crop_and_save_images_NEW_train.py

import os
import cv2
import shutil

# Load the cascade
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')

def get_cropped_image_if_2_eyes(image_path):
    img = cv2.imread(image_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    for (x,y,w,h) in faces:
        roi_gray = gray[y:y+h, x:x+w]
        roi_color = img[y:y+h, x:x+w]
        eyes = eye_cascade.detectMultiScale(roi_gray)
        if len(eyes) >= 1:
            return roi_color

def resize_image(image, width=224, height=224):
    return cv2.resize(image, (width, height))

path_to_data = 'train_classified'
path_to_cr_data = 'train_cropped'

# Create cropped directory if not exist
if not os.path.exists(path_to_cr_data):
    os.makedirs(path_to_cr_data)

# Function to process images in a directory
def process_images_in_directory(directory):
    for subdir, _, files in os.walk(directory):
        for file in files:
            img_path = os.path.join(subdir, file)
            cropped_image = get_cropped_image_if_2_eyes(img_path)
            if cropped_image is not None:
                # Resize to 224x224
                resized_image = resize_image(cropped_image)
                
                rel_path = os.path.relpath(subdir, path_to_data)
                target_dir = os.path.join(path_to_cr_data, rel_path)
                if not os.path.exists(target_dir):
                    os.makedirs(target_dir)
                cv2.imwrite(os.path.join(target_dir, file), resized_image)

# Process images in the 'train_small' directory
process_images_in_directory(path_to_data)

print("Images have been cropped, resized to 224x224, and saved in the 'cropped' directory.")