oculus cv2

years has been a rapid development, but in the corresponding experience design is still a long way to go. I spent a lot of time trying to find some practical VR interaction and interface design principles, but not much, only some documents from relevant companies, such as Google, Oculus, etc., as well as some of the design pioneers of the article. So we only learn while doing, and try to sum up their own thinking and practical experience. 　　Interact

software vendors, there will be more dedicated applications in the future. Three. Oculus Rift head wear monitor Reference price: About 300 USD (about 1852 RMB) Oculus Rift is not as wide as the use of smart glasses, but for game enthusiasts, it's definitely an immersive and inexpensive solution, and it's an official debut at CES this year, attracting a lot of people's attention. The head-wo

(v:value): The value range is 0 (black) ~255 (white); Effect Show Implementation ideas As shown above, all we have to do is identify the Green Piggy page in the video, and here's the identification step: The HSB value of the Piglet page color using PS is equivalent to the HSV of OpenCV, but the HSV (HSB) value of PS is: 0~360, 0~1, 0~1, and the HSV of OpenCV is: 0~180, 0~255, 0~255, so the HSV of PS is processed, H /2, sv*255; Use the OPENCV bit "and operation" to extract the color po

The arithmetic operation of the pattern.#-*-Coding:utf-8-*-"" "Created on Wed Sep-11:54:47 2016@author:administrator" "" "" Arithmetic (arithmetic) Operations on Imag Escv2.add (), cv2addweighted () ' Import numpy as Npimport cv2 ' ' There is a difference between OpenCV addition and NumPy Addi tion. OpenCV addition is a saturated operation and Numpy addition is a modulo operation. " x = Np.uint8 ([+]) y = np.uint8 ([10]) # Only when the result is more

Function:Create two sliders to control the size of the Gaussian kernel and σ The size of this program is changed on the threshold partition of the program. The threshold splitting program is in thisNote: because σ=0 , OpenCV calculates the window size based on the σ , so, sliding from 0 σ Slider, there is a clear and blurred first edgePYTHON+OPENCV Realization of threshold segmentationPython+opencv realize

, is really good things (big sense Bo main selfless):http://bbs.csdn.net/topics/390837851Http://pan.baidu.com/s/1jG62bhOThen there is the following Blogger series blog, very good, able to learn:http://blog.csdn.net/sunny2038/article/category/904451Under a lot of information, simply record your own study notes.(iii) Start from the beginning – Read the imageRead images using OpenCV under Python.First need to import OPENCV package, the above said that the Ipython does not have OPENCV package, so yo

The work requires the camera to be debugged, the Python platform greatly improves debugging efficiency.Find the segment code from the net, you can key out the blue from the camera image.Import Cv2Import NumPy as NPCap = Cv2. Videocapture (0)For I in range (0, 19):Print (Cap.get (i))while (1):RET, frame = Cap.read ()HSV = Cv2.cvtcolor (frame, cv2. COLOR_BGR2HSV)Lo

imutils.video import FPS import NumPy as NP Impo RT argparse Import imutils Import time import cv2 Videopath = "/home/user/desktop/test.mp4" # construct the argument pars E and parse the arguments ap = argparse. Argumentparser () ap.add_argument ("P", "--prototxt", default= '/home/user/desktop/mobilenetssd_deploy.prototxt ', help= "Path to Caffe ' deploy ' prototxt file") ap.add_argument ("-M", "--model", default= "/HOME/USER/DESKTOP/MOBILENETSSD

Prepare file: Python 3.5, PIP10, notepad++ 1. Download python3.5 Installation First Reference to: Liaoche tutorial Https://www.liaoxuefeng.com/wiki/0014316089557264a6b348958f449949df42a6d3a2e542c000/ 0014316090478912dab2a3a9e8f4ed49d28854b292f85bb000 2. Installation PIP10 (reference: https://blog.csdn.net/qq_14845119/article/details/52354394) For decompression, for example, I unzipped to the e-packing directory. cmd command into unpack directory, execute, python setup.py install Installation o

1, firstOriginal: Test1.pngMask diagram: Test-mask.pngResult diagram: Mask.png2. Code partImportCv2 fromPILImportImageImportNumPy as Npyuantu="Test1.png"masktu="Test1-mask.png"#overlay pictures with OpenCVIMG1 =Cv2.imread (yuantu) Img2=Cv2.imread (masktu) Alpha= 0.5Meta= 1-Alphagamma=0#cv2.imshow (' img1 ', IMG1)#cv2.i

Git:https://github.com/linyi0604/computer-vision1 #Coding:utf82 3 ImportCv24 ImportNumPy as NP5 6 7 #Read in Image8img = Cv2.imread (".. /data/line1.png")9 #Convert to Grayscale imageTenGray =Cv2.cvtcolor (IMG, Cv2. Color_bgr2gray) One #Canny edge detection Aedges = Cv2. Canny (Gray, 50, 100) - """ - Canny edge detecti

moment.I've never been too sure. Geometric moments can play a role in plant leaf identification, especially for occluded leaves. But just to learn here, try to calculate Hu moments under Python . The images used are the color images that were split up before:We've got a two value image of them:We use these two two-value images as the input image for the moment.#-*-coding:utf-8-*-import cv2import numpy as Np#import matplotlib.pyplot as Pltdir = ' F:\\projects\\src\\opencv\\imag Es\\cotton\\ '; #