COV Pipeline context and parts that have already been run through

Note: The COV data is stored on microcenter at /media/gqc/T7/cctv/COV/Data/Video_Lists.

Notebook Order	Notebook Name	Python File Name	Run for COV
1	cctv-video_list-creator.ipynb	video_list_creator.py	Was not run through video_list-creator since we did not have actual COV videos on hand. The videos table was populated using the video_lists csv which are present in COV/Data/Video_Lists
2	metadata_DB_extract_inspection_condition_data.ipynb	extract_inspection_condition_data.py	Was not run through metadata_DB_extract_inspection_condition_data since I(Dhananjay) did not have
3	cctv-import_supplementary_data.ipynb	import_suppl_data.py	This module import distance regions, video-types, Condition_code_standard, and all_conditions CSV files into Video_DB as separate tables. It also updates videos table's video-type column.
4	cctv-frame-extraction.ipynb	frame_extractor.py	This module extracts frames from a list of videos and saves them under a zip file.
5	cctv-full-frame-stitch-and-Azure-OCR.ipynb	stitch_and_ocr.py	This will do the following:Run Azure OCR on 6 stacked full frames,The OCR results will be saved to OCR_JSON column in the frames table which can be used for blurring in the next stage.
6	cctv-save-blurred-frames.ipynb	blur_frames.py	This module takes in the extracted frames and the CSV files generated by cctv-frame-extraction.ipynb utility and recognize the text present in the frames.
7	cctv-extract-distance-values.ipynb	extract_distance_values.py	This module takes in the OCR_JSON column in the frames table containing OCR results and extracts the distance values
8	cctv-distance-value-post-processing.ipynb	distance_postprocessor.py	This module processes the distance values detected by OCR to remove outliers and fill in the frames without distances by linear interpolation and extrapolation.
9	cctv-extract-condition_codes.ipynb	extract_condition_codes.py	This module takes in the OCR_JSON column in the frames table containing OCR results and extracts condition codes.
10	cctv_SD1_label_images.ipynb	SD1_label_images.py	This notebook is used to assign condition codes to each frame.
11	cctv-prepare-cctv-training-csv.ipynb	training_data_splitter.py	This module creates the correct data split between training, validation and test and writes the training, validation, and test into the sqlite3 database.