Count repeating "objects" in a picture

If you really do need to use a ML solution for this problem (as opposed to a strict computer vision solution), most (all?) frameworks will return a list of detections after running inference.

I'll use Tensorflow and this object detection tutorial as an example. If you run the colab and select the text cell with the headline "Visualizing the results" and run all the cells above, it will have downloaded a model and run inference on a picture when it's done.

Insert a code cell and add print(result["detection_classes"]) and run it. That should give output along the lines of:

[[38.  1.  1. 38. 38. 38.  1.  1. 38.  1.  1. 38.  1.  1.  1. 38. 42. 42.
 1. 38. 42. 38.  1.  1.  1. 31. 38. 38. 42.  1. 31. 27.  1.  1. 38.  1.
 2. 38.  1. 38. 37. 38. 38. 27. 38. 38. 38. 44. 38. 38. 27.  1.  1. 38.
 3.  1. 38. 42. 16. 38.  9.  1.  1. 27. 27. 38. 38.  1. 38. 38.  1.  1.
 4. 38. 31. 38.  1. 38. 38. 31. 42.  9. 38. 34. 40. 38. 38.  1. 38. 38.
 5.  3. 18. 27. 38. 18.  1. 31. 42. 38.]]

Each of the numbers in the list maps to one of the classes. In this case you will find the classes in the variable category_index.

Now you can use your favorite python method to count the occurrences. A simple implementation could look something like this:

def count_classes(detections: list) -> tuple:
  ret_tuple = {}
  for detection in detections:
    if detection in ret_tuple.keys():
      ret_tuple[detection] += 1
    else:
      ret_tuple[detection] = 1
  return ret_tuple

Running the above in the colab will give the following:

count_classes(result["detection_classes"][0])
...
{1.0: 30,
 3.0: 1,
 9.0: 2,
 16.0: 1,
 18.0: 3,
 27.0: 6,
 31.0: 5,
 34.0: 1,
 37.0: 1,
 38.0: 41,
 40.0: 1,
 42.0: 7,
 44.0: 1}