oculus cv2

Cv_cap_prop_rectification 18 Rectification mark for stereoscopic cameras (note: Only current support DC1394 v 2.x back end) code as follows: #coding: Utf-8import cv2import NumPy as NP "" "Function name: Cv2. Videocapture () function: Capture real-time image data return value via camera: Parameter one: Camera code, 0 is the default camera, notebook built-in camera is generally 0 or fill in the video name directly load

The rectangular border (bounding Rectangle) is said to wrap the found shape with a minimum rectangle. There is also a rectangle with a rotation, the area will be smaller, the effect seeOn the CodeFirst introduce the next Cv2.boundingrect (IMG) functionThis function is very simple, IMG is a binary graph, that is, its parameters;Returns four values, respectively, x,y,w,h;X, y is the coordinates of the top left of the matrix, W,h is the width and height

Nonsense not much to say directly on the code: Import NumPy as NP from Sklearn import datasets x,y = Datasets.make_classification (n_samples=100,n_features=2, n_redundant=0,n_classes=2,random_state=7816) print (x.shape,y.shape) X = X.astype (np.float32) y = y * 2-1 ' detach data ' from Sklearn import model_selection as Ms X_train, X_test, y_train, y_test = Ms.train_test_split ( X, y, test_size=0.2, random_state=42 ) import cv2 SVM =

Happy Shrimphttp://blog.csdn.net/lights_joy/Welcome reprint, but please keep the author informationAfter getting the foreground image of the green plants, we hope to further identify the cotton plants and weeds. The test image is still it:The first thing to do, of course, is to process the image in a sub-region. In the previous step we got a two-value image that identifies green plants, and a natural idea is to use the contour of this binary image for chunking.# to get the contour, our aim is to

1 ImportCv22 ImportNumPy as NP3 4 5 defOLBP (src):6DST = Np.zeros (src.shape,dtype=Src.dtype)7 forIinchRange (1,src.shape[0]-1):8 forJinchRange (1,src.shape[1]-1):9 PassTenCenter =src[i][j] oneCode =0; aCode |= (src[i-1][j-1] >= center) ; -Code |= (src[i-1][j] >= center) ; -Code |= (src[i-1][j+1] >= center) ; theCode |= (src[i][j+1] >= center) ; -Code |= (src[i+1][j+1] >= center) ; -Code |= (src[i+1][j] >= center) ; -Code |= (src[i+1][j-1] >= center) ; +Code |=

years, months, and days. With this, we can finally calculate the reflectivity directly. The rough code is as follows, because it is written to play, and there is no way to deal with it:However, when using remote sensing images for computing and output, you need to use a uint16 array (uint8 is not long enough ..)Some parameters involve floating-point number calculation. If there are extremely high requirements on the processing results, it is best to use a dedicated scientific computing Library

Using opencv in Python (2) (simple geometric image rendering) Simple geometric images generally include vertices, straight lines, matrices, circles, decimals, and polygon. First, let's take a look at the definition of pixels in opencv. One pixel of an image has one or three values. A gray image has a gray value, and a color image has three values to form a pixel value, which shows different colors.Points can form various polygon.(1) draw a straight line first Function:

calculate the matrix of N rows and 128 columns, the number of Star features extracted in each graph is different, so n is different. However, after sift calculation, the feature dimensions are changed to 128 dimensions. Returns the '''keypoints = xfeatures2d matrix of the 128 columns of N rows. stardetector_create (). detect (image) gray = cv2.cvtcolor (image, cv2.color _ bgr2gray) _, feature_vectors = xfe

Recently, because you want to implement a template match, you need to select a target in the video and then frame it (that is, as a template) to detect it using a template matching method. So we need to first select a frame in the video, but the only way I can think of it in the process of using the camera to read the video is:1. In the process of video playback, when you want to select which map to mark the target, press the pause key.2. Frame the target you want to detect.How can that be achie

0 reply content: Yes, divided into two parts 1. Camera input and visual analysis, via javasrit/jsfeat GitHub 2. WebGL 3D rendering and Model Animation, via mrdoob/three. js · GitHub The limitation is that browsers on mobile phones do not support cameras, so you have to wait. Js-sdk can get a single photo, but cannot get continuous camera videos, which means real-time AR cannot be implemented. If you are interested, you can follow the hacker and painter-zhihu column to see Liang Fengtai's AR

Sony PS VR with the mystery of the small black box is what exactly?After two years, Sony once again showed the PlayStation VR (previously called Project Morpheus) at GDC (Game Developers Conference). Sony also brought the final price of the PlayStation VR to $399 (about 2600 yuan), 44980 yen, 399 euros and 349 pounds, after Oculus Rift, HTC Vive and other star products were released. Visible, China's PS VR consumer version is still in the future.Sony

This guide provides a basic configuration for using Oculus Mobilesdk in Androidstudio and Gradle, and attempts to make up for related Android studio documentation.Migrating Eclipse Engineering to Android StudioHow to import an existing Eclipse project into Android Studio, see the instructions provided by Android: Http://developer.android.com/sdk/installing/migrate.html.Start Oculus native example: Import Gr

Simple geometric images generally include points, lines, matrices, circles, ellipses, polygons, and so on. First of all, recognize OpenCV's definition of pixel points. One pixel of the image has 1 or 3 values, a grayscale value for the grayscale image, and a pixel value of 3 values for the color image, showing a different color.Then there is a point to make a variety of polygons.(i) Draw a line firstFunction: Cv2.line (img,point pt1,point pt2,color,th

opencv_haystack =cv2.imread('woman2.bmp')opencv_needle =cv2.imread('face.bmp')ngrey = cv2.cvtColor(opencv_needle, cv2.COLOR_BGR2GRAY)hgrey = cv2.cvtColor(opencv_haystack, cv2.COLOR_BGR2GRAY)# build feature detector and descriptor

floating-point calculation, if the processing results are very high, it is best to use a specialized scientific database (like my slag school do not mind these) Import CV2 Import NumPy as NP import math Img1=cv2.imread (' F:\L71121040_04020030220_B10. TIF ') #图像格式转换 Img10=cv2.cvtcolor (Img1,cv2. Color_bgr2gray) #计算JD

. Cv_cap_prop_fram E_count number of frames in the video file. Cv_cap_prop_format FORMAT of the Matt objects returned by retrieve (). Cv_cap _prop_mode backend-specific value indicating the current capture Mode. cv_cap_prop_brightness brightness of the image (on ly for cameras). cv_cap_prop_contrast Contrast of the "image" (only for cameras). cv_cap_prop_saturation Saturation of the Image (only for cameras). Cv_cap_prop_hue HUE of the image (only for cameras). Cv_cap_prop_gain GAIN of the imag

Python learning-extract palm and some palm prints using opencv-python, The last time we successfully trained the palm reader http://www.cnblogs.com/take-fetter/p/8438747.html, we can get the recognition result. Next, we need to use opencv to obtain the palm of your hand and remove the background. here we need to use mask and region of interest, there are many websites. Extract the palm of your hand based on the frame part of the previous program (of course you can save it yourself-.-) as follow

Because the current plan is familiar with the language and library, and the image feature extraction theory is very boring, and it is likely to be inefficient, so the computer Vision feature extraction This Part skipped, direct start and deep learning with a closer target detection recognition part.This section describes the functions that extract the corner features of an image in OpenCV3:1# coding=utf-82 Import Cv23Import NumPy asNP4 5 6 " "Corner feature extraction of Harris algorithm" "7 8i

Simple geometric images typically include points, lines, matrices, circles, ellipses, polygons, and so on. First of all, recognize OpenCV's definition of pixel points.A pixel of an image has 1 or 3 values. Grayscale image has a gray value, the color image has 3 values to form a pixel value. They show a different color.Then there is a little talent to make up a variety of polygons.(i) Draw a line firstFunction: Cv2.line (img,point pt1,point pt2,color,t

The code below is a real-time image of the OpenCV grab camera in the PYTHON3 environment, and the histogram is calculated by OpenCV's calhist function and displayed in 3 different windows.Import Cv2Import NumPy as NPFrom matplotlib import Pyplot as PltImport timeCap = Cv2. Videocapture (0)For I in range (0, 19):Print (Cap.get (i))while (1):RET, frame = Cap.read ()# color = (' B ', ' G ', ' r ')color = ((255,0,0), (0,255,0), (0,0,255)) for I, col in E