图像增强套件 - 专业批量图像处理工具，无需Photoshop | 调整大小、水印、色彩校正

image-enhancement-suite by dkyazzentwatwa/chatgpt-skills

104 周安装量

44 GitHub Stars

GitHub

安装命令

npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill image-enhancement-suite

Python Web框架自动化图像处理

🇨🇳中文介绍

图像增强套件

专业的图像处理工具包，无需 Photoshop 或类似软件即可处理常见的图像任务。以一致的高质量结果处理单张图像或整个文件夹。

核心功能

调整大小与裁剪：保持宽高比的智能调整大小，裁剪至特定尺寸
水印：添加文本或图像水印，可控制位置和透明度
色彩校正：调整亮度、对比度、饱和度、锐度
格式转换：在 PNG、JPEG、WebP、BMP、TIFF、GIF 之间转换
压缩：使用质量预设优化文件大小
滤镜：应用预设滤镜（灰度、棕褐色、复古、模糊、锐化）
批量处理：使用相同设置处理多张图像

快速开始

from scripts.image_enhancer import ImageEnhancer

# 单张图像处理
enhancer = ImageEnhancer("photo.jpg")
enhancer.resize(width=800).sharpen(0.5).save("photo_enhanced.jpg")

# 批量处理
from scripts.image_enhancer import batch_process
batch_process(
    input_dir="raw_photos/",
    output_dir="processed/",
    operations=[
        ("resize", {"width": 1200}),
        ("watermark", {"text": "© 2024"}),
        ("compress", {"quality": 85})
    ]
)

操作参考

调整大小

# 按宽度调整（保持宽高比）
enhancer.resize(width=800)

# 按高度调整（保持宽高比）
enhancer.resize(height=600)

# 精确尺寸（可能失真）
enhancer.resize(width=800, height=600, maintain_aspect=False)

# 适应边界
enhancer.resize(max_width=1200, max_height=800)

# 按百分比缩放
enhancer.resize(scale=0.5)  # 50%

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# 从中心裁剪到特定尺寸
enhancer.crop(width=800, height=600)

# 带位置的裁剪
enhancer.crop(width=800, height=600, position='top-left')
# 位置：'center', 'top-left', 'top-right', 'bottom-left', 'bottom-right'

# 裁剪到精确坐标（左，上，右，下）
enhancer.crop(box=(100, 100, 900, 700))

# 智能裁剪（内容感知）
enhancer.smart_crop(width=800, height=600)

# 文本水印
enhancer.watermark(
    text="© 2024 Company",
    position='bottom-right',
    opacity=0.5,
    font_size=24,
    color='white'
)

# 图像水印
enhancer.watermark(
    image="logo.png",
    position='bottom-right',
    opacity=0.3,
    scale=0.2  # 主图像宽度的 20%
)

# 平铺水印
enhancer.watermark(
    text="DRAFT",
    tiled=True,
    opacity=0.1,
    rotation=45
)

# 单独调整（范围：-1.0 到 1.0，0 = 无变化）
enhancer.brightness(0.2)    # +20% 亮度
enhancer.contrast(0.3)      # +30% 对比度
enhancer.saturation(-0.2)   # -20% 饱和度
enhancer.sharpen(0.5)       # 锐化

# 组合调整
enhancer.adjust(
    brightness=0.1,
    contrast=0.2,
    saturation=0.1,
    sharpen=0.3
)

# 自动增强
enhancer.auto_enhance()

# 应用预设滤镜
enhancer.filter('grayscale')
enhancer.filter('sepia')
enhancer.filter('vintage')
enhancer.filter('blur')
enhancer.filter('sharpen')
enhancer.filter('edge_enhance')
enhancer.filter('emboss')

# 自定义模糊
enhancer.blur(radius=2)

# 高斯模糊
enhancer.gaussian_blur(radius=3)

# 转换为不同格式
enhancer.save("output.png")   # 根据扩展名自动检测
enhancer.save("output.webp")
enhancer.save("output.jpg")

# 显式指定格式
enhancer.convert('PNG').save("output.png")
enhancer.convert('WEBP').save("output.webp")

# JPEG 质量 (1-100)
enhancer.compress(quality=85).save("compressed.jpg")

# WebP 质量
enhancer.save("output.webp", quality=80)

# PNG 优化
enhancer.optimize_png().save("optimized.png")

# 目标文件大小
enhancer.compress_to_size(max_kb=500).save("sized.jpg")

# 网络优化
enhancer.preset('web')  # 最大 1200px，85 质量，WebP

# 社交媒体
enhancer.preset('instagram')  # 1080x1080，优化
enhancer.preset('twitter')    # 1200x675
enhancer.preset('facebook')   # 1200x630
enhancer.preset('linkedin')   # 1200x627

# 打印质量
enhancer.preset('print_4x6')   # 1800x1200，300dpi
enhancer.preset('print_8x10')  # 3000x2400，300dpi

# 缩略图
enhancer.preset('thumbnail')   # 150x150，中心裁剪
enhancer.preset('preview')     # 最大 400px，70 质量

from scripts.image_enhancer import batch_process

# 对所有图像应用相同操作
results = batch_process(
    input_dir="photos/",
    output_dir="processed/",
    operations=[
        ("resize", {"width": 1200}),
        ("watermark", {"text": "© 2024", "position": "bottom-right"}),
        ("compress", {"quality": 85})
    ],
    formats=['jpg', 'png'],  # 仅处理这些格式
    recursive=True           # 包含子目录
)

print(f"已处理: {results['success']} 张图像")
print(f"失败: {results['failed']} 张图像")

batch_process(
    input_dir="photos/",
    output_dir="processed/",
    operations=[("resize", {"width": 800})],
    rename_pattern="{name}_web_{index:03d}"
)
# 输出：photo_web_001.jpg, photo_web_002.jpg, ...

from scripts.image_enhancer import generate_sizes

# 从一张图像创建多种尺寸
sizes = generate_sizes(
    "hero.jpg",
    output_dir="responsive/",
    widths=[320, 640, 1024, 1920],
    format='webp'
)
# 输出：hero_320.webp, hero_640.webp, 等。

from scripts.image_enhancer import generate_icons

# 从单张图像生成网站图标和应用图标
icons = generate_icons(
    "logo.png",
    output_dir="icons/",
    sizes=[16, 32, 48, 64, 128, 256, 512]
)

# 读取 EXIF 数据
metadata = enhancer.get_metadata()
print(metadata['camera'])
print(metadata['date_taken'])
print(metadata['gps'])

# 剥离元数据（隐私）
enhancer.strip_metadata()

# 保留特定元数据
enhancer.strip_metadata(keep=['copyright', 'artist'])

# 单个文件
python image_enhancer.py input.jpg -o output.jpg --resize 800 --quality 85

# 批量处理
python image_enhancer.py photos/ -o processed/ --resize 1200 --watermark "© 2024"

# 应用预设
python image_enhancer.py photo.jpg --preset instagram

# 生成图标
python image_enhancer.py logo.png --icons icons/

格式	读取	写入	备注
JPEG	是	是	有损，最适合照片
PNG	是	是	无损，支持透明度
WebP	是	是	现代格式，压缩效果好
GIF	是	是	支持动画
BMP	是	是	未压缩
TIFF	是	是	高质量，文件大
ICO	是	是	图标格式
HEIC	是	否	iPhone 照片（仅读取）

from scripts.image_enhancer import ImageEnhancer, ImageError

try:
    enhancer = ImageEnhancer("photo.jpg")
    enhancer.resize(width=800).save("output.jpg")
except ImageError as e:
    print(f"图像错误: {e}")
except FileNotFoundError:
    print("未找到图像文件")

enhancer = ImageEnhancer("photo.jpg")

# 全局设置
enhancer.config.update({
    'default_quality': 85,
    'default_format': 'webp',
    'preserve_metadata': False,
    'color_profile': 'sRGB',
    'dpi': 72
})

大批量处理：使用 batch_process() 并设置 parallel=True
内存：对于非常大的文件，一次处理一张图像
速度：WebP 编码较慢但生成的文件更小
质量：从 85 质量开始，如果文件大小是关键则降低质量

pillow>=10.0.0
opencv-python>=4.8.0
numpy>=1.24.0

🇺🇸English

Image Enhancement Suite

Professional image processing toolkit that handles common image tasks without requiring Photoshop or similar software. Process single images or entire folders with consistent, high-quality results.

Core Capabilities

Resize & Crop: Smart resizing with aspect ratio preservation, crop to specific dimensions
Watermark : Add text or image watermarks with positioning and opacity control
Color Correction : Brightness, contrast, saturation, sharpness adjustments
Format Conversion : Convert between PNG, JPEG, WebP, BMP, TIFF, GIF
Compression : Optimize file size with quality presets
Filters : Apply preset filters (grayscale, sepia, vintage, blur, sharpen)
Batch Processing : Process multiple images with same settings

Quick Start

from scripts.image_enhancer import ImageEnhancer

# Single image processing
enhancer = ImageEnhancer("photo.jpg")
enhancer.resize(width=800).sharpen(0.5).save("photo_enhanced.jpg")

# Batch processing
from scripts.image_enhancer import batch_process
batch_process(
    input_dir="raw_photos/",
    output_dir="processed/",
    operations=[
        ("resize", {"width": 1200}),
        ("watermark", {"text": "© 2024"}),
        ("compress", {"quality": 85})
    ]
)

Operations Reference

Resize

# By width (maintain aspect ratio)
enhancer.resize(width=800)

# By height (maintain aspect ratio)
enhancer.resize(height=600)

# Exact dimensions (may distort)
enhancer.resize(width=800, height=600, maintain_aspect=False)

# Fit within bounds
enhancer.resize(max_width=1200, max_height=800)

# Scale by percentage
enhancer.resize(scale=0.5)  # 50%

Crop

# Crop to specific dimensions from center
enhancer.crop(width=800, height=600)

# Crop with position
enhancer.crop(width=800, height=600, position='top-left')
# Positions: 'center', 'top-left', 'top-right', 'bottom-left', 'bottom-right'

# Crop to exact coordinates (left, top, right, bottom)
enhancer.crop(box=(100, 100, 900, 700))

# Smart crop (content-aware)
enhancer.smart_crop(width=800, height=600)

Watermark

# Text watermark
enhancer.watermark(
    text="© 2024 Company",
    position='bottom-right',
    opacity=0.5,
    font_size=24,
    color='white'
)

# Image watermark
enhancer.watermark(
    image="logo.png",
    position='bottom-right',
    opacity=0.3,
    scale=0.2  # 20% of main image width
)

# Tiled watermark
enhancer.watermark(
    text="DRAFT",
    tiled=True,
    opacity=0.1,
    rotation=45
)

Color Adjustments

# Individual adjustments (range: -1.0 to 1.0, 0 = no change)
enhancer.brightness(0.2)    # +20% brightness
enhancer.contrast(0.3)      # +30% contrast
enhancer.saturation(-0.2)   # -20% saturation
enhancer.sharpen(0.5)       # Sharpen

# Combined adjustment
enhancer.adjust(
    brightness=0.1,
    contrast=0.2,
    saturation=0.1,
    sharpen=0.3
)

# Auto-enhance
enhancer.auto_enhance()

Filters

# Apply preset filters
enhancer.filter('grayscale')
enhancer.filter('sepia')
enhancer.filter('vintage')
enhancer.filter('blur')
enhancer.filter('sharpen')
enhancer.filter('edge_enhance')
enhancer.filter('emboss')

# Custom blur
enhancer.blur(radius=2)

# Gaussian blur
enhancer.gaussian_blur(radius=3)

Format Conversion

# Convert to different format
enhancer.save("output.png")   # Auto-detect from extension
enhancer.save("output.webp")
enhancer.save("output.jpg")

# Explicit format
enhancer.convert('PNG').save("output.png")
enhancer.convert('WEBP').save("output.webp")

Compression

# JPEG quality (1-100)
enhancer.compress(quality=85).save("compressed.jpg")

# WebP with quality
enhancer.save("output.webp", quality=80)

# PNG optimization
enhancer.optimize_png().save("optimized.png")

# Target file size
enhancer.compress_to_size(max_kb=500).save("sized.jpg")

Quality Presets

# Web optimized
enhancer.preset('web')  # 1200px max, 85 quality, WebP

# Social media
enhancer.preset('instagram')  # 1080x1080, optimized
enhancer.preset('twitter')    # 1200x675
enhancer.preset('facebook')   # 1200x630
enhancer.preset('linkedin')   # 1200x627

# Print quality
enhancer.preset('print_4x6')   # 1800x1200, 300dpi
enhancer.preset('print_8x10')  # 3000x2400, 300dpi

# Thumbnail
enhancer.preset('thumbnail')   # 150x150, center crop
enhancer.preset('preview')     # 400px max, 70 quality

Batch Processing

Process Directory

from scripts.image_enhancer import batch_process

# Apply same operations to all images
results = batch_process(
    input_dir="photos/",
    output_dir="processed/",
    operations=[
        ("resize", {"width": 1200}),
        ("watermark", {"text": "© 2024", "position": "bottom-right"}),
        ("compress", {"quality": 85})
    ],
    formats=['jpg', 'png'],  # Only process these formats
    recursive=True           # Include subdirectories
)

print(f"Processed: {results['success']} images")
print(f"Failed: {results['failed']} images")

Rename Pattern

batch_process(
    input_dir="photos/",
    output_dir="processed/",
    operations=[("resize", {"width": 800})],
    rename_pattern="{name}_web_{index:03d}"
)
# Output: photo_web_001.jpg, photo_web_002.jpg, ...

Generate Multiple Sizes

from scripts.image_enhancer import generate_sizes

# Create multiple sizes from one image
sizes = generate_sizes(
    "hero.jpg",
    output_dir="responsive/",
    widths=[320, 640, 1024, 1920],
    format='webp'
)
# Output: hero_320.webp, hero_640.webp, etc.

Icon Generation

from scripts.image_enhancer import generate_icons

# Generate favicon and app icons from single image
icons = generate_icons(
    "logo.png",
    output_dir="icons/",
    sizes=[16, 32, 48, 64, 128, 256, 512]
)

Metadata Operations

# Read EXIF data
metadata = enhancer.get_metadata()
print(metadata['camera'])
print(metadata['date_taken'])
print(metadata['gps'])

# Strip metadata (privacy)
enhancer.strip_metadata()

# Preserve specific metadata
enhancer.strip_metadata(keep=['copyright', 'artist'])

CLI Usage

# Single file
python image_enhancer.py input.jpg -o output.jpg --resize 800 --quality 85

# Batch processing
python image_enhancer.py photos/ -o processed/ --resize 1200 --watermark "© 2024"

# Apply preset
python image_enhancer.py photo.jpg --preset instagram

# Generate icons
python image_enhancer.py logo.png --icons icons/

Supported Formats

Format	Read	Write	Notes
JPEG	Yes	Yes	Lossy, best for photos
PNG	Yes	Yes	Lossless, supports transparency
WebP	Yes	Yes	Modern format, good compression
GIF	Yes	Yes	Animation support
BMP	Yes	Yes	Uncompressed
TIFF	Yes	Yes	High quality, large files
ICO	Yes	Yes	Icon format
HEIC

Error Handling

from scripts.image_enhancer import ImageEnhancer, ImageError

try:
    enhancer = ImageEnhancer("photo.jpg")
    enhancer.resize(width=800).save("output.jpg")
except ImageError as e:
    print(f"Image error: {e}")
except FileNotFoundError:
    print("Image file not found")

Configuration

enhancer = ImageEnhancer("photo.jpg")

# Global settings
enhancer.config.update({
    'default_quality': 85,
    'default_format': 'webp',
    'preserve_metadata': False,
    'color_profile': 'sRGB',
    'dpi': 72
})

Performance Tips

Large batches : Use batch_process() with parallel=True
Memory : Process one image at a time for very large files
Speed : WebP encoding is slower but produces smaller files
Quality : Start at 85 quality, reduce if file size is critical

Dependencies

pillow>=10.0.0
opencv-python>=4.8.0
numpy>=1.24.0

Weekly Installs

Repository

dkyazzentwatwa/…t-skills

GitHub Stars

First Seen

Jan 24, 2026

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