oculus cv2

since entering the 2016, the topic of VR has been on the go. In the first half, VR hosts and high-end graphics cards were also Vive as the two largest VR devices for HTC and Oculus Rift were sold. As we all know, rely on the PC to drive the VR head to the PC hardware requirements, especially the independent graphics, want to smooth drive VR head you need a power to the PC. Then, check your PC is not qualified when the time comes, if your PC does not m

Draw geometry with OpenCV.Import NumPy as Npimport cv2# Create a black imageimg = Np.zeros ((521,512,3), np.uint8) # Draw a diagonal blue line with th Ickness of 5 px# background data, straight start, line end, color, line weight img = Cv2.line (IMG, (0,0), (511,511), (255,0,0), 5) # Print (IMG) # Drawing rectangle# " Background (contains content previously drawn to IMG) ", upper left corner of rectangle, l

thinks that: where the new gray value is greater than or equal to the threshold of pixels is the edge point, this judgment is not reasonable, will cause the edge of the false, because many noise points of gray value is also very large.import cv2image=cv2.imread(‘E:\PyProjects\DataSet\FireAI/chair.jpg‘)# Sobel 算子进行图像边缘检测sobel_h=cv2.Sobel(image,cv2.CV_64F,1,0,ksiz

Python Image Processing (6): separation of soil and plants, python Image Processing Happy shrimp Http://blog.csdn.net/lights_joy/ Reprinted, but keep the author information Next we try to separate the soil and plants in the image. The goal is to get a green plant image and turn the soil background into black. Test image: First, use 2g-r-b to obtain a grayscale image and Its Histogram: #-*-Coding: UTF-8-*-import cv2import numpy as npimport matplotlib. pyplot as plt # Use 2g-r-b to separate s

#-*-coding:utf-8-*-#author:lyp TIME:2018/8/3ImportCv2ImportNumPy as Npimg= Cv2.imread ('lyp.jpg') grayimg=Cv2.cvtcolor (IMG, Cv2. Color_bgr2gray) Surf= Cv2.xfeatures2d.SURF_create (upright=True) KP, des=Surf.detectandcompute (grayimg, None) Img2= Cv2.drawkeypoints (IMG, KP,

OpenCV can open the camera via the head videocapture () methodCamera variable = cv2. Videocapture (n) n is an integer, the built-in camera is 0, if there are other cameras are 1,2,3,4, ...Cap = Cv2. Videocapture (0)Whether the camera is open can be judged by the isopened () methodCamera variables. isopened ()Returns True if Open returns false anywayCap.isopened ()Boolean variable, image variable = camera va

③ generates a hash code: The difference value is compared to 0. Greater than 0, 1; less than 0, 0. followed by a process with mean hash —————————————————————————————————————————— Second, code implementation: # coding:utf-8 Import CV2 import numpy as NP import time from Glob import Iglob class Hashtracker:def __init__ (sel F, Path): # Initialize Image self.img = cv2.imread (path) Self.gray =

* * output picture **Import the CV2 package in OpenCV, and then have functions like imread,imshow to read and display the picture. Cv2.waitkey (0) to keep the window, waiting for the keyboard input, if the keyboard does not enter, it will always wait.Cv2.destroyallwindows () deletes any window that we want to delete. Focus on Cv2.namedwindow ("image",

, interested students please Google Translate read: https://habrahabr.ru/company/ intel/blog/333612/ I think the DNN module will have a big impact on the OPENCV community. functions and Frames Using the deep learning pre-training model in OPENCV, the first step is to install OPENCV 3.3, and the installation process quantum bit is no longer described in detail ... Here are some of the functions we will use. To load a picture from a disk in DNN: Cv2.dnn

1 #Coding:utf82 ImportCv23 4 """5 Change BGR at (0, 0) to white pixels6 7 No. 0, Green 1th, Blue 2nd, Red8 Each of the IMG locations has a 3-length vector representing the GBR9 """Ten #img = Cv2.imread (".. /data/mm2.jpeg ") One #print (img[0, 0]) # [+] A #img[0, 0] = [255, 255, 255] - #cv2.imshow ("", IMG) - #cv2.waitkey (0) the - - """ - change the blue value

My study notes are mainly recorded in the study opencv-python-tutorials the book of the NotesThis evening learn how OpenCV for Python plots, mainly using these functions (these functions can be in: http://docs.opencv.org/modules/core/doc/drawing_functions.html Found):Start by copying the code from the book, the code is as follows:Import NumPy as NP Import = Np.zeros (512,512,3= Cv2.line (IMG, (), (510,510), (255,0,0), 5=

Use the socket to pass the camera image to the PC. Make sure that you have installed OPENCV and Python to determine your own server-side device IP: First is the server side: Import Socket Import cv2 import numpy def recvall (sock, count): buf = B ' while count:newbuf = SOCK.RECV (count) if not Newbuf:return None buf + = Newbuf count -= Len (newbuf) return bufconn, addr = S.accept () while 1:length = Recvall (conn,16) StringData = Recvall (conn, in

TensorFlow and OpenCV, read pictures, perform simple operations and display 1 OpenCV read into the picture, using TF. Variable initialized to tensor, loaded into TensorFlow to transpose the picture, then OpenCV shows the result of the transpose Import TensorFlow as tf import cv2 file_path = "/home/lei/desktop/" filename = "marshorchid.jpg" image = Cv2.imread (filename, 1)