Detect and Crop Head in Image

README.MD

Detect and Crop Head in Image

This Python script leverages the OpenCV library for face detection and the PIL (Pillow) library for image processing to detect and crop a human head in an image. The detect_and_crop_head function takes an input image file, detects the face using a pre-trained Haar Cascade classifier, and crops the image to include the entire head with a 1:1 aspect ratio. The cropping is achieved by specifying a factor that determines the size of the cropped region around the detected face. Adjust the factor parameter based on the specific requirements of your images.

Usage:

input_image_path = "path/to/your/input/image.jpg"
output_image_path = "path/to/save/cropped/head.jpg"
detect_and_crop_head(input_image_path, output_image_path, factor=1.5)

Feel free to experiment with different factor values to achieve optimal results for your use case.

detect_and_crop_head.py

import cv2
import numpy as np
from PIL import Image


def detect_and_crop_head(input_image_path, output_image_path, factor=1.7):
    # Load the pre-trained face detection model from OpenCV
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

    # Read the input image using PIL
    image = Image.open(input_image_path)

    # Convert PIL image to OpenCV format (BGR)
    cv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)

    # Convert the image to grayscale for face detection
    gray_image = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY)

    # Detect faces in the image
    faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.3, minNeighbors=5)

    if len(faces) > 0:
        # Assuming the first face is the target, you can modify this based on your requirements
        x, y, w, h = faces[0]

        # Calculate the new coordinates and dimensions for a 1:1 aspect ratio
        center_x = x + w // 2
        center_y = y + h // 2
        size = int(max(w, h) * factor)
        x_new = max(0, center_x - size // 2)
        y_new = max(0, center_y - size // 2)

        # Crop the head region with a 1:1 aspect ratio
        cropped_head = cv_image[y_new:y_new+size, x_new:x_new+size]

        # Convert the cropped head back to PIL format
        cropped_head_pil = Image.fromarray(cv2.cvtColor(cropped_head, cv2.COLOR_BGR2RGB))

        # Save the cropped head image
        cropped_head_pil.save(output_image_path)
        print("Cropped head saved successfully.")
    else:
        print("No faces detected in the input image.")



# Example usage
input_image_path = "./you_input_image_goes_here.jpg"
output_image_path = "./cropped_head.jpg"

detect_and_crop_head(input_image_path, output_image_path)