Neste tutorial, vamos treinar um modelo TensorFlow MobileNetV2 com o Keras para que ele possa ser aplicado ao nosso problema. Poderemos então utilizá-lo em tempo real para classificar novas imagens.
Para este tutorial, partimos do princípio de que seguiu os tutoriais anteriores: utilização de um modelo TensorFlow e preparação de uma base de dados para treino.
N.B.: Não encontrei o método correto para treinar o modelo ssd mobilenetV2, tal como está, com o tensorflow. Por isso, mudei para o Yolo. Se tiveres o método certo, não hesites em deixar um comentário.
Recuperação de uma base de dados de imagens
Descarregue uma das muitas bases de dados de imagens, como a de gatos e cães, ou crie a sua própria base de dados.
Descompactou a pasta em Tensorflow>data
Formação de modelos
Para treinar o modelo, pode utilizar o seguinte script:
- carregar e expandir a base de dados
- criar um modelo a partir do modelo MobileNetV2(base_model)
- impulsionar novos ganhos no modelo
- afinar os ganhos do modelo_base
import matplotlib.pyplot as plt
import numpy as np
import os
import tensorflow as tf
#_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'
#path_to_zip = tf.keras.utils.get_file('cats_and_dogs.zip', origin=_URL, extract=True)
#PATH = os.path.join(os.path.dirname(path_to_zip), 'cats_and_dogs_filtered')
PATH="./data/cats_and_dogs_filtered"
train_dir = os.path.join(PATH, 'train')
validation_dir = os.path.join(PATH, 'validation')
BATCH_SIZE = 32
IMG_SIZE = (160, 160)
#create train and validation sets
train_dataset = tf.keras.utils.image_dataset_from_directory(train_dir,
shuffle=True,
batch_size=BATCH_SIZE,
image_size=IMG_SIZE)
validation_dataset = tf.keras.utils.image_dataset_from_directory(validation_dir,
shuffle=True,
batch_size=BATCH_SIZE,
image_size=IMG_SIZE)
class_names = train_dataset.class_names
plt.figure(figsize=(10, 10))
for images, labels in train_dataset.take(1):
for i in range(9):
ax = plt.subplot(3, 3, i + 1)
plt.imshow(images[i].numpy().astype("uint8"))
plt.title(class_names[labels[i]])
plt.axis("off")
val_batches = tf.data.experimental.cardinality(validation_dataset)
test_dataset = validation_dataset.take(val_batches // 5)
validation_dataset = validation_dataset.skip(val_batches // 5)
print('Number of validation batches: %d' % tf.data.experimental.cardinality(validation_dataset))
print('Number of test batches: %d' % tf.data.experimental.cardinality(test_dataset))
#configure performance
AUTOTUNE = tf.data.AUTOTUNE
train_dataset = train_dataset.prefetch(buffer_size=AUTOTUNE)
validation_dataset = validation_dataset.prefetch(buffer_size=AUTOTUNE)
test_dataset = test_dataset.prefetch(buffer_size=AUTOTUNE)
#augmented data (usefull for small data sets)
data_augmentation = tf.keras.Sequential([
tf.keras.layers.RandomFlip('horizontal'),
tf.keras.layers.RandomRotation(0.2),
])
for image, _ in train_dataset.take(1):
plt.figure(figsize=(10, 10))
first_image = image[0]
for i in range(9):
ax = plt.subplot(3, 3, i + 1)
augmented_image = data_augmentation(tf.expand_dims(first_image, 0))
plt.imshow(augmented_image[0] / 255)
plt.axis('off')
preprocess_input = tf.keras.applications.mobilenet_v2.preprocess_input
rescale = tf.keras.layers.Rescaling(1./127.5, offset=-1)
# Create the base model from the pre-trained model MobileNet V2
IMG_SHAPE = IMG_SIZE + (3,)
base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,
include_top=False,
weights='imagenet')
#or load your own
#base_model= tf.saved_model.load("./pretrained_models/ssd_mobilenet_v2_320x320_coco17_tpu-8/saved_model")
image_batch, label_batch = next(iter(train_dataset))
feature_batch = base_model(image_batch)
print(feature_batch.shape)
base_model.trainable = False
base_model.summary()
#classification header
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
feature_batch_average = global_average_layer(feature_batch)
print(feature_batch_average.shape)
prediction_layer = tf.keras.layers.Dense(1)
prediction_batch = prediction_layer(feature_batch_average)
print(prediction_batch.shape)
#create new neural network based on MobileNet
inputs = tf.keras.Input(shape=(160, 160, 3))
x = data_augmentation(inputs)
x = preprocess_input(x)
x = base_model(x, training=False)
x = global_average_layer(x)
x = tf.keras.layers.Dropout(0.2)(x)
outputs = prediction_layer(x)
model = tf.keras.Model(inputs, outputs)
base_learning_rate = 0.0001
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate),
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics=['accuracy'])
initial_epochs = 10
loss0, accuracy0 = model.evaluate(validation_dataset)
print("initial loss: {:.2f}".format(loss0))
print("initial accuracy: {:.2f}".format(accuracy0))
history = model.fit(train_dataset,
epochs=initial_epochs,
validation_data=validation_dataset)
#plot learning curves
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
plt.figure(figsize=(8, 8))
plt.subplot(2, 1, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.ylabel('Accuracy')
plt.ylim([min(plt.ylim()),1])
plt.title('Training and Validation Accuracy')
plt.subplot(2, 1, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.ylabel('Cross Entropy')
plt.ylim([0,1.0])
plt.title('Training and Validation Loss')
plt.xlabel('epoch')
plt.show()
#fine tuning
base_model.trainable = True
# Let's take a look to see how many layers are in the base model
print("Number of layers in the base model: ", len(base_model.layers))
# Fine-tune from this layer onwards
fine_tune_at = 100
# Freeze all the layers before the `fine_tune_at` layer
for layer in base_model.layers[:fine_tune_at]:
layer.trainable = False
model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
optimizer = tf.keras.optimizers.RMSprop(learning_rate=base_learning_rate/10),
metrics=['accuracy'])
model.summary()
fine_tune_epochs = 10
total_epochs = initial_epochs + fine_tune_epochs
history_fine = model.fit(train_dataset,
epochs=total_epochs,
initial_epoch=history.epoch[-1],
validation_data=validation_dataset)
#plot fine learning curves
acc += history_fine.history['accuracy']
val_acc += history_fine.history['val_accuracy']
loss += history_fine.history['loss']
val_loss += history_fine.history['val_loss']
plt.figure(figsize=(8, 8))
plt.subplot(2, 1, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.ylim([0.8, 1])
plt.plot([initial_epochs-1,initial_epochs-1],
plt.ylim(), label='Start Fine Tuning')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(2, 1, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.ylim([0, 1.0])
plt.plot([initial_epochs-1,initial_epochs-1],
plt.ylim(), label='Start Fine Tuning')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.xlabel('epoch')
plt.show()
#evaluate
loss, accuracy = model.evaluate(test_dataset)
print('Test accuracy :', accuracy)
model.save('saved_models/my_model')
Utilizar o modelo treinado
Pode utilizar o modelo treinado para classificar novas imagens que contenham um único tipo de objeto por imagem. Para o fazer, basta carregar o modelo previamente guardado (modelos_salvados
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# ObjectRecognitionTFVideo.py
# Description:
# Use ModelNetV2-SSD model to detect objects on video
#
# www.aranacorp.com
# import packages
import sys
from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import argparse
import imutils
import time
import cv2
import tensorflow as tf
from PIL import Image
# load model from path
#model= tf.saved_model.load("./pretrained_models/ssd_mobilenet_v2_320x320_coco17_tpu-8/saved_model")
model= tf.saved_model.load("./saved_models/my_model")
#model.summary()
print("model loaded")
#load class names
#category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,use_display_name=True)
def read_label_map(label_map_path):
item_id = None
item_name = None
items = {}
with open(label_map_path, "r") as file:
for line in file:
line.replace(" ", "")
if line == "item{":
pass
elif line == "}":
pass
elif "id" in line:
item_id = int(line.split(":", 1)[1].strip())
elif "display_name" in line: #elif "name" in line:
item_name = line.split(":", 1)[1].replace("'", "").strip()
if item_id is not None and item_name is not None:
#items[item_name] = item_id
items[item_id] = item_name
item_id = None
item_name = None
return items
#class_names=read_label_map("./pretrained_models/ssd_mobilenet_v2_320x320_coco17_tpu-8/mscoco_label_map.pbtxt")
class_names = read_label_map("./saved_models/label_map.pbtxt")
class_names = list(class_names.values()) #convert to list
class_colors = np.random.uniform(0, 255, size=(len(class_names), 3))
print(class_names)
if __name__ == '__main__':
# Open image
#img= cv2.imread('./data/cats_and_dogs_filtered/train/cats/cat.1.jpg') #from image file
img= cv2.imread('./data/cats_and_dogs_filtered/train/dogs/dog.1.jpg') #from image file
img = cv2.resize(img,(160,160))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
#input_tensor = np.expand_dims(img, 0)
input_tensor = tf.convert_to_tensor(np.expand_dims(img, 0), dtype=tf.float32)
# predict from model
resp = model(input_tensor)
print("resp: ",resp)
score= tf.nn.sigmoid(resp).numpy()[0][0]*100
cls = int(score>0.5)
print("classId: ",int(cls))
print("score: ",score)
print("score: ",tf.nn.sigmoid(tf.nn.sigmoid(resp)))
# write classname for bounding box
cls=int(cls) #convert tensor to index
label = "{}".format(class_names[cls])
img = cv2.resize(img,(640,640))
cv2.putText(img, label, (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, class_colors[cls], 2)
# Show frame
cv2.imshow("Frame", img)
cv2.waitKey(0)
Aplicações
- reconhecer as diferentes raças de animais
- reconhecimento de diferentes tipos de objectos, como cartões electrónicos
Outros modelos de classificação a considerar
- vgg16
- vgg19
- resnet50
- resnet101
- resnet152
- densenet121
- densenet169
- densenet201
- inceptionresnetv2
- inceptionv3
- mobilenet
- mobilenetv2
- nasnetlarge
- nasnetmobile
- exceção