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This question already has answers here : Closed 2 years ago . overlay a smaller image on a larger image python OpenCv (6 answers) Hey everyone I'm trying to make a game using my webcam where I need some objects to fall down the screen while Im streaming video with my webcam (that stream being my background). The question is: how do I draw the image over this background? I know how to capture each frame and that I have to draw the image over the frames, but using what? For this I am using python and opencv and the cv2 module . Ive already searched and I discovered that you can use regions of

Currently, our organization does not practice Continuous Integration. In order for us to get an CI server up and running, I will need to produce a document demonstrating the return on the investment. Aside from cost savings by finding and fixing bugs early, I'm curious about other benefits/savings that I could stick into this document. My #1 reason for liking CI is that it helps prevent developers from checking in broken code which can sometimes cripple an entire team. Imagine if I make a significant check-in involving some db schema changes right before I leave for vacation. Sure, everything

1. Position Sensitive ROI-Pooling 简介 　　Position Sensitive ROI Pooling（位置敏感的候选区域池化）是检测框架R-FCN的主要创新点。一般来讲，网络越深，其具有的平移旋转不变性越强，这个性质对于保证分类模型的鲁棒性有积极意义。然而，在检测问题中，对物体的定位任务要求模型对位置信息有良好的感知能力，过度的平移旋转不变性会削弱这一性能。研究发现，对于较深的全卷积神经网络（Inception、ResNet 等），Faster-RCNN检测框架存在着一个明显的缺陷： 检测器对物体的位置信息的敏感度下降，检测准确度降低 　　有鉴于此，R-FCN 这篇文章的作者提出了一种全新的特征聚集方法：Position Sensitive ROI Pooling。其主要思想是在 特征聚集时人工引入位置信息，从而有效改善较深的神经网络对物体位置信息的敏感程度 。同时， R-FCN的大部分操作都直接对整张图片进行，这也大大优化了网络的运行速度 　　如上图，每一个候选区域(ROI)被平均分割成 k^2 个矩形单元,前序特征图先通过一层 1*1 的卷积核生成通道数为 k^2*(C+1) 的特征图。这里，k^2 代表一个ROI里所有矩形单元的数量，C+1 代表所有的类别数加上背景。这 k^2*(C+1) 张特征图每 C+1 张分成一组、共包含 k^2

论文大部分转载自：https://blog.csdn.net/WZZ18191171661/article/details/79439212 论文题目 ： Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks 论文链接 ： 论文链接 论文代码 ：Matlab版本 点击此处 ，Python版本 点击此处 作为一个目标检测领域的baseline算法， Faster-rcnn 值得去仔细理解里面的细节 按照 总分总 的顺序剖析。 图2 Faster-rcnn架构图（精简版） 图3 Faster-rcnn架构图（细节版） 一、目标检测的总体框架 图4 目标检测框架图 观察图4，你可以发现目标检测的框架中包含4个关键模块，包括region proposal（生成ROI）、feature extraction（特征提取网络）、classification（ROI分类）、regression（ROI回归）。而faster-rcnn利用一个神经网络将这4个模块结合起来，训练了一个端到端的网络。通过观察图1、图2、图3，我们可以得到如下的结论：Faster-rcnn主要包括4个关键模块， 特征提取网络、生成ROI、ROI分类、ROI回归 。 特征提取网络

Working on Face Detection and Recognition, and after successfully detecting a face, I just want to crop the face and save it somewhere in the drive to give it for the recognition code. I am having hard time doing the saving the Region of Interest as a new image. I have got some codes online but it is written in the previous version of OpenCV that uses IplImage* . I am using OpenCV 2.4.2 that uses cv::Mat . Heeeelp!!! I will post my codes(Face detection and Recognition per se) if you guys want it. #include <cv.h> #include <highgui.h> #include <math.h> // alphablend <imageA> <image B> <x> <y>

需求： 　　设置一个按钮，动态隐藏或展示数据表格的列。默认隐藏部分列，点击按钮进行显示，再次点击就隐藏。 　　基于 layui 的数据表格。 代码： 　　绑定按钮事件。 　　这里需要设置的列比较多，表格字段名格式：d1_roi、d1_ltv、d2_roi、d2_ltv... 　　原理：先清除每列的 layui-hide 的 class，然后给当前列每个格子加上固定宽度即可。 1 //拓展列切换 2 $(".js-showhide").click(function(){ 3 var roi_key = ''; 4 var ltv_key = ''; 5 var roi_cell_key = ''; 6 var ltv_cell_key = ''; 7 var days = [2,4,5,6,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,40,50,60,70,80,90]; 8 9 $.each(days, function (key, d) { 10 //每一列的 key 11 roi_key = "[data-field='d"+d+"_roi']"; 12 ltv_key = "[data-field='d"+d+"_ltv']"; 13 14 //每个单元格的 类 15 roi_cell

as the title says i'm trying to find the number of non-zero pixels in a certain area of a cv::Mat, namely within a RotatedRect. For a regular Rect one could simply use countNonZeroPixels on a ROI. However ROIs can only be regular (non rotated) rectangles. Another idea was to draw the rotated rectangle and use that as a mask. However openCV neither supports the drawing of rotated rectangles nor does countNonZeroPixels accept a mask. Does anyone have a solution for how to elegantly solve this ? Thank you ! Ok, so here's my first take at it. The idea is to rotate the image reverse to the