image-utils تایید شده

Use when performing classic image manipulation - resize, crop, composite, format conversion, watermarks, adjustments. Pillow-based utilities for deterministic pixel-level operations. Use alongside AI image generation (like Bria) for post-processing, or standalone for any image processing task.

74از ۱۰۰

این مهارت ممکن است از مخزن GitHub اصلی حذف یا جابه‌جا شده باشد. فایل‌ها از حافظه پنهان SkillHub ارائه می‌شوند و ممکن است قدیمی باشند.

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references/code-examples

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// نصب مهارت

نصب مهارت

مهارت‌ها کدهای شخص ثالث از مخازن عمومی GitHub هستند. SkillHub الگوهای مخرب شناخته‌شده را اسکن می‌کند اما نمی‌تواند امنیت را تضمین کند. قبل از نصب، کد منبع را بررسی کنید.

نصب سراسری (سطح کاربر):

npx skillhub install openclaw/skills/image-utils

نصب در پروژه فعلی:

npx skillhub install openclaw/skills/image-utils --project

skill.install.customTargetHelp

npx skillhub install openclaw/skills/image-utils --target-dir /path/to/skills

مسیر پیشنهادی: ~/.claude/skills/image-utils/

بررسی هوش مصنوعی

74
از ۱۰۰
کیفیت دستورالعمل72
دقت توضیحات68
کاربردی بودن78
صحت فنی80

Scored 74 for a production-quality Python image processing library with 30+ methods, proper error handling, and cross-platform support. The 27KB implementation is the main value — a complete image manipulation toolkit. Capped at 74 due to missing negative triggers in description and no formal workflow steps.

productionsimplepython-developersai-developersweb-developersimage-processingformat-conversionai-image-postprocessingbatch-image-processing
بررسی‌شده توسط claude-code در تاریخ ۱۴۰۵/۱/۲۵

محتوای SKILL.md

---
name: image-utils
description: Use when performing classic image manipulation - resize, crop, composite, format conversion, watermarks, adjustments. Pillow-based utilities for deterministic pixel-level operations. Use alongside AI image generation (like Bria) for post-processing, or standalone for any image processing task.
---

# Image Utilities

Pillow-based utilities for deterministic pixel-level image operations. Use for resize, crop, composite, format conversion, watermarks, and other standard image processing tasks.

## When to Use This Skill

- **Post-processing AI-generated images**: Resize, crop, optimize for web after generation
- **Format conversion**: PNG ↔ JPEG ↔ WEBP with quality control
- **Compositing**: Overlay images, paste subjects onto backgrounds
- **Batch processing**: Resize to multiple sizes, add watermarks
- **Web optimization**: Compress and resize for fast delivery
- **Social media preparation**: Crop to platform-specific aspect ratios

## Quick Reference

| Operation | Method | Description |
|-----------|--------|-------------|
| **Loading** | `load(source)` | Load from URL, path, bytes, or base64 |
| | `load_from_url(url)` | Download image from URL |
| **Saving** | `save(image, path)` | Save with format auto-detection |
| | `to_bytes(image, format)` | Convert to bytes |
| | `to_base64(image, format)` | Convert to base64 string |
| **Resizing** | `resize(image, width, height)` | Resize to exact dimensions |
| | `scale(image, factor)` | Scale by factor (0.5 = half) |
| | `thumbnail(image, size)` | Fit within size, maintain aspect |
| **Cropping** | `crop(image, left, top, right, bottom)` | Crop to region |
| | `crop_center(image, width, height)` | Crop from center |
| | `crop_to_aspect(image, ratio)` | Crop to aspect ratio |
| **Compositing** | `paste(bg, fg, position)` | Overlay at coordinates |
| | `composite(bg, fg, mask)` | Alpha composite |
| | `fit_to_canvas(image, w, h)` | Fit onto canvas size |
| **Borders** | `add_border(image, width, color)` | Add solid border |
| | `add_padding(image, padding)` | Add whitespace padding |
| **Transforms** | `rotate(image, angle)` | Rotate by degrees |
| | `flip_horizontal(image)` | Mirror horizontally |
| | `flip_vertical(image)` | Flip vertically |
| **Watermarks** | `add_text_watermark(image, text)` | Add text overlay |
| | `add_image_watermark(image, logo)` | Add logo watermark |
| **Adjustments** | `adjust_brightness(image, factor)` | Lighten/darken |
| | `adjust_contrast(image, factor)` | Adjust contrast |
| | `adjust_saturation(image, factor)` | Adjust color saturation |
| | `blur(image, radius)` | Apply Gaussian blur |
| **Web** | `optimize_for_web(image, max_size)` | Optimize for delivery |
| **Info** | `get_info(image)` | Get dimensions, format, mode |

## Requirements

```bash
pip install Pillow requests
```

## Basic Usage

```python
from image_utils import ImageUtils

# Load from URL
image = ImageUtils.load_from_url("https://example.com/image.jpg")

# Or load from various sources
image = ImageUtils.load("/path/to/image.png")         # File path
image = ImageUtils.load(image_bytes)                  # Bytes
image = ImageUtils.load("data:image/png;base64,...")  # Base64

# Resize and save
resized = ImageUtils.resize(image, width=800, height=600)
ImageUtils.save(resized, "output.webp", quality=90)

# Get image info
info = ImageUtils.get_info(image)
print(f"{info['width']}x{info['height']} {info['mode']}")
```

## Resizing & Scaling

```python
# Resize to exact dimensions
resized = ImageUtils.resize(image, width=800, height=600)

# Resize maintaining aspect ratio (fit within bounds)
fitted = ImageUtils.resize(image, width=800, height=600, maintain_aspect=True)

# Resize by width only (height auto-calculated)
resized = ImageUtils.resize(image, width=800)

# Scale by factor
half = ImageUtils.scale(image, 0.5)    # 50% size
double = ImageUtils.scale(image, 2.0)  # 200% size

# Create thumbnail
thumb = ImageUtils.thumbnail(image, (150, 150))
```

## Cropping

```python
# Crop to specific region
cropped = ImageUtils.crop(image, left=100, top=50, right=500, bottom=350)

# Crop from center
center = ImageUtils.crop_center(image, width=400, height=400)

# Crop to aspect ratio (for social media)
square = ImageUtils.crop_to_aspect(image, "1:1")      # Instagram
wide = ImageUtils.crop_to_aspect(image, "16:9")       # YouTube thumbnail
story = ImageUtils.crop_to_aspect(image, "9:16")      # Stories/Reels

# Control crop anchor
top_crop = ImageUtils.crop_to_aspect(image, "16:9", anchor="top")
bottom_crop = ImageUtils.crop_to_aspect(image, "16:9", anchor="bottom")
```

## Compositing

```python
# Paste foreground onto background
result = ImageUtils.paste(background, foreground, position=(100, 50))

# Alpha composite (foreground must have transparency)
result = ImageUtils.composite(background, foreground)

# Fit image onto canvas with letterboxing
canvas = ImageUtils.fit_to_canvas(
    image,
    width=1200,
    height=800,
    background_color=(255, 255, 255, 255),  # White
    position="center"  # or "top", "bottom"
)
```

## Format Conversion

```python
# Convert to different formats
png_bytes = ImageUtils.to_bytes(image, "PNG")
jpeg_bytes = ImageUtils.to_bytes(image, "JPEG", quality=85)
webp_bytes = ImageUtils.to_bytes(image, "WEBP", quality=90)

# Get base64 for data URLs
base64_str = ImageUtils.to_base64(image, "PNG")
data_url = ImageUtils.to_base64(image, "PNG", include_data_url=True)
# Returns: "data:image/png;base64,..."

# Save with format auto-detected from extension
ImageUtils.save(image, "output.png")
ImageUtils.save(image, "output.jpg", quality=85)
ImageUtils.save(image, "output.webp", quality=90)
```

## Watermarks

```python
# Text watermark
watermarked = ImageUtils.add_text_watermark(
    image,
    text="© 2024 My Company",
    position="bottom-right",  # bottom-left, top-right, top-left, center
    font_size=24,
    color=(255, 255, 255, 128),  # Semi-transparent white
    margin=20
)

# Logo/image watermark
logo = ImageUtils.load("logo.png")
watermarked = ImageUtils.add_image_watermark(
    image,
    watermark=logo,
    position="bottom-right",
    opacity=0.5,
    scale=0.15,  # 15% of image width
    margin=20
)
```

## Adjustments

```python
# Brightness (1.0 = original, <1 darker, >1 lighter)
bright = ImageUtils.adjust_brightness(image, 1.3)
dark = ImageUtils.adjust_brightness(image, 0.7)

# Contrast (1.0 = original)
high_contrast = ImageUtils.adjust_contrast(image, 1.5)

# Saturation (0 = grayscale, 1.0 = original, >1 more vivid)
vivid = ImageUtils.adjust_saturation(image, 1.3)
grayscale = ImageUtils.adjust_saturation(image, 0)

# Sharpness
sharp = ImageUtils.adjust_sharpness(image, 2.0)

# Blur
blurred = ImageUtils.blur(image, radius=5)
```

## Transforms

```python
# Rotate (counter-clockwise, degrees)
rotated = ImageUtils.rotate(image, 45)
rotated = ImageUtils.rotate(image, 90, expand=False)  # Don't expand canvas

# Flip
mirrored = ImageUtils.flip_horizontal(image)
flipped = ImageUtils.flip_vertical(image)
```

## Borders & Padding

```python
# Add solid border
bordered = ImageUtils.add_border(image, width=5, color=(0, 0, 0))

# Add padding (whitespace)
padded = ImageUtils.add_padding(image, padding=20)  # Uniform
padded = ImageUtils.add_padding(image, padding=(10, 20, 10, 20))  # left, top, right, bottom
```

## Web Optimization

```python
# Optimize for web delivery
optimized_bytes = ImageUtils.optimize_for_web(
    image,
    max_dimension=1920,  # Resize if larger
    format="WEBP",       # Best compression
    quality=85
)

# Save optimized
with open("optimized.webp", "wb") as f:
    f.write(optimized_bytes)
```

## Integration with AI Image Generation

Use with Bria AI or other image generation APIs:

```python
from bria_client import BriaClient
from image_utils import ImageUtils

client = BriaClient()

# Generate with AI
result = client.generate("product photo of headphones", aspect_ratio="1:1")
image_url = result['result']['image_url']

# Download and post-process
image = ImageUtils.load_from_url(image_url)

# Create multiple sizes for responsive images
sizes = {
    "large": ImageUtils.resize(image, width=1200),
    "medium": ImageUtils.resize(image, width=600),
    "thumb": ImageUtils.thumbnail(image, (150, 150))
}

# Save all as optimized WebP
for name, img in sizes.items():
    ImageUtils.save(img, f"product_{name}.webp", quality=85)
```

## Batch Processing Example

```python
from pathlib import Path
from image_utils import ImageUtils

def process_catalog(input_dir, output_dir):
    """Process all images in a directory."""
    output_path = Path(output_dir)
    output_path.mkdir(exist_ok=True)

    for image_file in Path(input_dir).glob("*.{jpg,png,webp}"):
        image = ImageUtils.load(image_file)

        # Crop to square
        square = ImageUtils.crop_to_aspect(image, "1:1")

        # Resize to standard size
        resized = ImageUtils.resize(square, width=800, height=800)

        # Add watermark
        final = ImageUtils.add_text_watermark(resized, "© My Brand")

        # Save optimized
        output_file = output_path / f"{image_file.stem}.webp"
        ImageUtils.save(final, output_file, quality=85)

process_catalog("./raw_images", "./processed")
```

## API Reference

See [image_utils.py](./references/code-examples/image_utils.py) for complete implementation with docstrings.

مجوز

مجوز اعلام‌شده: MIT

MIT License

Copyright (c) 2026 openclaw

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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