Skip to main content

AI image processing toolkit - resize, Flux Kontext/GPT image sizing, ratio analysis, prompt checker, ComfyUI manager and unified error codes

Project description

imgnorm

PyPI version Python License Documentation Status

AI图像处理工具集 —— 涵盖图片等比缩放、Flux Kontext / GPT 图像尺寸适配、比例分析、违禁词检测、ComfyUI服务管理等场景。


特性

  • 图片缩放与归一化

    • image_resize:等比缩放并居中放置到指定画布,支持自定义背景色
    • ImageTool:多功能比例调整,支持标准比例匹配、fill/resize双模式、自定义放大百分比
  • AI 模型尺寸适配

    • FluxKontextImageScale:自动匹配 17 个 Flux Kontext 优选分辨率并缩放
    • GPTImageSizeCalculator:根据比例 + 分辨率档位(1k/2k/4k)生成 gpt-image-2 合法尺寸
  • 图片比例分析

    • ImageRatioAnalyzer:获取精确最简比例(GCD 约分)或近似简单整数比例
  • 内容安全

    • PromptBanChecker:内置加密违禁词库开箱即用,支持自定义词库合并、排除词、Aho-Corasick 大词库加速
  • ComfyUI 服务管理

    • ComfyUIManager:一键触发重启 + 轮询等待服务恢复

安装

pip install imgnorm

可选扩展:

pip install imgnorm[fast]     # 大词库 Aho-Corasick 加速(推荐词库 >50 时安装)
pip install imgnorm[comfyui]  # ComfyUI 服务管理功能
pip install imgnorm[dev]      # 开发环境(包含全部扩展 + pytest)

使用示例

image_resize — 等比缩放居中

from PIL import Image
from imgnorm import image_resize

# 打开图片
img = Image.open("input.jpg")

# 缩放到 640x480,默认黑色背景
result = image_resize(img, 640, 480)
result.save("output.jpg")

# 自定义背景颜色(白色)
result = image_resize(img, 640, 480, bg_color=(255, 255, 255))
result.save("output_white.jpg")

FluxKontextImageScale — Flux Kontext 优选分辨率缩放

from PIL import Image
from imgnorm import FluxKontextImageScale

img = Image.open("input.jpg")

# 自动匹配最接近原图宽高比的 Flux Kontext 优选分辨率并缩放
scaler = FluxKontextImageScale(img)
result = scaler.start()
result.save("output_kontext.jpg")

ImageTool — 多功能比例调整工具

from imgnorm import ImageTool

# 读取图片二进制数据
with open("input.jpg", "rb") as f:
    image_bytes = f.read()

# 自动匹配最接近的标准比例,最小边放大 10%(默认)并填充透明背景
tool = ImageTool()
result_bytes = tool.adjust_size(image_bytes)

# 自定义放大百分比(放大 20%)
result_bytes = tool.adjust_size(image_bytes, expand_percent=20)

# 不缩放(expand_percent=0)
result_bytes = tool.adjust_size(image_bytes, expand_percent=0)

# 指定目标比例 3:4,使用 resize 模式
tool = ImageTool(proportion="3:4", need_adjust="resize")
result_bytes = tool.adjust_size(image_bytes)

# 指定目标比例 1:1,自定义填充色(白色不透明)
tool = ImageTool(proportion="1:1", need_adjust="fill", fill_color=(255, 255, 255, 255))
result_bytes, matched_ratio = tool.adjust_size(image_bytes, return_origin_ratio=True)
print(f"原图匹配比例: {matched_ratio}")

# 保存结果
with open("output.png", "wb") as f:
    f.write(result_bytes)

GPTImageSizeCalculator — gpt-image-2 尺寸计算器

from imgnorm import GPTImageSizeCalculator

# 计算 16:9 比例、2K 分辨率的合法尺寸
result = GPTImageSizeCalculator.calc_size(aspect_ratio="16:9", resolution="2k")
print(result)
# {'width': 2560, 'height': 1440, 'size': '2560x1440', 'pixels': 3686400, 'aspect_ratio': '16:9', 'resolution': '2k', 'valid': True}

# 计算 1:1 比例、4K 分辨率的合法尺寸
result = GPTImageSizeCalculator.calc_size(aspect_ratio="1:1", resolution="4k")
print(result['size'])  # 2880x2880

# 校验尺寸是否合法
is_valid = GPTImageSizeCalculator.is_valid_image_size(1024, 1024)  # True

# 比例超过 3:1 时,默认自动钳位到最近合法比例(保持横竖方向)
result = GPTImageSizeCalculator.calc_size(aspect_ratio="1:10", resolution="4k")
print(result['size'])  # 1280x3840(自动调整为 1:3)

# 严格模式:超限比例直接抛出异常
result = GPTImageSizeCalculator.calc_size(aspect_ratio="1:10", resolution="4k", strict=True)
# → ServiceError: 图像宽高比不合法, 相差太大

ImageRatioAnalyzer — 图片比例分析器

from imgnorm import ImageRatioAnalyzer

with open("input.jpg", "rb") as f:
    image_bytes = f.read()

analyzer = ImageRatioAnalyzer()

raw_ratio = analyzer.get_raw_simple_ratio(image_bytes)
# 精确比例: 74:55
print(f"精确比例: {raw_ratio}")

approx_ratio = analyzer.get_approx_simple_ratio(image_bytes)
# 近似比例: 4:3
print(f"近似比例: {approx_ratio}")

# 调整搜索范围(默认 1~10,增大可匹配更复杂比例)
analyzer = ImageRatioAnalyzer(approx_search_range=20)
# 19:14
print(analyzer.get_approx_simple_ratio(image_bytes))
# 74:55
print(analyzer.get_raw_simple_ratio(image_bytes))

ComfyUIManager — ComfyUI 服务管理

import logging
from imgnorm import ComfyUIManager

# 配置日志(可选)
logger = logging.getLogger("comfyui")
logging.basicConfig(level=logging.DEBUG)

# 创建管理器(默认连接本地 8188 端口)
manager = ComfyUIManager(logger=logger)

# 发起重启并等待服务恢复
success = manager.run_reboot_and_wait()
if success:
    print("服务重启成功")
else:
    print("服务重启超时")

# 自定义配置
manager = ComfyUIManager(
    base_url="http://127.0.0.1:8188",
    timeout=10,
    max_wait_seconds=120,
    poll_interval=3.0,
    logger=logger
)

PromptBanChecker — 提示词违禁词检测

from imgnorm import PromptBanChecker, PromptBanError

# 1. 开箱即用:自动加载内置加密违禁词库(1300+ 词)
checker = PromptBanChecker()

# 2. 内置词库 + 自定义文件合并
checker = PromptBanChecker(ban_word_file="my_words.txt")

# 3. 内置词库 + 运行时注入额外词
checker = PromptBanChecker(builtin_words=["自定义违禁词1", "自定义违禁词2"])

# 4. 排除内置词库中的某些词
checker = PromptBanChecker(exclude_words=["nude", "色情"])

# 5. 关闭内置词库,完全使用自己的
checker = PromptBanChecker(use_default=False, ban_word_file="my_words.txt")

# 校验提示词
try:
    checker.check("some prompt text here")
    print("校验通过")
except PromptBanError as e:
    print(f"违禁词拦截: {e.error_msg}")
    print(f"命中词: {e.extend_data}")

# 查找所有命中词(不抛异常)
hits = checker.find_matched_words("text to check")
print(f"命中: {hits}")

# 重新加载违禁词
checker.load_banned_words()

违禁词加载顺序: 包内置默认词库 → builtin_words 参数 → ban_word_file 用户文件,三者自动合并去重。

性能引擎: 词库 < 50 词自动使用正则预编译;≥ 50 词且已安装 pyahocorasick 时自动切换 Aho-Corasick 算法(O(n) 扫描,与词库大小无关)。

API 说明

image_resize(image, target_w, target_h, bg_color=(0, 0, 0))

将图片等比缩放并居中放置到指定尺寸的画布上。

参数 类型 说明
image PIL.Image.Image 输入图片对象
target_w int 目标宽度
target_h int 目标高度
bg_color tuple 画布背景颜色(RGB 元组),默认黑色 (0, 0, 0)

返回值: 缩放并居中后的 PIL.Image.Image 对象。

处理逻辑:

  1. 创建指定尺寸和背景色的画布
  2. 计算等比缩放比例(取宽、高缩放比的最小值),确保图片完整放入画布
  3. 使用 LANCZOS 算法高质量缩放图片
  4. 计算居中偏移量,将缩放后的图片粘贴到画布中央

FluxKontextImageScale(image)

根据原图宽高比,将图片缩放到最接近的 Flux Kontext 优选分辨率。

参数 类型 说明
image PIL.Image.Image 输入图片对象

方法 start() 计算原图宽高比,从 17 个优选分辨率中匹配最接近的一个,使用 LANCZOS 算法缩放并返回结果图片。

优选分辨率列表(宽×高): 672×1568 · 688×1504 · 720×1456 · 752×1392 · 800×1328 · 832×1248 · 880×1184 · 944×1104 · 1024×1024 · 1104×944 · 1184×880 · 1248×832 · 1328×800 · 1392×752 · 1456×720 · 1504×688 · 1568×672

返回值: 缩放后的 PIL.Image.Image 对象。

ImageTool(proportion=None, need_adjust="fill", fill_color=(255,255,255,0))

多功能图片尺寸调整工具,支持标准比例匹配和两种调整模式。

参数 类型 说明
proportion str | None 目标比例,如 "3:4" / "1:1";空字符串或 None 表示自动匹配
need_adjust str 调整模式:"fill"(画布填充)或 "resize"(拉伸)
fill_color tuple 填充背景色 RGBA,默认透明 (255, 255, 255, 0)

方法 adjust_size(image_bytes, return_origin_ratio=False, expand_percent=10)

参数 类型 说明
image_bytes bytes 图片二进制数据
return_origin_ratio bool 是否同时返回原图匹配的标准比例字符串
expand_percent float 放大百分比,默认 10(放大 10%);传 0 不缩放,负数缩小

返回值: bytes(处理后图片数据);当 return_origin_ratio=True 时返回 (bytes, str)

标准比例池: 1:1 · 2:3 · 3:2 · 3:4 · 4:3 · 4:5 · 5:4 · 9:16 · 16:9 · 21:9

处理逻辑:

  1. proportion 存在且与原图最接近标准比例不一致 → 直接返回原图
  2. proportion 存在且一致 → 反转比例后执行 resize/fill
  3. proportion 为空且 need_adjust="fill" → 最小边按 expand_percent 放大,画布填充
  4. proportion 为空且 need_adjust="resize" → 单边按 expand_percent 放大后拉伸

GPTImageSizeCalculator

gpt-image-2 尺寸计算器,根据比例和分辨率自动生成合法图片尺寸。

类方法:

方法 说明
calc_size(aspect_ratio="1:1", resolution="2k", strict=False) 根据比例和分辨率计算合法尺寸
is_valid_image_size(width, height) 校验尺寸是否合法
round_to_multiple(value, multiple=16) 四舍五入到指定倍数
floor_to_multiple(value, multiple=16) 向下对齐到指定倍数,用于像素超限时压缩

calc_size 参数:

参数 类型 说明
aspect_ratio str 宽高比,如 "1:1" / "16:9" / "4:3"
resolution str 分辨率档位:"1k" / "2k" / "4k"
strict bool 是否严格校验宽高比(≤3:1),默认 False(超限自动钳位到最近合法比例);True 时超限直接抛出 ServiceError

返回值: dict,包含 widthheightsizepixelsaspect_ratioresolutionvalid

合法性约束:

  • 最大边 ≤ 3840
  • 宽高为 16 的倍数
  • 宽高比 ≤ 3:1
  • 像素范围:655,360 ~ 8,294,400

ImageRatioAnalyzer(approx_search_range=10)

图片比例分析工具,获取图片的宽高比信息。

参数 类型 说明
approx_search_range int 近似比例搜索整数范围,默认 1~10

方法:

方法 说明
get_raw_simple_ratio(image_bytes) 获取精确最简宽高比(GCD 约分)
get_approx_simple_ratio(image_bytes) 自动拟合近似简单整数比例

get_raw_simple_ratio 通过最大公约数约分,返回精确的最简比例字符串,如 1920x1080"16:9"

get_approx_simple_ratio 遍历 1~approx_search_range 范围内的整数组合,找到与图片实际比例最接近的简单整数比,返回如 "2:3""16:9"

返回值: 比例字符串(str)。

ComfyUIManager

ComfyUI 服务管理工具,位于 imgnorm.comfyui 子模块。

参数 类型 说明
base_url str 服务地址,默认 "http://127.0.0.1:8188"
timeout int 请求超时秒数,默认 5
max_wait_seconds int 最长等待恢复时间,默认 60
poll_interval float 轮询间隔秒数,默认2.0
logger logging.Logger 日志记录器,默认使用模块logger

方法:

方法 说明
trigger_reboot() 发起重启请求,远程断开视为成功
wait_service_ready() 轮询检测服务是否恢复,超时返回False
run_reboot_and_wait() 完整流程:重启 + 等待,返回bool

工作原理:

  1. 调用 /api/manager/reboot 接口触发重启
  2. 轮询 /api/system_stats 接口,状态码 200 判定恢复
  3. 超时未恢复返回 False

安装: 使用 comfyui 功能需额外安装 requests

pip install imgnorm[comfyui]
# 或
pip install requests>=2.20.0

PromptBanChecker(ban_word_file=None, ignore_case=True, builtin_words=None, use_default=True, exclude_words=None)

提示词违禁词校验工具。内置加密违禁词库开箱即用,支持多来源词库合并、排除词、大词库自动加速。

参数 类型 说明
ban_word_file str | None 用户自定义违禁词文件路径(可选,支持 .txt / .pkl / .pickle
ignore_case bool 是否忽略大小写,默认 True
builtin_words list[str] | None 运行时注入的违禁词列表,与内置词库合并去重
use_default bool 是否加载包内置默认违禁词库,默认 True
exclude_words list[str] | None 从最终词库中排除的词列表

方法:

方法 说明
check(prompt) 校验提示词,命中则抛出 PromptBanError
find_matched_words(text) 查找所有命中词,返回列表
load_banned_words() 重新加载并合并所有来源的违禁词
create_pickle_file(words, output_path) 静态方法:将词列表序列化为 pickle 文件

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

imgnorm-1.0.1-cp314-cp314-win_amd64.whl (273.2 kB view details)

Uploaded CPython 3.14Windows x86-64

imgnorm-1.0.1-cp314-cp314-manylinux_2_17_x86_64.whl (1.7 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.17+ x86-64

imgnorm-1.0.1-cp313-cp313-win_amd64.whl (267.5 kB view details)

Uploaded CPython 3.13Windows x86-64

imgnorm-1.0.1-cp313-cp313-manylinux_2_17_x86_64.whl (1.7 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ x86-64

imgnorm-1.0.1-cp312-cp312-win_amd64.whl (271.7 kB view details)

Uploaded CPython 3.12Windows x86-64

imgnorm-1.0.1-cp312-cp312-manylinux_2_17_x86_64.whl (1.7 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ x86-64

imgnorm-1.0.1-cp311-cp311-win_amd64.whl (269.8 kB view details)

Uploaded CPython 3.11Windows x86-64

imgnorm-1.0.1-cp311-cp311-manylinux_2_17_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

imgnorm-1.0.1-cp310-cp310-win_amd64.whl (270.7 kB view details)

Uploaded CPython 3.10Windows x86-64

imgnorm-1.0.1-cp310-cp310-manylinux_2_17_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

imgnorm-1.0.1-cp39-cp39-win_amd64.whl (272.0 kB view details)

Uploaded CPython 3.9Windows x86-64

imgnorm-1.0.1-cp39-cp39-manylinux_2_17_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

imgnorm-1.0.1-cp38-cp38-win_amd64.whl (277.9 kB view details)

Uploaded CPython 3.8Windows x86-64

imgnorm-1.0.1-cp38-cp38-manylinux_2_17_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

File details

Details for the file imgnorm-1.0.1-cp314-cp314-win_amd64.whl.

File metadata

  • Download URL: imgnorm-1.0.1-cp314-cp314-win_amd64.whl
  • Upload date:
  • Size: 273.2 kB
  • Tags: CPython 3.14, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for imgnorm-1.0.1-cp314-cp314-win_amd64.whl
Algorithm Hash digest
SHA256 08d6aa74861ed8c2732a141838bd147a1fa1250fea9a5039728a84ab1429c2e5
MD5 ce6bf1bc282030e8c0ef6e54a6339f58
BLAKE2b-256 86e7943655176744fc3b063604fea76b6355014be325653c00677adfa41292ba

See more details on using hashes here.

Provenance

The following attestation bundles were made for imgnorm-1.0.1-cp314-cp314-win_amd64.whl:

Publisher: wheels.yml on zhenzi0322-package/imgnorm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file imgnorm-1.0.1-cp314-cp314-manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for imgnorm-1.0.1-cp314-cp314-manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 3ff40276df68ab34ce7cb257e156dd6aba01547c3713e5d9203cb5e4362cd7f1
MD5 8dc35dd3d6c4d4ecf9d50d424003016c
BLAKE2b-256 7d64ffaee12c7d10b761bec332696a3dc5aab93f54ad071d084a88669356b77d

See more details on using hashes here.

Provenance

The following attestation bundles were made for imgnorm-1.0.1-cp314-cp314-manylinux_2_17_x86_64.whl:

Publisher: wheels.yml on zhenzi0322-package/imgnorm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file imgnorm-1.0.1-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: imgnorm-1.0.1-cp313-cp313-win_amd64.whl
  • Upload date:
  • Size: 267.5 kB
  • Tags: CPython 3.13, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for imgnorm-1.0.1-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 aa46f07393571082c24d6f1b50a9c0c07ce290769c030804653f3e7e3d261f1d
MD5 601431fa76d0c168557106e277518bd5
BLAKE2b-256 e183cb95d1c4de171e768fbddf4c836ceda91c1571f0b368808d5c038386fe02

See more details on using hashes here.

Provenance

The following attestation bundles were made for imgnorm-1.0.1-cp313-cp313-win_amd64.whl:

Publisher: wheels.yml on zhenzi0322-package/imgnorm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file imgnorm-1.0.1-cp313-cp313-manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for imgnorm-1.0.1-cp313-cp313-manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 5e58520c7abe69767fc3c9689b5b07ed40005eda121c1d51fea0c1a9d8914285
MD5 f560ff1fac17f0a3c07faa0f00b40a25
BLAKE2b-256 bb1e87b51ca337de287031f3014e7b4470cb0c5715bdd7b170f1ebdc3956c2eb

See more details on using hashes here.

Provenance

The following attestation bundles were made for imgnorm-1.0.1-cp313-cp313-manylinux_2_17_x86_64.whl:

Publisher: wheels.yml on zhenzi0322-package/imgnorm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file imgnorm-1.0.1-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: imgnorm-1.0.1-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 271.7 kB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for imgnorm-1.0.1-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 48dce4196b70614922e6b00a0ddf7da49a34c735bf1a916de9da33e5543fb460
MD5 d6174d928a6e3b98d63a5b5e736e505f
BLAKE2b-256 24926c29ef052d6b1ea94d800a3772e8c8e79e4575d6ac3ec8d08bf1edfbc164

See more details on using hashes here.

Provenance

The following attestation bundles were made for imgnorm-1.0.1-cp312-cp312-win_amd64.whl:

Publisher: wheels.yml on zhenzi0322-package/imgnorm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file imgnorm-1.0.1-cp312-cp312-manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for imgnorm-1.0.1-cp312-cp312-manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 bcc3769db0214dbd4b7d33a20f2866e4f38c5a85c2cb7a085f44d2e971da895d
MD5 8b71bbaddfc9fc5cfb7386604824cd18
BLAKE2b-256 7c69256027998e489161c66072e7e3f60e149f96b66221136e3a41bc23489f35

See more details on using hashes here.

Provenance

The following attestation bundles were made for imgnorm-1.0.1-cp312-cp312-manylinux_2_17_x86_64.whl:

Publisher: wheels.yml on zhenzi0322-package/imgnorm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file imgnorm-1.0.1-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: imgnorm-1.0.1-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 269.8 kB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for imgnorm-1.0.1-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 8010e956a72fd564f094de2ab8ff8c97f6b2d1000a616a516d0bf25e5f57de46
MD5 726cd1b2b1c6665a7b9ae130a927b358
BLAKE2b-256 16c956c51bf59e176c1c0a8d0b3cdc06a79503d2eec41a8712a6ee14714366ea

See more details on using hashes here.

Provenance

The following attestation bundles were made for imgnorm-1.0.1-cp311-cp311-win_amd64.whl:

Publisher: wheels.yml on zhenzi0322-package/imgnorm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file imgnorm-1.0.1-cp311-cp311-manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for imgnorm-1.0.1-cp311-cp311-manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 f8df228ee0150b208cb9965c0bba1a20ea82644381f2a401286d63dd4417aa81
MD5 d106ff7c571b63d5384bfe9e6f0ed4c7
BLAKE2b-256 d2ed8b25a4edcc00cb58c022619837def7b3699ee7d573ff5e489fb433e30722

See more details on using hashes here.

Provenance

The following attestation bundles were made for imgnorm-1.0.1-cp311-cp311-manylinux_2_17_x86_64.whl:

Publisher: wheels.yml on zhenzi0322-package/imgnorm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file imgnorm-1.0.1-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: imgnorm-1.0.1-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 270.7 kB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for imgnorm-1.0.1-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 505072465f2a0d39a84fafe096c47d86a2049388974f367a87fd600e986fd083
MD5 5328995184fe48283c907d9f3940d625
BLAKE2b-256 044a9335bb3c2d1b5f08bfce1a90090abebd3a36386fde3b6199fea74a370dd7

See more details on using hashes here.

Provenance

The following attestation bundles were made for imgnorm-1.0.1-cp310-cp310-win_amd64.whl:

Publisher: wheels.yml on zhenzi0322-package/imgnorm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file imgnorm-1.0.1-cp310-cp310-manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for imgnorm-1.0.1-cp310-cp310-manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 1ab367a48ef1c4e22bf230d5f48b232fe8928359328a4c3c54d78b24c002ad4e
MD5 d839884978a004a0e72a42e426b93d6a
BLAKE2b-256 a9d5b32de2cdb0d99a94f89eb8910e720da2118f0c4fde90e5e868b82163a17c

See more details on using hashes here.

Provenance

The following attestation bundles were made for imgnorm-1.0.1-cp310-cp310-manylinux_2_17_x86_64.whl:

Publisher: wheels.yml on zhenzi0322-package/imgnorm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file imgnorm-1.0.1-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: imgnorm-1.0.1-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 272.0 kB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for imgnorm-1.0.1-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 5001e97ad7165b98d00e8ed227c3fbf011e4b887f5a3f95881ceb4b215e6543a
MD5 66d3bf584f4e200f3ef011000af05930
BLAKE2b-256 9a0e727780027755c64264644a23f7e9158a8c1f924984f4024a28ff5baa3a6a

See more details on using hashes here.

Provenance

The following attestation bundles were made for imgnorm-1.0.1-cp39-cp39-win_amd64.whl:

Publisher: wheels.yml on zhenzi0322-package/imgnorm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file imgnorm-1.0.1-cp39-cp39-manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for imgnorm-1.0.1-cp39-cp39-manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 2a6e15b14c45eab0d41f0bde4215b4609bb2158672a495ad959761f5ae10d1a8
MD5 88f4fb6f08a4651f8a3c7145ba82a672
BLAKE2b-256 547a33a18a233079de0770b7d5a4d94299123cd1a969cb241d46e6484e0482f1

See more details on using hashes here.

Provenance

The following attestation bundles were made for imgnorm-1.0.1-cp39-cp39-manylinux_2_17_x86_64.whl:

Publisher: wheels.yml on zhenzi0322-package/imgnorm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file imgnorm-1.0.1-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: imgnorm-1.0.1-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 277.9 kB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for imgnorm-1.0.1-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 8889d12a8449dbbaba66dd3de00340f1421acaf3ea4161db1a21436e3bcdbb7b
MD5 8bbec18b6ed7fa4c692f93eae492e4bc
BLAKE2b-256 f91cf431901d33d355da77ac66dcf90016d2bf806c66727927e0a4711738b2ae

See more details on using hashes here.

Provenance

The following attestation bundles were made for imgnorm-1.0.1-cp38-cp38-win_amd64.whl:

Publisher: wheels.yml on zhenzi0322-package/imgnorm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file imgnorm-1.0.1-cp38-cp38-manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for imgnorm-1.0.1-cp38-cp38-manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 2597166d0dc1fad2c17f86d2634fb878065c6a2f4244e1cddcee2e5717bb65a1
MD5 652eddf0862067ab3dbfa216d9108547
BLAKE2b-256 a1a202b893ff3fa87072392338f6642b4ae9ef88ac7c8dd29b1fbaa584e08c4a

See more details on using hashes here.

Provenance

The following attestation bundles were made for imgnorm-1.0.1-cp38-cp38-manylinux_2_17_x86_64.whl:

Publisher: wheels.yml on zhenzi0322-package/imgnorm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page