【持续更新】 | OpenCV 学习笔记

本文地址：http://www.cnblogs.com/QingHuan/p/7365732.html，转载请注明出处

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OpenCV的入门书籍有很多，这里选择的是《OpenCV 3计算机视觉-Python语言实现-第二版》

所有书上的源代码：https://github.com/techfort/pycv

安装过程请查看我的另一篇博客：

http://www.cnblogs.com/QingHuan/p/7354074.html

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第2章 处理文件、摄像头和图形用户界面

2.1 基本I／O脚本

2.1.5 捕获摄像头的帧

下面的代码实现了捕获摄像头10秒的视频信息，并将其写入一个AVI文件中

 import cv2

 cameraCapture = cv2.VideoCapture(0)
 fps = 30
 size = (int(cameraCapture.get(cv2.CAP_PROP_FRAME_WIDTH)),
         int(cameraCapture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
 videoWriter = cv2.VideoWriter(
     'MyOutputVid.avi', cv2.VideoWriter_fourcc('I', '', '', ''),
     fps, size)

 success, frame = cameraCapture.read()
 numFrameRemaining = 10*fps - 1
 while success and numFrameRemaining > 0:
     videoWriter.write(frame)
     success, frame = cameraCapture.read()
     numFrameRemaining -= 1

 cameraCapture.release()

2.1.6 在窗口显示图像

一般情况下使用imshow()来显示图片，图片会闪一下然后消失，下面代码可以是图片一直显示

 import cv2
 import numpy

 img = cv2.imread("data/aero1.jpg")
 cv2.imshow('Test Image', img)
 cv2.waitKey()
 cv2.destroyAllWindows()

虽然左上角没有关闭按钮，但是随便按一个按键，都会将其关闭

2.1.7 实时显示摄像头的内容

 import cv2
 import numpy

 clicked = False
 def onMouse(event, x, y, flags, param):
     global clicked
     if event == cv2.EVENT_FLAG_LBUTTON:
         clicked = True

 cameraCapture = cv2.VideoCapture(0)
 cv2.namedWindow('MyWindow')
 cv2.setMouseCallback('MyWindow', onMouse)

 print 'Show camera feed. Click window or press any key to stop'

 success, frame = cameraCapture.read()
 while success and cv2.waitKey(1) == -1 and not clicked:
     cv2.imshow('MyWindow', frame)
     success, frame = cameraCapture.read()

 cv2.destroyWindow('MyWindow')
 cameraCapture.release()

setMouseCallback可以获取鼠标的输入

namedWindow()用来创建窗口

imshow()用来显示窗口

destroyWindow()用来销毁窗口

cameraCapture.release()用来释放摄像头

cv2.waitKey(1) == -1 ：
参数为等待键盘触发的时间，返回值-1表示没有按键被按下

OpenCV不能处理窗口，当单击窗口的关闭按钮时，并不能将其关闭

2.3  一个小程序—— Cameo——面向对象的设计

2.3.1 使用 managers.CaptureManager 提取视频流

这里中文版讲的不太通顺，建议看看英文原版

# CaptureManager 高级I/O流接口

import cv2
import numpy
import time

# 初始化CaptureManager类

class CaptureManager(object):

    def __init__(self, capture, previewWindowManager = None,
                 shouldMirrorPreview = False):

        # 更新：前面有下划线的是非公有变量，主要与当前帧的状态与文件写入操作有关
        # self 类似于结构体，里面有很多变量，下面是在初始化这些变量
        # 类比于C++ 中学的"类"来比较
        self.previewWindowManager = previewWindowManager
        self.shouldMirrorPreview = shouldMirrorPreview

        self._capture = capture
        self._channel = 0
        self._enteredFrame = False
        self._frame = None
        self._imageFilename = None
        self._videoFilename = None
        self._videoEncoding = None
        self._videoWriter = None

        self._startTime = None
        self._frameElapsed = long(0)
        self._fpsEstimate = None

    # @ 符号的解释参见：http://gohom.win/2015/10/25/pyDecorator/
    @property
    def channel(self):
        return self._channel

    @channel.setter
    def channel(self, value):
        if self._channel != value
            self._channel = value
            self._frame = None

    @property
    def frame(self):
        if self._enteredFrame and self._frame is None:
            _, self._frame = self._capture.retrieve()
        return self._frame

    @property
    def isWritingImage (self):
        return self._imageFilename is not None

    @property
    def isWrtingVideo(self):
        return self._videoFilename is not None

    def enterFrame(self):
        """Capture the next frame, if any"""

暂停在书Page 30

代码暂时写到这里，因为这里根本不认真讲原理，就是简单的堆砌代码，

感觉学习不到知识，所以就不看了，跳到下一章

第3章 使用OpenCV 3 处理图像

3.2 傅立叶变换

下面是一个高通滤波器和低通滤波器的例子：注意看注释，写的很详细

import cv2
import numpy as np
from scipy import ndimage

# kernel defined by ourself
kernel_3x3 = np.array([[-1, -1, -1],
                       [-1, 8, -1],
                       [-1, -1, -1]])

kernel_5x5 = np.array([[-1, -1, -1, -1, -1],
                       [-1, 1, 2, 1, -1],
                       [-1, 2, 4, 2, -1],
                       [-1, 1, 2, 1, -1],
                       [-1, -1, -1, -1, -1]])

# http://docs.opencv.org/3.1.0/d4/da8/group__imgcodecs.html
# in func "imread", 0 means trans to gray image
img = cv2.imread("data/lena.jpg", 0)

k3 = ndimage.convolve(img, kernel_3x3)
k5 = ndimage.convolve(img, kernel_5x5)

"""Gaussian kernel: The function convolves the source image
 with the specified Gaussian kernel. 

 GaussianBlur(src, ksize, sigmaX[, dst[, sigmaY[, borderType]]]) -> dst

 @param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be
    .   positive and odd. Or, they can be zero's and then they are computed from sigma.
 @param sigmaX Gaussian kernel standard deviation in X direction."""

# here is a per-defined Gaussian Blur and the
# kernel is set to 11x11, start in X axis direction
# Attention: GaussianBlur is a low pass filter but the above two are high pass filters
# after minus with the origin image, finally equivalent to a high pass filter
blurred = cv2.GaussianBlur(img, (11, 11), 0)
g_hpf = img - blurred

cv2.imshow("3x3", k3)
cv2.imshow("5x5", k5)
cv2.imshow("g_hpf", g_hpf)
cv2.waitKey()
cv2.destroyAllWindows()

可以看一下处理效果：

原图：

高通滤波器：

3x3 kernel：

5x5 kernel：

用低通滤波器处理后的图片，与原图相减，得到高通滤波器的效果：（原理待查）

可以发现第三张图的效果最好

中间都跳过去了，书上讲的不好，所以只是大概看了一遍没有敲代码

读完这本书一定要换一本再补充一下

3.7 Canny 边缘检测

Canny函数可以非常方便的识别出边缘，

例子如下：

import cv2
import numpy as np
import filters
from scipy import ndimage

img = cv2.imread("data/lena.jpg", 0)
cv2.imwrite("lena_edge.jpg", cv2.Canny(img, 200, 300))

cv2.imshow("lena", cv2.imread("lena_edge.jpg"))
cv2.waitKey()
cv2.destroyAllWindows()

lena_edge.jpg：

3.8 简单的轮廓检测

首先创建了一个黑色图像，然后在中间放了一个白色方块。然后对方块寻找轮廓并把轮廓标注出来

（其实没有看懂代码）

# -*- coding: utf-8 -*
import cv2
import numpy as np
import filters
from scipy import ndimage

# dark, blank image
img = np.zeros((200, 200), dtype=np.uint8)
# assign the middle square white
img[50:150, 50:150] = 255

# 二值化
ret, thresh = cv2.threshold(img, 127, 255, 0)

image, contours, hierarchy = cv2.findContours(thresh,
                                              cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
color = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)

img = cv2.drawContours(color, contours, -1, (0, 255, 0), 2)

cv2.imshow("contours", color)
cv2.waitKey()
cv2.destroyAllWindows()

结果：

3.9 找到一个图形的边界框、最小矩形区域和最小闭圆的轮廓

# -*- coding: utf-8 -*
import cv2
import numpy as np

img = cv2.pyrDown(cv2.imread("pycv-master/chapter3/hammer.jpg", cv2.IMREAD_UNCHANGED))

ret, thresh = cv2.threshold(cv2.cvtColor(img.copy(), cv2.COLOR_BGR2GRAY) , 127, 255, cv2.THRESH_BINARY)
image, contours, hier = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

for c in contours:
  # find bounding box coordinates
  x,y,w,h = cv2.boundingRect(c)

  # 画一个正方形
  cv2.rectangle(img, (x,y), (x+w, y+h), (0, 255, 0), 2)

  # 画最贴近的正方形，有一定的旋转
  # find minimum area
  rect = cv2.minAreaRect(c)
  # calculate coordinates of the minimum area rectangle
  box = cv2.boxPoints(rect)
  # normalize coordinates to integers
  box = np.int0(box)
  # draw contours
  cv2.drawContours(img, [box], 0, (0,0, 255), 3)

  # 画一个圆正好包裹住
  # calculate center and radius of minimum enclosing circle
  (x,y),radius = cv2.minEnclosingCircle(c)
  # cast to integers
  center = (int(x),int(y))
  radius = int(radius)
  # draw the circle
  img = cv2.circle(img,center,radius,(0,255,0),2)

cv2.drawContours(img, contours, -1, (255, 0, 0), 1)
cv2.imshow("contours", img)

cv2.waitKey()
cv2.destroyAllWindows()

cv2.imwrite("hammer_contours.jpg", img)

结果：