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OpenCV-Python教程、源码迁移 2018年10月2日
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‎.gitignore

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wiki/

‎01. 简介与安装/cv2_contrast_brightness_time.cpp

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// More: http://ex2tron.top
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#include "stdafx.h"
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#include <opencv2\opencv.hpp>
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using namespace cv;
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using namespace std;
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int main()
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{
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// 开始计时
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int start = getTickCount();
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// 读入一张图片并调整对比度和亮度
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Mat gSrcImage = imread("lena.jpg");
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Mat gDstImage = Mat::zeros(gSrcImage.size(), gSrcImage.type());
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//1.访问每一个像素的方式
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for (int y = 0; y < gSrcImage.rows; y++)
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{
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for (int x = 0; x < gSrcImage.cols; x++)
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{
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for (int c = 0; c < 3; c++)
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{
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gDstImage.at<Vec3b>(y, x)[c] = saturate_cast<uchar>(0.8 * gSrcImage.at<Vec3b>(y, x)[c] + 80);
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}
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}
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}
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//2.最快速的方式
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//gSrcImage.convertTo(gDstImage, -1, 0.8, 80);
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// 停止计时
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int end = getTickCount();
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// 单位：s
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double time = (end - start) / getTickFrequency();
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printf("%f", time);
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getchar();
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return 0;
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}

‎01. 简介与安装/cv2_contrast_brightness_time.py

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# More: http://ex2tron.top
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import cv2
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import numpy as np
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# 开始计时
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start = cv2.getTickCount()
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# 读入一张图片并调整对比度和亮度
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img = cv2.imread('lena.jpg')
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res = np.uint8(np.clip((0.8 * img + 80), 0, 255))
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# 停止计时
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end = cv2.getTickCount()
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# 单位：s
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print((end - start) / cv2.getTickFrequency())

‎01. 简介与安装/cv2_read_image_time.cpp

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// More: http://ex2tron.top
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#include "stdafx.h"
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#include <opencv2\opencv.hpp>
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using namespace cv;
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using namespace std;
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int main()
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{
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// 开始计时
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int start = getTickCount();
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// 读入一张图片
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Mat gSrcImage = imread("lena.jpg");
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// 停止计时
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int end = getTickCount();
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// 单位：s
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double time = (end - start) / getTickFrequency();
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printf("%f", time);
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getchar();
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return 0;
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}

‎01. 简介与安装/cv2_read_image_time.py

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# More: http://ex2tron.top
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import cv2
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# 开始计时
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start = cv2.getTickCount()
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# 读入一张图片
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img = cv2.imread('lena.jpg')
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# 停止计时
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end = cv2.getTickCount()
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# 单位：s
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print((end - start) / cv2.getTickFrequency())

‎01. 简介与安装/lena.jpg

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‎02. 基本元素-图片/cv2_excises1.py

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# More: http://ex2tron.top
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import cv2
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img = cv2.imread('lena.jpg')
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cv2.imshow('lena', img)
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k = cv2.waitKey(0)
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# ord用来获取某个字符的编码
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if k == ord('s'):
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cv2.imwrite('lena_save.bmp', img)

‎02. 基本元素-图片/cv2_getting_start_with_images.py

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# More: http://ex2tron.top
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import cv2
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# 1.灰度图加载一张彩色图
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img = cv2.imread('lena.jpg', 0)
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# 2.显示图片
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cv2.imshow('lena', img)
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cv2.waitKey(0)
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# 先定义窗口，后显示图片
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cv2.namedWindow('lena2', cv2.WINDOW_NORMAL)
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cv2.imshow('lena2', img)
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cv2.waitKey(0)
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# 3.保存图片
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cv2.imwrite('lena_gray.jpg', img)

‎02. 基本元素-图片/lena.jpg

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‎03. 打开摄像头/cv2_capture_live_video_from_camera.py

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# More: http://ex2tron.top
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import cv2
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# 1.打开摄像头
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capture = cv2.VideoCapture(2)
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# 2.获取捕获的分辨率
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width, height = capture.get(3), capture.get(4)
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print(width, height)
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# 以原分辨率的一倍来捕获，
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# 参数1可以直接写数字，或者OpenCV符号表示
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capture.set(cv2.CAP_PROP_FRAME_WIDTH, width * 2)
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capture.set(cv2.CAP_PROP_FRAME_HEIGHT, height * 2)
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while(True):
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# 获取一帧
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ret, frame = capture.read()
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# 将这帧转换为灰度图
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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cv2.imshow('frame', gray)
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if cv2.waitKey(1) == ord('q'):
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break

‎03. 打开摄像头/cv2_exercise1.py

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# More: http://ex2tron.top
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import cv2
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def track_back(x):
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'''
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### 回调函数，x表示滑块的位置
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'''
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# 更改视频的帧位置
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capture.set(cv2.CAP_PROP_POS_FRAMES, x)
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cv2.namedWindow('window')
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capture = cv2.VideoCapture('demo_video.mp4')
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# 获取视频总共多少帧
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frames = capture.get(cv2.CAP_PROP_FRAME_COUNT)
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# 创建滑动条
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cv2.createTrackbar('process', 'window', 1, int(frames), track_back)
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while(capture.isOpened()):
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ret, frame = capture.read()
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cv2.imshow('window', frame)
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if cv2.waitKey(30) == ord('q'):
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break

‎03. 打开摄像头/cv2_play_video_from_file.py

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# More: http://ex2tron.top
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import cv2
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# 播放本地视频
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capture = cv2.VideoCapture('demo_video.mp4')
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while(capture.isOpened()):
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ret, frame = capture.read()
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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cv2.imshow('frame', gray)
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if cv2.waitKey(30) == ord('q'):
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break

‎03. 打开摄像头/cv2_save_video_to_file.py

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# More: http://ex2tron.top
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import cv2
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capture = cv2.VideoCapture(0)
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# 定义编码方式并创建VideoWriter对象
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fourcc = cv2.VideoWriter_fourcc(*'MJPG')
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outfile = cv2.VideoWriter('output.avi', fourcc, 25., (640, 480))
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while(capture.isOpened()):
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ret, frame = capture.read()
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if ret:
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# 写入文件
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outfile.write(frame)
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cv2.imshow('frame', frame)
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if cv2.waitKey(1) == ord('q'):
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break
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else:
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break

‎03. 打开摄像头/demo_video.mp4

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‎04. 图像基本操作/cv2_basic_operations.py

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# More: http://ex2tron.top
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import cv2
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img = cv2.imread('lena.jpg')
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# 1.获取像素的值
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px = img[100, 100]
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print(px) # [119 108 201]
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# 只获取蓝色blue通道的值
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px_blue = img[100, 100, 0]
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print(px_blue) # 119
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# 2.修改像素的值
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img[100, 100] = [255, 255, 255]
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print(img[100, 100]) # [255 255 255]
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# 3.图片形状
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print(img.shape) # (263, 263, 3)
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# 形状中包括高度、宽度和通道数
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height, width, channels = img.shape
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# img是灰度图的话：height, width = img.shape
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# 总像素数
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print(img.size) # 263*263*3=207507
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# 数据类型
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print(img.dtype) # uint8
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# 4.ROI截取
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face = img[100:200, 115:188]
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cv2.imshow('face', face)
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cv2.waitKey(0)
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# 5.通道分割与合并
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b, g, r = cv2.split(img)
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img = cv2.merge((b, g, r))
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# 更推荐的获取某一通道方式
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b = img[:, :, 0]
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cv2.imshow('b', b)
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cv2.waitKey(0)

‎04. 图像基本操作/cv2_exercise1.py

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# More: http://ex2tron.top
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import cv2
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img = cv2.imread('lena.jpg')
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# 帽子ROI的红色通道
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hat_r = img[25:120, 50:220, 2]
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cv2.imshow('hat', hat_r)
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cv2.waitKey(0)

‎04. 图像基本操作/lena.jpg

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‎05. 颜色空间转换/cv2_changing_color_space.py

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# More: http://ex2tron.top
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import cv2
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import numpy as np
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img = cv2.imread('lena.jpg')
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# 1.转成灰度图
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img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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cv2.imshow('img', img)
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cv2.imshow('gray', img_gray)
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cv2.waitKey(0)
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# 2.获取所有的转换模式
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flags = [i for i in dir(cv2) if i.startswith('COLOR_')]
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print(flags)
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# 蓝色的HSV值
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blue = np.uint8([[[255, 0, 0]]])
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hsv_blue = cv2.cvtColor(blue, cv2.COLOR_BGR2HSV)
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print(hsv_blue) # [[[120 255 255]]]
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# 3.追踪蓝色物体
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capture = cv2.VideoCapture(0)
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# 蓝色的范围，不同光照条件下不一样，可灵活调整
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lower_blue = np.array([100, 110, 110])
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upper_blue = np.array([130, 255, 255])
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while(True):
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# 1.捕获视频中的一帧
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ret, frame = capture.read()
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# 2.从BGR转换到HSV
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hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
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# 3.inRange()：介于lower/upper之间的为白色，其余黑色
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mask = cv2.inRange(hsv, lower_blue, upper_blue)
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# 4.只保留原图中的蓝色部分
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res = cv2.bitwise_and(frame, frame, mask=mask)
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cv2.imshow('frame', frame)
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cv2.imshow('mask', mask)
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cv2.imshow('res', res)
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if cv2.waitKey(1) == ord('q'):
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break

‎05. 颜色空间转换/cv2_exercises1.py

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# More: http://ex2tron.top
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import cv2
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import numpy as np
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# HSV中
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# Blue：[[[120 255 255]]]
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# Green：[[[ 60 255 255]]]
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# Red：[[[ 0 255 255]]]
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capture = cv2.VideoCapture(1)
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# 蓝色的范围
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lower_blue = np.array([100, 110, 110])
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upper_blue = np.array([130, 255, 255])
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# 绿色的范围
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lower_green = np.array([40, 90, 90])
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upper_green = np.array([70, 255, 255])
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# 红色的范围
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lower_red = np.array([160, 120, 120])
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upper_red = np.array([179, 255, 255])
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while(True):
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# 1.捕获视频中的一帧
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ret, frame = capture.read()
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# 2.从BGR转换到HSV
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hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
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mask_blue = cv2.inRange(hsv, lower_blue, upper_blue)
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mask_green = cv2.inRange(hsv, lower_green, upper_green)
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mask_red = cv2.inRange(hsv, lower_red, upper_red)
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# 3.将所有的mask相加，就可以同时显示了
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mask = mask_blue + mask_green + mask_red
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# 4.保留原图中的三种颜色部分
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res = cv2.bitwise_and(frame, frame, mask=mask)
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cv2.imshow('frame', frame)
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cv2.imshow('mask', mask)
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cv2.imshow('res', res)
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if cv2.waitKey(1) == ord('q'):
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break

‎05. 颜色空间转换/lena.jpg

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