Reputation: 143
I want to improve my project which is designed for object detection.
Firstly, to get my actual result I use absdiff
, and next I use the following operations are in my code below:
cv::threshold(subtractionResultEdges, threshold, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
Sobel(threshold, sobel, CV_32F, 1, 0);
minMaxLoc(sobel, &minVal, &maxVal);
sobel.convertTo(sobel, CV_8U, 255.0 / (maxVal - minVal), -minVal * 255.0 / (maxVal - minVal));
dilate(subtractionResultEdges, subtractionResultEdges, verticalStructreMat, Point(-1, -1));
erode(subtractionResultEdges, filteredResult, verticalStructreMat, Point(-1, -1));
Canny(filteredResult, filteredResult, 33, 100, 3);
My last operation is findContours(canny_output, *contours, *hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
This is my result after use every function and foreground which I get with using accumulate
function (20 frames) :
foreground: http://j71i.imgup.net/foregroundc3dc.PNG
subtraction: http://p81i.imgup.net/subtractio2866.PNG
Sobel: http://g51i.imgup.net/sobela1fb.PNG
threshold: http://p46i.imgup.net/treshold14c9.PNG
dilate, erode and Canny:
http://q68i.imgup.net/canny2e1a.PNG
findContours: http://v76i.imgup.net/contours6845.PNG
Background is also obtained from accumulate
function.
Could you help me get better corner or contours detection? I need it, to get object size in pixels.
Thanks in advance!
Upvotes: 1
Views: 673
Reputation: 6822
Use a larger kernel for dilate/erode part, maybe (11, 11) or even bigger, or alternatively do multiple iterations (this can be set as a parameter. This should connect the individual parts of your detected object better and then you'll have less contours.
To calculate area, you can then use contourArea()
Upvotes: 1