treecounter-ML VS OpenCV

You can take a look at an example implementation of the code here. I was wondering how I can extract the feature maps from a particular layer using the dnn module. I thought of using the net.forward() to do it, but I am not sure if this is the correct way, and more importantly, I do not want to run two forward passes on the same image(I do not think it will be an elegant implementation).

Overlapping Bounding Boxes: As you can see from the image, I have quite a lot of overlapping bounding boxes around each detected object. I did some reading online, and found that I needed to use Non Maxima Suppression to filter the detections. I did that, and adjusted the NMS_THRESH parameter(The current results are at confidence threshold of 95% and NMS threshold of 100%). I am perplexed by this, and what I would like to have is some sort of a filter, where say if I already have a box at (x, y, w, h), then there should be like a barrier around the surrounding n pixels, where even if a box is detected, it will not be placed. If you want to take a look at my code, you can find the repository here and the particular file here.

I am not being able to work the Sort tracker into my current application code. It would be great if any of you could help me out here. The program crashes when I use the tracker.update() method.

Referenceshttps https://www.kaggle.com/datasets/riyaelizashaju/skin-disease-image-dataset-balanced?fbclid=IwAR3wbTp8l5yo_5fx6HAX8Vd2-9cca3khAc8EiBGFObaALfdVid29IuB_rYE https://keras.io/api/applications/vgg/ https://www.tensorflow.org/tutorials/images/cnn?hl=th https://opencv.org/

Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.

You'll need to plot the predictions. There are a few open source tools to do that, supervision is one you can use (https://github.com/roboflow/supervision) and opencv is another common option (https://github.com/opencv/opencv)

You might be able to achieve this with scripting tools like AutoHotkey or Python with libraries for GUI automation and image recognition (e.g., PyAutoGUI https://pyautogui.readthedocs.io/en/latest/, OpenCV https://opencv.org/).

The OpenCV4NodeJs A.I. library provides an interface for calling OpenCV routines in NodeJS.