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.
Related
I have the a list of images (each of these image is a separate file), let's say they are some jigsaw puzzle pieces and, for each of them, I know it's position (x,y) and rotation in the complete puzzle.
How can I show the complete puzzle by stitching each of these pieces together in a single image (given that i know where to put each of them)?
I don't know if this is important but the pieces are not of regular shape (e.g. they are not squares), and they are all of different sizes
EDIT:
For the moments it seems to be working without the rotation but there is another problem, the pieces seems to not have a transparent background but rather a black one.
I have loaded them with opencv2 in the following way:
import glob
folder = './img/2/frag_eroded/'
frags = []
files = glob.glob(folder+"/*.png")
for file in files:
image = cv2.imread(file, cv2.IMREAD_UNCHANGED)
image = cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA)
frags.append(image)
Example of resulting image, you can kinda see the the squares around each piece and see how the pieces overlap with their "background" that should be transparent rather then black
This depends on how you want to handle it when there's an overlapping transparent area.
Suppose all pixels are either transparent or opaque, and
Suppose each image has RGBA (4-channels including alpha),
then you can set all RGB values to zero whenever the pixel is transparent.
Then proceed to add the smaller images to a bigger canvas (initialized to be all zeros RGB). The canvas can either have an alpha layer or not, depending on your preference.
Beware the canvas is big enough to contain all of them. So the first step here would be to make a large enough matrix / opencv image.
How to add images: https://stackoverflow.com/a/68878529/19042045
My goal is to draw a rectangle border around the face by removing the neck area connected to the whole face area. All positive values here represent skin color pixels. Here I have so far filtered out the binary image using OpenCV and python. Code so far skinid.py
Below is the test image.
Noise removals have also been applied to this binary image
Up to this point, I followed this paper Face segmentation using skin-color map in videophone applications. And for the most of it, I used custom functions rather than using built-in OpenCV functions because I kind of wanted to do it from scratch. (although some erosion, opening, closing were used to tune it up)
I want to know a way to split the neck from the whole face area and remove it like this,
as I am quite new to the whole image processing area.
Perform a distance transform (built into opencv or you could write by hand its a pretty fun and easy one to write using the erode function iteratively, and adding the result into another matrix each round, lol slow but conceptually easy). On the binary image you presented above, the highest value in a distance transform (and tbh I think pretty generalized across any mug shots) will be the center of the face. So that pixel is the center of your box, but also that value (value of that pixel after the distance transform) will give you a pretty solid approx face size (since it is going to be the pixel distance from the center of the face to the horizontal edges of the face). Depending on what you are after, you may just be able to multiply that distance by say 1.5 or so (figure out standard face width to height ratio and such to choose your best multiplier), set that as your circle radius (or half side width for a box) and call it a day. Comment if you need anything clarified as I am pretty confident in this answer and would be happy to write up some quick code (in c++ opencv) if you need/ it would help.
(alt idea). You could tweak your color filter a bit to reject darker areas (this will at least in the image presented) create a nice separation between your face and neck due to the shadowing of the chin. (you may have to dial back your dilate/ closing op tho)
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.
Working on object detection in Python with opencv.
I have two pictures
The reference picture with no object in it.
Picture with object.
The result of the images is:
The problem is, the pattern of the reference image is now on my objects. I want to remove this pattern and I don't know how to do it. For further image processing I need the the correct outline of the objects.
Maybe you know how to fix it, or have better ideas to exctract the object.
I would be glad for your help.
Edit: 4. A black object:
As #Mark Setchell commented, the difference of the two images shows which pixels contain the object, you shouldn't try to use it as the output. Instead, find the pixels with a significant difference, and then read those pixels directly from the input image.
Here, I'm using Otsu thresholding to find what "significant difference" is. There are many other ways to do this. I then use the inverse of the mask to blank out pixels in the input image.
import PyDIP as dip
bg = dip.ImageReadTIFF('background.tif')
bg = bg.TensorElement(1) # The image has 3 channels, let's use just the green one
fg = dip.ImageReadTIFF('object.tif')
fg = fg.TensorElement(1)
mask = dip.Abs(bg - fg) # Difference between the two images
mask, t = dip.Threshold(mask, 'otsu') # Find significant differences only
mask = dip.Closing(mask, 7) # Smooth the outline a bit
fg[~mask] = 0 # Blank out pixels not in the mask
I'm using PyDIP above, not OpenCV, because I don't have OpenCV installed. You can easily do the same with OpenCV.
An alternative to smoothing the binary mask as I did there, is to smooth the mask image before thresholding, for example with dip.Gauss(mask,[2]), a Gaussian smoothing.
Edit: The black object.
What happens with this image, is that its illumination has changed significantly, or you have some automatic exposure settings in your camera. Make sure you have turned all of that off so that every image is exposed exactly the same, and that you use the raw images directly off of the camera for this, not images that have gone through some automatic enhancement procedure or even JPEG compression if you can avoid it.
I computed the median of the background image divided by the object image (fg in the code above, but for this new image), which came up to 1.073. That means that the background image is 7% brighter than the object image. I then multiplied fg by this value before computing the absolute difference:
mask = dip.Abs(fg * dip.Median(bg/fg)[0][0] - bg)
This helped a bit, but it showed that the changes in contrast are not consistent across the image.
Next, you can change the threshold selection method. Otsu assumes a bimodal histogram, and works well if you have a significant number of pixels in each group (foreground and background). Here we'll have fewer pixels belonging to the object, because only some of the object pixels have a different color from the background. The 'triangle' method is suitable in this case:
mask, t = dip.Threshold(mask, 'triangle')
This will lead to a mask that contains only some of the object pixels. You'll have to add some additional knowledge about your object (i.e. it is a rotated square) to find the full object. There are also some isolated background pixels that are being picked up by the threshold, those are easy to eliminate using a bit of blurring before the threshold or a small opening after.
Getting the exact outline of the object in this case will be impossible with your current setup. I would suggest you improve your setup by either:
making the background more uniform in illumination,
using color (so that there are fewer possible objects that match the background color so exactly as in this case),
using infrared imaging (maybe the background could have different properties from all the objects to be detected in infrared?),
using back-illumination (this is the best way if your aim is to measure the objects).
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.