imagecrop.py

import cv2
import numpy as np
import os

def write_log_no_face_detected(image_path, log_file="no_face_detected_log.txt"):
    with open(log_file, "a") as log:
        log.write(image_path + "\n")

def crop_to_face(image_path):
    image = cv2.imread(image_path)
    
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
    
    faces = face_cascade.detectMultiScale(image, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
    
    if len(faces) == 0:
        write_log_no_face_detected(image_path)  
        print("No faces detected in", image_path)
        return None
    
    x, y, w, h = faces[0]
    
    face_cropped = image[y:y+h, x:x+w]
    
    height, width = face_cropped.shape[:2]
    scale_factor = max(768 / height, 768 / width)
    
    face_resized = cv2.resize(face_cropped, None, fx=scale_factor, fy=scale_factor, interpolation=cv2.INTER_AREA)
    
    final_image = np.zeros((768, 768, 3), dtype=np.uint8)
    
    center_x, center_y = (768 - face_resized.shape[1]) // 2, (768 - face_resized.shape[0]) // 2
    
    final_image[center_y:center_y+face_resized.shape[0], center_x:center_x+face_resized.shape[1]] = face_resized
    
    return final_image

def process_images(input_folder, output_folder):
    if not os.path.exists(output_folder):
        os.makedirs(output_folder)
    
    for filename in os.listdir(input_folder):
        if filename.lower().endswith(('.png', '.jpg', '.jpeg')):
            input_path = os.path.join(input_folder, filename)
            output_path = os.path.join(output_folder, filename)
            
            cropped_image = crop_to_face(input_path)
            if cropped_image is not None:
                cv2.imwrite(output_path, cropped_image)
                print(f"Processed and saved: {filename}")
            else:
                print(f"Skipped (No face detected): {filename}")

input_folder = './bilder'
output_folder = './output_crop'
process_images(input_folder, output_folder)