演示视频
0.配置环境
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)
- 升级pip
PowerShell管理员模式运行
python -m pip install -U pip
- 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
- 等待完成即可
1.训练模型
- 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'<b>%s</b>' % 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"<p><b>%s</b></p>"
u"<p>Make sure <i>%s</i> 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"<p>Make sure <i>%s</i> 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,
'<p><b>%s</b></p>%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())
- train.py( 需要点击此处跳转Github下载配置权重 )
"""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
- Qt项目( 点击此处进行项目下载 )
- 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
