[PYTHON:TODAY]
Python скрипты, нейросети, боты, автоматизация. Всё бесплатно! Приват: https://boosty.to/pythontoday YouTube: https://clck.ru/3LfJhM Канал админа: @akagodlike Чат: @python2day_chat Сотрудничество: @web_runner Канал в РКН: https://clck.ru/3GBFVm
显示更多📈 Telegram 频道 [PYTHON:TODAY] 的分析概览
频道 [PYTHON:TODAY] (@python2day) 俄语 语言赛道中的 是活跃参与者。目前社区聚集了 63 875 名订阅者,在 技术与应用 类别中位列第 1 991,并在 俄罗斯 地区排名第 9 309 位。
📊 受众指标与增长动态
自 невідомо 创建以来,项目保持高速增长,吸引了 63 875 名订阅者。
根据 29 八月, 2026 的最新数据,频道保持稳定运转。过去 30 天订阅人数变化为 -80,过去 24 小时变化为 -19,整体触达仍然可观。
- 认证状态: 未认证
- 互动率 (ER): 平均受众互动率为 14.92%。内容发布后 24 小时内通常能获得 7.71% 的反应,占订阅者总量。
- 帖子覆盖: 每篇帖子平均可获得 9 527 次浏览,首日通常累积 4 926 次浏览。
- 互动与反馈: 受众积极参与,单帖平均反应数为 57。
- 主题关注点: 内容集中在 github, soft, install, pip, docker 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台:
“Python скрипты, нейросети, боты, автоматизация. Всё бесплатно!
Приват: https://boosty.to/pythontoday
YouTube: https://clck.ru/3LfJhM
Канал админа: @akagodlike
Чат: @python2day_chat
Сотрудничество: @web_runner
Канал в РКН: https://clck.ru/3GBFVm”
凭借高频更新(最新数据采集于 30 八月, 2026),频道始终保持新鲜度与高覆盖。分析显示受众积极互动,使其成为 技术与应用 类别中的关键影响点。
pip install retina-face
Примеры использования:
from retinaface import RetinaFace
resp = RetinaFace.detect_faces("img1.jpg")
print(resp)
{
"face_1": {
"score": 0.9993440508842468,
"facial_area": [155, 81, 434, 443],
"landmarks": {
"right_eye": [257.82974, 209.64787],
"left_eye": [374.93427, 251.78687],
"nose": [303.4773, 299.91144],
"mouth_right": [228.37329, 338.73193],
"mouth_left": [320.21982, 374.58798]
}
}
}
♎️ GitHub/Инструкция
#python #soft #githubgit clone https://github.com/DrewThomasson/ebook2audiobook.git
cd ebook2audiobook
pip install -r requirements.txt
Запуск:
python app.py
⚙️ GitHub/Инструкция
Сохраняй, пока бесплатно 👍
#python #soft #githubAnalyze the provided images and create a "brand style profile" in the form of a JSON object. This profile should extract and describe the visual identity, structure, and aesthetic rules shown in the images, in a way that allows an AI to recreate similar visuals in the same style, but for entirely different content. Do not include or reference any specific subjects, logos, products, people, text, or brand names present in the input. Your job is to isolate and document the style, layout principles, and design system used, so it can be reapplied to different content while maintaining the same visual language. The JSON should include, but not be limited to: Color usage: dominant tones, gradients, or palettes Typography style: font mood (bold, modern, playful, clean, etc.), placement, and usage hierarchy Lighting & vibe: energetic / moody / clean / vibrant / soft / etc. Subject placement: centered / floating / grouped / perspective / rotated / etc. Background style: abstract / gradient / textured / scenic / etc. Composition layout: symmetrical / rule of thirds / collage / exploded view / etc. Branding elements: use of overlays, strokes, glows, shapes, burst effects, or other visual motifs Visual tone: bold / casual / premium / loud / minimal / playful / etc. Post-processing style: contrast, saturation, shadows, glow, noise, etc. General style tags: genre/feel, e.g., "sports aesthetic", "editorial look", "clean tech", "pop art", etc. The output must be a well-structured JSON that an AI can use to recreate visuals in the same stylistic language, regardless of the actual subject matter.#nn #soft
