Develop Reference

How to detect only specific objects like cardboard boxes?

Imagine a factory warehouse, there are different sized boxes which loaded with products. I want to measure these.boxes with a camera. There is no background, background is natural factory warehouse. I have a code for measuring. But this code measuring everything. I want to measure only boxes.
I have code for measuring objects but How to detect only cardboard boxes with opencv?
Should i detect with color filter or with yolo?
Also maybe user will measure other objects instead of cardboard boxes like industrial machines etc. Maybe i need more general solution...
camera facing with width,height(180degrees).
As you see code measuring everything but I want only cardboard Boxes. I have tried filter colors with Hue, Saturation, Volume. But it didnt work because I'm using Aruco perimeter. Aruco perimeter is Black and White. When i lost other colors, Aruco perimeter is lost too. And maybe there would be different colored boxes.

You can try detecting rectangular or quadrilateral contours in a copy of B/W frame and then correlate the contours to the original(colored) frame. Thus you can apply color filtering on it to detect cardboard box. The advantage of this over DL would be DL might take more processing power.

Did your use any deep learning(DL) methods for cardboardboxes detection? If not, I recommend you to use yolov5 method based DL or choose some machine learning methods such as HOG with SVM. The advantage you use DL methods is that you only need label this boxes and pass the data(images and annotations) to model without worrying about whatever other object.

I tagged the cells using Labelme software (you can tag any object with it), and then I trained yolact's model with the images and annotations. Figure 1 shows the results that the model predicted a new image.

Magnify image based on rectangle points selected on image

I am working on a code in python and I came across a figure in a report that I would like to replicate.
Basically I would like to create a 'bounding' box onto the original image, and then subsequently crop and display the part of the image that has the bounding box on it. (basically to 'magnify' that section)
I've been googling but I can't seem to find the correct function to use so that I can achieve this. Currently, opencv is used to read my image, but if there is a function in matplotlib that does this, then you can suggest that too.
Thank you for your help!

Get Image Bounding box from Image of Page with Python

Say I have an image of a book page (something similar to what is pictured below) and want to generate a bounding box for the central image (outlined in green). How might I do this with python? I've tried the normal edge detection route but have found it to be too slow and that it picks up too many edges within the actual image of interest. Meanwhile libraries like detecto attempt to look for objects within the images rather than just detect some rectangular image. I have about 100 of these that I'd like to process and generate bounding boxes for.
100 is too few for me too want to train any kind of AI model, but too many to just do manually. Any thoughts on an approach?

How to detect edge of object using OpenCV

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.

Homography of soccer field

Okay so i am trying to find homography of a soccer match. What i have till now is
Read images from a folder which is basically many cropped images of a template soccer field. Basically this has images for center circle and penalty lines etc.
Read video stream from a file and crop it into many smaller segments.
Loop inside the images in video stream and inside that another loop for images that i read from folder.
Now in the two images that i get through iteration , i applied a green filter because of my assumption that field is green
Use orb to find points and then find matches.
Now the Problem is that because of players and some noise from croud, i am unable to find proper matches for homography. Also removing them is a problem because that also tends to hide the soccer field lines that i need to calculate the homography on.
Any suggestions on this is greatly appreciated. Also below are some sample code and images that i am using.
"Code being used"
Sample images
Output that i am getting
The image on right of output is a frame from video and that on left is the same sample image that i uploaded after filterGreen function as can be seen from the code.
Finally what i want is for the image to properly map to center circle so i can draw a cube in center, Somewhat similar to "This example" . Thanks in advance for helping me out.

An interesting technique to throw at this problem is RASL. It computes homographies that align stacks of related images. It does not require that you specify corresponding points on the images, but operates directly on the image pixels. It is robust against image occlusions (eg, players moving in the foreground).
I've just released a Python implementation here: https://github.com/welch/rasl
(there are also links there to the original RASL paper, MATLAB implementation, and data).
I am unsure if you'd want to crop the input images to that center circle, or if the entire frames can be aligned. Try both and see.