I have an image which has roots like this:
I want to crop each root individually out.
I initially thought of heavy dilation followed by erosion and contour detection of the blob, but since the roots are thin, it does not work well.
I also directly applied canny edge and contour detection like this image below. It has around 62000 contours, but I cannot use it to get the outline of each root.
I also thresholded the image using HSV followed by some median blurring. But it did not reduce much noise. Further blurring only leads to losing the root features.
Can anyone suggest me a better approach to tackle this problem? Will any Machine Learning based approach work better? Thanks
Use Cv2.BoundingRect(), it gets the rectangle area of your contour. Also you can use MedianFilter to get rid of "salt and pepper" noise in your picture.
First step detect big root, by user select or big erosion.
Second step base on previous big area center coordinate, make a fill algorithm (like Paint Bucket Tool in Paint) on this original threshold image
Related
The LCD display cannot be cleaned, the light conditions cannot be changed and the background can be tricky. So, I cannot use any kind of segmentation by color, search for rectangles and use Otsu. MSER doesn't give a good result. I even tried to locate display relatively to "DEXP" logo. The logo turned out to be too small to do this with sufficient accuracy. Bilateral filtering or gaussian blur helps, but not much. Even supposing that I found ROI, local thresholding gives too noisy results. Morphological transformations don't help. Is there a way to extract digits for further OCR?
I'm facing some general problems regarding the edge detection in an image (the image should be irrelevant for my question).
I want the canny edge detector to ignore a certain pixel value. For example: It should only look for edges if the gray value is not 0. Otherwise there will be "false edges" detected.
I usually use the cv2.canny function which works quite fast and well. Problem is, it is not customizable. So I took this code of a custom canny edge detector (https://rosettacode.org/wiki/Canny_edge_detector#Python) in order to customize it. It works but it's calculating the edges way too slow (It takes several minutes, whereas the cv2.canny function takes a fraction of a second).
This is my first problem.
Is there another way to make the cv2.canny function "ignore" pixels of a certein value. Imagine somewhere in the picture is a area filled with black (soo the image below). I don't want the edge detector to detect the edge of this black area.
Once I have some clear edges detected in my image, I want to create masks based on those edges. I couldn't find any examples for this online. So if anyone knows where to find a good tutorial on how to create masks from edges it would be great if you could help me out.
Thanks in advance
Here's an approach:
Calculate your Canny as usual using the fast OpenCV function.
Now locate all the black pixels in the image - you can do that with _,thr = cv2.threshold(im,1,255,cv2.THRESH_BINARY) and dilate those areas by 1 pixel with morphology to allow edges to be offset a little as they often are.
Multiply the normal Canny image with the mask you created so that anything it found in the black areas gets multiplied by zero, i.e. lost.
I am trying to use OpenCV to measure size of filament ( that plastic material used for 3D printing)
What I am trying to do is measuring filament size ( that plastic material used for 3D printing ). The idea is that I use led panel to illuminate filament, then take image with camera, preprocess the image, apply edge detections and calculate it's size. Most filaments are fine made of one colour which is easy to preprocess and get fine results.
The problem comes with transparent filament. I am not able to get useful results. I would like to ask for a little help, or if someone could push me the right directions. I have already tried cropping the image to heigh that is a bit higher than filament, and width just a few pixels and calculating size using number of pixels in those images, but this did not work very well. So now I am here and trying to do it with edge detections
works well for filaments of single colour
not working for transparent filament
Code below is working just fine for common filaments, the problem is when I try to use it for transparent filament. I have tried adjusting tresholds for Canny function. I have tried different colour-spaces. But I am not able to get the results.
Images that may help to understand:
https://imgur.com/gallery/CIv7fxY
image = cv.imread("../images/img_fil_2.PNG") # load image
gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY) # convert image to grayscale
edges = cv.Canny(gray, 100, 200) # detect edges of image
You can use the assumption that the images are taken under the same conditions.
Your main problem is that the reflections in the transparent filament are detected as edges. But, since the image is relatively simple, without any other edges, you can simply take the upper and the lower edge, and measure the distance between them.
A simple way of doing this is to take 2 vertical lines (e.g. image sides), find the edges that intersect the line (basically traverse a column in the image and find edge pixels), and connect the highest and the lowest points to form the edges of the filament. This also removes the curvature in the filament, which I assume is not needed for your application.
You might want to use 3 or 4 vertical lines, for robustness.
I am working on an application where I need feature like Cam Scanner where document is to be detected in an image. For that I am using Canny Edge detection followed by Hough Transform.
The results look promising but the text in the document is creating issues as explained via images below:
Original Image
After canny edge detection
After hough transform
My issue lies in the third image, the text in original mage near the bottom has forced hough transform to detect the horizontal line(2nd cluster from bottom).
I know I can take the largest quadrilateral and that would work fine in most cases, but still I want to know any other ways where in this processing I can ignore the effect of text on the edges.
Any help would be appreciated.
I solved the issue of text with the help of median filter of size 15(square) in an image of 500x700.
Median filter doesn't affect the boundaries of the paper, but can help eliminate the text completely.
Using that I was able to get much more effective boundaries.
Another approach you could try is to use thresholding to find the paper boundaries. This would create a binary image. You can then examine the blobs of white pixels and see if any are large enough to be the paper and have the right dimensions. If it fits the criteria, you can find the min/max points of this blob to represent the paper.
There are several ways to do the thresholding, including iterative, otsu, and adaptive.
Also, for best results you may have to dilate the binary image to close the black lines in the table as shown in your example.
I need to blur faces to protect the privacy of people in street view images like Google does in Google Street View. The blur should not make the image aesthetically unpleasant. I read in the paper titled Large-scale Privacy Protection in Google Street View by Google (link) that Google does the following to blur the detected faces.
We chose to apply a combination of noise and aggressive Gaussian blur that we alpha-blend smoothly with the background starting at the edge of the box.
Can someone explain how to perform this task? I understand Gaussian Blur, but how to blend it with the background?
Code will be helpful but not required
My question is not how to blur a part of image?, it is how to blend the blurred portion with the background so that blur is not unpleasant? Please refer to the quote I provided from the paper.
I have large images and a lot of them. An iterative process as in the possible duplicate will be time consuming.
EDIT
If someone ever wants to do something like this, I wrote a Python implementation. It isn't exactly what I was asking for but it does the job.
Link: pyBlur
I'm reasonably sure the general idea is:
Create a shape for the area you want to blur (say a rectangle).
Extend your shape by X pixels outwards.
Apply a gradient on alpha from 0.0 .. 1.0 (or similar) over the extended area.
Apply blur the extended area (ignoring alpha)
Now use an alpha-blend to apply the modified image to the original image.
Adding noise in a similar way to the original image would make it further less obvious that it's been blurred (because the blur will of course also blur away the noise).
I don't know the exact parameters for how much to grow, what values to use for the alpha gradient, etc, but that's what I understand from the quoted text.