I am trying to crop a picture on right on along the contour. The object is detected using surf features and than i want to crop the image of extactly as detected.
When using crop some outside boundaries of other object is includes. I want to crop along the green line below. OpenCV has RotatedRect but i am unsure if its good for cropping.
Is there way to perfectly crop along the green line
I assume you get you get your example from http://docs.opencv.org/doc/tutorials/features2d/feature_homography/feature_homography.html, so what you can do is to find the minimum axis aligned bounding box around the green bounding box, crop it from the image, use the inverted homography (H.inv()) matrix to transform that sub image into a new image (call cv::warpPerspective), and then crop your green bounding box (it should be axis aligned in your new image).
You can get the equations of the lines from the end points for each. Use these equations to check whether any given pixel lies within the green box or not i.e. does it lie between the left and right lines and between the top and bottom lines. Run this over the entire image and reset anything that doesn't lie within the box to black.
Not sure about in-built functionality to do this, but this simple methodology is guaranteed to work. For higher accuracy, you may want to consider sub-pixel checks.
Related
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 want to fit an image of a clown like face into a contour of another face (a person).
I am detecting the persons face and getting a elliptical-like contour.
I can figure out the center, radius, highest, lowest, left-most and right-most points.
How do I fit the clown face (a square image which I can make elliptical by cutting the face out of the empty background of a png and then detecting the contour) into the persons face?
Or at the least, how do I fit a polygon into another polygon.
I can fit a rectangular image into a rectangular contour with ease, but faces aren't that shape.
Python preferable, but C++ is also manageable, thank you.
Edit: Visual representation as requested:
I have
and I want to make it like this:
but I want the clown face to stretch over the guys face and fit within the blue contour.
I think the keyword you are looking for is Active Appearance Models. First, you need to fit a model to first face (such as this one), which lays inside the contour. Then, you should fit the same model to the clown face. After that, since you have fitted same model to both faces, you can stretch it as you need.
I haven't use AAM myself and I'm not an expert about it, so my explanation might not be enough or might not be exactly correct, but I'm sure it will give you some insight.
A simple and good answer to this question is to find the extreme top, bottom, left, and right points on your contour (head) and then resize your mask to match the aspect ration and place it to cover the 4 points.
Because human heads are elliptical you can use fitEllipse() to give you those 4 points. This will automagically fix any problems with the person tilting their head because regardless of the angle you will know which point is top, bottom, left, and right.
The relevant code for finding the ellipse is:
vector<Point> contour;
// Do whatever you are doing to populate this vector
RotatedRect ellipse = fitEllipse(Mat(contour));
There is also an example as well as documentation for RotatedRect.
// Resize your mask with these sizes for optimum fit
ellipse.size.width
ellipse.size.height
You can rotate your image like this.
UPDATE:
You may also want to find the contour's extreme points to know how much you need to scale your image to ensure that all of the face is covered.
I'm going to find the most look-like rectangles among shapes. The first image is the original image with shapes which possibly be rectangles but they are not. The green rectangles in the second image is what I want. So is there a way to do this with opencv? I've tried hough lines but the result's not good
The source image:
And what I want is to find out the most look-like rectangle among these shapes, like the rectangles in green.
What I want:
A very simple approach is, after you have a rectangle bounding box around your shape, count the percentage of pixels inside the box which are white.
The higher the percentage of white pixels, the closest to a rectangle it is.
To get the bounding boxes you should take a look at either findContours from opencv, or some Blob extracting algorithm, you will find plenty of questions regarding those.
Edit:
Maybe you should first get the Minimum bounding rectangles of the shapes and then do this kind of heuristic:
Shrink the rectangle dimensions until the white-pixel percentage inside the rectangle reaches some threshold defined by you (like 90% of white pixels inside the rectangle).
To get the Minimum bounding rectangle (the smallest rectangle which contains the whole shape), you might check this tutorial:
http://docs.opencv.org/doc/tutorials/imgproc/shapedescriptors/bounding_rects_circles/bounding_rects_circles.html
One thing that might also help is doing the difference of sizes from the minimum bounding rectangle and the maximum inner rectangle (the biggest rectangle you can fit inside the white shape). The less difference there is between those rectangle's properties (width, height, area, center coordinates) the closest is the shape to a rectangle.
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.
The application PhotoFiltre has an option to stretch part of an image. You select a rectangular shape and you can then grab and move the vertexes somewhere else to make any quadrangle. The image part which you selected will stretch along. Hopefully these images make my point a little clearer:
Is there a general algorithm which can handle this? I would like to obtain the same effect on HTML5 canvas - given an image and the resulting corner points, I would like to be able to draw the stretched image in such a way that it fills the new quadrangle neatly.
A while ago I asked something similar, where the solution was to divide the image up in triangles and stretch each triangle so that each three points correspond to the three points on the original image. This technique turned out to be rather exprensive and I would like if there is a more general method of accomplishing this.
I would like to use this in a 3D renderer, but I would like to work with a (2D) quadrangle.
I don't know whether PhotoFiltre internally also uses triangles, or whether it uses another (cheaper) algorithm to stretch an image like this.
Does someone perhaps know if there is a cheaper or more general method/algorithm to stretch a rectangular image, so that it fills a quadrangle given four points?
The normal method is to start with the destination, pick an appropriate grid size and then for each point in the new shape calculate the corresponding point in the source image (possibly with interpolation depending on the quality you need)
Affine transform.
Given four points for the "stretched" figure and four points for the figure it should match (e.g. a rectangle), an affine transform provides the spatial mapping you need. For each point (x1,y1) in the original image there is a corresponding point (x2,y2) in the second, "stretched" image.
For each integer-valued pixel (x2, y2) in the stretched image, use the affine transform to find the corresponding real-valued point (x1, y1) in the original image and apply its color to (x2,y2).
http://demonstrations.wolfram.com/AffineTransform/
You'll find sample code for Java and other languages online. .NET has the Matrix class.