opencv color detection

the arc center. It is usually set to 1. The fourth parameter represents the minimum distance between two different circles. Because the distance is calculated by the center of the circle, the detection of concentric circles is powerless. Note that the Circle Detection Function can use grayscale images. The Circle Detection code is as follows: // Circle

through the second stage of detection. If a window passes all the tests, the window is considered to be the face area.This plan is not very handsome!!!The author divides more than 6,000 features into 38 stages, and the first five phases feature numbers of 1,10,25,25 and 50 respectively. (The two features in fact are the best features obtained from Adaboost).According to authors, on a average, features out of 6000+is evaluated per Sub-window.The above

; ", Source_window, maxcorners, Maxtrackbar, Cornershitomasi_demo); Namedwindow (Corners_window, cv_window_autosize); Namedwindow (Source_window, cv_window_autosize); Imshow (Source_window, SRC); Cornerharris_demo ( 0 , 0 ); Cornershitomasi_demo ( 0 , 0 ); It is also necessary to say that the OpenCV 2.4.2 in the corner detection of the sample code to trace some of the problem is that the surf shou

OpenCV provides four different edge detection operators, or High-pass filters: Sobel,scharr and Laplacian operators and canny operators, the specific detection steps are as follows: image filtering : The algorithm of edge detection is based on first and second derivative of image gray value, but the derivative is usual

algorithm to detect the edge of the image, the principle of the canny algorithm is not introduced in detail, we can consult the information on the Internet, this paper directly using the OPENCV with the detection function to detect. Edge detection for a single image imgprocess:: edgedetect: First, the image is converted from a

The second chapter describes how to implement the calibration-based Augmented reality on the iOS platform, including the following four aspects: 1. Build opencv project on iOS platform 2. Marker Detection and Recognition 3. Camera Calibration and marker pose estimation 4. rendering a 3D virtual object based on marker The first part is the development of the iOS platform. I am not too concerned about it. The

The interaction between humans and robots constantly evolve and adopt different tools and software to increase the comfort of humans. In this article, I explore nine tutorials which show you different methods to detect and recognize hand. The OpenCV library is not enough to start your project. This library provides for you the software side, but for you also need hardware. In the hardware category enters a developed platform able to run the

color area can be implemented using the following rules: Reference Papers of the above algorithms: Adaptive Skin Color modeling using the skin locus.pdf A novel method for detecting lips, eyes and faces in real time Articles related to Baidu Library: A Rapid face detection algorithm based on the mixed skin color mode

Cvcanny function of Image edge detection--OPENCV Category: C/ void Cvcanny (const cvarr* image, cvarr* edges, double threshold1, double threshold2, int aperture_size=3); Image single-channel input images. Edges the output image of a single-channel storage edge threshold1 The first threshold threshold2 the second threshold Aperture_sizesobel operator kernel size (see Cvsobel). The function Cvcanny uses the C

Tags: des style blog HTTP color OS ar use Feature Points, also known as points of interest and key points, are highlighted and representative points in the image. Through these points, we can identify images, perform image registration, and perform 3D reconstruction. This article mainly introduces several functions in opencv to locate and represent key points. I. Harris corner A corner is the most bas

. cmake_exe_linker_flags:string=-LPTHREAD-LRT Continue make The following error may occur Code: Select All CMake Error at/home/xlab/zhouhua/opencv/opencv-2.4.9/cmake/cl2cpp.cmake:50 (String): String does not recognize Sub-command MD5 MAKE[2]: * * * [modules/ocl/opencl_kernels.cpp] Error 1 MAKE[1]: * * * [Modules/ocl/cmakefiles/opencv_ocl.dir/all] Error 2 Make: * * * [ALL] Error 2 Delete the contents o

Ycrcb Ycrcb is widely used in video compression and digital image processing. It is a color space based on human eyes. In opencv, the formula for converting from RGB space to ycrcb space is as follows: Note that the formula from RGB to ycrcb is not unique. Here, opencv only uses the formula in Intel IPP. The Delta value is as follows: Here we use 8-bit R

points) result = Cv2.blur (IMG, (5,5)) cv2.imshow (' 2 ', result) #灰度化, is the de-color (similar to vintage photos) Gray=cv2.cvtcolor (result,cv2. Color_bgr2gray) cv2.imshow (' 3 ', gray) #param1的具体实现 for edge detection canny = cv2. Canny (IMG, max, cv2.imshow) (' 4 ', Canny) #霍夫变换圆检测circles = Cv2. Houghcircles (Gray,cv2. HOUGH_GRADIENT,1,50,PARAM1=80,PARAM2=30,MINRADIUS=15,MAXRADIUS=20) #输出返回值 for e

Reference: Pedestrian detection using hog features and SVM Classifier:Http://blog.csdn.net/carson2005/article/details/7841443 Hog + SVM has excellent Pedestrian detection effects due to its characteristics, but it also has good effects on other targets. Here we will expand the scope. Carson2005's blog article describes how to use opencv to implement sample trai

threshold value. This point is the corner point.To avoid calculating eigenvalues (time-consuming), use the following formula instead:The determinant of the Det matrix, traces of the trace matrix. It has been proved that the value of this equation is higher when two eigenvalues are high. The parameter k is generally set to 0.05~0.5.Harris Corner detection, can be improved by maximal value inhibition, that is, to ensure that the strength of the corner

I have previously introduced a Article on target detection using cascading classifier skip. We found that the Haar features in the library of opencv are only human faces, human organs, and human bodies. Recently, we wanted to perform a human hand detection. We used color features to make it very unreliable, try again

error of the model depends on the training data, the perfect form of the model is a defective model based on the perfect database. At the same time, it is more intuitive to see the shape of the skin color in the RGB colour space than to test the complexion with a picture. I found it. Experimental results of the training data set: The color space is determined by the possibility of 90%,80%,70% and 60% in or

Note: This article is translated from: pedestrian detection OpenCV. Do you know the built-in pedestrian detection method inside the OpenCV? In OpenCV, there is a hog+ linear SVM model that can detect pedestrians in images and videos. If you are not familiar with the directio

Note that the axis of the OPENCV, the x-axis to the right, and the width corresponding to the Y axis downward, and the height of the corresponding; 1. MOG2 and KNN Mog:mixture of Gaussian Import cv2 cap = Cv2. Videocapture ('./data/video/768x576.avi ') knn_sub = CV2.CREATEBACKGROUNDSUBTRACTORKNN () mog2_sub = CV2.CREATEBACKGROUNDSUBTRACTORMOG2 () while True: ret, frame = Cap.read () if not ret: break Mog_ Sub_mask = mog2_sub.app

than the minimum confidence If confidence > args["confidence"]: # Extract the index of the class label from the ' detections ', # then compute the (x, y)-coordinates of the bounding box for # the object idx = Int (det Ections[0, 0, I, 1]) box = detections[0, 0, I, 3:7] * Np.array ([w, H, W, H]) (StartX, Starty, EndX , EndY) = Box.astype ("int") # Display the prediction label = ' {}: {:. 2f}% '. Format (Classes[idx], C Onfidence *) Print ("[INFO] {}". Format (label)) Cv2.rectangle (f