From be5ab673f1dd1e811490a517cbf5fdeb6b58db80 Mon Sep 17 00:00:00 2001
From: waelkh <waelkhlifi12@gmail.com>
Date: Tue, 19 Mar 2024 03:14:05 +0100
Subject: [PATCH] Minor changes , mainly added comments
---
Dataset_tools/Rename_vgg_face_dataset.py | 2 +-
Dataset_tools/Split_celeba_in_folders.py | 15 +++++---
Dataset_tools/crop_celeba.py | 20 +++++++----
Dataset_tools/crop_vggface.py | 33 +++++++++--------
Siamese_model.py | 11 ++++++
face_image_quality/face_image_quality.py | 31 +++++++++++++---
functions.py | 30 +++++++++++++++-
requirements.txt | Bin 0 -> 2852 bytes
test_model/capture_image.py | 14 ++++++--
test_model/convert_tf_lite.py | 5 +++
test_model/test_model.py | 44 ++++++++++++++++++++---
test_model/use_model.py | 20 +++++++----
test_model/use_tflite.py | 12 +++++++
train_model_tripletloss.py | 11 +++---
14 files changed, 198 insertions(+), 50 deletions(-)
create mode 100644 requirements.txt
diff --git a/Dataset_tools/Rename_vgg_face_dataset.py b/Dataset_tools/Rename_vgg_face_dataset.py
index 1f7b1d3..0bad9d6 100644
--- a/Dataset_tools/Rename_vgg_face_dataset.py
+++ b/Dataset_tools/Rename_vgg_face_dataset.py
@@ -1,5 +1,5 @@
import os
-import shutil
+
diff --git a/Dataset_tools/Split_celeba_in_folders.py b/Dataset_tools/Split_celeba_in_folders.py
index 1193780..275ea77 100644
--- a/Dataset_tools/Split_celeba_in_folders.py
+++ b/Dataset_tools/Split_celeba_in_folders.py
@@ -3,8 +3,15 @@ import os
import shutil
-def Read_Two_Column_File(file_name):
- with open(file_name, 'r') as f_input:
+def Read_Two_Column_File(file_path):
+ """
+
+ :param file_path: This is the file path
+ :return: Two lists containing the labels and the filenames corresponding to each label.
+ Y is the labels
+ X is the filenames
+ """
+ with open(file_path, 'r') as f_input:
csv_input = csv.reader(f_input, delimiter=' ', skipinitialspace=True)
x = []
y = []
@@ -16,8 +23,8 @@ def Read_Two_Column_File(file_name):
x, y = Read_Two_Column_File(r'/home/khlifi/Downloads/identity_CelebA.txt')
-path = r'/home/khlifi/Documents/newdat'
-src_folder = r"/home/khlifi/Documents/datasets"
+path = 'destination path'
+src_folder = 'source folder path'
for i in range(len(x)):
b=0
for j in range(len(x)):
diff --git a/Dataset_tools/crop_celeba.py b/Dataset_tools/crop_celeba.py
index 7190e99..aaf9d0e 100644
--- a/Dataset_tools/crop_celeba.py
+++ b/Dataset_tools/crop_celeba.py
@@ -6,6 +6,12 @@ from matplotlib.pyplot import imshow
import os
import shutil
def crop_image(image_path):
+ """
+
+ :param image_path: Path to the input image
+ :return: tuple containing a boolean indicating successful detection and the cropped
+ image if successful otherwise (False, None)
+ """
detector = MTCNN()
img=cv2.imread(image_path)
data=detector.detect_faces(img)
@@ -13,23 +19,25 @@ def crop_image(image_path):
if data !=[]:
for faces in data:
box=faces['box']
- # calculate the area in the image
+ # calculate the area of the bounding box in the image
area = box[3] * box[2]
+ #Checks if the current detected face is the largest
if area>biggest:
biggest=area
bounding_box=box
+ # Ensure bounding box coordinates are non-negative
bounding_box[0]= 0 if bounding_box[0]<0 else bounding_box[0]
bounding_box[1]= 0 if bounding_box[1]<0 else bounding_box[1]
- img =img[bounding_box[1]: bounding_box[1]+bounding_box[3],bounding_box[0]: bounding_box[0]+ bounding_box[2]]
- cropped_face = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # convert from bgr to rgb
+ #crop the face region from the imgae
+ cropped_img =img[bounding_box[1]: bounding_box[1]+bounding_box[3],bounding_box[0]: bounding_box[0]+ bounding_box[2]]
+ cropped_face = cv2.cvtColor(cropped_img, cv2.COLOR_BGR2RGB) # convert from bgr to rgb
return (True, cropped_face)
else:
return (False, None)
#path = r"G:\datasets\celeb-A\img_align_celeba\000060.jpg"
-Path2 = r'/home/khlifi/Downloads/img_align_celeba'
-dst_path= r"/home/khlifi/Documents/datasets"
+Path2 = 'replace with source path'
+dst_path= 'replace with destination path'
all_files = os.listdir(Path2)
-print(len(all_files))
for files in all_files:
img_path = os.path.join(Path2, files)
dst_path3 = os.path.join(dst_path, files)
diff --git a/Dataset_tools/crop_vggface.py b/Dataset_tools/crop_vggface.py
index 37386a7..6f22c40 100644
--- a/Dataset_tools/crop_vggface.py
+++ b/Dataset_tools/crop_vggface.py
@@ -1,10 +1,13 @@
from mtcnn import MTCNN
import cv2
-#import matplotlib.pyplot as plt
-#from matplotlib.pyplot import imshow
import os
-import shutil
def crop_image(image_path):
+ """
+
+ :param image_path: Path to the input image
+ :return: tuple containing a boolean indicating successful detection and the cropped
+ image if successful otherwise (False, None)
+ """
detector = MTCNN()
img=cv2.imread(image_path)
data=detector.detect_faces(img)
@@ -12,23 +15,25 @@ def crop_image(image_path):
if data !=[]:
for faces in data:
box=faces['box']
- # calculate the area in the image
+ # calculate the area of the bounding box in the image
area = box[3] * box[2]
+ #Checks if the current detected face is the largest
if area>biggest:
biggest=area
- bbox=box
- bbox[0]= 0 if bbox[0]<0 else bbox[0]
- bbox[1]= 0 if bbox[1]<0 else bbox[1]
- img =img[bbox[1]: bbox[1]+bbox[3],bbox[0]: bbox[0]+ bbox[2]]
- img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # convert from bgr to rgb
- return (True, img)
+ bounding_box=box
+ # Ensure bounding box coordinates are non-negative
+ bounding_box[0]= 0 if bounding_box[0]<0 else bounding_box[0]
+ bounding_box[1]= 0 if bounding_box[1]<0 else bounding_box[1]
+ #crop the face region from the imgae
+ cropped_img =img[bounding_box[1]: bounding_box[1]+bounding_box[3],bounding_box[0]: bounding_box[0]+ bounding_box[2]]
+ cropped_face = cv2.cvtColor(cropped_img, cv2.COLOR_BGR2RGB) # convert from bgr to rgb
+ return (True, cropped_face)
else:
return (False, None)
-#path = r"G:\datasets\celeb-A\img_align_celeba\000060.jpg"
-Path2 = r"/home/khlifi/Documents/vgg_face/train"
-dst_path = r"/home/khlifi/Documents/vggface_cropped"
+
+Path2 = 'replace with source directory'
+dst_path = 'replace with destination directory'
all_folders = os.listdir(Path2)
-print(len(all_folders))
result = False
for folders in all_folders:
diff --git a/Siamese_model.py b/Siamese_model.py
index fa47c77..75370a2 100644
--- a/Siamese_model.py
+++ b/Siamese_model.py
@@ -11,9 +11,15 @@ from keras.models import Model, Sequential
def image_embedder(input_shape):
+ """
+
+ :param input_shape: Shape of th expected input
+ :return: the encoder
+ """
"this function creates a CNN that will be used to generate embeddings of the images"
"the layers until the 27th layer will be frozen"
+
pretrained_model = Xception(
input_shape=input_shape,
weights='imagenet',
@@ -36,6 +42,11 @@ def image_embedder(input_shape):
def get_siamese_network(input_shape):
+ """
+
+ :param input_shape: shape of the input expected by the network
+ :return: the encoder and the siamse network.
+ """
encoder = image_embedder(input_shape)
# Define the input layers of the model for the inputs
diff --git a/face_image_quality/face_image_quality.py b/face_image_quality/face_image_quality.py
index a61c566..2046548 100644
--- a/face_image_quality/face_image_quality.py
+++ b/face_image_quality/face_image_quality.py
@@ -6,28 +6,43 @@ from numpy.linalg import norm
+image_path = r'C:\Users\waelk\PycharmProjects\facial_recognition\facial_recognition\test_model\database\1\0.jpg'
-
-image_path = cv2.imread(r'G:\datasets\new_dat\1\006439.jpg')
+image = cv2.imread(image_path)
#gray = cv2.imread(r'/home/khlifi/Documents/test_bgg/n000017/53.jpg', 0)
def brightness(img):
+ """
+
+ :param img: Image path
+ :return: Euclidean norm
+ """
# A good value for brightness would be 80
if len(img.shape) == 3:
# Colored RGB or BGR (*Do Not* use HSV images with this function)
- # create brightness with euclidean norm
+ # create brightness with Euclidean norm
return np.average(norm(img, axis=2)) / np.sqrt(3)
else:
# Grayscale
return np.average(img)
#print(f"brightness is: {brightness(img)}")
def blurr_detection(img):
+ """
+
+ :param img: the image path
+ :return: the variance of the laplacian of an image
+ """
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
variance_of_laplacian = cv2.Laplacian(gray, cv2.CV_64F).var()
return variance_of_laplacian
def resolution_measurement(img):
+ """
+
+ :param img: takes the image path as input
+ :return: returns the width and height
+ """
wid = img.shape[1]
hgt = img.shape[0]
print(str(wid) + "x" + str(hgt))
@@ -35,12 +50,18 @@ def resolution_measurement(img):
#resolution_measurement(img)
def check_image_quality(image_path):
+ """
+
+ :param image_path: takes the image_path as input, to check the quality of the images
+ :return: 1 if the image passed quality check, 0 if not
+ """
img = cv2.imread(image_path)
- if resolution_measurement(img) == (383,524) and blurr_detection(img)>100 and brightness(img)>90:
+ #if resolution_measurement(img) == (383,524) and blurr_detection(img)>100 and brightness(img)>90:
+ if blurr_detection(img) > 100 and brightness(img) > 90:
print("image quality is acceptable")
return 1
else:
print('image was not accepted')
return 0
-check_image_quality()
+check_image_quality(image_path)
diff --git a/functions.py b/functions.py
index db5019e..4436061 100644
--- a/functions.py
+++ b/functions.py
@@ -5,6 +5,14 @@ from keras.applications.inception_v3 import preprocess_input
import random
def get_image(image_path, preprocessing=False, resize=False, shape= (224,224,3)):
+ """
+
+ :param image_path: Path to the image file
+ :param preprocessing: if it's set to true, preprocessing will be performed, it's by default false
+ :param resize: Set to true if resizing is needed
+ :param shape: Image size for resizing
+ :return: the 3d tensor containing the image information
+ """
image_string = tf.io.read_file(image_path)
image = tf.image.decode_jpeg(image_string, channels=3)
@@ -20,6 +28,12 @@ def preprocess_images(anchor_img, positive_img, negative_img):
def split_dataset(directory, split=0.9):
+ """
+
+ :param directory: Path to the directory containing the dataset
+ :param split: The percentage of data that will be used in training.
+ :return: returns two dictionaries in the following shape : {'label', 'number of images corresponding to that label}
+ """
all_files = os.listdir(directory)
len_train = int(len(all_files) * split)
random.shuffle(all_files)
@@ -47,7 +61,15 @@ def split_dataset(directory, split=0.9):
return train_list, test_list
def create_triplets(directory, folder_list, max_files=20):
- "Create triplets from the generated dataset lists."
+ """
+
+ :param directory: Path to the directory of the dataset
+ :param folder_list: this is the dictionary given by the 'split dataset function'
+ :param max_files: maximum number of files that can be used for triplet creation from each label
+ :return: list of tuples, each tuple containing 3 tuples (anchor, positive, negative).
+ Tuples are in the following form : ('label', 'filename')
+ """
+ "Creates triplets from the generated dataset lists."
triplets = []
list_folders = list(folder_list.keys())
@@ -73,6 +95,12 @@ def create_triplets(directory, folder_list, max_files=20):
return triplets
def Generate_dataset(list, Path):
+ """
+
+ :param list: This is the triplet list
+ :param Path: Path to the dataset
+ :return: returns a tensorflow dataset of images.
+ """
"this function will create a tf dataset "
anchor_label = []
positive_label = []
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
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diff --git a/test_model/capture_image.py b/test_model/capture_image.py
index a5131aa..c432df1 100644
--- a/test_model/capture_image.py
+++ b/test_model/capture_image.py
@@ -25,14 +25,21 @@ while True:
#if you press space
img_name= f"test_image_new{img_counter}.jpg"
img = cv2.imwrite(img_name, frame)
- image_path = directory_path + f"test_image_new{img_counter}.jpg"
- print(image_path)
+
print(f"image {img_name}was saved")
img_counter+= 1
cam.release()
cv2.destroyAllWindows()
-print(img)
+image_path = os.path.join(directory_path, img_name)
+print(image_path)
+
def crop_image(image_path):
+ """
+
+ :param image_path: Path to the image
+ :return: tuple containing a boolean indicating successful detection and the cropped
+ image if successful otherwise (False, None)
+ """
detector = MTCNN()
img = cv2.imread(image_path)
data = detector.detect_faces(img)
@@ -59,4 +66,5 @@ if x==1 :
print("image was accepted")
cv2.imwrite("test_image_new.jpg", img)
else:
+
print('image was not accepted')
\ No newline at end of file
diff --git a/test_model/convert_tf_lite.py b/test_model/convert_tf_lite.py
index b4d145a..3f43b04 100644
--- a/test_model/convert_tf_lite.py
+++ b/test_model/convert_tf_lite.py
@@ -8,6 +8,11 @@ from keras.applications import Xception
from keras.models import Model, Sequential
#os.chdir(r"C:\Users\waelk\PycharmProjects\facial_recognition\facial_recognition\test_model\model\encoder.keras")
def image_embedder(input_shape):
+ """
+
+ :param input_shape: take the input shape which the CNN will expect
+ :return: The convolutional neural network that will be used in embedding.
+ """
""" Returns the convolutional neural network that will generate the encodings of each picture """
pretrained_model = Xception(
diff --git a/test_model/test_model.py b/test_model/test_model.py
index 81d432a..f2581a4 100644
--- a/test_model/test_model.py
+++ b/test_model/test_model.py
@@ -13,26 +13,34 @@ from pathlib import Path
from sklearn.metrics import accuracy_score, confusion_matrix,ConfusionMatrixDisplay
-#from sklearn.metrics.ConfusionMatrixDisplay import plot_confusion_matrix
+
directory_path = os.getcwd()
Path = os.path.join(directory_path, 'Extracted Faces')
print(Path)
-#Path = r'C:\Users\waelk\PycharmProjects\facial_recognition\facial_recognition\Dataset_tools\Extracted Faces'
-#Path =r'G:\datasets\new_dat'
random.seed(5)
np.random.seed(5)
tf.random.set_seed(5)
folders = os.listdir(Path)
def read_imagee(index):
+ """
+
+ :param index: takes the index of an image list .
+ :return:a 3D tensor of the following shape will be returned [height, width, channels]
+ """
image_path = os.path.join(Path, index[0], index[1])
image_string = tf.io.read_file(image_path)
image = tf.image.decode_jpeg(image_string, channels=3)
image = tf.image.convert_image_dtype(image, tf.float32)
- #image = tf.image.resize(image, (224,224))
+ image = tf.image.resize(image, (224,224))
def read_image(index):
+ """
+
+ :param index: takes the index to an image that contains the filename and path.
+ :return: a 3D tensor of the following shape will be returned [height, width, channels]
+ """
path = os.path.join(Path, index[0], index[1])
image = cv2.imread(path, cv2.COLOR_BGR2RGB)
@@ -52,6 +60,13 @@ test_triplet = create_triplets(Path, test_list, max_files=2)
print(f'this is the Triplet list {test_triplet}')
def batch_generator(triplet_list, batch_size=256, preprocessing = False):
+ """
+
+ :param triplet_list: this is list containing the tripelets
+ :param batch_size: size of the image batch
+ :param preprocessing: specifies if any preprocessing is applied
+ :return: returns a batch of images (images are in numpy array form).
+ """
batch_step = len(triplet_list)//batch_size
for i in range(batch_step+1):
@@ -76,6 +91,11 @@ def batch_generator(triplet_list, batch_size=256, preprocessing = False):
yield ([anchor, positive, negative])
def data_generator(triplet_list):
+ """
+
+ :param triplet_list: list of triplets in the following form (anchor, positive, negative)
+ :return: returns a tuple containing the images that are in numpy array form.
+ """
batch_step = len(triplet_list)
anchor = []
positive = []
@@ -97,12 +117,20 @@ def data_generator(triplet_list):
encoder = image_embedder((224,224,3))
-encoder.load_weights(r'C:\Users\waelk\PycharmProjects\facial_recognition\facial_recognition\test_model\model\encoder.keras')
+encoder.load_weights(os.path.join('model', 'encoder.keras'))
def classify_images(face_list1, face_list2, threshold=1.2):
+ """
+
+ :param face_list1: This is a list of images can be anchor, positive or negative
+ :param face_list2: this is a list of images, can be anchor , positive or negative
+ :param threshold: A value for the euclidean distance that determines if two images
+ belong to the same class.
+ :return:
+ """
# Getting the encodings for the passed faces
embeddings1 = encoder.predict(face_list1)
embeddings2 = encoder.predict(face_list2)
@@ -113,6 +141,12 @@ def classify_images(face_list1, face_list2, threshold=1.2):
def ModelMetrics(pos_list, neg_list):
+ """
+
+ :param pos_list: Takes a list containing the predictions of the model on anchor and positive samples
+ :param neg_list:Takes a list containing the predictions of the model on anchor and negative samples
+ :return: a confusion matrix
+ """
true = np.array([0] * len(pos_list) + [1] * len(neg_list))
pred = np.append(pos_list, neg_list)
diff --git a/test_model/use_model.py b/test_model/use_model.py
index 5151b0e..1347f07 100644
--- a/test_model/use_model.py
+++ b/test_model/use_model.py
@@ -14,10 +14,11 @@ from facial_recognition.Siamese_model import image_embedder, get_siamese_network
directory_path = os.getcwd()
database_path = os.path.join(directory_path, 'database')
+encoder_path = os.path.join(directory_path, 'encoder.keras')
-#parser = argparse.ArgumentParser(description='gives filename')
-#parser.add_argument('file_name', metavar='file_name', type=str, help='gives filename')
-#args = parser.parse_args()
+parser = argparse.ArgumentParser(description='gives filename')
+parser.add_argument('file_name', metavar='file_name', type=str, help='gives filename')
+args = parser.parse_args()
def read_image(Path):
@@ -33,8 +34,15 @@ def read_image(Path):
encoder = image_embedder((224,224,3))
-encoder.load_weights(r'C:\Users\waelk\PycharmProjects\facial_recognition\facial_recognition\test_model\model\encoder.keras')
+encoder.load_weights(encoder_path)
def classify_images(face_list1, face_list2, threshold=1.3):
+ """
+
+ :param face_list1:
+ :param face_list2:
+ :param threshold:
+ :return:
+ """
# Getting the encodings for the passed faces
tensor1 = encoder.predict(face_list1)
tensor2 = encoder.predict(face_list2)
@@ -45,8 +53,8 @@ def classify_images(face_list1, face_list2, threshold=1.3):
return 1
else:
return 0
-#file_name = args.file_name
-test_path = os.path.join(directory_path, 'test_image_2.jpg')
+file_name = args.file_name
+test_path = os.path.join(directory_path, file_name)
img1 = np.array([read_image(Path=test_path)])
result = {}
for persons in os.listdir(database_path):
diff --git a/test_model/use_tflite.py b/test_model/use_tflite.py
index 4c903d3..ec24d0b 100644
--- a/test_model/use_tflite.py
+++ b/test_model/use_tflite.py
@@ -28,6 +28,11 @@ database_path = os.path.join(directory_path, 'database')
def read_image(Path):
+ """
+
+ :param Path: Path to the image that will be read.
+ :return: Returns a 3D tensor containing the image data in the following shape [widht,height,channels].
+ """
image = cv2.imread(Path, cv2.COLOR_BGR2RGB)
@@ -93,6 +98,13 @@ output_data2 = interpreter.get_tensor(output_details[0]['index'])
print(output_data2)
print(output_data)
def classify_images(face_list1, face_list2, threshold=1.2):
+ """
+
+ :param face_list1: a list of labeled imgaes
+ :param face_list2:
+ :param threshold:
+ :return:
+ """
# Getting the encodings for the passed faces
tensor1 = interpreter.set_tensor(input_details[0]['index'], [face_list1])
diff --git a/train_model_tripletloss.py b/train_model_tripletloss.py
index 468851b..091ef57 100644
--- a/train_model_tripletloss.py
+++ b/train_model_tripletloss.py
@@ -10,7 +10,7 @@ from functions import split_dataset, Generate_dataset, create_triplets
random.seed(5)
np.random.seed(5)
tf.random.set_seed(5)
-Path = "replace this with link to dataset"
+Path = "replace this with Path to dataset"
physical_devices = tf.config.list_physical_devices('GPU')
for gpu_instance in physical_devices:
@@ -23,6 +23,7 @@ train_dataset = Generate_dataset(Path=Path,list=train_triplet)
train_dataset = train_dataset.shuffle(buffer_size=1024)
train_dataset = train_dataset.batch(2048, drop_remainder=False)
train_dataset = train_dataset.prefetch(tf.data.AUTOTUNE)
+print(type(train_dataset))
@@ -34,23 +35,23 @@ strategy = tf.distribute.MirroredStrategy(gpus)
with strategy.scope():
emb_mod, model= get_siamese_network([224 ,224, 3])
- checkpoint_path = r"/home/khlifi/Documents/model_semihard_triplet_loss/all/allweights_1024b_preprocessing/max_55.keras"
+ checkpoint_path = 'replace with path to checkpoint'
model.compile(optimizer=tf.keras.optimizers.Adam(0.0001),loss=tfa.losses.TripletSemiHardLoss(margin=0.3))
cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
save_weights_only=True,
verbose=1)
history = model.fit(train_dataset, epochs=15, callbacks=[cp_callback])
hist_df = pd.DataFrame(history.history)
- hist_json_file = '/home/khlifi/Documents/more_data_preprocessing_on/all/history_2048_50.json'
+ hist_json_file = 'replace with path to history'
with open(hist_json_file, mode='w') as f:
hist_df.to_json(f)
- hist_csv_file = '/home/khlifi/Documents/more_data_preprocessing_on/all/history_2048_50.csv'
+ hist_csv_file = 'replace with path to history'
with open(hist_csv_file, mode='w') as f:
hist_df.to_csv(f)
-model.save('/home/khlifi/Documents/model_semihard_triplet_loss/final/2048_batch_pre_on_max_50.keras')
+model.save('replace with path to directory ')
--
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