[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
Show more📈 Analytical overview of Telegram channel [PYTHON:TODAY]
Channel [PYTHON:TODAY] (@python2day) in the Russian language segment is an active participant. Currently, the community unites 64 158 subscribers, ranking 2 046 in the Technologies & Applications category and 9 511 in the Russia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 64 158 subscribers.
According to the latest data from 08 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 199 over the last 30 days and by 3 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 16.29%. Within the first 24 hours after publication, content typically collects 9.48% reactions from the total number of subscribers.
- Post reach: On average, each post receives 10 454 views. Within the first day, a publication typically gains 6 081 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 64.
- Thematic interests: Content is focused on key topics such as github, soft, install, pip, docker.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“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”
Thanks to the high frequency of updates (latest data received on 09 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
git clone https://github.com/RishiDesai/FaceEnhance
cd FaceEnhance
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python install.py
😰 Протестировать можно одной командой:
python test.py --input examples/dany_gpt_1.png --ref examples/dany_face.jpg --out examples/dany_enhanced.png
⌨️ Есть даже веб-интерфейс на Gradio (python demo.py) для быстрого теста без кода.
5️⃣ GitHub/Инструкция
#python #soft #codeimport cv2
from typing import Tuple
def load_image(path: str) -> cv2.Mat:
"""Загружает изображение с указанного пути."""
image = cv2.imread(path)
if image is None:
raise FileNotFoundError(f"Изображение не найдено: {path}")
return image
def detect_faces(image: cv2.Mat) -> Tuple[Tuple[int, int, int, int], ...]:
"""Находит лица на изображении с помощью каскада Хаара."""
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
face_cascade = cv2.CascadeClassifier(
cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
)
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
return tuple(faces)
def blur_faces(image: cv2.Mat, faces: Tuple[Tuple[int, int, int, int], ...]) -> cv2.Mat:
"""Размывает найденные лица на изображении."""
for (x, y, w, h) in faces:
face_roi = image[y:y+h, x:x+w]
blurred = cv2.GaussianBlur(face_roi, (99, 99), 30)
image[y:y+h, x:x+w] = blurred
return image
def save_image(image: cv2.Mat, path: str) -> None:
"""Сохраняет изображение на диск."""
cv2.imwrite(path, image)
if __name__ == "__main__":
input_path = "/home/kali/Coding/TEST/man.png"
output_path = "blurred_faces.jpg"
img = load_image(input_path)
found_faces = detect_faces(img)
print(f"👀 Найдено лиц: {len(found_faces)}")
blurred_img = blur_faces(img, found_faces)
save_image(blurred_img, output_path)
print(f"✅ Сохранено: {output_path}")
👨💻 Возможности:
🖱 Автоматическое обнаружение лиц;
🖱 Размытие без потери качества остальной части фото;
🖱 Работает за считанные секунды;
🖱 Код полностью на Python + OpenCV.
📂 Сохраняй и экспериментируй! Лойс не забудь поставить 👍
#python #soft #code
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