深海游弋的鱼 – 第 68 页

Visual Studio Code 1.41.0调试python代码时添加参数(args)

前提：代码中设置了arg paser，需要手动设置，VS code的debug没有简洁的添加参数的方式。

解决方式如下：

打开Debug->Open Configurations/ Add Configurations

继续阅读Visual Studio Code 1.41.0调试python代码时添加参数(args)

在用BeautifulSoup解析HTML前对其中以JavaScript渲染部分的处理

在命令行运行：

# Python2
$ pip install requests-html

# Python3
# pip3 install requests-html

# Python2

$ pip install requests-html

# Python3

# pip3 install requests-html

即可安装该模块。

例子如下：

#coding=utf-8
from bs4 import BeautifulSoup
import requests
from requests_html import HTMLSession
 
#使用requests抓取页面内容，并将响应赋值给page变量
html = requests.get('https://xcx.xzlzq.net/#/liftDetail?registerCode=31103301042010020002')
 
session = HTMLSession()
first_page = session.get('https://xcx.xzlzq.net/#/liftDetail?registerCode=31103301042010020002')
first_page.html.render(sleep=5)

#使用content属性获取页面的源页面
#使用BeautifulSoap解析，内容传递到BeautifulSoap类
soup = BeautifulSoup(first_page.html.html,'lxml')
links = soup.find_all('div',class_='content')
 
#link的内容就是div，我们取它的span内容就是我们需要段子的内容
for link in links:
    print(link.span.get_text())

#coding=utf-8

from bs4 import BeautifulSoup

import requests

from requests_html import HTMLSession

#使用requests抓取页面内容，并将响应赋值给page变量

html = requests.get('https://xcx.xzlzq.net/#/liftDetail?registerCode=31103301042010020002')

session = HTMLSession()

first_page = session.get('https://xcx.xzlzq.net/#/liftDetail?registerCode=31103301042010020002')

first_page.html.render(sleep=5)

#使用content属性获取页面的源页面

#使用BeautifulSoap解析，内容传递到BeautifulSoap类

soup = BeautifulSoup(first_page.html.html,'lxml')

links = soup.find_all('div',class_='content')

#link的内容就是div，我们取它的span内容就是我们需要段子的内容

for link in links:

print(link.span.get_text())

参考链接

Samsung Magician 6.0升级固件失败“在选定的驱动器上无法更新固件。”("Failed to update firmware on the selected drive.")

最近的三星固态硬盘提示固件有升级，但是固件升级失败，提示“在选定的驱动器上无法更新固件。”或者"Failed to update firmware on the selected drive."

如下图所示：

继续阅读Samsung Magician 6.0升级固件失败“在选定的驱动器上无法更新固件。”("Failed to update firmware on the selected drive.")

ubuntu 18.04安装Vulkan依赖的工具链SPIR-V Toolchain(glslangValidator)

开发 Vulkan 的时候，需要使用 glslangValidator 编译 Vulkan 代码。

如果是 ubuntu 19.10 版本，可以直接执行:

$ sudo apt-get install vulkan-tools

# 目前ubuntu 20.04 最新需要使用如下命令 sudo apt-get install glslang-tools

$ sudo apt-get install vulkan-tools

# 目前ubuntu 20.04 最新需要使用如下命令 sudo apt-get install glslang-tools

如果低于这个版本，则只能从源代码编译安装了，如下：

# 编译安装 glslang
$ git clone https://github.com/KhronosGroup/glslang.git

# 也可本站下载一份拷贝 wget https://www.mobibrw.com/wp-content/uploads/2018/12/glslang.zip
 
$ cd glslang
 
# 目前（2018.12.17）的正式版，最稳定的版本，试过最新的版本，编译部分代码存在问题
$ git checkout 7.10.2984
 
# 下载 spirv-tools 部分的功能代码
$ python update_glslang_sources.py 
 
$ mkdir build
 
$ cd build
 
$ cmake ..
 
$ make
 
$ sudo make install

# 编译安装 glslang

$ git clone https://github.com/KhronosGroup/glslang.git

# 也可本站下载一份拷贝 wget https://www.mobibrw.com/wp-content/uploads/2018/12/glslang.zip

$ cd glslang

# 目前（2018.12.17）的正式版，最稳定的版本，试过最新的版本，编译部分代码存在问题

$ git checkout 7.10.2984

# 下载 spirv-tools 部分的功能代码

$ python update_glslang_sources.py

$ mkdir build

$ cd build

$ cmake ..

$ make

$ sudo make install

Python tkinter实现图片标注

安装依赖

$ python -m pip install --upgrade pip --user

$ python -m pip install Pillow --user

$ python -m pip install Numpy --user

$ python -m pip install OpenCV-Python --user

$ python -m pip install --upgrade pip --user

$ python -m pip install Pillow --user

$ python -m pip install Numpy --user

$ python -m pip install OpenCV-Python --user

源代码：

#!/usr/bin/python
# -*- coding: UTF-8 -*-

import os
import sys
if sys.version_info < (3, 0):  
    import Tkinter as tk  # 导入 Tkinter 库
    from tkFileDialog import askopenfilename, askdirectory
else :
    import tkinter as tk  # 导入 Tkinter 库
    from tkinter.filedialog import askopenfilename, askdirectory
from PIL import Image, ImageTk, ImageDraw
from time import sleep
import numpy as np
import cv2 as cv
import collections

DEF_WIDTH = 1080
DEF_HEIGHT = 720
IMAGE_HEIGHT = 720
FRAME_LEFT_WIDTH = 360
# 太小的选定区域我们需要丢弃，防止误操作
MINI_RECT_AREA = 20 

class RawImageEditor:

    def __init__(self, win, img, rects):
        #变量X和Y用来记录鼠标左键按下的位置
        self.X = tk.IntVar(value=0)
        self.Y = tk.IntVar(value=0)

        self.sel = False
        self.lastDraw = None
        self.lastDraws = []
        self.imageScale = 1.0
        self.dispWidth = DEF_WIDTH  # 图片显示区域的最大高度，宽度
        self.dispHeight = DEF_HEIGHT
        self.rawImage = img
        self.calcImageScale(self.rawImage)
        self.dispWidth = int(self.imageScale * self.rawImage.width)
        self.dispHeight = int(self.imageScale * self.rawImage.height)
        # 图片缩放
        self.dispImage = self.rawImage.resize((self.dispWidth, self.dispHeight))
        # 选择区域
        self.selPositions = []
        for r in rects :
            self.selPositions.append((r[0] * self.imageScale, r[1] * self.imageScale, r[2] * self.imageScale, r[3] * self.imageScale))
        #创建顶级组件容器
        self.top = tk.Toplevel(win, width=self.dispWidth, height=self.dispHeight)
        #不显示最大化、最小化按钮
        self.top.overrideredirect(True)
        #center window on desktop
        nScreenWid, nScreenHei = self.top.maxsize()
        winPos = '%sx%s+%s+%s' % (int(self.top.winfo_reqwidth()), int(self.top.winfo_reqheight()), int((nScreenWid - self.top.winfo_reqwidth())/2), int((nScreenHei - self.top.winfo_reqheight())/2))
        self.top.geometry(winPos)
        # Make topLevelWindow remain on top until destroyed, or attribute changes.
        self.top.attributes('-topmost', 'true')
        self.canvas = tk.Canvas(self.top, bg='white', width=self.dispWidth, height=self.dispHeight)

        self.tkImage = ImageTk.PhotoImage(self.dispImage)
        self.canvas.create_image(self.dispWidth//2, self.dispHeight//2, image=self.tkImage)
        for r in self.selPositions :
            draw = self.canvas.create_rectangle(r[0], r[1], r[2], r[3], outline='green')
            self.lastDraws.append(draw)

        #鼠标左键按下的位置
        def onLeftButtonDown(event):
            self.X.set(event.x)
            self.Y.set(event.y)
            #开始截图
            self.sel = True

            #重新绘制已经选择的区域
            for draw in self.lastDraws :
                self.canvas.delete(draw)
            self.lastDraws = []
            for r in self.selPositions :
                draw = self.canvas.create_rectangle(r[0], r[1], r[2], r[3], outline='green')
                self.lastDraws.append(draw)

        self.canvas.bind('<Button-1>', onLeftButtonDown)
        #鼠标左键移动，显示选取的区域
        def onLeftButtonMove(event):
            if not self.sel:
                return
            try:
                #删除刚画完的图形，要不然鼠标移动的时候是黑乎乎的一片矩形
                self.canvas.delete(self.lastDraw)
            except Exception as e:
                pass
            self.lastDraw = self.canvas.create_rectangle(self.X.get(), self.Y.get(), event.x, event.y, outline='green')
        self.canvas.bind('<B1-Motion>', onLeftButtonMove)
        #获取鼠标左键抬起的位置，保存区域截图
        def onLeftButtonUp(event):
            if not self.sel:
                return
            self.sel = False
            sleep(0.1)
            #考虑鼠标左键从右下方按下而从左上方抬起的截图
            left, right = sorted([self.X.get(), event.x])
            top, bottom = sorted([self.Y.get(), event.y])
            if (right - left) * (bottom - top) > MINI_RECT_AREA :
                self.selPositions.append((left,top,right,bottom))
            #self.top.destroy()
        
        #鼠标右键按下
        def onRightButtonDown(event):
            self.sel = False
            self.top.destroy()
        self.canvas.bind('<Button-2>', onRightButtonDown)

        self.canvas.bind('<ButtonRelease-1>', onLeftButtonUp)
        self.canvas.pack(fill=tk.BOTH, expand=tk.YES)

    def calcImageScale(self, image) :
        w = image.width
        h = image.height
        self.imageScale = 1.0
        # 计算最小的缩放比例，保证原始宽高比
        if w > self.dispWidth and h > self.dispHeight :
            ws = self.dispWidth * 1.0 / w
            hs = self.dispHeight * 1.0 / h
            if ws < hs :
                self.imageScale = ws
            else :
                self.imageScale = hs
        elif w > self.dispWidth and h < self.dispHeight :
            self.imageScale = self.dispWidth * 1.0 / w
        elif w < self.dispWidth and h > self.dispHeight :
            self.imageScale = self.dispHeight * 1.0 / h

    def waitForWindow(self, win) :            
        win.wait_window(self.top)

    def selectedPositions(self) : 
        # 转换为原始像素位置
        realPos = []
        for r in self.selPositions :
            realPos.append((r[0] / self.imageScale, r[1] / self.imageScale, r[2] / self.imageScale, r[3] / self.imageScale))
        return realPos      

class MainWin(tk.Tk):

    def __init__(self):
        if sys.version_info >= (3, 0):
            super().__init__()
        else : 
            tk.Tk.__init__(self)
        self.title('图像处理工具')
        self.geometry('{}x{}'.format(DEF_WIDTH, DEF_HEIGHT))
        self.rawImagePath = ''
        self.rawImage = None  # self.rawImage 原始图像，未经过缩放处理
        self.transRawImage = None  # self.transRawImage 经过转换处理之后的原始图像，没有经过缩放处理
        self.dispImage = None  # self.dispImage 显示图像，可能经过缩放处理
        self.imageScale = 1.0  # 图片缩放比例，根据缩放比例进行显示的时候的缩放处理，后期选择区域的时候，需要进行缩放还原
        self.leftFrameWidth = FRAME_LEFT_WIDTH
        self.frameDispHeight = DEF_HEIGHT # 整个窗口的高度
        self.labelTextHeight = 18 # 文本标签的高度
        self.btnHeight = 30 # 按钮的高度
        self.brightnessScale = tk.StringVar() # 亮度比
        self.brightnessScale.set('1.0')
        self.defSavePath = '' # 默认保存路径
        self.imageDispWidth = IMAGE_HEIGHT  # 图片显示区域的最大高度，宽度
        self.imageDispHeight = self.frameDispHeight / 2 - self.labelTextHeight * 2
        self.liRect = collections.OrderedDict() # 选择区域
        self.rawImageEditor = None
        self.currentListBoxSelIdx = None #当前选择的项目

        self.setupUI()

    def scaleDisplayImage(self, image) :
        w = image.width
        h = image.height
        self.imageScale = 1.0
        # 计算最小的缩放比例，保证原始宽高比
        if w > self.imageDispWidth and h > self.imageDispHeight :
            ws = self.imageDispWidth * 1.0 / w
            hs = self.imageDispHeight * 1.0 / h
            if ws < hs :
                self.imageScale = ws
            else :
                self.imageScale = hs
        elif w > self.imageDispWidth and h < self.imageDispHeight :
            self.imageScale = self.imageDispWidth * 1.0 / w
        elif w < self.imageDispWidth and h > self.imageDispHeight :
            self.imageScale = self.imageDispHeight * 1.0 / h
        # 图片缩放
        return image.resize((int(self.imageScale * w), int(self.imageScale * h)))

    def loadImageCfgFile(self, imf):
        (path, name) = os.path.split(imf)
        cfgname = imf + '.txt'
        if (not os.path.exists(cfgname)) or (not os.path.isfile(cfgname)) :
            cfgname = os.path.join(path, 'mask', name) + '.txt'
        if (not os.path.exists(cfgname)) or (not os.path.isfile(cfgname)) :
            cfgname = os.path.join(path, 'cfg', name) + '.txt'            
        self.liRect.clear()
        if os.path.exists(cfgname) and os.path.isfile(cfgname) :
            with open(cfgname, "r") as f:
                lines = f.readlines()
                for line in lines:
                    rs = line.split(',')
                    r = (float(rs[0].strip()), float(rs[1].strip()), float(rs[2].strip()), float(rs[3].strip()))
                    if len(rs) > 4 :
                        self.liRect[r] = rs[4].strip()
                    else :
                        self.liRect[r] = ''

    # 打开图片时使用，传值（图）给展示函数
    def openAndDisplayImage(self):
        imF = self.selectImageFile()
        if '' != imF :
            self.rawImagePath = imF
            self.loadImageCfgFile(self.rawImagePath)
            self.drawListBox()
            self.rawImage = Image.open(self.rawImagePath)    
            self.rawImage = self.rawImage.convert('RGBA')
            self.drawRawImageDisp()
            self.image_l_trans.image = None
            self.transRawImage = None

    def drawListBox(self):
        self.l_box.delete(0, tk.END)
        for r in self.liRect.keys():
            r = '{},{},{},{}'.format(round(r[0],1), round(r[1],1), round(r[2],1), round(r[3],1))
            self.l_box.insert(0, r)

    def drawRawImageDisp(self, selItems=[]):            
        self.dispImage = self.scaleDisplayImage(self.rawImage)
        self.dispImage = self.dispImage.convert('RGB')
        draw = ImageDraw.Draw(self.dispImage)
        rs = list(self.liRect.keys())
        for i in range(len(rs)) :
            r = rs[i]
            if i in selItems :
                draw.rectangle((r[0] * self.imageScale, r[1] * self.imageScale, r[2] * self.imageScale, r[3] * self.imageScale), outline = "red")
            else :
                draw.rectangle((r[0] * self.imageScale, r[1] * self.imageScale, r[2] * self.imageScale, r[3] * self.imageScale), outline = "green")
        img = ImageTk.PhotoImage(self.dispImage)
        self.image_l_raw.config(image=img)
        self.image_l_raw.image = img

    def deleteSelectedItemFromListBox(self):
        #print(self.l_box.get(self.l_box.curselection()))
        idx = self.l_box.curselection()
        if len(idx) > 0 and (None == self.rawImageEditor) :
            ro = collections.OrderedDict()
            rs = self.liRect.keys()
            for i in range(len(rs)) :
                if i not in idx :
                    r = rs[i]
                    ro[r] = self.liRect[r]
            self.liRect = ro
            self.drawListBox() 
            self.drawRawImageDisp()  

    # 打开图片时使用，获得地址
    def selectImageFile(self):
        path = tk.StringVar()
        file_entry = tk.Entry(self, state='readonly', text=path)
        path_ = askopenfilename()
        path.set(path_)
        return file_entry.get()

    def rawImageLabelClicked(self, event):
        if (None != self.rawImage) and (None == self.rawImageEditor) :
            self.rawImageEditor = RawImageEditor(self, self.rawImage, self.liRect.keys())
            self.rawImageEditor.waitForWindow(self.image_l_raw)
            rs = self.rawImageEditor.selectedPositions()
            trs = collections.OrderedDict()
            for k in rs :
                if k in self.liRect :
                    trs[k] = self.liRect[k]
                else:
                    trs[k] = ''
            self.liRect = trs
            self.rawImageEditor = None
            self.drawListBox()
            self.drawRawImageDisp()

    def onRectListboxSelect(self, event):
        idx = self.l_box.curselection()
        if len(idx) > 0 and (self.currentListBoxSelIdx != idx) :
            self.currentListBoxSelIdx = idx
            ctx = ''
            rs = list(self.liRect.keys())
            for i in range(len(self.currentListBoxSelIdx)) :
                v = self.currentListBoxSelIdx[i]
                k = rs[v]
                ctx = ctx + self.liRect[k]
                if i < len(self.currentListBoxSelIdx)-1 :
                    ctx = ctx + ','
            self.edtListBoxSel.delete(0, tk.END)
            self.edtListBoxSel.insert(0, ctx)
            self.drawRawImageDisp(idx)

    def onEdtListBoxComplete(self, event):
        idx = self.l_box.curselection()
        if (len(idx) > 0) and (self.currentListBoxSelIdx == idx) :
            ctx = self.edtListBoxSel.get().strip()
            ctx = ctx.split(',')
            while len(idx) > len(ctx) :
                ctx.append('')
            rs = list(self.liRect.keys())
            n = 0
            for i in idx :
                r = rs[i]
                self.liRect[r] = ctx[n]
                n = n + 1        

    def drawTransImageDisp(self):            
        transImage = self.scaleDisplayImage(self.transRawImage)
        transImage = transImage.convert('L')
        img = ImageTk.PhotoImage(transImage)
        self.image_l_trans.config(image=img)
        self.image_l_trans.image = img

    def doTransRawImage(self):
        self.transRawImage = Image.new('L', (self.rawImage.width, self.rawImage.height))
        rs = self.liRect.keys()
        for r in rs :
            im = self.rawImage.crop(r)
            cv_im = cv.cvtColor(np.asarray(im), cv.COLOR_RGB2BGR)
            hsv = cv.cvtColor(cv_im, cv.COLOR_BGR2HSV)
            _, _, v = cv.split(hsv)
            avg = np.average(v.flatten())
            pixels = im.load()
            scale = float(self.brightnessScale.get())
            for j in range(im.height) :
                for i in range(im.width) :
                    hv = v[j,i]
                    if hv < avg * scale:
                        #im.putpixel((i, j), 0) #
                        pixels[i, j] = 0
                    '''else :
                        im.putpixel((i, j), (255, 255, 255, 255))'''
            self.transRawImage.paste(im, (int(r[0]),int(r[1])), mask = None) 

        self.drawTransImageDisp()

    def onTransRawImageBtnClicked(self):
        if None != self.rawImage :
            self.doTransRawImage()

    def saveTransCfg(self, pth):
        (path,name) = os.path.split(self.rawImagePath)
        cfg = os.path.join(pth, 'cfg')
        if os.path.exists(cfg) and os.path.isdir(cfg) :
            cfgname = os.path.join(cfg, name) + '.txt'
        else :
            cfgname = os.path.join(pth, name) + '.txt'
        with open(cfgname, "w") as f:
            for r in self.liRect.keys() :
                v = self.liRect[r]
                line = '{},{},{},{},{}\n'.format(r[0], r[1], r[2], r[3], v)
                f.write(line) 

    def saveCombinedImage(self, pth):
        im_A = cv.cvtColor(np.array(self.rawImage), cv.COLOR_RGB2BGR)
        im_B = cv.cvtColor(np.array(self.transRawImage), cv.COLOR_RGB2BGR)
        im_AB = np.concatenate([im_A, im_B], 1)
        ext = os.path.splitext(self.rawImagePath)[-1]
        ext = ext.lower()
        name = os.path.basename(self.rawImagePath)
        sp = name.split('.')[:-1]
        comb = os.path.join(pth, 'comb')
        if os.path.exists(comb) and os.path.isdir(comb) :
            name = sp[0] + ext
            pth = comb
        else :
            name = sp[0] + '_comb' + ext
        path_AB = os.path.join(pth, name)
        cv.imwrite(path_AB, im_AB)

    def saveTransImage(self, path):
        ext = os.path.splitext(self.rawImagePath)[-1]
        ext = ext.lower()
        (path,name) = os.path.split(self.rawImagePath)
        mask = os.path.join(path, 'mask')
        fn = name.split('.')[:-1]
        if os.path.exists(mask) and os.path.isdir(mask) : 
            fn = fn[0] + ext
        else :
            fn = fn[0] + '_mask' + ext
        im = os.path.join(mask, fn)
        if not im.endswith(ext) :
            im = im + ext
        self.transRawImage.save(im)

    def saveTransInformation(self, path):
        if '' != path :
           self.saveTransImage(path)
           self.saveTransCfg(path)
           self.saveCombinedImage(path)

    def onSaveTransRawImageBtnClicked(self):
        if None != self.transRawImage :
            (path,name) = os.path.split(self.rawImagePath)
            dirname = askdirectory(initialdir = os.path.expanduser(path), title = '保存结果')
            if '' != dirname :
                self.defSavePath = dirname
                self.saveTransInformation(self.defSavePath)

    def onDefaultSaveTransRawImageBtnClicked(self) :
        if (None != self.transRawImage) and ('' != self.defSavePath) :
            self.saveTransInformation(self.defSavePath)

    def setupUI(self):
        # 左边菜单栏
        left_f = tk.Frame(self, height=self.frameDispHeight, width=self.leftFrameWidth)
        left_f.pack(side=tk.LEFT)

        # 各种功能按钮名称及位置
        btnOpen = tk.Button(left_f, text='打开图像', command=self.openAndDisplayImage)
        btnOpen.place(y=25, x=30, width=300, height=self.btnHeight)
        btnTrans = tk.Button(left_f, text='处理图像', command=self.onTransRawImageBtnClicked)
        btnTrans.place(y=75, x=30, width=300, height=self.btnHeight)

        l_selRect = tk.Label(left_f, text = '鼠标选定区域')
        l_selRect.place(x=0, y=125, width=self.leftFrameWidth, height=self.labelTextHeight)
        
        '''列表'''
        self.l_box = tk.Listbox(left_f, exportselection=False)  # 创建两个列表组件
        self.l_box.place(x=1, y=125+self.labelTextHeight, width=self.leftFrameWidth-2, height=270)
        self.l_box.bind('<<ListboxSelect>>', self.onRectListboxSelect)
        self.drawListBox()

        # 选中列表注释
        ledt = tk.Label(left_f, text = '选中区域内容注释（回车结束）：', anchor = 'w')
        ledt.place(x=1, y=420, width=self.leftFrameWidth-2)
        self.edtListBoxSel = tk.Entry(left_f)
        self.edtListBoxSel.place(x=1, y=440, width=self.leftFrameWidth-2)
        self.edtListBoxSel.bind('<Return>', self.onEdtListBoxComplete)

        # 亮度比例调节 
        tkScale = tk.Scale(left_f, from_=0.2, to=1.8, orient=tk.HORIZONTAL, resolution=0.1, length=self.leftFrameWidth-2, variable=self.brightnessScale, label = '亮度比:')
        tkScale.place(x=1, y=480)

        # 删除选定项
        btnDel = tk.Button(left_f, text='删除选定项', command=self.deleteSelectedItemFromListBox)
        btnDel.place(y=550, x=30, width=300, height=self.btnHeight)

        btnSave = tk.Button(left_f, text='保存结果', command=self.onSaveTransRawImageBtnClicked)
        btnSave.place(y=600, x=30, width=300, height=self.btnHeight)

        btnDefSave = tk.Button(left_f, text='默认或上次位置保存', command=self.onDefaultSaveTransRawImageBtnClicked)
        btnDefSave.place(y=650, x=30, width=300, height=self.btnHeight)

        # 右侧图像显示栏
        right_f = tk.Frame(self, height=self.frameDispHeight, width=self.imageDispWidth)
        right_f.pack(side=tk.RIGHT)

        l_rawT = tk.Label(right_f, text = '原始图片')
        l_rawT.place(x=0, y=0, width=self.imageDispWidth, height=self.labelTextHeight)

        self.image_l_raw = tk.Label(right_f, relief='ridge')
        self.image_l_raw.place(x=0, y=self.labelTextHeight, width=self.imageDispWidth, height=self.imageDispHeight)
        self.image_l_raw.bind("<Button-1>",self.rawImageLabelClicked)

        l_transT = tk.Label(right_f, text = '处理后图片')
        l_transT.place(x=0, y=self.labelTextHeight + self.imageDispHeight, width=self.imageDispWidth, height=self.labelTextHeight)

        self.image_l_trans = tk.Label(right_f, relief='ridge')
        self.image_l_trans.place(x=0, y=self.labelTextHeight + self.imageDispHeight + self.labelTextHeight, width=self.imageDispWidth, height=self.imageDispHeight)

if __name__ == '__main__' :
    win = MainWin()
    # 进入消息循环
    win.mainloop()

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#!/usr/bin/python

# -*- coding: UTF-8 -*-

import os

import sys

if sys.version_info < (3, 0):

import Tkinter as tk # 导入 Tkinter 库

from tkFileDialog import askopenfilename, askdirectory

else :

import tkinter as tk # 导入 Tkinter 库

from tkinter.filedialog import askopenfilename, askdirectory

from PIL import Image, ImageTk, ImageDraw

from time import sleep

import numpy as np

import cv2 as cv

import collections

DEF_WIDTH = 1080

DEF_HEIGHT = 720

IMAGE_HEIGHT = 720

FRAME_LEFT_WIDTH = 360

# 太小的选定区域我们需要丢弃，防止误操作

MINI_RECT_AREA = 20

class RawImageEditor:

def __init__(self, win, img, rects):

#变量X和Y用来记录鼠标左键按下的位置

self.X = tk.IntVar(value=0)

self.Y = tk.IntVar(value=0)

self.sel = False

self.lastDraw = None

self.lastDraws = []

self.imageScale = 1.0

self.dispWidth = DEF_WIDTH # 图片显示区域的最大高度，宽度

self.dispHeight = DEF_HEIGHT

self.rawImage = img

self.calcImageScale(self.rawImage)

self.dispWidth = int(self.imageScale * self.rawImage.width)

self.dispHeight = int(self.imageScale * self.rawImage.height)

# 图片缩放

self.dispImage = self.rawImage.resize((self.dispWidth, self.dispHeight))

# 选择区域

self.selPositions = []

for r in rects :

self.selPositions.append((r[0] * self.imageScale, r[1] * self.imageScale, r[2] * self.imageScale, r[3] * self.imageScale))

#创建顶级组件容器

self.top = tk.Toplevel(win, width=self.dispWidth, height=self.dispHeight)

#不显示最大化、最小化按钮

self.top.overrideredirect(True)

#center window on desktop

nScreenWid, nScreenHei = self.top.maxsize()

winPos = '%sx%s+%s+%s' % (int(self.top.winfo_reqwidth()), int(self.top.winfo_reqheight()), int((nScreenWid - self.top.winfo_reqwidth())/2), int((nScreenHei - self.top.winfo_reqheight())/2))

self.top.geometry(winPos)

# Make topLevelWindow remain on top until destroyed, or attribute changes.

self.top.attributes('-topmost', 'true')

self.canvas = tk.Canvas(self.top, bg='white', width=self.dispWidth, height=self.dispHeight)

self.tkImage = ImageTk.PhotoImage(self.dispImage)

self.canvas.create_image(self.dispWidth//2, self.dispHeight//2, image=self.tkImage)

for r in self.selPositions :

draw = self.canvas.create_rectangle(r[0], r[1], r[2], r[3], outline='green')

self.lastDraws.append(draw)

#鼠标左键按下的位置

def onLeftButtonDown(event):

self.X.set(event.x)

self.Y.set(event.y)

#开始截图

self.sel = True

#重新绘制已经选择的区域

for draw in self.lastDraws :

self.canvas.delete(draw)

self.lastDraws = []

for r in self.selPositions :

draw = self.canvas.create_rectangle(r[0], r[1], r[2], r[3], outline='green')

self.lastDraws.append(draw)

self.canvas.bind('<Button-1>', onLeftButtonDown)

#鼠标左键移动，显示选取的区域

def onLeftButtonMove(event):

if not self.sel:

return

try:

#删除刚画完的图形，要不然鼠标移动的时候是黑乎乎的一片矩形

self.canvas.delete(self.lastDraw)

except Exception as e:

pass

self.lastDraw = self.canvas.create_rectangle(self.X.get(), self.Y.get(), event.x, event.y, outline='green')

self.canvas.bind('<B1-Motion>', onLeftButtonMove)

#获取鼠标左键抬起的位置，保存区域截图

def onLeftButtonUp(event):

if not self.sel:

return

self.sel = False

sleep(0.1)

#考虑鼠标左键从右下方按下而从左上方抬起的截图

left, right = sorted([self.X.get(), event.x])

top, bottom = sorted([self.Y.get(), event.y])

if (right - left) * (bottom - top) > MINI_RECT_AREA :

self.selPositions.append((left,top,right,bottom))

#self.top.destroy()

#鼠标右键按下

def onRightButtonDown(event):

self.sel = False

self.top.destroy()

self.canvas.bind('<Button-2>', onRightButtonDown)

self.canvas.bind('<ButtonRelease-1>', onLeftButtonUp)

self.canvas.pack(fill=tk.BOTH, expand=tk.YES)

def calcImageScale(self, image) :

w = image.width

h = image.height

self.imageScale = 1.0

# 计算最小的缩放比例，保证原始宽高比

if w > self.dispWidth and h > self.dispHeight :

ws = self.dispWidth * 1.0 / w

hs = self.dispHeight * 1.0 / h

if ws < hs :

self.imageScale = ws

else :

self.imageScale = hs

elif w > self.dispWidth and h < self.dispHeight :

self.imageScale = self.dispWidth * 1.0 / w

elif w < self.dispWidth and h > self.dispHeight :

self.imageScale = self.dispHeight * 1.0 / h

def waitForWindow(self, win) :

win.wait_window(self.top)

def selectedPositions(self) :

# 转换为原始像素位置

realPos = []

for r in self.selPositions :

realPos.append((r[0] / self.imageScale, r[1] / self.imageScale, r[2] / self.imageScale, r[3] / self.imageScale))

return realPos

class MainWin(tk.Tk):

def __init__(self):

if sys.version_info >= (3, 0):

super().__init__()

else :

tk.Tk.__init__(self)

self.title('图像处理工具')

self.geometry('{}x{}'.format(DEF_WIDTH, DEF_HEIGHT))

self.rawImagePath = ''

self.rawImage = None # self.rawImage 原始图像，未经过缩放处理

self.transRawImage = None # self.transRawImage 经过转换处理之后的原始图像，没有经过缩放处理

self.dispImage = None # self.dispImage 显示图像，可能经过缩放处理

self.imageScale = 1.0 # 图片缩放比例，根据缩放比例进行显示的时候的缩放处理，后期选择区域的时候，需要进行缩放还原

self.leftFrameWidth = FRAME_LEFT_WIDTH

self.frameDispHeight = DEF_HEIGHT # 整个窗口的高度

self.labelTextHeight = 18 # 文本标签的高度

self.btnHeight = 30 # 按钮的高度

self.brightnessScale = tk.StringVar() # 亮度比

self.brightnessScale.set('1.0')

self.defSavePath = '' # 默认保存路径

self.imageDispWidth = IMAGE_HEIGHT # 图片显示区域的最大高度，宽度

self.imageDispHeight = self.frameDispHeight / 2 - self.labelTextHeight * 2

self.liRect = collections.OrderedDict() # 选择区域

self.rawImageEditor = None

self.currentListBoxSelIdx = None #当前选择的项目

self.setupUI()

def scaleDisplayImage(self, image) :

w = image.width

h = image.height

self.imageScale = 1.0

# 计算最小的缩放比例，保证原始宽高比

if w > self.imageDispWidth and h > self.imageDispHeight :

ws = self.imageDispWidth * 1.0 / w

hs = self.imageDispHeight * 1.0 / h

if ws < hs :

self.imageScale = ws

else :

self.imageScale = hs

elif w > self.imageDispWidth and h < self.imageDispHeight :

self.imageScale = self.imageDispWidth * 1.0 / w

elif w < self.imageDispWidth and h > self.imageDispHeight :

self.imageScale = self.imageDispHeight * 1.0 / h

# 图片缩放

return image.resize((int(self.imageScale * w), int(self.imageScale * h)))

def loadImageCfgFile(self, imf):

(path, name) = os.path.split(imf)

cfgname = imf + '.txt'

if (not os.path.exists(cfgname)) or (not os.path.isfile(cfgname)) :

cfgname = os.path.join(path, 'mask', name) + '.txt'

if (not os.path.exists(cfgname)) or (not os.path.isfile(cfgname)) :

cfgname = os.path.join(path, 'cfg', name) + '.txt'

self.liRect.clear()

if os.path.exists(cfgname) and os.path.isfile(cfgname) :

with open(cfgname, "r") as f:

lines = f.readlines()

for line in lines:

rs = line.split(',')

r = (float(rs[0].strip()), float(rs[1].strip()), float(rs[2].strip()), float(rs[3].strip()))

if len(rs) > 4 :

self.liRect[r] = rs[4].strip()

else :

self.liRect[r] = ''

# 打开图片时使用，传值（图）给展示函数

def openAndDisplayImage(self):

imF = self.selectImageFile()

if '' != imF :

self.rawImagePath = imF

self.loadImageCfgFile(self.rawImagePath)

self.drawListBox()

self.rawImage = Image.open(self.rawImagePath)

self.rawImage = self.rawImage.convert('RGBA')

self.drawRawImageDisp()

self.image_l_trans.image = None

self.transRawImage = None

def drawListBox(self):

self.l_box.delete(0, tk.END)

for r in self.liRect.keys():

r = '{},{},{},{}'.format(round(r[0],1), round(r[1],1), round(r[2],1), round(r[3],1))

self.l_box.insert(0, r)

def drawRawImageDisp(self, selItems=[]):

self.dispImage = self.scaleDisplayImage(self.rawImage)

self.dispImage = self.dispImage.convert('RGB')

draw = ImageDraw.Draw(self.dispImage)

rs = list(self.liRect.keys())

for i in range(len(rs)) :

r = rs[i]

if i in selItems :

draw.rectangle((r[0] * self.imageScale, r[1] * self.imageScale, r[2] * self.imageScale, r[3] * self.imageScale), outline = "red")

else :

draw.rectangle((r[0] * self.imageScale, r[1] * self.imageScale, r[2] * self.imageScale, r[3] * self.imageScale), outline = "green")

img = ImageTk.PhotoImage(self.dispImage)

self.image_l_raw.config(image=img)

self.image_l_raw.image = img

def deleteSelectedItemFromListBox(self):

#print(self.l_box.get(self.l_box.curselection()))

idx = self.l_box.curselection()

if len(idx) > 0 and (None == self.rawImageEditor) :

ro = collections.OrderedDict()

rs = self.liRect.keys()

for i in range(len(rs)) :

if i not in idx :

r = rs[i]

ro[r] = self.liRect[r]

self.liRect = ro

self.drawListBox()

self.drawRawImageDisp()

# 打开图片时使用，获得地址

def selectImageFile(self):

path = tk.StringVar()

file_entry = tk.Entry(self, state='readonly', text=path)

path_ = askopenfilename()

path.set(path_)

return file_entry.get()

def rawImageLabelClicked(self, event):

if (None != self.rawImage) and (None == self.rawImageEditor) :

self.rawImageEditor = RawImageEditor(self, self.rawImage, self.liRect.keys())

self.rawImageEditor.waitForWindow(self.image_l_raw)

rs = self.rawImageEditor.selectedPositions()

trs = collections.OrderedDict()

for k in rs :

if k in self.liRect :

trs[k] = self.liRect[k]

else:

trs[k] = ''

self.liRect = trs

self.rawImageEditor = None

self.drawListBox()

self.drawRawImageDisp()

def onRectListboxSelect(self, event):

idx = self.l_box.curselection()

if len(idx) > 0 and (self.currentListBoxSelIdx != idx) :

self.currentListBoxSelIdx = idx

ctx = ''

rs = list(self.liRect.keys())

for i in range(len(self.currentListBoxSelIdx)) :

v = self.currentListBoxSelIdx[i]

k = rs[v]

ctx = ctx + self.liRect[k]

if i < len(self.currentListBoxSelIdx)-1 :

ctx = ctx + ','

self.edtListBoxSel.delete(0, tk.END)

self.edtListBoxSel.insert(0, ctx)

self.drawRawImageDisp(idx)

def onEdtListBoxComplete(self, event):

idx = self.l_box.curselection()

if (len(idx) > 0) and (self.currentListBoxSelIdx == idx) :

ctx = self.edtListBoxSel.get().strip()

ctx = ctx.split(',')

while len(idx) > len(ctx) :

ctx.append('')

rs = list(self.liRect.keys())

n = 0

for i in idx :

r = rs[i]

self.liRect[r] = ctx[n]

n = n + 1

def drawTransImageDisp(self):

transImage = self.scaleDisplayImage(self.transRawImage)

transImage = transImage.convert('L')

img = ImageTk.PhotoImage(transImage)

self.image_l_trans.config(image=img)

self.image_l_trans.image = img

def doTransRawImage(self):

self.transRawImage = Image.new('L', (self.rawImage.width, self.rawImage.height))

rs = self.liRect.keys()

for r in rs :

im = self.rawImage.crop(r)

cv_im = cv.cvtColor(np.asarray(im), cv.COLOR_RGB2BGR)

hsv = cv.cvtColor(cv_im, cv.COLOR_BGR2HSV)

_, _, v = cv.split(hsv)

avg = np.average(v.flatten())

pixels = im.load()

scale = float(self.brightnessScale.get())

for j in range(im.height) :

for i in range(im.width) :

hv = v[j,i]

if hv < avg * scale:

#im.putpixel((i, j), 0) #

pixels[i, j] = 0

'''else :

im.putpixel((i, j), (255, 255, 255, 255))'''

self.transRawImage.paste(im, (int(r[0]),int(r[1])), mask = None)

self.drawTransImageDisp()

def onTransRawImageBtnClicked(self):

if None != self.rawImage :

self.doTransRawImage()

def saveTransCfg(self, pth):

(path,name) = os.path.split(self.rawImagePath)

cfg = os.path.join(pth, 'cfg')

if os.path.exists(cfg) and os.path.isdir(cfg) :

cfgname = os.path.join(cfg, name) + '.txt'

else :

cfgname = os.path.join(pth, name) + '.txt'

with open(cfgname, "w") as f:

for r in self.liRect.keys() :

v = self.liRect[r]

line = '{},{},{},{},{}\n'.format(r[0], r[1], r[2], r[3], v)

f.write(line)

def saveCombinedImage(self, pth):

im_A = cv.cvtColor(np.array(self.rawImage), cv.COLOR_RGB2BGR)

im_B = cv.cvtColor(np.array(self.transRawImage), cv.COLOR_RGB2BGR)

im_AB = np.concatenate([im_A, im_B], 1)

ext = os.path.splitext(self.rawImagePath)[-1]

ext = ext.lower()

name = os.path.basename(self.rawImagePath)

sp = name.split('.')[:-1]

comb = os.path.join(pth, 'comb')

if os.path.exists(comb) and os.path.isdir(comb) :

name = sp[0] + ext

pth = comb

else :

name = sp[0] + '_comb' + ext

path_AB = os.path.join(pth, name)

cv.imwrite(path_AB, im_AB)

def saveTransImage(self, path):

ext = os.path.splitext(self.rawImagePath)[-1]

ext = ext.lower()

(path,name) = os.path.split(self.rawImagePath)

mask = os.path.join(path, 'mask')

fn = name.split('.')[:-1]

if os.path.exists(mask) and os.path.isdir(mask) :

fn = fn[0] + ext

else :

fn = fn[0] + '_mask' + ext

im = os.path.join(mask, fn)

if not im.endswith(ext) :

im = im + ext

self.transRawImage.save(im)

def saveTransInformation(self, path):

if '' != path :

self.saveTransImage(path)

self.saveTransCfg(path)

self.saveCombinedImage(path)

def onSaveTransRawImageBtnClicked(self):

if None != self.transRawImage :

(path,name) = os.path.split(self.rawImagePath)

dirname = askdirectory(initialdir = os.path.expanduser(path), title = '保存结果')

if '' != dirname :

self.defSavePath = dirname

self.saveTransInformation(self.defSavePath)

def onDefaultSaveTransRawImageBtnClicked(self) :

if (None != self.transRawImage) and ('' != self.defSavePath) :

self.saveTransInformation(self.defSavePath)

def setupUI(self):

# 左边菜单栏

left_f = tk.Frame(self, height=self.frameDispHeight, width=self.leftFrameWidth)

left_f.pack(side=tk.LEFT)

# 各种功能按钮名称及位置

btnOpen = tk.Button(left_f, text='打开图像', command=self.openAndDisplayImage)

btnOpen.place(y=25, x=30, width=300, height=self.btnHeight)

btnTrans = tk.Button(left_f, text='处理图像', command=self.onTransRawImageBtnClicked)

btnTrans.place(y=75, x=30, width=300, height=self.btnHeight)

l_selRect = tk.Label(left_f, text = '鼠标选定区域')

l_selRect.place(x=0, y=125, width=self.leftFrameWidth, height=self.labelTextHeight)

'''列表'''

self.l_box = tk.Listbox(left_f, exportselection=False) # 创建两个列表组件

self.l_box.place(x=1, y=125+self.labelTextHeight, width=self.leftFrameWidth-2, height=270)

self.l_box.bind('<<ListboxSelect>>', self.onRectListboxSelect)

self.drawListBox()

# 选中列表注释

ledt = tk.Label(left_f, text = '选中区域内容注释（回车结束）：', anchor = 'w')

ledt.place(x=1, y=420, width=self.leftFrameWidth-2)

self.edtListBoxSel = tk.Entry(left_f)

self.edtListBoxSel.place(x=1, y=440, width=self.leftFrameWidth-2)

self.edtListBoxSel.bind('<Return>', self.onEdtListBoxComplete)

# 亮度比例调节

tkScale = tk.Scale(left_f, from_=0.2, to=1.8, orient=tk.HORIZONTAL, resolution=0.1, length=self.leftFrameWidth-2, variable=self.brightnessScale, label = '亮度比:')

tkScale.place(x=1, y=480)

# 删除选定项

btnDel = tk.Button(left_f, text='删除选定项', command=self.deleteSelectedItemFromListBox)

btnDel.place(y=550, x=30, width=300, height=self.btnHeight)

btnSave = tk.Button(left_f, text='保存结果', command=self.onSaveTransRawImageBtnClicked)

btnSave.place(y=600, x=30, width=300, height=self.btnHeight)

btnDefSave = tk.Button(left_f, text='默认或上次位置保存', command=self.onDefaultSaveTransRawImageBtnClicked)

btnDefSave.place(y=650, x=30, width=300, height=self.btnHeight)

# 右侧图像显示栏

right_f = tk.Frame(self, height=self.frameDispHeight, width=self.imageDispWidth)

right_f.pack(side=tk.RIGHT)

l_rawT = tk.Label(right_f, text = '原始图片')

l_rawT.place(x=0, y=0, width=self.imageDispWidth, height=self.labelTextHeight)

self.image_l_raw = tk.Label(right_f, relief='ridge')

self.image_l_raw.place(x=0, y=self.labelTextHeight, width=self.imageDispWidth, height=self.imageDispHeight)

self.image_l_raw.bind("<Button-1>",self.rawImageLabelClicked)

l_transT = tk.Label(right_f, text = '处理后图片')

l_transT.place(x=0, y=self.labelTextHeight + self.imageDispHeight, width=self.imageDispWidth, height=self.labelTextHeight)

self.image_l_trans = tk.Label(right_f, relief='ridge')

self.image_l_trans.place(x=0, y=self.labelTextHeight + self.imageDispHeight + self.labelTextHeight, width=self.imageDispWidth, height=self.imageDispHeight)

if __name__ == '__main__' :

win = MainWin()

# 进入消息循环

win.mainloop()

参考链接

在ubuntu 18.04(GeForce GTX 760 4GB显存)使用Pytorch Pix2PixGAN(CUDA-10.1)

1. 参照 pytorch 1.0.1在ubuntu 18.04(GeForce GTX 760)编译(CUDA-10.1) 建立 pytorch 1.0.1 的编译环境，并解决编译时遇到的问题。

2. 依旧是推荐在 Anaconda 上建立独立的编译环境，然后执行编译：

$ sudo apt-get install git

# conda remove -n Pix2Pix --all

$ conda create -n Pix2Pix -y python=3.6.8 pip

$ source activate Pix2Pix

$ conda install numpy pyyaml mkl=2019.1 mkl-include=2019.1 setuptools cmake cffi typing pybind11

$ conda install ninja

# magma-cuda90 magma-cuda91 magma-cuda92 会编译失败 
$ conda install -c pytorch magma-cuda101

$ git clone https://github.com/pytorch/pytorch

$ cd pytorch

# pytorch 1.0.1 版本支持“Compute Capability” 低于3.0版本的硬件，pytorch 1.2.0需要至少3.5版本的硬件才可以正常运行
# https://github.com/pytorch/pytorch/blob/v1.3.0/torch/utils/cpp_extension.py
$ git checkout v1.0.1 -b v1.0.1

$ git submodule sync

$ git submodule update --init --recursive

$ export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}

# 如果不需要使用cuda的话，这里还要加上一句：export NO_CUDA=1

$ python setup.py clean

# 卸载以前安装的pytorch
$ conda uninstall pytorch

# 从Nvidia开发网站查询到自己硬件对应的“Compute Capability” 
# 比如 “GeForce GTX 760” 对应 “3.0” 计算能力，能力不正确会导致运行异常
# RuntimeError: cuda runtime error (48) : no kernel image is available for execution on the device

$ python setup.py install

# 对于开发者模式，可以使用
# python setup.py build develop

# 一定要退出 pytorch 的编译目录，在pytorch代码目录下执行命令会出现异常
$ cd ..

# 退出环境 
$ conda deactivate

$ sudo apt-get install git

# conda remove -n Pix2Pix --all

$ conda create -n Pix2Pix -y python=3.6.8 pip

$ source activate Pix2Pix

$ conda install numpy pyyaml mkl=2019.1 mkl-include=2019.1 setuptools cmake cffi typing pybind11

$ conda install ninja

# magma-cuda90 magma-cuda91 magma-cuda92 会编译失败

$ conda install -c pytorch magma-cuda101

$ git clone https://github.com/pytorch/pytorch

$ cd pytorch

# pytorch 1.0.1 版本支持“Compute Capability” 低于3.0版本的硬件，pytorch 1.2.0需要至少3.5版本的硬件才可以正常运行

# https://github.com/pytorch/pytorch/blob/v1.3.0/torch/utils/cpp_extension.py

$ git checkout v1.0.1 -b v1.0.1

$ git submodule sync

$ git submodule update --init --recursive

$ export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}

# 如果不需要使用cuda的话，这里还要加上一句：export NO_CUDA=1

$ python setup.py clean

# 卸载以前安装的pytorch

$ conda uninstall pytorch

# 从Nvidia开发网站查询到自己硬件对应的“Compute Capability”

# 比如 “GeForce GTX 760” 对应 “3.0” 计算能力，能力不正确会导致运行异常

# RuntimeError: cuda runtime error (48) : no kernel image is available for execution on the device

$ python setup.py install

# 对于开发者模式，可以使用

# python setup.py build develop

# 一定要退出 pytorch 的编译目录，在pytorch代码目录下执行命令会出现异常

$ cd ..

# 退出环境

$ conda deactivate

编译出错信息，参考 pytorch 1.0.1在ubuntu 18.04(GeForce GTX 760)编译(CUDA-10.1) 里面的介绍解决。

3. 编译安装 TorchVision

$ sudo apt-get install git

# 进入运行环境
$ source activate Pix2Pix

$ git clone https://github.com/pytorch/vision.git

# 也可本站下载一份拷贝 wget https://www.mobibrw.com/wp-content/uploads/2019/11/vision.zip

$ cd vision

$ git checkout v0.2.1 -b v0.2.1

$ python setup.py install

# 退出环境 
$ conda deactivate

$ sudo apt-get install git

# 进入运行环境

$ source activate Pix2Pix

$ git clone https://github.com/pytorch/vision.git

# 也可本站下载一份拷贝 wget https://www.mobibrw.com/wp-content/uploads/2019/11/vision.zip

$ cd vision

$ git checkout v0.2.1 -b v0.2.1

$ python setup.py install

# 退出环境

$ conda deactivate

4. 检出 CycleGAN and pix2pix in PyTorch 的代码，并安装依赖

# 进入运行环境
$ source activate Pix2Pix

$ git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix.git

# 也可本站下载 wget https://www.mobibrw.com/wp-content/uploads/2019/12/pytorch-CycleGAN-and-pix2pix.zip

$ cd pytorch-CycleGAN-and-pix2pix

# 下载人脸替换部分的数据集
$ bash datasets/download_pix2pix_dataset.sh facades

# 也可本站下载然后自己参照脚本解压缩到指定目录 https://www.mobibrw.com/wp-content/uploads/2019/12/facades.tar.gz

# 安装依赖
$ pip install pillow==6.2.1
$ pip install dominate==2.4.0
$ pip install visdom==0.1.8.9

# 修正错误 models/networks.py
# TypeError: cuda() got an unexpected keyword argument 'device_id'
$ sed -i "s/netG\.cuda(device_id=gpu_ids\[0\])/netG.cuda(gpu_ids[0])/g" models/networks.py

$ sed -i "s/netD\.cuda(device_id=gpu_ids\[0\])/netD.cuda(gpu_ids[0])/g" models/networks.py

$ sed -i "s/network\.cuda(device_id=gpu_ids\[0\])/network.cuda(gpu_ids[0])/g" models/base_model.py

# 开启WEB服务,主要是第一次运行需要下载部分辅助软件包，
# 训练之前需要执行，否则下面训练的时候会报错
$ python -m visdom.server & 

# 等待屏幕上出现 “You can navigate to http://localhost:8097” 代表服务启动成功

# 执行训练
$ bash scripts/train_pix2pix.sh

# 进入运行环境

$ source activate Pix2Pix

$ git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix.git

# 也可本站下载 wget https://www.mobibrw.com/wp-content/uploads/2019/12/pytorch-CycleGAN-and-pix2pix.zip

$ cd pytorch-CycleGAN-and-pix2pix

# 下载人脸替换部分的数据集

$ bash datasets/download_pix2pix_dataset.sh facades

# 也可本站下载然后自己参照脚本解压缩到指定目录 https://www.mobibrw.com/wp-content/uploads/2019/12/facades.tar.gz

# 安装依赖

$ pip install pillow==6.2.1

$ pip install dominate==2.4.0

$ pip install visdom==0.1.8.9

# 修正错误 models/networks.py

# TypeError: cuda() got an unexpected keyword argument 'device_id'

$ sed -i "s/netG\.cuda(device_id=gpu_ids\[0\])/netG.cuda(gpu_ids[0])/g" models/networks.py

$ sed -i "s/netD\.cuda(device_id=gpu_ids\[0\])/netD.cuda(gpu_ids[0])/g" models/networks.py

$ sed -i "s/network\.cuda(device_id=gpu_ids\[0\])/network.cuda(gpu_ids[0])/g" models/base_model.py

# 开启WEB服务,主要是第一次运行需要下载部分辅助软件包，

# 训练之前需要执行，否则下面训练的时候会报错

$ python -m visdom.server &

# 等待屏幕上出现 “You can navigate to http://localhost:8097” 代表服务启动成功

# 执行训练

$ bash scripts/train_pix2pix.sh

执行训练的时候，如果出现如下错误：

Traceback (most recent call last):
  File "train.py", line 47, in <module>
    errors = model.get_current_errors()
  File "~/pytorch-CycleGAN-and-pix2pix/models/pix2pix_model.py", line 122, in get_current_errors
    return OrderedDict([('G_GAN', self.loss_G_GAN.data[0]),
IndexError: invalid index of a 0-dim tensor. Use tensor.item() to convert a 0-dim tensor to a Python number

Traceback (most recent call last):

File "train.py", line 47, in <module>

errors = model.get_current_errors()

File "~/pytorch-CycleGAN-and-pix2pix/models/pix2pix_model.py", line 122, in get_current_errors

return OrderedDict([('G_GAN', self.loss_G_GAN.data[0]),

IndexError: invalid index of a 0-dim tensor. Use tensor.item() to convert a 0-dim tensor to a Python number

这个原因是由于 PyTorch 版本差异造成的,（作者在 Pytorch 0.4.1 版本上测试，我们在 Pytorch 1.0.1 版本上测试），执行如下命令修复：

#loss_G_GAN.data[0] 替换为 loss_G_GAN.item()

$ sed -i "s/self\.loss_G_GAN\.data\[0]/self.loss_G_GAN.item()/g" models/pix2pix_model.py

$ sed -i "s/self\.loss_G_L1\.data\[0]/self.loss_G_L1.item()/g" models/pix2pix_model.py

$ sed -i "s/self\.loss_D_real\.data\[0]/self.loss_D_real.item()/g" models/pix2pix_model.py

$ sed -i "s/self\.loss_D_fake\.data\[0]/self.loss_D_fake.item()/g" models/pix2pix_model.py

#loss_G_GAN.data[0] 替换为 loss_G_GAN.item()

$ sed -i "s/self\.loss_G_GAN\.data\[0]/self.loss_G_GAN.item()/g" models/pix2pix_model.py

$ sed -i "s/self\.loss_G_L1\.data\[0]/self.loss_G_L1.item()/g" models/pix2pix_model.py

$ sed -i "s/self\.loss_D_real\.data\[0]/self.loss_D_real.item()/g" models/pix2pix_model.py

$ sed -i "s/self\.loss_D_fake\.data\[0]/self.loss_D_fake.item()/g" models/pix2pix_model.py

5. 测试训练结果

$ bash scripts/test_pix2pix.sh

# 观察结果需要打开 ./results/facades_pix2pix/test_latest/index.html

$ bash scripts/test_pix2pix.sh

# 观察结果需要打开 ./results/facades_pix2pix/test_latest/index.html

参考链接

格力中央空调线控器上没有“制热”模式

在这寒冷的天气，家用多联机就要发挥作用了，但是当你打开空调，发现在线控器上怎么调都没有“制热模式”的太阳呢？只有制冷、送风和除湿，难道买的是单冷机吗？

继续阅读格力中央空调线控器上没有“制热”模式

在ubuntu 18.04(GeForce GTX 760 4GB显存)使用MaskTextSpotter(CUDA-10.1)进行训练

参考在ubuntu 18.04(GeForce GTX 760 4GB显存)编译/测试MaskTextSpotter(CUDA-10.1) 建立能运行的测试环境。

由于测试集使用的是 icdar2013 ,因此，务必保证已经可以在 icdar2013 数据集中进行测试。

接下来就是进行数据训练：

1. 修改训练脚本，默认情况下，训练脚本中使用了 8 张卡进行训练，我们只有一张卡，因此要调整训练参数

$ cd MaskTextSpotter

$ export ROOT_PATH=`pwd`

$ sed -i 's/nproc_per_node=8/nproc_per_node=1/g' train.sh

$ cd MaskTextSpotter

$ export ROOT_PATH=`pwd`

$ sed -i 's/nproc_per_node=8/nproc_per_node=1/g' train.sh

2. 下载训练集 MaskTextSpotter 默认使用的是 SynthText 数据集进行训练，需要先下载这个数据集，大约 40GB

$ mkdir datasets

$ cd datasets

$ sudo apt-get install aria2

$ aria2c -c -j16 -s16 -x16 --follow-torrent=mem -o 'hyperai.torrent' 'https://hyper.ai/tracker/download?torrent=7783'

# 也可下载种子文件 wget https://www.mobibrw.com/wp-content/uploads/2019/11/SynthText.zip

$ mkdir datasets

$ cd datasets

$ sudo apt-get install aria2

$ aria2c -c -j16 -s16 -x16 --follow-torrent=mem -o 'hyperai.torrent' 'https://hyper.ai/tracker/download?torrent=7783'

# 也可下载种子文件 wget https://www.mobibrw.com/wp-content/uploads/2019/11/SynthText.zip

3. 解压缩 SynthText 数据集到指定目录

$ mkdir synthtext

$ unzip SynthText/data/SynthText.zip -d synthtext

# 目录改名
$ mv synthtext/SynthText synthtext/train_images

$ mkdir synthtext

$ unzip SynthText/data/SynthText.zip -d synthtext

# 目录改名

$ mv synthtext/SynthText synthtext/train_images

4. 下载转换后的 SynthText 数据集索引文件，上面解压缩出来的索引是 .mat 扩展名的文件，我们需要转换成 MaskTextSpotter 需要的数据索引文件，作者提供了一份已经转换好的文件，我们直接下载并使用这个文件即可,这个文件大概要 1.6GB 的样子。

$ aria2 -c https://1drv.ms/u/s!ArsnjfK83FbXgb5vgOOVPYywgCWuQw?e=UPuNTa

# 解压缩到指定目录
$ tar -xvf SynthText_GT_E2E.tar.gz -C synthtext

# 目录改名
$ mv synthtext/SynthText_GT_E2E synthtext/train_gts

$ aria2 -c https://1drv.ms/u/s!ArsnjfK83FbXgb5vgOOVPYywgCWuQw?e=UPuNTa

# 解压缩到指定目录

$ tar -xvf SynthText_GT_E2E.tar.gz -C synthtext

# 目录改名

$ mv synthtext/SynthText_GT_E2E synthtext/train_gts

5. 生成训练文件 train_list.txt

import os

path = 'train_images'

train_list = 'train_list.txt'

tf = open(train_list, 'w')

for root, dirs, files in os.walk(path):
    files = [f for f in files if not f[0] == '.']
    dirs[:] = [d for d in dirs if not d[0] == '.']
    # use files and dirs
    for file_name in files:
        fn = os.path.join(root, file_name)
        fn = fn.replace('./', '')
        fn = fn.replace(path + '/', '')
        ext = os.path.splitext(fn)[1]
        if '.jpg' == ext :  
            tf.write(fn + '\n')

tf.close()

import os

path = 'train_images'

train_list = 'train_list.txt'

tf = open(train_list, 'w')

for root, dirs, files in os.walk(path):

files = [f for f in files if not f[0] == '.']

dirs[:] = [d for d in dirs if not d[0] == '.']

# use files and dirs

for file_name in files:

fn = os.path.join(root, file_name)

fn = fn.replace('./', '')

fn = fn.replace(path + '/', '')

ext = os.path.splitext(fn)[1]

if '.jpg' == ext :

tf.write(fn + '\n')

tf.close()

执行脚本，生成文件

$ cd synthtext

$ python gen_train.py

$ cd synthtext

$ python gen_train.py

执行测试

$ cd $ROOT_PATH

# 减少一次性加载图片数量，解决“OSError: [Errno 24] Too many open files”
# 参数设置为 0 代表从主进程加载图片资源
$ sed -i "s/NUM_WORKERS: 4/NUM_WORKERS: 0/g" configs/pretrain.yaml

# 调整训练参数，对于单个GPU来说，默认参数太大了，会导致GPU内存不足
# 解决 “RuntimeError: CUDA out of memory.”
$ sed -i "s/IMS_PER_BATCH: 8/IMS_PER_BATCH: 1/g" configs/pretrain.yaml

# 修正错误 “AttributeError: module 'torch' has no attribute 'bool'”
# 从Pytorch 1.2开始，torch.uint8被修改为torch.bool,如果是低于 Pytorch 1.2的版本
# 需要修改为torch.uint8
$ sed -i "s/torch.bool/torch.uint8/g" maskrcnn_benchmark/modeling/rpn/inference.py
$ sed -i "s/torch.bool/torch.uint8/g" maskrcnn_benchmark/modeling/balanced_positive_negative_sampler.py


# 修改SOLVER设置上的GPU相关参数
# https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14
# 官方参考建议单个GPU的学习速率是0.0025但是实际运行中会报错，调整为0.0015可以正常运行
$ sed -i "s/BASE_LR: 0.01/BASE_LR: 0.0015/g" configs/pretrain.yaml

# 4GB 显存设置为 8 ，8GB显存可以设置为64/128 
$ sed -i "s/MASK_BATCH_SIZE_PER_IM: 512/MASK_BATCH_SIZE_PER_IM: 8/g" configs/pretrain.yaml

# 目前在RTX 2070 Super 8GB显存版本上测试来看，使用
# “WEIGHT: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl” 
# 的配置情况下，BASE_LR可以设置为 0.0025 , MASK_BATCH_SIZE_PER_IM 可以设置为 128 

# 进入运行环境
$ source activate MaskTextSpotter

$ bash train.sh

$ cd $ROOT_PATH

# 减少一次性加载图片数量，解决“OSError: [Errno 24] Too many open files”

# 参数设置为 0 代表从主进程加载图片资源

$ sed -i "s/NUM_WORKERS: 4/NUM_WORKERS: 0/g" configs/pretrain.yaml

# 调整训练参数，对于单个GPU来说，默认参数太大了，会导致GPU内存不足

# 解决 “RuntimeError: CUDA out of memory.”

$ sed -i "s/IMS_PER_BATCH: 8/IMS_PER_BATCH: 1/g" configs/pretrain.yaml

# 修正错误 “AttributeError: module 'torch' has no attribute 'bool'”

# 从Pytorch 1.2开始，torch.uint8被修改为torch.bool,如果是低于 Pytorch 1.2的版本

# 需要修改为torch.uint8

$ sed -i "s/torch.bool/torch.uint8/g" maskrcnn_benchmark/modeling/rpn/inference.py

$ sed -i "s/torch.bool/torch.uint8/g" maskrcnn_benchmark/modeling/balanced_positive_negative_sampler.py

# 修改SOLVER设置上的GPU相关参数

# https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14

# 官方参考建议单个GPU的学习速率是0.0025但是实际运行中会报错，调整为0.0015可以正常运行

$ sed -i "s/BASE_LR: 0.01/BASE_LR: 0.0015/g" configs/pretrain.yaml

# 4GB 显存设置为 8 ，8GB显存可以设置为64/128

$ sed -i "s/MASK_BATCH_SIZE_PER_IM: 512/MASK_BATCH_SIZE_PER_IM: 8/g" configs/pretrain.yaml

# 目前在RTX 2070 Super 8GB显存版本上测试来看，使用

# “WEIGHT: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl”

# 的配置情况下，BASE_LR可以设置为 0.0025 , MASK_BATCH_SIZE_PER_IM 可以设置为 128

# 进入运行环境

$ source activate MaskTextSpotter

$ bash train.sh

注意，我们在 configs/pretrain.yaml 加载的权重文件是 "WEIGHT: "./outputs/finetune/model_finetune.pth" ,这个权重文件是从 SynthText 训练得来的，那么这个"model_finetune.pth"是怎么生成的呢？

作者没有详细介绍，我们从 masktextspotter.caffe2 项目的配置文件中可以知道，这个文件其实是从 " WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl" 开始生成的。这个文件也可以从本站下载 R-50.pkl

R-50.pkl: converted copy of MSRA’s original ResNet-50 model

具体配置文件内容参考如下：

MODEL:
  TYPE: generalized_rcnn
  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
  NUM_CLASSES: 2
  FASTER_RCNN: True
  MASK_ON: True
  NAME: shrink++
NUM_GPUS: 1
SOLVER:
  WEIGHT_DECAY: 0.0001
  LR_POLICY: steps_with_decay
  BASE_LR: 0.005   #synth
  GAMMA: 0.1
  MAX_ITER: 200000
  STEPS: [0, 120000]
FPN:
  FPN_ON: True
  MULTILEVEL_ROIS: True
  MULTILEVEL_RPN: True
  USE_DEFORMABLE: False
  RPN_ASPECT_RATIOS: (0.5, 1, 2)
FAST_RCNN:
  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
  ROI_XFORM_METHOD: RoIAlign
  ROI_XFORM_RESOLUTION: 7
  ROI_XFORM_SAMPLING_RATIO: 2
MRCNN:
  ROI_MASK_HEAD: text_mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
  RESOLUTION: 28  # (output mask resolution) default 14
  RESOLUTION_H: 32
  RESOLUTION_W: 128
  ROI_XFORM_METHOD: RoIAlign
  ROI_XFORM_RESOLUTION: 14  # default 7
  ROI_XFORM_RESOLUTION_H: 16
  ROI_XFORM_RESOLUTION_W: 64
  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
  DILATION: 1  # default 2
  CONV_INIT: MSRAFill  # default GaussianFill
  IS_E2E: True
  MASK_BATCH_SIZE_PER_IM: 16
  WEIGHT_LOSS_MASK: 1.0
  WEIGHT_LOSS_CHAR_BOX: 1.0
  WEIGHT_WH: True  ## default is false 

TRAIN:
  BATCH_SIZE_PER_IM: 512
  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
  AUTO_RESUME: True
  SNAPSHOT_ITERS: 10000

  ##################### pre-train on synth ##########################
  WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
  DATASETS: ('synth_train', )
  SCALES: (800,)
  MAX_SIZE: 1333
  IMS_PER_BATCH: 2
  ASPECT_GROUPING: True
  MIX_TRAIN: False
  USE_CHARANNS: [True]

  ######################  Fine tune   #################################
  # MIX_TRAIN: True
  # WEIGHTS: ./train/synth_train/pretrain_model/model_iter159999.pkl
  # DATASETS: ('totaltext_train', 'scut-eng-char_train', 'synth_train', 'icdar2013_train', 'icdar2015_train')
  # USE_CHARANNS: [False, True, True, True, False]
  # # the ratios of synth, icdar2013, icdar2015 is 2:1:1, defaultly
  # # MIX_RATIOS: [0.125, 0.125, 0.5, 0.125, 0.125]
  # MIX_RATIOS: [1.0 / 6, 1.0 / 6, 1.0 / 3, 1.0 / 6, 1.0 / 6]
  # SCALES: (600, 800, 1000)
  # MAX_SIZE: 1333
  # # # SCALES: (800,)
  # # # MAX_SIZE: 1333
  # IMS_PER_BATCH: 1
  # ASPECT_GROUPING: False

IMAGE:
  aug: False
  saturation_prob: 0.5
  saturation_lower: 0.5
  saturation_upper: 1.5
  hue_prob: 0.5
  hue_delta: 18
  lighting_noise_prob: 0.5
  contrast_prob: 0.5
  contrast_lower: 0.5
  contrast_upper: 1.5
  brightness_prob: 0.5
  brightness_delta: 32
  rotate_prob: 0.5
  rotate_delta: 15
TEST:
  OUTPUT_POLYGON: False # only set to True for totaltext
  WEIGHTS: ./train/shrink++_finetune/model_iter79999.pkl
  DATASETS: ('icdar2015_test',)
  SCALES: (1000,)
  MAX_SIZE: 3333
  NMS: 0.5
  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
  RPN_POST_NMS_TOP_N: 1000
  VIS: False
  SCORE_THRESH: 0.2
  BBOX_AUG:
    ENABLED: False
    SCORE_HEUR: UNION  # AVG NOTE: cannot use AVG for e2e model
    COORD_HEUR: UNION  # AVG NOTE: cannot use AVG for e2e model
    H_FLIP: False
    SCALES: (800,)
    MAX_SIZE: 2000
    SCALE_H_FLIP: False
    SCALE_SIZE_DEP: False
    AREA_TH_LO: 2500   # 50^2
    AREA_TH_HI: 32400  # 180^2
    ASPECT_RATIOS: ()
    ASPECT_RATIO_H_FLIP: False
  MASK_AUG:
    ENABLED: False
    HEUR: SOFT_AVG
    H_FLIP: False
    SCALES: (1600,)
    MAX_SIZE: 3333
    SCALE_H_FLIP: False
    SCALE_SIZE_DEP: False
    AREA_TH: 32400  # 180^2
    ASPECT_RATIOS: ()
    ASPECT_RATIO_H_FLIP: False
  BBOX_VOTE:
    ENABLED: True
    VOTE_TH: 0.9
  SOFT_NMS:
    ENABLED: False
OUTPUT_DIR: .

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MODEL:

TYPE: generalized_rcnn

CONV_BODY: FPN.add_fpn_ResNet50_conv5_body

NUM_CLASSES: 2

FASTER_RCNN: True

MASK_ON: True

NAME: shrink++

NUM_GPUS: 1

SOLVER:

WEIGHT_DECAY: 0.0001

LR_POLICY: steps_with_decay

BASE_LR: 0.005 #synth

GAMMA: 0.1

MAX_ITER: 200000

STEPS: [0, 120000]

FPN:

FPN_ON: True

MULTILEVEL_ROIS: True

MULTILEVEL_RPN: True

USE_DEFORMABLE: False

RPN_ASPECT_RATIOS: (0.5, 1, 2)

FAST_RCNN:

ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head

ROI_XFORM_METHOD: RoIAlign

ROI_XFORM_RESOLUTION: 7

ROI_XFORM_SAMPLING_RATIO: 2

MRCNN:

ROI_MASK_HEAD: text_mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs

RESOLUTION: 28 # (output mask resolution) default 14

RESOLUTION_H: 32

RESOLUTION_W: 128

ROI_XFORM_METHOD: RoIAlign

ROI_XFORM_RESOLUTION: 14 # default 7

ROI_XFORM_RESOLUTION_H: 16

ROI_XFORM_RESOLUTION_W: 64

ROI_XFORM_SAMPLING_RATIO: 2 # default 0

DILATION: 1 # default 2

CONV_INIT: MSRAFill # default GaussianFill

IS_E2E: True

MASK_BATCH_SIZE_PER_IM: 16

WEIGHT_LOSS_MASK: 1.0

WEIGHT_LOSS_CHAR_BOX: 1.0

WEIGHT_WH: True ## default is false

TRAIN:

BATCH_SIZE_PER_IM: 512

RPN_PRE_NMS_TOP_N: 2000 # Per FPN level

AUTO_RESUME: True

SNAPSHOT_ITERS: 10000

##################### pre-train on synth ##########################

WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl

DATASETS: ('synth_train', )

SCALES: (800,)

MAX_SIZE: 1333

IMS_PER_BATCH: 2

ASPECT_GROUPING: True

MIX_TRAIN: False

USE_CHARANNS: [True]

###################### Fine tune #################################

# MIX_TRAIN: True

# WEIGHTS: ./train/synth_train/pretrain_model/model_iter159999.pkl

# DATASETS: ('totaltext_train', 'scut-eng-char_train', 'synth_train', 'icdar2013_train', 'icdar2015_train')

# USE_CHARANNS: [False, True, True, True, False]

# # the ratios of synth, icdar2013, icdar2015 is 2:1:1, defaultly

# # MIX_RATIOS: [0.125, 0.125, 0.5, 0.125, 0.125]

# MIX_RATIOS: [1.0 / 6, 1.0 / 6, 1.0 / 3, 1.0 / 6, 1.0 / 6]

# SCALES: (600, 800, 1000)

# MAX_SIZE: 1333

# # # SCALES: (800,)

# # # MAX_SIZE: 1333

# IMS_PER_BATCH: 1

# ASPECT_GROUPING: False

IMAGE:

aug: False

saturation_prob: 0.5

saturation_lower: 0.5

saturation_upper: 1.5

hue_prob: 0.5

hue_delta: 18

lighting_noise_prob: 0.5

contrast_prob: 0.5

contrast_lower: 0.5

contrast_upper: 1.5

brightness_prob: 0.5

brightness_delta: 32

rotate_prob: 0.5

rotate_delta: 15

TEST:

OUTPUT_POLYGON: False # only set to True for totaltext

WEIGHTS: ./train/shrink++_finetune/model_iter79999.pkl

DATASETS: ('icdar2015_test',)

SCALES: (1000,)

MAX_SIZE: 3333

NMS: 0.5

RPN_PRE_NMS_TOP_N: 1000 # Per FPN level

RPN_POST_NMS_TOP_N: 1000

VIS: False

SCORE_THRESH: 0.2

BBOX_AUG:

ENABLED: False

SCORE_HEUR: UNION # AVG NOTE: cannot use AVG for e2e model

COORD_HEUR: UNION # AVG NOTE: cannot use AVG for e2e model

H_FLIP: False

SCALES: (800,)

MAX_SIZE: 2000

SCALE_H_FLIP: False

SCALE_SIZE_DEP: False

AREA_TH_LO: 2500 # 50^2

AREA_TH_HI: 32400 # 180^2

ASPECT_RATIOS: ()

ASPECT_RATIO_H_FLIP: False

MASK_AUG:

ENABLED: False

HEUR: SOFT_AVG

H_FLIP: False

SCALES: (1600,)

MAX_SIZE: 3333

SCALE_H_FLIP: False

SCALE_SIZE_DEP: False

AREA_TH: 32400 # 180^2

ASPECT_RATIOS: ()

ASPECT_RATIO_H_FLIP: False

BBOX_VOTE:

ENABLED: True

VOTE_TH: 0.9

SOFT_NMS:

ENABLED: False

OUTPUT_DIR: .

其实我们直接删除或者注释掉权重文件加载部分也是可以的。只是，如果想要复现原作者的测试成果的话，我们最好使用相同的配置信息。

对于 4GB 显存的机器来说，由于显存非常有限，导致非常可能在运行的途中出现 "RuntimeError: CUDA out of memory." ,目前测试来看，继续执行命令即可。

训练结果存储在 outputs/pretrain 目录下，训练结果会在训练到一定阶段之后，存储到这个目录下。

如果出现类似如下错误，请适当减少学习速率 BASE_LR

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [32,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
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~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [36,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [37,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [38,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [39,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [40,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
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~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [42,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [43,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [44,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [45,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [46,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [47,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [48,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [2,0,0], thread: [49,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
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Traceback (most recent call last):
  File "tools/train_net.py", line 173, in <module>
    main()
  File "tools/train_net.py", line 166, in main
    model = train(cfg, args.local_rank, args.distributed)
  File "tools/train_net.py", line 76, in train
    cfg,
  File "~/MaskTextSpotter/maskrcnn_benchmark/engine/trainer.py", line 66, in do_train
    loss_dict = model(images, targets)
  File "~/.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py", line 50, in forward
    proposals, proposal_losses = self.rpn(images, features, targets)
  File "~/.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/rpn.py", line 94, in forward
    return self._forward_train(anchors, objectness, rpn_box_regression, targets)
  File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/rpn.py", line 110, in _forward_train
    anchors, objectness, rpn_box_regression, targets
  File "~/.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/inference.py", line 138, in forward
    sampled_boxes.append(self.forward_for_single_feature_map(a, o, b))
  File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/inference.py", line 113, in forward_for_single_feature_map
    boxlist = remove_small_boxes(boxlist, self.min_size)
  File "~/MaskTextSpotter/maskrcnn_benchmark/structures/boxlist_ops.py", line 46, in remove_small_boxes
    (ws >= min_size) & (hs >= min_size)
RuntimeError: copy_if failed to synchronize: cudaErrorAssert: device-side assert triggered

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~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [4,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [5,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [6,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [7,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [8,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [9,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [10,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [11,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [12,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [13,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [14,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [15,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [16,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [17,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [18,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [19,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [20,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [21,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [22,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [23,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [24,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [25,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [26,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [27,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [28,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [29,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [30,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [3,0,0], thread: [31,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [0,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [1,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [2,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [3,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [4,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [5,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [6,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [7,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [8,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [9,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [10,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [11,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [12,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [13,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [14,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [15,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [16,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [17,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [18,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [19,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [20,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [21,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [22,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [23,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [24,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [25,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [26,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [27,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [28,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [29,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [30,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [31,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

Traceback (most recent call last):

File "tools/train_net.py", line 173, in <module>

main()

File "tools/train_net.py", line 166, in main

model = train(cfg, args.local_rank, args.distributed)

File "tools/train_net.py", line 76, in train

cfg,

File "~/MaskTextSpotter/maskrcnn_benchmark/engine/trainer.py", line 66, in do_train

loss_dict = model(images, targets)

File "~/.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__

result = self.forward(*input, **kwargs)

File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py", line 50, in forward

proposals, proposal_losses = self.rpn(images, features, targets)

File "~/.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__

result = self.forward(*input, **kwargs)

File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/rpn.py", line 94, in forward

return self._forward_train(anchors, objectness, rpn_box_regression, targets)

File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/rpn.py", line 110, in _forward_train

anchors, objectness, rpn_box_regression, targets

File "~/.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__

result = self.forward(*input, **kwargs)

File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/inference.py", line 138, in forward

sampled_boxes.append(self.forward_for_single_feature_map(a, o, b))

File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/inference.py", line 113, in forward_for_single_feature_map

boxlist = remove_small_boxes(boxlist, self.min_size)

File "~/MaskTextSpotter/maskrcnn_benchmark/structures/boxlist_ops.py", line 46, in remove_small_boxes

(ws >= min_size) & (hs >= min_size)

RuntimeError: copy_if failed to synchronize: cudaErrorAssert: device-side assert triggered

参考链接

HTML实现点击图片放大功能

<html>
    <head>
        <style>
            .over {position: fixed; left:0; top:0; width:100%; z-index:100;}
            .tempContainer {position:fixed; width:100%; margin-right:0px; margin-left:0px; text-align:center; z-index:101;}
        </style>
        <script src="http://code.jquery.com/jquery-1.8.0.min.js"></script>
    </head>
    <body>
        <div class="over"></div><!--背景层-->
        <div class="amplifyImg"><!--注意：此处的amlifyImg不可少-->
            <img src="test.jpg"/><!-- 此处是引入图片的路径 -->
        </div>
        <script>
            $(document).ready(function () {
                var imgsObj = $('.amplifyImg img');//需要放大的图像
                if(imgsObj){
                    $.each(imgsObj,function(){
                        $(this).click(function(){
                            var currImg = $(this);
                            coverLayer(1);
                            var tempContainer = $('<div class=tempContainer></div>');//图片容器
                            with(tempContainer){//width方法等同于$(this)
                                appendTo("body");
                                var windowWidth=$(window).width();
                                var windowHeight=$(window).height();
                                //获取图片原始宽度、高度
                                var orignImg = new Image();
                                orignImg.src =currImg.attr("src") ;
                                var currImgWidth= orignImg.width;
                                var currImgHeight = orignImg.height;
                                if(currImgWidth<windowWidth){//为了让图片不失真，当图片宽度较小的时候，保留原图
                                    if(currImgHeight<windowHeight){
                                        var topHeight=(windowHeight-currImgHeight)/2;
                                        if(topHeight>35){/*此处为了使图片高度上居中显示在整个手机屏幕中：因为在android,ios的微信中会有一个title导航，35为title导航的高度*/
                                            topHeight=topHeight-35;
                                            css('top',topHeight);
                                        }else{
                                            css('top',0);
                                        }
                                        html('<img border=0 src=' + currImg.attr('src') + '>');
                                    }else{
                                        css('top',0);
                                        html('<img border=0 src=' + currImg.attr('src') + ' height='+windowHeight+'>');
                                    }
                                }else{
                                    var currImgChangeHeight=(currImgHeight*windowWidth)/currImgWidth;
                                    if(currImgChangeHeight<windowHeight){
                                        var topHeight=(windowHeight-currImgChangeHeight)/2;
                                        if(topHeight>35){
                                            topHeight=topHeight-35;
                                            css('top',topHeight);
                                        }else{
                                            css('top',0);
                                        }
                                        html('<img border=0 src=' + currImg.attr('src') + ' width='+windowWidth+';>');
                                    }else{
                                        css('top',0);
                                        html('<img border=0 src=' + currImg.attr('src') + ' width='+windowWidth+'; height='+windowHeight+'>');
                                    }
                                }
                            }
                            tempContainer.click(function(){
                                $(this).remove();
                                coverLayer(0);
                            });
                        });
                    });
                }
                else{
                    return false;
                }
                //使用禁用蒙层效果
                function coverLayer(tag){
                    with($('.over')){
                        if(tag==1){
                            css('height',$(document).height());
                            css('display','block');
                            css('opacity',1);
                            css("background-color","#FFFFFF");
                            css("background-color","rgba(0,0,0,0.7)" );  //蒙层透明度
                        }
                        else{
                            css('display','none');
                        }
                    }
                }
            });
        </script>
    </body>
</html>

<html>

<head>

<style>

.over {position: fixed; left:0; top:0; width:100%; z-index:100;}

.tempContainer {position:fixed; width:100%; margin-right:0px; margin-left:0px; text-align:center; z-index:101;}

</style>

</head>

<body>

</div>

$(document).ready(function () {

var imgsObj = $('.amplifyImg img');//需要放大的图像

if(imgsObj){

$.each(imgsObj,function(){

$(this).click(function(){

var currImg = $(this);

coverLayer(1);

var tempContainer = $('<div class=tempContainer></div>');//图片容器

with(tempContainer){//width方法等同于$(this)

appendTo("body");

var windowWidth=$(window).width();

var windowHeight=$(window).height();

//获取图片原始宽度、高度

var orignImg = new Image();

orignImg.src =currImg.attr("src") ;

var currImgWidth= orignImg.width;

var currImgHeight = orignImg.height;

if(currImgWidth<windowWidth){//为了让图片不失真，当图片宽度较小的时候，保留原图

if(currImgHeight<windowHeight){

var topHeight=(windowHeight-currImgHeight)/2;

if(topHeight>35){/*此处为了使图片高度上居中显示在整个手机屏幕中：因为在android,ios的微信中会有一个title导航，35为title导航的高度*/

topHeight=topHeight-35;

css('top',topHeight);

}else{

css('top',0);

}

html('<img border=0 src=' + currImg.attr('src') + '>');

}else{

css('top',0);

html('<img border=0 src=' + currImg.attr('src') + ' height='+windowHeight+'>');

}

}else{

var currImgChangeHeight=(currImgHeight*windowWidth)/currImgWidth;

if(currImgChangeHeight<windowHeight){

var topHeight=(windowHeight-currImgChangeHeight)/2;

if(topHeight>35){

topHeight=topHeight-35;

css('top',topHeight);

}else{

css('top',0);

}

html('<img border=0 src=' + currImg.attr('src') + ' width='+windowWidth+';>');

}else{

css('top',0);

html('<img border=0 src=' + currImg.attr('src') + ' width='+windowWidth+'; height='+windowHeight+'>');

}

tempContainer.click(function(){

$(this).remove();

coverLayer(0);

});

}

else{

return false;

}

//使用禁用蒙层效果

function coverLayer(tag){

with($('.over')){

if(tag==1){

css('height',$(document).height());

css('display','block');

css('opacity',1);

css("background-color","#FFFFFF");

css("background-color","rgba(0,0,0,0.7)" ); //蒙层透明度

}

else{

css('display','none');

}

});

</script>

</body>

</html>

参考链接

html实现点击图片放大功能

HTTPS建立连接的详细过程

简介

HTTPS是在HTTP的基础上和SSL/TLS证书结合起来的一种协议,保证了传输过程中的安全性,减少了被恶意劫持的可能.很好的解决了解决了HTTP的三个缺点（被监听、被篡改、被伪装）

继续阅读HTTPS建立连接的详细过程

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