Remove circles from technical drawing image - opencv

When OCRing technical drawing, most (all?) ocr engines have problems with surrounding geometry and sometimes falsely interpret a line as letter.
To improve the quality of the OCR, I first want to remove certain elements from the drawing, mainly circles and rectangels, from the image.
The drawings are all black & white and look very similar to the below example.
What is the best way to achieve this? I've played around with image magick and opencv with little success...


Here's a partial solution. This problem can be broken up into two steps:
1) Remove rectangles by removing horizontal + vertical lines
We create vertical and horizontal kernels then perform morph close to detect the lines. From here we use bitwise operations to remove the lines.
Detected vertical lines (left) and horizontal lines (right)
Removed lines
import cv2
image = cv2.imread('1.jpg')
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,15))
remove_vertical = 255 - cv2.morphologyEx(image, cv2.MORPH_CLOSE, vertical_kernel)
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15,1))
remove_horizontal = 255 - cv2.morphologyEx(image, cv2.MORPH_CLOSE, horizontal_kernel)
result = cv2.add(cv2.add(remove_vertical, remove_horizontal), image)
cv2.imshow('result', result)
cv2.waitKey()
2) Detect/remove circles
There are several approaches to remove the circles
Use cv2.HoughCircles(). Here's a good tutorial to detect circles in images using Hough Circles
Construct a cv2.MORPH_ELLIPSE kernel using cv2.getStructuringElement() then perform morphological operations to isolate the circle contours
Use simple shape detection with contour approximation and contour filtering to detect the circles. This method uses cv2.arcLength() and cv2.approxPolyDP() for contour approximation. One tradeoff with this method is that it only works with "perfect" shapes. Take a look at detect simple geometric shapes and opencv shape detection

Related

Find contour without negative area inside shape

As a follow-up question to finding contiguous black pixels in image, I use OpenCV's findContours() to detect shapes of black on white (I invert the colors for the function to work better). In the images below, OpenCV detects the outer shape of the "g" and the inner bowl as different shapes:
I could use the hierarchy to discard shapes inside other shapes, but I would rather avoid it in case OpenCV detects an overarching contour around the whole image. Does findContours have some tuning that cause it to find only contiguous pixels and not the inside negative shape?

Find shapes on white background. Thinning the lines

I have the following image as a test image:
I attempt to find the shapes on the image (and other images). My approch right now is the following:
Gaussian blur with a 3x3 kernel
Canny edge detection using
list (to get all shapes)
Morphology with MorphOp.Close to close
the edges
FindContours to find contours
Iteration of each contour:
Find ApproxPolyDP
Find ConvexHull
Discard if
hull size < 2, approx area < 200 or hull size > 50000, or arclength
of the approx < 100
Draw convexhull
This method yields the following images where the convex hulls are drawn:
This is almost perfect, but notice that the lines are seen as a contours events->suppliers and events->documents). When looking at the edge information, it becomes apparent why this is so:
The lines are detected as a contour. How could I prepare/find the shapes so the lines are not detected? I though of some thinning algorithm, but since I also work on real life images it is difficult to find a threshold that works. Here is an example of a real life image where thinning is difficult to do because thinning typically requires the images to be monochrome in black and white.
How would you do it? Is there some method to determine if the contour/convex hull is a line, rectangle or something like this?

I ended up using a mix of overlapping test and convexity scan. The convexity scans for the error between the convex hull and the actual contour. If this error exceeds a certain amount, the hull is ignored. The overlapping simply use bitwise and to detech if two convex hull's overlap. If they overlap more than 95% percent, one of them is ignored.

Finding ROI for a periodic repetative fringe pattern

I am trying to detect ROI for a fixed repetitive pattern in an image using opencv C++.
The ROI which I am trying to find - is shown with red boundary as shown in the pic:
I tried canny edge detection after blurring but it detects edge of the vertical/horizontal black and white lines. This is not something I am trying to detect.
What is the best approach to my problem?

Since you're starting with a binary image you could use
findContours()
to get the contours for the individual strips. Since there are a couple of solitary pixels from noise you should then filter for size using
contourArea(contour)
and merge the points of all contours meeting your size criteria into a combined contour. Then get the bounding box for the combined contour:
boundingRect(combinedContour)

Using OpenCV to detect clothing buttons on a piece of paper

I have no background in computer vision, but I was curious to know how I could use OpenCV library to achieve the following:
I have a jar of spare buttons, assorted in colour, style and diameter. For the most part they are circular. I evenly scatter them on a piece of white paper, and under good lighting, take a fairly high resolution picture with your average digital camera. How would I got about slicing this image to grab each button individually as a separate object/image?
Thanks in advance.

Two possible ways:
1) Using the circle hough transform
You run some edge detector (canny/sobel) and then the circle hough transform. You'll get the circles.
2) Using contours
Seperate the button and background using thresholding. Detect contours in this thresholded image and you have the buttons!
Articles that might help:
Contours: http://aishack.in/tutorials/an-introduction-to-contours/
Thresholding: http://aishack.in/tutorials/thresholding/
Hough circles: http://aishack.in/tutorials/hough-circles-in-opencv/
Disclaimer: Those are links to my website.

I think the simplest thing you could try is: run the Canny edge detector and apply a Hough transform to detect circles and generate a separate image from each of the circles.

I've been doing some dish recognition and it worked pretty good. do this:
Do some thresholding (buttons should be shiner than background) to leave only the buttons,
then cvFindContours
for each contour:
run cvFitEllipse, it will return you both axis (a,b) of the fitted ellipse.
check that the area of an ellipse PIab is similar to the Area of the contour using cvContourArea and also that both axis are similar a = b. (this will leave only circles)
then you can do whatever you need.
printContour, using cvPrintContour, use cvMinAreaRect2 to get button bounding box, etc
Hough transform is also possible but it is quite more expensive.

Finding location of rectangles in an image with OpenCV

I'm trying to use OpenCV to "parse" screenshots from the iPhone game Blocked. The screenshots are cropped to look like this:
I suppose for right now I'm just trying to find the coordinates of each of the 4 points that make up each rectangle. I did see the sample file squares.c that comes with OpenCV, but when I run that algorithm on this picture, it comes up with 72 rectangles, including the rectangular areas of whitespace that I obviously don't want to count as one of my rectangles. What is a better way to approach this? I tried doing some Google research, but for all of the search results, there is very little relevant usable information.

The similar issue has already been discussed:
How to recognize rectangles in this image?
As for your data, rectangles you are trying to find are the only black objects. So you can try to do a threshold binarization: black pixels are those ones which have ALL three RGB values less than 40 (I've found it empirically). This simple operation makes your picture look like this:
After that you could apply Hough transform to find lines (discussed in the topic I referred to), or you can do it easier. Compute integral projections of the black pixels to X and Y axes. (The projection to X is a vector of x_i - numbers of black pixels such that it has the first coordinate equal to x_i). So, you get possible x and y values as the peaks of the projections. Then look through all the possible segments restricted by the found x and y (if there are a lot of black pixels between (x_i, y_j) and (x_i, y_k), there probably is a line probably). Finally, compose line segments to rectangles!

Here's a complete Python solution. The main idea is:
Apply pyramid mean shift filtering to help threshold accuracy
Otsu's threshold to get a binary image
Find contours and filter using contour approximation
Here's a visualization of each detected rectangle contour
Results
import cv2
image = cv2.imread('1.png')
blur = cv2.pyrMeanShiftFiltering(image, 11, 21)
gray = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.015 * peri, True)
if len(approx) == 4:
x,y,w,h = cv2.boundingRect(approx)
cv2.rectangle(image,(x,y),(x+w,y+h),(36,255,12),2)
cv2.imshow('thresh', thresh)
cv2.imshow('image', image)
cv2.waitKey()

I wound up just building on my original method and doing as Robert suggested in his comment on my question. After I get my list of rectangles, I then run through and calculate the average color over each rectangle. I check to see if the red, green, and blue components of the average color are each within 10% of the gray and blue rectangle colors, and if they are I save the rectangle, if they aren't I discard it. This process gives me something like this:
From this, it's trivial to get the information I need (orientation, starting point, and length of each rectangle, considering the game window as a 6x6 grid).

The blocks look like bitmaps - why don't you use simple template matching with different templates for each block size/color/orientation?

Since your problem is the small rectangles I would start by removing them.
Since those lines are much thinner than the borders of the rectangles I would start by applying morphological operations on the image.
Using a structural element that looks like this:
element = [ 1 1
1 1 ]
should remove lines that are less than two pixels wide. After the small lines are removed the rectangle finding algorithm of OpenCV will most likely do the rest of the job for you.
The erosion can be done in OpenCV by the function cvErode

Try one of the many corner detectors like harris corner detector. also it is in general a good idea to try that at multiple resolutions : so do some preprocessing of of varying magnification.
It appears that you want some sort of color dominated square then you can suppress the other colors, by first using something like cvsplit .....and then thresholding the color...so only that region remains....follow that with a cropping operation ...I think that could work as well ....

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