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new_image.py

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+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")