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.
Related
I'm trying to blindly detect signals in a spectra.
one way that came to my mind is to detect rectangles in the waterfall (a 2D matrix that can be interpret as an image) .
Is there any fast way (in the order of 0.1 second) to find center and width of all of the horizontal rectangles in an image? (heights of rectangles are not considered for me).
an example image will be uploaded (Note I know that all rectangles are horizontal.
I would appreciate it if you give me any other suggestion for this purpose.
e.g. I want the algorithm to give me 9 center and 9 coordinates for the above image.
Since the rectangle are aligned, you can do that quite easily and efficiently (this is not the case with unaligned rectangles since they are not clearly separated). The idea is first to compute the average color of each line and for each column. You should get something like that:
Then, you can subtract the background color (blue), compute the luminance and then compute a threshold. You can remove some artefact using a median/blur before.
Then, you can just scan the resulting 1D array filled with binary values so to locate where each rectangle start/stop. The center of each rectangle is ((x_start+x_end)/2, (y_start+y_end)/2).
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()
I am trying to find a reliable method to calculate the corner points of a container. From these corner point’s idea is to calculate the center point of the container for the localization of robot, it means that the calculated center point will be the destination of robot in order to pick the container. For this I am looking for any suggestions to calculate the corner points or may be if any possibility to calculate the center point directly. Up to this point PCL library C/C++ is used for the processing of the 3D data.
The image below is the screenshot of the container.
thanks in advance.
afterApplyingPassthrough
I did the following things:
I binarized the image (black pixels = 0, green pixels = 1),
inverted the image (black pixels = 1, green pixels = 0),
eroded the image with 3x3 kernel N-times and dilated it with same kernel M-times.
Left: N=2, M=1;Right: N=6, M=6
After that:
I computed contours of all non-zero areas and
removed the contour that surrounded entire image.
This are the contours that remained:
I do not know how "typical" input image looks like in your case. Since I only have access to one sample image, I would rather not speculate about "general solution" that will be suitable for you. But to solve this particular case, you could analyze every contour in the following way:
compute rotatated rectangle that fits best around your contour (you need something similar to minAreaRect from OpenCV)
compute areas of rectangle and contour interior
if the difference between contour area and the area of the rotated bounding rectangle is small, the contour has approximately rectangular shape
find the contour that is both rectangular and satisfies some other condition (for example: typical area of the container). Assume that this belongs to container and compute its center.
I am not claiming that this is a solution that will work well in real world scenarios. It is also not fast. You should view it as a "sketch" that shows how to extract some useful information.
I assume the wheels maintain the cart a known offset from the floor and you can identify the floor. Filter out all points which are too close to the floor (this will remove wheels and everything but cart which will help limit data and simplify later steps.
If you isolate the cart, you could apply a simple average point (centroid), alternately, if that is not precise, you could try finding the bounding box of the isolated cart (min max in primary directions) and then take the centroid of that bounding box (this should be more accurate, but will still need a slight vertical offset due to the top handles).
If you can not isolate the cart or the other methods are not working well, you could try using PCL sample consensus specifically SACMODEL_LINE. This will be an involved strategy, but will give very solid results, basically run through and find each line and subtract its members from the cloud so as to find the next best line. After you have your 4 primary cart lines, use their parameters to find your centroid. *this would also be robust against random items being in or on the cart as well as carts of various sizes (assuming they always had linear perpendicular walls)
I am learning JavaCV and want to extract part of images dynamically based on color.
As identification I am outlining the region which I need to extract with a color. Is there anyway I can do extract ROI based on color outline. Any help appreciated.
Here is the Sample Image
it is quite simple. Since your figure has 4 corners hence you ought to follow the following steps.
1.identify the orientation of the image and store the points in a MatofPoint2f in a specific order.
(clock wise or anti clockwise- For this you can use Math.atan2(p1(y)-centerpoint(y),p1(x)-centerpoint(x)) and then sort the points according to the result of the equation. find the center point by finding the avg all the xcoords and y coords or any method you prefer).
2.Create a MatofPoint2f containing the corner coords of the result image size you want the cropped image in.
3.use Imgproc.getPerspectiveTransform() to perform the cropping.
4.Finally use Imgproc.warpPerspective() to obtain the output that is desired.
And for creating the border of the ROI the best way to go is to threshold the image by using some specific range so as to extract only those parts of the spectrum which is required.
Currently i am trying to read a square card by using an OCR engine. But before processing image, i want during capturing card image, user should only capture card not other surrounding noise. So for that i looked for overlay & able to create a overlay on camera screen but it is not that useful. So right now i am looking forward some help, how to draw a contour / a outline around a square card when user see it in camera eye as this example.
for ex.
Any body has done this before ?
At first use cvCanny to detect all contours on your image.
Then you can use Standard Hough Line Transform for detection of all lines on the image.
Then you can calculate their intersections and find 4 points: the leftmost and the rightmost of the top and the bottom of the image.
You can ignore small lines which are on the the left and right borders of the image by changing the property of threshold.