My code looks like this:

hope you are doing well today, i am working with a personal project, im using some cables to find the edges, find the center and sample the colors in hsv to then figure out the color and list it in a final image drawn on the frame, yes this is real time.

I have an image of an object I want to put in a setting, and I’ve explored a few different approaches and here are my initial impressions

How we can count triangles in this image:

I have a document in the form of an image. The content of the document is some information in the form of a table. Most often, these are some scanned documents, that is, black and white. However, sometimes there may be originals, where there may be a blue seal, or a company logo of any color (perhaps this information is important for solving my issue).

here the code:

How can the location of the paper rectangle be detected as a rotated in these images using opencv? The big problem is the background which makes it hard to extract only the paper.

I’m currently working on a project that requires detecting the orientation of a QR code. I’m using the qrcode library to detect the center of the QR code and then trying to orient it using a line. However, I’m facing issues due to lighting conditions affecting the detection for certain colors. The line is evident, but the parameters I’m using to detect the lines don’t seem to be effective.My objective is to detect the angle for the piece with the x axis.

I have a code that compares two geometric figures. I set the threshold for comparison to 0.1 (you will see it below in the code), and in principle the results are more or less good.

I’m working on a project to visualize annotations made using LabelImg on a set of images. However, when I display these images with the annotated bounding boxes using OpenCV in Python, the bounding boxes appear to be consistently offset from their correct positions.