how can I identify the presence or absence of regular stripes of different colors, but ranging from very very very very light pink to black inside of a scanned image (bitmap 200x200dpi 24-bit).
Carry a few examples.
Example 1
Example 2 (the lines are in all the columns except 7 in the second row of the last column)
For now try to identify (using python language) whether or not there is at least 5-10 pixels for the presence of different color from white to each strip, however, does not always work because the scanned image is not of high quality and the strip changes color very similar to color that surrounds it.
Thanks.
This looks to me a connected component labeling in an image to identify discrete regions of certain color range. You can have a look to cvBlobLib. Some pre-processing would be required to merge the pixels if there are holes or small variations between neighbors.
Not going to happen. The human visual system is far better than any image processing system, and I don't see anything in the 2nd row of #3. #1 and #5 are also debatable.
You need to find some way to increase the optical quality of your input.
Search for segmentation algorithm ,with a low threshold.
It should give you good results as the edges are sharp.
Sobel would be a good start ;)
Related
I am a beginner in python & image processing.I have the following image.
I would like to detect the changes between this picture and another one where the other one may be
taken from a slightly different angle or the object is translated
have different light conditions
and the changes may be
a change in color in part of the object
an extra or missing part
After various searches I thought about using ORB to detect the matching parts and remove them from the picture then use contour to extract and compare the difference.
But I cannot seem to find a way to remove the matching parts from the two images.
I am open for all suggestions / better way to approach the problem.
edit:
Sorry , forgot to mention that the color change could either be white or pink
Looking at your image, it appears there are three dominant colors. If this is always the case the first thing that comes to mind is to apply a color K mean algorithm with three clusters, like explained here.
The center color of each cluster would then give you information on the color of the tubes, and the size of each cluster (# of pixels belonging to that cluster) would give you if there are extra or missing parts.
I was looking for ways to classify the different colours present in the bands of the resistor using openCV and python.
What algorithm can be used to segment the image into different bands etc. I tried using the watershed algorithm but i couldnt get the markers right and didnt get the desired results.
Sample Image I used:
The possible colors of resistor codes are known apriori.
You could simply scan across the resistor and check which of those colors are present in which order.
The final implementation would of course depend on many things.
If you just have a random snapshot of a resistor it will be more difficult than having the same orientation, position, perspective and scale every time.
Finding the resistor and its main axis should be rather simple, then all you need is a scan line.
Another option: Transform the image to HUE, then use the resistors body colour and the background colour for two threshold operations, which should leave you with the colour bands.
I've got the following image.
Other Samples
I want to detect the six square-shaped green portions and the one circular portion above them. I basically want a binary image with these portions marked 1 (white) and everything else 0 (black).
What have I done so far?
I found a range of H, S, and V within which these colors fall which works fine for a single image, but I've got multiple such images, some under different illumination (brightness) conditions and the ranges do not work in those cases. What should I do to make the thresholding as invariant to brightness as possible? Is there a different approach I should take for thresholding?
What you did was manually analyze the values you need for thresholding for a specific image, and then apply that. What you see is that analysis done on one image doesn't necessarily fit other images.
The solution is to do the analysis automatically for each image. This can be achieved by creating a histogram for each of the channels, and if you're working in HSV, I'm guessing that the H channel would be pretty much useless in this case.
Anyway, once you have the histograms, you should analyze the threshold using something like Lloyd-Max, which is basically a K-Means type clustering of intensities. This should give the centroids for the intensity of the white background, and the other colors. Then you choose the threshold based on the cluster standard deviation.
For example, in the image you gave above, the histogram of the S channel looks like:
You can see the large blob near 0 is the white background that has the lowest saturation.
I'm newbie in computer vision. My goal is to distinguish individual cells on a set of pictures like this: Example
Basically, I blur whole image, find region maximum on it and use it like seed in watershed algorithm on distance tranfsform of threesholded blurred image. In fact I'm following tutorial which you can find here:
github/luispedro/python-image-tutorial
(sorry, can't post more than 2 links).
My problem is that some cells in my set have very distinguishable dark nucleus (which you can see on the example) and my algorithm produce results like this which are cleary wrong.
Of course it's possible to fix it by increasing strength of gaussian blur but it will merge some other cells toghether which is even worse.
What can be done to solve this problem? What are other possibilites if watershed just isn't situable for this case (keeping in mind that my set is pretty small and learning seems impossible)?
The watershed tends to over-segment if you don't use a watershed with markers.
Usually, we start with DNA/DAPI segmentation that is easy, and it provides the number of cells and the inner markers for the watershed.
If you blur the images, you smooth all the patterns. You should use an alternate sequential filter (opening / closing) in order to simplify each zone, and then try an ultimate eroded in order to find the number of inner seed for your watershed.
Given an image of a connect-4 board I'd like to recognize and output the board's state (a 6 by 7 matrix). The first approach I tried was based on finding the circles and then looking for a grid pattern in their centroids.
This is the open-cv function I'm using:
circles = cv2.HoughCircles(bw_im,
cv2.cv.CV_HOUGH_GRADIENT,
dp=DP,
minDist=MIN_DIST,
minRadius=MIN_RADIUS,
maxRadius=MAX_RADIUS)
I add non-maximum suppression, but the results are not great.
Is there a better way than dealing with Hough circles directly, perhaps there is some sort of filled circularity morphological operation that I don't know of.
Here's an example input image:
You can assume that the input image has been cropped and has similar margins as above (I have another piece of code that takes care of this).
If Hough isn't a requirement, Id suggest implementing a ray-casting algorithm as described here: https://en.wikipedia.org/wiki/Point_in_polygon
The general steps are:
Create a mask for the red circles
Run ray-casting on x columns spaced y apart to determine # and position of reds
Repeat steps 1 & 2 for yellow
Since you're working in RGB, the color contrast should be enough to give you good results.
Assuming your grid will maintain its position the easiest way would be to setup a fixed region of interest for every slot and measure their hue values every time you change something.