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Repository: AhmetHamzaEmra/Intelegent_Lock
Branch: master
Commit: 1c52c5e49420
Files: 16
Total size: 59.8 MB
Directory structure:
gitextract_uj0rtyli/
├── .vscode/
│ └── settings.json
├── LICENSE
├── README.md
├── common.py
├── livenessmodel.py
├── main_code.py
├── model/
│ ├── model.h5
│ └── model.json
└── v1/
├── liveness_detection/
│ ├── create_data.py
│ ├── labels.py
│ └── liveness_data.csv
├── main_code.py
└── model/
├── checkpoint
├── my_model.tflearn.data-00000-of-00001
├── my_model.tflearn.index
└── my_model.tflearn.meta
================================================
FILE CONTENTS
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================================================
FILE: .vscode/settings.json
================================================
{
"python.pythonPath": "/Library/Frameworks/Python.framework/Versions/3.5/bin/python3"
}
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2018 Ahmet Hamza Emra
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
# Intelegent_Lock_2.0
As Andrej Karpathy said, Software2.0 is creating revalatrion in the field of software engineering. Now AI is taking most of the heavy lifting in complexity and giving incredible results in the industry. One of this application is locks with face recognition. Although it sounds simple to combine these two technologies, it is more complex than most other applications.
First of all, we have a problem with a dataset. Which is it is very hard to collect more than a couple of pictures of every person that we need to give access. If we can get a couple hundred pictures per person, One can easily train a simple model to do classification. But let us say we have 1000 employees that we want to give access. we need to get ~1.000.000 employee pictures and couple hundred pictures from other people in the World so the model can know if unknown people to lock to the door. On top of this, if we hire anybody else, We would need to train again. Screw that, we need another method. So we need a method that we can just use one picture per person to classify all of them and doesn't need to train when someone changes. That method is Landmark technique. Let's mark specific points in the face. Then we can use this transformation to classify people. Since everybody will have a different distance between two eyes or eyes and notice (except the twins!) we can tread this new measurement as embedding and if they are too close to each other in the multidimensional space we can they are the same person, vice versa. This is kind of an oversimplification, If you want to read more please check this [blog post](https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78) from Adam Geitgey.
Okay, since we solved the face recognition, we have another problem. What happens if someone holds a picture of me to get in. or How can we make sure they are live people and not someone with a mask. So we just need to classify if the input frames contain live people. For this stage, we can use a 3D convolutional network in our model. Why 3D you asked? Cause a frame does not contain some very useful features like eye movement, winkings etc. RNN also would work great but that part on you if you want to try out. (Please don't forget to send it to me 😄)
Now, these two big problems should be solved. Please note that these two methods are not the best but they get the job done, but just keep in mind that I wouldn't use this project on my houses entrance 😅 Just use it in the meeting room or something 😄
### TLDR:
- AI & ML good
- Landmark model for face recognition is crucial
- Liveness is also crucial
- Not the safest lock tho (at least for now)
## What does it do?
The structure of the program is;
1. takes image and checks with pretrained neular network if what it sees, alive human or printout, img from phone etc.
2. if it is more than %95 confidente that it is an alive thing than it checks it if anyone from known people who have access
3. if it is someone the system knows than unlock the door :D
## How to run:
Please install this modules:
```
pip3 install keras, face_recognition, opencv-python
python3 main_code.py
```
## References
* [Software 2.0, Andrej Karpathy](https://medium.com/@karpathy/software-2-0-a64152b37c35)
* [Adam Geitgey](https://medium.com/@ageitgey)
**!WARNING!**
Old version is store in v1 directory. It works faster but you wont get the same result. FYI!
**Important Not:**
Due to containging many of personal pictures of my friends, I cannot share the dataset!
================================================
FILE: common.py
================================================
import face_recognition
from os import listdir
from os.path import isfile, join
from glob import glob
def get_users():
known_names=[]
known_encods=[]
for i in glob("people/*.jpg"):
img = face_recognition.load_image_file(i)
encoding = face_recognition.face_encodings(img)[0]
known_encods.append(encoding)
known_names.append(i[7:-4])
return known_names, known_encods
================================================
FILE: livenessmodel.py
================================================
import numpy as np
import keras
from keras.models import model_from_json
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv3D, MaxPooling3D
from keras import backend as K
def get_liveness_model():
model = Sequential()
model.add(Conv3D(32, kernel_size=(3, 3, 3),
activation='relu',
input_shape=(24,100,100,1)))
model.add(Conv3D(64, (3, 3, 3), activation='relu'))
model.add(MaxPooling3D(pool_size=(2, 2, 2)))
model.add(Conv3D(64, (3, 3, 3), activation='relu'))
model.add(MaxPooling3D(pool_size=(2, 2, 2)))
model.add(Conv3D(64, (3, 3, 3), activation='relu'))
model.add(MaxPooling3D(pool_size=(2, 2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
return model
================================================
FILE: main_code.py
================================================
import face_recognition
import cv2
import numpy as np
from livenessmodel import get_liveness_model
from common import get_users
font = cv2.FONT_HERSHEY_DUPLEX
# Get the liveness network
model = get_liveness_model()
# load weights into new model
model.load_weights("model/model.h5")
print("Loaded model from disk")
# Read the users data and create face encodings
known_names, known_encods = get_users()
video_capture = cv2.VideoCapture(0)
video_capture.set(3, 640)
video_capture.set(4, 480)
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
input_vid = []
while True:
# Grab a single frame of video
if len(input_vid) < 24:
ret, frame = video_capture.read()
liveimg = cv2.resize(frame, (100,100))
liveimg = cv2.cvtColor(liveimg, cv2.COLOR_BGR2GRAY)
input_vid.append(liveimg)
else:
ret, frame = video_capture.read()
liveimg = cv2.resize(frame, (100,100))
liveimg = cv2.cvtColor(liveimg, cv2.COLOR_BGR2GRAY)
input_vid.append(liveimg)
inp = np.array([input_vid[-24:]])
inp = inp/255
inp = inp.reshape(1,24,100,100,1)
pred = model.predict(inp)
input_vid = input_vid[-25:]
if pred[0][0]> .95:
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Only process every other frame of video to save time
if process_this_frame:
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(small_frame)
face_encodings = face_recognition.face_encodings(small_frame, face_locations)
name = "Unknown"
face_names = []
for face_encoding in face_encodings:
for ii in range(len(known_encods)):
# See if the face is a match for the known face(s)
match = face_recognition.compare_faces([known_encods[ii]], face_encoding)
if match[0]:
name = known_names[ii]
face_names.append(name)
process_this_frame = not process_this_frame
unlock = False
for n in face_names:
if n != 'Unknown':
unlock=True
# Display the results
for (top, right, bottom, left), name in zip(face_locations, face_names):
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top *= 4
right *= 4
bottom *= 4
left *= 4
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
if unlock:
cv2.putText(frame, 'UNLOCK', (frame.shape[1]//2, frame.shape[0]//2), font, 1.0, (255, 255, 255), 1)
else:
cv2.putText(frame, 'LOCKED!', (frame.shape[1]//2, frame.shape[0]//2), font, 1.0, (255, 255, 255), 1)
else:
cv2.putText(frame, 'WARNING!', (frame.shape[1]//2, frame.shape[0]//2), font, 1.0, (255, 255, 255), 1)
# Display the liveness score in top left corner
cv2.putText(frame, str(pred[0][0]), (20, 20), font, 1.0, (255, 255, 0), 1)
# Display the resulting image
cv2.imshow('Video', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
================================================
FILE: model/model.json
================================================
{"class_name": "Sequential", "config": [{"class_name": "Conv3D", "config": {"name": "conv3d_1", "trainable": true, "batch_input_shape": [null, 24, 100, 100, 1], "dtype": "float32", "filters": 32, "kernel_size": [3, 3, 3], "strides": [1, 1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1, 1], "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Conv3D", "config": {"name": "conv3d_2", "trainable": true, "filters": 64, "kernel_size": [3, 3, 3], "strides": [1, 1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1, 1], "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling3D", "config": {"name": "max_pooling3d_1", "trainable": true, "pool_size": [2, 2, 2], "padding": "valid", "strides": [2, 2, 2], "data_format": "channels_last"}}, {"class_name": "Conv3D", "config": {"name": "conv3d_3", "trainable": true, "filters": 64, "kernel_size": [3, 3, 3], "strides": [1, 1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1, 1], "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling3D", "config": {"name": "max_pooling3d_2", "trainable": true, "pool_size": [2, 2, 2], "padding": "valid", "strides": [2, 2, 2], "data_format": "channels_last"}}, {"class_name": "Conv3D", "config": {"name": "conv3d_4", "trainable": true, "filters": 64, "kernel_size": [3, 3, 3], "strides": [1, 1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1, 1], "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling3D", "config": {"name": "max_pooling3d_3", "trainable": true, "pool_size": [2, 2, 2], "padding": "valid", "strides": [2, 2, 2], "data_format": "channels_last"}}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "rate": 0.25, "noise_shape": null, "seed": null}}, {"class_name": "Flatten", "config": {"name": "flatten_1", "trainable": true, "data_format": "channels_last"}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "units": 128, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dropout", "config": {"name": "dropout_2", "trainable": true, "rate": 0.5, "noise_shape": null, "seed": null}}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 2, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}], "keras_version": "2.2.0", "backend": "tensorflow"}
================================================
FILE: v1/liveness_detection/create_data.py
================================================
import cv2
import numpy as np
cap = cv2.VideoCapture(0)
xx = 0
while 1:
ret, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if xx < 100:
cv2.imwrite('img-not-live/' +str(900+ xx) + ".jpg", img )
else:
break
cv2.imshow('img',img)
k = cv2.waitKey(30) % 0xff
xx+=1
if k == 27:
break
================================================
FILE: v1/liveness_detection/labels.py
================================================
import pandas as pd
import cv2
images = []
labels = []
for i in range(400):
img = cv2.imread("img-live/" + str(i)+".jpg")
img = cv2.resize(img, (120,120))
images.append(img)
labels.append(0)
for i in range(400):
img = cv2.imread("img-not-live/" + str(i)+".jpg")
img = cv2.resize(img, (120,120))
images.append(img)
labels.append(1)
d = {'images': images, 'labels': labels}
csv = pd.DataFrame(data=d)
csv.to_csv('liveness_data.csv', index=False)
================================================
FILE: v1/liveness_detection/liveness_data.csv
================================================
images,labels
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[11
gitextract_uj0rtyli/
├── .vscode/
│ └── settings.json
├── LICENSE
├── README.md
├── common.py
├── livenessmodel.py
├── main_code.py
├── model/
│ ├── model.h5
│ └── model.json
└── v1/
├── liveness_detection/
│ ├── create_data.py
│ ├── labels.py
│ └── liveness_data.csv
├── main_code.py
└── model/
├── checkpoint
├── my_model.tflearn.data-00000-of-00001
├── my_model.tflearn.index
└── my_model.tflearn.meta
SYMBOL INDEX (2 symbols across 2 files) FILE: common.py function get_users (line 6) | def get_users(): FILE: livenessmodel.py function get_liveness_model (line 10) | def get_liveness_model():
Condensed preview — 16 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (582K chars).
[
{
"path": ".vscode/settings.json",
"chars": 92,
"preview": "{\n \"python.pythonPath\": \"/Library/Frameworks/Python.framework/Versions/3.5/bin/python3\"\n}"
},
{
"path": "LICENSE",
"chars": 1073,
"preview": "MIT License\n\nCopyright (c) 2018 Ahmet Hamza Emra\n\nPermission is hereby granted, free of charge, to any person obtaining "
},
{
"path": "README.md",
"chars": 3770,
"preview": "\n\n\n\n# Intelegent_Lock_2.0\nAs Andrej K"
},
{
"path": "common.py",
"chars": 419,
"preview": "import face_recognition\nfrom os import listdir\nfrom os.path import isfile, join\nfrom glob import glob\n\ndef get_users():\n"
},
{
"path": "livenessmodel.py",
"chars": 932,
"preview": "import numpy as np \nimport keras\nfrom keras.models import model_from_json\nfrom keras.models import Sequential\nfrom keras"
},
{
"path": "main_code.py",
"chars": 4020,
"preview": "\nimport face_recognition\nimport cv2\nimport numpy as np \nfrom livenessmodel import get_liveness_model\nfrom common import "
},
{
"path": "model/model.json",
"chars": 4414,
"preview": "{\"class_name\": \"Sequential\", \"config\": [{\"class_name\": \"Conv3D\", \"config\": {\"name\": \"conv3d_1\", \"trainable\": true, \"batc"
},
{
"path": "v1/liveness_detection/create_data.py",
"chars": 314,
"preview": "import cv2\nimport numpy as np \n\n\ncap = cv2.VideoCapture(0)\nxx = 0\nwhile 1:\n\tret, img = cap.read()\n\tgray = cv2.cvtColor(i"
},
{
"path": "v1/liveness_detection/labels.py",
"chars": 459,
"preview": "import pandas as pd \nimport cv2\n\nimages = []\nlabels = []\nfor i in range(400):\n\timg = cv2.imread(\"img-live/\" + str(i)+\".j"
},
{
"path": "v1/liveness_detection/liveness_data.csv",
"chars": 517614,
"preview": "images,labels\n\"[[[ 91 106 119]\n [ 74 96 112]\n [ 74 97 112]\n ..., \n [ 81 89 111]\n [ 92 100 119]\n [ 93 99 113]]\n"
},
{
"path": "v1/main_code.py",
"chars": 6545,
"preview": "\nimport face_recognition\nimport cv2\nimport numpy as np \nfont = cv2.FONT_HERSHEY_DUPLEX\nimport tflearn\nfrom tflearn.layer"
},
{
"path": "v1/model/checkpoint",
"chars": 211,
"preview": "model_checkpoint_path: \"/Users/ahmethamzaemra/Documents/github/Intelegent_Lock/model/my_model.tflearn\"\nall_model_checkpo"
}
]
// ... and 4 more files (download for full content)
About this extraction
This page contains the full source code of the AhmetHamzaEmra/Intelegent_Lock GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 16 files (59.8 MB), approximately 285.3k tokens, and a symbol index with 2 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.