def preprocessing_image(image_path):
image = load_img(image_path, target_size=(260, 270))
image = img_to_array(image)
#add another dimension
image = np.expand_dims(image, axis=0)
image = preprocess_input(image, data_format="channels_last")
return image
def gen_batch_data(dataset, x, y, batch_size):
'''
批数据生成器
:param x:
:param y:
:param batch_size:
:return:
'''
indices = np.arange(x.shape[0])
random.shuffle(indices)
x = x[indices]
y = y[indices]
i = 0
while True:
bi = i * batch_size
ei = min(i * batch_size + batch_size, len(indices))
if ei == len(indices):
i = 0
else:
i += 1
x_data = x[bi:ei]
y_data = y[bi:ei]
x_batch = dataset.processor_x(x_data)
x_batch = preprocess_input(x_batch, **kwargs)
y_batch = dataset.processor_y(y_data)
y_batch = label_smoothing(y_batch)
yield x_batch, y_batch
def predict(self, **data):
if self.model is None:
self.model = keras.models.load_model(self.model_path)
# 获取需要预测的图像数据， predict_data 方法默认会去调用 processor.py 中的 input_x 方法
x_data = self.dataset.predict_data(**data)
x_data = preprocess_input(x_data,**kwargs)
predict = self.model.predict(x_data)
# 将预测数据转换成对应标签 to_categorys 会去调用 processor.py 中的 output_y 方法
prediction = self.dataset.to_categorys(predict)
return prediction
def preprocess_input(*args, **kwargs): return densenet.preprocess_input(*args, **kwargs)
def preprocess_input(*args, **kwargs): return densenet.preprocess_input(*args, **kwargs)
数据训练次数大体一致
'''
for i_in in range(RATIO):
x_train_big, y_train_big = dataset.next_train_batch()
# 数据增强器
imageGen = ImageDataGenerator(
horizontal_flip=True,
zoom_range=[0.7, 1.3],
rotation_range=45,
)
small_step = 0
batch_size_small_train = 32
for x_train_small, y_train_small in imageGen.flow(
x_train_big, y_train_big, batch_size=batch_size_small_train):
x_train_small = preprocess_input(x_train_small, **kwargs)
train_loss_and_metrics = mymodel.train_on_batch(
x_train_small, y_train_small)
small_step += 1
# 保证扩充数量不超过此批数据的1倍
if small_step > 1 * (x_train_big.shape[0] /
batch_size_small_train):
if (i_in + 1) % 5 == 0: # 减少打印次数。
print(
'step: %d/%d, train_loss: %f， train_acc: %f, ' %
(i + 1, dataset.get_step() // RATIO,
train_loss_and_metrics[0], train_loss_and_metrics[1]))
break
# args.BATCH 准备设为 2560
iter_num = dataset.get_validation_length() // 6
def batch_preprocess_input(x_batch, network):
if network == 'Xception':
x_batch = xception.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'VGG16':
x_batch = vgg16.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'VGG19':
x_batch = vgg19.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'ResNet50' or network == 'ResNet101' or network == 'ResNet152':
x_batch = resnet.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'ResNet50V2' or network == 'ResNet101V2' or network == 'ResNet152V2':
x_batch = resnet_v2.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'ResNeXt50' or network == 'ResNeXt101':
x_batch = resnext.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'InceptionV3':
x_batch = inception_v3.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'InceptionResNetV2':
x_batch = inception_resnet_v2.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'MobileNet':
x_batch = mobilenet.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'MobileNetV2':
x_batch = mobilenet_v2.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'DenseNet121' or network == 'DenseNet169' or network == 'DenseNet201':
x_batch = densenet.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'NASNetMobile' or network == 'NASNetLarge':
x_batch = nasnet.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif 'EfficientNet' in network:
x_batch = efficientnet.preprocess_input(x_batch)
else:
return None
return x_batch
def load_data(parent_directory,
data_type,
image_preprocess,
image_shape,
five_mats,
crop=False,
scio_extended=False,
test_set=False):
materials = [
'metal', 'plastic', 'wood', 'paper', 'fabric', 'glass', 'ceramic',
'foam'
]
measurements_per_object = 100
materials_objects = []
materials_objects.extend([['metal', obj] for obj in [
'alumfoil', 'bowl', 'canlarge', 'canrockstar', 'cupwithhandle',
'cyltall', 'muggt', 'panbot', 'pot', 'potlarge', 'spraycan',
'traytall', 'waterbottle'
]])
materials_objects.extend([['plastic', obj] for obj in [
'bag', 'blackcontainer', 'bottlesip', 'clorox', 'coffeemate',
'cupblack', 'cupblue', 'cups', 'folder', 'plate', 'poncho',
'redcokebottle', 'smartwater'
]])
materials_objects.extend([['wood', obj] for obj in [
'blocksides', 'bowlcylash', 'bowlwhiteshell', 'boxflagus', 'crate',
'cutboard', 'plank', 'platter', 'pothepta', 'pyramid', 'smallblocks',
'soapblock', 'tray'
]])
materials_objects.extend([['paper', obj] for obj in [
'bagamazon', 'bagstarbucks', 'cups', 'eggcarton', 'folder', 'napkins',
'newspaper', 'notebookgraph', 'packaging', 'printer', 'saltcontainer',
'tissuebox', 'towelbox'
]])
materials_objects.extend([['fabric', obj] for obj in [
'beanie', 'cardiganred', 'clothblue', 'collarcheckered', 'collargray',
'jeans', 'shirtgray', 'shirtgt', 'shirtwhite', 'socks', 'sweaterknit',
'towelwhite', 'waistcoat'
]])
materials_objects.extend([['glass', obj] for obj in [
'bowl', 'cupcurved', 'cuptall', 'cuptallpatterned', 'cuptumbler',
'halfliter', 'jartall', 'mason', 'measuringcup', 'pitcher', 'plate',
'tray', 'waterbottle'
]])
materials_objects.extend([['ceramic', obj] for obj in [
'bowl', 'bowlgreen', 'bowllarge', 'bowlscratched', 'mugblue', 'mugtan',
'mugwhite', 'plate', 'platecorelle', 'platepatterned', 'potorange',
'trayblack', 'traytall'
]])
materials_objects.extend([['foam', obj] for obj in [
'blacktwo', 'blockstyro', 'cup', 'drygreen', 'piecewhite', 'plate',
'plushies', 'sandal', 'soundpointy', 'spongepatterned', 'styrohead',
'tubes', 'yogamat'
]])
if test_set:
materials_objects = []
materials_objects.extend([['metal', obj] for obj in [
'bowllarge', 'cancampbell', 'mugbubba', 'trayshort',
'traywithhandles'
]])
materials_objects.extend(
[['plastic', obj] for obj in
['bagtarget', 'bottlechew', 'bottlegt', 'coffeered', 'vaseline']])
materials_objects.extend([[
'wood', obj
] for obj in ['bowllarge', 'boxsoap', 'ladle', 'pot', 'servingbowl']])
materials_objects.extend([['paper', obj] for obj in [
'book', 'googlecardboard', 'napkinsbrown', 'plate', 'starbuckscup'
]])
materials_objects.extend([['fabric', obj] for obj in [
'hospitalgown', 'pantsblue', 'pillowcase', 'shirtirim',
'shortscargo'
]])
materials_objects.extend([['glass', obj] for obj in [
'bottlebeer', 'jarfrosted', 'jarrect', 'platepatterned', 'stagioni'
]])
materials_objects.extend(
[['ceramic', obj] for obj in
['muggt', 'potgray', 'traycurved', 'trayshort', 'trayterracota']])
materials_objects.extend(
[['foam', obj] for obj in
['evasheet', 'sheet', 'sheetyellow', 'sponge', 'yogablock']])
data_filename = os.path.join(
parent_directory,
'traintest_spectral_data.h5' if data_type == 'spectral' else
'traintest_spectral_image_%s_data.h5' % image_preprocess)
if test_set:
data_filename = os.path.join(
parent_directory,
'testset_spectral_data.h5' if data_type == 'spectral' else
'testset_spectral_image_%s_data.h5' % image_preprocess)
if os.path.isfile(data_filename):
hf = h5py.File(data_filename, 'r')
X_spectral = np.array(hf.get('X_scio'))
X_image = np.array(hf.get('X_image'))
y = np.array(hf.get('y'))
objs = np.array([n.decode('ascii') for n in hf.get('objs')])
wavelengths = np.array(hf.get('wavelengths'))
hf.close()
# If only five_mats, then delete the glass, ceramic, and foam objects
if five_mats:
indices = [
i for i, key in enumerate(objs)
if ('glass' not in key and 'ceramic' not in key
and 'foam' not in key)
]
X_spectral = X_spectral[indices]
if len(X_image) > 0:
X_image = X_image[indices]
y = y[indices]
objs = objs[indices]
X_spectral = np.array(X_spectral, dtype=np.float32)
if 'image' in data_type:
X_image = np.array(X_image, dtype=np.float32)
else:
X_image = np.array(X_spectral, dtype=np.float32)
return X_spectral, X_image, y, objs, wavelengths
# Needed for downloading Keras resnet and densenet pretrained models
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
import keras, cv2
from keras.layers import Flatten, GlobalAveragePooling2D, AveragePooling2D
from keras_applications import resnet50, densenet, resnext, inception_resnet_v2, nasnet, vgg19, xception, resnet_v2, resnet
# Load spectral data
filename = os.path.join(parent_directory, 'spectraldata.csv')
spectral_data = dict()
wavelength_count = 331
wavelengths = None
with open(filename, 'r') as f:
reader = csv.reader(f)
for i, row in enumerate(reader):
if i < 10 or i == 11:
continue
if i == 10:
# Header row
wavelengths = [
float(r.strip().split('_')[-1].split()[0]) + 740.0
for r in row[10:wavelength_count + 10]
]
if scio_extended:
wavelengths.extend([
float(r.strip().split('_')[-1].split()[0]) + 740.0
for r in row[672:wavelength_count + 672]
])
continue
obj = row[3].strip()
material = row[4].strip()
key = material + '_' + obj
if key not in spectral_data:
spectral_data[key] = []
spectral_data[key].append(
[float(v) for v in row[10:wavelength_count + 10]])
if scio_extended:
spectral_data[key][-1].extend(
[float(v) for v in row[672:wavelength_count + 672]])
# Load spectral data and images
X_spectral = []
X_image = []
y = []
objs = []
if 'image' in data_type and 'none' not in image_preprocess:
if 'resnet50' in image_preprocess:
preprocess_model = resnet.ResNet50(weights='imagenet',
include_top=False,
input_shape=(image_shape[0],
image_shape[1], 3),
pooling='avg',
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif 'vgg19' in image_preprocess:
preprocess_model = vgg19.VGG19(weights='imagenet',
include_top=False,
input_shape=(image_shape[0],
image_shape[1], 3),
pooling='avg',
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif 'densenet201' in image_preprocess:
preprocess_model = densenet.DenseNet201(
weights='imagenet',
include_top=False,
input_shape=(image_shape[0], image_shape[1], 3),
pooling='avg',
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif 'resnext101' in image_preprocess:
preprocess_model = resnext.ResNeXt101(weights='imagenet',
include_top=False,
input_shape=(image_shape[0],
image_shape[1],
3),
pooling='avg',
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif 'resnet152' in image_preprocess:
preprocess_model = resnet.ResNet152(weights='imagenet',
include_top=False,
input_shape=(image_shape[0],
image_shape[1],
3),
pooling='avg',
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
for material, obj in materials_objects:
key = material + '_' + obj
X_image_raw = []
for iteration in range(measurements_per_object):
if 'image' in data_type:
filename = os.path.join(
parent_directory, 'data_sets_ABCD', material, obj,
'spectrovis_%s_%s_%04d.png' % (material, obj, iteration))
if not os.path.isfile(filename):
print('Image file not found!', filename)
exit()
image = cv2.imread(filename, cv2.IMREAD_COLOR)
if crop:
h, w, _ = np.shape(image)
X_image_raw.append(
np.copy(image[h // 2 - image_shape[0] // 2:h // 2 +
image_shape[0] // 2,
w // 2 - image_shape[1] // 2:w // 2 +
image_shape[1] // 2]))
else:
X_image_raw.append(
cv2.resize(image, (image_shape[1], image_shape[0])))
# cv2.imshow('Image', X_image[-1])
# cv2.waitKey(0)
# if len(X_image) % 50 == 0:
# print(len(X_image))
# sys.stdout.flush()
X_spectral.append(spectral_data[key][iteration])
y.append(materials.index(material))
objs.append(key)
if len(X_image_raw) >= 100:
if 'image' in data_type and 'none' not in image_preprocess:
print('X_image_raw:', len(X_image_raw),
np.shape(X_image_raw))
if 'resnet50' in image_preprocess:
X_image_raw = resnet.preprocess_input(
np.array(X_image_raw),
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif 'vgg19' in image_preprocess:
X_image_raw = vgg19.preprocess_input(
np.array(X_image_raw),
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif 'densenet201' in image_preprocess:
X_image_raw = densenet.preprocess_input(
np.array(X_image_raw),
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif 'resnext101' in image_preprocess:
X_image_raw = resnext.preprocess_input(
np.array(X_image_raw),
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif 'resnet152' in image_preprocess:
X_image_raw = resnet.preprocess_input(
np.array(X_image_raw),
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
X_image.extend(preprocess_model.predict(X_image_raw))
print('X_image:', len(X_image), np.shape(X_image))
sys.stdout.flush()
X_image_raw = []
X_spectral = np.array(X_spectral)
X_image = np.array(X_image)
y = np.array(y)
objs = np.array(objs)
wavelengths = np.array(wavelengths)
print('Data loaded. Preprocessing images')
print(np.shape(X_image))
sys.stdout.flush()
hf = h5py.File(data_filename, 'w')
hf.create_dataset('X_scio', data=X_spectral)
hf.create_dataset('X_image', data=X_image)
hf.create_dataset('y', data=y)
hf.create_dataset('objs', data=[n.encode('ascii') for n in objs])
hf.create_dataset('wavelengths', data=wavelengths)
hf.close()
# If only five_mats, then delete the glass, ceramic, and foam objects
if five_mats:
indices = [
i for i, key in enumerate(objs) if
('glass' not in key and 'ceramic' not in key and 'foam' not in key)
]
X_spectral = X_spectral[indices]
if len(X_image) > 0:
X_image = X_image[indices]
y = y[indices]
objs = objs[indices]
X_spectral = np.array(X_spectral, dtype=np.float32)
if 'image' in data_type:
X_image = np.array(X_image, dtype=np.float32)
else:
X_image = np.array(X_spectral, dtype=np.float32)
return X_spectral, X_image, y, objs, wavelengths