opencv-contour

This must be simple for C++ developers using OpenCV directly. However what I'm using is Emgu (an OpenCV wrapper for .NET) and in the latest version we have the method CvInvoke.FindContours returning void, the output result is passed by parameter reference and is of type VectorOfVectorOfPoint . Here is a simple call: //outputResult is a VectorOfVectorOfPoint CvInvoke.FindContours(inputImage, outputResult, null, RetrType.Tree, ChainApproxMethod.ChainApproxSimple); For RetrType.List mode, we can just convert the result to some array of arrays and loop through all the contours easily. However here

I have used an adaptive thresholding technique to create a picture like the one below: The code I used was: image = cv2.adaptiveThreshold(image, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 45, 0) Then, I use this code to get contours: cnt = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[0] My goal is to generate a mask using all the pixels within the outer contour, so I want to fill in all pixels within the object to be white. How can I do this? I have tried the code below to create a mask, but the resulting mask seems no different then the image after

I am working with openCv and python and I am dealing with Structural Analysis and Shape Descriptors. I have found this blog: http://opencvpython.blogspot.it/2012/06/contours-2-brotherhood.html that's very helpful and I have tried with a black and white image to drawing a bounding rectangle and it works. But now from an image i extract, for example, the yellow color and on that i would like to draw a bounding rectangle. The problem is that the black and white image is not uniform it has some noise and like that the code doesn't recognize the whole shape. And this is the code: import numpy as np

I am trying to detect the biggest/larger rectangular shape and draw bounding box to the detected area. In my use case, very often (and not always) the object that represent the rectangle shape is in color white and the background is also in color very similar to white. Before detecting contours, I have preprocessed the image in order to detect perfect edge. My problem is that I can't detect edges perfectly and i have a lot of noise even after blurring and using 'adaptive threshold' or 'threshold'. The original image i have used for contours detection I have tried different way to detect

My simple python code is this import cv2 img=cv2.imread('Materials/shapes.png') blur=cv2.GaussianBlur(img,(3,3),0) gray=cv2.cvtColor(blur,cv2.COLOR_BGR2GRAY) returns,thresh=cv2.threshold(gray,80,255,cv2.THRESH_BINARY) ret,contours,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) for cnt in contours: area=cv2.contourArea(cnt) #contour area if (area>1220): cv2.drawContours(img,[cnt],-1,(0,255,0),2) cv2.imshow('RGB',img) cv2.waitKey(1000) print(len(cnt)) import numpy as np contours=np.array(contours) print(contours) This worked fine. But recently without me even making any

Here is the original code from matlab: % Calculate each separated object area cDist=regionprops(bwImg, 'Area'); cDist=[cDist.Area]; % Label each object [bwImgLabeled, ~]=bwlabel(bwImg); % Calculate min and max object size based on assumptions on the color % checker size maxLabelSize = prod(size(imageData)./[4 6]); minLabelSize = prod(size(imageData)./[4 6]./10); % Find label indices for objects that are too large or too small remInd = find(cDist > maxLabelSize); remInd = [remInd find(cDist < minLabelSize)]; % Remove over/undersized objects for n=1:length(remInd) ri = bwImgLabeled == remInd(n);