update

new_image.py

@@ -1,143 +0,0 @@
-import os
-import cv2
-import pickle
-import shutil
-import numpy as np
-from PIL import Image
-from mtcnn import MTCNN
-from numpy import asarray
-from keras.preprocessing import image
-from keras_vggface.vggface import VGGFace
-from keras_vggface.utils import preprocess_input
-import tensorflow as tf
-
-# Configure GPU memory growth for TensorFlow
-physical_devices = tf.config.list_physical_devices('GPU')
-if physical_devices:
-    for device in physical_devices:
-        tf.config.experimental.set_memory_growth(device, True)
-
-def feature_extractor(img_path):
-    model = VGGFace(model='resnet50', include_top=False, input_shape=(224, 224, 3), pooling='avg')
-    img = image.load_img(img_path, target_size=(224, 224))
-    img_array = image.img_to_array(img)
-    expanded_img = np.expand_dims(img_array, axis=0)
-    preprocessed_img = preprocess_input(expanded_img)
-    result = model.predict(preprocessed_img).flatten()
-    return result
-
-def extract_faces_mtcnn(input_folder, output_folder, required_size=(224, 224)):
-    detector = MTCNN()
-    # if not os.path.exists(output_folder):
-    #     os.makedirs(output_folder)
-    os.makedirs(output_folder, exist_ok=True)
-
-    for filename in os.listdir(input_folder):
-        img_path = os.path.join(input_folder, filename)
-        img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
-
-        if img is None:
-            print(f"Error reading image: {img_path}")
-            continue
-
-        print(img.shape)
-
-        clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
-        clahe_image = clahe.apply(img)
-        ConvertedImage = cv2.cvtColor(clahe_image, cv2.COLOR_GRAY2BGR)
-
-        faces = detector.detect_faces(ConvertedImage)
-        if faces is None or not faces:
-            continue
-        else:
-            for i, face_info in enumerate(faces):
-                x, y, w, h = face_info['box']
-                x, y = max(x, 0), max(y, 0)
-                face = img[y:y + h, x:x + w]
-                image_obj = Image.fromarray(face)
-                image_obj = image_obj.resize(required_size)
-                face_array = asarray(image_obj)
-                face_filename = f"{filename}"
-                output_path = os.path.join(output_folder, face_filename)
-                cv2.imwrite(output_path, face_array)
-
-def delete_files_in_folder(folder_path):
-    try:
-        files = os.listdir(folder_path)
-        for file_name in files:
-            file_path = os.path.join(folder_path, file_name)
-            if os.path.isfile(file_path):
-                os.remove(file_path)
-    except Exception as e:
-        print(f"An error occurred: {e}")
-
-def new_pickle_file(new_image_dir, update_face_dir):
-
-    pickle_file_dir = "/home/bikas/Desktop/IR/pickle"
-    filenames_pickle_path = os.path.join(pickle_file_dir, 'FinalFilenames.pkl')
-    features_pickle_path = os.path.join(pickle_file_dir, 'FeatureEmbeddings.pkl')
-
-    # Initialize existing data
-    if os.path.exists(filenames_pickle_path) and os.path.exists(features_pickle_path):
-        existing_filenames = pickle.load(open(filenames_pickle_path, 'rb'))
-        existing_features = pickle.load(open(features_pickle_path, 'rb'))
-    else:
-        existing_filenames = []
-        existing_features = []
-
-    image_files = os.listdir(new_image_dir)
-
-    if len(image_files) == 0:
-        # No valid image files found in the directory.
-        return
-
-    # Filter out already processed files
-    new_files = [file for file in image_files if file not in existing_filenames]
-
-    if not new_files:
-        # All files have already been processed
-        return
-
-    extract_faces_mtcnn(new_image_dir, update_face_dir)
-    filenames = []
-    features = []
-
-    image_files = os.listdir(update_face_dir)
-    if len(image_files) == 0:
-        # No valid image files found in the update face directory.
-        return
-
-    # Filter out already processed files
-    new_files = [file for file in image_files if file not in existing_filenames]
-
-    if not new_files:
-        # All files have already been processed
-        return
-
-    for person_file_name in new_files:
-        filenames.append(person_file_name)
-
-    # Update the existing pickle files with new data
-    existing_filenames.extend(filenames)
-
-    for file in filenames:
-        features.append(feature_extractor(os.path.join(update_face_dir, file)))
-
-    # Convert features to numpy array
-    if existing_features:
-        existing_features = np.array(existing_features)
-        features = np.array(features)
-        updated_features = np.vstack((existing_features, features))
-    else:
-        updated_features = np.array(features)
-
-    pickle.dump(existing_filenames, open(filenames_pickle_path, 'wb'))
-    pickle.dump(updated_features, open(features_pickle_path, 'wb'))
-
-    # Clean up the new_pickle and new_extract_face directories
-    delete_files_in_folder(update_face_dir)
-    # Delete the directory and all its contents
-    # shutil.rmtree(update_face_dir)
-
-# Example usage
-new_pickle_file("/home/hajj_images", "new_extract_face")