├── .idea
    ├── Face Detection.iml
    ├── misc.xml
    ├── modules.xml
    └── workspace.xml
├── AddNewFace.py
├── LICENSE
├── PrepareDataset.py
├── README.md
├── RecogniseFace.py
├── __pycache__
    ├── AddNewFace.cpython-36.pyc
    └── PrepareDataset.cpython-36.pyc
├── _config.yml
├── people
    ├── mahatma gandhi
    │   ├── 1.jpg
    │   ├── 10.jpg
    │   ├── 100.jpg
    │   ├── 101.jpg
    │   ├── 102.jpg
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    │   ├── 3.jpg
    │   ├── 30.jpg
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    │   ├── 4.jpg
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    │   ├── 6.jpg
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    │   ├── 8.jpg
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    │   ├── 9.jpg
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    │   ├── 96.jpg
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    │   ├── 98.jpg
    │   └── 99.jpg
    └── unknown
    │   ├── 1.jpg
    │   ├── 10.jpg
    │   ├── 100.jpg
    │   ├── 101.jpg
    │   ├── 102.jpg
    │   ├── 103.jpg
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    │   ├── 109.jpg
    │   ├── 11.jpg
    │   ├── 110.jpg
    │   ├── 111.jpg
    │   ├── 112.jpg
    │   ├── 113.jpg
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    │   ├── 115.jpg
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    │   ├── 12.jpg
    │   ├── 120.jpg
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    │   ├── 13.jpg
    │   ├── 130.jpg
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    │   ├── 14.jpg
    │   ├── 140.jpg
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    │   ├── 149.jpg
    │   ├── 15.jpg
    │   ├── 150.jpg
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    │   ├── 16.jpg
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    │   ├── 170.jpg
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    │   ├── 18.jpg
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    │   ├── 19.jpg
    │   ├── 190.jpg
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    │   ├── 199.jpg
    │   ├── 2.jpg
    │   ├── 20.jpg
    │   ├── 201.jpg
    │   ├── 202.jpg
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    │   ├── 22.jpg
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    │   ├── 27.jpg
    │   ├── 28.jpg
    │   ├── 29.jpg
    │   ├── 3.jpg
    │   ├── 30.jpg
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    │   ├── 4.jpg
    │   ├── 40.jpg
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    │   ├── 44.jpg
    │   ├── 45.jpg
    │   ├── 46.jpg
    │   ├── 47.jpg
    │   ├── 48.jpg
    │   ├── 49.jpg
    │   ├── 5.jpg
    │   ├── 50.jpg
    │   ├── 51.jpg
    │   ├── 52.jpg
    │   ├── 53.jpg
    │   ├── 54.jpg
    │   ├── 55.jpg
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    │   ├── 6.jpg
    │   ├── 60.jpg
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    │   ├── 7.jpg
    │   ├── 70.jpg
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    │   ├── 8.jpg
    │   ├── 80.jpg
    │   ├── 82.jpg
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    │   ├── 84.jpg
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    │   ├── 86.jpg
    │   ├── 87.jpg
    │   ├── 88.jpg
    │   ├── 89.jpg
    │   ├── 9.jpg
    │   ├── 90.jpg
    │   ├── 91.jpg
    │   ├── 92.jpg
    │   ├── 93.jpg
    │   ├── 94.jpg
    │   ├── 95.jpg
    │   ├── 96.jpg
    │   ├── 97.jpg
    │   ├── 98.jpg
    │   └── 99.jpg
├── requirements.txt
├── svc_linear_face.pkl
└── xml
    └── frontal_face.xml


/.idea/Face Detection.iml:
--------------------------------------------------------------------------------
 1 | <?xml version="1.0" encoding="UTF-8"?>
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10 |     <option name="PROJECT_TEST_RUNNER" value="pytest" />
11 |   </component>
12 | </module>


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/.idea/misc.xml:
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1 | <?xml version="1.0" encoding="UTF-8"?>
2 | <project version="4">
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/.idea/modules.xml:
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1 | <?xml version="1.0" encoding="UTF-8"?>
2 | <project version="4">
3 |   <component name="ProjectModuleManager">
4 |     <modules>
5 |       <module fileurl="file://$PROJECT_DIR$/.idea/Face Detection.iml" filepath="$PROJECT_DIR$/.idea/Face Detection.iml" />
6 |     </modules>
7 |   </component>
8 | </project>


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/.idea/workspace.xml:
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206 |       <option name="PARAMETERS" value="" />
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210 |       <option name="REDIRECT_INPUT" value="false" />
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212 |       <method v="2" />
213 |     </configuration>
214 |     <configuration name="open" type="PythonConfigurationType" factoryName="Python" temporary="true">
215 |       <module name="Face Detection" />
216 |       <option name="INTERPRETER_OPTIONS" value="" />
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218 |       <envs>
219 |         <env name="PYTHONUNBUFFERED" value="1" />
220 |       </envs>
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233 |       <method v="2" />
234 |     </configuration>
235 |     <configuration name="t2s" type="PythonConfigurationType" factoryName="Python" temporary="true">
236 |       <module name="Face Detection" />
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239 |       <envs>
240 |         <env name="PYTHONUNBUFFERED" value="1" />
241 |       </envs>
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281 |       <option name="presentableId" value="Default" />
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--------------------------------------------------------------------------------
/AddNewFace.py:
--------------------------------------------------------------------------------
 1 | from PrepareDataset import *
 2 | import os
 3 | 
 4 | 
 5 | def add_face():
 6 |     cam = cv2.VideoCapture(0)
 7 | 
 8 |     folder = "people/" + input('Person:').lower()
 9 |     try:
10 |         os.mkdir(folder)
11 | 
12 |         flag_start_capturing = False
13 |         sample = 1
14 |         cv2.namedWindow("Face", cv2.WINDOW_AUTOSIZE)
15 | 
16 |         while True:
17 |             ret, frame = cam.read()
18 | 
19 |             faces_coord = detect_face(frame)
20 | 
21 |             if len(faces_coord):
22 |                 faces = normalize_faces(frame, faces_coord)
23 |                 cv2.imwrite(folder + '/' + str(sample) + '.jpg', faces[0])
24 | 
25 |                 if flag_start_capturing:
26 |                     sample += 1
27 | 
28 |             draw_rectangle(frame, faces_coord)
29 |             cv2.imshow('Face', frame)
30 |             keypress = cv2.waitKey(1)
31 | 
32 |             if keypress == ord('c'):
33 | 
34 |                 if not flag_start_capturing:
35 |                     flag_start_capturing = True
36 | 
37 |             if sample > 150:
38 |                 break
39 | 
40 |         cam.release()
41 |         cv2.destroyAllWindows()
42 |     except FileExistsError:
43 |         print("Already exists")
44 | 
45 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2018 Ritik Gupta
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/PrepareDataset.py:
--------------------------------------------------------------------------------
 1 | import cv2
 2 | 
 3 | 
 4 | def detect_face(frame):
 5 | 
 6 |         if frame.ndim != 2:
 7 |             frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
 8 | 
 9 |         detector = cv2.CascadeClassifier("xml/frontal_face.xml")
10 | 
11 |         faces = detector.detectMultiScale(frame, 1.2, 5)
12 | 
13 |         return faces
14 | 
15 | 
16 | def gray_scale(image):
17 |         if image.ndim != 2:
18 |             image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
19 | 
20 |         return image
21 | 
22 | 
23 | def cut_faces(image, faces_coord):
24 |         faces = []
25 | 
26 |         for (x, y, w, h) in faces_coord:
27 |             faces.append(image[y: y + h, x: x + w])
28 | 
29 |         return faces
30 | 
31 | 
32 | def normalize_intensity(images):
33 |         images_norm = []
34 |         for image in images:
35 |             is_color = len(image.shape) == 3
36 |             if is_color:
37 |                 image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
38 |             images_norm.append(cv2.equalizeHist(image))
39 |         return images_norm
40 | 
41 | 
42 | def resize(images, size=(47, 62)):
43 |         image_resize = []
44 | 
45 |         for image in images:
46 |             if image.shape < size:
47 |                 img_size = cv2.resize(image, size, interpolation=cv2.INTER_CUBIC)
48 |             else:
49 |                 img_size = cv2.resize(image, size, interpolation=cv2.INTER_AREA)
50 |             image_resize.append(img_size)
51 | 
52 |         return image_resize
53 | 
54 | 
55 | def normalize_faces(frame, faces_coord):
56 |     gray_frame = gray_scale(frame)
57 |     faces = cut_faces(gray_frame, faces_coord)
58 |     faces = normalize_intensity(faces)
59 | 
60 |     faces = resize(faces)
61 |     return faces
62 | 
63 | 
64 | def draw_rectangle(image, coords):
65 |     for (x, y, w, h) in coords:
66 | 
67 |         cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)
68 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Python face-recognition using open-cv + machine learning
 2 | 
 3 | - This is a simple python face recogniton which can detect multiple faces at the same time.
 4 | - Based on open-cv and sklearn library this is a program that detects face and display name on the window.
 5 | - It has the ability to add a new face to detect. Simply type 'yes' while running the program
 6 | 
 7 | # How to run:
 8 | 
 9 | ## NOTE: If you have Anaconda installed then open "conda prompt". Then:
10 | 
11 | - type "pip install opencv-python" instead of "pip install -r requirements.txt"
12 | ## NOTE2: For linux users make sure you run program with "python3" instead of simply python and "pip3" instead of pip.
13 | 
14 | 
15 | ## Steps
16 | - download all the files to a folder
17 | - open command prompt as administrator
18 | - change directory to that folder (cd directory-full-path)
19 | - pip install -r requirements.txt (it will install all the required modules)
20 | - (for linux use sudo pip install -r requirements.txt)
21 | - run RecogniseFace.py
22 | - follow instructions
23 | - Note: To create dataset press 'c'
24 | - finally press 'Esc' to exit from the window
25 | 
26 | 
27 | 


--------------------------------------------------------------------------------
/RecogniseFace.py:
--------------------------------------------------------------------------------
 1 | import numpy as np
 2 | from PrepareDataset import *
 3 | import AddNewFace
 4 | import cv2
 5 | import os
 6 | from sklearn.decomposition import PCA
 7 | from sklearn.preprocessing import StandardScaler
 8 | from sklearn.svm import SVC
 9 | from sklearn.model_selection import GridSearchCV, KFold
10 | import pickle
11 | 
12 | 
13 | images = []
14 | labels = []
15 | labels_dic = {}
16 | 
17 | choice = input("Do you want to add new face? (Yes or No) ")
18 | if choice == 'yes':
19 |     AddNewFace.add_face()
20 | 
21 | 
22 | def collect_dataset():
23 | 
24 |     people = [person for person in os.listdir("people/")]
25 | 
26 |     for i, person in enumerate(people):
27 |         labels_dic[i] = person
28 |         for image in os.listdir("people/" + person):
29 |             if image.endswith('.jpg'):
30 |                 images.append(cv2.imread("people/" + person + '/' + image, 0))
31 |                 labels.append(i)
32 |     return images, np.array(labels), labels_dic
33 | 
34 | 
35 | images, labels, labels_dic = collect_dataset()
36 | 
37 | X_train = np.asarray(images)
38 | train = X_train.reshape(len(X_train), -1)
39 | 
40 | sc = StandardScaler()
41 | X_train_sc = sc.fit_transform(train.astype(np.float64))
42 | pca1 = PCA(n_components=.97)
43 | new_train = pca1.fit_transform(X_train_sc)
44 | kf = KFold(n_splits=5,shuffle=True)
45 | param_grid = {'C': [.0001, .001, .01, .1, 1, 10]}
46 | gs_svc = GridSearchCV(SVC(kernel='linear', probability=True), param_grid=param_grid, cv=kf, scoring='accuracy')
47 | gs_svc.fit(new_train, labels)
48 | clf = gs_svc.best_estimator_
49 | filename = 'svc_linear_face.pkl'
50 | f = open(filename, 'wb')
51 | pickle.dump(clf, f)
52 | f.close()
53 | 
54 | filename = 'svc_linear_face.pkl'
55 | svc1 = pickle.load(open(filename, 'rb'))
56 | 
57 | cam = cv2.VideoCapture(0)
58 | font = cv2.FONT_HERSHEY_PLAIN
59 | cv2.namedWindow("opencv_face ", cv2.WINDOW_AUTOSIZE)
60 | 
61 | while True:
62 |     ret, frame = cam.read()
63 | 
64 |     faces_coord = detect_face(frame)  # detect more than one face
65 |     if len(faces_coord):
66 |         faces = normalize_faces(frame, faces_coord)
67 | 
68 |         for i, face in enumerate(faces):  # for each detected face
69 | 
70 |             t = face.reshape(1, -1)
71 |             t = sc.transform(t.astype(np.float64))
72 |             test = pca1.transform(t)
73 |             prob = svc1.predict_proba(test)
74 |             confidence = svc1.decision_function(test)
75 |             print(confidence)
76 |             print(prob)
77 | 
78 |             pred = svc1.predict(test)
79 |             print(pred, pred[0])
80 | 
81 |             name = labels_dic[pred[0]].capitalize()
82 |             print(name)
83 | 
84 |             cv2.putText(frame, name, (faces_coord[i][0], faces_coord[i][1] - 10),
85 |                         cv2.FONT_HERSHEY_PLAIN, 2, (66, 53, 243), 2)
86 | 
87 |         draw_rectangle(frame, faces_coord)  # rectangle around face
88 | 
89 |     cv2.putText(frame, "ESC to exit", (5, frame.shape[0] - 5), cv2.FONT_HERSHEY_PLAIN, 1.3, (66, 53, 243), 2,
90 |                 cv2.LINE_AA)
91 | 
92 |     cv2.imshow("opencv_face", frame)  # live feed in external
93 |     if cv2.waitKey(5) == 27:
94 |         break
95 | 
96 | cam.release()
97 | cv2.destroyAllWindows()
98 | 


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2 | numpy
3 | sklearn
4 | scipy
5 | 


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