I am working on an image to find contours, I am successfully able to identify almost all contours in the below image. But I am not able to find certain contours like the two grey boxes on the left side. I have tried all the contour methods like Tree, List, External and also tried with thresholding.
Please suggest If I am missing something or can do to improve it
img = cv2.imread(input_image, 0)
kernel = np.ones((5, 5),np.uint8)
morphological_img = cv2.morphologyEx(img, cv2.MORPH_GRADIENT, kernel)
canny_img = cv2.Canny(morphological_img, 200, 300)
input_imag, contours, hierarchy =
cv2.findContours(canny_img,cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # get contours
Your question does not show or link to the image that it refers to. But here are the steps to improve this:
Unless your image is already binary, the standard method is to apply thresholding first, and then use morphological operations.
If you are interested in only the contours, you can first do canny edge detection and then use morphological operations to enhance the contour that you want to detect.
If canny_img does not show the edge corresponding to the desired contour (try cv2.imshow), findContours want be of much use, if any.
If you can edit the answer to include the image, we will be able to provide more specific answers.
Related
I am fairly new to OpenCV and am currently learning how to extract digits from images using contours. The image I am referring to can be found here:
The problem I'm running into is that the grayscale values of the numbers and lines/dots can be quite similar and as a result, their contours are merged. How would I resolve this?
The code I have so far looks like this:
img = cv2.imread(filename=('data/6737,K.png'))
imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY);
thresh = cv2.adaptiveThreshold(imgray,255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
If OpenCV has an option that allows separating contours per grayscale value, it might be possible to extract individual digits. Or maybe there's another solution to this that I haven't tried?
Any help would be appreciated.
Solved by setting pixels from columns with a low summed grayscale value to the background color (i.e. cutting the horizontal line). Thanks!
I'm trying solve the recognition problem with a help OpenCV library for C++.
I have a some text(below) and i want to separate each symbol in this text using by cvFindContours(...) function. After, I want to send each separated symbol on the input of neural network for recognition it. It's all ok. I will can get all contours in my image and i can drawn it on my image with a help cvDrawContours(...) function(below). But cvFindContours(...) returns unordered sequence(pointer on the first contour in this sequence) where contains all the found contours. For my task order is very important.
CVAPI(int) cvFindContours( CvArr* image, CvMemStorage* storage, CvSeq** first_contour,
int header_size CV_DEFAULT(sizeof(CvContour)),
int mode CV_DEFAULT(CV_RETR_LIST),
int method CV_DEFAULT(CV_CHAIN_APPROX_SIMPLE),
CvPoint offset CV_DEFAULT(cvPoint(0,0)));
-image- source image
-storage- for storing where contains contours
-first_contour- pointer to the first contour in the storage
-mode- mode of search (I use the CV_RETR_EXTERNAL for search external contours)
-method- method of approximation (I'm using the CV_CHAIN_APPROX_SIMPLE by default)
How can I make the cvFindContours(...) function that returns the contours in the order in which they in the picture? Is it possible?
Thanks!
You can't directly force findContours to yield contours in a certain order (I mean there is no parameter to tune this in the function call).
To sort your contours in a "read text" order, you could do a loop which goes through all your contours and retrieves for each contour the top-leftest point, either by directly going through all points in each contour object, or by using a boundingbox (see minAreaRect for example).
Once you have all these points, sort them from left to right and bottom to top (some adjustments will probably have to be made, like detecting all contours starting within a range of heights to be all part of the same text line)
You have found bounding rectangles for all the contours present in your image. Instead of going about with the left-most point approach, you can sort your contours based on the centroid of each contour, which is more robust since your approach is being for text.
THIS ANSWER from the OpenCV community might help provide a start
my matlab code is:
h = fspecial('average', filterSize);
imageData = imfilter(imageData, h, 'replicate');
bwImg = im2bw(imageData, grayThresh);
cDist=regionprops(bwImg, 'Area');
cDist=[cDist.Area];
opencv code is:
cv::blur(dst, dst,cv::Size(filterSize,filterSize));
dst = im2bw(dst, grayThresh);
cv::vector<cv::vector<cv::Point> > contours;
cv::vector<cv::Vec4i> hierarchy;
cv::findContours(dst,contours,hierarchy,CV_RETR_CCOMP, CV_CHAIN_APPROX_NONE);
here is my image2blackand white function
cv::Mat AutomaticMacbethDetection::im2bw(cv::Mat src, double grayThresh)
{
cv::Mat dst;
cv::threshold(src, dst, grayThresh, 1, CV_THRESH_BINARY);
return dst;
}
I'm getting an exception in findContours() C++ exception: cv::Exception at memory location 0x0000003F6E09E0A0.
Can you please explain what am I doing wrong.
dst is cv::Mat and I used it all along it has my original values.
Update here is my matrix written into *.txt file:
http://www.filedropper.com/gili
UPDATE 2:
I have added dst.convertTo(dst,CV_8U); like Micka suggested, I no longer have an exception. however values are nothing like expected.
Take a look at this question which has a similar problem to what you're encountering: Matlab and OpenCV calculate different image moment m00 for the same image.
Basically, the OP in the linked post is trying to find the zeroth image moment for both x and y of all closed contours - which is actually just the area, by using findContours in OpenCV and regionprops in MATLAB. In MATLAB, that can be accessed by the Area property from regionprops, and judging from your MATLAB code, you wish to find the same quantity.
From the post, there is most certainly a difference between how OpenCV and MATLAB finds contours in an image. This boils down to the way both platforms consider what is a "connected pixel". OpenCV only uses a four-pixel neighbourhood while MATLAB uses an eight-pixel neighbourhood.
As such, there is nothing wrong with your implementation, and converting to 8UC1 is good. However, the areas (and ultimately the total number of connected components and contours themselves) between both contours found with MATLAB and OpenCV are not the same. The only way for you to get exactly the same result is if you manually draw the contours found by findContours on a black image, and using the cv::moments function directly on this image.
However, because of the differing implementations of cv::blur() in comparison to fspecial with an averaging mask that is even, you still may not be able to get the same results along the borders of the image. If there are no important contours around the borders of your image, then hopefully this will give you the right result.
Good luck!
For my college project I need to identify a species of a plant from plant leaf shape by detecting edges of a leaf. (I use OpenCV 2.4.9 and C++), but the source image has taken in the real environment of the plant and has more than one leaf. See the below example image. So here I need to extract the edge pattern of just one leaf to process further.
Using Canny Edge Detector I can identify edges of the whole image.
But I don't know how to proceed from here to extract edge pattern of just one leaf, may be more clear and complete leaf. I don't know even if this is possible also. Can anyone please tell me if this is possible how to extract edges of one leaf I just want to know the image peocessing steps that I need to apply to the image. I don't want any code samples. I'm new to image processing and OpenCV and learning by doing experiments.
Thanks in advance.
Edit
As Luis said said I have done Morphological close to the image after doing edge detection using Canny edge detection, but it seems still it is difficult me to find the largest contour from the image.
Here are the steps I have taken to process the image
Apply Bilateral Filter to reduce noise
bilateralFilter(img_src, img_blur, 31, 31 * 2, 31 / 2);
Adjust contrast by histogram equaliztion
cvtColor(img_blur,img_equalized,CV_BGR2GRAY);
Apply Canny edge detector
Canny(img_equalized, img_edge_detected, 20, 60, 3);
Threshold binary image to remove some background data
threshold(img_edge_detected, img_threshold, 1, 255,THRESH_BINARY_INV);
Morphological close of the image
morphologyEx(img_threshold, img_closed, MORPH_CLOSE, getStructuringElement(MORPH_ELLIPSE, Size(2, 2)));
Following are the resulting images I'm getting.
This result I'm getting for the above original image
Source image and result for second image
Source :
Result :
Is there any way to detect the largest contour and extract it from the image ?
Note that my final target is to create a plant identification system using real environmental image, but here I cannot use template matching or masking kind of things because the user has to take an image and upload it so the system doesn't have any prior idea about the leaf.
Here is the full code
#include <opencv\cv.h>
#include <opencv\highgui.h>
using namespace cv;
int main()
{
Mat img_src, img_blur,img_gray,img_equalized,img_edge_detected,img_threshold,img_closed;
//Load original image
img_src = imread("E:\\IMAG0196.jpg");
//Apply Bilateral Filter to reduce noise
bilateralFilter(img_src, img_blur, 31, 31 * 2, 31 / 2);
//Adjust contrast by histogram equaliztion
cvtColor(img_blur,img_equalized,CV_BGR2GRAY);
//Apply Canny edge detector
Canny(img_equalized, img_edge_detected, 20, 60, 3);
//Threshold binary image to remove some background data
threshold(img_edge_detected, img_threshold, 15, 255,THRESH_BINARY_INV);
//Morphological close of the image
morphologyEx(img_threshold, img_closed, MORPH_CLOSE, getStructuringElement(MORPH_ELLIPSE, Size(2, 2)));
imshow("Result", img_closed);
waitKey(0);
return 0;
}
Thank you.
Well there is a similar question that was asked here:
opencv matching edge images
It seems that edge information is not a good descriptor for the image, still if you want to try it I'll do the following steps:
Load image and convert it to grayscale
Detect edges - Canny, Sobel try them and find what it suits you best
Set threshold to a given value that eliminates most background - Binarize image
Close the image - Morphological close dont close the window!
Count and identify objects in the image (Blobs, Watershed)
Check each object for a shape (assuming you have described shapes of the leaf you could find before or a standard shape like an ellipse) features like:
http://docs.opencv.org/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html
http://www.math.uci.edu/icamp/summer/research_11/park/shape_descriptors_survey.pdf
If a given object has a given shape that you described as a leaf then you detected the leaf!.
I believe that given images are taken in the real world these algorithm will perform poorly but it's a start. Well hope it helps :).
-- POST EDIT 06/07
Well since you have no prior information about the leaf, I think the best we could do is the following:
Load image
Bilateral filter
Canny
Extract contours
Assume: that the contour with the largest perimeter is the leaf
Convex hull the 3 or 2 largest contours (the blue line is the convex hull done)
Use this convex hull to do a graph cut on the image and segmentate it
If you do those steps, you'll end up with images like these:
I won't post the code here, but you can check it out in my messy github. I hope you don't mind it was made in python.
Leaf - Github
Still, I have a couple of things to finish that could improve the result.. Roadmap would be:
Define the mask in the graphcut (like its described in the doc)
Apply region grow may give a better convex hull
Remove all edges that touch the border of the image can help to identify larger edges
Well, again, I hope it helps
I am currently working on image processing project. I am using Opencv2.3.1 with VC++.
I have written the code such that, the input image is filtered to only blue color and converted to a binary image. The binary image has some small objects which I don't want. I wanted to eliminate those small objects, so i used openCV's cvFindContours() method to detect contours in Binary image. but the problem is I cant eliminate the small objects in the image output. I used cvContourArea() function , but didn't work properly.. , erode function also didn't work properly.
So please someone help me with this problem..
The binary image which I obtained :
The result/output image which I want to obtain :
Ok, I believe your problem could be solved with the bounding box demo recently introduced by OpenCV.
As you have probably noticed, the object you are interested at should be inside the largest rectangle draw in the picture. Luckily, this code is not very complex and I'm sure you can figure it all out by investigating and experimenting with it.
Here is my solution to eliminate small contours.
The basic idea is check the length/area for each contour, then delete the smaller one from vector container.
normally you will get contours like this
Mat canny_output; //example from OpenCV Tutorial
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
Canny(src_img, canny_output, thresh, thresh*2, 3);//with or without, explained later.
findContours(canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0,0));
With Canny() pre-processing, you will get contour segments, however each segment is stored with boundary pixels as a closed ring. In this case, you can check the length and delete the small one like
for (vector<vector<Point> >::iterator it = contours.begin(); it!=contours.end(); )
{
if (it->size()<contour_length_threshold)
it=contours.erase(it);
else
++it;
}
Without Canny() preprocessing, you will get contours of objects.
Similarity, you can also use area to define a threshold to eliminate small objects, as OpenCV tutorial shown
vector<Point> contour = contours[i];
double area0 = contourArea(contour);
this contourArea() is the number of non-zero pixels
Are you sure filtering by small contour area didn't work? It's always worked for me. Can we see your code?
Also, as sue-ling mentioned, it's a good idea to use both erode and dilate to approximately preserve area. To remove small noisy bits, use erode first, and to fill in holes, use dilate first.
And another aside, you may want to check out the new C++ versions of the cv* functions if you weren't aware of them already (documentation for findContours). They're much easier to use, in my opinion.
Judging by the before and after images, you need to determine the area of all the white areas or blobs, then apply a threshold area value. This would eliminate all areas less than the value and leave only the large white region which is seen in the 2nd image. After using the cvFindContours function, try using 0 order moments. This would return the area of the blobs in the image. This link might be helpful in implementing what I've just described.
http://www.aishack.in/2010/07/tracking-colored-objects-in-opencv/
I believe you can use morphological operators like erode and dilate (read more here)
You need to perform erosion with a kernel size near to the radius of the circle on the right (the one you want to eliminate).
followed by dilation using the same kernel to fill the gaps created by the erosion step.
FYI erosion followed by dilation using the same kernel is called opening.
the code will be something like this
int erosion_size = 30; // adjust with you application
Mat erode_element = getStructuringElement( MORPH_ELLIPSE,
Size( 2*erosion_size + 1, 2*erosion_size+1 ),
Point( erosion_size, erosion_size ) );
erode( binary_img, binary_img, erode_element );
dilate( binary_img, binary_img, erode_element );
It is not a fast way but may be usefull in some cases.
There is a new function in OpencCV 3.0 - connectedComponentsWithStats. With it we can get area of connected components and eliminate unnecessary. So we can easy remove circle with holes, with the same bounding box as solid circle.