Process a folder of jump and squat videos

Coaches can do this entirely in the browser. There is no script to install or command to type.

  1. Open Process a folder from the Jump + Squat Field Test in desktop Chrome or Edge.
  2. Choose a folder and allow the browser to read and write it.
  3. Choose the movement (double-leg jump, single-leg hop, double-leg squat or single-leg squat), the capture FPS and the athlete’s height. Override the FPS for individual videos if needed.
  4. Press Process videos and keep this tab open.
  5. Find the MP4 highlight, PDF report and Markdown text beside each original. Review each row for warnings or failures.
Process a folder

What gets saved

For jump.mp4, the app creates results_jump.mp4, results_jump.pdf and results_jump.md. A results_jump.json receipt records the settings, actual frame rates, analyzed range and output sizes. The MP4 is a metric highlight reel, with 30 FPS output.

Originals are not changed. Existing result files are skipped, including incomplete sets. Move old or partial results before retrying. Videos with the same base name, such as jump.mp4 and jump.mov, must be renamed first. Files beginning results_ or reslts_ are excluded. Subfolders are not processed.

Recording settings that matter

Local processing and recovery

Video analysis and export happen in your browser. Static app code and model files may download from the internet. Folder permissions are granted by you in the browser, and you may need to grant them again after reopening the page.

One video runs at a time. A failure does not stop the rest. Stop batch cancels the active analysis/export; a save already in progress finishes before the queue stops. Closing the browser can leave partial results. Move these before retrying. Avoid concurrent programs writing to the same folder.

If the folder picker is unavailable, use a supported desktop browser such as Chrome or Edge, or return to the single-video app. MP4 export requires H.264 WebCodecs support; an unsupported encoder produces an error. Results are the same estimates and coaching guidance offered by the Jump + Squat Field Test’s individual-video workflow.

For AI agents and developers: browser interface

Everything runs in the browser at https://apopt.com/squat/automation.html. There is nothing to download or install. An AI agent or script that controls a browser (for example a browser-automation tool) can use the page’s versioned interface, window.apoptAutomation, instead of the folder picker.

  1. Open https://apopt.com/squat/automation.html and wait until window.apoptAutomation.version === 1.
  2. Put one local video on the file input #automation-video; the browser reads it locally.
  3. Start the job, poll until it finishes, then read the Markdown or download the outputs.
const api = window.apoptAutomation;
api.capabilities();   // app "squat", options, defaults, outputs (no view option)
const id = api.start({
  movementType: "double_leg_jump", captureFps: 120, heightMeters: 1.82
});
api.getJob(id);       // state: running → succeeded / failed / cancelled
// After state === "succeeded":
await api.markdown(id);   // the text results
api.download(id, "mp4");  // also "pdf" and "md"
api.cancel(id);           // stops a running job
OptionValues
captureFps"auto" or the recorded FPS, 1–2000
movementTypedouble_leg_jump, single_leg_hop, double_leg_squat or single_leg_squat; default double_leg_jump
heightMeters1.0–2.5; default 1.8
modelmovenet_thunder, blazepose, or yolo26n_pose to yolo26x_pose
outputFormatvertical or person-crop; metricCycleSeconds 2–10

One job runs at a time, and only the latest job’s outputs are kept, so save every output before starting another. Unknown options and out-of-range values are rejected before processing. Video stays in the browser; an agent that reads the Markdown receives that derived text.

Example agent request: “Open the Jump + Squat automation page, analyze jump.mp4 as a double-leg jump at 120 capture FPS with height 1.82 meters, save the three outputs and read the Markdown.”