This Python script scans a directory for images and automatically deletes those that contain human figures. It uses OpenCV's Haar Cascade Classifier to detect humans in images.

• Detects human presence in images using a pre-trained model.
• Deletes images that contain humans.
• Supports common image formats (.jpg, .jpeg, .png).
• Fast and efficient processing using OpenCV.

Ensure you have Python installed and install the required dependencies using:

pip install opencv-python

1. Clone this repository or download the script.
2. Place all your images inside a folder.
3. Modify the script to set the correct image_directory path.

image_directory = 'path_to_your_images'

4. Run the script:

python detect_and_delete.py

1. The script loads a Haar Cascade Classifier (haarcascade_fullbody.xml) to detect human figures.
2. It converts each image to grayscale and scans for human-like shapes.
3. If a human is detected, the image is deleted from the directory.

Image1.jpg does not contain a human.
Deleted image: Image2.jpg
Image3.png does not contain a human.

• If you want to detect faces instead of full-body figures, replace:

  cascade_path = cv2.data.haarcascades + 'haarcascade_fullbody.xml'

  with:

  cascade_path = cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'

• The script is optimized for general human detection, but for higher accuracy, consider using deep learning models like YOLO, SSD, or Faster R-CNN.

This project is open-source and available under the MIT License.