本文主要是介绍目标检测学习笔记（二）——图片取样及数据集制作，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
前言
参考：
从视频中提取图片：如何将视频逐帧提取成为一张张图片？
图片重命名：python文件批量改名
制作数据集：睿智的目标检测12——使用labeling进行目标检测数据集标注
批量修改标签名称：如何批量修改Pascal VOC数据集中xml标签中的标签名称
拍摄视频并提取图片
拍摄视频
getvedio.py
'''
python 录制mp4视频 by 郑瑞国
'''
import numpy as np
import cv2cap = cv2.VideoCapture(0)## some videowriter props
sz = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))fps = 20
# fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
# fourcc = cv2.VideoWriter_fourcc('m', 'p', 'e', 'g')
fourcc = cv2.VideoWriter_fourcc(*'mpeg')## open and set props
out = cv2.VideoWriter()
out.open('getvedio/16.mp4', fourcc, fps, sz, True)while (True):ret, frame = cap.read()out.write(frame)cv2.imshow('frame', frame)if cv2.waitKey(1) & 0xff == ord('q'):breakout.release()
cap.release()
cv2.destroyAllWindows()
提取图片
方法1：视频帧差法提取图片
frame_extract.py
# -*- coding: utf-8 -*-import cv2
import numpy as np
import os
import urllibdef frame_difference(frame1, frame2): #求取侦差# f1, f2 f1 = cv2.resize(frame1, (416, 416))f2 = cv2.resize(frame2, (416, 416))f1 = cv2.cvtColor(f1, cv2.COLOR_BGR2GRAY)f2 = cv2.cvtColor(f2, cv2.COLOR_BGR2GRAY) # 灰度化f1 = cv2.threshold(f1, 128, 255, cv2.THRESH_BINARY)[1] # 二值化f2 = cv2.threshold(f2, 128, 255, cv2.THRESH_BINARY)[1]diff = cv2.absdiff(f1, f2) #求取差值p = cv2.countNonZero(diff) / float(416 * 416)return pif __name__ == "__main__":root = r'F:\0-0\Myyolo\vedio_img\getvedio'threshold = 0.09 #阙值frame_interval = 15 #帧间隔save_root = r'F:\0-0\Myyolo\vedio_img\getvedio\img'for c, path in enumerate(os.listdir(root)):path = os.path.join(root, path)video = cv2.VideoCapture(path)count = 0st, frame1 = video.read() #st,frame是获cv2.VideoCapture.read()方法的两个返回值。st, frame2 = video.read() #其中st是布尔值，如果读取帧是正确的则返回True，如果文件读取到结尾，它的返回值就为False。frame就是每一帧的图像，是个三维矩阵。while st:if count % frame_interval == 0:st, frame2 = video.read()p = frame_difference(frame1, frame2) #求取侦差print(p)if p > threshold:save_dir = os.path.join(save_root, os.path.splitext(os.path.basename(path))[0])if not os.path.exists(save_dir): #如果目录不存在os.makedirs(save_dir) #创建目录print(save_dir)#cv2.imencode(os.path.join(save_dir, str(count)+'.jpg'), frame1)#cv2.imencode(str(count)+'.jpg', frame1)[1].tofile(save_dir)cv2.imwrite(os.path.join(save_dir, str(count) + '.jpg'), frame1)print('保存图片')frame1 = frame2st, frame2 = video.read()count += 1if st:cv2.imshow("video%d"%c, frame2) #函数可以在窗口中显示图像。该窗口和图像的原始大小自适应（自动调整到原始尺寸）。cv2.waitKey(100) #是一个和键盘绑定的函数，它的作用是等待一个键盘的输入（因为我们创建的图片窗口如果没有这个函数的话会闪一下就消失了，所以如果需要让它持久输出，我们可以使用该函数）。它的参数是毫秒级。该函数等待任何键盘事件的指定毫秒。如果您在此期间按下任何键，程序将继续进行。我们也可以将其设置为一个特定的键。video.release() #发布软件资源 / 释放硬件资源cv2.destroyAllWindows() #销毁我们创建的所有窗口。如果要销毁任何特定窗口，请使用函数cv2.destroyWindow()，其中传递确切的窗口名称作为参数。(应该是使用创建窗口时所使用的窗口名称，字符串类型。)print("video%d"%c, "done!")方法2：定时从视频截图
使用img_extract.py
# 每隔 多少 秒 提取视频中的图片到文件夹# This is a sample Python script.# Press Shift+F10 to execute it or replace it with your code.
# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.def print_hi(name):# Use a breakpoint in the code line below to debug your script.print(f'Hi, {name}') # Press Ctrl+F8 to toggle the breakpoint.# Press the green button in the gutter to run the script.
if __name__ == '__main__':print_hi('PyCharm')# See PyCharm help at https://www.jetbrains.com/help/pycharm/
# -*- coding: utf-8 -*-
import os
import cv2 ##加载OpenCV模块def video2frames(pathIn: object = '',pathOut: object = '',only_output_video_info: object = False,extract_time_points: object = None,initial_extract_time: object = 0,end_extract_time: object = None,extract_time_interval: object = -1,output_prefix: object = 'frame',jpg_quality: object = 100,isColor: object = True) -> object:'''pathIn：视频的路径，比如：F:\python_tutorials\test.mp4pathOut：设定提取的图片保存在哪个文件夹下，比如：F:\python_tutorials\frames1\。如果该文件夹不存在，函数将自动创建它only_output_video_info：如果为True，只输出视频信息（长度、帧数和帧率），不提取图片extract_time_points：提取的时间点，单位为秒，为元组数据，比如，(2, 3, 5)表示只提取视频第2秒， 第3秒，第5秒图片initial_extract_time：提取的起始时刻，单位为秒，默认为0（即从视频最开始提取）end_extract_time：提取的终止时刻，单位为秒，默认为None（即视频终点）extract_time_interval：提取的时间间隔，单位为秒，默认为-1（即输出时间范围内的所有帧）output_prefix：图片的前缀名，默认为frame，图片的名称将为frame_000001.jpg、frame_000002.jpg、frame_000003.jpg......jpg_quality：设置图片质量，范围为0到100，默认为100（质量最佳）isColor：如果为False，输出的将是黑白图片'''cap = cv2.VideoCapture(pathIn) ##打开视频文件n_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) ##视频的帧数fps = cap.get(cv2.CAP_PROP_FPS) ##视频的帧率dur = n_frames/fps ##视频的时间##如果only_output_video_info=True, 只输出视频信息，不提取图片if only_output_video_info:print('only output the video information (without extract frames)::::::')print("Duration of the video: {} seconds".format(dur))print("Number of frames: {}".format(n_frames))print("Frames per second (FPS): {}".format(fps))##提取特定时间点图片elif extract_time_points is not None:if max(extract_time_points) > dur: ##判断时间点是否符合要求raise NameError('the max time point is larger than the video duration....')try:os.mkdir(pathOut)except OSError:passsuccess = Truecount = 0while success and count < len(extract_time_points):cap.set(cv2.CAP_PROP_POS_MSEC, (1000*extract_time_points[count]))success,image = cap.read()if success:if not isColor:image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) ##转化为黑白图片print('Write a new frame: {}, {}th'.format(success, count+1))cv2.imwrite(os.path.join(pathOut, "{}_{:06d}.jpg".format(output_prefix, count+1)), image, [int(cv2.IMWRITE_JPEG_QUALITY), jpg_quality]) # save frame as JPEG filecount = count + 1else:##判断起始时间、终止时间参数是否符合要求if initial_extract_time > dur:raise NameError('initial extract time is larger than the video duration....')if end_extract_time is not None:if end_extract_time > dur:raise NameError('end extract time is larger than the video duration....')if initial_extract_time > end_extract_time:raise NameError('end extract time is less than the initial extract time....')##时间范围内的每帧图片都输出if extract_time_interval == -1:if initial_extract_time > 0:cap.set(cv2.CAP_PROP_POS_MSEC, (1000*initial_extract_time))try:os.mkdir(pathOut)except OSError:passprint('Converting a video into frames......')if end_extract_time is not None:N = (end_extract_time - initial_extract_time)*fps + 1success = Truecount = 0while success and count < N:success,image = cap.read()if success:if not isColor:image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)print('Write a new frame: {}, {}/{}'.format(success, count+1, n_frames))cv2.imwrite(os.path.join(pathOut, "{}_{:06d}.jpg".format(output_prefix, count+1)), image, [int(cv2.IMWRITE_JPEG_QUALITY), jpg_quality]) # save frame as JPEG filecount = count + 1else:success = Truecount = 0while success:success,image = cap.read()if success:if not isColor:image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)print('Write a new frame: {}, {}/{}'.format(success, count+1, n_frames))cv2.imwrite(os.path.join(pathOut, "{}_{:06d}.jpg".format(output_prefix, count+1)), image, [int(cv2.IMWRITE_JPEG_QUALITY), jpg_quality]) # save frame as JPEG filecount = count + 1##判断提取时间间隔设置是否符合要求elif extract_time_interval > 0 and extract_time_interval < 1/fps:raise NameError('extract_time_interval is less than the frame time interval....')elif extract_time_interval > (n_frames/fps):raise NameError('extract_time_interval is larger than the duration of the video....')##时间范围内每隔一段时间输出一张图片else:try:os.mkdir(pathOut)except OSError:passprint('Converting a video into frames......')if end_extract_time is not None:N = (end_extract_time - initial_extract_time)/extract_time_interval + 1success = Truecount = 0while success and count < N:cap.set(cv2.CAP_PROP_POS_MSEC, (1000*initial_extract_time+count*1000*extract_time_interval))success,image = cap.read()if success:if not isColor:image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)print('Write a new frame: {}, {}th'.format(success, count+1))cv2.imwrite(os.path.join(pathOut, "{}_{:06d}.jpg".format(output_prefix, count+1)), image, [int(cv2.IMWRITE_JPEG_QUALITY), jpg_quality]) # save frame as JPEG filecount = count + 1else:success = Truecount = 0while success:cap.set(cv2.CAP_PROP_POS_MSEC, (1000*initial_extract_time+count*1000*extract_time_interval))success,image = cap.read()if success:if not isColor:image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)print('Write a new frame: {}, {}th'.format(success, count+1))cv2.imwrite(os.path.join(pathOut, "{}_{:06d}.jpg".format(output_prefix, count+1)), image, [int(cv2.IMWRITE_JPEG_QUALITY), jpg_quality]) # save frame as JPEG filecount = count + 1##### 测试
# pathIn = 'pathIn/1.1.avi'# video2frames(pathIn, only_output_video_info = True) #只输出视频信息（长度、帧数和帧率），不提取图片
#
# pathOut = './frames1/'
# video2frames(pathIn, pathOut)
#
# pathOut = './frames2'
# video2frames(pathIn, pathOut, extract_time_points=(1, 2, 5)) #提取的时间点，单位为秒，为元组数据，比如，(2, 3, 5)表示只提取视频第2秒， 第3秒，第5秒图片
#
# pathOut = './frames3'
# video2frames(pathIn, pathOut,
# initial_extract_time=1, #提取的起始时刻，单位为秒，默认为0（即从视频最开始提取）
# end_extract_time=3, #提取的终止时刻，单位为秒，默认为None（即视频终点）
# extract_time_interval = 0.5) #提取的时间间隔，单位为秒，默认为-1（即输出时间范围内的所有帧）
#
# pathOut = './frames4/'
# video2frames(pathIn, pathOut, extract_time_points=(0.3, 2), isColor = False) #isColor：如果为False，输出的将是黑白图片
#
#
# pathOut = './frames5/'
# video2frames(pathIn, pathOut, extract_time_points=(0.3, 2), jpg_quality=50) #jpg_quality：设置图片质量，范围为0到100，默认为100（质量最佳）#输出
# pathIn = 'pathIn/1.1.avi'
pathIn = '0'
pathOut = 'pathOut/1/'
video2frames(pathIn, pathOut, extract_time_interval = 5, jpg_quality=100)
整理文件并重命名
先设置文件夹名称，再用img_rename.py
import os#将多个文件夹文件调到一个文件里面，并重命名
m = 0 # 文件夹名称
#for i in range(1,15): #对14个文件夹中的 图片 调用到一个文件夹中
for i in range(1,2):m = 15m += 1# 输入路径# path = input('请输入文件路径(结尾加上/)：')path = 'getvedio/img/' + str(m) + '/'# 输出路径path2 = 'getvedio/img_rename/'# 获取该目录下所有文件，存入列表中f = os.listdir(path)#重命名n = 1for i in f:# 设置旧文件名（就是路径+文件名）oldname = path + f[n - 1]# 设置新文件名newname = path2 +str(m) + '_' + str(n) + '.JPG'# 用os模块中的rename方法对文件改名os.renames(oldname, newname)print(oldname, '======>', newname)n += 1# #
# m = 1
# n = 1
# #输入路径
# # path = input('请输入文件路径(结尾加上/)：')
# path = 'img_pre/' + str(m) + '/'
# #输出路径
# path2 = 'img_ed/'
# # 获取该目录下所有文件，存入列表中
# f = os.listdir(path)
#
# for i in f:
# # 设置旧文件名（就是路径+文件名）
# oldname = path + f[n - 1]
#
# # 设置新文件名
# newname = path2 + str(n) + '.JPG'
#
# # 用os模块中的rename方法对文件改名
# os.renames(oldname, newname)
# print(oldname, '======>', newname)
# n += 1数据集制作
labelimg的安装及使用
- 安装及启动
安装：
pip install labelimg
启动命令：
labelimg
- 标记图片
JPEGimages储存原图片
数据集（.xml文件 ）保存至 Annotations
然后 复制到 VOCdevkit/VOC2007 文件夹中。
批量修改标签名称
参考：如何批量修改Pascal VOC数据集中xml标签中的标签名称
运行xml_rename.py
import os
import xml.etree.ElementTree as ET#程序功能：批量修改VOC数据集中xml标签文件的标签名称
def changelabelname(inputpath):listdir = os.listdir(inputpath)for file in listdir:if file.endswith('xml'):file = os.path.join(inputpath,file)tree = ET.parse(file)root = tree.getroot()for object1 in root.findall('object'):for sku in object1.findall('name'): #查找需要修改的名称if (sku.text == 'preName'): #‘preName’为修改前的名称sku.text = 'TESTNAME' #‘TESTNAME’为修改后的名称tree.write(file,encoding='utf-8') #写进原始的xml文件并避免原始xml中文字符乱码else:passelse:passif __name__ == '__main__':inputpath = 'E:/Research/Dataset/Test/Annotations' #此处替换为自己的路径changelabelname(inputpath)这篇关于目标检测学习笔记（二）——图片取样及数据集制作的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
