computer vision udemy

experiment with the CPU and # CPU_ONLY := 1 remove the previous # number. If you use the GPU and have cudnn acceleration , # USE_CUDNN := 1 Remove the previous # number. If you use Openblas, it will be BLAS := atlas changed and BLAS := open added BLAS_INCLUDE := /usr/include/openblas (the default matrix operations library in Caffe is Atlas, but Openblas has some performance optimizations, so it is recommended to change Openblas) Not to be continued ... Deep Learning and

+ dilate1_out + dilate2_out + dilate3_out return out Ocnet:object Context Network for Scene parsing For semantic segmentation, the model needs both the contextual information of high latitude (global information) and the resolution capability (that is, the local information of the picture). Unet through concatenate to improve the image of the local information. So how do you get better global information? The center block in the middle of the unet structure is discussed in Ocnet pap

Particle filtering steps1. Initialize randomly select N points, weights uniform assignment 1/n2. Select the target feature, color histogram, etc., to obtain a priori probability density, compare the similarity degree3. Determine the state transition matrix for predicting the next frame target locationCycle start4. According to the state transfer matrix, for each particle, predict the target new position5. Obtain the system observations and calculate the characteristics at the observation locatio

("Background", mask);Charc = (Char) Waitkey ( -);if(c = = -) Break; }return 0; }ResourcesMixed Gaussian background model and OPENCV implementationThe principle of mixed Gaussian algorithm in OpenCVreprint Please indicate the author Jason Ding and its provenanceGitcafe Blog Home page (http://jasonding1354.gitcafe.io/)GitHub Blog Home page (http://jasonding1354.github.io/)CSDN Blog (http://blog.csdn.net/jasonding1354)Jane Book homepage (http://www.jianshu.com/users/2bd9b48f6ea8/latest_articles)Ba

Linefinder.h#if!defined linef#define linef#includeMain.cpp#include OPENCV2 implementation of multiple picture road signs (lines and circles) detection and the processing of the picture synthesis video _ Computer Vision Big Job 2

introduction of noise for normalized operation and model training, which can increase the robustness of the model, but if the original distribution of each mini-batch is very different, then the data will be transformed differently Mini-batch This increases the difficulty of the model training.BN is more suitable for the scenario is: each mini-batch larger, the data distribution is relatively close. Before the training, to do a good job of shuffle, otherwise the effect will be much worse.In add

[Cars, pedestrians, bicycles, buildings, trees, skies, roads, sidewalks, and stores]Labelme Dataset: over 150,000 marked photos.Muhavi: multicamera human action video dataa large body of human action video data using 8 cameras. Includes manually annotated silhouette data. datasets used to test human behaviorINRIA Xmas motion acquisition sequences (ixmas): multiview dataset for view-invariant human action recognition.I-lids Datasets: UK Government benchmark datasets for automatic surveillance.Th

: control – The optional input controlStep 3: Call the correct method of the Kalman class to obtain the state variable value matrix after the observed value correction is added.The formula is as follows:Corrected state (x (k): X (K) = x' (k) + K (k)(Z (k)-HX' (k ))Here, x' (k) is the result calculated in step 2, and Z (k) is the current measurement value, which is the input vector after the external measurement. H initializes the given measurement matrix for the Kalman class. K (k) is the Kal

I. Overview of mask generation At the end of the previous section, we have obtained the classification and regression information of the image to be detected. we extract the regression information (that is, the border information of the target to be detected) separately, and combine the pyramid feature mrcnn_feature_maps, generate a mask. # Detections # output is [batch, num_detections, (y1, x1, y2, x2, class_id, score)] in # normalized coordinates

Python Computer Vision: Chapter 1 Image Processing basics, python Image ProcessingChapter 1 basics of image processing

after filtering, divided into M regions.application II. TrackingThe target tracking algorithm based on mean shift is used to calculate the eigenvalue probability of the pixels in the target region and the candidate region, and then the target model and the candidate model are described, then the similarity function is applied to measure the similarities between the initial frame target model and the current frame candidate region. Select the candidate model with the maximal similarity function

Chen Yu Si Yuan[Http://yuhuazou.sinaapp.com] FoundA good academic conference calendar siteHttp://www.confsearch.orgYou can also use the embedded framework (embeddedIFRAME) integrated into your own web page, easy to use. Some computer vision-related meetings were selected here, which were updated from time to time, The csdn blog is really a weakness and cannot be embedded. If you want to learn more, please

To protect the vision of the revolution, especially for office workers in front of the computer, pay attention to the following points: The distance from the eye to the screen is kept above 60 centimeters. The farther the better, if you can't see the screen, increase the font. The vertical position of the screen is between 15 degrees and 50 degrees under the eye horizontal line. It is not only good fo

: flannbasedmatcher to optimize the training process and create an index tree for the descriptor, this operation will play a huge role in matching a large amount of data (for example, searching for matching images in a data set of hundreds of images ). Brute-force matcher does not operate in this process, but stores train descriptors in the memory.Sample Code #include Lab results References FLANN project HomepageFLANN manual PDFLearning opencv -- SURF (feature points) FLANNOpencv documentation

criteria for the underlying meanShift());Termcriteria template class This class is used as the termination condition of the iteration algorithm. Its constructor requires three parameters:One is type, the second parameter is the maximum number of iterations, And the last parameter is a specific threshold. TermCriteria(int type, int maxCount, double epsilon); The types include cv_termcrit_iter, cv_termcrit_eps, cv_termcrit_iter + cv_termcrit_eps, which indicate that the iteration termination co

rewrite the above code -img = Cv2.imread ('Beauti.jpeg', Cv2. Imread_grayscale) - PrintImg.shape - #Img[0][0] = 0 in PrintImg.item (0,0) - Img.itemset ((0,0), 0) toCv2.imwrite ('Mypic-gray.png', IMG) + - #Remove the green channel theimg = Cv2.imread ('Beauti.jpeg') *img[:,:,1] =0 $Cv2.imwrite ('No_green.png', IMG)Panax Notoginseng PrintImg.shape,img.size,img.dtype - theimg = Cv2.imread ('Beauti.jpeg') + #To display a picture, you must enter two parameters ACv2.imshow ('My Image', IMG) the #Wi

://mmlab.ie.cuhk.edu.hk/projects/srcnn.htmlCode:http://mmlab.ie.cuhk.edu.hk/projects/srcnn.htmlpeople(1) Ross B. girshick-the Author of Rcnn, FAST-RCNNwebsite:http://www.cs.berkeley.edu/~rbg/#girshick2014rcnnGithub:https://github.com/rbgirshick(2) shaoqing ren-the Author of FASTER-RCNN, spp-netwebsite:http://home.ustc.edu.cn/~sqren/Github:https://github.com/shaoqingren(3) Georg nebehay-the Author of CMTWebsite:http://www.gnebehay.comGithub:https://github.com/gnebehay(4) Jianchao yang-the Author

()Mask =numpy.uint8 (Numpy.ones (gray.shape)) keypoints=S.detect (Gray, mask)#displaying images and feature pointsVis =Cv2.cvtcolor (Gray, Cv2. COLOR_GRAY2BGR) forKinchKeypoints[::10]: cv2.circle (Vis, (int (k.pt[0)), int (k.pt[1]), 2, (0, 255, 0), 1) cv2.circle (Vis, (int (k.pt[0)), int (k.pt[1])), int (k.size), (0, 255, 0), 2) Cv2.imshow ('Local descriptors', Vis) Cv2.waitkey () Cv2.imwrite ('c:/users/public/pictures/sample pictures/koala2.jpg', Vis)②duang~ jumping out of a picture like this

that the human eye is not sensitive to the image due to insufficient illumination.　　McCann AlgorithmThis algorithm is intended to produce a better estimate of the uneven illumination, the extraction of the control information is no longer the weighted mode of the Gaussian convolution, but the intensity of a spiral is selected to be weighted, compared with the Gaussian weighting so that the greater range of illumination information can be obtained, and the specified number of iterations based on

fromMatplotlib Import Pyplot aspltimg=cv2.imread ("1.jpg") Gray=Cv2.cvtcolor (Img,cv2. Color_bgr2gray) #颜色转为灰度ret, Thresh=cv2.threshold (Gray,0,255, Cv2. thresh_binary_inv+Cv2. Thresh_otsu) #可为图像设一个阈值kernel=np.ones ((3,3), np.uint8) opening=cv2.morphologyex (Thresh,cv2. morph_open,kernel,iterations=2) #去除噪声sure_bg=cv2.dilate (opening,kernel,iterations=3) Dist_transform=cv2.distancetransform (Opening,cv2. DIST_L2,5) #可以通过distanceTransform来获取确定的前景区域. That is, this is the most likely foreground ar