# py-img-processor
[](https://github.com/SkylerHu/py-img-processor)
[](https://github.com/SkylerHu/py-img-processor)
[](https://github.com/SkylerHu/py-img-processor)
[](https://github.com/SkylerHu/py-img-processor)
[](https://github.com/SkylerHu/py-img-processor)
[](https://github.com/SkylerHu/py-img-processor)
[](https://github.com/SkylerHu/py-img-processor)
[](https://github.com/SkylerHu/py-img-processor)
Image editor using Python and Pillow.
依赖Pillow开发的Python库，用于图像编辑处理。
## 1. 安装
pip install py-img-processor
依赖：
- `Python >= 3.9`
- `Pillow >= 8.0.0`
可查看版本变更记录 [ChangeLog](https://github.com/SkylerHu/py-img-processor/blob/master/docs/CHANGELOG-1.x.md)
## 2. 使用(Usage)
具体使用说明查看 [readthedocs](https://py-img-processor.readthedocs.io) 。
## 2.1 运行配置
可以通过指定环境变量`PY_SETTINGS_MODULE`加载配置文件：
export PY_SETTINGS_MODULE=${your_project.settings_file.py}
支持的配置项有：
| 配置项 | 类型 | 说明 | 默认值 |
| - | - | - | - |
| DEBUG | bool | 是否debug开发模式 | False |
| PROCESSOR_MAX_FILE_SIZE | int | 处理原图的大小限制， 单位 MB | 20 |
| PROCESSOR_MAX_W_H | int | 处理图像，原图宽高像素限制 | 30000 |
| PROCESSOR_MAX_PIXEL | int | width x height总像素3亿，处理前后的值都被此配置限制 | 300000000 |
| PROCESSOR_DEFAULT_QUALITY | int | 图像处理后的默认质量 | 75 |
| PROCESSOR_TEXT_FONT | str | 默认字体文件，默认从系统中寻找；也可以直接传递字体文件路径 | Arial Unicode.ttf |
> `注意`：`PROCESSOR_TEXT_FONT` 字体的设置是文字水印必要参数，需保证系统已安装该字体。默认值 `Arial Unicode.ttf` 是MacOS系统存在的字体，建议设置字体文件路径。
## 2.2 图像处理
测试图片 `lenna-400x225.jpg` (像素400x225)
### 处理函数
`process_image_by_path(input_path, out_path, params)`
参数说明：
- `input_path` str，输入图像文件路径
- `out_path` str, 输出图像保存路径
- `params` str or json，图像处理参数，参数说明详见 [Reference.md](https://github.com/SkylerHu/py-img-processor/blob/master/docs/Reference.md)
### 图像处理参数为字符串
- 斜线 `/` 隔开，区分不同的操作；
- 逗号 `,` 隔开，区分操作中不同的参数；
- 下划线 `_` 隔开，`key_value` 的形式，区分参数的Key和Value；
- `value`是复杂参数时，需要进行`base64url_encode`，是否需要encode查看文档参数详细说明；
```python
from imgprocessor.utils import base64url_encode
from imgprocessor.processor import process_image_by_path
process_image_by_path(
"docs/imgs/lenna-400x225.jpg",
"/tmp/output.png",
# 对图片缩放、裁剪、生成圆角、并转成png存储
f"resize,s_200/crop,w_200,h_200,g_center/watermark,text_{base64url_encode('Hello 世界')},color_FFF,size_20/circle,r_10/format,png",
)
```
输出图像 (像素200x200)：
### 图像处理参数为JSON
- 只是形式不同，参数和字符串形式无本质区别；
- `format`、`quality`、`interlace`三个值在JSON第一层，直接按照`key: value`的形式设置；
- 其他参数都放在 `actions` 数组中；
```python
process_image_by_path(
"docs/imgs/lenna-400x225.jpg",
"/tmp/output.png",
{
"actions": [
{"key": "resize", "s": 200},
{"key": "crop", "w": 200, "h": 200, "g": "center"},
# JSON形式参数, text无需encode
{"key": "watermark", "text": "Hello 世界", "color": "FFF", "size": 20},
{"key": "circle", "r": 10},
],
"format": "png",
},
)
```
该操作与上述字符串示例参数等效。
## 命令行
安装库后 有可执行命令 `img-processor` 可以使用，通过 `img-processor -h` 查看参数说明。
```shell
usage: img-processor [-h] [-V] -P PATH [--action ACTION [ACTION ...]] -O OUTPUT [--overwrite]
图像处理
optional arguments:
-h, --help show this help message and exit
-V, --version show program's version number and exit
-P PATH, --path PATH 输入图像的文件路径/目录，若是目录则批量处理目录下所有图像
--action ACTION [ACTION ...]
操作参数，可对同一个文件多组操作
-O OUTPUT, --output OUTPUT
输出图像路径，多个图像或多个操作时请设置已存在的目录
--overwrite 是否覆盖输出路径中已有文件
```
示例：
```shell
# 对单个图像进行多个操作，actions有2个参数，会输出2个图像文件
img-processor -P docs/imgs/lenna-400x225.jpg -O /tmp/ --action resize,s_200/format,webp resize,s_225/crop,w_225,h_225,g_center/circle/format,png --overwrite
```
> 注意：action参数仅支持字符串表达形式。
会输出2个图像文件：
`/tmp/lenna-400x225-0.webp` (像素355x200)
`/tmp/lenna-400x225-1.png` (像素225x225)
## 提取图像主色调
```python
from imgprocessor.processor import extract_main_color
extract_main_color("docs/imgs/lenna-400x225.jpg")
# 输出： "905C4C"
```
Raw data
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"description": "# py-img-processor\n\n[](https://github.com/SkylerHu/py-img-processor)\n[](https://github.com/SkylerHu/py-img-processor)\n[](https://github.com/SkylerHu/py-img-processor)\n[](https://github.com/SkylerHu/py-img-processor)\n[](https://github.com/SkylerHu/py-img-processor)\n[](https://github.com/SkylerHu/py-img-processor)\n[](https://github.com/SkylerHu/py-img-processor)\n[](https://github.com/SkylerHu/py-img-processor)\n\n\nImage editor using Python and Pillow.\n\n\u4f9d\u8d56Pillow\u5f00\u53d1\u7684Python\u5e93\uff0c\u7528\u4e8e\u56fe\u50cf\u7f16\u8f91\u5904\u7406\u3002\n\n\n## 1. \u5b89\u88c5\n\n\tpip install py-img-processor\n\n\u4f9d\u8d56\uff1a\n\n- `Python >= 3.9`\n- `Pillow >= 8.0.0`\n\n\u53ef\u67e5\u770b\u7248\u672c\u53d8\u66f4\u8bb0\u5f55 [ChangeLog](https://github.com/SkylerHu/py-img-processor/blob/master/docs/CHANGELOG-1.x.md)\n\n## 2. \u4f7f\u7528(Usage)\n\n\u5177\u4f53\u4f7f\u7528\u8bf4\u660e\u67e5\u770b [readthedocs](https://py-img-processor.readthedocs.io) \u3002\n\n## 2.1 \u8fd0\u884c\u914d\u7f6e\n\u53ef\u4ee5\u901a\u8fc7\u6307\u5b9a\u73af\u5883\u53d8\u91cf`PY_SETTINGS_MODULE`\u52a0\u8f7d\u914d\u7f6e\u6587\u4ef6\uff1a\n\n export PY_SETTINGS_MODULE=${your_project.settings_file.py}\n\n\u652f\u6301\u7684\u914d\u7f6e\u9879\u6709\uff1a\n\n| \u914d\u7f6e\u9879 | \u7c7b\u578b | \u8bf4\u660e | \u9ed8\u8ba4\u503c |\n| - | - | - | - |\n| DEBUG | bool | \u662f\u5426debug\u5f00\u53d1\u6a21\u5f0f | False |\n| PROCESSOR_MAX_FILE_SIZE | int | \u5904\u7406\u539f\u56fe\u7684\u5927\u5c0f\u9650\u5236\uff0c \u5355\u4f4d MB | 20 |\n| PROCESSOR_MAX_W_H | int | \u5904\u7406\u56fe\u50cf\uff0c\u539f\u56fe\u5bbd\u9ad8\u50cf\u7d20\u9650\u5236 | 30000 |\n| PROCESSOR_MAX_PIXEL | int | width x height\u603b\u50cf\u7d203\u4ebf\uff0c\u5904\u7406\u524d\u540e\u7684\u503c\u90fd\u88ab\u6b64\u914d\u7f6e\u9650\u5236 | 300000000 |\n| PROCESSOR_DEFAULT_QUALITY | int | \u56fe\u50cf\u5904\u7406\u540e\u7684\u9ed8\u8ba4\u8d28\u91cf | 75 |\n| PROCESSOR_TEXT_FONT | str | \u9ed8\u8ba4\u5b57\u4f53\u6587\u4ef6\uff0c\u9ed8\u8ba4\u4ece\u7cfb\u7edf\u4e2d\u5bfb\u627e\uff1b\u4e5f\u53ef\u4ee5\u76f4\u63a5\u4f20\u9012\u5b57\u4f53\u6587\u4ef6\u8def\u5f84 | Arial Unicode.ttf |\n\n> `\u6ce8\u610f`\uff1a`PROCESSOR_TEXT_FONT` \u5b57\u4f53\u7684\u8bbe\u7f6e\u662f\u6587\u5b57\u6c34\u5370\u5fc5\u8981\u53c2\u6570\uff0c\u9700\u4fdd\u8bc1\u7cfb\u7edf\u5df2\u5b89\u88c5\u8be5\u5b57\u4f53\u3002\u9ed8\u8ba4\u503c `Arial Unicode.ttf` \u662fMacOS\u7cfb\u7edf\u5b58\u5728\u7684\u5b57\u4f53\uff0c\u5efa\u8bae\u8bbe\u7f6e\u5b57\u4f53\u6587\u4ef6\u8def\u5f84\u3002\n\n## 2.2 \u56fe\u50cf\u5904\u7406\n\n\u6d4b\u8bd5\u56fe\u7247 `lenna-400x225.jpg` (\u50cf\u7d20400x225)\n\n\n\n\n### \u5904\u7406\u51fd\u6570\n`process_image_by_path(input_path, out_path, params)`\n\n\u53c2\u6570\u8bf4\u660e\uff1a\n\n- `input_path` str\uff0c\u8f93\u5165\u56fe\u50cf\u6587\u4ef6\u8def\u5f84\n- `out_path` str, \u8f93\u51fa\u56fe\u50cf\u4fdd\u5b58\u8def\u5f84\n- `params` str or json\uff0c\u56fe\u50cf\u5904\u7406\u53c2\u6570\uff0c\u53c2\u6570\u8bf4\u660e\u8be6\u89c1 [Reference.md](https://github.com/SkylerHu/py-img-processor/blob/master/docs/Reference.md)\n\n\n### \u56fe\u50cf\u5904\u7406\u53c2\u6570\u4e3a\u5b57\u7b26\u4e32\n\n- \u659c\u7ebf `/` \u9694\u5f00\uff0c\u533a\u5206\u4e0d\u540c\u7684\u64cd\u4f5c\uff1b\n- \u9017\u53f7 `,` \u9694\u5f00\uff0c\u533a\u5206\u64cd\u4f5c\u4e2d\u4e0d\u540c\u7684\u53c2\u6570\uff1b\n- \u4e0b\u5212\u7ebf `_` \u9694\u5f00\uff0c`key_value` \u7684\u5f62\u5f0f\uff0c\u533a\u5206\u53c2\u6570\u7684Key\u548cValue\uff1b\n- `value`\u662f\u590d\u6742\u53c2\u6570\u65f6\uff0c\u9700\u8981\u8fdb\u884c`base64url_encode`\uff0c\u662f\u5426\u9700\u8981encode\u67e5\u770b\u6587\u6863\u53c2\u6570\u8be6\u7ec6\u8bf4\u660e\uff1b\n\n```python\nfrom imgprocessor.utils import base64url_encode\nfrom imgprocessor.processor import process_image_by_path\n\nprocess_image_by_path(\n \"docs/imgs/lenna-400x225.jpg\",\n \"/tmp/output.png\",\n # \u5bf9\u56fe\u7247\u7f29\u653e\u3001\u88c1\u526a\u3001\u751f\u6210\u5706\u89d2\u3001\u5e76\u8f6c\u6210png\u5b58\u50a8\n f\"resize,s_200/crop,w_200,h_200,g_center/watermark,text_{base64url_encode('Hello \u4e16\u754c')},color_FFF,size_20/circle,r_10/format,png\",\n)\n```\n\n\u8f93\u51fa\u56fe\u50cf (\u50cf\u7d20200x200)\uff1a\n\n\n\n### \u56fe\u50cf\u5904\u7406\u53c2\u6570\u4e3aJSON\n- \u53ea\u662f\u5f62\u5f0f\u4e0d\u540c\uff0c\u53c2\u6570\u548c\u5b57\u7b26\u4e32\u5f62\u5f0f\u65e0\u672c\u8d28\u533a\u522b\uff1b\n- `format`\u3001`quality`\u3001`interlace`\u4e09\u4e2a\u503c\u5728JSON\u7b2c\u4e00\u5c42\uff0c\u76f4\u63a5\u6309\u7167`key: value`\u7684\u5f62\u5f0f\u8bbe\u7f6e\uff1b\n- \u5176\u4ed6\u53c2\u6570\u90fd\u653e\u5728 `actions` \u6570\u7ec4\u4e2d\uff1b\n\n```python\nprocess_image_by_path(\n \"docs/imgs/lenna-400x225.jpg\",\n \"/tmp/output.png\",\n {\n \"actions\": [\n {\"key\": \"resize\", \"s\": 200},\n {\"key\": \"crop\", \"w\": 200, \"h\": 200, \"g\": \"center\"},\n # JSON\u5f62\u5f0f\u53c2\u6570, text\u65e0\u9700encode\n {\"key\": \"watermark\", \"text\": \"Hello \u4e16\u754c\", \"color\": \"FFF\", \"size\": 20},\n {\"key\": \"circle\", \"r\": 10},\n ],\n \"format\": \"png\",\n },\n)\n```\n\u8be5\u64cd\u4f5c\u4e0e\u4e0a\u8ff0\u5b57\u7b26\u4e32\u793a\u4f8b\u53c2\u6570\u7b49\u6548\u3002\n\n## \u547d\u4ee4\u884c\n\u5b89\u88c5\u5e93\u540e \u6709\u53ef\u6267\u884c\u547d\u4ee4 `img-processor` \u53ef\u4ee5\u4f7f\u7528\uff0c\u901a\u8fc7 `img-processor -h` \u67e5\u770b\u53c2\u6570\u8bf4\u660e\u3002\n\n```shell\nusage: img-processor [-h] [-V] -P PATH [--action ACTION [ACTION ...]] -O OUTPUT [--overwrite]\n\n\u56fe\u50cf\u5904\u7406\n\noptional arguments:\n -h, --help show this help message and exit\n -V, --version show program's version number and exit\n -P PATH, --path PATH \u8f93\u5165\u56fe\u50cf\u7684\u6587\u4ef6\u8def\u5f84/\u76ee\u5f55\uff0c\u82e5\u662f\u76ee\u5f55\u5219\u6279\u91cf\u5904\u7406\u76ee\u5f55\u4e0b\u6240\u6709\u56fe\u50cf\n --action ACTION [ACTION ...]\n \u64cd\u4f5c\u53c2\u6570\uff0c\u53ef\u5bf9\u540c\u4e00\u4e2a\u6587\u4ef6\u591a\u7ec4\u64cd\u4f5c\n -O OUTPUT, --output OUTPUT\n \u8f93\u51fa\u56fe\u50cf\u8def\u5f84\uff0c\u591a\u4e2a\u56fe\u50cf\u6216\u591a\u4e2a\u64cd\u4f5c\u65f6\u8bf7\u8bbe\u7f6e\u5df2\u5b58\u5728\u7684\u76ee\u5f55\n --overwrite \u662f\u5426\u8986\u76d6\u8f93\u51fa\u8def\u5f84\u4e2d\u5df2\u6709\u6587\u4ef6\n```\n\n\u793a\u4f8b\uff1a\n```shell\n# \u5bf9\u5355\u4e2a\u56fe\u50cf\u8fdb\u884c\u591a\u4e2a\u64cd\u4f5c\uff0cactions\u67092\u4e2a\u53c2\u6570\uff0c\u4f1a\u8f93\u51fa2\u4e2a\u56fe\u50cf\u6587\u4ef6\nimg-processor -P docs/imgs/lenna-400x225.jpg -O /tmp/ --action resize,s_200/format,webp resize,s_225/crop,w_225,h_225,g_center/circle/format,png --overwrite\n```\n\n> \u6ce8\u610f\uff1aaction\u53c2\u6570\u4ec5\u652f\u6301\u5b57\u7b26\u4e32\u8868\u8fbe\u5f62\u5f0f\u3002\n\n\u4f1a\u8f93\u51fa2\u4e2a\u56fe\u50cf\u6587\u4ef6\uff1a\n\n`/tmp/lenna-400x225-0.webp` (\u50cf\u7d20355x200)\n\n\n\n`/tmp/lenna-400x225-1.png` (\u50cf\u7d20225x225)\n\n\n\n\n## \u63d0\u53d6\u56fe\u50cf\u4e3b\u8272\u8c03\n```python\nfrom imgprocessor.processor import extract_main_color\n\nextract_main_color(\"docs/imgs/lenna-400x225.jpg\")\n# \u8f93\u51fa\uff1a \"905C4C\"\n```\n",
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