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
1 import cv2
2
3 cameraCapture = cv2.VideoCapture(0)
4 fps = 30
5 size = (int(cameraCapture.get(cv2.CAP_PROP_FRAME_WIDTH)),
6 int(cameraCapture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
7 videoWriter = cv2.VideoWriter(
8 'MyOutputVid.avi', cv2.VideoWriter_fourcc('I', '4', '2', '0'),
9 fps, size)
10
11 success, frame = cameraCapture.read()
12 numFrameRemaining = 10*fps - 1
13 while success and numFrameRemaining > 0:
14 videoWriter.write(frame)
15 success, frame = cameraCapture.read()
16 numFrameRemaining -= 1
17
18 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
1 import cv2
2 import numpy
3
4 img = cv2.imread("data/aero1.jpg")
5 cv2.imshow('Test Image', img)
6 cv2.waitKey()
7 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
1 import cv2
2 import numpy
3
4 clicked = False
5 def onMouse(event, x, y, flags, param):
6 global clicked
7 if event == cv2.EVENT_FLAG_LBUTTON:
8 clicked = True
9
10 cameraCapture = cv2.VideoCapture(0)
11 cv2.namedWindow('MyWindow')
12 cv2.setMouseCallback('MyWindow', onMouse)
13
14 print 'Show camera feed. Click window or press any key to stop'
15
16 success, frame = cameraCapture.read()
17 while success and cv2.waitKey(1) == -1 and not clicked:
18 cv2.imshow('MyWindow', frame)
19 success, frame = cameraCapture.read()
20
21 cv2.destroyWindow('MyWindow')
22 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"""
View Code
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:
转载于:https://www.cnblogs.com/QingHuan/p/7365732.html