Bilderkennung mit TensorFlow

TensorFlow enthält eine spezielle Funktion zur Bilderkennung. Diese Bilder werden in einem bestimmten Ordner gespeichert. Mit relativ gleichen Bildern ist es aus Sicherheitsgründen einfach, diese Logik zu implementieren.

Die Ordnerstruktur der Implementierung des Bilderkennungscodes ist wie folgt:

Das Dataset-Bild enthält die zugehörigen Bilder, die geladen werden müssen. Wir konzentrieren uns auf die Bilderkennung mit unserem darin definierten Logo. Die Bilder werden mit dem Skript "load_data.py" geladen, mit dessen Hilfe Sie verschiedene Bilderkennungsmodule in ihnen notieren können.

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()

Das Training von Bildern hilft beim Speichern der erkennbaren Muster in einem bestimmten Ordner.

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")

Die obige Codezeile generiert eine Ausgabe wie unten gezeigt -