182 lines
8.6 KiB
Python
182 lines
8.6 KiB
Python
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# Copyright (c) <2015-Present> Tzutalin
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# Copyright (C) 2013 MIT, Computer Science and Artificial Intelligence Laboratory. Bryan Russell, Antonio Torralba,
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# William T. Freeman. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
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# associated documentation files (the "Software"), to deal in the Software without restriction, including without
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# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
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# Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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# The above copyright notice and this permission notice shall be included in all copies or substantial portions of
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# the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
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# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
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# SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
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# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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# THE SOFTWARE.
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from math import sqrt
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from libs.ustr import ustr
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import hashlib
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import re
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import sys
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import cv2
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import numpy as np
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try:
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from PyQt5.QtGui import *
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from PyQt5.QtCore import *
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from PyQt5.QtWidgets import *
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except ImportError:
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from PyQt4.QtGui import *
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from PyQt4.QtCore import *
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def newIcon(icon, iconSize=None):
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if iconSize is not None:
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return QIcon(QIcon(':/' + icon).pixmap(iconSize,iconSize))
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else:
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return QIcon(':/' + icon)
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def newButton(text, icon=None, slot=None):
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b = QPushButton(text)
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if icon is not None:
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b.setIcon(newIcon(icon))
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if slot is not None:
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b.clicked.connect(slot)
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return b
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def newAction(parent, text, slot=None, shortcut=None, icon=None,
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tip=None, checkable=False, enabled=True, iconSize=None):
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"""Create a new action and assign callbacks, shortcuts, etc."""
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a = QAction(text, parent)
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if icon is not None:
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if iconSize is not None:
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a.setIcon(newIcon(icon, iconSize))
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else:
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a.setIcon(newIcon(icon))
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if shortcut is not None:
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if isinstance(shortcut, (list, tuple)):
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a.setShortcuts(shortcut)
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else:
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a.setShortcut(shortcut)
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if tip is not None:
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a.setToolTip(tip)
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a.setStatusTip(tip)
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if slot is not None:
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a.triggered.connect(slot)
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if checkable:
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a.setCheckable(True)
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a.setEnabled(enabled)
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return a
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def addActions(widget, actions):
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for action in actions:
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if action is None:
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widget.addSeparator()
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elif isinstance(action, QMenu):
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widget.addMenu(action)
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else:
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widget.addAction(action)
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def labelValidator():
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return QRegExpValidator(QRegExp(r'^[^ \t].+'), None)
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class struct(object):
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def __init__(self, **kwargs):
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self.__dict__.update(kwargs)
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def distance(p):
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return sqrt(p.x() * p.x() + p.y() * p.y())
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def fmtShortcut(text):
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mod, key = text.split('+', 1)
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return '<b>%s</b>+<b>%s</b>' % (mod, key)
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def generateColorByText(text):
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s = ustr(text)
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hashCode = int(hashlib.sha256(s.encode('utf-8')).hexdigest(), 16)
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r = int((hashCode / 255) % 255)
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g = int((hashCode / 65025) % 255)
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b = int((hashCode / 16581375) % 255)
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return QColor(r, g, b, 100)
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def have_qstring():
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'''p3/qt5 get rid of QString wrapper as py3 has native unicode str type'''
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return not (sys.version_info.major >= 3 or QT_VERSION_STR.startswith('5.'))
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def util_qt_strlistclass():
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return QStringList if have_qstring() else list
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def natural_sort(list, key=lambda s:s):
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"""
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Sort the list into natural alphanumeric order.
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"""
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def get_alphanum_key_func(key):
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convert = lambda text: int(text) if text.isdigit() else text
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return lambda s: [convert(c) for c in re.split('([0-9]+)', key(s))]
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sort_key = get_alphanum_key_func(key)
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list.sort(key=sort_key)
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def get_rotate_crop_image(img, points):
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try:
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img_crop_width = int(
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max(
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np.linalg.norm(points[0] - points[1]),
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np.linalg.norm(points[2] - points[3])))
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img_crop_height = int(
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max(
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np.linalg.norm(points[0] - points[3]),
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np.linalg.norm(points[1] - points[2])))
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pts_std = np.float32([[0, 0], [img_crop_width, 0],
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[img_crop_width, img_crop_height],
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[0, img_crop_height]])
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M = cv2.getPerspectiveTransform(points, pts_std)
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dst_img = cv2.warpPerspective(
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img,
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M, (img_crop_width, img_crop_height),
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borderMode=cv2.BORDER_REPLICATE,
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flags=cv2.INTER_CUBIC)
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dst_img_height, dst_img_width = dst_img.shape[0:2]
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if dst_img_height * 1.0 / dst_img_width >= 1.5:
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dst_img = np.rot90(dst_img)
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return dst_img
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except Exception as e:
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print(e)
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def stepsInfo(lang='en'):
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if lang == 'ch':
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msg = "1. 安装与运行：使用上述命令安装与运行程序。\n" \
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"2. 打开文件夹：在菜单栏点击 “文件” - 打开目录 选择待标记图片的文件夹.\n"\
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"3. 自动标注：点击 ”自动标注“，使用PPOCR超轻量模型对图片文件名前图片状态为 “X” 的图片进行自动标注。\n" \
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"4. 手动标注：点击 “矩形标注”（推荐直接在英文模式下点击键盘中的 “W”)，用户可对当前图片中模型未检出的部分进行手动" \
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"绘制标记框。点击键盘P，则使用四点标注模式（或点击“编辑” - “四点标注”），用户依次点击4个点后，双击左键表示标注完成。\n" \
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"5. 标记框绘制完成后，用户点击 “确认”，检测框会先被预分配一个 “待识别” 标签。\n" \
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"6. 重新识别：将图片中的所有检测画绘制/调整完成后，点击 “重新识别”，PPOCR模型会对当前图片中的**所有检测框**重新识别。\n" \
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"7. 内容更改：双击识别结果，对不准确的识别结果进行手动更改。\n" \
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"8. 保存：点击 “保存”，图片状态切换为 “√”，跳转至下一张。\n" \
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"9. 删除：点击 “删除图像”，图片将会被删除至回收站。\n" \
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"10. 标注结果：关闭应用程序或切换文件路径后，手动保存过的标签将会被存放在所打开图片文件夹下的" \
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"*Label.txt*中。在菜单栏点击 “PaddleOCR” - 保存识别结果后，会将此类图片的识别训练数据保存在*crop_img*文件夹下，" \
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"识别标签保存在*rec_gt.txt*中。\n"
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else:
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msg = "1. Build and launch using the instructions above.\n" \
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"2. Click 'Open Dir' in Menu/File to select the folder of the picture.\n"\
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"3. Click 'Auto recognition', use PPOCR model to automatically annotate images which marked with 'X' before the file name."\
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"4. Create Box:\n"\
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"4.1 Click 'Create RectBox' or press 'W' in English keyboard mode to draw a new rectangle detection box. Click and release left mouse to select a region to annotate the text area.\n"\
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"4.2 Press 'P' to enter four-point labeling mode which enables you to create any four-point shape by clicking four points with the left mouse button in succession and DOUBLE CLICK the left mouse as the signal of labeling completion.\n"\
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"5. After the marking frame is drawn, the user clicks 'OK', and the detection frame will be pre-assigned a TEMPORARY label.\n"\
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"6. Click re-Recognition, model will rewrite ALL recognition results in ALL detection box.\n"\
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"7. Double click the result in 'recognition result' list to manually change inaccurate recognition results.\n"\
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"8. Click 'Save', the image status will switch to '√',then the program automatically jump to the next.\n"\
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"9. Click 'Delete Image' and the image will be deleted to the recycle bin.\n"\
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"10. Labeling result: After closing the application or switching the file path, the manually saved label will be stored in *Label.txt* under the opened picture folder.\n"\
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" Click PaddleOCR-Save Recognition Results in the menu bar, the recognition training data of such pictures will be saved in the *crop_img* folder, and the recognition label will be saved in *rec_gt.txt*.\n"
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return msg
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