├── .github └── FUNDING.yml ├── cascades └── data │ ├── __init__.py │ ├── __pycache__ │ └── __init__.cpython-36.pyc │ └── haarcascade_licence_plate_rus_16stages.xml ├── images ├── ORL │ ├── p1 │ │ ├── 10.jpg │ │ ├── 3.jpg │ │ ├── 4.jpg │ │ ├── 5.jpg │ │ ├── 8.jpg │ │ ├── 9.jpg │ │ ├── 1_001.jpg │ │ ├── 2_001.jpg │ │ ├── 6_001.jpg │ │ └── 7_001.jpg │ ├── p10 │ │ ├── 6.jpg │ │ ├── 10_001.jpg │ │ ├── 1_001.jpg │ │ ├── 2_001.jpg │ │ ├── 3_001.jpg │ │ ├── 4_001.jpg │ │ ├── 5_001.jpg │ │ ├── 7_001.jpg │ │ ├── 8_001.jpg │ │ └── 9_001.jpg │ ├── p11 │ │ ├── 2.jpg │ │ ├── 3.jpg │ │ ├── 5.jpg │ │ ├── 6.jpg │ │ ├── 10.jpg │ │ ├── 1_001.jpg │ │ ├── 4_001.jpg │ │ ├── 7_001.jpg │ │ ├── 8_001.jpg │ │ └── 9_001.jpg │ ├── p12 │ │ ├── 1.jpg │ │ ├── 4.jpg │ │ ├── 7.jpg │ │ ├── 8.jpg │ │ ├── 9.jpg │ │ ├── 10_001.jpg │ │ ├── 2_001.jpg │ │ ├── 3_001.jpg │ │ ├── 5_001.jpg │ │ └── 6_001.jpg │ ├── p13 │ │ ├── 1.jpg │ │ ├── 2.jpg │ │ ├── 3.jpg │ │ ├── 4.jpg │ │ ├── 5.jpg │ │ ├── 7.jpg │ │ ├── 8.jpg │ │ ├── 9.jpg │ │ ├── 10.jpg │ │ └── 6_001.jpg │ ├── p14 │ │ ├── 6.jpg │ │ ├── 10_001.jpg │ │ ├── 1_001.jpg │ │ ├── 2_001.jpg │ │ ├── 3_001.jpg │ │ ├── 4_001.jpg │ │ ├── 5_001.jpg │ │ ├── 7_001.jpg │ │ ├── 8_001.jpg │ │ └── 9_001.jpg │ ├── p15 │ │ ├── 1.jpg │ │ ├── 2.jpg │ │ ├── 3.jpg │ │ ├── 4.jpg │ │ ├── 5.jpg │ │ ├── 7.jpg │ │ ├── 8.jpg │ │ ├── 9.jpg │ │ ├── 10.jpg │ │ └── 6_001.jpg │ ├── p16 │ │ ├── 6.jpg │ │ ├── 10_001.jpg │ │ ├── 1_001.jpg │ │ ├── 2_001.jpg │ │ ├── 3_001.jpg │ │ ├── 4_001.jpg │ │ ├── 5_001.jpg │ │ ├── 7_001.jpg │ │ ├── 8_001.jpg │ │ └── 9_001.jpg │ ├── p17 │ │ ├── 1.jpg │ │ ├── 2.jpg │ │ ├── 3.jpg │ │ ├── 4.jpg │ │ ├── 5.jpg │ │ ├── 7.jpg │ │ ├── 8.jpg │ │ ├── 9.jpg │ │ ├── 10_001.jpg │ │ └── 6_001.jpg │ ├── p18 │ │ ├── 6.jpg │ │ ├── 10.jpg │ │ ├── 1_001.jpg │ │ ├── 2_001.jpg │ │ ├── 3_001.jpg │ │ ├── 4_001.jpg │ │ ├── 5_001.jpg │ │ ├── 7_001.jpg │ │ ├── 8_001.jpg │ │ └── 9_001.jpg │ ├── p19 │ │ ├── 1.jpg │ │ ├── 2.jpg │ │ ├── 3.jpg │ │ ├── 7.jpg │ │ ├── 8.jpg │ │ ├── 9.jpg │ │ ├── 10_001.jpg │ │ ├── 4_001.jpg │ │ ├── 5_001.jpg │ │ └── 6_001.jpg │ ├── p2 │ │ ├── 1.jpg │ │ ├── 2.jpg │ │ ├── 6.jpg │ │ ├── 7.jpg │ │ ├── 10_001.jpg │ │ ├── 3_001.jpg │ │ ├── 4_001.jpg │ │ ├── 5_001.jpg │ │ ├── 8_001.jpg │ │ └── 9_001.jpg │ ├── p20 │ │ ├── 4.jpg │ │ ├── 5.jpg │ │ ├── 6.jpg │ │ ├── 10.jpg │ │ ├── 1_001.jpg │ │ ├── 2_001.jpg │ │ ├── 3_001.jpg │ │ ├── 7_001.jpg │ │ ├── 8_001.jpg │ │ └── 9_001.jpg │ ├── p21 │ │ ├── 1.jpg │ │ ├── 2.jpg │ │ ├── 3.jpg │ │ ├── 4.jpg │ │ ├── 5.jpg │ │ ├── 7.jpg │ │ ├── 8.jpg │ │ ├── 9.jpg │ │ ├── 10.jpg │ │ └── 6_001.jpg │ ├── p22 │ │ ├── 6.jpg │ │ ├── 10_001.jpg │ │ ├── 1_001.jpg │ │ ├── 2_001.jpg │ │ ├── 3_001.jpg │ │ ├── 4_001.jpg │ │ ├── 5_001.jpg │ │ ├── 7_001.jpg │ │ ├── 8_001.jpg │ │ └── 9_001.jpg │ ├── p23 │ │ ├── 1.jpg │ │ ├── 2.jpg │ │ ├── 3.jpg │ │ ├── 4.jpg │ │ ├── 5.jpg │ │ ├── 7.jpg │ │ ├── 8.jpg │ │ ├── 9.jpg │ │ ├── 10_001.jpg │ │ └── 6_001.jpg │ ├── p24 │ │ ├── 6.jpg │ │ ├── 10.jpg │ │ ├── 1_001.jpg │ │ ├── 2_001.jpg │ │ ├── 3_001.jpg │ │ ├── 4_001.jpg │ │ ├── 5_001.jpg │ │ ├── 7_001.jpg │ │ ├── 8_001.jpg │ │ └── 9_001.jpg │ ├── p25 │ │ ├── 4.jpg │ │ ├── 6.jpg │ │ ├── 8.jpg │ │ ├── 9.jpg │ │ ├── 10.jpg │ │ ├── 1_001.jpg │ │ ├── 2_001.jpg │ │ ├── 3_001.jpg │ │ ├── 5_001.jpg │ │ └── 7_001.jpg │ ├── p26 │ │ ├── 1.jpg │ │ ├── 2.jpg │ │ ├── 3.jpg │ │ ├── 5.jpg │ │ ├── 7.jpg │ │ ├── 10_001.jpg │ │ ├── 4_001.jpg │ │ ├── 6_001.jpg │ │ ├── 8_001.jpg │ │ └── 9_001.jpg │ ├── p27 │ │ ├── 1.jpg │ │ ├── 2.jpg │ │ ├── 3.jpg │ │ ├── 4.jpg │ │ ├── 5.jpg │ │ ├── 6.jpg │ │ ├── 7.jpg │ │ ├── 8.jpg │ │ ├── 9.jpg │ │ └── 10.jpg │ ├── p29 │ │ ├── 1.jpg │ │ ├── 4.jpg │ │ ├── 6.jpg │ │ ├── 7.jpg │ │ ├── 8.jpg │ │ ├── 9.jpg │ │ ├── 10_001.jpg │ │ ├── 2_001.jpg │ │ ├── 3_001.jpg │ │ └── 5_001.jpg │ ├── p3 │ │ ├── 1.jpg │ │ ├── 10.jpg │ │ ├── 2.jpg │ │ ├── 3.jpg │ │ ├── 4.jpg │ │ ├── 5.jpg │ │ ├── 7.jpg │ │ ├── 8.jpg │ │ ├── 9.jpg │ │ └── 6_001.jpg │ ├── p30 │ │ ├── 2.jpg │ │ ├── 3.jpg │ │ ├── 5.jpg │ │ ├── 10.jpg │ │ ├── 1_001.jpg │ │ ├── 4_001.jpg │ │ ├── 6_001.jpg │ │ ├── 7_001.jpg │ │ ├── 8_001.jpg │ │ └── 9_001.jpg │ ├── p31 │ │ ├── 5.jpg │ │ ├── 6.jpg │ │ ├── 10.jpg │ │ ├── 1_001.jpg │ │ ├── 2_001.jpg │ │ ├── 3_001.jpg │ │ ├── 4_001.jpg │ │ ├── 7_001.jpg │ │ ├── 8_001.jpg │ │ └── 9_001.jpg │ ├── p32 │ │ ├── 1.jpg │ │ ├── 2.jpg │ │ ├── 3.jpg │ │ ├── 4.jpg │ │ ├── 7.jpg │ │ ├── 8.jpg │ │ ├── 9.jpg │ │ ├── 10_001.jpg │ │ ├── 5_001.jpg │ │ └── 6_001.jpg │ ├── p33 │ │ ├── 1.jpg │ │ ├── 2.jpg │ │ ├── 3.jpg │ │ ├── 4.jpg │ │ ├── 5.jpg │ │ ├── 6.jpg │ │ ├── 7.jpg │ │ ├── 8.jpg │ │ ├── 9.jpg │ │ └── 10.jpg │ ├── p35 │ │ ├── 1.jpg │ │ ├── 2.jpg │ │ ├── 3.jpg │ │ ├── 4.jpg │ │ ├── 5.jpg │ │ ├── 6.jpg │ │ ├── 7.jpg │ │ ├── 8.jpg │ │ ├── 9.jpg │ │ └── 10.jpg │ ├── p37 │ │ ├── 1.jpg │ │ ├── 2.jpg │ │ ├── 3.jpg │ │ ├── 4.jpg │ │ ├── 5.jpg │ │ ├── 7.jpg │ │ ├── 8.jpg │ │ ├── 9.jpg │ │ ├── 10_001.jpg │ │ └── 6_001.jpg │ ├── p38 │ │ ├── 6.jpg │ │ ├── 10.jpg │ │ ├── 1_001.jpg │ │ ├── 2_001.jpg │ │ ├── 3_001.jpg │ │ ├── 4_001.jpg │ │ ├── 5_001.jpg │ │ ├── 7_001.jpg │ │ ├── 8_001.jpg │ │ └── 9_001.jpg │ ├── p4 │ │ ├── 6.jpg │ │ ├── 10_001.jpg │ │ ├── 1_001.jpg │ │ ├── 2_001.jpg │ │ ├── 3_001.jpg │ │ ├── 4_001.jpg │ │ ├── 5_001.jpg │ │ ├── 7_001.jpg │ │ ├── 8_001.jpg │ │ └── 9_001.jpg │ ├── p6 │ │ ├── 1.jpg │ │ ├── 10.jpg │ │ ├── 2.jpg │ │ ├── 3.jpg │ │ ├── 4.jpg │ │ ├── 5.jpg │ │ ├── 6.jpg │ │ ├── 7.jpg │ │ ├── 8.jpg │ │ └── 9.jpg │ ├── p7 │ │ ├── 6.jpg │ │ ├── 10_001.jpg │ │ ├── 1_001.jpg │ │ ├── 2_001.jpg │ │ ├── 3_001.jpg │ │ ├── 4_001.jpg │ │ ├── 5_001.jpg │ │ ├── 7_001.jpg │ │ ├── 8_001.jpg │ │ └── 9_001.jpg │ ├── p8 │ │ ├── 1.jpg │ │ ├── 10.jpg │ │ ├── 2.jpg │ │ ├── 3.jpg │ │ ├── 4.jpg │ │ ├── 5.jpg │ │ ├── 6.jpg │ │ ├── 7.jpg │ │ ├── 8.jpg │ │ └── 9.jpg │ ├── p9 │ │ ├── 1.jpg │ │ ├── 10.jpg │ │ ├── 2.jpg │ │ ├── 3.jpg │ │ ├── 4.jpg │ │ ├── 5.jpg │ │ ├── 7.jpg │ │ ├── 8.jpg │ │ ├── 9.jpg │ │ └── 6_001.jpg │ ├── p28 │ │ ├── 10_001.jpg │ │ ├── 1_001.jpg │ │ ├── 2_001.jpg │ │ ├── 3_001.jpg │ │ ├── 4_001.jpg │ │ ├── 5_001.jpg │ │ ├── 6_001.jpg │ │ ├── 7_001.jpg │ │ ├── 8_001.jpg │ │ └── 9_001.jpg │ ├── p34 │ │ ├── 10_001.jpg │ │ ├── 1_001.jpg │ │ ├── 2_001.jpg │ │ ├── 3_001.jpg │ │ ├── 4_001.jpg │ │ ├── 5_001.jpg │ │ ├── 6_001.jpg │ │ ├── 7_001.jpg │ │ ├── 8_001.jpg │ │ └── 9_001.jpg │ ├── p36 │ │ ├── 10_001.jpg │ │ ├── 1_001.jpg │ │ ├── 2_001.jpg │ │ ├── 3_001.jpg │ │ ├── 4_001.jpg │ │ ├── 5_001.jpg │ │ ├── 6_001.jpg │ │ ├── 7_001.jpg │ │ ├── 8_001.jpg │ │ └── 9_001.jpg │ └── p5 │ │ ├── 10_001.jpg │ │ ├── 1_001.jpg │ │ ├── 2_001.jpg │ │ ├── 3_001.jpg │ │ ├── 4_001.jpg │ │ ├── 5_001.jpg │ │ ├── 6_001.jpg │ │ ├── 7_001.jpg │ │ ├── 8_001.jpg │ │ └── 9_001.jpg └── Group │ └── group_image.jpg ├── FaceExtractor ├── ExtractFacesFromImages.py └── image_face_detector_by_folder.py ├── images_to_matrix.py ├── images_matrix_for_2d_square_pca.py ├── dataset.py ├── README.md ├── TwoDPCA.py ├── TwoD_Square_PCA.py ├── PCA.py ├── Face_Recognition.py └── LICENSE /.github/FUNDING.yml: -------------------------------------------------------------------------------- 1 | custom: ['https://www.buymeacoffee.com/parveshmonu'] 2 | -------------------------------------------------------------------------------- /cascades/data/__init__.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | haarcascades = os.path.join(os.path.dirname(__file__), '') 4 | -------------------------------------------------------------------------------- /images/ORL/p1/10.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Parveshdhull/FaceRecognitionUsing-PCA-2D-PCA-And-2D-Square-PCA/HEAD/images/ORL/p1/10.jpg -------------------------------------------------------------------------------- /images/ORL/p1/3.jpg: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/Parveshdhull/FaceRecognitionUsing-PCA-2D-PCA-And-2D-Square-PCA/HEAD/cascades/data/__pycache__/__init__.cpython-36.pyc -------------------------------------------------------------------------------- /images_to_matrix.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | 4 | 5 | class images_to_matrix_class: 6 | 7 | 8 | def __init__(self, images_name, img_width, img_height): 9 | 10 | self.images_name = images_name 11 | self.img_width = img_width 12 | self.img_height = img_height 13 | self.img_size = (img_width * img_height) 14 | 15 | 16 | 17 | def get_matrix(self): 18 | 19 | col = len(self.images_name) 20 | img_mat = np.zeros((self.img_size, col)) 21 | 22 | i = 0 23 | for name in self.images_name: 24 | gray = cv2.imread(name, 0) 25 | gray = cv2.resize(gray, (self.img_height, self.img_width)) 26 | mat = np.asmatrix(gray) 27 | img_mat[:, i] = mat.ravel() 28 | i += 1 29 | return img_mat 30 | -------------------------------------------------------------------------------- /images_matrix_for_2d_square_pca.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | 4 | 5 | class images_to_matrix_class_for_two_d: 6 | 7 | 8 | def __init__(self, images_name, img_width, img_height): 9 | 10 | self.images_name = images_name 11 | self.img_width = img_width 12 | self.img_height = img_height 13 | self.img_size = (img_width * img_height) 14 | 15 | 16 | 17 | def get_matrix(self): 18 | 19 | img_mat = np.zeros( 20 | (len(self.images_name), self.img_height ,self.img_width), 21 | dtype=np.uint8) 22 | 23 | i = 0 24 | for name in self.images_name: 25 | # print("Name", name) 26 | gray = cv2.imread(name, 0) 27 | gray = cv2.resize(gray, (self.img_height, self.img_width)) 28 | mat = np.asmatrix(gray) 29 | img_mat[i, :, :] = mat 30 | i += 1 31 | print("Matrix Size:", img_mat.shape) 32 | return img_mat 33 | -------------------------------------------------------------------------------- /dataset.py: -------------------------------------------------------------------------------- 1 | 2 | import os 3 | 4 | 5 | class dataset_class: 6 | 7 | def __init__(self, required_no): 8 | 9 | #Dataset Name 10 | self.dir = ("images/ORL") 11 | 12 | self.images_name_for_train = [] 13 | self.target_name_as_array= [] 14 | self.target_name_as_set = {} 15 | self.y_for_train = [] 16 | self.no_of_elements_for_train = [] 17 | 18 | self.images_name_for_test = [] 19 | self.y_for_test = [] 20 | self.no_of_elements_for_test = [] 21 | 22 | 23 | per_no = 0 24 | for name in os.listdir(self.dir): 25 | dir_path = os.path.join(self.dir, name) 26 | if os.path.isdir(dir_path): 27 | if len(os.listdir(dir_path)) >= required_no: 28 | i = 0 29 | for img_name in os.listdir(dir_path): 30 | img_path = os.path.join(dir_path, img_name) 31 | 32 | 33 | if i < required_no: 34 | self.images_name_for_train += [img_path] 35 | self.y_for_train += [per_no] 36 | if len(self.no_of_elements_for_train) > per_no: 37 | self.no_of_elements_for_train[per_no] += 1 38 | else: 39 | self.no_of_elements_for_train += [1] 40 | 41 | if i is 0: 42 | self.target_name_as_array += [name] 43 | self.target_name_as_set[per_no] = name 44 | 45 | else: 46 | self.images_name_for_test += [img_path] 47 | self.y_for_test += [per_no] 48 | if len(self.no_of_elements_for_test) > per_no: 49 | self.no_of_elements_for_test[per_no] += 1 50 | else: 51 | self.no_of_elements_for_test += [1] 52 | 53 | 54 | 55 | i += 1 56 | per_no += 1 57 | 58 | 59 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # FaceRecognitionUsing-PCA-2D-PCA-And-2D-Square-PCA 2 | Implementation of PCA/2D-PCA/2D(Square)-PCA in Python for recognizing Faces: 3 | 1. Single Person Image 4 | 2. Group Image 5 | 3. Recognize Face In Video 6 | # Accuracy on ORL dataset 7 | - PCA(93.42%) 8 | - 2D-PCA(96.05%) 9 | - 2D(Square)-PCA(97.36%) 10 | # Requirements 11 | 1. numpy 12 | 2. opencv 13 | 3. scipy 14 | # Usage 15 | 1. In Face_Recognition class use algo_type from (pca, 2d-pca, 2d2-pca) 16 | 2. In Face_Recognition class use reco_type as 17 | - for single image = 0 18 | - for video = 1 19 | - for group image = 2 20 | 3. The project uses ORL dataset, You can put your dataset in the images folder and change the name of the dataset in the dataset.py file 21 | (You can create new dataset by extracting faces using FaceExtractor provided) 22 | 4. Run Face_Recognition and Enjoy Project 23 | 24 | # Videos 25 | - [How to run my Face Recognition Project](https://www.youtube.com/watch?v=kJFiKRu5yIc) 26 | 27 | - [PCA-Face Recognition(Principal Component Analysis) Playlist](https://www.youtube.com/playlist?list=PLgWKOWHJlDUM_cog-ujJgYoRCJ6LcAhtU) 28 | 29 | - [2D-PCA-Face Recognition(Two Dimensional Principal Component Analysis) Playlist](https://www.youtube.com/playlist?list=PLgWKOWHJlDUOgp8mj5pnTmA9j_i3RRJbD) 30 | 31 | - [2D(Square)-PCA-Face Recognition(Two Directional Two Dimensional Principal Component Analysis) Playlist](https://www.youtube.com/playlist?list=PLgWKOWHJlDUNNtPxO82kJyotdGtxTzzoX) 32 | 33 | - [How to detect Face Mask in Video or Image using PCA](https://www.youtube.com/watch?v=6R8EgLI9Ovg) 34 | 35 | - [A Complete Understanding of PCA(Principal Component Analysis)](https://www.youtube.com/watch?v=NP5zbFLcQYM) 36 | 37 | - [A Complete Understanding of Singular Value Decomposition](https://www.youtube.com/watch?v=GnZQ704UDvM) 38 | 39 | ## Liked my work? 40 | Buy Me A Coffee 41 | 42 | ## Websites 43 | https://github.com/Parveshdhull 44 |
https://twitter.com/ParveshMonu 45 |
https://youtube.com/right2trick 46 | -------------------------------------------------------------------------------- /FaceExtractor/image_face_detector_by_folder.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | import os 4 | from os.path import dirname, abspath 5 | 6 | 7 | class face_extractor: 8 | 9 | def __init__(self): 10 | 11 | d = dirname(dirname(abspath(__file__))) 12 | self.face_cascade1 = cv2.CascadeClassifier(d + '/cascades/data/haarcascade_frontalface_alt2.xml') 13 | self.face_cascade2 = cv2.CascadeClassifier(d + '/cascades/data/haarcascade_frontalface_alt.xml') 14 | self.face_cascade3 = cv2.CascadeClassifier(d + '/cascades/data/haarcascade_profileface.xml') 15 | self.min_width = 2000 16 | self.min_height = 2000 17 | 18 | self.dir = d + r'/Persons' # Set Images Folders Location, It will find images in all folders 19 | print("Location:", self.dir) 20 | self.faces = [] 21 | self.names = [] 22 | 23 | 24 | def find_faces(self): 25 | 26 | for name in os.listdir(self.dir): 27 | temp_dir = os.path.join(self.dir, name) 28 | if os.path.isdir(temp_dir): 29 | if name in ["Cropped", "Originals", "Group Photos"]: # Folders to execulde 30 | continue 31 | for file_name in os.listdir(temp_dir): 32 | temp = temp_dir + "/" +file_name 33 | temp2 = temp_dir + "/" + "_cropped" + file_name 34 | img = cv2.imread(temp) 35 | gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 36 | faces = self.face_cascade1.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=3) 37 | if len(faces) == 0: 38 | faces = self.face_cascade2.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=3) 39 | if len(faces) == 0: 40 | faces = self.face_cascade3.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=3) 41 | if len(faces) == 0: 42 | continue 43 | i = 0 44 | for face in faces: 45 | x, y, w, h = face 46 | cropped = gray[y: y + h, x: x + w] 47 | if i == 0: 48 | self.names += [temp2] 49 | i += 1 50 | else: 51 | self.names += [temp2 + str(i) + ".png"] 52 | i += 1 53 | self.faces += [cropped] 54 | if self.min_width > w: 55 | self.min_width = w 56 | if self.min_height > h: 57 | self.min_height = h 58 | 59 | i = 0 60 | for face in self.faces: 61 | print("Face", self.names[i]) 62 | resized_image = cv2.resize(face, (228, 228)) 63 | cv2.imwrite(self.names[i], resized_image) 64 | i += 1 65 | 66 | self.faces = [] 67 | self.names = [] 68 | -------------------------------------------------------------------------------- /TwoDPCA.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2 3 | import scipy.linalg as s_linalg 4 | 5 | 6 | class two_d_pca_class: 7 | 8 | #function used for finding value of p for covering 95% of image information 9 | def give_p(self, d): 10 | 11 | #sum of all eigen values 12 | sum = np.sum(d) 13 | sum_95 = 0.95 * sum 14 | temp = 0 15 | p = 0 16 | while temp < sum_95: 17 | temp += d[p] 18 | p += 1 19 | return p 20 | 21 | def reduce_dim(self): 22 | 23 | no_of_images = self.images.shape[0] 24 | mat_height = self.images.shape[1] 25 | 26 | #creating emptymatrix for find covarience matrix 27 | g_t = np.zeros((mat_height, mat_height)) 28 | 29 | for i in range(no_of_images): 30 | 31 | #multiplying net subtracted image with its transpose and adding in gt 32 | temp = np.dot(self.images_mean_subtracted[i].T, self.images_mean_subtracted[i]) 33 | g_t += temp 34 | 35 | #dividing by total number of images 36 | g_t /= no_of_images 37 | 38 | #finding eigen values and eigen vectors 39 | d_mat, p_mat = np.linalg.eig(g_t) 40 | 41 | #finding first p important vectors 42 | p = self.give_p(d_mat) 43 | self.new_bases = p_mat[:, 0:p] 44 | 45 | #finding new coordinates using dot product new bases 46 | self.new_coordinates = np.dot(self.images, self.new_bases) 47 | 48 | #returning new coordinates matrix 49 | return self.new_coordinates 50 | 51 | 52 | def __init__(self, images, y, target_names): 53 | self.images = np.asarray(images) 54 | self.y = y 55 | self.target_names = target_names 56 | 57 | #finding means of image 58 | self.mean_face = np.mean(self.images, 0) 59 | 60 | #subtracting mean face from images 61 | self.images_mean_subtracted = self.images - self.mean_face 62 | 63 | 64 | def original_data(self, new_coordinates): 65 | return (np.dot(new_coordinates, self.new_bases.T)) 66 | 67 | 68 | def new_cord(self, name, img_height, img_width): 69 | img = cv2.imread(name, 0) 70 | cv2.imshow("Recognize Image", img) 71 | cv2.waitKey() 72 | gray = cv2.resize(img, (img_height, img_width)) 73 | return np.dot(gray, self.new_bases) 74 | 75 | def new_cord_for_image(self, image): 76 | return np.dot(image, self.new_bases) 77 | 78 | 79 | def recognize_face(self, new_cord): 80 | no_of_images = len(self.y) 81 | distances = [] 82 | for i in range(no_of_images): 83 | temp_imgs = self.new_coordinates[i] 84 | dist = np.linalg.norm(new_cord - temp_imgs) 85 | distances += [dist] 86 | 87 | print("Distances", distances) 88 | min = np.argmin(distances) 89 | per = self.y[min] 90 | per_name = self.target_names[per] 91 | 92 | print("Person", per, ":", min, self.target_names[per], "Dist:", distances[min]) 93 | return per_name 94 | 95 | 96 | 97 | 98 | 99 | 100 | -------------------------------------------------------------------------------- /TwoD_Square_PCA.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2 3 | import scipy.linalg as s_linalg 4 | 5 | 6 | class two_d_square_pca_class: 7 | 8 | 9 | def give_p(self, d): 10 | print("D", d) 11 | sum = np.sum(d) 12 | sum_85 = 0.95 * sum 13 | temp = 0 14 | p = 0 15 | while temp < sum_85: 16 | temp += d[p] 17 | p += 1 18 | return p 19 | 20 | def reduce_dim(self): 21 | 22 | no_of_images = self.images.shape[0] 23 | mat_height = self.images.shape[1] 24 | mat_width = self.images.shape[2] 25 | g_t = np.zeros((mat_height, mat_height)) 26 | h_t = np.zeros((mat_width, mat_width)) 27 | 28 | for i in range(no_of_images): 29 | temp = np.dot(self.images_mean_subtracted[i].T, self.images_mean_subtracted[i]) 30 | g_t += temp 31 | h_t += np.dot(self.images_mean_subtracted[i], self.images_mean_subtracted[i].T) 32 | 33 | g_t /= no_of_images 34 | h_t /= no_of_images 35 | 36 | #For G_T 37 | d_mat, p_mat = np.linalg.eig(g_t) 38 | p_1 = self.give_p(d_mat) 39 | self.new_bases_gt = p_mat[:, 0:p_1] 40 | 41 | #For H_T 42 | d_mat, p_mat = np.linalg.eig(h_t) 43 | p_2 = self.give_p(d_mat) 44 | self.new_bases_ht = p_mat[:, 0:p_2] 45 | 46 | 47 | new_coordinates_temp = np.dot(self.images, self.new_bases_gt) 48 | 49 | self.new_coordinates = np.zeros((no_of_images, p_2, p_1)) 50 | 51 | for i in range(no_of_images): 52 | self.new_coordinates[i, :, :] = np.dot(self.new_bases_ht.T, new_coordinates_temp[i]) 53 | 54 | return self.new_coordinates 55 | 56 | 57 | def __init__(self, images, y, target_names): 58 | self.images = np.asarray(images) 59 | self.y = y 60 | self.target_names = target_names 61 | self.mean_face = np.mean(self.images, 0) 62 | self.images_mean_subtracted = self.images - self.mean_face 63 | 64 | 65 | def original_data(self, new_coordinates): 66 | return np.dot(self.new_bases_ht, np.dot(new_coordinates, self.new_bases_gt.T)) 67 | 68 | def new_cord(self, name, img_height, img_width): 69 | img = cv2.imread(name, 0) 70 | cv2.imshow("Recognize Image",img) 71 | cv2.waitKey() 72 | gray = cv2.resize(img, (img_height, img_width)) 73 | return np.dot(self.new_bases_ht.T, np.dot(gray, self.new_bases_gt)) 74 | 75 | def new_cord_for_image(self, image): 76 | return np.dot(self.new_bases_ht.T, np.dot(gray, self.new_bases_gt)) 77 | 78 | 79 | 80 | 81 | def recognize_face(self, new_cord): 82 | 83 | no_of_images = len(self.y) 84 | distances = [] 85 | for i in range(no_of_images): 86 | temp_imgs = self.new_coordinates[i] 87 | dist = np.linalg.norm(new_cord - temp_imgs) 88 | distances += [dist] 89 | 90 | min = np.argmin(distances) 91 | per = self.y[min] 92 | per_name = self.target_names[per] 93 | if distances[min] < 14975: 94 | print("Person", per, ":", min, self.target_names[per], "Dist:", distances[min]) 95 | return per_name 96 | else: 97 | print("Person", per, ":", min, 'Unknown', "Dist:", distances[min]) 98 | return 'Un' 99 | 100 | 101 | 102 | 103 | 104 | 105 | -------------------------------------------------------------------------------- /PCA.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2 3 | import scipy.linalg as s_linalg 4 | 5 | 6 | class pca_class: 7 | 8 | 9 | def give_p(self, d): 10 | sum = np.sum(d) 11 | sum_85 = self.quality_percent * sum/100 12 | temp = 0 13 | p = 0 14 | while temp < sum_85: 15 | temp += d[p] 16 | p += 1 17 | return p 18 | 19 | def reduce_dim(self): 20 | 21 | p, d, q = s_linalg.svd(self.images, full_matrices=True) 22 | p_matrix = np.matrix(p) 23 | d_diag = np.diag(d) 24 | q_matrix = np.matrix(q) 25 | p = self.give_p(d) 26 | self.new_bases = p_matrix[:, 0:p] 27 | self.new_coordinates = np.dot(self.new_bases.T, self.images) 28 | return self.new_coordinates.T 29 | 30 | 31 | def __init__(self, images, y, target_names, no_of_elements, quality_percent): 32 | self.no_of_elements = no_of_elements 33 | self.images = np.asarray(images) 34 | self.y = y 35 | self.target_names = target_names 36 | mean = np.mean(self.images, 1) 37 | self.mean_face = np.asmatrix(mean).T 38 | self.images = self.images - self.mean_face 39 | self.quality_percent = quality_percent 40 | 41 | def original_data(self, new_coordinates): 42 | return self.mean_face + (np.dot(self.new_bases, new_coordinates.T)) 43 | 44 | 45 | def show_eigen_face(self, height, width, min_pix_int, max_pix_int, eig_no): 46 | ev = self.new_bases[:, eig_no:eig_no + 1] 47 | min_orig = np.min(ev) 48 | max_orig = np.max(ev) 49 | ev = min_pix_int + (((max_pix_int - min_pix_int)/(max_orig - min_orig)) * ev) 50 | ev_re = np.reshape(ev, (height, width)) 51 | cv2.imshow("Eigen Face " + str(eig_no), cv2.resize(np.array(ev_re, dtype = np.uint8),(200, 200))) 52 | cv2.waitKey() 53 | 54 | def new_cord(self, name, img_height, img_width): 55 | img = cv2.imread(name) 56 | gray = cv2.resize(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), (img_height, img_width)) 57 | img_vec = np.asmatrix(gray).ravel() 58 | img_vec = img_vec.T 59 | new_mean = ((self.mean_face * len(self.y)) + img_vec)/(len(self.y) + 1) 60 | img_vec = img_vec - new_mean 61 | return np.dot(self.new_bases.T, img_vec) 62 | 63 | def new_cord_for_image(self, image): 64 | img_vec = np.asmatrix(image).ravel() 65 | img_vec = img_vec.T 66 | new_mean = ((self.mean_face * len(self.y)) + img_vec) / (len(self.y) + 1) 67 | img_vec = img_vec - new_mean 68 | return np.dot(self.new_bases.T, img_vec) 69 | 70 | def recognize_face(self, new_cord_pca, k=0): 71 | classes = len(self.no_of_elements) 72 | start = 0 73 | distances = [] 74 | for i in range(classes): 75 | temp_imgs = self.new_coordinates[:, int(start): int(start + self.no_of_elements[i])] 76 | mean_temp = np.mean(temp_imgs, 1) 77 | start = start + self.no_of_elements[i] 78 | dist = np.linalg.norm(new_cord_pca - mean_temp) 79 | distances += [dist] 80 | min = np.argmin(distances) 81 | 82 | #Temp Threshold 83 | threshold = 100000 84 | if distances[min] < threshold: 85 | print("Person", k, ":", min, self.target_names[min]) 86 | return self.target_names[min] 87 | else: 88 | print("Person", k, ":", min, 'Unknown') 89 | return 'Unknown' 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | -------------------------------------------------------------------------------- /Face_Recognition.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import time 3 | import numpy as np 4 | 5 | # importing algorithms 6 | from PCA import pca_class 7 | from TwoDPCA import two_d_pca_class 8 | from TwoD_Square_PCA import two_d_square_pca_class 9 | 10 | # importing feature extraction classes 11 | from images_to_matrix import images_to_matrix_class 12 | from images_matrix_for_2d_square_pca import images_to_matrix_class_for_two_d 13 | from dataset import dataset_class 14 | 15 | # Algo Type (pca, 2d-pca, 2d2-pca) 16 | algo_type = "pca" 17 | 18 | 19 | #for single image = 0 20 | #for video = 1 21 | #for group image = 2 22 | reco_type = 0 23 | 24 | #No of images For Training(Left will be used as testing Image) 25 | no_of_images_of_one_person = 8 26 | dataset_obj = dataset_class(no_of_images_of_one_person) 27 | 28 | 29 | #Data For Training 30 | images_names = dataset_obj.images_name_for_train 31 | y = dataset_obj.y_for_train 32 | no_of_elements = dataset_obj.no_of_elements_for_train 33 | target_names = dataset_obj.target_name_as_array 34 | 35 | #Data For Testing 36 | images_names_for_test = dataset_obj.images_name_for_test 37 | y_for_test = dataset_obj.y_for_test 38 | no_of_elements_for_test = dataset_obj.no_of_elements_for_test 39 | 40 | 41 | training_start_time = time.process_time() 42 | img_width, img_height = 50, 50 43 | 44 | if algo_type == "pca": 45 | i_t_m_c = images_to_matrix_class(images_names, img_width, img_height) 46 | else: 47 | i_t_m_c = images_to_matrix_class_for_two_d(images_names, img_width, img_height) 48 | 49 | scaled_face = i_t_m_c.get_matrix() 50 | 51 | if algo_type == "pca": 52 | cv2.imshow("Original Image" , cv2.resize(np.array(np.reshape(scaled_face[:,1],[img_height, img_width]), dtype = np.uint8),(200, 200))) 53 | cv2.waitKey() 54 | else: 55 | cv2.imshow("Original Image" , cv2.resize(scaled_face[0],(200, 200))) 56 | cv2.waitKey() 57 | 58 | #Algo 59 | if algo_type == "pca": 60 | my_algo = pca_class(scaled_face, y, target_names, no_of_elements, 90) 61 | elif algo_type == "2d-pca": 62 | my_algo = two_d_pca_class(scaled_face, y, target_names) 63 | else: 64 | my_algo = two_d_square_pca_class(scaled_face, y, target_names) 65 | 66 | 67 | new_coordinates = my_algo.reduce_dim() 68 | if algo_type == "pca": 69 | my_algo.show_eigen_face(img_width, img_height, 50, 150, 0) 70 | 71 | if algo_type == "pca": 72 | cv2.imshow("After PCA Image", cv2.resize(np.array(np.reshape(my_algo.original_data(new_coordinates[1, :]), [img_height, img_width]), dtype = np.uint8), (200, 200))) 73 | cv2.waitKey() 74 | else: 75 | cv2.imshow("After PCA Image", cv2.resize(np.array(my_algo.original_data(new_coordinates[0]), dtype = np.uint8), (200, 200))) 76 | cv2.waitKey() 77 | 78 | 79 | training_time = time.process_time() - training_start_time 80 | 81 | 82 | #Reco 83 | if reco_type == 0: 84 | time_start = time.process_time() 85 | 86 | correct = 0 87 | wrong = 0 88 | i = 0 89 | net_time_of_reco = 0 90 | 91 | for img_path in images_names_for_test: 92 | 93 | time_start = time.process_time() 94 | find_name = my_algo.recognize_face(my_algo.new_cord(img_path, img_height, img_width)) 95 | time_elapsed = (time.process_time() - time_start) 96 | net_time_of_reco += time_elapsed 97 | rec_y = y_for_test[i] 98 | rec_name = target_names[rec_y] 99 | if find_name is rec_name: 100 | correct += 1 101 | print("Correct", " Name:", find_name) 102 | else: 103 | wrong +=1 104 | print("Wrong:", " Real Name:", rec_name, "Rec Y:", rec_y, "Find Name:", find_name) 105 | i+=1 106 | 107 | print("Correct", correct) 108 | print("Wrong", wrong) 109 | print("Total Test Images", i) 110 | print("Percent", correct/i*100) 111 | print("Total Person", len(target_names)) 112 | print("Total Train Images", no_of_images_of_one_person * len(target_names)) 113 | print("Total Time Taken for reco:", time_elapsed) 114 | print("Time Taken for one reco:", time_elapsed/i) 115 | print("Training Time", training_time) 116 | 117 | 118 | 119 | #For Video 120 | 121 | if reco_type == 1: 122 | face_cascade = cv2.CascadeClassifier('cascades/data/haarcascade_frontalface_alt2.xml') 123 | 124 | cap = cv2.VideoCapture(0) 125 | 126 | while True: 127 | ret, frame = cap.read() 128 | 129 | gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) 130 | 131 | 132 | faces = face_cascade.detectMultiScale(gray, scaleFactor=1.5, minNeighbors=7) 133 | 134 | i = 0 135 | for(x, y, w, h) in faces: 136 | roi_gray = gray[y:y+h, x:x+w] 137 | scaled = cv2.resize(roi_gray, (img_height, img_width)) 138 | rec_color = (255, 0, 0) 139 | rec_stroke = 2 140 | cv2.rectangle(frame, (x, y), (x+w, y+h), rec_color, rec_stroke) 141 | 142 | new_cord = my_algo.new_cord_for_image(scaled) 143 | name = my_algo.recognize_face(new_cord) 144 | font = cv2.FONT_HERSHEY_SIMPLEX 145 | font_color = (255, 255, 255) 146 | font_stroke = 2 147 | cv2.putText(frame, name + str(i), (x, y), font, 1, font_color, font_stroke, cv2.LINE_AA) 148 | i += 1 149 | 150 | 151 | 152 | cv2.imshow('Colored Frame', frame) 153 | if cv2.waitKey(20) & 0xFF == ord('q'): 154 | break 155 | 156 | 157 | cap.release() 158 | cv2.destroyAllWindows() 159 | 160 | 161 | #For Image 162 | 163 | if reco_type == 2: 164 | face_cascade = cv2.CascadeClassifier('cascades/data/haarcascade_frontalface_alt2.xml') 165 | dir = r'images/Group/' 166 | 167 | 168 | frame = cv2.imread(dir+ "group_image.jpg") 169 | 170 | 171 | gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) 172 | 173 | 174 | faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=3) 175 | 176 | 177 | i = 0 178 | 179 | for(x, y, w, h) in faces: 180 | roi_gray = gray[y:y+h, x:x+w] 181 | scaled = cv2.resize(roi_gray, (img_height, img_width)) 182 | rec_color = (0, 255, 0) 183 | rec_stroke = 5 184 | cv2.rectangle(frame, (x, y), (x+w, y+h), rec_color, rec_stroke) 185 | 186 | new_cord = my_algo.new_cord_for_image(scaled) 187 | print("New Cord PCA"+str(i), new_cord) 188 | name = my_algo.recognize_face(new_cord) 189 | font = cv2.FONT_HERSHEY_SIMPLEX 190 | font_color = (255, 0, 0) 191 | font_stroke = 5 192 | cv2.putText(frame, name + str(i), (x, y), font, 8, font_color, font_stroke, cv2.LINE_AA) 193 | i += 1 194 | # cv2.imshow('Face', scaled) 195 | # cv2.waitKey() 196 | 197 | 198 | frame = cv2.resize(frame, (1080, 568)) 199 | cv2.imshow('Colored Frame', frame) 200 | cv2.waitKey() 201 | 202 | 203 | 204 | 205 | 206 | 207 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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-------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 64 16 7 | 8 | <_> 9 | 10 | 11 | <_> 12 | 13 | <_> 14 | 15 | 16 | 17 | <_> 18 | 32 2 8 6 -1. 19 | <_> 20 | 32 4 8 2 3. 21 | 0 22 | 1.6915600746870041e-002 23 | -9.5547717809677124e-001 24 | 8.9129137992858887e-001 25 | <_> 26 | 27 | <_> 28 | 29 | 30 | 31 | <_> 32 | 0 4 6 10 -1. 33 | <_> 34 | 3 4 3 10 2. 35 | 0 36 | 2.4228349328041077e-002 37 | -9.2089319229125977e-001 38 | 8.8723921775817871e-001 39 | <_> 40 | 41 | <_> 42 | 43 | 44 | 45 | <_> 46 | 55 0 8 6 -1. 47 | <_> 48 | 55 0 4 3 2. 49 | <_> 50 | 59 3 4 3 2. 51 | 0 52 | -1.0168660432100296e-002 53 | 8.8940089941024780e-001 54 | -7.7847331762313843e-001 55 | <_> 56 | 57 | <_> 58 | 59 | 60 | 61 | <_> 62 | 44 7 4 9 -1. 63 | <_> 64 | 44 10 4 3 3. 65 | 0 66 | 2.0863260142505169e-003 67 | -8.7998157739639282e-001 68 | 5.8651781082153320e-001 69 | -2.0683259963989258e+000 70 | -1 71 | -1 72 | <_> 73 | 74 | 75 | <_> 76 | 77 | <_> 78 | 79 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1266 | -1. 1267 | <_> 1268 | 1269 | <_> 1270 | 1271 | 1272 | 1273 | <_> 1274 | 8 5 30 9 -1. 1275 | <_> 1276 | 8 8 30 3 3. 1277 | 0 1278 | -7.0475979009643197e-004 1279 | 1. 1280 | -9.9976968765258789e-001 1281 | <_> 1282 | 1283 | <_> 1284 | 1285 | 1286 | 1287 | <_> 1288 | 3 12 33 3 -1. 1289 | <_> 1290 | 14 12 11 3 3. 1291 | 0 1292 | 2.1271279547363520e-003 1293 | -9.9694627523422241e-001 1294 | 1.0002720355987549e+000 1295 | <_> 1296 | 1297 | <_> 1298 | 1299 | 1300 | 1301 | <_> 1302 | 0 0 3 2 -1. 1303 | <_> 1304 | 1 0 1 2 3. 1305 | 0 1306 | -2.4224430671893060e-004 1307 | 1. 1308 | -1. 1309 | <_> 1310 | 1311 | <_> 1312 | 1313 | 1314 | 1315 | <_> 1316 | 46 4 3 8 -1. 1317 | <_> 1318 | 47 4 1 8 3. 1319 | 0 1320 | 7.4700301047414541e-004 1321 | -9.9108231067657471e-001 1322 | 9.9941182136535645e-001 1323 | -1.0991690158843994e+000 1324 | 13 1325 | -1 1326 | <_> 1327 | 1328 | 1329 | <_> 1330 | 1331 | <_> 1332 | 1333 | 1334 | 1335 | <_> 1336 | 1 2 6 5 -1. 1337 | <_> 1338 | 3 2 2 5 3. 1339 | 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1405 | --------------------------------------------------------------------------------