'1.png just has one contour'
img = cv2.imread('1.png')
retval,dst = cv2.threshold(img,120,255,cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(dst,cv2.RETR_EXTERANL,cv2.CHAIN_APPROX_SIMPLE)
print cv2.contourArea(contours[0],False)
The image just has one contour, then contours is a list. When I change contours[0] to contours[3] or other numbers, there still have a area. I have no ideas about the question, only one contour.
Why appear so many values? It is the problem of the found thresh? Need your help?
The issue is that what OpenCV considers to be a contour is only part of what you consider to be a contour. In other words, OpenCV is splitting up your contour into pieces. It's possible you could get the full area by adding the areas of the individual contours, but this method is not reliable.
Something you could try is Image Morphology. Using this, you could "grow" the contours so that they overlap more, which would mean that the chance of OpenCV recognizing them as a single contour would be greater.
However, this method would result in a loss of precision. Therefore, if you need an exact area, you will have to rely on other methods. For complex geometries, this is not a simple task.
One more "quick and dirty" solution I've used is to create a fresh one-channel Mat (Mat::zeros), draw the contours filled with a color value of 255 (drawContours), and sum the contents (in c++ this is cv::sum and for you I think it's cv2.sum), then divide the result by 255. This would get you the area in pixels, and would be more reliable than summing the areas of individual contours because it would account for overlap between them.
Related
I don't know much about image processing so please bear with me if this is not possible to implement.
I have several sets of aerial images of the same area originating from different sources. The pictures have been taken during different seasons, under different lighting conditions etc. Unfortunately some images look patchy and suffer from discolorations or are partially obstructed by clouds or pix-elated, as par example picture1 and picture2
I would like to take as an input several images of the same area and (by some kind of averaging them) produce 1 picture of improved quality. I know some C/C++ so I could use some image processing library.
Can anybody propose any image processing algorithm to achieve it or knows any research done in this field?
I would try with a "color twist" transform, i.e. a 3x3 matrix applied to the RGB components. To implement it, you need to pick color samples in areas that are split by a border, on both sides. You should fing three significantly different reference colors (hence six samples). This will allow you to write the nine linear equations to determine the matrix coefficients.
Then you will correct the altered areas by means of this color twist. As the geometry of these areas is intertwined with the field patches, I don't see a better way than contouring the regions by hand.
In the case of the second picture, the limits of the regions are blurred so that you will need to blur the region mask as well and perform blending.
In any case, don't expect a perfect repair of those problems as the transform might be nonlinear, and completely erasing the edges will be difficult. I also think that colors are so washed out at places that restoring them might create ugly artifacts.
For the sake of illustration, a quick attempt with PhotoShop using manual HLS adjustment (less powerful than color twist).
The first thing I thought of was a kernel matrix of sorts.
Do a first pass of the photo and use an edge detection algorithm to determine the borders between the photos - this should be fairly trivial, however you will need to eliminate any overlap/fading (looks like there's a bit in picture 2), you'll see why in a minute.
Do a second pass right along each border you've detected, and assume that the pixel on either side of the border should be the same color. Determine the difference between the red, green and blue values and average them along the entire length of the line, then divide it by two. The image with the lower red, green or blue value gets this new value added. The one with the higher red, green or blue value gets this value subtracted.
On either side of this line, every pixel should now be the exact same. You can remove one of these rows if you'd like, but if the lines don't run the length of the image this could cause size issues, and the line will likely not be very noticeable.
This could be made far more complicated by generating a filter by passing along this line - I'll leave that to you.
The issue with this could be where there was development/ fall colors etc, this might mess with your algorithm, but there's only one way to find out!
I am looking for algorithms to, given an image containing a crossword
crop the image to just the crossword
distinguish between regular and barred crosswords
extract the grid size and the positions of the black squares/bars
The crossword itself can be assumed to be regular (i.e. I am interested in crosswords that have been generated by some program and published as an image, rather than scanned paper-based crosswords), and I would like the program to run without needing any inputs other than the image bitmap.
I can think of some brute-force multi-pass ways to do this (essentially using variants of imagemagick's hit-and-miss filter and then looping over the image looking for leftover dots) but I'm hoping for better ideas from people who actually know about image processing.
This is a really broad question, but I wil try to give you some pointers.
These are the steps you need to take:
Detect the position of the crossword.
Detect the grid of the crossword. For this, you will need some Computer Vision algorithm (for example the Hough lines detector).
For each cell, you need to find if it have a character or not. To do so, you just simply analize the "amount" of white color does the cell have
For the cells containing a character you need to recognize it. To do so, you need an OCR, and I recommend you Tesseract.
Create your own algorithm for solving the crosswords. You can use this.
And here (1,2,3) you have an example of a Sudoku Solver in Python. The first steps are common to your problem so you can use OpenCV to solve it like this:
import cv2
import numpy as np
#Load the Black and White image
img = cv2.imread('sudoku.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray,(5,5),0)
thresh = cv2.adaptiveThreshold(gray,255,1,1,11,2)
#Detect the lines of the sudoku
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
#Detect the square of the Sudoku
biggest = None
max_area = 0
for i in contours:
area = cv2.contourArea(i)
if area > 100:
peri = cv2.arcLength(i,True)
approx = cv2.approxPolyDP(i,0.02*peri,True)
if area > max_area and len(approx)==4:
biggest = approx
max_area = area
Using a screenshot of the linked crossword as example, I assume that:
the crossword grid is crisp, i.e. the horizontal and vertical grid lines are drawn at exact pixels with a constant dark colour and that there is no noise inside the grid cells,
the crossword is black or another relatively dark colour ("black") on white or light grey ("white"),
the clue numbers are written in the top left corner,
the crossword is rectangular and regular.
You can then scan the image from top to bottom to find horizontal black lines of sufficient length. A line starts with a black pixel and ends with a white pixel. Other pixels are indicators that it is not a line. (This is to weed out text and buttons.) Do the same for vertical lines.
Ideally, you now have the crossword lines. If your image is not cropped to the crossword, you might have false positives, such as the button borders. To find the crossword lines, sort them by length and look for the largest contiguous block of the same length. These should be your crossword lines unless you hae some degenerate cases
Now do a nested loop of horizontal and vertical lines, but skip the first line. Look two or three pixels to the northwest of the intersection of the lines. If the pixel is dark, that's a blank. If it is light, it's a cell. This heuristic seems to work well. I say dark and light here, bacause some crosswords use grey cells to save on ink when printing and some cell are highlighted in the screenshot.
If you end up with no blanks, you have a barred crossword. You can find the bars by checking whether one of the pixels to the left and right of a cell border is black.
Lastly, a tip: If you want to use your algorithm to find the cells in a crossword generated with the Crossword Compiler, look at the source. You will find a link to a Javascript file /puzzles/sample/cryptic_demo/cryptic_demo_xml.js, which contans the crossword as XML string, which also gives you the clues as a bonus.
Older versions of the Crossword Compiler, such as the one used for the Independent Cryptic hide their data in a file loaded from an applet. The format of that file is binary, but not too hard to read if you know the original data.
Try hough transform to find squares and when you get the squares check using histogram whether it is a dark or white square using threshold on its gray scale values
Thinking of an alternative way to do this.
This is similar in many respects to object recongition, computer vision
One way would be to use a framework like openCV which, trained with some samples of what you want to detect, can detect any similar results
(a javascript library for object detection based on Viola-Jones algorithm, used also by openCV and of which am the author is HAAR.js)
Apart from this (or a similar alternative to this) there is a possibility of constructing a "visual" template of a crossword you want to detect (in a scale-invariant way)
and scan the images looking for correlations of parts of the image with the template (complexity O(N*M), N size of image, M size of template)
Since crossword grids have relatively constant shapes (especially fixed outputs of crossword compilers) it should be relative easy to create a prototype template and have success in matching (and aligning) the detected regions to extract the shape information
Lets think, I would take a picture of a sheet on a table in an angle, that is not frontal. Of course, I will have a perspectivly stretched image.
Does anyone know an easy algorithm to "normalize" the area again to a sqared one, when all 4 edges/edgepoints in the source (taken photo) are defined/maybe clicked by the user?
The interpolation may be easy, I do not need algorithms to have smooth borders, nearest neighbour is enough (so, simply copying the pixel from the source position, which meets the rounded value from the calculated according pixel, ignoring the after-commas).
OpenCV has it all.
Basically, you get the 4 points and use
http://docs.opencv.org/modules/imgproc/doc/geometric_transformations.html#getperspectivetransform
(which basically inverts a matrix inside) to obtain the perspective transformation matrix, and then use
http://docs.opencv.org/modules/imgproc/doc/geometric_transformations.html#warpperspective
to apply the perspective transformation on the image.
The algorithms inside the latter can be found in http://en.wikipedia.org/wiki/Texture_mapping
i'm working in a project to recognize a bit code from an image like this, where black rectangle represents 0 bit, and white (white space, not visible) 1 bit.
Somebody have any idea to process the image in order to extract this informations? My project is written in java, but any solution is accepted.
thanks all for support.
I'm not an expert in image processing, I try to apply Edge Detection using Canny Edge Detector Implementation, free java implementation find here. I used this complete image [http://img257.imageshack.us/img257/5323/colorimg.png], reduce it (scale factor = 0.4) to have fast processing and this is the result [http://img222.imageshack.us/img222/8255/colorimgout.png]. Now, how i can decode white rectangle with 0 bit value, and no rectangle with 1?
The image have 10 line X 16 columns. I don't use python, but i can try to convert it to Java.
Many thanks to support.
This is recognising good old OMR (optical mark recognition).
The solution varies depending on the quality and consistency of the data you get, so noise is important.
Using an image processing library will clearly help.
Simple case: No skew in the image and no stretch or shrinkage
Create a horizontal and vertical profile of the image. i.e. sum up values in all columns and all rows and store in arrays. for an image of MxN (width x height) you will have M cells in horizontal profile and N cells in vertical profile.
Use a thresholding to find out which cells are white (empty) and which are black. This assumes you will get at least a couple of entries in each row or column. So black cells will define a location of interest (where you will expect the marks).
Based on this, you can define in lozenges in the form and you get coordinates of lozenges (rectangles where you have marks) and then you just add up pixel values in each lozenge and based on the number, you can define if it has mark or not.
Case 2: Skew (slant in the image)
Use fourier (FFT) to find the slant value and then transform it.
Case 3: Stretch or shrink
Pretty much the same as 1 but noise is higher and reliability less.
Aliostad has made some good comments.
This is OMR and you will find it much easier to get good consistent results with a good image processing library. www.leptonica.com is a free open source 'C' library that would be a very good place to start. It could process the skew and thresholding tasks for you. Thresholding to B/W would be a good start.
Another option would be IEvolution - http://www.hi-components.com/nievolution.asp for .NET.
To be successful you will need some type of reference / registration marks to allow for skew and stretch especially if you are using document scanning or capturing from a camera image.
I am not familiar with Java, but in Python, you can use the imaging library to open the image. Then load the height and the widths, and segment the image into a grid accordingly, by Height/Rows and Width/Cols. Then, just look for black pixels in those regions, or whatever color PIL registers that black to be. This obviously relies on the grid like nature of the data.
Edit:
Doing Edge Detection may also be Fruitful. First apply an edge detection method like something from wikipedia. I have used the one found at archive.alwaysmovefast.com/basic-edge-detection-in-python.html. Then convert any grayscale value less than 180 (if you want the boxes darker just increase this value) into black and otherwise make it completely white. Then create bounding boxes, lines where the pixels are all white. If data isn't terribly skewed, then this should work pretty well, otherwise you may need to do more work. See here for the results: http://imm.io/2BLd
Edit2:
Denis, how large is your dataset and how large are the images? If you have thousands of these images, then it is not feasible to manually remove the borders (the red background and yellow bars). I think this is important to know before proceeding. Also, I think the prewitt edge detection may prove more useful in this case, since there appears to be less noise:
The previous method of segmenting may be applied, if you do preprocess to bin in the following manner, in which case you need only count the number of black or white pixels and threshold after some training samples.
I'm searching for an certain object in my photograph:
Object: Outline of a rectangle with an X in the middle. It looks like a rectangular checkbox. That's all. So, no fill, just lines. The rectangle will have the same ratios of length to width but it could be any size or any rotation in the photograph.
I've looked a whole bunch of image recognition approaches. But I'm trying to determine the best for this specific task. Most importantly, the object is made of lines and is not a filled shape. Also, there is no perspective distortion, so the rectangular object will always have right angles in the photograph.
Any ideas? I'm hoping for something that I can implement fairly easily.
Thanks all.
You could try using a corner detector (e.g. Harris) to find the corners of the box, the ends and the intersection of the X. That simplifies the problem to finding points in the right configuration.
Edit (response to comment):
I'm assuming you can find the corner points in your image, the 4 corners of the rectangle, the 4 line endings of the X and the center of the X, plus a few other corners in the image due to noise or objects in the background. That simplifies the problem to finding a set of 9 points in the right configuration, out of a given set of points.
My first try would be to look at each corner point A. Then I'd iterate over the points B close to A. Now if I assume that (e.g.) A is the upper left corner of the rectangle and B is the lower right corner, I can easily calculate, where I would expect the other corner points to be in the image. I'd use some nearest-neighbor search (or a library like FLANN) to see if there are corners where I'd expect them. If I can find a set of points that matches these expected positions, I know where the symbol would be, if it is present in the image.
You have to try if that is good enough for your application. If you have too many false positives (sets of corners of other objects that accidentially form a rectangle + X), you could check if there are lines (i.e. high contrast in the right direction) where you would expect them. And you could check if there is low contrast where there are no lines in the pattern. This should be relatively straightforward once you know the points in the image that correspond to the corners/line endings in the object you're looking for.
I'd suggest the Generalized Hough Transform. It seems you have a fairly simple, fixed shape. The generalized Hough transform should be able to detect that shape at any rotation or scale in the image. You many need to threshold the original image, or pre-process it in some way for this method to be useful though.
You can use local features to identify the object in image. Feature detection wiki
For example, you can calculate features on some referent image which contains only the object you're looking for and save the results, let's say, to a plain text file. After that you can search for the object just by comparing newly calculated features (on images with some complex scenes containing the object) with the referent ones.
Here's some good resource on local features:
Local Invariant Feature Detectors: A Survey