gray

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I am trying to implement an algorithm in computer vision and I want to try it on a set of pictures. The pictures are all in color, but I don't want to deal with that. I want to convert them to grayscale which is enough for testing the algorithm. How can I convert a color image to grayscale? I'm reading it with: x = imread('bla.jpg'); Is there any argument I can add to imread to read it as grayscale? Is there any way I change x to grayscale after reading it? 回答1: Use rgb2gray to strip hue and saturation (ie, convert to grayscale).

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 由 翻译 强力驱动 问题: I'm new to OpenCV platform. I've installed and successfully run some tutorial codes(inbuilt). But I'm facing some issues with the codes involving haarcascade.The code builds perfectly and also the webcam seems to flash light and work but the code displays blank window as attached. image Can anyone suggest some rectification for the problem? (I'm using opencv version 3.0.0 and Visual Studios 2013.) #include "stdafx.h" #include "opencv2/objdetect.hpp" #include "opencv2/videoio.hpp" #include "opencv2/highgui.hpp" #include "opencv2

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I have a QImage and I need to convert it to grayscale, then later paint over that with colors. I found an allGray() and isGrayScale() function to check if an image is already grayscale, but no toGrayScale() or similarly-named function. Right now I'm using this code, but it's does not have a very good performance: for (int ii = 0; ii What would be the best way, performance-wise, to convert a QImage to grayscale? 回答1: Rather than using the slow functions QImage::pixel and QImage::setPixel , use QImage::scanline to access the data. Pixels on a

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: So I know how to do a basic box shadow with CSS3. You can see that in the top of the graphic below. The effect I'm trying to achieve is a 3D box shadow, as shown in the bottom of the graphic below. Any ideas on how to do this with CSS3 box shadows? 回答1: Unfortunately box shadows are effectively just flat layers. However you can apply multiple box shadows to create this effect. .box-shadow-3d{ box-shadow: 1px 1px 0px #999, 2px 2px 0px #999, 3px 3px 0px #999, 4px 4px 0px #999, 5px 5px 0px #999, 6px 6px 0px #999; } 回答2: you can use pseudo

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I am learning image processing using OpenCV for a realtime application. I did some thresholding on an image and want to label the contours in green, but they aren't showing up in green because my image is in black and white. Early in the program I used gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) to convert from RGB to grayscale, but to go back I am confused, and the function backtorgb = cv2.cvtColor(gray,cv2.CV_GRAY2RGB) is giving AttributeError: 'module' object has no attribute 'CV_GRAY2RGB'. The code below does not appear to be drawing

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I am trying to make a face tracker that combines Haar Cascade Classification with Lucas Kanade good feature detection. However, I keep getting an error that I cannot figure out what it means nor how to solve it. Can anyone help me here? Error: line 110, in <module> cv2.imshow('frame',img) error: /build/buildd/opencv-2.4.8+dfsg1/modules/highgui/src/window.cpp:269: error: (-215)size.width>0 && size.height>0 in function imshow Code: from matplotlib import pyplot as plt import numpy as np import cv2 face_classifier = cv2.CascadeClassifier(

opencv 图片特效 更多干货 分布式实战（干货） spring cloud 实战（干货） mybatis 实战（干货） spring boot 实战（干货） React 入门实战（干货） 构建中小型互联网企业架构（干货） python 学习持续更新 ElasticSearch 笔记 kafka storm 实战 (干货) scala 学习持续更新 RPC 深度学习 灰度处理 方法1 imread #imread #方法1 imread import cv2 img0 = cv2.imread('image0.jpg',0) img1 = cv2.imread('image0.jpg',1) print(img0.shape) print(img1.shape) cv2.imshow('src',img0) cv2.waitKey(0) 方法2 cvtColor #方法2 cvtColor import cv2 img = cv2.imread('image0.jpg',1) dst = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)# 颜色空间转换 1 data 2 BGR gray cv2.imshow('dst',dst) cv2.waitKey(0) 方法3 RGB R=G=B = gray (R+G+B)/3 import cv2 import

有些特征用轮廓并无法来检测，比如脸，在内容上显示的特征就不能用边缘检测了，要想把这些关键点检测出来就需要用star检测器， import numpy as np import cv2 as cv image = cv.imread( '/Users/youkechaung/Desktop/算法/数据分析/AI/day02/day02/data/table.jpg' ) #显示内部细节的检测，不是边缘检测而是特征检测， #颜色并不是很重要，因此把图转换为灰度图 cv.imshow( 'Original' ,image) gray = cv.cvtColor(image,cv.COLOR_BGR2GRAY) cv.imshow( 'Gray' ,gray) detector = cv.xfeatures2d.StarDetector_create() #特征检测器 keypoints = detector.detect(gray) #返回关键点问题 # print(keypoints) 是一个矢量，不是很明显表示 cv.drawKeypoints(image,keypoints,image,flags = cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) #画在原图上，第一个是原图，第二个image是目标图，这个flags既包括位置又包括方向

这次介绍的是优达学城的无人驾驶工程师的P1项目，利用车的前摄像头来识别当前车道的左右两边两条的车道线。 一开始先倒包 #importing some useful packages import matplotlib.pyplot as plt import matplotlib.image as mpimg import numpy as np import cv2 %matplotlib inline 开始读取图片 #reading in an image image = mpimg.imread('test_images/solidWhiteRight.jpg') #printing out some stats and plotting print('This image is:', type(image), 'with dimensions:', image.shape) plt.imshow(image) # if you wanted to show a single color channel image called 'gray', for example, call as plt.imshow(gray, cmap='gray') 下面是一些辅助函数（选用，可以根据自己想法添加删除其他方法） import math def grayscale(img): ""

也许更好的阅读体验 \(\mathcal{Description}\) 定义一种字符串 \(gray\) 串满足： 长度为奇数 正中间的字母只出现一次 左右两端相同，左右两端也是gray串 一个 \(gray\) 串的贡献为这个串长度的平方 需要注意的是一个长度为 \(7\) 的 \(gray\) 串是包含了长度为 \(1,3\) 的 \(gray\) 的 现给你一个长为 \(n(n<=100,000)\) 的字符串，你可以修改至多一个字母，使得总贡献值最大 输入方式为一个字符串 \(\mathcal{Solution}\) 考虑怎么去求修改字符的贡献 我们修改一个字符时应该考虑两点: 一是该字符原本造成的贡献没有了 二是将其修改后会得到新的贡献 当我们对第 \(i\) 个字符修改时 用 \(cost[i]\) 表示 \(i\) 位置原本的贡献 用 \(bef[i][x](benefit)\) 表示将 \(i\) 位置的字符修改为 \(x\) 所得到的贡献,特别的,设i位置本来的字符为 \(x\) ,那么 \(cost[i]=bef[i][x]\) 那么将 \(i\) 位置的字符修改为 \(x\) 得到的贡献应为 \(bef[i][x]-cost[i]\) 根据 \(gray\) 串的定义,我们知道一个 \(gray\) 串的长度应该是 \(1,3,7,15,31\dots(2^x