'Face lock using JavaScript without using any third-party frameworks including TensorFlow.js
Whenever I'm working on any web project, I always want to develop everything on my own without using any third-party framewoks or libraries. I had actually designed password based authentication using sessions. Now I would like to implement face authentication. How can I do that?
How can I train my JS detecting user face? What is the logic behind this? I don't want to use any third-party framewoks or libraries even from trusted companies. For instance, if a third-party JavaScript framework can help us in doing this, then why can't we do that? Even that framework is also coded in JavaScript right?
Solution 1:[1]
It is possible in many ways, using C++ libraries, Tensorflow, Tensorflow Lite, Tensorflow JS, and authenticating process as your intention. Communication is a single point target with the response of information and targets. ( JS is simply https://js.tensorflow.org/api/latest/ )
Face recognition is easy you can do it without API but with API they can adjust easily and have those parameters interfaces. I simply an example this codes many times but it is working when the face recognition is possible with Tensorflow and Python. The exchange parameters are only authentication and your camera access with return some parameter told you to do next process or with messages errors return.
[ Sample - simple objects ]: Object recognition, and face recognition required object observing, crop image attention, colors and contrast, and personal recording of data behaviors.
import cv2
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import tensorflow as tf
import os
from os.path import exists
import numpy as np
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
None
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
physical_devices = tf.config.experimental.list_physical_devices('GPU')
assert len(physical_devices) > 0, "Not enough GPU hardware devices available"
config = tf.config.experimental.set_memory_growth(physical_devices[0], True)
print(physical_devices)
print(config)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
global stacks_frames
stacks_frames = [ ]
stacks_frames.append( tf.zeros( [ 29, 39, 3 ] ).numpy() )
stacks_frames.append( tf.zeros( [ 29, 39, 3 ] ).numpy() )
stacks_frames.append( tf.zeros( [ 29, 39, 3 ] ).numpy() )
stacks_frames.append( tf.zeros( [ 29, 39, 3 ] ).numpy() )
fig = plt.figure()
image = plt.imread( "F:\\datasets\\downloads\\cats_name\\train\\Symbols\\01.jpg" )
im = plt.imshow( image )
list_actual_label = [ 'Shoes', 'Duck' ]
global video_capture_0
video_capture_0 = cv2.VideoCapture(0)
checkpoint_path = "F:\\models\\checkpoint\\" + os.path.basename(__file__).split('.')[0] + "\\TF_DataSets_01.h5"
checkpoint_dir = os.path.dirname(checkpoint_path)
loggings = "F:\\models\\checkpoint\\" + os.path.basename(__file__).split('.')[0] + "\\loggings.log"
if not exists(checkpoint_dir) :
os.mkdir(checkpoint_dir)
print("Create directory: " + checkpoint_dir)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
Functions
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
def f1( picture ):
global stacks_frames
image = tf.constant( picture ).numpy()
stacks_frames.append( image )
stacks_frames = stacks_frames[-4:]
image_1 = stacks_frames[0][:,:,0:1] + ( 255 - stacks_frames[3][:,:,0:1] )
image_2 = stacks_frames[0][:,:,1:2] + ( 255 - stacks_frames[3][:,:,1:2] )
image_3 = stacks_frames[0][:,:,2:3] + ( 255 - stacks_frames[3][:,:,2:3] )
image = tf.keras.layers.Concatenate(axis=2)([image_1, image_2])
image = tf.keras.layers.Concatenate(axis=2)([image, image_3])
image = tf.where( tf.greater_equal( image[:,:,0:1], tf.ones( [ 29, 39, 1 ] ) * 100 ), [255] , [0], name=None ).numpy()
list_cells_values = [ ]
list_cells_pos_values = [ ]
for i in range(4):
for j in range(4):
cropped_image = tf.image.crop_to_bounding_box(image, 2 * i, 2 * j, 15, 16)
list_cells_values.append([ tf.math.count_nonzero(cropped_image) ])
list_cells_pos_values.append([ 2 * i, 2 * j, 15, 16 ])
width = list_cells_pos_values[ int(tf.math.argmax( list_cells_values ).numpy()) ][0]
height = list_cells_pos_values[ int(tf.math.argmax( list_cells_values ).numpy()) ][1]
print( 'width: ' + str( width ) )
print( 'height: ' + str( height ) )
img = tf.constant( picture, shape=( 1, 29, 39, 3 ) ).numpy()
box = np.array([0.0625 * width, 0.0625 * height, 1 - ( 0.0625 * width ), 1 - ( 0.0625 * height )])
boxes = box.reshape([1, 1, 4])
colors = np.array([[1.0, 0.0, 0.0]])
image = tf.image.draw_bounding_boxes(img, boxes, colors).numpy()
image = tf.constant( image, shape=( 29, 39, 3 ) ).numpy()
image = tf.keras.preprocessing.image.array_to_img(
image,
data_format=None,
scale=True
)
return image
def animate( i ):
ret0, frame0 = video_capture_0.read()
if (ret0):
frame0 = tf.image.resize(frame0, [29, 39]).numpy()
temp = img_array = tf.keras.preprocessing.image.img_to_array(frame0[:,:,2:3])
temp2 = img_array = tf.keras.preprocessing.image.img_to_array(frame0[:,:,1:2])
temp3 = img_array = tf.keras.preprocessing.image.img_to_array(frame0[:,:,0:1])
temp = tf.keras.layers.Concatenate(axis=2)([temp, temp2])
temp = tf.keras.layers.Concatenate(axis=2)([temp, temp3])
temp = f1( temp )
im.set_array( temp )
result = predict_action( temp )
print( list_actual_label[result] )
return im,
def predict_action ( image ) :
predictions = model.predict(tf.constant(image, shape=(1, 29, 39, 3) , dtype=tf.float32))
result = tf.math.argmax(predictions[0])
return result
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Initialize
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model = tf.keras.models.Sequential([
tf.keras.layers.InputLayer(input_shape=( 29, 39, 3 )),
tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Reshape((234, 32)),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32, return_sequences=True, return_state=False)),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),
])
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(64))
model.add(tf.keras.layers.Dense(2))
model.summary()
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
DataSet
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
list_picture = []
list_label = []
path_1 = "F:\\datasets\\downloads\\Duck_Shoe_2\\Duck\\"
path_2 = "F:\\datasets\\downloads\\Duck_Shoe_2\\Shoes\\"
for file in os.listdir( path_1 ):
image = plt.imread( path_1 + file )
image = tf.image.resize(image, [29, 39]).numpy()
for i in range ( 40 ) :
if i % 6 == 0 :
layer = tf.keras.layers.RandomZoom(.5, .2)
image = layer( image ).numpy()
list_picture.append( image )
elif i % 5 == 0 :
image = tf.image.random_hue(image, 0.2).numpy()
image = tf.image.random_flip_up_down(image, 1).numpy()
list_picture.append( image )
elif i % 4 == 0 :
image = tf.image.random_saturation(image, 5, 10, 1).numpy()
image = tf.image.random_flip_left_right(image, 1).numpy()
list_picture.append( image )
elif i % 3 == 0 :
image = tf.image.random_flip_up_down(image, 1).numpy()
image = tf.image.random_saturation(image, 5, 10, 1).numpy()
list_picture.append( image )
elif i % 2 == 0 :
image = tf.image.random_flip_left_right(image, 1).numpy()
image = tf.image.random_hue(image, 0.2).numpy()
list_picture.append( image )
else :
list_picture.append( image )
list_label.append( 1 )
for file in os.listdir( path_2 ):
image = plt.imread( path_2 + file )
image = tf.image.resize(image, [29, 39]).numpy()
for i in range ( 40 ) :
if i % 6 == 0 :
layer = tf.keras.layers.RandomZoom(.5, .2)
image = layer( image ).numpy()
list_picture.append( image )
elif i % 5 == 0 :
image = tf.image.random_hue(image, 0.2).numpy()
image = tf.image.random_flip_up_down(image, 1).numpy()
list_picture.append( image )
elif i % 4 == 0 :
image = tf.image.random_saturation(image, 5, 10, 1).numpy()
image = tf.image.random_flip_left_right(image, 1).numpy()
list_picture.append( image )
elif i % 3 == 0 :
image = tf.image.random_flip_up_down(image, 1).numpy()
image = tf.image.random_saturation(image, 5, 10, 1).numpy()
list_picture.append( image )
elif i % 2 == 0 :
image = tf.image.random_flip_left_right(image, 1).numpy()
image = tf.image.random_hue(image, 0.2).numpy()
list_picture.append( image )
else :
list_picture.append( image )
list_label.append( 0 )
dataset = tf.data.Dataset.from_tensor_slices((tf.constant([list_picture], shape=(len(list_picture), 1, 29, 39, 3), dtype=tf.float32),tf.constant([list_label], shape=(len(list_picture), 1, 1, 1), dtype=tf.int64)))
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Optimizer
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
optimizer = tf.keras.optimizers.Nadam( learning_rate=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, name='Nadam' )
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Loss Fn
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
lossfn = tf.keras.losses.MeanSquaredLogarithmicError(reduction=tf.keras.losses.Reduction.AUTO, name='mean_squared_logarithmic_error')
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Summary
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model.compile(optimizer=optimizer, loss=lossfn, metrics=['accuracy'])
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: FileWriter
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
if exists(checkpoint_path) :
model.load_weights(checkpoint_path)
print("model load: " + checkpoint_path)
input("Press Any Key!")
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Training
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
history = model.fit(dataset, epochs=2 ,validation_data=(dataset))
while True:
ani = animation.FuncAnimation(fig, animate, interval=50, blit=True)
plt.show()
# When everything is done, release the capture
video_capture_0.release()
cv2.destroyAllWindows()
input('...')
[ Output ]:
Sources
This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.
Source: Stack Overflow
| Solution | Source |
|---|---|
| Solution 1 | Jirayu Kaewprateep |