I am trying to detect the regions of traffic signs. Using OpenCV, my approach is as follows:
The color image:
Using the TanTriggs Preprocessing get rid of the illumination variances:
Equalize histogram:
And binarize (Cv2.Threshold(blobs, blobs, 127, 255, ThresholdTypes.BinaryInv):
Iterate each blob using ConnectedComponents and get the mean color value using the blob as mask. If it is a red color then it may be a red sign.
Then get contours of this blob using FindContours.
Simplify the contours using ApproxPolyDP and check the points of each contour:
If 3 points then triangle shape is acceptable --> candidate for triangle sign
If 4 points then shape is acceptable --> candidate
If more than 4 points, BBox dimensions are acceptable and most of the points are on the ellipse fitted (FitEllipse) --> candidate
This approach works for the separated blobs in the binary image, like the circular 100km sign in my example. However if there is a connection to the outside objects, like the triangle left bottom part in the binary image, it fails.
Because, the mean value of this blob is far from red!
Using Erosion helps in some cases, however makes it worse in many of the other images.
Using different threshold values for the binarization also works for some, but fails on many; like the erosion.
Using HoughCircle is just very slow and I couldn't manage to get good results playing with the parameters.
I have tried using matchShapes but couldn't get good results.
Can anybody show me another way the achieve what I want (with a reasonable computational time)?
Any information, or code in any language is wellcome.
Edit:
Using circularity measure (C=P^2/4πA) or the approach I have described above, triangle and ellips shapes can be found when they are separated. However when the contour is like this for example:
I could not find a robust way to extract the triangle piece. If I could, I would check the mean color, and decide if its a red sign candidate.
Sorry, I don't have the kudos to comment, but can't you use the red colour?
import common
myshow = common.myshow
img = cv2.imread("ms0QB.png")
grey = np.zeros(img.shape[:2],np.uint8)
hsv = cv2.cvtColor(img,cv2.COLOR_mask = np.logical_or(hsv[:,:,0]>160,hsv[:,:,0]<10 )
grey[mask] = 255
cv2.imshow("160<hue<182",grey)
cv2.waitKey()
Related
I'm trying to extract the geometries of the papers in the image below, but I'm having some trouble with grabbing the contours. I don't know which threshold algorithm to use (here I used static threshold = 10, which is probably not ideal.
And as you can see, I can get the correct number of images, but I can't get the proper bounds using this method.
Simply applying Otsu just doesn't work, it doesn't capture the geometries.
I assume I need to apply some edge detection, but I'm not sure what to do once I apply Canny or some other.
I also tried sobel in both directions (+ve and -ve in x and y), but unsure how to extract these contours from there.
How do I grab these contours?
Below is some previews of the images in the process of the final convex hull results.
**Original Image** **Sharpened**
**Dilate,Sharpen,Erode,Sharpen** **Convex Of Approximated Polygons Hulls (which doesn't fully capture desired regions)**
Sorry in advance about the horrible formatting, I have no idea how to make images smaller or title them nicely in SOF
I'm working on a project which locates the Machine Readable Zone on ID cards.
For this I need to do some pre processing to extract the ID card from a scanned image which typically are randomly disposed on a white page. I'm able to locate the majority of the cards by using a Histogram equalization with CLAHE before a contour detection. But in some cases the border around the MRZ is totally invisible (white on white) as shown on the attached image.
I'd like to detect rectangle of a predefined shape as I know the shape of the ID card will be always the same but so far I wasn't able to find a way do do something like this with OpenCV.
Basically what I need is to find two rectangle of a fixed ratio that best match the 2 cards on the scan.
I'm wondering if I need to try OpenCV matchers or if there is a simpler way to accomplish this kind of detection.
The solution to you problem is likely going to be matrix transformations. The concept is to pinpoint 4 coordinates on the card that can be easily detected using opencv, such as the the rectangle colored in blue & cyan.
Have coordinates of the card with the predefined shape stored in an array, where a corner of the card is at the 0, 0. Also store the coordinates of the blue * cyan rectangle in an array. With the two arrays you can find the perspective transform of the two arrays using the cv2.getPerspectiveTransform method.
Using the perspective transform found, you can detect the coordinates of the whole card every time you detect the coordinates of the blue & cyan rectangle.
I am trying to do some image processing for which I need to remove the facial features like eyes, nose, lips etc.
I have the following contour points
I am doing inpainting on this image::
Now I have to remove the facial features namely eyes, nose, lips and have the skin in place or that. The thing is that I don't have to do just for this image but for a general image that the user uploads.
I am trying using inpainting but it does create some problem, especially around lips where its having beards in neighbouring pixels and it gives a blackish output like this ::
I tried different contour points and shapes but somewhere or the other its causing problem mainly because of hair or beards. So how to achieve what I am trying to?
Code ::
Photo.inpaint(finalImage,imageROIGRAY,imageROIDest,8,Photo.INPAINT_NS);
I have done dilation also on the mask, but doesn't work.
Showing mask for one of the shape formed using the contour points ::
I am looking for a fast idea/algorithm letting me to find squares (as mark points) in the image file. It shouldn't be so much challenge, however...
I started doing this by changing the color of the source image to a grey scale image and scanning each line of the image looking for two, three longest lines (pixel by pixel).
Then having an array of "lines" I am finding elements which may create the desire square.
The better idea would be to find the pattern with known traits, like: it is square, beyond of the square there are no distortion (there is just white space) etc.
The goal is to analyze the image 5000 X 5000 px in less than 1-2s.
Is it possible?
One of the OpenCV samples squares.cpp does just this, see here for code, . Alternatively you could look up the Hough transform to detect all lines in your image, and then test for two lines intersecting at right angles.
There are also a number of resources on this site which may help you:
OpenCV C++/Obj-C: Detecting a sheet of paper / Square Detection
Are there any opencv function like "cvHoughCircles()" for square detection?
square detection, image processing
I'm sure there are others, these are just the first few I came across.
See Scale-invariant feature transform, template matching, and Hough transform. A quick and inaccurate guess may be to make a histogram of color and compare it. If the image is complicated enough, you might be able to distinguish between several sets of images.
To make the matter simple, assume we have three buckets for R, G, and B. A completely white image would have (100%, 100%, 100%) for (R, G, B). A completely red image would have (100%, 0%, 0%). A complicated image might have something like (23%, 53%, 34%). If you take the distance between the points in that (R, G, B) space, you can compare which one is "closer".
I guess links by chris solved the question :)
I'm new to image processing and I'm working on detecting lines in a document image. I read the theory of Hough line transform but I can't see why I must use Canny before calling that function in opencv like being said in many tutorials. What's the point of finding edges in this case? The fact is that if I don't use Canny or threshold before HoughLines() the results will be very messy. I hope someone will explain for me the reason why.
2 of the tutorials I've read:
Imgproc Feature Detection
Hough Line Transform
Short Answer
cvCanny is used to detect Edges, as well as increase contrast and remove image noise.
HoughLines which uses the Hough Transform is used to determine whether those edges are lines or not. Hough Transform requires edges to be detected well in order to be efficient and provide meaning results.
Long Answer
The Limitations of the Hough Transform are described in more detail on Wikipedia.
The efficiency of the Hough Transform relies of the bin of acculumated pixel being distinct, e.g. a direct contrast between a pixel and its surrounding neighbours or if using a mask region a pixel region and its surrounds regions. If all pixels had similar acculumated values nothing would stand out as a line or circle. This leads to the reduction of colour (colour to grayscale, grayscale to black and white) in order to increase contract.
The number of parameters to the Hough Transform also increase the spread of votes in the pixel bins and increase the complexity of the transform, which mean that normally only lines or circles are reliably detected using it as they have less than 3 parameters.
The edges need to be detected well before running the Hough Transform otherwise its efficiency suffers further. Also noisy images don't work well with Hough transform unless the noise is removed before hand.
First of all, to detect lines you need to work on a boolean matrix image (or binary), I mean: the color is black or white, there's no grayscale.
HoughLines()'s requirement to work properly is to have this kind of image as input. That's the reason you have to use Canny or Treshold, to convert the colored image matrix into a boolean one.
Hough transformation
A line in one picture is actually an edge. Hough transform scans the whole image and using a transformation that converts all white pixel cartesian coordinates in polar coordinates; the black pixels are left out. So you won't be able to get a line if you first don't detect edges, because HoughLines() don't know how to behave when there's a grayscale.
Theoretically, you are correct. Finding edges is not absolutely required for the Hough Line algorithm to work.
The way the Hough works is basically it takes every point and connects it to every other point, and whatever points have the most lines going through them, those lines stay. For this, we need points. The Canny creates those points. Theoretically you could use any sort of filter - isolate all blue or purple points and connect them, whatever - but edges works well.
The Hough also does not weight its lines or points. To the Hough, an image is binary - made up of either 1s or 0, points or not points. There is no need for greyscale, and the canny conveniently returns binary images.
Thus is the Canny always part of the Hough.
all is about processing binary data,
complex data -> (a binary data, b binary data, c binary data, ..) (using canny(),sobel(), etc)
a binary data -> function1() (using houghlines())
b binary data -> function2()
c binary data -> function3() ..
a binary data -X-> function2() ..
complex data -X-> function1() ..
HTH