social-media-slideshow-video

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SKILL.md (first lines)

# Social Media Slideshow Video Generator

## When to use

Use when users request: product review videos, promotional slideshow videos, TikTok/Reels/Shorts content from static images, photo-based video with text overlays, hijab/fashion/beauty review videos, unboxing recap videos, or any image-to-video social media content with designed slides.

## Stack

| Layer | Tool | Purpose |
|-------|------|---------|
| Imaging | Pillow (PIL) | Slide design, text rendering, image manipulation |
| Encoding | ffmpeg (CLI) | Frame sequence → MP4 encoding |
| Core | Python 3 | Orchestration |

No GPU, no moviepy, no heavy dependencies needed.

## Architecture: File-Based Frame Pipeline

**Critical: Do NOT store all frames as numpy arrays in memory.** A 15-second 1080×1920 video at 24fps = 360 frames × ~6MB each = 2.1GB RAM → OOM kill.

### Correct approach:
```
1. Render each SLIDE as a static PIL Image (5-10 slides in memory is fine)
2. For each slide, generate per-frame variations (fade, zoom) and SAVE AS PNG to tmpdir
3. Feed the PNG sequence to ffmpeg via -i pattern
4. Clean up temp files
```

### Why not pipe to ffmpeg stdin?
Piping raw RGB frames to ffmpeg stdin causes deadlocks and broken pipe errors in many environments. The file-based approach is robust and debuggable.

## Resolution Presets

| Platform | Resolution | Aspect | FPS |
|----------|-----------|--------|-----|
| TikTok / Reels / Shorts | 1080×1920 | 9:16 | 24 |
| YouTube landscape | 1920×1080 | 16:9 | 24-30 |
| Instagram square | 1080×1080 | 1:1 | 24 |
| Story/Status | 1080×1920 | 9:16 | 24 |

## Slide Types & Design Patterns

### 1. Title Slide
- Gradient background (soft, matching product color)
- Hero photo in rounded-rect or circular frame with colored border
- Product name + subtitle text centered below
- Tag/category text above photo

### 2. Detail/Review Slide
- Blurred photo as background (`GaussianBlur(radius=20)` + dark blend at 0.55)
- Photo at top with gradient fade mask at bottom edge
- Semi-transparent card overlay (`RGBA` with alpha ~220) with rounded corners
- Bullet points + rating text

### 3. Verdict/Score Slide
- Gradient background
- Circular photo with colored border
- Large score text (e.g., "9.5/10")
- Verdict text + CTA

## Key Techniques

### Gradient Background
```python
def gradient_bg(w, h, c1, c2):
    img = Image.new("RGB", (w, h))
    px = img.load()
    for y in range(h):
        r = y / h
        for x in range(w):
            px[x, y] = (int(c1[0]*(1-r)+c2[0]*r), ...)
    return img
```

### Photo with Rounded/Circular Mask
```python
mask = Image.new("L", (size, size), 0)
ImageDraw.Draw(mask).rounded_rectangle([0, 0, size, size], radius=35, fill=255)
# or .ellipse([0, 0, size, size], fill=255) for circular
frame.paste(photo, (x, y), mask)
```

### Colored Border Around Photo
```python
bdr = 8
border_img = Image.new("RGB", (size+bdr*2, size+bdr*2), border_color)
border_mask = Image.new("L", border_img.size, 0)
ImageDraw.Draw(border_mask).rounded_rectangle([0,0,...], radius=40, fill=255)
frame.paste(border_img, (x-bdr, y-bdr), border_mask)
frame.paste(photo, (x, y), photo_mask)  # photo on top
```

### Semi-Transparent Card Overlay
```python
card = Image.new("RGBA", (cw, ch), (255, 248, 240, 220))
cmask = Image.new("L", (cw, ch), 0)
ImageDraw.Draw(cmask).rounded_rectangle([0,0,cw,ch], radius=25, fill=220)
frame_rgba = frame.convert("RGBA")
frame_rgba.paste(card, (x, y), cmask)
frame = frame_rgba.convert("RGB")
```

### Blurred Photo Background
```python
bg = prepare_photo(WIDTH, HEIGHT, zoom=1.5)
bg = bg.filter(ImageFilter.GaussianBlur(radius=20))
dark = Image.new("RGB", (WIDTH, HEIGHT), (40, 30, 30))
frame = Image.blend(bg, dark, 0.55)
```

### Gradient Fade Mask (photo fading to transparent at bottom)
```python
gmask = Image.new("L", (w, h), 255)
gd = ImageDraw.Draw(gmask)
for y in range(h - fade_height, h):
    alpha = int(255 * (1 - (y - (h - fade_height)) / fade_height))
    gd.rectangle([(0, y), (w, y)], fill=alpha)
frame.paste(photo, (0, 0), gmask)
```

From kevinnft/ai-agent-skills, MIT.

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