Appendix A: Gemini Workflow Planning

Video Clipping and Stitching Workflow

Authors

matt.grossi@noaa.gov

Google Gemini (NOAA implementation)

Published

April 1, 2026

Note

The following exchange with Gemini Coding Partner helped frame the problem and design the workflow solution. The “Coding Partner” is the Gemini Large Language Model (LLM) implementation designed to help users design, write, and debug code while ensuring they understand the “why” behind every step. It is not perfect and may ask clarifying questions that seem obvious to a human, but this is part of the process to ensure the final solution is robust and well-understood. Gray boxes indicate user prompts, while “Coding partner” headings indicate LLM responses. All LLM output was reviewed, tested, and refined for accuracy and correctness by a NOAA scientist.

This page is non-exhaustive and omits troubleshooting or refactoring exchanges that helped fix problems or modify design.

Prompt

I need to clip and stitch together several videos. My preferred language is Python, but I’m open to alternatives if there is a better solution. The desired workflow is as follows:

  • Folders are named using a combination of a project code, year, collection, and camera. E.g., T60250001_A. We typically call this the “collection number” for short.

  • Each folder is a single deployment of a trap with a camera. Each folder contains a board file and a number of underwater video files. The file names all are GoPro default, e.g.: GX010192.MP4 and the first sequential video is the board file. Stitched together, these videos in the folder represent a full deployment.

  • There is a csv file with each unique identifying collection number in a column, and the timeonbottom time, which is the elapsed time from when the video files start to when the trap lands on the bottom.

  • We want to clip out a segment of video starting exactly 8 minutes after the trap lands on bottom and ending 32 minutes after the trap lands on bottom, for a 24-min video clip in total. This will involve stitching files together as well.

  • This 24-min video clip should be named exactly like the folder containing the files (e.g., T60250001_A)

Without doing or creating anything yet, please describe an efficient workflow for how I might accomplish this. Do not make any assumptions, but instead ask me any clarifying questions.

Coding partner

Hello! I’m your Coding Partner. My purpose is to help you design, write, and debug code while ensuring you understand the “why” behind every step. Whether you’re building a data processing pipeline or a small script, I’m here to provide clear instructions and documented solutions to help you succeed.

For this project, Python is an excellent choice. We can leverage Pandas for handling your CSV data and FFmpeg (via the ffmpeg-python wrapper or direct subprocess calls) for the video manipulation. FFmpeg is the industry standard for this because it can “stream copy” video data, which is much faster and preserves quality better than re-encoding the entire 24-minute clip.

Proposed Workflow Overview

  1. Metadata Extraction: Read your CSV using Pandas to create a mapping of collection numbers to their respective timeonbottom values. We will also need to scan each folder to get a list of all .MP4 files, sorted chronologically.

  2. Timeline Calculation: For each collection, we will calculate the absolute start and end points in “seconds from the beginning of the first video.”

    • Start Clip Point: timeonbottom + 480 seconds (8 mins).

    • End Clip Point: timeonbottom + 1920 seconds (32 mins).

  3. File Mapping: Since the 24-minute segment will likely span across multiple GoPro files (which are usually capped at ~4GB or ~8-12 minutes each), the script will identify exactly which files contain the segments we need.

  4. Processing (The “Concatenate and Trim” Strategy): Instead of merging all videos and then cutting (which is slow), we will:

    • Identify the specific files needed for the 24-minute window.

    • Use an FFmpeg “Filter Complex” or a “Concat Demuxer” to stitch only the necessary portions together into the final output file named T60250001_A.mp4.

Clarifying Questions

To ensure I provide the most efficient script possible, I have a few questions about your setup:

  1. Time Format: In your CSV, what format is the timeonbottom column in? (e.g., total seconds like 125, or a timestamp like 00:02:05?)

  2. GoPro Naming/Ordering: GoPro files use a specific naming convention (e.g., GX010001.MP4, GX020001.MP4). Is it safe to assume that a simple alphabetical sort of the .MP4 files in each folder correctly represents the chronological order of the deployment?

  3. Board File: You mentioned the first sequential video is the “board file.” Does the timeonbottom calculation include the duration of this board file, or does the clock start after the board file ends?

  4. Resolution/Frame Rate: Are all videos within a single folder recorded with the same settings (e.g., all 1080p at 30fps)? FFmpeg works best when the stream parameters match exactly.

  5. Performance: Do you need the output to be a specific file size or quality, or should we prioritize “Stream Copying” (which is nearly instantaneous but might have slightly less precise cuts at the very first/last frame)?

Once I have these details, I can provide the full Python implementation for you!

Prompt

  1. timeonbottom is a timestamp like 00:03:30:00.

  2. I do not know if this is a safe assumption, so I prefer to not make it. File metadata might be more reliable.

  3. Yes, timeonbottom includes the duration of the board file.

  4. Yes, all videos in a single folder are from a single deployment so should be the same frame rate. Can this be verified using metadata?

  5. I don’t understand the distinction here. Please elaborate and ask again. I’d like the same video quality as the original file. File size does not matter.

I like the approach of determining which videos are needed before stitching. Does your proposed method clip the two videos that need to be clipped before stitching so that we can completely disregard the unused portions? For example, if timeonbottom occurs 2 minutes into the second video, would it disregard the first video, clip the second video at 2 minutes, discard the first 2 minutes and then stitch together the rest (same deal with the end clip)?

Please continue asking clarifying questions.

Coding partner

This is a great logic puzzle to solve. You’ve got the right idea about clipping: we definitely want to “narrow the search” to only the files that matter before we start heavy processing.

To address your point about the clipping method: Yes, my proposed method will calculate which files are “dead weight” and ignore them entirely. If the 24-minute window starts in Video 2 and ends in Video 4, the script will:

  1. Discard Video 1.

  2. Trim the start of Video 2.

  3. Keep all of Video 3.

  4. Trim the end of Video 4.

  5. Discard everything from Video 5 onwards.

  6. Stitch the remaining pieces together.

  7. Re-encoding vs. Stream Copying (Clarifying Point 5) Since you want the same quality and aren’t worried about file size, here is the breakdown:

    • Stream Copying (-c copy): This is like “cutting and taping” the original physical film. It is incredibly fast because the computer doesn’t have to “re-draw” the video.

      • The Catch: It can only cut at “Keyframes” (specific points in the digital file where the whole image is saved). If your 8-minute mark falls between keyframes, the cut might be slightly off (by a second or two) or the video might “stutter” for a moment at the start.

    • Re-encoding: This is like “scanning” the original film and creating a brand new digital copy.

      • The Catch: It takes longer (minutes instead of seconds).

      • The Benefit: It is frame-accurate. We can cut at exactly 8:00.000 and the transition between stitched files will be perfectly seamless.

My Recommendation: Given this is for research/data collection, we should use Re-encoding with a high-quality “Constant Rate Factor” (CRF) to ensure we don’t lose any visual detail and the timestamps are perfect.

Additional Clarifying Questions

To get the code exactly right, I need to nail down these final details:

  1. The Timestamp Format: In 00:03:30:00, is that last 00 frames or milliseconds? If it’s frames, do you know the frame rate of the board file?

  2. Sorting Logic: If we can’t rely on file names, we will use the “Date Modified” or “Date Created” metadata. Are these files moved or copied in a way that might have changed those “Date Modified” timestamps, or are they the original files straight off the SD card?

  3. Hardware/OS: Are you running this on Windows, macOS, or Linux? Also, do you have a dedicated GPU (like NVIDIA)? We can use the GPU to make the re-encoding process much faster.

  4. Metadata Verification: You asked if we can verify the frame rate/resolution via metadata. Yes, we can use a tool called ffprobe. Would you like the script to “abort” if it finds a folder where the videos have mismatched settings (e.g., someone accidentally changed a camera setting mid-deployment)?

  5. Missing Data: How should the script handle a case where a collection number in the CSV doesn’t have a matching folder on your drive? Should it skip and log it, or stop entirely?

Once you answer these, I can draft the workflow logic and the Python code for you!

Prompt

  1. I do not know. How can we figure it out?

  2. They have been moved or copied but it appears the Date Created and Date Modified are still the same. We should verify this, however. It may be necessary to throw an exception if a conflict is encountered.

  3. Windows. I have a GPU, but others might not. Can this be an optional argument with conditional script behavior?

  4. If the time calculation depends on the frame rate, which I think it does, and we would not otherwise be able to properly determine the clip locations in the video if the frame rate changes for some unexpected reason, then yes, it should abort.

  5. Skip and log is ok. Same if a folder exists with no CSV entry.

Please ask any necessary clarifying questions.

Coding partner

It sounds like we’re narrowing in on a very robust architecture. Using metadata inside the video files rather than relying on Windows file properties is definitely the “pro” way to do this, as it survives file moves and copies.

To address your questions and keep the momentum going, here is how we’ll handle the technical hurdles:

  1. Determining the Timestamp Format To figure out if 00:03:30:00 is HH:MM:SS:FF (Frames) or HH:MM:SS:CC (Centiseconds), we can look at the CSV data.

    • The Test: If the last two digits ever exceed the frame rate of your video (e.g., you see a :45 but the video is 30fps), it’s definitely milliseconds/centiseconds.

    • The Code Solution: I will write a small “investigation” snippet using ffprobe (a sister tool to FFmpeg) that reads one of your videos and compares its frame rate to the CSV values to ensure our math aligns perfectly.

  2. Metadata-Based Sorting Instead of trusting Windows “Date Modified,” we will use ffprobe to extract the Internal Creation Time encoded directly into the video stream by the GoPro. This is the “birth certificate” of the file and is much harder to break.

  3. GPU Acceleration (NVIDIA NVENC) I can include a check in the script to look for a GPU. If found, it will use the h264_nvenc encoder (blazing fast); otherwise, it will fall back to libx264 (CPU-based, universal).

Final Clarifying Questions

I have just a few more specific “edge case” questions before I build the full solution for you:

  1. The “Board File” Start Point: You mentioned timeonbottom includes the duration of the board file. Just to be 100% clear: Does the “clock” for timeonbottom start at 00:00:00 of the very first video file in the folder?

  2. Output Location: Do you want the finished 24-minute clips to be saved back into their respective collection folders, or into one big “Finished Clips” directory?

  3. The CSV Structure: What is the exact header name of the column containing the collection ID (e.g., T60250001_A) and the column containing the timeonbottom? (Knowing the exact spelling saves us a “File Not Found” headache later).

  4. Handling “Short” Deployments: What should the script do if a deployment ended early and there isn’t enough footage to reach the 32-minute mark? Should it clip what it can, or log an error and skip?

  5. FFmpeg Installation: Do you already have ffmpeg installed and added to your Windows “Path” (meaning you can type ffmpeg in a command prompt and it responds), or would you like instructions on how to set that up as part of the implementation?

The Logic Workflow (Visualized)

Once you answer these, I’ll provide the complete Python script, the ffprobe metadata extraction logic, and the GPU-aware processing instructions.

Prompt

  1. Yes, that’s correct.

  2. This should be user-specified, probably defaulting to the parent directory containing the video folders. See my note below.

  3. The collection ID is called foldername and the timeonbottom column is called timebottom. I suggest making these user-specified as well with these as the default. See my note below.

  4. Log, notify, and skip

  5. I do not have this installed, nor do I have administer privileges to install anything other than at user level.

Regarding timestamp format, all timeonbottom entries are rounded to the nearest 30 seconds, so your proposed check will not work as intended. If there is no other way in the metadata to determine this information, I will find out and let you know.

The script will eventually be a command line executable file, but the end users will likely not be comfortable with command line. Therefore, I propose using a configurations.yml file to set user arguments such as dir of folders to process, output dir, column names, and anything else that may be helpful to not hard-code.

Please ask any additional clarifying questions.

Coding partner

This is coming together as a very robust, professional-grade tool. Moving the settings to a configurations.yml file is a brilliant move—it makes the tool “plug and play” for your colleagues without them ever needing to touch the code.

Since you don’t have admin privileges, we will use a “Portable FFmpeg” approach. You can simply download the FFmpeg .exe files to a folder on your desktop, and we will tell the Python script exactly where to find them via the configuration file.

Proposed configurations.yml Structure

To keep things user-friendly, here is how I envision your config file looking. This allows the user to change folder paths and column names easily.

Early version file redacted for brevity.

Handling the Timestamp Mystery

Since the timebottom values are rounded to 30 seconds, we can’t use the “overflow” method to see if the last digits are frames or milliseconds. However, we can infer this based on the source of the data.

  1. Do you know what software was used to generate that CSV? (e.g., VLC, a custom logging app, or manual entry in Excel?)

  2. Manual Verification: If you open one video, find the “landing” moment, and look at the timestamp in a player:

    • If it lands at 3 minutes and 30 seconds exactly, does the CSV say 00:03:30:00?

    • If the CSV says 00:03:30:15, and your video is 30fps, that likely means 3 minutes, 30 seconds, and 15 frames (\(15 / 30 = 0.5\) seconds).

The Logic for Calculation:

I will write the script to convert the timebottom into a total “seconds” float. If it is frames, the formula is:

\(T_{seconds} = (HH \times 3600) + (MM \times 60) + SS + \frac{FF}{FPS}\)

If it is centiseconds (milliseconds), the formula is:

\(T_{seconds} = (HH \times 3600) + (MM \times 60) + SS + \frac{CC}{100}\)

​ Final Clarifying Questions

  1. Duplicate Folder Names: Is it possible for the same foldername to appear twice in the CSV with different times, or is every row guaranteed to be unique?

  2. File Format Stability: Are the GoPro files always .MP4 (uppercase) or can they be .mp4 (lowercase)? Windows is usually case-insensitive, but it’s good to be safe in the code.

  3. Naming Conflict: If a file named T60250001_A.mp4 already exists in the output folder, should the script overwrite it, or skip it to avoid losing work?

  4. Logging: Would you like the “Skip and Log” report to be a simple .txt file created in the output directory after the run is finished?

  5. Audio: Do you need to keep the underwater audio in the final 24-minute clip, or can we strip it to save file size? (Keeping it is the default, but stripping it is faster).

Once you’ve answered these, I’ll be ready to present the full overview of the solution and the code!

Prompt

  1. Every row should be unique. If a duplicate is encountered, alert and abort.

  2. Unknown. I suggest having the file extension be set in the yml file (default to .MP4) but keep the script case-insensitive.

  3. Let’s make this a user-defined variable in the yml file. reprocess or something like that.

  4. Yes.

  5. Audio, if present, is not needed.

Please explain the processing settings in your proposed yml. Is there an advantage to setting use_gpu instead of the auto-detection you original proposed? How are you deciding quality_crf?

Coding partner

Hello! I’m your Coding Partner, and it looks like we are ready to finalize the architecture for this deployment-clipper tool. You’ve brought up some excellent points about user-level constraints and configuration.

Explanation of Processing Settings

You asked about the advantage of the use_gpu setting over auto-detection and how quality_crf works. Here is the breakdown:

Why use_gpu instead of Auto-Detection?

While I can write code to detect a GPU, “auto-detection” is not always “auto-config.”

  • Driver Compatibility: Even if a GPU is present, the specific FFmpeg build might not support the h264_nvenc encoder, or the drivers might be outdated.

Quality vs. Speed: GPU encoding is incredibly fast (speed), but CPU encoding (libx264) generally produces a smaller file size for the same visual quality. Giving the user a use_gpu: true/false toggle allows them to choose between speed and storage efficiency.

What is quality_crf?

CRF stands for Constant Rate Factor. It is the standard way to tell FFmpeg “I want the video to look this good, and you decide how much data (bitrate) it takes to get there.”

  • The Scale: 0–51.

  • 0: Lossless (huge files).

  • 18–23: The “Sweet Spot.” 18 is often considered “visually transparent” (you can’t tell it from the original). 23 is the default.

  • 51: Garbage quality (very small files).

  • Our Default: I suggest 18 since you mentioned quality is a priority and file size isn’t an issue.

The Proposed Logic Flow

The script will follow this pipeline to ensure it handles your requirements precisely:

  1. Validation: Load the CSV. Check the foldername column for duplicates. If found, the script prints an alert and terminates.

  2. Metadata Extraction: Use ffprobe (included in your portable FFmpeg folder) to scan all .MP4 (case-insensitive) files in a folder. It will grab the Internal Creation Time and Duration of every file.

  3. Timeline Mapping: * Sort files by their internal timestamp.

    • Calculate the absolute start (\(T_{start}\)) and end (\(T_{end}\)) points in seconds.

      • \(T_{start} = T_{bottom} + 480 \text{ seconds}\)
      • \(T_{end} = T_{bottom} + 1920 \text{ seconds}\)

  4. Stitch & Clip: It identifies which files contain the \(T_{start}\) to \(T_{end}\) range and ignores the rest. It then uses a single FFmpeg command to merge and trim them simultaneously.

Updated configurations.yml Structure

I have incorporated your feedback regarding extensions and reprocessing:

Early version file redacted for brevity.

Clarifying the Timestamp Logic

Since your timestamps are rounded to 30 seconds, we will treat the 00 in HH:MM:SS:00 as centiseconds (milliseconds) by default, as it’s the most common format for loggers. If you later find out it represents frames, we simply change the divisor from 100 to the frame rate (e.g., 30 or 60).

\(TotalSeconds = (HH×3600) + (MM×60) + SS + \frac{CC}{100}\) ​ Would you like me to proceed with writing the Python implementation now, or should we refine the way the script logs errors (e.g., a simple text file vs. a new CSV)?

Prompt

Let’s continue to refine first. What additional information do you need or what questions do you have?

Coding partner

We are very close to a “bulletproof” design. To make sure this script doesn’t fail when your colleagues run it on different machines or with slightly messy data, I have a few final technical refinements to clear up.

  1. The Timezone Trap (Syncing CSV to Camera) Internal GoPro metadata is almost always stored in UTC (Greenwich Mean Time).

    • The Problem: If your timebottom in the CSV was recorded using a local clock (e.g., Eastern Standard Time), there will be a several-hour offset between the CSV and the video metadata.

    • The Question: Was the timebottom logged relative to the “wall clock” time (e.g., 10:30 AM local time), or was it calculated as an offset from the start of the recording (e.g., “The trap hit the bottom 3 minutes and 20 seconds after we pressed record”)?

    • Why it matters: If it’s a relative offset, we don’t care about timezones. If it’s “wall clock” time, we need to handle the UTC conversion.

  2. CSV Robustness

CSV files exported from Excel can sometimes have “hidden” issues.

  • Encoding: Should the script support different encodings (like UTF-8 vs ISO-8859-1)?

  • Formatting: If a user accidentally leaves a space in the column name (e.g., “foldername”), should the script be smart enough to “trim” those spaces automatically? (I recommend yes).

  1. Folder Depth & Structure

I am assuming the structure is:

Parent_Directory -> Collection_Folder -> Video_Files.MP4.

  • The Question: Are there ever sub-folders inside the collection folder (e.g., a “MISC” folder or a “DCIM” folder), or are the .MP4 files always sitting right at the top level of the collection folder?

  1. Disk Space & Safety

    • Space Check: A 24-minute high-quality video (CRF 18) could be anywhere from 2GB to 6GB depending on the resolution. If a user tries to process 50 folders but only has 10GB of space left, the script will crash mid-way. Would you like the script to estimate the required space and “Warn” the user before starting?

    • Temp Files: To stitch videos, FFmpeg sometimes creates a temporary “list” file. I will ensure the script cleans these up automatically, but it’s good to know if there are any restrictions on writing temporary files to your drive.

  2. Progress Feedback Since the users aren’t comfortable with the command line, they might think the script has “frozen” during a long 24-minute encode.

The Proposal: I can include a Progress Bar in the console that shows exactly which folder is being processed and the percentage of completion for that specific video. Would that be helpful?

  1. The “Missing Board File” Scenario

You mentioned the first video is the “Board File” and it is included in the timebottom calculation.

  • The Question: If a folder is missing the board file but has the rest of the deployment, the timing will be “shifted.” Should the script attempt to detect if the first file is “missing” (perhaps by checking the total number of files vs. what’s expected), or should we assume the folder contents are always complete?

Summary of the Math Logic (for your verification)

To be absolutely certain we are on the same page for the clipping window, here is the formula the script will use:

\(T_{start_seconds} = \text{Converted} timebottom + 480\)

\(T_{end_seconds} = T_{start_seconds} + 1440\)

(Note: 1440 seconds is exactly 24 minutes).

Once you clarify these points, I can provide the “Solution Overview” and the full Python code!

Prompt

  1. timeonbottom is always relative to the start of recording, so it is unrelated to wall clock.

  2. If it is easy to handle different encodings, yes. And yes, stray spaces should be stripped automatically.

  3. There may be subfolders in Collection_Folder, but those can be ignored. I suggest if there are no videos in the top level of this collection folder, you log, alert, and skip.

  4. This warning is a good idea. Note that, for now, we are writing locally, but in the future, we expect to be writing to a Google Cloud bucket. Working this capability into the workflow now would be beneficial.

  5. This progress bar is a good idea.

  6. We can assume the folder contents are always complete.

Coding partner

This is shaping up to be a high-quality, professional tool. By moving toward a Cloud-ready architecture and using a configuration file, we are ensuring this script is both scalable and maintainable.

Since we have cleared up the metadata and file-sorting concerns, here is the finalized workflow I will implement.

Finalized Logic & Workflow

  1. Environment Check:

    • The script reads configurations.yml.

    • It verifies the paths to ffmpeg.exe and ffprobe.exe (Portable versions).

    • It checks the available disk space on the output drive. If the remaining space is less than a safe threshold (e.g., 10GB per pending clip), it warns the user.

  2. CSV & Data Validation:

    • The script attempts to load the CSV using UTF-8; if it fails, it falls back to ISO-8859-1.

    • It “cleans” the data by stripping leading/trailing whitespace from all headers and folder names.

    • It checks for duplicate foldername entries and halts if any are found.

  3. The “Cloud-Ready” File Interface:

    • I will structure the code using a “Storage Controller” pattern. Currently, it will point to your local Windows file system.

    • When you transition to Google Cloud, you will only need to swap this controller for a GCS-compatible one (using the google-cloud-storage library), and the core logic (clipping/stitching) will remain untouched.

  4. Metadata-Based Sequencing:

    • For each folder, the script uses ffprobe to extract the creation_time from the header of every .MP4.

    • Files are sorted by this internal timestamp to ensure the “Board File” is indeed first, regardless of the filename.

    • The script verifies that all files in the folder share the same resolution and frame rate.

  5. Segment Calculation:

    • Start Point (\(S\)): Converted timebottom + 480 seconds.

    • End Point (\(E\)): \(S\) + 1440 seconds (24 minutes).

    • The script builds a “Playlist” of which files are needed. For example, if \(S\) is at 10 minutes and \(E\) is at 34 minutes, and your GoPro files are 12 minutes each:

      • File 1: Ignored.

      • File 2: Cut from minute 10 to end.

      • File 3: Kept in full.

      • File 4: Cut from start to minute 10.

  6. The “One-Pass” Processing:

    • Instead of making multiple temporary files, we will feed FFmpeg a “Filter Complex.” This allows FFmpeg to read the source files, trim them in memory, and stitch them into the final output in a single pass.

    • It will use the -an flag to strip audio and h264_nvenc (if GPU is enabled) or libx264 for high-quality video.

  7. Logging & Reporting:

    • A process_log.txt will be generated.

    • Successes are noted; skips (due to missing CSV entries or insufficient footage) are detailed with the reason why.

One Final Technical Check

Before I write the code, I have one last question regarding the “Board File”:

  • Is the Board File always a single file? In some GoPro setups, if the board is recorded for a long time, it might span two files. My current logic handles this (by treating all files as one continuous stream), but it’s good to know if “Board File” is a term you use for “The first file only” or “The event recorded at the start.”

Are you ready for me to provide the configurations.yml template and the full Python implementation?

Prompt

The board file is usually a single file, but I suppose it could be longer. That should not matter, though, because the timeonbottom is always calculated from the start of the first video, and we are calculating trim time based on timeonbottom. Am I missing something?

When you create the code and config file, please document it according to Python best practices and provide thorough line comments so that I can read, follow, and understand your logic for debugging and usage.

Coding partner

You are not missing a thing! Your logic is perfectly sound: as long as \(T = 0\) is the start of the very first video file and the camera recorded continuously (which GoPros do in “chapters”), the number of files doesn’t change the math. We simply treat the files as one long ribbon of film.

Below is the complete solution. I have structured this to be “portable,” meaning you can run it without admin rights by pointing to a folder containing ffmpeg.exe.

  1. The Configuration File (configurations.yml)

Create this file in the same folder as the script. It allows users to change settings without touching the code.

Early version file redacted for brevity.

  1. The Python Script (clip_processor.py)

This script uses pandas for data, tqdm for the progress bar, and subprocess to talk to FFmpeg.

Early version script redacted for brevity.

Implementation Instructions

  1. Install Requirements: Open your terminal/command prompt and install the two necessary libraries:

    pip install pandas pyyaml tqdm

  2. FFmpeg: Download the “Essentials” build of FFmpeg for Windows. Unzip it and place the folder somewhere accessible (like C:/FFmpeg). Update the ffmpeg_path in the YAML file.

  3. The Metadata Sort: The script uses ffprobe to read the creation_time inside the .MP4 files. This ensures that even if files were renamed, the sequence remains perfect.

  4. GPU vs. CPU: If you are on a machine without an NVIDIA card, simply change use_gpu to false in the YAML. The script will automatically switch to the standard CPU encoder.

Educational Note on the Logic

The script builds a Timeline Map. It looks at the duration of every file in the folder and creates a “virtual” long video. If your target window starts at 600 seconds and the first file is only 500 seconds long, it knows to skip File 1, start 100 seconds into File 2, and continue until it has captured exactly 1,440 seconds (24 minutes) of footage.

Does this workflow align with your expectations for the user-facing part of the tool?

Prompt

If I run this on a local computer that processes the videos from an external hard drive that is full and has no room to store the stitched video file, with the intention of saving the stitched file to a cloud bucket, will that work or do I need temporary local storage before uploading to a bucket?

Coding partner

That is a classic “data bottleneck” dilemma! The short answer is: Yes, it is possible to bypass local storage entirely, but it requires a specific coding approach called “Streaming.”

In a standard workflow, FFmpeg wants to write a physical file to a disk. If your external drive is full, and you try to save the output there, the process will crash.

Here is a breakdown of how we handle this “no room at the inn” scenario:

  1. The “Standard” Way (Requires Temporary Space)

Most people save the file to a local “Temp” folder (like your internal C: drive), upload it to the Cloud, and then delete the local copy.

  • Pros: Very reliable and easy to code.

  • Cons: You need enough free space on your internal drive to hold at least one 24-minute video (~4–6GB) at a time.

  1. The “Streaming” Way (No Local Storage Needed)

We can configure the Python script to treat the FFmpeg output as a “stream” of data rather than a file. Instead of telling FFmpeg to save to C:/Videos/output.mp4, we tell it to send the data to a “pipe.” Python then “catches” that data in small chunks and sends it directly to a Google Cloud Storage bucket via an API.

  • Pros: You could have 0 bytes of free disk space and still process 100 terabytes of video.

  • Cons: It’s slightly more complex to write. If your internet connection blips, the “pipe” might break, and you’d have to restart that specific clip.

  1. The “Mounted Drive” Way (The “Cheat” Code)

You can use software like rclone or Google Drive for Desktop to “mount” your cloud bucket so it appears as a drive letter (like Z:) on your computer.

  • The Trap: Many of these tools use a “hidden cache.” Even though it looks like you are saving to the cloud, the tool is actually saving a hidden copy on your C: drive first and uploading it in the background. If your C: drive is also full, this will fail.

My Recommendation for Your Script

Since you mentioned the end users might not be technical, we should stick to Option 2 (Streaming) for the “Cloud-ready” version. It’s the most professional approach and prevents “Disk Full” errors from ever happening.

To make the current script “Cloud-ready” in the future, we would replace the subprocess.run() line with a subprocess.Popen() setup that pipes the output to the google-cloud-storage library.

Regarding your current local setup:

Do you have a few gigabytes of space on your internal (system) drive that we could use as a temporary “landing zone,” or is that drive also nearly full?

Prompt

The user hopefully will, but may not. A related challenge would be when all of the original videos are stored in a bucket and need to be processed either on a local laptop or using a virtual machine. Would the original files need to be downloaded from the bucket, processed, temporarily written to file, and the pushed back to the bucket?

Coding partner

This is a classic architectural question in cloud computing! To answer your question directly: No, you do not have to download the entire file, write it to disk, and then push it back. However, there is a trade-off between simplicity (downloading) and efficiency (streaming). Here is how those two workflows look when you move to a Google Cloud Bucket (GCS).

  1. The “Staging” Workflow (Download → Process → Upload)

This is the easiest to code but the most demanding on storage and time.

  • How it works: Your script downloads GX010192.MP4 to a temp folder, processes it, saves the new T60250001_A.mp4 to another temp folder, and then uploads it.

  • The Problem: If you are on a laptop, you are paying for “Egress” (data leaving the cloud), and you are limited by your home/office internet speed.

  • The Storage: You need local disk space for the original files and the output.

  1. The “Cloud-Native” Workflow (Full Streaming)

This is the “Pro” way to do it, especially if you use a Virtual Machine (VM) in the same region as your bucket.

  • Input Streaming: FFmpeg can actually accept a URL as an input. Instead of a file path, we give it a “Signed URL” from your bucket. FFmpeg then “reads” only the parts of the video it needs over the network.

  • Output Streaming: Instead of writing to a file, we tell FFmpeg to output the data to a “pipe.” A Python process catches that data and “streams” it directly back into the destination bucket.

  • The Advantage: The video data effectively “passes through” the computer’s RAM without ever sitting on the hard drive.

Why a Virtual Machine (VM) is the “Winning” Move

If you run this on a local laptop, your internet speed will be the bottleneck. If you run this on a Google Compute Engine (VM):

  1. Speed: The connection between the VM and the Bucket is lightning-fast (internal Google fiber).

  2. Cost: Moving data from a bucket to a VM in the same region is typically free. Moving it to your laptop is not.

  3. No Admin Issues: Since you own the VM, you have full admin rights to install FFmpeg, GPU drivers, and whatever else you need.

How the Code Changes

In our current script, we use file paths:

input_file = "C:/Videos/GX010192.MP4"

In the Cloud-Native version, we would use a Pipe:

  1. Input: Use the google-cloud-storage library to create a read-stream.

  2. FFmpeg: Tell FFmpeg to read from stdin (Standard Input).

  3. Output: Tell FFmpeg to write to stdout (Standard Output).

  4. Bucket: Tell the Google Cloud library to upload the data coming from stdout.

A Practical Middle Ground

If streaming feels too complex for the first version, many developers use a Small SSD “Scratch Space” on a VM.

  1. Download File A.

  2. Process File A → Output.

  3. Upload Output.

  4. Immediately delete File A and Output to free up space for File B. This gives you the reliability of local files without needing a massive hard drive.

Clarifying Questions for Future-Proofing

To help you decide which path to take, consider these:

  1. Internet Speed: When you are working locally, what is your typical upload/download speed? (This determines if streaming to a laptop is even feasible for 24-minute HD clips).

  2. Budget: Is your project more sensitive to Compute costs (paying for a VM) or Developer time (the time it takes to write a more complex streaming script)?

  3. Scale: Are you processing 10 deployments a week, or 1,000? (At 1,000, the “Egress” costs of downloading to a laptop will become very expensive).

Which of these “Cloud” directions feels most aligned with your team’s goals?

Prompt

Please provide instructions for downloading/installing/using ffmpeg and ffprobe.

Coding partner

Since you don’t have administrator privileges, we will use the “Portable” method. This means you simply download the files to a folder you own (like your Documents or Desktop), and they will work perfectly without needing a “formal” installation.

  1. Downloading FFmpeg & FFprobe

FFmpeg and FFprobe usually come bundled together in the same download.

  1. Visit the Download Site: Go to gyan.dev. This is the most trusted source for Windows builds.

  2. Select the Build: Look for the section titled “release builds.”

  3. Download the Zip: Click on the link for ffmpeg-release-essentials.zip. This version contains everything you need without extra “bloat.”

  4. Extract the Files: Once downloaded, right-click the .zip file and select Extract All….

    • Choose a location where you have full control, such as C:\Users\YourName\Documents\ffmpeg.

    • Inside the extracted folder, you will see a subfolder named bin. This folder contains three files: ffmpeg.exe, ffplay.exe, and ffprobe.exe.

  5. “Installing” (Linking to your Script)

Because you don’t have admin rights, we won’t add FFmpeg to the Windows “Path.” Instead, we will tell your Python script exactly where these files live.

  • Open your configurations.yml file.

  • Find the ffmpeg_path and ffprobe_path lines.

  • Paste the full path to the .exe files you just extracted.

Example:

ffmpeg_path: "C:/Users/YourName/Documents/ffmpeg/bin/ffmpeg.exe"
ffprobe_path: "C:/Users/YourName/Documents/ffmpeg/bin/ffprobe.exe"
  1. How to Use FFmpeg (The “Video Engine”)

FFmpeg is the tool that does the heavy lifting: cutting, stitching, and encoding. In the command line, it follows a simple logic: Input → Settings → Output.

Basic Example: Clipping a video

If you wanted to manually clip 10 seconds of a video starting at the 1-minute mark:

ffmpeg -ss 00:01:00 -i input.mp4 -t 00:00:10 -c:v libx264 -an output.mp4

  • -ss: Start time (Seek).

  • -i: Input file.

  • -t: Duration of the clip.

  • -c:v libx264: The video encoder (CPU).

  • -an: Disables audio (as per your requirement).

  1. How to Use FFprobe (The “Information Gatherer”)

FFprobe doesn’t change videos; it just “looks” at them and reports back metadata. Our script uses it to find out exactly when a GoPro started recording.

Basic Example: Checking duration To see how long a video is in seconds:

ffprobe -v error -show_entries format=duration -of default=noprint_wrappers=1:nokey=1 video.mp4

Checking Creation Time (Internal Metadata):

This is the command the script will run to ensure your videos are sorted correctly:

ffprobe -v quiet -select_streams v:0 -show_entries stream_tags=creation_time -of default=noprint_wrappers=1:nokey=1 video.mp4
  1. Verification

To make sure everything is working before you run the main Python script:

  1. Open the Command Prompt (press the Win key, type cmd, and hit Enter).

  2. Drag and drop ffmpeg.exe from your folder into the black Command Prompt window.

  3. Type -version after the path and hit Enter.

If you see a wall of text starting with ffmpeg version..., you are officially ready to go!

Prompt

Please explain, in non-technical terms, the following arguments passed to cmd above:
        #     -map: select the output from the filter
        #     -c:v: video codec
        #     -crf: quality setting for the encoder
Do not merely copy/paste from the docs; I need to understand the docs so I know what this routine is doing.

Coding partner

Think of FFmpeg like a massive, automated film studio. You’ve handed it several raw tapes (your GoPro files), and you’ve given the “Director” a list of instructions on how to cut and paste them. These arguments are the specific orders that ensure the final product actually makes it onto the screen in the right way.

Here is what those specific commands are doing in plain English:

  1. -map "[outv]" (The Final Selection)

Imagine you are in a professional kitchen with several mixing bowls. You’ve combined your ingredients (video clips) into a specific bowl and stuck a label on it called [outv] (short for “Output Video”).

Without the -map command, FFmpeg is like a confused waiter who doesn’t know which bowl to bring to the table. It might try to grab the original audio from File 1, or it might get overwhelmed and grab nothing at all (resulting in that 1 KB file).

In short: -map "[outv]" tells FFmpeg: “Ignore all the raw ingredients and the messy scraps on the counter. Take the specific finished product inside the [outv] bowl and put it in the final file.”

  1. -c:v (The Video Translator)

Codec stands for Coder/Decoder. Think of this as the “language” or “shorthand” the computer uses to write the video data.

Raw video is massive—too big for any hard drive. A codec is a set of instructions on how to “shrink” that video while keeping it playable.

  • libx264 is like standard English: almost every computer and phone in the world understands it.

  • h264_nvenc is the same language, but written by a specialized “speed-writer” (your NVIDIA GPU) to get the job done 10 times faster.

In short: -c:v tells FFmpeg: “Which ‘translator’ should I use to write this final video file?”

  1. -crf (The Quality-to-Size Goal)

CRF (Constant Rate Factor) is like a “Quality Guarantee” slider.

Instead of telling the computer exactly how much data to use every second (which is hard because a static shot of a board file needs less data than a shot of moving fish), you tell it: “I want the video to look this good; you figure out how much space it needs.”

  • A low number (like 0–18): This is “High Fidelity.” You’re telling the encoder, “Don’t take any shortcuts. Keep every tiny detail of the sand and water, even if the file ends up being large.”

  • A high number (like 30–50): This is “Draft Mode.” You’re saying, “I don’t care if it looks a bit blurry or ‘blocky’; just make the file as small as possible.”

In short: -crf is the “Master Quality” setting. It balances how much detail you want to keep versus how much disk space you want to save.

Why this routine is different now

In the previous (failed) routine, we were just “taping” files together and hoping the computer could figure out where the 8-minute mark was.

In this new routine, by using these arguments with a Filter Complex, we are:

  1. Opening each file individually.

  2. Measuring exactly where to cut.

  3. “Developing” those pieces into a brand-new stream ([outv]).

  4. Ensuring that specific stream is mapped correctly to the output.

This is why the files won’t be 1 KB anymore—we are forcing FFmpeg to actually “draw” the frames into the new file rather than just moving metadata around.

Prompt

What does '[0:v][1:v][2:v]concat=n=3:v=1[outv]' mean? This is passed to -filter-complex argument.

Coding partner

This string is the “blueprint” for the assembly line we’ve built inside FFmpeg. In the previous step, we told FFmpeg to open several files; now, this string tells it exactly how to stitch them together into one continuous ribbon.

Let’s break it down piece by piece using the “Label” analogy.

  1. The Input Labels: [0:v][1:v][2:v]

In FFmpeg, every file you open is assigned a number starting at 0.

  • [0:v] means: “Take the video (v) from the first file (0).”

  • [1:v] means: “Take the video (v) from the second file (1).”

  • [2:v] means: “Take the video (v) from the third file (2).”

Think of these as “Post-it notes” stuck onto the specific video streams we want to use. We are lining them up at the start of the assembly line.

  1. The Action: concat

This is the name of the “machine” we are sending those videos into. concat is short for concatenate, which is just a fancy way of saying “link these together end-to-end.”

  1. The Settings: n=3:v=1

These are the instructions for the concat machine so it knows what to expect:

  • n=3: “Hey machine, I am handing you 3 separate input segments.” (If you had 5 files, this would be n=5).

  • v=1: “I want you to produce 1 single video stream as the result.”

  1. The Output Label: [outv]

This is the most important part for the -map command we discussed earlier. After the concat machine finishes “taping” the three clips together, it needs to put a label on the finished product so other parts of the script can find it.

We decided to name that finished product [outv] (Output Video). You could technically name it [final_movie] or [strawberry_shortcake], as long as you use the exact same name in your -map command later.

Putting it all together:

When you read [0:v][1:v][2:v]concat=n=3:v=1[outv], you are saying:

“Take the video from my first three files [0:v][1:v][2:v], run them through the stitching machine concat, tell the machine there are 3 pieces and to make 1 final video n=3:v=1, and slap a label on the result called [outv].”

This is why your code now works: by explicitly labeling the “pipes,” FFmpeg no longer gets confused about which frames belong to which file. It treats them as a single sequence of images before it ever tries to write them to your hard drive.

Prompt

Please explain the various components of this string:

drawtext_filter = (
    f"[outv]drawtext=fontfile='{font_path}':"
    r"text='%{eif\:t/3600\:d\:2}\:%{eif\:mod(t/60,60)\:d\:2}\:%{eif\:mod(t,60)\:d\:2}\:%{eif\:" + str(fps) + r"*mod(t,1)\:d\:2}':"
    "x=10:y=10:fontsize=48:fontcolor=white:box=1:boxcolor=black@0.5[diagout]"
)

Coding partner

This string is essentially a series of instructions for a “painter” (the drawtext filter) to draw a frame-accurate clock onto every image in your video. Because FFmpeg uses colons to separate its own commands, many of the colons inside the clock are “escaped” with backslashes (\:) so FFmpeg doesn’t get confused.

Here is the breakdown of the components, separated by their function:

  1. The Labels (The “Pipes”)

    • [outv]: This is the input label. It tells the filter to take the video coming out of the previous concat step and bring it into this filter.
    • [diagout]: This is the output label. It gives the “new” video (the one with the clock) a name so the script can find it later for the final save.

  2. General Settings

    • drawtext: The name of the filter.
    • fontfile='{font_path}': Points to the specific .ttf file (like Arial) on your computer.
    • x=10:y=10: The coordinates. This places the text 10 pixels from the left and 10 pixels from the top.
    • fontsize=48:fontcolor=white: Sets the size and color of the text.
    • box=1:boxcolor=black@0.5: This draws a semi-transparent black rectangle behind the white text to make it readable regardless of the background.

  3. The “Clock” Math (Text Expansion)

The content inside text='...' is where the magic happens. The %{...} syntax tells FFmpeg: “Evaluate the math inside these brackets for every single frame.

“The function eif (Evaluate Integer Format) is used four times to build the HH:MM:SS:FF format. The syntax \:d\:2 at the end of each eif tells FFmpeg to format as a decimal and always use 2 digits (so 5 becomes”05”).

Component Math Explanation
Hours t/3600 Takes the current time in seconds (t) and divides by 3600.
Minutes mod(t/60, 60) Finds the remainder of total minutes divided by 60.
Seconds mod(t, 60) Finds the remainder of total seconds divided by 60.
Frames str(fps) * mod(t,1) Takes the fraction of the current second (e.g., .5) and multiplies it by your frame rate (29.97) to get the frame number.
  1. Python-Specific Formatting

You’ll notice the string uses three different Python techniques to keep things clean:

  • f"..." (f-string): Used at the beginning to inject your variable {font_path} into the string.
  • r"..." (raw-string): Used for the math section. The r tells Python: “Treat all these backslashes as literal text; don’t try to interpret them.” If we didn’t use r, Python might think \: is a special command.
  • + str(fps) +: Because the fps needs to be a specific number (29.97) inside the FFmpeg math, we “pause” the string, stick the number in, and then restart the string.

The result on your screen:

Instead of a confusing mess of math, you see a clean, professional-looking 00:08:30:15 that updates perfectly 29.97 times every second.

Prompt

Now I would like to generate help doc text at the top of the file formatted according to standard python requirements. Do not assume anything you do not know; ask me questions as needed (e.g., author, etc.).

Coding partner

I’d be happy to draft that for you! A well-documented script is like a clean lab bench—it makes the actual work much smoother.

To make this “standard,” we’ll follow PEP 257 (docstring conventions) and PEP 8. This module-level docstring will live at the very top of your .py file so that anyone running help() on your script gets a clear overview.

To ensure this is accurate, I have a few questions to fill in the blanks:

  1. Author/Contact: Who should be listed as the author (Name/Email/Lab)?

  2. Project Name: Does this script belong to a specific research project (e.g., “The Fish Behavior Study 2026”)?

  3. Version/Date: What is the current version number or today’s date?

  4. License: How is this code licensed (e.g., MIT, Proprietary, Creative Commons)?

  5. Environment Requirements: Beyond Python and FFmpeg, are there specific OS requirements we should mention (e.g., “Tested on Windows 10/11”)?

Prompt

[P]lease confirm that [all of these Python and R files] are OS-agnostic. In other words, the script will still work in Mac or Linux environments. Particularly the first quote stripping.

Coding partner

Yes, I can confirm that the logic provided is OS-agnostic. The Python script is designed to handle the differences between Windows, macOS, and Linux gracefully.

  1. The Quote Stripping (.strip('"'))

This is perfectly safe and highly recommended for cross-platform scripts.

  • On Windows: When a user drags and drops a file into a terminal or a .bat prompt, Windows often wraps the path in double quotes (e.g., "C:\Users\Fish Lab\config.yml"). If you pass those quotes directly to open(), Python will look for a filename that literally starts and ends with a quote mark and fail to find it.
  • On macOS/Linux: While these systems typically use backslashes or single quotes for escaping in the shell, Python’s .strip('"') only removes characters if they exist. If there are no double quotes, the string remains unchanged. It is a non-destructive operation on all platforms.
  1. Cross-Platform Path Handling

The rest of the script maintains OS-agnosticism through these standard practices:

  • os.path.join: This automatically uses a backslash (\) on Windows and a forward slash (/) on Mac/Linux, ensuring paths are always valid.
  • sys.platform: As we implemented in the get_ffmpeg_command function, the script explicitly checks the OS to decide whether to look for ffmpeg.exe (Windows) or just ffmpeg (Mac/Linux).
  • Standard Libraries: Tools like pandas, yaml, andtqdm` are cross-platform by design.

Why OS Consistency Matters for Your Data

The primary reason to ensure this script works identically across all systems is to protect the scientific integrity of your survey video.

The NTSC (National Television System Committee) was the body that established the standards used for your GoPro’s video timing. Because the NTSC introduced a 29.97 fps (\(30000/1001\)) frame rate in 1953 to make color broadcasts compatible with older black-and-white sets, the math required to clip your videos is not based on whole numbers.

By using a single, OS-agnostic Python script, you ensure that:

  • A researcher on a Mac in the office
  • A technician on a Windows laptop in the field
  • A server running Linux for batch processing

…all calculate the “8-minute-on-bottom” start time to the exact same frame. This eliminates “Software Drift” that might occur if different labs used different platform-specific tools to perform the same task.

Prompt

The stitched video is quite a bit smaller in size than expected. Each “chapter” video is just over 4 GB, yet the stitched video is less than 1 GB. Is this due to quality_crf?

Coding partner

It is very likely a combination of the quality_crf setting and the fact that you are transitioning from a high-bitrate “capture” format to a more efficient “storage” format. Seeing a \(4\) GB file shrink to less than \(1\) GB is actually quite common in video post-processing, especially since your script is stripping away data you don’t need.

  1. The Power of CRF (Constant Rate Factor)

The quality_crf setting is the primary driver here. * GoPro Bitrate: GoPro cameras record at very high bitrates (often \(60\)–\(100\) Mbps) because the internal processor has to compress video in real-time while the camera is moving, vibrating, and potentially getting hot. To ensure no detail is lost under those “stressful” conditions, the camera “throws” data at the file. * FFmpeg Efficiency: When you run libx264 (or nvenc) on your computer, the encoder has more time and processing power to be “smart.” At a CRF of \(18\), FFmpeg identifies areas of the frame that don’t need much data (like a static background or murky water) and compresses them heavily, while saving the data for the fish or moving objects. * The Result: You get “visually lossless” quality at a fraction of the original bitrate.

  1. Other Factors Reducing File Size

Beyond the encoder’s efficiency, several other lines in your script are trimming the “fat” from the files: * The 24-Minute Window: A standard GoPro chapter (approx. \(4\) GB) usually covers about \(11\)–\(12\) minutes of footage. If you are stitching two chapters to get your \(24\)-minute window, you start with roughly \(8\) GB of raw data. * Stripping Audio (-an): GoPro files contain multiple audio tracks (sometimes stereo + 5.1 surround data). By using the -an flag in your cmd list, you are deleting \(24\) minutes of high-bitrate audio data, which can account for hundreds of megabytes. * NTSC Timing Logic: Because your script uses specific trim and fps filters to maintain the \(29.97\) fps (\(30000/1001\)) NTSC (National Television System Committee) standard, it is discarding “junk” frames or overlap that might exist between chapter seams.

  1. How to Verify Quality

If you are worried the quality is too low for your research, you can perform a quick “Eyeball Test”:

  1. Open the original GoPro file and your stitched file side-by-side.
  2. Zoom in on a high-detail area (like the scales of a fish or the texture of the sea floor).
  3. If you see “blockiness” or “pixelation” in the stitched version, change quality_crf in your configurations.yml from \(18\) to a lower number like \(12\) or \(10\). Note: \(0\) is technically “lossless” but will create files larger than the originals.

Prompt

I do not want any video quality loss whatsoever. Audio is not needed, but the final video quality needs to be the same as the original. Is there any information in the extracted metadata that can help with setting crf?

Coding partner

To achieve mathematically lossless video where the quality is identical to the original, you cannot rely solely on metadata to “guess” a perfect crf. While metadata provides the Bitrate, which tells you how much data the GoPro used, the only way to guarantee zero further loss during re-encoding is to use CRF 0.

  1. Metadata that influences Quality

Your get_video_metadata function already extracts key information that impacts how the video is reconstructed, but two specific fields are most relevant to quality:

  • Bitrate (bit_rate): This is found in data['format']['bit_rate']. It tells you the total data per second. If your output bitrate is lower than the source, data has been discarded.
  • Chroma Subsampling (pix_fmt): Most GoPros use yuv420p. To maintain quality, the encoder must match this format.
  1. The “True Lossless” Solution: CRF 0

If your end user insists on no quality loss whatsoever, you should change the quality_crf in your configurations.yml to 0.

  • CRF 18: Considered “visually lossless.” The human eye usually cannot tell the difference between this and the original.

  • CRF 0: This is mathematically lossless for the libx264 encoder. It ensures that every pixel coming out of the GoPro file is preserved exactly in the stitched file.

    Warning: Using crf: 0 will result in file sizes that are likely larger than the original GoPro files. This is because the original files were compressed using the camera’s hardware constraints; a lossless re-encode must store all that data without any of the clever shortcuts the camera used.

  1. Comparison of Quality Settings
Setting Quality Level File Size Best Use Case
CRF 0 Mathematically Lossless Extremely Large Archival, High-precision AI analysis
CRF 12-15 Near-Lossless Large High-detail benthic surveys
CRF 18 Visually Lossless Moderate Standard SEFIS survey analysis
CRF 23 Standard Small Quick review or sharing via cloud

Recommendation

If the user truly wants “no loss,” update your configurations.yml:

configurations.yml
quality_crf: 0
use_gpu: false  # Note: NVIDIA's nvenc (GPU) often does not support true CRF 0; use libx264 for perfect losslessness.

Edited for brevity.