├── .idea ├── inspectionProfiles │ └── Project_Default.xml ├── libraries │ └── R_User_Library.xml ├── misc.xml ├── modules.xml ├── opencv3.iml ├── vcs.xml └── workspace.xml ├── 01_处理文件、摄像头和图形用户界面 ├── 011_读写图像文件.py ├── 012_图像与原始字节转换.py ├── 013_使用numpy访问图像数据.py ├── 014_视频文件的读写.py ├── 015_捕获摄像头的帧.py ├── 016_窗口显示图像.py ├── 017_窗口显示摄像头的帧.py ├── mm1_gray.png ├── randomColor.png └── randomGray.png ├── 02_Cameo项目(人脸跟踪和图像处理) ├── __init__.py ├── cameo.py ├── depth.py ├── filters.py ├── filters.pyc ├── managers.py ├── managers.pyc ├── utils.py └── utils.pyc ├── 03_处理图像 ├── 001_滤波器.py ├── 002_canny边缘检测.py ├── 003_轮廓检测.py ├── 004_矩形、圆形边界框.py ├── 005_凸轮廓与douglas-peucher算法.py ├── 006_直线检测.py ├── 007_圆检测.py └── 008_在图像上写字.py ├── 04_深度估计与分割 ├── 001_普通摄像头进行深度估计.py ├── 002_grabcut前景检测.py ├── 003_分水岭算法图像分割.py └── __init__.py ├── 05_人脸检测和识别 ├── 001_静态图像人脸检测.py ├── 002_视频中检测人脸.py ├── 003_人脸识别.py └── data │ └── faces │ ├── s0 │ ├── 0.pgm │ ├── 1.pgm │ ├── 10.pgm │ ├── 11.pgm │ ├── 12.pgm │ ├── 13.pgm │ ├── 14.pgm │ ├── 15.pgm │ ├── 16.pgm │ ├── 17.pgm │ ├── 18.pgm │ ├── 19.pgm │ ├── 2.pgm │ ├── 20.pgm │ ├── 21.pgm │ ├── 22.pgm │ ├── 23.pgm │ ├── 24.pgm │ ├── 25.pgm │ ├── 26.pgm │ ├── 27.pgm │ ├── 28.pgm │ ├── 29.pgm │ ├── 3.pgm │ ├── 30.pgm │ ├── 31.pgm │ ├── 32.pgm │ ├── 33.pgm │ ├── 34.pgm │ ├── 35.pgm │ ├── 36.pgm │ ├── 37.pgm │ ├── 38.pgm │ ├── 39.pgm │ ├── 4.pgm │ ├── 40.pgm │ ├── 41.pgm │ ├── 42.pgm │ ├── 43.pgm │ ├── 44.pgm │ ├── 45.pgm │ ├── 46.pgm │ ├── 47.pgm │ ├── 48.pgm │ ├── 49.pgm │ ├── 5.pgm │ ├── 50.pgm │ ├── 51.pgm │ ├── 52.pgm │ ├── 53.pgm │ ├── 54.pgm │ ├── 55.pgm │ ├── 56.pgm │ ├── 57.pgm │ ├── 58.pgm │ ├── 59.pgm │ ├── 6.pgm │ ├── 60.pgm │ ├── 61.pgm │ ├── 62.pgm │ ├── 63.pgm │ ├── 64.pgm │ ├── 65.pgm │ ├── 66.pgm │ ├── 67.pgm │ ├── 68.pgm │ ├── 69.pgm │ ├── 7.pgm │ ├── 70.pgm │ ├── 71.pgm │ ├── 72.pgm │ ├── 73.pgm │ ├── 74.pgm │ ├── 75.pgm │ ├── 76.pgm │ ├── 77.pgm │ ├── 78.pgm │ ├── 79.pgm │ ├── 8.pgm │ ├── 80.pgm │ ├── 81.pgm │ ├── 82.pgm │ ├── 83.pgm │ ├── 84.pgm │ ├── 85.pgm │ └── 9.pgm │ ├── s1 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s10 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s11 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s12 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s13 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s14 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s15 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s16 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s17 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s18 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s19 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s2 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s20 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s21 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s22 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s23 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s24 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s25 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s26 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s27 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s28 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s29 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s3 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s30 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s31 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s32 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s33 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s34 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s35 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s36 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s37 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s38 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s39 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s4 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s40 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s5 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s6 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s7 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ ├── s8 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm │ └── s9 │ ├── 1.pgm │ ├── 10.pgm │ ├── 2.pgm │ ├── 3.pgm │ ├── 4.pgm │ ├── 5.pgm │ ├── 6.pgm │ ├── 7.pgm │ ├── 8.pgm │ └── 9.pgm ├── 06_图像检测和搜索 ├── 001_检测角点特征.py ├── 002_DoG和SIFT特征提取与描述.py ├── 003_hessian算法surf提取和检测.py ├── 004_基于ORB的特征检测和BF暴力匹配.py ├── 005_k近邻匹配.py ├── 006_FLANN匹配.py ├── 007_FLANN单应性匹配.py └── 008_基于文身取证的应用.py ├── 07_目标检测与识别 ├── 001_检测人.py └── 002_汽车检测.py ├── data ├── album1.png ├── chess1.jpg ├── circle.jpg ├── haarcascade_eye.xml ├── haarcascade_frontalface_default.xml ├── item2.jpg ├── line1.png ├── logo1.png ├── mm1.jpg ├── mm2.jpeg ├── mm3.jpg ├── people1.jpg ├── people2.jpg ├── person1.jpg └── walez1.jpg └── requirements.txt /.idea/inspectionProfiles/Project_Default.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 14 | -------------------------------------------------------------------------------- /.idea/libraries/R_User_Library.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 6 | 7 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/opencv3.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 13 | -------------------------------------------------------------------------------- /.idea/vcs.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /01_处理文件、摄像头和图形用户界面/011_读写图像文件.py: -------------------------------------------------------------------------------- 1 | # coding:utf8 2 | import cv2 3 | 4 | 5 | # 读取一张图片, 第二个参数可选 6 | image = cv2.imread("../data/mm1.jpg") 7 | # 设置窗口 8 | cv2.namedWindow("show", cv2.WINDOW_AUTOSIZE) 9 | # 图像窗口显示 10 | cv2.imshow("show", image) 11 | # 等待按键 12 | cv2.waitKey(0) 13 | 14 | 15 | ''' 16 | cv2.IMREAD_ANYCOLOR = 4 17 | cv2.IMREAD_ANYDEPTH = 2 18 | cv2.IMREAD_COLOT = 1 19 | cv2.IMREAD_GRAYSCALE = 0 20 | cv2.IMREAD_LOAD_GDAL = 8 21 | cv2.IMREAD_UNCHANGED = -1 22 | ''' 23 | 24 | # 加载一个图像为灰度图像（会丢失颜色） 再保存为png 25 | image = cv2.imread("../data/mm1.jpg", cv2.IMREAD_GRAYSCALE) 26 | # 保存图片 根据拓展名自动转换保存格式 保存一定是BGR图像 27 | cv2.imwrite("mm1_gray.png", image) 28 | 29 | -------------------------------------------------------------------------------- /01_处理文件、摄像头和图形用户界面/012_图像与原始字节转换.py: -------------------------------------------------------------------------------- 1 | # coding:utf8 2 | import cv2 3 | import numpy 4 | import os 5 | 6 | 7 | """ 8 | 随机字节的bytearray转为灰度图像和BGR图像 9 | """ 10 | randomByteArray = bytearray(os.urandom(120000)) 11 | flatNumpyArray = numpy.array(randomByteArray) 12 | # 转换成400*300的灰度图像 13 | grayImage = flatNumpyArray.reshape(400, 300) 14 | cv2.imwrite("randomGray.png", grayImage) 15 | 16 | """ 17 | 随机字节的bytearray转为400*100的彩色图像 18 | """ 19 | bgrImage = flatNumpyArray.reshape(400, 100, 3) 20 | cv2.imwrite("randomColor.png", bgrImage) -------------------------------------------------------------------------------- /01_处理文件、摄像头和图形用户界面/013_使用numpy访问图像数据.py: -------------------------------------------------------------------------------- 1 | # coding:utf8 2 | import cv2 3 | 4 | """ 5 | 将bgr在(0, 0)处改为白色像素 6 | 7 | 0号为green 1号为blue 2号为red 8 | img的每一个位置存一个 3个长度的向量 分别表示gbr 9 | """ 10 | # img = cv2.imread("../data/mm2.jpeg") 11 | # print(img[0, 0]) # [49 65 11] 12 | # img[0, 0] = [255, 255, 255] 13 | # cv2.imshow("", img) 14 | # cv2.waitKey(0) 15 | 16 | 17 | """ 18 | 将坐标(150, 120) 的蓝色值改为255 19 | 20 | 建议使用itemset函数完成， 这能避免访问原始索引 21 | """ 22 | # img = cv2.imread("../data/mm2.jpeg") 23 | # print(img.item(150, 120, 0)) # 打印出这个坐标的blue值 24 | # cv2.imshow("", img) 25 | # img.itemset((150, 120, 0), 255) # 将这个位置的blue值设为255 26 | # print(img.item(150, 120, 0)) # 打印这个坐标的blue值 27 | 28 | 29 | """ 30 | 将图像所有的green值都设置为0 31 | 32 | 不推荐使用循环，，使用索引方式能提高程序实现的效率 33 | """ 34 | # img = cv2.imread("../data/mm1.jpg") 35 | # img[:, :, 1] = 0 36 | # cv2.imshow("", img) 37 | # cv2.waitKey(0) 38 | 39 | 40 | """ 41 | 将某个区域与变量绑定，将值分配给第二个区域 42 | """ 43 | # img = cv2.imread("../data/mm3.jpg") 44 | # my_roi = img[0: 100, 0:100] # 选定宽和高都是0到100的区域为感兴趣的区域 45 | # img[300: 400, 300: 400] = my_roi # 将my_roi区域的值赋给 宽和搞300到400像素的位置 46 | # cv2.imshow("", img) 47 | # cv2.waitKey() 48 | 49 | """ 50 | 查看图像的一些属性 51 | shape: 宽度 高度 和 通道数 52 | size： 图像像素的大小 53 | datatype： 图像的数据类型 一般为无符号整型 54 | """ 55 | img = cv2.imread("../data/mm3.jpg") 56 | print(img.shape) # (750, 1000, 3) 57 | print(img.size) # 2250000 58 | print(img.dtype) # uint8 59 | 60 | -------------------------------------------------------------------------------- /01_处理文件、摄像头和图形用户界面/014_视频文件的读写.py: -------------------------------------------------------------------------------- 1 | # coding:utf8 2 | import cv2 3 | 4 | """ 5 | 读取视频文件的帧， 采用yuv颜色编码写入到另一个帧 6 | 7 | VideoCapture和VideoWriter用于视频文件的读写 8 | VideoCapture读的每一帧都是一个bgr格式的图像 9 | """ 10 | videoCapture = cv2.VideoCapture("../data/demo3.mp4") 11 | fps = videoCapture.get(cv2.CAP_PROP_FPS) 12 | size = ( 13 | int(videoCapture.get(cv2.CAP_PROP_FRAME_WIDTH)), 14 | int(videoCapture.get(cv2.CAP_PROP_FRAME_HEIGHT)) 15 | ) 16 | 17 | videoWriter = cv2.VideoWriter( 18 | "output.mp4", 19 | cv2.VideoWriter_fourcc("I", "4", "2", "0"), # 编码器 20 | fps, 21 | size 22 | ) 23 | ''' 24 | 编码器常用的几种： 25 | cv2.VideoWriter_fourcc("I", "4", "2", "0") 26 | 压缩的yuv颜色编码器，4:2:0色彩度子采样 兼容性好，产生很大的视频 avi 27 | cv2.VideoWriter_fourcc("P", I", "M", "1") 28 | 采用mpeg-1编码，文件为avi 29 | cv2.VideoWriter_fourcc("X", "V", "T", "D") 30 | 采用mpeg-4编码，得到视频大小平均 拓展名avi 31 | cv2.VideoWriter_fourcc("T", "H", "E", "O") 32 | Ogg Vorbis， 拓展名为ogv 33 | cv2.VideoWriter_fourcc("F", "L", "V", "1") 34 | FLASH视频，拓展名为.flv 35 | ''' 36 | 37 | success, frame = videoCapture.read() 38 | while success: # 循环直到没有帧了 39 | videoWriter.write(frame) 40 | success, frame = videoCapture.read() -------------------------------------------------------------------------------- /01_处理文件、摄像头和图形用户界面/015_捕获摄像头的帧.py: -------------------------------------------------------------------------------- 1 | # coding:utf8 2 | 3 | import cv2 4 | 5 | 6 | """ 7 | 捕获摄像头10s的视频信息 写入一个avi文件 8 | """ 9 | 10 | cameraCapture = cv2.VideoCapture(0) # 传入0代表0号摄像头 11 | fps = 30 12 | size = ( 13 | int(cameraCapture.get(cv2.CAP_PROP_FRAME_WIDTH)), 14 | int(cameraCapture.get(cv2.CAP_PROP_FRAME_HEIGHT)) 15 | ) 16 | 17 | videoWriter = cv2.VideoWriter( 18 | "outputVid.avi", 19 | cv2.VideoWriter_fourcc("I", "4", "2", "0"), 20 | fps, 21 | size 22 | ) 23 | 24 | success, frame = cameraCapture.read() 25 | numFramesRemaining = 10 * fps - 1 26 | while success and numFramesRemaining: 27 | videoWriter.write(frame) 28 | success, frame = cameraCapture.read() 29 | numFramesRemaining -= 1 30 | 31 | cameraCapture.release() 32 | 33 | 34 | """ 35 | 如果使用一组摄像头或多个摄像头 36 | 用grab和retrieve方法代替 37 | 38 | success0 = cameraCapture.grab() 39 | success1 = cameraCapture.grab() 40 | if success0 and success1: 41 | frame0 = cameraCapture0.retrieve() 42 | frame1 = cameraCapture1.retrieve() 43 | 44 | 45 | """ 46 | -------------------------------------------------------------------------------- /01_处理文件、摄像头和图形用户界面/016_窗口显示图像.py: -------------------------------------------------------------------------------- 1 | # coding:utf8 2 | 3 | import cv2 4 | 5 | """ 6 | 显示一张图像 7 | """ 8 | img = cv2.imread("../data/mm2.jpeg") # 读取一张图像 9 | cv2.imshow("my image", img) # 显示图片窗口 10 | cv2.waitKey() # 阻塞等待按键 11 | cv2.destroyAllWindows() # 销毁资源 12 | 13 | -------------------------------------------------------------------------------- /01_处理文件、摄像头和图形用户界面/017_窗口显示摄像头的帧.py: -------------------------------------------------------------------------------- 1 | # coding:utf8 2 | 3 | import cv2 4 | 5 | 6 | """ 7 | 在窗口显示摄像头帧 8 | 9 | namedWindow() 指定窗口名 10 | imshow() 创建窗口 11 | DestroyWindow() 销毁所有窗口 12 | waitKey() 获取键盘输入 13 | setMouseCallback() 获取鼠标输入 14 | 15 | """ 16 | """ 17 | opencv窗口只有调用waitKey()后才能实时更新 18 | waitKey() 只有窗口创建后才能捕获键盘 19 | """ 20 | 21 | clicked = False 22 | 23 | 24 | def onMouse(event, x, y, flags, param): 25 | global clicked 26 | if event == cv2.EVENT_LBUTTONUP: 27 | clicked = True 28 | 29 | 30 | cameraCapture = cv2.VideoCapture(0) # 传入摄像头设备的索引 31 | cv2.namedWindow("MyWindow") # 设置窗口名称 32 | cv2.setMouseCallback("MyWindow", onMouse) # 传入窗口名称和响应捕获的函数 33 | """ 34 | setMouseCallback() 第二个参数接收一个回调函数 35 | 回调事件可以取值如下： 36 | cv2.EVENT_MOUSEMOVE 鼠标移动 37 | cv2.EVENT_LBUTTONDOWN 左按键按下 38 | cv2.EVENT_RBUTTONDOWN 右按键按下 39 | cv2.EVENT_MBUTTONDOWN 中间键按下 40 | cv2.EVENT_LBUTTONDBLCLK 双击左键 41 | cv2.EVENT_RBUTTONDBLCLK 双击右键 42 | cv2.EVENT_MBUTTONDBLCLK 双击中间 43 | 44 | 鼠标回调的标志参数可能是以下事件的桉位组合： 45 | cv2.EVENT_FLAG_LBUTTON 按下鼠标左键 46 | cv2.EVENT_FLAG_RBUTTON 按下鼠标右键 47 | cv2.EVENT_FLAG_MBUTTON 按下中间 48 | cv2.EVENT_FLAG_CTRLKEY 按下ctrl键 49 | cv2.EVENT_FLAG_SHITKEY 按下shift键 50 | cv2.EVENT_FLAG_ALTKEY 按下alt键 51 | """ 52 | 53 | print("点击窗口或者按键停止") 54 | 55 | success, frame = cameraCapture.read() 56 | while success and cv2.waitKey(1) == -1 and not clicked: 57 | cv2.imshow("MyWindow", frame) 58 | success, frame = cameraCapture.read() 59 | 60 | cv2.destroyAllWindows() 61 | cameraCapture.release() -------------------------------------------------------------------------------- /01_处理文件、摄像头和图形用户界面/mm1_gray.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/linyi0604/Computer-Vision/e2ec411bcf78b1d1bc353b721cabe250b1b28778/01_处理文件、摄像头和图形用户界面/mm1_gray.png -------------------------------------------------------------------------------- /01_处理文件、摄像头和图形用户界面/randomColor.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/linyi0604/Computer-Vision/e2ec411bcf78b1d1bc353b721cabe250b1b28778/01_处理文件、摄像头和图形用户界面/randomColor.png -------------------------------------------------------------------------------- /01_处理文件、摄像头和图形用户界面/randomGray.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/linyi0604/Computer-Vision/e2ec411bcf78b1d1bc353b721cabe250b1b28778/01_处理文件、摄像头和图形用户界面/randomGray.png -------------------------------------------------------------------------------- /02_Cameo项目(人脸跟踪和图像处理)/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/linyi0604/Computer-Vision/e2ec411bcf78b1d1bc353b721cabe250b1b28778/02_Cameo项目(人脸跟踪和图像处理)/__init__.py -------------------------------------------------------------------------------- /02_Cameo项目(人脸跟踪和图像处理)/cameo.py: -------------------------------------------------------------------------------- 1 | # coding:utf8 2 | 3 | import cv2 4 | from managers import WindowManger, CaptureManager 5 | import filters 6 | import depth 7 | """ 8 | Cameo项目 面向对象方式 实现一个 人脸跟踪和图像处理 9 | """ 10 | 11 | 12 | class Cameo(object): 13 | def __init__(self): 14 | # 窗口管理器 15 | self._windowManager = WindowManger( 16 | "Cameo", 17 | self.onKeyPress 18 | ) 19 | # 捕获管理器 20 | self._captureManager = CaptureManager( 21 | cv2.VideoCapture(0), 22 | self._windowManager, 23 | True, 24 | ) 25 | 26 | self._curveFilter = filters.BGRPortraCurveFilter() 27 | 28 | def run(self): 29 | self._windowManager.createWindow() 30 | while self._windowManager.isWindowCreated: 31 | self._captureManager.enterFrame() 32 | frame = self._captureManager.frame 33 | 34 | # 进行边缘检测 35 | filters.strokeEdge(frame, frame) 36 | self._curveFilter.apply(frame, frame) 37 | 38 | self._captureManager.exitFrame() 39 | self._windowManager.processEvents() 40 | 41 | def onKeyPress(self, keycode): 42 | if keycode == 32: # 空格 进行截屏 43 | self._captureManager.writeImage("screenshot.png") 44 | elif keycode == 9: # tab 录像 45 | if not self._captureManager.isWritingVideo: 46 | self._captureManager.startWritingVideo("screencast.avi") 47 | else: 48 | self._captureManager.stopWritingVideo() 49 | elif keycode == 27: # esc键退出 50 | self._windowManager.destroyWindow() 51 | 52 | 53 | if __name__ == '__main__': 54 | Cameo().run() 55 | -------------------------------------------------------------------------------- /02_Cameo项目(人脸跟踪和图像处理)/depth.py: -------------------------------------------------------------------------------- 1 | # coding:utf8 2 | 3 | import numpy 4 | import cv2 5 | import utils 6 | 7 | 8 | def createMedianMask(disparityMap, validDepthMask, rect=None): 9 | if rect is not None: 10 | x, y, w, h = rect 11 | disparityMap = disparityMap[y: y+h, x: x+w] 12 | validDepthMask = validDepthMask[y: y+h, x: x+w] 13 | # 获得一个数组的中位数 14 | median = numpy.median(disparityMap) 15 | # 获得一个卷集合，如果条件为真 返回1 否则为0 16 | # 得到一个跟输入一样的矩阵 由 0 1 组成 17 | return numpy.where( 18 | (validDepthMask == 0) | 19 | (abs(disparityMap - median) < 12), 20 | 1.0, 0.0) 21 | 22 | 23 | def copyRect(src, dst, srcRect, dstRect, mask=None, interpolation=cv2.INTER_LINEAR): 24 | x0, y0, w0, h0 = srcRect 25 | x1, y1, w1, h1 = dstRect 26 | 27 | if mask is None: 28 | dst[y1: y1+h1, x1:x1+w1] = cv2.resize( 29 | src[y0: y0+h0, x0:x0+w0], 30 | (w1, h1), 31 | interpolation=interpolation 32 | ) 33 | else: 34 | if not utils.isGray(src): 35 | mask = mask.repeat(3).reshape(h0, w0, 3) 36 | dst[y1:y1+h1, x1:x1+w1] = numpy.where( 37 | cv2.resize(mask, (w1, h1), interpolation=cv2.INTER_NEAREST), 38 | cv2.resize(src[y0: y0+h0, x0:x0+w0], 39 | (w1, h1), interpolation=interpolation), 40 | dst[y1:y1+h1, x1:x1+w1] 41 | ) 42 | 43 | 44 | def swapRects(src, dst, rects, masks=None, interpolation=cv2.INTER_LINEAR): 45 | if dst is not src: 46 | dst[:] = src 47 | numRects = len(rects) 48 | if numRects < 2: 49 | return 50 | if masks is None: 51 | masks = [None] * numRects 52 | x, y, w, h = rects[numRects - 1] 53 | temp = src[y:y+h, x:x+w].copy() 54 | i = numRects - 2 55 | while i >= 0: 56 | copyRect(src, dst, rects[i], rects[i+1], masks[i],interpolation) 57 | i -= 1 58 | copyRect(temp, dst, [0, 0, w, h], rects[0], masks[numRects-1], interpolation) 59 | -------------------------------------------------------------------------------- /02_Cameo项目(人脸跟踪和图像处理)/filters.py: -------------------------------------------------------------------------------- 1 | # coding:utf8 2 | 3 | import cv2 4 | import numpy 5 | import utils 6 | import scipy 7 | """ 8 | 边缘检测： 9 | 先平滑消除噪声 10 | 再转为灰度图像 11 | 再边缘检测 12 | 13 | 1 模糊消除噪声 14 | 2 转为灰度图像 15 | 3 边缘检测 转为边缘亮色 非背景黑色 16 | 4 将边缘检测结果转换为黑色边缘 被色背景的图像 17 | 5 原图像边缘变黑 18 | """ 19 | 20 | 21 | def strokeEdge(src, dst, blurKsize=7, edgeKsize=5): 22 | if blurKsize >= 3: 23 | # 算数平均卷积消除噪声 24 | blurredSrc = cv2.medianBlur(src, blurKsize) 25 | # 转为灰度图像 26 | graySrc = cv2.cvtColor(blurredSrc, cv2.COLOR_BGR2GRAY) 27 | else: 28 | # 如果卷积核太小，就直接转为灰度图像 29 | graySrc = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY) 30 | # Laplacian边缘检测 会将非边缘变成黑色，边缘变成其他颜色 31 | cv2.Laplacian(graySrc, cv2.CV_8U, graySrc, ksize=edgeKsize) 32 | normalizedInverseAlpha = (1.0 / 255) * (255 - graySrc) 33 | channels = cv2.split(src) 34 | for channel in channels: 35 | channel[:] = channel * normalizedInverseAlpha 36 | cv2.merge(channels, dst) 37 | 38 | 39 | # 一般的卷积滤波器 40 | class VConvolutionFilter(object): 41 | def __init__(self, kernel): 42 | self._kernel = kernel 43 | 44 | def apply(self, src, dst): 45 | # 做卷积运算， -1表示和输入图像的深度是一样的 46 | cv2.filter2D(src, -1, self._kernel, dst) 47 | 48 | 49 | # 锐化滤波器 50 | class SharpenFilter(VConvolutionFilter): 51 | def __init__(self): 52 | kernel = numpy.array([ 53 | [-1, -1, -1], 54 | [-1, 9, -1], 55 | [-1, -1, -1] 56 | ]) 57 | VConvolutionFilter.__init__(self, kernel) 58 | 59 | 60 | # 检测边缘滤波器 边缘为白色 非边缘为黑色 61 | class FindEdgesFilter(VConvolutionFilter): 62 | def __init__(self): 63 | kernel = numpy.array([ 64 | [-1, -1, -1], 65 | [-1, 8, -1], 66 | [-1, -1, -1] 67 | ]) 68 | VConvolutionFilter.__init__(self, kernel) 69 | 70 | 71 | # 模糊滤波器 72 | class BlurFilter(VConvolutionFilter): 73 | def __init__(self): 74 | kernel = numpy.array([ 75 | [0.04, 0.04, 0.04, 0.04, 0.04], 76 | [0.04, 0.04, 0.04, 0.04, 0.04], 77 | [0.04, 0.04, 0.04, 0.04, 0.04], 78 | [0.04, 0.04, 0.04, 0.04, 0.04], 79 | [0.04, 0.04, 0.04, 0.04, 0.04], 80 | ]) 81 | VConvolutionFilter.__init__(self, kernel) 82 | 83 | 84 | # 浮雕效果滤波器 85 | class EmbossFilter(VConvolutionFilter): 86 | def __init__(self): 87 | kernel = numpy.array([ 88 | [-2, -1, 0], 89 | [-1, 1, 1], 90 | [0, 1, 2] 91 | ]) 92 | VConvolutionFilter.__init__(self, kernel) 93 | 94 | 95 | """ 96 | 原书第三版 缺少部分 97 | """ 98 | 99 | 100 | class BGRFuncFilter(object): 101 | 102 | def __init__(self, vFunc=None, bFunc=None, gFunc=None, rFunc=None, dtype=numpy.uint8): 103 | 104 | length = numpy.iinfo(dtype).max + 1 105 | self._bLookupArray = utils.createLookupArray(utils.createCompositeFunc(bFunc, vFunc), length) 106 | self._gLookupArray = utils.createLookupArray(utils.createCompositeFunc(gFunc, vFunc), length) 107 | self._rLookupArray = utils.createLookupArray(utils.createCompositeFunc(rFunc, vFunc), length) 108 | 109 | def apply(self, src, dst) : 110 | 111 | """Apply the filter with a BGR source/destination.""" 112 | b, g, r = cv2.split(src) 113 | utils.applyLookupArray(self._bLookupArray, b, b) 114 | utils.applyLookupArray(self._gLookupArray, g, g) 115 | utils.applyLookupArray(self._rLookupArray, r, r) 116 | cv2.merge([ b, g, r ], dst) 117 | 118 | 119 | class BGRCurveFilter(BGRFuncFilter): 120 | 121 | def __init__(self, vPoints = None, bPoints = None,gPoints = None, rPoints = None, dtype = numpy.uint8): 122 | BGRFuncFilter.__init__(self, utils.createCurveFunc(vPoints), utils.createCurveFunc(bPoints), utils.createCurveFunc(gPoints), utils.createCurveFunc(rPoints), dtype) 123 | 124 | 125 | class BGRPortraCurveFilter(BGRCurveFilter): 126 | def __init__(self, dtype = numpy.uint8): 127 | BGRCurveFilter.__init__( 128 | self, 129 | vPoints = [ (0, 0), (23, 20), (157, 173), (255, 255) ], 130 | bPoints = [ (0, 0), (41, 46), (231, 228), (255, 255) ], 131 | gPoints = [ (0, 0), (52, 47), (189, 196), (255, 255) ], 132 | rPoints = [ (0, 0), (69, 69), (213, 218), (255, 255) ], 133 | dtype = dtype) -------------------------------------------------------------------------------- /02_Cameo项目(人脸跟踪和图像处理)/filters.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/linyi0604/Computer-Vision/e2ec411bcf78b1d1bc353b721cabe250b1b28778/02_Cameo项目(人脸跟踪和图像处理)/filters.pyc -------------------------------------------------------------------------------- /02_Cameo项目(人脸跟踪和图像处理)/managers.py: -------------------------------------------------------------------------------- 1 | # coding:utf8 2 | 3 | """ 4 | 对CaptureManager的实现 5 | """ 6 | 7 | import cv2 8 | import time 9 | import numpy 10 | 11 | 12 | class CaptureManager(object): 13 | 14 | def __init__(self, capture, 15 | previewWindowManager=None, 16 | shouldMirrorPreview=False): 17 | """ 18 | :param capture: 19 | :param previewWindowManager: 20 | :param shouldMirrorPreview: 设置显示的图像水平镜像翻转 21 | """ 22 | self.previewWindowManager = previewWindowManager 23 | self.shouldMirrorPreview = shouldMirrorPreview 24 | 25 | self._capture = capture 26 | self._chanel = 0 27 | self._enteredFrame = False 28 | self._frame = None 29 | self._imageFilename = None 30 | self._videoFilename = None 31 | self._videoEncoding = None 32 | self._videoWriter = None 33 | self._startTime = None 34 | self._framesElapsed = long(0) 35 | self._fpsEstimate = None 36 | 37 | @property 38 | def channel(self): 39 | return self._chanel 40 | 41 | @channel.setter 42 | def channel(self, value): 43 | if self._chanel != value: 44 | self._chanel = value 45 | self._frame = None 46 | 47 | @property 48 | def frame(self): 49 | if self._enteredFrame and self._frame is None: 50 | _, self._frame = self._capture.retrieve() 51 | return self._frame 52 | 53 | @property 54 | def isWritingImage(self): 55 | 56 | return self._imageFilename is not None 57 | 58 | @property 59 | def isWritingVideo(self): 60 | return self._videoFilename is not None 61 | 62 | def enterFrame(self): 63 | """下一帧""" 64 | assert not self._enteredFrame, \ 65 | "previous enterFrame() had no matching exitFrame()" 66 | if self._capture is not None: 67 | self._enteredFrame = self._capture.grab() 68 | 69 | def exitFrame(self): 70 | """写到窗口，写入文件，释放帧""" 71 | # 检查是否能获取下一帧 72 | if self.frame is None: 73 | self._enteredFrame = False 74 | return 75 | 76 | # 更新fps 和 参数 77 | if self._framesElapsed == 0: 78 | self._startTime = time.time() 79 | else: 80 | timeElapsed = time.time() - self._startTime 81 | self._fpsEstimate = self._framesElapsed / timeElapsed 82 | self._framesElapsed += 1 83 | 84 | # 画到窗口 85 | if self.previewWindowManager is not None: 86 | if self.shouldMirrorPreview: 87 | mirroredFrame = numpy.fliplr(self._frame).copy() 88 | self.previewWindowManager.show(mirroredFrame) 89 | else: 90 | self.previewWindowManager.show(self._frame) 91 | # 写到文件 92 | if self.isWritingImage: 93 | cv2.imwrite(self._imageFilename, self._frame) 94 | self._imageFilename = None 95 | 96 | # 写到视频文件 97 | self._writeVideoFrame() 98 | self._frame = None 99 | self._enteredFrame = False 100 | 101 | def writeImage(self, filename): 102 | self._imageFilename = filename 103 | 104 | def startWritingVideo(self, 105 | filename, 106 | encoding=cv2.VideoWriter_fourcc("I","4","2", "0")): 107 | self._videoFilename = filename 108 | self._videoEncoding = encoding 109 | 110 | def stopWritingVideo(self): 111 | self._videoFilename = None 112 | self._videoEncoding = None 113 | self._videoWriter = None 114 | 115 | def _writeVideoFrame(self): 116 | 117 | if not self.isWritingVideo: 118 | return 119 | 120 | if self._videoWriter is None: 121 | fps = self._capture.get(cv2.CAP_PROP_FPS) 122 | if fps == 0.0: 123 | if self._framesElapsed < 20: 124 | return 125 | else: 126 | fps = self._fpsEstimate 127 | size = ( 128 | int(self._capture.get(cv2.CAP_PROP_FRAME_WIDTH)), 129 | int(self._capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) 130 | ) 131 | self._videoWriter = cv2.VideoWriter( 132 | self._videoFilename, self._videoEncoding, 133 | fps, size 134 | ) 135 | self._videoWriter.write(self._frame) 136 | 137 | 138 | class WindowManger(object): 139 | 140 | def __init__(self, 141 | windowName, 142 | keypressCallback=None): 143 | self.keypressCallback = keypressCallback 144 | self._windowName = windowName 145 | self._isWindowCreated = False 146 | 147 | 148 | @property 149 | def isWindowCreated(self): 150 | return self._isWindowCreated 151 | 152 | def createWindow(self): 153 | cv2.namedWindow(self._windowName) 154 | self._isWindowCreated = True 155 | 156 | def show(self, frame): 157 | cv2.imshow(self._windowName, frame) 158 | 159 | def destroyWindow(self): 160 | cv2.destroyWindow(self._windowName) 161 | self._isWindowCreated = False 162 | 163 | def processEvents(self): 164 | keycode = cv2.waitKey(1) 165 | if self.keypressCallback is not None and keycode != -1: 166 | keycode &= 0xFF 167 | self.keypressCallback(keycode) -------------------------------------------------------------------------------- /02_Cameo项目(人脸跟踪和图像处理)/managers.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/linyi0604/Computer-Vision/e2ec411bcf78b1d1bc353b721cabe250b1b28778/02_Cameo项目(人脸跟踪和图像处理)/managers.pyc -------------------------------------------------------------------------------- /02_Cameo项目(人脸跟踪和图像处理)/utils.py: -------------------------------------------------------------------------------- 1 | # coding:utf8 2 | 3 | import cv2 4 | import numpy 5 | import scipy.interpolate 6 | 7 | 8 | def createCurveFunc(points): 9 | """Return a function derived from control points.""" 10 | if points is None: 11 | return None 12 | num_points = len(points) 13 | if num_points < 2: 14 | return None 15 | xs, ys = zip(*points) 16 | if num_points < 4: 17 | kind = 'linear' 18 | # 'quadratic' is not implemented. 19 | else: 20 | kind = 'cubic' 21 | return scipy.interpolate.interp1d(xs, ys, kind,bounds_error = False) 22 | 23 | 24 | def createLookupArray(func, length = 256): 25 | """Return a lookup for whole-number inputs to a function. The lookup values are clamped to [0, length - 1].""" 26 | if func is None: 27 | return None 28 | lookup_array = numpy.empty(length) 29 | i = 0 30 | while i < length: 31 | func_i = func(i) 32 | lookup_array[i] = min(max(0, func_i), length - 1) 33 | i += 1 34 | return lookup_array 35 | 36 | def applyLookupArray(lookup_array, src, dst): 37 | """Map a source to a destination using a lookup.""" 38 | if lookup_array is None: 39 | return 40 | dst[:] = lookup_array[src] 41 | 42 | 43 | def createCompositeFunc(func0, func1): 44 | """Return a composite of two functions.""" 45 | if func0 is None: 46 | return func1 47 | if func1 is None: 48 | return func0 49 | return lambda x: func0(func1(x)) 50 | 51 | 52 | def createFlatView(array): 53 | """Return a 1D view of an array of any dimensionality.""" 54 | flat_view = array.view() 55 | flat_view.shape = array.size 56 | return flat_view 57 | 58 | -------------------------------------------------------------------------------- /02_Cameo项目(人脸跟踪和图像处理)/utils.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/linyi0604/Computer-Vision/e2ec411bcf78b1d1bc353b721cabe250b1b28778/02_Cameo项目(人脸跟踪和图像处理)/utils.pyc -------------------------------------------------------------------------------- /03_处理图像/001_滤波器.py: -------------------------------------------------------------------------------- 1 | # coding:utf8 2 | 3 | import cv2 4 | import numpy as np 5 | from scipy import ndimage 6 | 7 | # 3*3 的高通卷积核 8 | kernel_3x3 = np.array([ 9 | [-1, -1, -1], 10 | [-1, 8, -1], 11 | [-1, -1, -1] 12 | ]) 13 | 14 | # 5*5 高通卷积核 15 | kernel_5x5 = np.array([ 16 | [-1, -1, -1, -1, -1], 17 | [-1, 1, 2, 1, -1], 18 | [-1, 3, 4, 2, -1], 19 | [-1, 1, 2, 1, -1], 20 | [-1, -1, -1, -1, -1] 21 | ]) 22 | 23 | # 按灰度值读入图像 24 | img = cv2.imread("../data/mm1.jpg", 0) 25 | 26 | 27 | # 进行卷积运算 28 | k3 = ndimage.convolve(img, kernel_3x3) 29 | k5 = ndimage.convolve(img, kernel_5x5) 30 | """ 31 | 高通滤波器： 根据像素与临近像素的亮度差值来提升像素的亮度 32 | """ 33 | 34 | 35 | # 原图像运用高斯低通滤波器 36 | blurred = cv2.GaussianBlur(img, (11, 11), 0) 37 | """ 38 | 低通滤波器： 像素周围亮度小于一个特定值时候，平滑该像素的亮度，主要用于去噪和模糊化 39 | 高斯滤波器是最常用的模糊滤波器之一，他是一个削弱强度的低通滤波器 40 | """ 41 | # 原图像减去低通 42 | g_hpf = img - blurred 43 | 44 | cv2.imshow("3x3", k3) 45 | cv2.imshow("5x5", k5) 46 | cv2.imshow("g_hpf", g_hpf) 47 | cv2.imshow("origin", img) 48 | cv2.waitKey() 49 | cv2.destroyAllWindows() -------------------------------------------------------------------------------- /03_处理图像/002_canny边缘检测.py: -------------------------------------------------------------------------------- 1 | # coding:utf8 2 | 3 | import cv2 4 | """ 5 | canny边缘检测： 6 | 有五个步骤： 7 | 1 高斯滤波器降噪 8 | 2 计算梯度 9 | 3 边缘上使用非最大抑制 nms 10 | 4 边缘上使用双阈值去除假阳性 11 | 5 分析所有边缘连接 消除不明显的边缘 12 | """ 13 | 14 | img = cv2.imread("../data/mm2.jpeg", 0) # 按照灰度值读入 15 | canny = cv2.Canny(img, 200, 300) 16 | cv2.imshow("origin", img) 17 | cv2.imshow("canny", canny) 18 | cv2.waitKey() 19 | cv2.destroyAllWindows() 20 | -------------------------------------------------------------------------------- /03_处理图像/003_轮廓检测.py: -------------------------------------------------------------------------------- 1 | # coding:utf8 2 | 3 | import cv2 4 | import numpy as np 5 | 6 | # 创建一个200*200 的黑色空白图像 7 | img = np.zeros((200, 200), dtype=np.uint8) 8 | # 在图像的中央位置 放置一个100*100的白色方块 9 | img[50:150, 50: 150] = 255 10 | 11 | cv2.imshow("image", img) 12 | # 二值化操作 13 | ret, thresh = cv2.threshold(img, 127, 255, 0) 14 | """ 15 | ret, dst = cv2.threshold(src, thresh, value, type) 16 | 参数: 17 | src: 原图像 18 | thresh: 阈值 19 | value: 新值 大于或小于阈值的值将赋新值 20 | type: 方法类型，有如下取值： 21 | cv2.THRESH_BINARY 黑白二值 22 | cv2.THRESH_BINARY_INV 黑白二值翻转 23 | cv2.THRESH_TRUNC 得到多像素值 24 | cv2.THRESH_TOZERO 25 | cv2.THRESH_TOZERO_INV 26 | 返回值： 27 | ret: 得到的阈值值 28 | dst: 阈值化后的图像 29 | """ 30 | 31 | # 得到 修改后的图像， 轮廓， 轮廓的层次 32 | image, contours, hierarchy = cv2.findContours( 33 | thresh, 34 | cv2.RETR_TREE, 35 | cv2.CHAIN_APPROX_SIMPLE 36 | ) 37 | 38 | """ 39 | cv.findContours() 40 | 参数： 41 | 1 要寻找轮廓的图像 只能传入二值图像，不是灰度图像 42 | 2 轮廓的检索模式，有四种： 43 | cv2.RETR_EXTERNAL表示只检测外轮廓 44 | cv2.RETR_LIST检测的轮廓不建立等级关系 45 | cv2.RETR_CCOMP建立两个等级的轮廓，上面的一层为外边界， 46 | 里面的一层为内孔的边界信息。 47 | 如果内孔内还有一个连通物体，这个物体的边界也在顶层 48 | cv2.RETR_TREE建立一个等级树结构的轮廓 49 | 3 轮廓的近似办法 50 | cv2.CHAIN_APPROX_NONE存储所有的轮廓点， 51 | 相邻的两个点的像素位置差不超过1， 52 | 即max（abs（x1-x2），abs（y2-y1））==1 53 | cv2.CHAIN_APPROX_SIMPLE压缩水平方向，垂直方向，对角线方向的元素， 54 | 只保留该方向的终点坐标，例如一个矩形轮廓只需4个点来保存轮廓信息 55 | 返回值: 56 | contours:一个列表，每一项都是一个轮廓， 不会存储轮廓所有的点，只存储能描述轮廓的点 57 | hierarchy:一个ndarray, 元素数量和轮廓数量一样， 58 | 每个轮廓contours[i]对应4个hierarchy元素hierarchy[i][0] ~hierarchy[i][3]， 59 | 分别表示后一个轮廓、前一个轮廓、父轮廓、内嵌轮廓的索引编号，如果没有对应项，则该值为负数 60 | """ 61 | # 原图像转换成bgr图像 62 | color = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) 63 | # 用绿色 在原图像上画出轮廓 64 | img = cv2.drawContours(color, contours, -1, (0, 255, 255), 2) 65 | 66 | cv2.imshow("contours", color) 67 | cv2.waitKey() 68 | cv2.destroyAllWindows() -------------------------------------------------------------------------------- /03_处理图像/004_矩形、圆形边界框.py: -------------------------------------------------------------------------------- 1 | # coding:utf8 2 | 3 | import cv2 4 | import numpy as np 5 | # 读入图像 6 | img = cv2.imread("../data/mm1.jpg", cv2.IMREAD_UNCHANGED) 7 | # 转化为分别率更低的图像 8 | img = cv2.pyrDown(img) 9 | 10 | # 二值化， 黑白二值化 11 | ret, thresh = cv2.threshold( 12 | cv2.cvtColor(img.copy(), cv2.COLOR_BGR2GRAY), # 转换为灰度图像, 13 | 127, 255, # 大于127的改为255 否则改为0 14 | cv2.THRESH_BINARY) # 黑白二值化 15 | """ 16 | ret, dst = cv2.threshold(src, thresh, value, type) 17 | 参数: 18 | src: 原图像 19 | thresh: 阈值 20 | value: 新值 大于或小于阈值的值将赋新值 21 | type: 方法类型，有如下取值： 22 | cv2.THRESH_BINARY 黑白二值 23 | cv2.THRESH_BINARY_INV 黑白二值翻转 24 | cv2.THRESH_TRUNC 得到多像素值 25 | cv2.THRESH_TOZERO 26 | cv2.THRESH_TOZERO_INV 27 | 返回值： 28 | ret: 得到的阈值值 29 | dst: 阈值化后的图像 30 | """ 31 | 32 | # 搜索轮廓 33 | image, contours, hierarchy = cv2.findContours( 34 | thresh, 35 | cv2.RETR_EXTERNAL, 36 | cv2.CHAIN_APPROX_SIMPLE) 37 | """ 38 | cv.findContours() 39 | 参数： 40 | 1 要寻找轮廓的图像 只能传入二值图像，不是灰度图像 41 | 2 轮廓的检索模式，有四种： 42 | cv2.RETR_EXTERNAL表示只检测外轮廓 43 | cv2.RETR_LIST检测的轮廓不建立等级关系 44 | cv2.RETR_CCOMP建立两个等级的轮廓，上面的一层为外边界， 45 | 里面的一层为内孔的边界信息。 46 | 如果内孔内还有一个连通物体，这个物体的边界也在顶层 47 | cv2.RETR_TREE建立一个等级树结构的轮廓 48 | 3 轮廓的近似办法 49 | cv2.CHAIN_APPROX_NONE存储所有的轮廓点， 50 | 相邻的两个点的像素位置差不超过1， 51 | 即max（abs（x1-x2），abs（y2-y1））==1 52 | cv2.CHAIN_APPROX_SIMPLE压缩水平方向，垂直方向，对角线方向的元素， 53 | 只保留该方向的终点坐标，例如一个矩形轮廓只需4个点来保存轮廓信息 54 | 返回值: 55 | contours:一个列表，每一项都是一个轮廓， 不会存储轮廓所有的点，只存储能描述轮廓的点 56 | hierarchy:一个ndarray, 元素数量和轮廓数量一样， 57 | 每个轮廓contours[i]对应4个hierarchy元素hierarchy[i][0] ~hierarchy[i][3]， 58 | 分别表示后一个轮廓、前一个轮廓、父轮廓、内嵌轮廓的索引编号，如果没有对应项，则该值为负数 59 | """ 60 | 61 | for c in contours: 62 | 63 | x, y, w, h = cv2.boundingRect(c) 64 | """ 65 | 传入一个轮廓图像，返回 x y 是左上角的点， w和h是矩形边框的宽度和高度 66 | """ 67 | cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2) 68 | """ 69 | 画出矩形 70 | img 是要画出轮廓的原图 71 | (x, y) 是左上角点的坐标 72 | (x+w, y+h) 是右下角的坐标 73 | 0,255,0）是画线对应的rgb颜色 74 | 2 是画出线的宽度 75 | """ 76 | 77 | # 获得最小的矩形轮廓 可能带旋转角度 78 | rect = cv2.minAreaRect(c) 79 | # 计算最小区域的坐标 80 | box = cv2.boxPoints(rect) 81 | # 坐标规范化为整数 82 | box = np.int0(box) 83 | # 画出轮廓 84 | cv2.drawContours(img, [box], 0, (0, 0, 255), 3) 85 | 86 | # 计算最小封闭圆形的中心和半径 87 | (x, y), radius = cv2.minEnclosingCircle(c) 88 | # 转换成整数 89 | center = (int(x), int(y)) 90 | radius = int(radius) 91 | # 画出圆形 92 | img = cv2.circle(img, center, radius, (0, 255, 0), 2) 93 | 94 | # 画出轮廓 95 | cv2.drawContours(img, contours, -1, (255, 0, 0), 1) 96 | cv2.imshow("contours", img) 97 | cv2.waitKey() 98 | cv2.destroyAllWindows() 99 | -------------------------------------------------------------------------------- /03_处理图像/005_凸轮廓与douglas-peucher算法.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/linyi0604/Computer-Vision/e2ec411bcf78b1d1bc353b721cabe250b1b28778/03_处理图像/005_凸轮廓与douglas-peucher算法.py -------------------------------------------------------------------------------- /03_处理图像/006_直线检测.py: -------------------------------------------------------------------------------- 1 | # coding:utf8 2 | 3 | import cv2 4 | import numpy as np 5 | 6 | 7 | # 读入图像 8 | img = cv2.imread("../data/line1.png") 9 | # 转为灰度图像 10 | gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 11 | # Canny边缘检测 12 | edges = cv2.Canny(gray, 50, 100) 13 | """ 14 | canny边缘检测： 15 | 有五个步骤： 16 | 1 高斯滤波器降噪 17 | 2 计算梯度 18 | 3 边缘上使用非最大抑制 nms 19 | 4 边缘上使用双阈值去除假阳性 20 | 5 分析所有边缘连接 消除不明显的边缘 21 | """ 22 | 23 | minLineLength = 20 24 | maxLineGap = 5 25 | lines = cv2.HoughLinesP(edges, 1, np.pi/180, 100, minLineLength, maxLineGap) 26 | """ 27 | cv2.HoughLinesP 28 | 作用：标准霍夫线变换， 找到图像中的所有直线 29 | 参数： 30 | 1 二值图 31 | 2 半径精度 32 | 3 角度精度 33 | 4 最短检测长度 34 | 5 允许的最大缺口 35 | 返回： 36 | 一个列表，每一项是一个四元组，分别是直线两个端点的坐标 37 | """ 38 | for line in lines: 39 | for x1, y1, x2, y2 in line: 40 | # 在图片上画直线 41 | cv2.line(img, (x1, y1), (x2, y2), (0, 255, 0), 2) 42 | 43 | cv2.imshow("edges", edges) 44 | cv2.imshow("lines", img) 45 | cv2.waitKey() 46 | cv2.destroyAllWindows() -------------------------------------------------------------------------------- /03_处理图像/007_圆检测.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | 4 | img_origin = cv2.imread("../data/circle.jpg") 5 | img_gray = cv2.cvtColor(img_origin, cv2.COLOR_BGR2GRAY) 6 | # 低同滤波进行平滑图像 7 | img = cv2.medianBlur(img_gray, 5) 8 | cimg = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) 9 | 10 | circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1, 120, 11 | param1=100,param2=30, 12 | minRadius=0, maxRadius=0) 13 | """ 14 | cv2.HoughCircles(image, method, dp, 15 | minDist, circles, param1, param2, 16 | minRadius, maxRadius) 17 | 参数： 18 | image:　输入图像　必须是灰度图像 19 | method:检测方法,常用CV_HOUGH_GRADIENT 20 | dp:检测内侧圆心的累加器图像的分辨率于输入图像之比的倒数， 21 | 如dp=1，累加器和输入图像具有相同的分辨率，如果dp=2， 22 | 累计器便有输入图像一半那么大的宽度和高度 23 | minDist: 两个圆心之间的最小距离 24 | param1: 默认100, 是method方法的参数 25 | 在CV_HOUGH_GRADIENT表示传入canny边缘检测的阈值 26 | param2： 默认100,method的参数， 27 | 对当前唯一的方法霍夫梯度法cv2.HOUGH_GRADIENT， 28 | 它表示在检测阶段圆心的累加器阈值， 29 | 它越小，就越可以检测到更多根本不存在的圆， 30 | 而它越大的话，能通过检测的圆就更加接近完美的圆形了 31 | minRadius:默认值0，圆半径的最小值 32 | maxRadius:默认值0，圆半径的最大值 33 | 返回值： 34 | 35 | """ 36 | # 整数化 37 | circles = np.uint16(np.around(circles)) 38 | 39 | for i in circles[0, :]: 40 | # 画出外边圆 41 | cv2.circle(img_origin, (i[0], i[1]), i[2], (0, 255, 0), 2) 42 | # 画出圆心 43 | cv2.circle(img_origin, (i[0], i[1]), 2, (0, 0, 255), 3) 44 | 45 | 46 | cv2.imshow("", img_origin) 47 | cv2.waitKey() 48 | cv2.destroyAllWindows() -------------------------------------------------------------------------------- /03_处理图像/008_在图像上写字.py: -------------------------------------------------------------------------------- 1 | # coding:utf-8 2 | 3 | import cv2 4 | 5 | img = cv2.imread("../data/mm1.jpg", cv2.IMREAD_GRAYSCALE) 6 | 7 | img = cv2.putText(img, "hello world!", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 255, 0), 2) 8 | """ 9 | cv2.putText(图像, 文字, (x, y), 字体, 大小, (b, g, r), 宽度) 10 | """ 11 | 12 | cv2.imshow("", img) 13 | cv2.waitKey() 14 | cv2.destroyAllWindows() 15 | 16 | -------------------------------------------------------------------------------- /04_深度估计与分割/001_普通摄像头进行深度估计.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/linyi0604/Computer-Vision/e2ec411bcf78b1d1bc353b721cabe250b1b28778/04_深度估计与分割/001_普通摄像头进行深度估计.py -------------------------------------------------------------------------------- /04_深度估计与分割/002_grabcut前景检测.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2 3 | import matplotlib.pyplot as plt 4 | 5 | # 读入图片 6 | img = cv2.imread("../data/mm2.jpeg") 7 | # 创建一个和加载图像一样形状的 填充为0的掩膜 8 | mask = np.zeros(img.shape[:2], np.uint8) 9 | 10 | # 创建以0填充的前景和背景模型 11 | bgdModel = np.zeros((1, 65), np.float64) 12 | fgdModel = np.zeros((1, 65), np.float64) 13 | 14 | # 定义一个矩形 15 | rect = (100, 50, 421, 378) 16 | 17 | cv2.grabCut(img, mask, rect, bgdModel, fgdModel, 5, cv2.GC_INIT_WITH_RECT) 18 | 19 | """ 20 | cv2.grabCut() 21 | 参数： 22 | img: 输入图像 23 | mask: 蒙板图像，确定前景区域，背景区域，不确定区域， 24 | 可以设置为cv2.GC_BGD,cv2.GC_FGD,cv2.GC_PR_BGD,cv2.GC_PR_FGD， 25 | 也可以输入0,1,2,3 26 | rect: 包含前景的矩形 格式(x, y, w, h) 27 | bdgModel: 算法内部使用的数组. 你只需要创建两个大小为 (1,65)，数据类型为 np.float64 的数组 28 | fgdModel: 算法内部使用的数组. 你只需要创建两个大小为 (1,65)，数据类型为 np.float64 的数组 29 | iterCount: 算法的迭代次数 30 | mode: 可以设置为 cv2.GC_INIT_WITH_RECT 或 cv2.GC_INIT_WITH_MASK，也可以联合使用。 31 | 这是用来确定我们进行修改的方式，矩形模式或者掩模模式 32 | 33 | 算法会修改掩模图像，在新的掩模图像中， 34 | 所有的像素被分为四类：背景，前景，可能是背景/前景使用 4 个不同的标签标记。 35 | 然后我们来修改掩模图像， 36 | 所有的 0 像素和 1 像素都被归为 0（例如背景），所有的 1 像素和 3 像素都被归为 1（前景）。 37 | 我们最终的掩模图像就这样准备好了。用它和输入图像相乘就得到了分割好的图像 38 | 39 | 原理： 40 | 1 输入矩形框，矩形框外部区域都是背景。内部一定包含前景。 41 | 2 电脑对输入图像进行初始化，标记前景和背景的像素。 42 | 3 使用高斯混合模型（GMM）对前景和背景建模。 43 | 4 根据输入，GMM会学习并创建新的像素分布。 44 | 对未知的像素（前景或背景不确定），根据他们与已知的分类像素关系进行分类。（类似聚类操作） 45 | 5 这样会根据像素的分布创建一幅图，图中节点是像素。 46 | 除了像素点是节点以外，还有Source_node和Sink_node两个节点。 47 | 所有的前景图像都与Source_node相连。背景与Sink_node相连。 48 | 6 像素是否连接到Source_node/end_node依赖于权值， 49 | 这个权值由像素属于同一类，也就是前景或者背景的概率来决定。 50 | 如果像素的颜色有很大区别，那么他们之间的权重就很小。 51 | 7 使用mincut算法对图像进行分割。 52 | 它会根据最小代价方程对图像分成source_node和sink_node。 53 | 代价方程是指裁剪所有边上权重的和。 54 | 裁剪完成后，所有连接到source_node的判定为前景，sink_node上的为背景。 55 | 继续此过程，直到分类收敛。 56 | 57 | 58 | 59 | """ 60 | mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype("uint8") 61 | img = img*mask2[:, :, np.newaxis] 62 | 63 | plt.subplot(121), plt.imshow(img) 64 | plt.title("grabcut"), plt.xticks([]), plt.yticks([]) 65 | plt.subplot(122), plt.imshow(cv2.cvtColor(cv2.imread("../data/mm2.jpeg"), cv2.COLOR_BGR2RGB)) 66 | plt.title("original"), plt.xticks([]), plt.yticks([]) 67 | plt.show() 68 | -------------------------------------------------------------------------------- /04_深度估计与分割/003_分水岭算法图像分割.py: -------------------------------------------------------------------------------- 1 | # coding:utf8 2 | 3 | import numpy as np 4 | import cv2 5 | from matplotlib import pyplot as plt 6 | 7 | """ 8 | 分水岭算法： 9 | 10 | """ 11 | 12 | # 读入图像 13 | img = cv2.imread("../data/item2.jpg") 14 | # 转成灰度图像 15 | gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 16 | # 黑白二值化 17 | ret, thresh = cv2.threshold( 18 | gray, 19 | 0, 255, 20 | cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU 21 | ) 22 | 23 | # 使用morphologyEx变换来去除噪声， 对图像先膨胀再腐蚀 24 | kernel = np.ones((3, 3), np.uint8) 25 | opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2) 26 | # morphologyEx变换后的图像进行膨胀，可以得到大部分都是背景的区域 27 | sure_bg = cv2.dilate(opening, kernel, iterations=3) 28 | # 通过distanceTransform来确定前景区域 结合阈值来决定哪些是前景 29 | dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 5) 30 | 31 | ret, sure_fg = cv2.threshold(dist_transform, 0.7*dist_transform.max(), 255, 0) 32 | 33 | # 用sure_bg 和 sure_fg 相减确定前景和背景重合区域 34 | sure_fg = np.uint8(sure_fg) 35 | unkown = cv2.subtract(sure_bg, sure_fg) 36 | 37 | # 定义栅栏 分割不同的山谷 38 | ret, markers = cv2.connectedComponents(sure_fg) 39 | # 背景区域加上1， unkown区域设为0 40 | markers = markers + 1 41 | markers[unkown == 255] = 0 42 | 43 | # 打开栅栏 把栅栏设置成红色 44 | markers = cv2.watershed(img, markers) 45 | img[markers == -1] = [255, 0, 0] 46 | plt.imshow(img) 47 | plt.show() 48 | -------------------------------------------------------------------------------- /04_深度估计与分割/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/linyi0604/Computer-Vision/e2ec411bcf78b1d1bc353b721cabe250b1b28778/04_深度估计与分割/__init__.py -------------------------------------------------------------------------------- /05_人脸检测和识别/001_静态图像人脸检测.py: -------------------------------------------------------------------------------- 1 | # coding:utf-8 2 | 3 | import cv2 4 | 5 | filename = "../data/mm3.jpg" 6 | 7 | 8 | def detect(filename): 9 | # 创建检测人脸的对象 10 | face_cascade = cv2.CascadeClassifier("../data/haarcascade_frontalface_default.xml") 11 | # 读取图像 12 | img = cv2.imread(filename) 13 | # 转为灰度图像 14 | gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 15 | # 进行人脸检测 16 | faces = face_cascade.detectMultiScale(gray, 1.3, 5) 17 | """ 18 | faces = face_cascade.detectMultiScale(img, scaleFactor, minNeighbors) 19 | 参数： 20 | img: 识别的原图 21 | scaleFactor： 迭代时图像的压缩率 22 | minNeighbors: 每个人脸矩形保留近邻数目的最小值 23 | 24 | 返回值： 25 | 一个列表，列表里边每一项是一个框起人脸的矩形(x, y, w, h) 26 | 27 | """ 28 | print(faces) 29 | for (x, y, w, h) in faces: 30 | # 画出矩形框 31 | img = cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2) 32 | 33 | cv2.imshow("Vikings Detected", img) 34 | cv2.waitKey() 35 | 36 | 37 | detect(filename) 38 | -------------------------------------------------------------------------------- /05_人脸检测和识别/002_视频中检测人脸.py: -------------------------------------------------------------------------------- 1 | # coding:utf8 2 | 3 | import cv2 4 | 5 | 6 | def detect(): 7 | # 创建人脸检测的对象 8 | face_cascade = cv2.CascadeClassifier("../data/haarcascade_frontalface_default.xml") 9 | # 创建眼睛检测的对象 10 | eye_cascade = cv2.CascadeClassifier("../data/haarcascade_eye.xml") 11 | # 连接摄像头的对象 0表示摄像头的编号 12 | camera = cv2.VideoCapture(0) 13 | 14 | while True: 15 | # 读取当前帧 16 | ret, frame = camera.read() 17 | # 转为灰度图像 18 | gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) 19 | # 检测人脸 返回列表 每个元素都是(x, y, w, h)表示矩形的左上角和宽高 20 | faces = face_cascade.detectMultiScale(gray, 1.3, 5) 21 | # 画出人脸的矩形 22 | for (x, y, w, h) in faces: 23 | # 画矩形 在frame图片上画， 传入左上角和右下角坐标 矩形颜色 和线条宽度 24 | img = cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2) 25 | # 把脸单独拿出来 26 | roi_gray = gray[y: y+h, x: x+w] 27 | # 在脸上检测眼睛 (40, 40)是设置最小尺寸，再小的部分会不检测 28 | eyes = eye_cascade.detectMultiScale(roi_gray, 1.03, 5, 0, (40, 40)) 29 | # 把眼睛画出来 30 | for(ex, ey, ew, eh) in eyes: 31 | cv2.rectangle(img, (x+ex, y+ey), (x+ex+ew, y+ey+eh), (0, 255, 0), 2) 32 | 33 | cv2.imshow("camera", frame) 34 | if cv2.waitKey(5) & 0xff == ord("q"): 35 | break 36 | 37 | camera.release() 38 | cv2.destroyAllWindows() 39 | 40 | 41 | if __name__ == '__main__': 42 | detect() 43 | -------------------------------------------------------------------------------- /05_人脸检测和识别/003_人脸识别.py: -------------------------------------------------------------------------------- 1 | # coding:utf-8 2 | 3 | import cv2 4 | import os 5 | import numpy as np 6 | 7 | 8 | # 1 生成人脸识别数据 9 | # 图像是灰度格式，后缀名.pgm 10 | # 图像是正方形 图像大小要一样 在这里使用200*200 11 | def generate(): 12 | # 加载检测图像中人脸位置的对象， xml文件需要去opencv文件夹里面找， 放到项目里面来引入 13 | face_cascade = cv2.CascadeClassifier("../data/haarcascade_frontalface_default.xml") 14 | # 调用本机摄像头 15 | camera = cv2.VideoCapture(0) 16 | count = 0 17 | # 读取摄像头 18 | while True: 19 | # 读入 帧 20 | ret, frame = camera.read() 21 | # 变为灰度图像 22 | gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) 23 | # 检测人脸位置 24 | faces = face_cascade.detectMultiScale(gray, 1.3, 5) 25 | # 将图片中人脸位置单独拿出来改变成200*200大小的图片 存入本地 作为数据集 26 | for (x, y, w, h) in faces: 27 | img = cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2) 28 | f = cv2.resize(gray[y:y+h, x:x+w], (200, 200)) 29 | cv2.imwrite("./data/%s.pgm" % str(count), f) 30 | count += 1 31 | 32 | cv2.imshow("camera", frame) 33 | # 如果按键q就退出 否则等50毫秒 34 | if cv2.waitKey(50) & 0xff == ord("q"): 35 | break 36 | 37 | camera.release() 38 | cv2.destroyAllWindows() 39 | 40 | 41 | # 读取生成好的数据 在我项目目录下整理好的 42 | def readImages(): 43 | x, y = [], [] 44 | path = "./data/faces/" 45 | image_file = os.listdir(path) 46 | image_files = [path + i for i in image_file] 47 | for file in image_files: 48 | images = os.listdir(file) 49 | label = file.split("/")[-1][1:] 50 | for i in images: 51 | img = cv2.imread(file + "/" + i, cv2.IMREAD_GRAYSCALE) 52 | img = cv2.resize(img, (200, 200)) 53 | x.append(np.asarray(img, dtype=np.uint8)) 54 | y.append(int(label)) 55 | 56 | y = np.asarray(y, dtype=np.int32) 57 | return x, y 58 | 59 | 60 | # 检测人脸 61 | def face_rec(): 62 | # 获取数据 63 | x, y = readImages() 64 | 65 | # 人脸识别的模型 66 | model = cv2.face.EigenFaceRecognizer_create() 67 | # fisherfaces算法的模型 68 | # model = cv2.face.FisherFaceRecognizer_create() 69 | # LBPH算法的模型 70 | # model = cv2.face.LBPHFaceRecognizer_create() 71 | """ 72 | Eigenfaces和Fisherfaces 预测时候产生0到20000的评分 73 | 低于4000 5000 的评分都是相当可靠的 74 | LBPH 产生评分不同，低于50是可靠的 高于80是不可靠的 75 | """ 76 | 77 | # 训练模型 78 | model.train(np.asarray(x), np.asarray(y)) 79 | 80 | # 开摄像头 81 | camera = cv2.VideoCapture(0) 82 | # 加载检测人脸对象 83 | face_cascade = cv2.CascadeClassifier("../data/haarcascade_frontalface_default.xml") 84 | while True: 85 | # 读取当前帧 86 | read, img = camera.read() 87 | # 当前帧下检测人脸 88 | faces = face_cascade.detectMultiScale(img, 1.3, 5) 89 | for (x, y, w, h) in faces: 90 | # 画出人脸 91 | img 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-------------------------------------------------------------------------------- 1 | # coding:utf-8 2 | 3 | import cv2 4 | import numpy as np 5 | 6 | img = cv2.imread("../data/chess1.jpg") 7 | gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 8 | gray = np.float32(gray) 9 | # 角点检测 10 | dst = cv2.cornerHarris(gray, 2, 23, 0.04) 11 | """ 12 | 角点也是处在一个无论框框往哪边移动　框框内像素值都会变化很大的情况而定下来的点 13 | cv2.cornerHarris() 14 | img - 数据类型为 float32 的输入图像。 15 | blockSize - 角点检测中要考虑的领域大小。 16 | ksize - Sobel 求导中使用的窗口大小 17 | k - Harris 角点检测方程中的自由参数,取值参数为 [0,04,0.06]. 18 | """ 19 | 20 | # 将检测到角点的位置标记为红色 21 | img[dst > 0.01 * dst.max()] = (0, 0, 255) 22 | 23 | cv2.imshow("corners", img) 24 | cv2.waitKey() 25 | cv2.destroyAllWindows() -------------------------------------------------------------------------------- /06_图像检测和搜索/002_DoG和SIFT特征提取与描述.py: -------------------------------------------------------------------------------- 1 | # coding:utf-8 2 | 3 | import cv2 4 | 5 | # 读取图片 6 | img = cv2.imread("../data/walez1.jpg") 7 | # 转为灰度图像 8 | gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 9 | # 创建一个sift对象 并计算灰度图像 10 | sift = cv2.xfeatures2d.SIFT_create() 11 | keypoints, descriptor = sift.detectAndCompute(gray, None) 12 | """ 13 | sift对象会使用DoG检测关键点，对关键点周围的区域计算向量特征，检测并计算 14 | 返回 关键点和描述符 15 | 关键点是点的列表 16 | 描述符是检测到的特征的局部区域图像列表 17 | 18 | 关键点的属性： 19 | pt: 点的x y坐标 20 | size： 表示特征的直径 21 | angle: 特征方向 22 | response: 关键点的强度 23 | octave: 特征所在金字塔层级 24 | 算法进行迭代的时候， 作为参数的图像尺寸和相邻像素会发生变化 25 | octave属性表示检测到关键点所在的层级 26 | ID： 检测到关键点的ID 27 | 28 | """ 29 | # 在图像上绘制关键点 30 | # DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS表示对每个关键点画出圆圈和方向 31 | img = cv2.drawKeypoints(image=img, outImage=img, keypoints=keypoints, 32 | flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS, 33 | color=(51, 163, 236)) 34 | 35 | cv2.imshow("sift_keypoints", img) 36 | cv2.waitKey() 37 | cv2.destroyAllWindows() -------------------------------------------------------------------------------- /06_图像检测和搜索/003_hessian算法surf提取和检测.py: -------------------------------------------------------------------------------- 1 | # coding:utf-8 2 | 3 | import cv2 4 | 5 | img = cv2.imread("../data/walez1.jpg") 6 | gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 7 | 8 | surf = cv2.xfeatures2d.SURF_create(8000) 9 | """ 10 | 创建surf对象，设置阈值，阈值越高检测到的特征就越少， 11 | 通过调整阈值得到合适的关键点 12 | """ 13 | # 检测图像中的关键点和描述 14 | keypoints, descriptor = surf.detectAndCompute(gray, None) 15 | """ 16 | sift对象会使用DoG检测关键点，对关键点周围的区域计算向量特征，检测并计算 17 | 返回 关键点和描述符 18 | 关键点是点的列表 19 | 描述符是检测到的特征的局部区域图像列表 20 | 21 | 关键点的属性： 22 | pt: 点的x y坐标 23 | size： 表示特征的直径 24 | angle: 特征方向 25 | response: 关键点的强度 26 | octave: 特征所在金字塔层级 27 | 算法进行迭代的时候， 作为参数的图像尺寸和相邻像素会发生变化 28 | octave属性表示检测到关键点所在的层级 29 | ID： 检测到关键点的ID 30 | 31 | """ 32 | # 将关键点画在原图像上 33 | cv2.drawKeypoints(image=img, outImage=img, 34 | keypoints=keypoints,flags=4, 35 | color=(51, 163, 236)) 36 | 37 | cv2.imshow("surf_detected", img) 38 | cv2.waitKey() 39 | cv2.destroyAllWindows() 40 | 41 | -------------------------------------------------------------------------------- /06_图像检测和搜索/004_基于ORB的特征检测和BF暴力匹配.py: -------------------------------------------------------------------------------- 1 | # coding:utf-8 2 | 3 | import cv2 4 | 5 | """ 6 | orb特征检测和匹配 7 | 两幅图片分别是 乐队的logo 和包含该logo的专辑封面 8 | 利用orb进行检测后进行匹配两幅图片中的logo 9 | 10 | """ 11 | # 按照灰度图像的方式读入两幅图片 12 | img1 = cv2.imread("../data/logo1.png", cv2.IMREAD_GRAYSCALE) 13 | img2 = cv2.imread("../data/album1.png", cv2.IMREAD_GRAYSCALE) 14 | 15 | # 创建ORB特征检测器和描述符 16 | orb = cv2.ORB_create() 17 | # 对两幅图像检测特征和描述符 18 | keypoint1, descriptor1 = orb.detectAndCompute(img1, None) 19 | keypoint2, descriptor2 = orb.detectAndCompute(img2, None) 20 | """ 21 | keypoint 是一个包含若干点的列表 22 | descriptor 对应每个点的描述符 是一个列表， 每一项都是检测到的特征的局部图像 23 | 24 | 检测的结果是关键点 25 | 计算的结果是描述符 26 | 27 | 可以根据监测点的描述符 来比较检测点的相似之处 28 | 29 | """ 30 | # 获得一个暴力匹配器的对象 31 | bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True) 32 | # 利用匹配器 匹配两个描述符的相近成都 33 | maches = bf.match(descriptor1, descriptor2) 34 | # 按照相近程度 进行排序 35 | maches = sorted(maches, key=lambda x: x.distance) 36 | # 画出匹配项 37 | img3 = cv2.drawMatches(img1, keypoint1, img2, keypoint2, maches[: 30], img2, flags=2) 38 | 39 | cv2.imshow("matches", img3) 40 | cv2.waitKey() 41 | cv2.destroyAllWindows() -------------------------------------------------------------------------------- /06_图像检测和搜索/005_k近邻匹配.py: -------------------------------------------------------------------------------- 1 | # coding:utf-8 2 | 3 | import cv2 4 | 5 | # 按照灰度图像读入两张图片 6 | img1 = cv2.imread("../data/logo1.png", cv2.IMREAD_GRAYSCALE) 7 | img2 = cv2.imread("../data/album1.png", cv2.IMREAD_GRAYSCALE) 8 | 9 | # 获取特征提取器对象 10 | orb = cv2.ORB_create() 11 | # 检测关键点和特征描述 12 | keypoint1, desc1 = orb.detectAndCompute(img1, None) 13 | keypoint2, desc2 = orb.detectAndCompute(img2, None) 14 | """ 15 | keypoint 是关键点的列表 16 | desc 检测到的特征的局部图的列表 17 | """ 18 | # 获得knn检测器 19 | bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True) 20 | matches = bf.knnMatch(desc1, desc2, k=1) 21 | """ 22 | knn 匹配可以返回k个最佳的匹配项 23 | bf返回所有的匹配项 24 | """ 25 | # 画出匹配结果 26 | img3 = cv2.drawMatchesKnn(img1, keypoint1, img2, keypoint2, matches, img2, flags=2) 27 | cv2.imshow("matches", img3) 28 | cv2.waitKey() 29 | cv2.destroyAllWindows() -------------------------------------------------------------------------------- /06_图像检测和搜索/006_FLANN匹配.py: -------------------------------------------------------------------------------- 1 | # coding:utf-8 2 | 3 | import cv2 4 | 5 | 6 | """ 7 | FLANN是类似最近邻的快速匹配库 8 | 它会根据数据本身选择最合适的算法来处理数据 9 | 比其他搜索算法快10倍 10 | """ 11 | # 按照灰度图片读入 12 | img1 = cv2.imread("../data/logo1.png", cv2.IMREAD_GRAYSCALE) 13 | img2 = cv2.imread("../data/album1.png", cv2.IMREAD_GRAYSCALE) 14 | # 创建sift检测器 15 | sift = cv2.xfeatures2d.SIFT_create() 16 | # 查找监测点和匹配符 17 | kp1, des1 = sift.detectAndCompute(img1, None) 18 | kp2, des2 = sift.detectAndCompute(img2, None) 19 | """ 20 | keypoint是检测到的特征点的列表 21 | descriptor是检测到特征的局部图像的列表 22 | """ 23 | # 获取flann匹配器 24 | FLANN_INDEX_KDTREE = 0 25 | indexParams = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) 26 | searchParams = dict(checks=50) 27 | flann = cv2.FlannBasedMatcher(indexParams, searchParams) 28 | 29 | """ 30 | flann = cv2.FlannBasedMatcher(indexParams, searchParams) 31 | 传入两个参数以字典的形式 32 | FLANN内部会决定如何处理索引和搜索对象 33 | 可以选择 LinearIndex KTreeIndex KMeansIndex CompositeIndex 和 AutotunelIndex 34 | KTreeIndex配置索引简单 只需要指定待处理核密度树的数量 最理想的数量是1到16 KTreeIndex非常灵活 35 | searchParams字段包含一个checks 用来指定索引树要被遍历的次数 次数越高花费时间越长 结果越准确 36 | 37 | """ 38 | 39 | # 进行匹配 40 | matches = flann.knnMatch(des1, des2, k=2) 41 | # 准备空的掩膜 画好的匹配项 42 | matchesMask = [[0, 0] for i in range(len(matches))] 43 | 44 | for i, (m, n) in enumerate(matches): 45 | if m.distance < 0.7*n.distance: 46 | matchesMask[i] = [1, 0] 47 | 48 | drawPrams = dict(matchColor=(0, 255, 0), 49 | singlePointColor=(255, 0, 0), 50 | matchesMask=matchesMask, 51 | flags=0) 52 | # 匹配结果图片 53 | img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, matches, None, **drawPrams) 54 | 55 | 56 | cv2.imshow("matches", img3) 57 | cv2.waitKey() 58 | cv2.destroyAllWindows() -------------------------------------------------------------------------------- /06_图像检测和搜索/007_FLANN单应性匹配.py: -------------------------------------------------------------------------------- 1 | # coding:utf-8 2 | 3 | """ 4 | 单应性匹配： 5 | 两幅图像中的一幅 出现投影畸变的时候，他们还能彼此匹配 6 | """ 7 | 8 | import cv2 9 | import numpy as np 10 | # 最小匹配数量设为10个， 大于这个数量从中筛选出10个最好的 11 | MIN_MATCH_COUNT = 10 12 | 13 | # 读入两幅图片 图片中有相同部分 14 | img1 = cv2.imread("../data/logo1.png", cv2.IMREAD_GRAYSCALE) 15 | img2 = cv2.imread("../data/album1.png", cv2.IMREAD_GRAYSCALE) 16 | 17 | # 获取sift特征检测器 18 | sift = cv2.xfeatures2d.SIFT_create() 19 | # 检测关键点 计算描述符 20 | kp1, des1 = sift.detectAndCompute(img1, None) 21 | kp2, des2 = sift.detectAndCompute(img2, None) 22 | 23 | # kdtree建立索引方式的常量参数 24 | FLANN_INDEX_KDTREE = 0 25 | index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) 26 | search_params = dict(checks=50) # checks指定索引树要被遍历的次数 27 | flann = cv2.FlannBasedMatcher(index_params, search_params) 28 | # 进行匹配搜索 29 | matches = flann.knnMatch(des1, des2, k=2) 30 | 31 | # 寻找距离近的放入good列表 32 | good = [] 33 | for m, n in matches: 34 | if m.distance < 0.7 * n.distance: 35 | good.append(m) 36 | 37 | # 如果足够多 就筛选 38 | if len(good) > MIN_MATCH_COUNT: 39 | # 通过距离近的描述符 找到两幅图片的关键点 40 | src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2) 41 | dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2) 42 | 43 | # 单应性匹配图关键点匹配线。。不懂啥意思 44 | M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0) 45 | """ 46 | 参数： 47 | src_pts: 第一张图片的检测特征点 48 | dst_pts: 第二张图片的检测特征点 49 | 第三个参数： 50 | cv2.RANSAC RANSAC-based robust method 51 | cv2.LMEDS least-median robust method 52 | 第四个参数： 1到10之间 阈值， 原图像点经过变换后超过误差就舍弃 53 | 返回值: 54 | H: 变换矩阵 55 | mask: 掩膜 56 | """ 57 | matchesMask = mask.ravel().tolist() 58 | 59 | h, w = img1.shape 60 | 61 | # 计算第二张图相对于第一张图的畸变 其实不太理解这是咋回事 62 | pts = np.float32([[0, 0], [0, h-1], [w-1, 0]]).reshape(-1, 1, 2) 63 | dst = cv2.perspectiveTransform(pts, M) 64 | img2 = cv2.polylines(img2, [np.int32(dst)], True, 255, 3, cv2.LINE_AA) 65 | else: 66 | matchesMask = None 67 | 68 | draw_params = dict( 69 | matchColor=(0, 255, 0), 70 | singlePointColor=None, 71 | matchesMask=matchesMask, 72 | flags=2 73 | ) 74 | 75 | img3 = cv2.drawMatches(img1, kp1, img2, kp2, good, None, **draw_params) 76 | cv2.imshow("", img3) 77 | cv2.waitKey() 78 | -------------------------------------------------------------------------------- /06_图像检测和搜索/008_基于文身取证的应用.py: -------------------------------------------------------------------------------- 1 | # coding:utf-8 2 | 3 | import cv2 4 | import numpy as np 5 | 6 | """ 7 | 1 将描述符保存到文件 8 | 方便图片进行匹配和单应性分析 9 | """ 10 | 11 | 12 | 13 | 14 | 15 | """ 16 | 2 扫描匹配 17 | 检查图像 创建描述符 18 | 扫描文件夹中的描述符 19 | FLANN来计算匹配 20 | 如果匹配超过了阈值， 就包含 21 | 匹配数量最多的图像是取证对象 22 | """ 23 | -------------------------------------------------------------------------------- /07_目标检测与识别/001_检测人.py: -------------------------------------------------------------------------------- 1 | # coding:utf-8 2 | 3 | import cv2 4 | 5 | 6 | # 检测i方框 包含o方框 7 | def is_inside(o, i): 8 | ox, oy, ow, oh = o 9 | ix, iy, iw, ih = i 10 | return ox > ix and ox + ow < ix + iw and oy + oh < iy + ih 11 | 12 | 13 | # 将人外面的方框画出来 14 | def draw_person(image, person): 15 | x, y, w, h = person 16 | cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 255), 2) 17 | 18 | 19 | # 读入图片 20 | img = cv2.imread("../data/people2.jpg") 21 | # 获取hog检测器对象 22 | hog = cv2.HOGDescriptor() 23 | # 设置检测人的默认检测器 24 | hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector()) 25 | # 在图片中检测人， 26 | # 返回found列表 每个元素是一个(x, y, w, h)的矩形，w是每一个矩形的置信度 27 | found, w = hog.detectMultiScale(img) 28 | found_filtered = [] 29 | # 如果方框有包含，只留下内部的小方块 30 | for ri, r in enumerate(found): 31 | for qi, q in enumerate(found): 32 | if ri != qi and is_inside(r, q): 33 | break 34 | else: 35 | found_filtered.append(r) 36 | 37 | # 将每一个方块画出来 38 | for person in found_filtered: 39 | draw_person(img, person) 40 | 41 | 42 | cv2.imshow("person detection", img) 43 | cv2.waitKey() 44 | cv2.destroyAllWindows() -------------------------------------------------------------------------------- /07_目标检测与识别/002_汽车检测.py: -------------------------------------------------------------------------------- 1 | # coding:utf-8 2 | 3 | import cv2 4 | import numpy as np 5 | 6 | datapath = "" 7 | 8 | 9 | def path(cls, i): 10 | return "%s/%s%d/pgm" % (datapath, cls, i+1) 11 | 12 | 13 | pos, neg = "pos-", "neg-" 14 | 15 | detect = cv2.xfeatures2d.SIFT_create() 16 | extract = cv2.xfeatures2d.SIFT_create() 17 | 18 | flann_params = dict( 19 | algorithm=1, 20 | trees=5) 21 | flann = cv2.FlannBasedMatcher(flann_params, {}) 22 | 23 | bow_kmeans_trainer = cv2.BOWKMeansTrainer(40) 24 | extract_bow = cv2.BOWImgDescriptorExtractor(extract, flann) 25 | 26 | 27 | def extract_sift(fn): 28 | im = cv2.imread(fn, cv2.IMREAD_GRAYSCALE) 29 | return extract.compute(im, detect(im))[1] 30 | 31 | 32 | for i in range(8): 33 | bow_kmeans_trainer.add(extract_sift(path(pos, i))) 34 | bow_kmeans_trainer.add(extract_sift(path(neg, i))) 35 | 36 | 37 | voc = bow_kmeans_trainer.cluster() 38 | extract_bow.setVocabulary(voc) 39 | 40 | 41 | def bow_features(fn): 42 | im = cv2.imread(fn, cv2.IMREAD_GRAYSCALE) 43 | return extract_bow.compute(im, detect.detect(im)) 44 | 45 | 46 | traindata, trainlabels = [], [] 47 | for i in range(20): 48 | traindata.extend(bow_features(path(pos, i))) 49 | trainlabels.append(i) 50 | traindata.extend(bow_features(path(neg, i))) 51 | trainlabels.append(-1) 52 | 53 | svm = cv2.ml.SVM_create() 54 | svm.train(np.array(traindata), cv2.ml.ROW_SAMPLE, np.array(trainlabels)) 55 | 56 | 57 | def predict(fn): 58 | f = bow_features(fn) 59 | p = svm.predict(f) 60 | print(fn, p[1][0][0]) 61 | return p 62 | 63 | 64 | car, notcar = "", "" 65 | car_img = cv2.imread(car) 66 | notcar_img = cv2.imread(notcar) 67 | car_predict = predict(car) 68 | notcar_predict = predict(notcar) 69 | 70 | font = cv2.FONT_HERSHEY_SIMPLEX 71 | if car_predict[1][0][0] == 1.0: 72 | cv2.putText(car_img, "car", (10, 30), font, 1, (0, 255, 0), 2, cv2.LINE_AA) 73 | 74 | cv2.imshow("", car_img) 75 | cv2.imshow("1", notcar_img) 76 | cv2.waitKey() 77 | cv2.destroyAllWindows() -------------------------------------------------------------------------------- /data/album1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/linyi0604/Computer-Vision/e2ec411bcf78b1d1bc353b721cabe250b1b28778/data/album1.png -------------------------------------------------------------------------------- /data/chess1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/linyi0604/Computer-Vision/e2ec411bcf78b1d1bc353b721cabe250b1b28778/data/chess1.jpg 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https://raw.githubusercontent.com/linyi0604/Computer-Vision/e2ec411bcf78b1d1bc353b721cabe250b1b28778/data/person1.jpg -------------------------------------------------------------------------------- /data/walez1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/linyi0604/Computer-Vision/e2ec411bcf78b1d1bc353b721cabe250b1b28778/data/walez1.jpg -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | backports.functools-lru-cache==1.5 2 | cycler==0.10.0 3 | kiwisolver==1.0.1 4 | matplotlib==2.2.2 5 | numpy==1.15.0 6 | opencv-python==3.4.2.17 7 | pyparsing==2.2.0 8 | python-dateutil==2.7.3 9 | pytz==2018.5 10 | scipy==1.1.0 11 | six==1.11.0 12 | subprocess32==3.5.2 13 | --------------------------------------------------------------------------------