rbobjecttracking 3.0.0

A python library for single-object tracking.

rbobjecttracking is based on an algorithm I invented called RBOT. it stands for ROI Based Object Tracking.

using rbobjecttracking, you can get around 50-70 FPS on a CPU To use rbobjecttracking, first create your custom dataset using rbot.dataCollector.

from rbot.dataCollector import DataCollector # Import the DataCollector class

data_collector = DataCollector() # Create the DataCollector object
data_collector.collectData() # This will open a GUI window using customtkinter and opencv.

After filling in the text fields and collecting the image samples, train a small model using rbot.trainer.

from rbot.trainer import Trainer # Import the Trainer class

trainer = Trainer(dataset_path="<dataset name that you created>", img_size=(128, 128), model_path="<path you want to save your model>") # Create the Trainer object
                  
trainer.train() # This will train and save the model to your specified model path

Training should usually take around 10seconds to a minute on your GPU depending on your dataset size, and image size.

After training, you can need to get the color mask of the object with rbot.colorExtractor.

from rbot.colorExtractor import ColorRangeSelector # import the ColorRangeSelector class

rangeSelector = ColorRangeSelector() # Create the class object
rangeSelector.select_color_range() # call the function to select your object's color range

This will open a GUI with the masked webcam, and some sliders. You need to adjust the sliders so that everything but your object turns black in the webcam view.

After selecting your object's color range, you can then start the object detection with rbot.rbot

from rbot.rbot import RBOT # Import the RBOT class
import cv2 # Import opencv for accessing the webcam

rbot = RBOT(hsvValues="", image_size=(256, 256), minimum_confidence=0.9) # Create the RBOT object
cap = cv2.VideoCapture(0) # Access the webcam

while True:
    ret, frame = cap.read() # Get a frame from the webcam

    processed_frame = rbot.track_object(frame=frame, color=(0, 255, 0), width=2) # Process the frame

    cv2.imshow("processed frame", processed_frame) # Display the proccesed frame
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()