Reconnaissance d'image à l'aide de TensorFlow

TensorFlow inclut une fonction spéciale de reconnaissance d'image et ces images sont stockées dans un dossier spécifique. Avec des images relativement identiques, il sera facile d'implémenter cette logique à des fins de sécurité.

La structure de dossier de l'implémentation du code de reconnaissance d'image est comme indiqué ci-dessous -

Dataset_image inclut les images associées, qui doivent être chargées. Nous nous concentrerons sur la reconnaissance d'image avec notre logo défini. Les images sont chargées avec le script «load_data.py», ce qui permet de garder une note sur les différents modules de reconnaissance d'image en leur sein.

import pickle
from sklearn.model_selection import train_test_split
from scipy import misc

import numpy as np
import os

label = os.listdir("dataset_image")
label = label[1:]
dataset = []

for image_label in label:
   images = os.listdir("dataset_image/"+image_label)
   
   for image in images:
      img = misc.imread("dataset_image/"+image_label+"/"+image)
      img = misc.imresize(img, (64, 64))
      dataset.append((img,image_label))
X = []
Y = []

for input,image_label in dataset:
   X.append(input)
   Y.append(label.index(image_label))

X = np.array(X)
Y = np.array(Y)

X_train,y_train, = X,Y

data_set = (X_train,y_train)

save_label = open("int_to_word_out.pickle","wb")
pickle.dump(label, save_label)
save_label.close()

La formation des images aide à stocker les modèles reconnaissables dans le dossier spécifié.

import numpy
import matplotlib.pyplot as plt

from keras.layers import Dropout
from keras.layers import Flatten
from keras.constraints import maxnorm
from keras.optimizers import SGD
from keras.layers import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
from keras import backend as K

import load_data
from keras.models import Sequential
from keras.layers import Dense

import keras
K.set_image_dim_ordering('tf')

# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)

# load data
(X_train,y_train) = load_data.data_set

# normalize inputs from 0-255 to 0.0-1.0
X_train = X_train.astype('float32')

#X_test = X_test.astype('float32')
X_train = X_train / 255.0

#X_test = X_test / 255.0
# one hot encode outputs
y_train = np_utils.to_categorical(y_train)

#y_test = np_utils.to_categorical(y_test)
num_classes = y_train.shape[1]

# Create the model
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), padding = 'same', 
   activation = 'relu', kernel_constraint = maxnorm(3)))

model.add(Dropout(0.2))
model.add(Conv2D(32, (3, 3), activation = 'relu', padding = 'same', 
   kernel_constraint = maxnorm(3)))

model.add(MaxPooling2D(pool_size = (2, 2)))
model.add(Flatten())
model.add(Dense(512, activation = 'relu', kernel_constraint = maxnorm(3)))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation = 'softmax'))

# Compile model
epochs = 10
lrate = 0.01
decay = lrate/epochs
sgd = SGD(lr = lrate, momentum = 0.9, decay = decay, nesterov = False)
model.compile(loss = 'categorical_crossentropy', optimizer = sgd, metrics = ['accuracy'])
print(model.summary())

#callbacks = [keras.callbacks.EarlyStopping(
   monitor = 'val_loss', min_delta = 0, patience = 0, verbose = 0, mode = 'auto')]
callbacks = [keras.callbacks.TensorBoard(log_dir='./logs', 
   histogram_freq = 0, batch_size = 32, write_graph = True, write_grads = False, 
   write_images = True, embeddings_freq = 0, embeddings_layer_names = None, 
   embeddings_metadata = None)]

# Fit the model

model.fit(X_train, y_train, epochs = epochs, 
   batch_size = 32,shuffle = True,callbacks = callbacks)

# Final evaluation of the model
scores = model.evaluate(X_train, y_train, verbose = 0)
print("Accuracy: %.2f%%" % (scores[1]*100))

# serialize model to JSONx
model_json = model.to_json()
with open("model_face.json", "w") as json_file:
   json_file.write(model_json)

# serialize weights to HDF5
model.save_weights("model_face.h5")
print("Saved model to disk")

La ligne de code ci-dessus génère une sortie comme indiqué ci-dessous -