full or partial)
 for k, v in model.named_parameters():
 v.requires_grad = True # train all layers
 if any(x in k for x in freeze):
 print('freezing %s' % k)
 v.requires_grad = False
 
 # Optimizer
 nbs = 64 # nominal batch size
 accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing
 hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay
 logger.info(f"Scaled weight_decay = {hyp['weight_decay']}")
 
 pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
 for k, v in model.named_modules():
 if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
 pg2.append(v.bias) # biases
 if isinstance(v, nn.BatchNorm2d):
 pg0.append(v.weight) # no decay
 elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
 pg1.append(v.weight) # apply decay
 
 if opt.adam:
 optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
 else:
 optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
 
 optimizer.add_param_group({ 'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
 optimizer.add_param_group({ 'params': pg2}) # add pg2 (biases)
 logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
 del pg0, pg1, pg2
 
 # Scheduler https://arxiv.org/pdf/1812.01187.pdf
 # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
 if opt.linear_lr:
 lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear
 else:
 lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf']
 scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
 # plot_lr_scheduler(optimizer, scheduler, epochs)
 
 # EMA
 ema = ModelEMA(model) if RANK in [-1, 0] else None
 
 # Resume
 start_epoch, best_fitness = 0, 0.0
 if pretrained:
 # Optimizer
 if ckpt['optimizer'] is not None:
 optimizer.load_state_dict(ckpt['optimizer'])
 best_fitness = ckpt['best_fitness']
 
 # EMA
 if ema and ckpt.get('ema'):
 ema.ema.load_state_dict(ckpt['ema'].float().state_dict())
 ema.updates = ckpt['updates']
 
 # Results
 if ckpt.get('training_results') is not None:
 results_file.write_text(ckpt['training_results']) # write results.txt
 
 # Epochs
 start_epoch = ckpt['epoch'] + 1
 if resume:
 assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs)
 if epochs < start_epoch:
 logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
 (weights, ckpt['epoch'], epochs))
 epochs += ckpt['epoch'] # finetune additional epochs
 
 del ckpt, state_dict
 
 # Image sizes
 gs = max(int(model.stride.max()), 32) # grid size (max stride)
 nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj'])
 imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples
 
 # DP mode
 if cuda and RANK == -1 and torch.cuda.device_count() > 1:
 logging.warning('DP not recommended, instead use torch.distributed.run for best DDP Multi-GPU results.\n'
 'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.')
 model = torch.nn.DataParallel(model)
 
 # SyncBatchNorm
 if opt.sync_bn and cuda and RANK != -1:
 model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
 logger.info('Using SyncBatchNorm()')
 
 # Trainloader
 dataloader, dataset = create_dataloader(train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls,
 hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=RANK,
 workers=workers,
 image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: '))
 mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
 nb = len(dataloader) # number of batches
 assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, data, nc - 1)
 
 # Process 0
 if RANK in [-1, 0]:
 testloader = create_dataloader(test_path, imgsz_test, batch_size // WORLD_SIZE * 2, gs, single_cls,
 hyp=hyp, cache=opt.cache_images and not notest, rect=True, rank=-1,
 workers=workers,
 pad=0.5, prefix=colorstr('val: '))[0]
 
 if not resume:
 labels = np.concatenate(dataset.labels, 0)
 c = torch.tensor(labels[:, 0]) # classes
 # cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
 # model._initialize_biases(cf.to(device))
 if plots:
 plot_labels(labels, names, save_dir, loggers)
 if loggers['tb']:
 loggers['tb'].add_histogram('classes', c, 0) # TensorBoard
 
 # Anchors
 if not opt.noautoanchor:
 check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
 model.half().float() # pre-reduce anchor precision
 
 # DDP mode
 if cuda and RANK != -1:
 model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
 
 # Model parameters
 hyp['box'] *= 3. / nl # scale to layers
 hyp['cls'] *= nc / 80. * 3. / nl # scale to classes and layers
 hyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl # scale to image size and layers
 hyp['label_smoothing'] = opt.label_smoothing
 model.nc = nc # attach number of classes to model
 model.hyp = hyp # attach hyperparameters to model
 model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou)
 model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights
 model.names = names
 
 # Start training
 t0 = time.time()
 nw = max(round(hyp['warmup_epochs'] * nb), 1000) # number of warmup iterations, max(3 epochs, 1k iterations)
 # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
 last_opt_step = -1
 maps = np.zeros(nc) # mAP per class
 results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
 scheduler.last_epoch = start_epoch - 1 # do not move
 scaler = amp.GradScaler(enabled=cuda)
 compute_loss = ComputeLoss(model) # init loss class
 logger.info(f'Image sizes {imgsz} train, {imgsz_test} test\n'
 f'Using {dataloader.num_workers} dataloader workers\n'
 f'Logging results to {save_dir}\n'
 f'Starting training for {epochs} epochs...')
 for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
 model.train()
 
 # Update image weights (optional)
 if opt.image_weights:
 # Generate indices
 if RANK in [-1, 0]:
 cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights
 iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights
 dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx
 # Broadcast if DDP
 if RANK != -1:
 indices = (torch.tensor(dataset.indices) if RANK == 0 else torch.zeros(dataset.n)).int()
 dist.broadcast(indices, 0)
 if RANK != 0:
 dataset.indices = indices.cpu().numpy()
 
 # Update mosaic border
 # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
 # dataset.mosaic_border = [b - imgsz, -b] # height, width borders
 
 mloss = torch.zeros(4, device=device) # mean losses
 if RANK != -1:
 dataloader.sampler.set_epoch(epoch)
 pbar = enumerate(dataloader)
 logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size'))
 if RANK in [-1, 0]:
 pbar = tqdm(pbar, total=nb) # progress bar
 optimizer.zero_grad()
 for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
 ni = i + nb * epoch # number integrated batches (since train start)
 imgs = imgs.to(device, non_blocking=True).float() / 255.0 # uint8 to float32, 0-255 to 0.0-1.0
 
 # Warmup
 if ni <= nw:
 xi = [0, nw] # x interp
 # model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
 accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round())
 for j, x in enumerate(optimizer.param_groups):
 # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
 x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
 if 'momentum' in x:
 x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
 
 # Multi-scale
 if opt.multi_scale:
 sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size
 sf = sz / max(imgs.shape[2:]) # scale factor
 if sf != 1:
 ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)
 imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
 
 # Forward
 with amp.autocast(enabled=cuda):
 pred = model(imgs) # forward
 loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size
 if RANK != -1:
 loss *= WORLD_SIZE # gradient averaged between devices in DDP mode
 if opt.quad:
 loss *= 4.
 
 # Backward
 scaler.scale(loss).backward()
 
 # Optimize
 if ni - last_opt_step >= accumulate:
 scaler.step(optimizer) # optimizer.step
 scaler.update()
 optimizer.zero_grad()
 if ema:
 ema.update(model)
 last_opt_step = ni
 
 # Print
 if RANK in [-1, 0]:
 mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
 mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB)
 s = ('%10s' * 2 + '%10.4g' * 6) % (
 f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1])
 pbar.set_description(s)
 
 # Plot
 if plots and ni < 3:
 f = save_dir / f'train_batch{ni}.jpg' # filename
 Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()
 if loggers['tb'] and ni == 0: # TensorBoard
 with warnings.catch_warnings():
 warnings.simplefilter('ignore') # suppress jit trace warning
 loggers['tb'].add_graph(torch.jit.trace(de_parallel(model), imgs[0:1], strict=False), [])
 elif plots and ni == 10 and loggers['wandb']:
 wandb_logger.log({ 'Mosaics': [loggers['wandb'].Image(str(x), caption=x.name) for x in
 save_dir.glob('train*.jpg') if x.exists()]})
 
 # end batch ------------------------------------------------------------------------------------------------
 
 # Scheduler
 lr = [x['lr'] for x in optimizer.param_groups] # for loggers
 scheduler.step()
 
 # DDP process 0 or single-GPU
 if RANK in [-1, 0]:
 # mAP
 ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights'])
 final_epoch = epoch + 1 == epochs
 if not notest or final_epoch: # Calculate mAP
 wandb_logger.current_epoch = epoch + 1
 results, maps, _ = test.run(data_dict,
 batch_size=batch_size // WORLD_SIZE * 2,
 imgsz=imgsz_test,
 model=ema.ema,
 single_cls=single_cls,
 dataloader=testloader,
 save_dir=save_dir,
 save_json=is_coco and final_epoch,
 verbose=nc < 50 and final_epoch,
 plots=plots and final_epoch,
 wandb_logger=wandb_logger,
 compute_loss=compute_loss)
 
 # Write
 with open(results_file, 'a') as f:
 f.write(s + '%10.4g' * 7 % results + '\n') # append metrics, val_loss
 
 # Log
 tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', # train loss
 'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
 'val/box_loss', 'val/obj_loss', 'val/cls_loss', # val loss
 'x/lr0', 'x/lr1', 'x/lr2'] # params
 for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
 if loggers['tb']:
 loggers['tb'].add_scalar(tag, x, epoch) # TensorBoard
 if loggers['wandb']:
 wandb_logger.log({ tag: x}) # W&B
 
 # Update best mAP
 fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
 if fi > best_fitness:
 best_fitness = fi
 wandb_logger.end_epoch(best_result=best_fitness == fi)
 
 # Save model
 if (not nosave) or (final_epoch and not evolve): # if save
 ckpt = { 'epoch': epoch,
 'best_fitness': best_fitness,
 'training_results': results_file.read_text(),
 'model': deepcopy(de_parallel(model)).half(),
 'ema': deepcopy(ema.ema).half(),
 'updates': ema.updates,
 'optimizer': optimizer.state_dict(),
 'wandb_id': wandb_logger.wandb_run.id if loggers['wandb'] else None}
 
 # Save last, best and delete
 torch.save(ckpt, last)
 if best_fitness == fi:
 torch.save(ckpt, best)
 if loggers['wandb']:
 if ((epoch + 1) % opt.save_period == 0 and not final_epoch) and opt.save_period != -1:
 wandb_logger.log_model(last.parent, opt, epoch, fi, best_model=best_fitness == fi)
 del ckpt
 
 # end epoch ----------------------------------------------------------------------------------------------------
 # end training -----------------------------------------------------------------------------------------------------
 if RANK in [-1, 0]:
 logger.info(f'{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.\n')
 if plots:
 plot_results(save_dir=save_dir) # save as results.png
 if loggers['wandb']:
 files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]]
 wandb_logger.log({ "Results": [loggers['wandb'].Image(str(save_dir / f), caption=f) for f in files
 if (save_dir / f).exists()]})
 
 if not evolve:
 if is_coco: # COCO dataset
 for m in [last, best] if best.exists() else [last]: # speed, mAP tests
 results, _, _ = test.run(data_dict,
 batch_size=batch_size // WORLD_SIZE * 2,
 imgsz=imgsz_test,
 conf_thres=0.001,
 iou_thres=0.7,
 model=attempt_load(m, device).half(),
 single_cls=single_cls,
 dataloader=testloader,
 save_dir=save_dir,
 save_json=True,
 plots=False)
 
 # Strip optimizers
 for f in last, best:
 if f.exists():
 strip_optimizer(f) # strip optimizers
 if loggers['wandb']: # Log the stripped model
 loggers['wandb'].log_artifact(str(best if best.exists() else last), type='model',
 name='run_' + wandb_logger.wandb_run.id + '_model',
 aliases=['latest', 'best', 'stripped'])
 wandb_logger.finish_run()
 
 torch.cuda.empty_cache()
 return results
 
 
 def parse_opt(known=False):
 parser = argparse.ArgumentParser()
 parser.add_argument('--weights', type=str, default='yolov5s.pt', help='initial weights path')
 parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
 parser.add_argument('--data', type=str, default='data/coco128.yaml', help='dataset.yaml path')
 parser.add_argument('--hyp', type=str, default='data/hyps/hyp.scratch.yaml', help='hyperparameters path')
 parser.add_argument('--epochs', type=int, default=300)
 parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs')
 parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')
 parser.add_argument('--rect', action='store_true', help='rectangular training')
 parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
 parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
 parser.add_argument('--notest', action='store_true', help='only test final epoch')
 parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
 parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
 parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
 parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
 parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
 parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
 parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
 parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
 parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
 parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
 parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')
 parser.add_argument('--project', default='runs/train', help='save to project/name')
 parser.add_argument('--entity', default=None, help='W&B entity')
 parser.add_argument('--name', default='exp', help='save to project/name')
 parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
 parser.add_argument('--quad', action='store_true', help='quad dataloader')
 parser.add_argument('--linear-lr', action='store_true', help='linear LR')
 parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')
 parser.add_argument('--upload_dataset', action='store_true', help='Upload dataset as W&B artifact table')
 parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval for W&B')
 parser.add_argument('--save_period', type=int, default=-1, help='Log model after every "save_period" epoch')
 parser.add_argument('--artifact_alias', type=str, default="latest", help='version of dataset artifact to be used')
 parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
 opt = parser.parse_known_args()[0] if known else parser.parse_args()
 return opt
 
 
 def main(opt):
 set_logging(RANK)
 if RANK in [-1, 0]:
 print(colorstr('train: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
 check_git_status()
 check_requirements(exclude=['thop'])
 
 # Resume
 wandb_run = check_wandb_resume(opt)
 if opt.resume and not wandb_run: # resume an interrupted run
 ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path
 assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist'
 with open(Path(ckpt).parent.parent / 'opt.yaml') as f:
 opt = argparse.Namespace(**yaml.safe_load(f)) # replace
 opt.cfg, opt.weights, opt.resume = '', ckpt, True # reinstate
 logger.info('Resuming training from %s' % ckpt)
 else:
 # opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml')
 opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp) # check files
 assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
 opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test)
 opt.name = 'evolve' if opt.evolve else opt.name
 opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve))
 
 # DDP mode
 device = select_device(opt.device, batch_size=opt.batch_size)
 if LOCAL_RANK != -1:
 from datetime import timedelta
 assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
 torch.cuda.set_device(LOCAL_RANK)
 device = torch.device('cuda', LOCAL_RANK)
 dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo", timeout=timedelta(seconds=60))
 assert opt.batch_size % WORLD_SIZE == 0, '--batch-size must be multiple of CUDA device count'
 assert not opt.image_weights, '--image-weights argument is not compatible with DDP training'
 
 # Train
 if not opt.evolve:
 train(opt.hyp, opt, device)
 if WORLD_SIZE > 1 and RANK == 0:
 _ = [print('Destroying process group... ', end=''), dist.destroy_process_group(), print('Done.')]
 
 # Evolve hyperparameters (optional)
 else:
 # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
 meta = { 'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)
 'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
 'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1
 'weight_decay': (1, 0.0, 0.001), # optimizer weight decay
 'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok)
 'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum
 'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr
 'box': (1, 0.02, 0.2), # box loss gain
 'cls': (1, 0.2, 4.0), # cls loss gain
 'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight
 'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels)
 'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight
 'iou_t': (0, 0.1, 0.7), # IoU training threshold
 'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold
 'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore)
 'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)
 'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction)
 'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)
 'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction)
 'degrees': (1, 0.0, 45.0), # image rotation (+/- deg)
 'translate': (1, 0.0, 0.9), # image translation (+/- fraction)
 'scale': (1, 0.0, 0.9), # image scale (+/- gain)
 'shear': (1, 0.0, 10.0), # image shear (+/- deg)
 'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
 'flipud': (1, 0.0, 1.0), # image flip up-down (probability)
 'fliplr': (0, 0.0, 1.0), # image flip left-right (probability)
 'mosaic': (1, 0.0, 1.0), # image mixup (probability)
 'mixup': (1, 0.0, 1.0), # image mixup (probability)
 'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability)
 
 with open(opt.hyp) as f:
 hyp = yaml.safe_load(f) # load hyps dict
 assert LOCAL_RANK == -1, 'DDP mode not implemented for --evolve'
 opt.notest, opt.nosave = True, True # only test/save final epoch
 # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
 yaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml' # save best result here
 if opt.bucket:
 os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists
 
 for _ in range(300): # generations to evolve
 if Path('evolve.txt').exists(): # if evolve.txt exists: select best hyps and mutate
 # Select parent(s)
 parent = 'single' # parent selection method: 'single' or 'weighted'
 x = np.loadtxt('evolve.txt', ndmin=2)
 n = min(5, len(x)) # number of previous results to consider
 x = x[np.argsort(-fitness(x))][:n] # top n mutations
 w = fitness(x) - fitness(x).min() + 1E-6 # weights (sum > 0)
 if parent == 'single' or len(x) == 1:
 # x = x[random.randint(0, n - 1)] # random selection
 x = x[random.choices(range(n), weights=w)[0]] # weighted selection
 elif parent == 'weighted':
 x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
 
 # Mutate
 mp, s = 0.8, 0.2 # mutation probability, sigma
 npr = np.random
 npr.seed(int(time.time()))
 g = np.array([x[0] for x in meta.values()]) # gains 0-1
 ng = len(meta)
 v = np.ones(ng)
 while all(v == 1): # mutate until a change occurs (prevent duplicates)
 v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
 for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
 hyp[k] = float(x[i + 7] * v[i]) # mutate
 
 # Constrain to limits
 for k, v in meta.items():
 hyp[k] = max(hyp[k], v[1]) # lower limit
 hyp[k] = min(hyp[k], v[2]) # upper limit
 hyp[k] = round(hyp[k], 5) # significant digits
 
 # Train mutation
 results = train(hyp.copy(), opt, device)
 
 # Write mutation results
 print_mutation(hyp.copy(), results, yaml_file, opt.bucket)
 
 # Plot results
 plot_evolution(yaml_file)
 print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n'
 f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}')
 
 
 def run(**kwargs):
 # Usage: import train; train.run(imgsz=320, weights='yolov5s.pt')
 opt = parse_opt(True)
 for k, v in kwargs.items():
 setattr(opt, k, v)
 main(opt)
 
 
 if __name__ == "__main__":
 opt = parse_opt()
 main(opt)

## 2.检测 ##

1.  Qt项目( [点击此处进行项目下载][Link 1] )
2.  detect3.py( 使用前先打开Qt项目-启动Services)

"""Run inference with a YOLOv5 model on images, videos, directories, streams Usage: $ python path/to/detect.py --source path/to/img.jpg --weights yolov5l.pt --img 640 """
    import time
    from datetime import datetime
    import socket
    import argparse
    import sys
    import time
    from pathlib import Path
    
    import cv2
    import torch
    import torch.backends.cudnn as cudnn
    
    FILE = Path(__file__).absolute()
    sys.path.append(FILE.parents[0].as_posix())  # add yolov5/ to path
    
    from models.experimental import attempt_load
    from utils.datasets import LoadStreams, LoadImages
    from utils.general import check_img_size, check_requirements, check_imshow, colorstr, non_max_suppression, \
        apply_classifier, scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box
    from utils.plots import colors, plot_one_box
    from utils.torch_utils import select_device, load_classifier, time_synchronized
    
    
    @torch.no_grad()
    def run(weights='yolov5l.pt',  # model.pt path(s)
            #source='data/images', # file/dir/URL/glob, 0 for webcam
            source=0,  # webcam
            imgsz=640,  # inference size (pixels)
            conf_thres=0.25,  # confidence threshold
            iou_thres=0.45,  # NMS IOU threshold
            max_det=1000,  # maximum detections per image
            device='0',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
            view_img=False,  # show results
            save_txt=False,  # save results to *.txt
            save_conf=False,  # save confidences in --save-txt labels
            save_crop=False,  # save cropped prediction boxes
            nosave=False,  # do not save images/videos
            classes=None,  # filter by class: --class 0, or --class 0 2 3
            agnostic_nms=False,  # class-agnostic NMS
            augment=False,  # augmented inference
            update=False,  # update all models
            project='runs/detect',  # save results to project/name
            name='exp',  # save results to project/name
            exist_ok=False,  # existing project/name ok, do not increment
            line_thickness=3,  # bounding box thickness (pixels)
            hide_labels=False,  # hide labels
            hide_conf=False,  # hide confidences
            half=False,  # use FP16 half-precision inference
            ):
        save_img = not nosave and not source.endswith('.txt')  # save inference images
        webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
            ('rtsp://', 'rtmp://', 'http://', 'https://'))
    
        # Directories
        save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
        (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
    
        # Initialize
        set_logging()
        device = select_device(device)
        half &= device.type != 'cpu'  # half precision only supported on CUDA
    
        # Load model
        model = attempt_load(weights, map_location=device)  # load FP32 model
        stride = int(model.stride.max())  # model stride
        imgsz = check_img_size(imgsz, s=stride)  # check image size
        names = model.module.names if hasattr(model, 'module') else model.names  # get class names
        if half:
            model.half()  # to FP16
    
        # Second-stage classifier
        classify = False
        if classify:
            modelc = load_classifier(name='resnet50', n=2)  # initialize
            modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval()
    
        # Set Dataloader
        vid_path, vid_writer = None, None
        if webcam:
            view_img = check_imshow()
            cudnn.benchmark = True  # set True to speed up constant image size inference
            dataset = LoadStreams(source, img_size=imgsz, stride=stride)
        else:
            dataset = LoadImages(source, img_size=imgsz, stride=stride)
    
        # Run inference
        if device.type != 'cpu':
            model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
        t0 = time.time()
        for path, img, im0s, vid_cap in dataset:
            img = torch.from_numpy(img).to(device)
            img = img.half() if half else img.float()  # uint8 to fp16/32
            img /= 255.0  # 0 - 255 to 0.0 - 1.0
            if img.ndimension() == 3:
                img = img.unsqueeze(0)
    
            # Inference
            t1 = time_synchronized()
            pred = model(img, augment=augment)[0]
    
            # Apply NMS
            pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
            t2 = time_synchronized()
    
            # Apply Classifier
            if classify:
                pred = apply_classifier(pred, modelc, img, im0s)
    
            # Process detections
            for i, det in enumerate(pred):  # detections per image
                if webcam:  # batch_size >= 1
                    p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
                else:
                    p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)
    
                p = Path(p)  # to Path
                save_path = str(save_dir / p.name)  # img.jpg
                txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
                s += '%gx%g ' % img.shape[2:]  # print string
                gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
                imc = im0.copy() if save_crop else im0  # for save_crop
                if len(det):
                    # Rescale boxes from img_size to im0 size
                    det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
    
                    # Print results
                    for c in det[:, -1].unique():
                        n = (det[:, -1] == c).sum()  # detections per class
                        s = f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
    
                        server_ip = "127.0.0.1" 
                        server_port = 9090
                        client_num = 1
                        # 保存所有已成功连接的客户端TCP socket
                        client_socks = []
    
                        for i in range(client_num):
                            sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
                            sock.connect((server_ip, server_port))
                            client_socks.append(sock)
                            print('Client {}[ID: {}] has connected to {}'.format(sock, i, (server_ip, server_port)))
                        
                        for s in client_socks:
                            data = str(int(c)).encode('utf-8')
                            s.send(data)
                            print('Client {} has sent {} to {}'.format(s, data, (server_ip, server_port))) 
                            time.sleep(2)
                    # Write results
                    for *xyxy, conf, cls in reversed(det):
                        if save_txt:  # Write to file
                            xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                            line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
                            with open(txt_path + '.txt', 'a') as f:
                                f.write(('%g ' * len(line)).rstrip() % line + '\n')
    
                        if save_img or save_crop or view_img:  # Add bbox to image
                            c = int(cls)  # integer class
                            label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
                            plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=line_thickness)
                            if save_crop:
                                save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
    
                # Print time (inference + NMS)
                print(f'{s}Done. ({t2 - t1:.3f}s)')
    
    
                server_ip = "127.0.0.1" 
                server_port = 9090
                client_num = 1
                # 保存所有已成功连接的客户端TCP socket
                client_socks = []
    
                for i in range(client_num):
                    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
                    sock.connect((server_ip, server_port))
                    client_socks.append(sock)
                        
                for s in client_socks:
                    data = str('7').encode('utf-8')
                    s.send(data)
                    print('Client {} has sent {} to {}'.format(s, data, (server_ip, server_port))) 
                    time.sleep(2)
    
                # Stream results
                if view_img:
                    cv2.imshow(str(p), im0)
                    cv2.waitKey(1)  # 1 millisecond
    
                # Save results (image with detections)
                if save_img:
                    if dataset.mode == 'image':
                        cv2.imwrite(save_path, im0)
                    else:  # 'video' or 'stream'
                        if vid_path != save_path:  # new video
                            vid_path = save_path
                            if isinstance(vid_writer, cv2.VideoWriter):
                                vid_writer.release()  # release previous video writer
                            if vid_cap:  # video
                                fps = vid_cap.get(cv2.CAP_PROP_FPS)
                                w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                                h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                            else:  # stream
                                fps, w, h = 30, im0.shape[1], im0.shape[0]
                                save_path += '.mp4'
                            vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                        vid_writer.write(im0)
    
        if save_txt or save_img:
            s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
            print(f"Results saved to {save_dir}{s}")
    
        if update:
            strip_optimizer(weights)  # update model (to fix SourceChangeWarning)
    
        print(f'Done. ({time.time() - t0:.3f}s)')
        sendtcp = 7
    
    
    
    def parse_opt():
        parser = argparse.ArgumentParser()
        parser.add_argument('--weights', nargs='+', type=str, default='runs/train/exp2/weights/last.pt', help='yolov5l.pt')
        parser.add_argument('--source', type=str, default='0', help='0')
        parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
        parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
        parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
        parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
        parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
        parser.add_argument('--view-img', action='store_true', help='show results')
        parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
        parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
        parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
        parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
        parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
        parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
        parser.add_argument('--augment', action='store_true', help='augmented inference')
        parser.add_argument('--update', action='store_true', help='update all models')
        parser.add_argument('--project', default='runs/detect', help='save results to project/name')
        parser.add_argument('--name', default='exp', help='save results to project/name')
        parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
        parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
        parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
        parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
        parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
        opt = parser.parse_args()
        return opt
    
    
    def main(opt):
        print(colorstr('detect: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
        check_requirements(exclude=('tensorboard', 'thop'))
        run(**vars(opt))
    
    
    if __name__ == "__main__":
        opt = parse_opt()
        main(opt)

## 3.效果 ##

![在这里插入图片描述][865b75405f7b12570c590149c372f568.png_pic_center]  
![在这里插入图片描述][5f3ade5d040b5448fb18cf976fc22cb1.png_pic_center]  
![在这里插入图片描述][d20b8062dceb68545faf197fb0e9e586.png_pic_center]  
![在这里插入图片描述][a35e37d971480514d5bba94fec65bb4c.png_pic_center]  
![在这里插入图片描述][d20c09dfcd409ee2cd40e9b317fdbe49.png_pic_center]  
![在这里插入图片描述][feb59d12398741c48a72cb38d96ec0aa.png_pic_center]

[anaconda_python3.8.8_-]: https://download.csdn.net/download/GodOuO/20368584
[CUDA]: https://developer.nvidia.com/cuda-10.2-download-archive?target_os=Windows&target_arch=x86_64&target_version=10
[Github]: https://github.com/ultralytics/yolov5/releases
[Link 1]: https://download.csdn.net/download/GodOuO/20389249
[865b75405f7b12570c590149c372f568.png_pic_center]: /images/20220829/763c005425254637a6218e833564e846.png
[5f3ade5d040b5448fb18cf976fc22cb1.png_pic_center]: /images/20220829/50a8135a43ad45efabdabc5c9ff85667.png
[d20b8062dceb68545faf197fb0e9e586.png_pic_center]: /images/20220829/d3aad2849779452c8fc6134cdd1ba91a.png
[a35e37d971480514d5bba94fec65bb4c.png_pic_center]: /images/20220829/74cd0d696f794f5a93da236866348b21.png
[d20c09dfcd409ee2cd40e9b317fdbe49.png_pic_center]: /images/20220829/47a238d01ebf4917ba4240877a33e4b7.png
[feb59d12398741c48a72cb38d96ec0aa.png_pic_center]: /images/20220829/1c9a2e6dc80947e483c6885b41761459.png