I am attempting to develop software that will determine the confluency of the cells we are producing on microcarriers. To do that you need to determine the area occupied by the cells divided by the area available for the cells to grow. So 100% confluency would mean the microcarriers are completely covered.
In order to do that, we need to identify the microcarriers which are spherical but the edges don't show up with one of our imaging techniques.
I've played around with most of the variables in imfindcircles() but I am having no luck with these images.
Any suggestions on how to detect circles when the edges are bumpy and incomplete along with the corresponding radius and center? (Green = cells)
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I'm making a game, where levels are grid based.
At first, I have rectangle MxM cells. Then within this rectangle I have to put rooms. Room is another rectangle which is AxB cells, where 2 <= A, B <= 4. Besides, It's necessary to put N rooms in each row and N ones in each column. They should fill all the space, there can't be empty space between them. In other words, I have to feel rectangle with other rectangles the way that there will no be empty space between them and they will form a grid with N rows and N columns.
What I did:
I store information about rooms in form of their left-top corner, calculate it and then put rooms based on their and neighbor's corners. To do that:
Divide grid on rooms 3x3
In each of 3x3 rooms define area which is obligatory floor (2x2 square, let's call it red area)
In loop for each room count it's neighbor x and y corner position the way that it doesn't cross none of the obligatory floor ares. For that:
a. Get red area of current room and it's neighbors. Set corner somewhere between them, making sure the dimensions of the room are within range above.
b. Resolve collisions, when it's not possible to set random corner. For instance, if x position of room above isn't equal to our room, then we can't put horizontal wall between to rooms righter them in random y position, because in that case these rooms will overlap each other.
Some other stuff with converting information about corners to rooms themselves
So, what's the problem? My code with a lot of if-statements and crutches became so unreadable and huge that it almost impossible to test and find bugs. Approach I used seems to work but it's impossible to control the way it's working or not working.
Another issue is that I want to have more control on how it looks like. Grid must be interesting, which means that neighbor rooms are preferably not of the same size. There's an example (grid) of such a grid (with red areas that are gray there), which is not bad.
Is there some alternative to solve this? In other questions I saw a lot of similar solutions, but all of them doesn't assume that there's fixed amount of rows and columns.
Recommend me some articles I haven't managed to find, probably, literature devoted to this topic, or point the direction where to move and find a working solution.
A traditional method of generating grids containing rooms is to use Binary-Space-Partition trees.
One thing about that method is that it often produces grids that are less densely populated than your example. You might be able to modify some BSP example code and make the map more dense though.
Another possible approach would be to generate the rectangles first, (perhaps with a border along two edges for the gap) then try to pack them using a rectangle packing algorithm. This previous answer has several potential packing algorithms.
Here are some images taken from experiments which show a bubble caused by spheres moving in liquid.
Now I want to get the area of the bubble from every image using Matlab. The first thing come to my mind is edge detection. So I tried using the following code:
A = imread('D:\1.jpg');
BW1 = edge(A,'sobel');
figure, imshow(BW1)
to get the cavity edge of the picture which was then cropped manually, as the picture show, the result (below) doesn't satisfy requirements. Also, I still don't know how to get the area of the bubble.
So, can someone tell me what should I do?
I think you should use background subtraction and try a simple segmentation.
You could use regionprops to get the area of the bubble:
https://www.mathworks.com/help/images/ref/regionprops.html
I feel like it should work pretty well. If you have a hard time obtaining a clean segmentation you could probably improve the experimental setup to increase the contrast of the bubble with respect to the background by choosing a background as dark as possible and using some lateral illumination to leverage the diffusion of the light by the bubble.
Finally the segmentation should be performed in a region of interest (ROI) since you know the bubble is confined within the tank
As for the issue of getting an accurate cavity edges, the computer vision system toolbox has the vision.ForegroundDetector object, which implements a variant of Stauffer and Grimson's GMM background subtraction. The implementation is very fast, leveraging multiple cores. Check out this example of how to use background subtraction.
As for the issue of finding the area of the bubble, use the bwarea command. https://www.mathworks.com/help/images/ref/bwarea.html, it will sum up all the white pixels in the image.
I believe background subtraction is the most efficient method to calculate this bubble area. Note that you may need to use opening and closing techniques afterwards to filter other regions see (imopen imclose) at: https://uk.mathworks.com/help/images/ref/imopen.html , and afterwards, you can apply bwarea to calculate area. You could also use impixelinfo command to compare intensity level of bubbles and other areas, and therefore, threshold image to extract bubbles. It works only when you have same threshold level for all images. Further, it is possible to combine all these techniques which is completely depended on your images to achieve better results.
Other shape-based techniques also can be used to extract bubble region area.
how to find distance between black points in a image using image processing image is taken by web camera and it is a snap of moving belt which is cover by white paper having black dots
Lots of way of doing it.
Firstly you need to identify the dots. Use Otsu thresholding to separate foreground from background. Then convent to binary, and label connected components. Eliminate everything that is smaller or larger than a threshold, or anything that isn't roughly circular.
Then you get a list of frames, so you need a blob-following algorithm. Eliminate any stationary blob (not on the paper).
Finally output the distances based on the blob identifications.
I am developing a project of detecting vehicles' headlights in night scene. I am working on a demo on MATLAB. My problem is that I need to find region of interest (ROI) to get low computing requirement. I have researched in many papers and they just use a fixed ROI like this one, the upper part is ignored and the bottom is used to analysed later.
However, if the camera is not stable, I think this approach is inappropriate. I want to find a more flexible one, which alternates in each frame. My experiments images are shown here:
If anyone has any idea, plz give me some suggestions.
I would turn the problem around and say that we are looking for headlights
ABOVE a certain line rather than saying that the headlights are below a certain line i.e. the horizon,
Your images have a very high reflection onto the tarmac and we can use that to our advantage. We know that the maximum amount of light in the image is somewhere around the reflection and headlights. We therefore look for the row with the maximum light and use that as our floor. Then look for headlights above this floor.
The idea here is that we look at the profile of the intensities on a row-by-row basis and finding the row with the maximum value.
This will only work with dark images (i.e. night) and where the reflection of the headlights onto the tarmac is large.
It will NOT work with images taking in daylight.
I have written this in Python and OpenCV but I'm sure you can translate it to a language of your choice.
import matplotlib.pylab as pl
import cv2
# Load the image
im = cv2.imread('headlights_at_night2.jpg')
# Convert to grey.
grey_image = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
Smooth the image heavily to mask out any local peaks or valleys
We are trying to smooth the headlights and the reflection so that there will be a nice peak. Ideally, the headlights and the reflection would merge into one area
grey_image = cv2.blur(grey_image, (15,15))
Sum the intensities row-by-row
intensity_profile = []
for r in range(0, grey_image.shape[0]):
intensity_profile.append(pl.sum(grey_image[r,:]))
Smooth the profile and convert it to a numpy array for easy handling of the data
window = 10
weights = pl.repeat(1.0, window)/window
profile = pl.convolve(pl.asarray(intensity_profile), weights, 'same')
Find the maximum value of the profile. That represents the y coordinate of the headlights and the reflection area. The heat map on the left show you the distribution. The right graph shows you the total intensity value per row.
We can clearly see that the sum of the intensities has a peak.The y-coordinate is 371 and indicated by a red dot in the heat map and a red dashed line in the graph.
max_value = profile.max()
max_value_location = pl.where(profile==max_value)[0]
horizon = max_value_location
The blue curve in the right-most figure represents the variable profile
The row where we find the maximum value is our floor. We then know that the headlights are above that line. We also know that most of the upper part of the image will be that of the sky and therefore dark.
I display the result below.
I know that the line in both images are on almost the same coordinates but I think that is just a coincidence.
You may try downsampling the image.
I have an image which looks like this:
I have a task in which I should circle all the bottles around their opening. I created a simple algorithm and started working it. My algorithm follows:
Threshold the original image
Do some morphological opening in it
Fill the empty holes
Separate the portion of the image using region props such that only the area equivalent to the mouth of the bottles is selected.
Find the centroid for each and draw circle around each bottle.
I did according to the algorithm above and but I have some portion of the image around which I draw a circle. This is because I have selected the area since the area of the mouth of bottle and the remained noise is almost same. And so I yielded a figure like this.
The processing applied on the image look like this:
And my final image after plotting the circle over the original image is like this:
I think I can deal with the extra circle, that is, because of some white portion of the image remained as shown in the figure 2 below. This can be filtered out using regionproping for eccentricity. Is that a good idea or there are some other approaches to this? How would I deal with other bottles behind the glass and select them?
Nice example images you provide for your question!
One thing you can use to detect the remaining bottles (if there are any) is the well defined structure of the placement of the bottles.
The 4 by 5 grid of the bottle should be relatively easy to locate, and when the grid is located you can test if a bottle is detected at each expected bottle location.
With respect to the extra detected bottle, you can use shape features like
eccentricity,
the first Hu moment
a ratio between the perimeter length squared over the area (which is minimized for a circle) details here
If you are able to detect the grid, it should be easy to located it as an outlier (far from an expected bottle location) and discard accordingly.
Good luck with your project!
I've used the same approach as midtiby's third suggestion using the ratio between area and perimeter called shape factor:
4π * Area /perimeter^2
to detect circles from a contour traced image (from the thresholded image) to great success;
http://www.empix.com/NE%20HELP/functions/glossary/morphometric_param.htm
Regarding the 4 unfound bottles, this is rather tricky without some a priori knowledge of what it is you're looking at (as discussed using the 4 x 5 grid, then looking from the centre of each cell). I did think that from the list of contours, most would be of the bottle tops (which you can test using the shape factor stuff), however, one would be of a large rectangle. If you could find the extremities of the rectangle (from the largest contour in terms of area), then remove it from the third image, you'd be left with partial circles. If you then contour traced those partial circles and used a mixture of shape factor/curve detection etc. may help? And yes, good luck again!