This repo contains the code that can be used to detect "a ballpoint or a gel pen" that is placed in front of the webcam.
The generate_annotations.py file calls a function create_xml() of generate_xml.py file which generates a "xml" file that is in-turn used for generating the annotations of objects(which in our case "pens"). You can even use this repo's scripts to train your own model to detect any custom object.
First download this(YOLO_Custom_Object_Detection) repo and clone the darkflow repo(https://github.com/thtrieu/darkflow). Now, add the files of YOLO_Custom_Object_Detection repo inside the darkflow repo as we need the yolo model for the detection, also add tiny-yolo-voc-1c.cfg file of this repo to darkflow repo's "cfg/" folder.
Now, you can use imageWebScrapper.py file to download and save images into your local system by just entering the search term, for each search-term an individual forlder gets created. Once you download all the required images to their respective folders, you need to change the images_dir value and "folder names to take images from" in images_concatenation.py file. Now, change the value of img_dir value in generate_annotations.py file to the one you specified above and also change the savedir, anno_dir, obj values to something you like/something on which you are training on.
That is everything, now all you have to do is run the generate_annnotations.py file and create some annotations (just by clicking and dragging on the images) as these will be used to train our custom yolo model. After creating annotations, run the command python flow --model cfg/tiny-yolo-voc-1c.cfg --load bin/tiny-yolo-voc.weights --train --annotation <your_annotations-folder-name> --dataset <your_images_dataset_folder_name> --gpu 1.0 --epoch 250
This will start the training process, once the process completes the weights will be updated and you can now change the "load" value inside yolo_detection.py file to the no. of checkpoints created(Note: you can check them in "ckpt/" directory), also add your object name to labels.txt file and run yolo_detection.py. By, running the file you can see a webcam window popped up, all that is left is to keep your trained object in front of the webcam and "boom!!" you can see the object along with its confidence score gets detected.
PS, training the model takes lot of computational power, so train it on some external GPU or cloud.