Here’s a practical guide for **blurring part of a video** (specific region of frames) so you can create focus / emphasis effects. ### 1. Quick & free method with FFmpeg (recommended on Ubuntu) This is the most precise and scriptable way. You crop the area you want to blur, blur it, then overlay it back. #### Blur a fixed rectangular region (whole video) ```bash ffmpeg -i input.mp4 \ -filter_complex "[0:v]crop=W:H:X:Y,boxblur=10[blurred];[0:v][blurred]overlay=X:Y" \ -c:a copy -c:v libx264 -crf 18 -preset medium \ output.mp4 ``` **Replace:** - `W:H` → width and height of the blur box (e.g. `300:200`) - `X:Y` → top-left corner of the box (e.g. `100:50`) - `boxblur=10` → blur strength (try 5–20) **Example** (blur a 400×300 area starting at x=200, y=150): ```bash ffmpeg -i input.mp4 \ -filter_complex "[0:v]crop=400:300:200:150,boxblur=12[blurred];[0:v][blurred]overlay=200:150" \ -c:a copy -c:v libx264 -crf 18 output.mp4 ``` #### Blur only during a time range Add `enable='between(t,START,END)'` to the overlay: ```bash ffmpeg -i input.mp4 \ -filter_complex "[0:v]crop=400:300:200:150,boxblur=12[blurred];[0:v][blurred]overlay=200:150:enable='between(t,5,15)'" \ -c:a copy -c:v libx264 -crf 18 output.mp4 ``` (Blurs only from 5s to 15s) #### Opposite effect: blur **everything except** a sharp focus region ```bash ffmpeg -i input.mp4 \ -filter_complex "[0:v]boxblur=15[bg];[0:v]crop=400:300:200:150[fg];[bg][fg]overlay=200:150" \ -c:a copy -c:v libx264 -crf 18 output.mp4 ``` This creates a nice “focus” look — the subject stays sharp while the rest is blurred. #### Stronger / smoother blur options - Soft Gaussian: replace `boxblur=12` with `gblur=sigma=15` - Pixelated (mosaic): `crop=W:H:X:Y,scale=W/10:H/10,scale=W:H:flags=neighbor` --- ### 2. How to find the X/Y coordinates 1. Extract a frame: ```bash ffmpeg -ss 00:00:05 -i input.mp4 -frames:v 1 frame.png ``` 2. Open `frame.png` in any image viewer / GIMP / Gwenview and note the pixel coordinates of the top-left corner and size of the area you want to blur. --- ### 3. GUI tools (easier for moving / tracking blur) If the area moves across frames (you want the blur to follow something), use a visual editor: | Tool | Best for | Ubuntu install | |-------------------|-----------------------------------|---------------------------------| | **Kdenlive** | Free, good tracking masks | `sudo apt install kdenlive` | | **Shotcut** | Simple filters + keyframes | `sudo apt install shotcut` | | **DaVinci Resolve** | Professional tracking + power windows | Download from Blackmagic | | CapCut / Premiere | Very easy face/object tracking | (if you prefer commercial) | **Typical workflow in Kdenlive / Shotcut / Resolve:** 1. Add the video to the timeline. 2. Apply a **Blur** / **Gaussian Blur** / **Mosaic** effect. 3. Use a **Mask** (rectangle, ellipse, or free-form) on the effect. 4. Animate the mask position with keyframes (or use motion tracking if available). --- ### Quick recommendation - **Static region** → use the FFmpeg command above (fastest & highest quality). - **Moving subject / need focus tracking** → open the video in **Kdenlive** or **Shotcut** and animate a mask. Would you like me to give you a ready-to-run command if you tell me: - Approximate position/size of the area, or - Whether you want the region **blurred** or the **background** blurred (focus effect)? --- **`crop` and `overlay` work as a pair** in the selective-blur command. ### Simple mental model 1. **`crop`** → cuts out a rectangular piece of the video 2. **`boxblur` (or gblur)** → blurs that piece 3. **`overlay`** → pastes the blurred piece **back** onto the original video at the same position That’s why the numbers must match. ### Breakdown of the filter ```bash [0:v]crop=W:H:X:Y,boxblur=10[blurred]; [0:v][blurred]overlay=X:Y ``` | Part | What it does | Example values | |-----------------------------|---------------------------------------------------|---------------------| | `crop=W:H:X:Y` | Cut a rectangle of size **W×H** starting at position **(X, Y)** | `crop=400:300:200:150` | | `boxblur=10` | Blur the cut piece | strength 10 | | `[blurred]` | Give the blurred piece a name | — | | `[0:v][blurred]overlay=X:Y` | Take original video + blurred piece and paste the blurred piece at position **(X, Y)** | same `200:150` | ### Why the coordinates must be the same - `crop=...:X:Y` → “cut from this location” - `overlay=X:Y` → “paste back to this location” If the numbers are different, the blurred region will appear in the wrong place (or partially off-screen). ### Visual flow ``` Original video ──┐ ├──► overlay ──► final video Cropped + Blurred ┘ ``` ### Quick example ```bash # Blur a 300×200 box whose top-left corner is at (100, 50) ffmpeg -i input.mp4 \ -filter_complex "[0:v]crop=300:200:100:50,boxblur=12[blurred];[0:v][blurred]overlay=100:50" \ -c:a copy output.mp4 ``` - `crop=300:200:100:50` → cut 300 wide × 200 high, starting 100 px from left and 50 px from top - `overlay=100:50` → put that blurred rectangle back at exactly the same place That’s the whole relationship. --- ffmpeg -i input.mkv -filter_complex "[0:v]crop=1550:1050:300:20,boxblur=9[blurred];[0:v][blurred]overlay=300:18" -c:a copy -c:v libx264 -crf 18 output.mkv --- # 3, head to NN animation **Here’s a practical way to turn a side-view head portrait into a neural-network animation.** ### Recommended Approaches (from easiest to most controllable) ### 1. Fastest – AI Image-to-Video tools (best quality / least work) These tools are currently the strongest for this kind of morph: | Tool | Strength for this effect | Notes | |-------------------|-------------------------------------------|-------| | **Runway Gen-3** | Excellent morphing & structure retention | Best overall right now | | **Kling AI** | Very good motion + detail | Strong alternative | | **Luma Dream Machine** | Smooth cinematic motion | Good free tier | | **Pika / Haiper** | Fast iterations | Quicker but sometimes less coherent | **Prompt template that works well:** ``` Side profile portrait of a person slowly transforms into a glowing neural network, the head silhouette becomes a network of luminous nodes and connecting edges, organic to digital transition, elegant and scientific style, soft blue and purple neon lines, cinematic lighting, highly detailed ``` Tips: - Upload your side-view photo as the starting image (Image-to-Video mode) - Add “maintain head shape and silhouette throughout the transformation” - Generate 4–6 seconds first, then extend if needed - Use “Camera locked / static camera” if you want pure morph without camera movement ### 2. More control – Hybrid workflow (recommended if you want precision) This gives better artistic control: 1. **Generate keyframes** with an image model (Flux, SD3, Midjourney, etc.) - Frame 1: Original photo - Frame 2–3: Partial transformation (face still visible, some nodes appearing) - Frame 4: Full neural network in the shape of the head 2. **Animate between them** using: - Runway / Kling (Image-to-Video with multiple keyframes) - Or **Stable Video Diffusion / AnimateDiff** locally - Or **EbSynth** + interpolated frames 3. Optional polish in **DaVinci Resolve** or **Blender** (add particle connections, glow, etc.) ### 3. Fully technical / free route (Blender + Geometry Nodes) If you want maximum control and zero paid AI: 1. Import the photo as a plane or use it as a texture. 2. Trace / extract the head silhouette (or use a 3D head model and project the photo). 3. Convert the silhouette into a point cloud or mesh. 4. Use **Geometry Nodes** to: - Distribute points (nodes) - Create edges between nearby points - Animate the points emerging from the photo and the connections growing 5. Add emission materials + bloom for the neural-network look. This is more work but looks very intentional and unique. --- ### Quick Decision Guide | Goal | Best choice | |-----------------------------------|--------------------------| | Fast & beautiful result | Runway Gen-3 or Kling | | Good quality + free options | Luma + some manual keyframes | | Maximum artistic control | Keyframes + Blender | | Purely free & local | Blender Geometry Nodes | --- Would you like me to give you: 1. Ready-to-use prompts for Runway/Kling, or 2. A more detailed step-by-step for the Blender method, or 3. A hybrid pipeline using open-source tools (ComfyUI + AnimateDiff)? Also tell me: - Do you prefer a realistic → abstract transition or a more stylized/sci-fi look? - Roughly how long should the animation be (3s / 5s / 8s+)? --- # generate image ImageMagick is the better fit for this — it's built for exactly this kind of text-to-image generation, while ffmpeg is more oriented around video/audio processing (though it can technically do it via the `drawtext` filter). **ImageMagick (recommended):** ```bash convert -size 800x400 xc:black -gravity center \ -fill "#888888" -font DejaVu-Sans-Bold -pointsize 48 \ -annotate +0+0 "Your Text Here" \ output.png ``` If you're on ImageMagick 7 (likely default on Ubuntu 26.04), use `magick` instead of `convert`: ```bash magick -size 800x400 xc:black -gravity center \ -fill "#888888" -font DejaVu-Sans-Bold -pointsize 48 \ -annotate +0+0 "Your Text Here" \ output.png ``` Notes: - `-size 800x400` — change to whatever resolution you need - `-fill "#888888"` — grey; adjust hex for lighter/darker grey (e.g. `#AAAAAA` lighter, `#555555` darker) - Check available fonts with `magick -list font` if `DejaVu-Sans-Bold` isn't installed — GNOME/Ubuntu usually ships with DejaVu and Noto families by default - For multi-line text, add `\n` in the string or use `-size` with word wrap via `caption:` instead of `-annotate` **ffmpeg alternative** (useful if this is part of a video pipeline you're already building, e.g. for OBS overlays): ```bash ffmpeg -f lavfi -i color=c=black:s=800x400 \ -vf "drawtext=text='Your Text Here':fontcolor=#888888:fontsize=48:x=(w-text_w)/2:y=(h-text_h)/2" \ -frames:v 1 output.png ``` Given you're already knee-deep in OBS/PipeWire, if this is for a title card or overlay in your recording workflow, ffmpeg's `drawtext` might be more convenient since you're probably already scripting around ffmpeg for other things. But for a one-off static image, ImageMagick's syntax is simpler. Want me to adjust either command for a specific use case — like a lower-third overlay with transparency, or a wrapped multi-line caption? --- # 1. Extract the audio track as lossless PCM (video is h264, audio is aac/stereo/48kHz) ffmpeg -i "$IN" -vn -acodec pcm_s24le "/tmp/${BASE}_audio_raw.wav" # 2. Same treatment as vrecord.sh (skip the mono->stereo pan step — this track is already stereo) ffmpeg -i "/tmp/${BASE}_audio_raw.wav" -af " highpass=f=100, afftdn=nr=12:nf=-50, acompressor=threshold=-18dB:ratio=3:attack=20:release=250, loudnorm=I=-16:TP=-1.5:LRA=11 " -ar 48000 "/tmp/${BASE}_audio_clean.wav" # 3. Remux: original video stream copied untouched, cleaned audio re-encoded to AAC ffmpeg -i "$IN" -i "/tmp/${BASE}_audio_clean.wav" \ -map 0:v:0 -map 1:a:0 -c:v copy -c:a aac -b:a 192k \ "${BASE}_clean.mkv" Notes: - -c:v copy in step 3 mem isn't re-encoded at all — fast and lossless, o - Output lands as kb-intmkv next to wherever you run the command; origi - Tune NR (the nr=12 in iss, same as the NR env var in vrecord.sh. Want me to run this now t into a script(vprocess.sh) alongside e recordings? --- Correct — and this matters more than it might seem. If both your Mic and Desktop Audio sources feed OBS's default single audio track (the normal "Simple" output mode setup), OBS mixes them down into one stereo PCM stream *at record time*. Once that's baked into the file, there's no separating voice from desktop audio afterward — ffmpeg filters operate on the whole mixed signal, they can't isolate "just the voice part" out of an already-merged track. So yes: once you mix in desktop audio, our current pipeline (`highpass` → `afftdn` → `acompressor` → `loudnorm`) would apply to everything equally, uniformly. That's a real problem once desktop audio carries actual content (game sound, music, app audio) rather than being muted: `afftdn`'s noise-floor suppression and `loudnorm`'s -16 LUFS speech target aren't tuned for that material and could distort or flatten it. **The fix, and worth setting up before you start mixing in desktop audio**: use OBS's multi-track recording so the two sources land as *separate* audio streams in the same file, not pre-mixed: - OBS → Settings → Output → Output Mode: **Advanced** - Recording tab → check 2 (or more) audio tracks - On each source → right-click → **Properties** (or the mixer gear icon) → **Advanced Audio Properties** → assign Mic to **Track 1**, Desktop Audio to **Track 2** - Recording tab's "Audio Track" selector controls which track(s) get baked into the file — with Advanced mode you can keep them as independent streams Then our pipeline changes to: extract Track 1 (voice) and Track 2 (desktop) separately, run the voice treatment only on Track 1, leave Track 2 alone (or apply lighter/different treatment), then mix both down together in the final merge step — instead of one uniform pass over an already-fused signal. Want me to update the extract/process/merge commands (or `vprocess.sh` if we build it) to handle two input tracks like this? # background removal, character segmentation You do not need a green screen. With your setup, the easiest approach is **Kdenlive’s AI Object Mask**, followed by manual correction only where necessary. ### Recommended: Kdenlive Object Mask Recent Kdenlive versions include local background removal based on **SAM2 object segmentation**. Processing stays on your computer. [Kdenlive Object Mask documentation](https://docs.kdenlive.org/en/effects_and_filters/video_effects/alpha_mask_keying/object_mask.html) 1. Import the teacher video into the **Project Bin**. 2. Select the clip in the Project Bin—not merely on the timeline. 3. Open Kdenlive’s plugin settings and install/configure **Object Detection / SAM2**. 4. In the Clip Monitor, set **In** and **Out** points around the section you need. 5. Choose **Create Object Mask** or **Remove Background**. 6. Draw a rectangle around the teacher, then click the teacher. 7. Use: * `Shift+click` to include missing areas. * `Ctrl+click` to exclude background areas. 8. Click **Generate Mask**. 9. Preview the result and add correction points where tracking fails. 10. Select the completed mask and click **Apply Mask**. 11. Put the replacement background on the track underneath. Kdenlive automatically recognizes the resulting alpha information, allowing lower video tracks to show through. [Kdenlive alpha-channel documentation](https://docs.kdenlive.org/en/compositing/alpha_channels.html) ### Hardware considerations * **Nvidia GPU:** Kdenlive can use CUDA and should process considerably faster. * **AMD/Intel GPU:** SAM2 normally runs on the CPU in Kdenlive. * For long footage, process it in shorter sections—perhaps **30 seconds to two minutes each**. * Enable **Offload video to CPU** if GPU memory runs out. [Kdenlive plugin configuration](https://docs.kdenlive.org/en/getting_started/configure_kdenlive/configuration_plugins.html) ### When a Python workflow is better For a long, mostly stationary talking-teacher video, I would also test **Robust Video Matting (RVM)**. Unlike running `rembg` independently on every frame, RVM remembers previous frames, reducing flickering around hair, arms, and clothing. It accepts normal video without a green screen or clean background reference. [Official RVM repository](https://github.com/PeterL1n/RobustVideoMatting) A good pipeline would be: ```text Original MP4 ↓ RVM Python processing ↓ transparent PNG sequence or alpha video ↓ FFmpeg encoding ↓ Kdenlive editing and new background ``` Avoid ordinary per-frame background-removal scripts for the final result. They often produce edges that change slightly on every frame, creating visible “boiling” or flickering. For your first attempt, use **Kdenlive SAM2**. It is already integrated into your editor and lets you correct particular frames interactively. RVM becomes attractive if Kdenlive is too slow or you need to process many similar teacher videos.