@author: ladvien """ import cv2 import os import sys import matplotlib.pyplot as plt image_tools_path = "/home/ladvien/deep_arcane/" sys.path.append(image_tools_path) from image_utils import ImageUtils iu = ImageUtils() ############# # Parameters ############# input_path = "/home/ladvien/deep_arcane/images/0_raw/2_black_and_white/" output_path = "/home/ladvien/deep_arcane/images/0_raw/3_all_white/" threshold = 80 samples = 10 ############# # Extract #############
# 如果写入tensorboard
if training_options['output_board']:
merged = tf.summary.merge_all() # tensorflow >= 0.12
writer = tf.summary.FileWriter(training_options['log_path'],
sess.graph) # tensorflow >=0.12
# 获得saver对象，可以保存model以及读取model
saver = tf.train.Saver()
# 如果已经存在model副本就直接读取，否则就初始化神经网络参数
if os.path.exists(training_options['model_path'] + '/checkpoint'):
saver.restore(
sess,
training_options['model_path'] + training_options['model_name'])
else:
sess.run(tf.global_variables_initializer())
# 初始化 imageUtils类，获得所有训练，测试数据
imageUtils = ImageUtils()
train_data = imageUtils.train_data
train_label = imageUtils.train_label
test_data = imageUtils.test_data
test_label = imageUtils.test_label
# 开始训练啦
for episode in range(training_options['total_episode']):
# 随机获得定义好数量的训练数据
sample_datas, sample_labels = imageUtils.sample(
len(train_data), training_options['batch_size'], train_data,
train_label)
# 训练神经网络
global_step, _, loss = sess.run(
[train['global_step'], train['train'], train['loss']],
feed_dict={
image: sample_datas,
def post_img(bi_img):
response = requests.post("http://10.40.16.226:11111/photo-service/photo", data=ImageUtils.resize_to_16v9(bi_img))
url = response.json()['url']
avatar_url = "http://webapi-rings.shabikplus.mozat.com/" + re.sub("__", "0", url)
logging.info("post a faked user avatar, url=%s", avatar_url)
return avatar_url
import sys import matplotlib.pyplot as plt from random import randint import numpy as np from PIL import Image import wandb # Import Keras import tensorflow.keras image_tools_path = "/home/ladvien/deep_arcane/" sys.path.append(image_tools_path) from image_utils import ImageUtils iu = ImageUtils() ################################# # TODO: Make experiment folder ################################# """ DONE: 1. Add WandB 2. Test results. 4. Train with dropout. TODO: 3. Split train / test images. """ #################################
def main():
if (prepareData):
imgUtils = ImageUtils(imgSize,
useAditional=useAditional,
keepAspectRatio=keepAspectRatio,
useKaggleData=useKaggleData)
imgUtils.dataPreparation()
K.set_image_data_format('channels_first')
K.set_floatx('float32')
np.random.seed(seed)
print("\nLoading train data...\n" + SEPARATOR)
if (keepAspectRatio):
if (useAditional):
train_data = np.load('saved_data/trainExtra' + str(imgSize) +
'_OrigAspectRatio.npy')
train_target = np.load('saved_data/trainExtra_target.npy')
else:
train_data = np.load('saved_data/train' + str(imgSize) +
'_OrigAspectRatio.npy')
train_target = np.load('saved_data/train_target.npy')
else:
if (useAditional):
train_data = np.load('saved_data/trainExtra' + str(imgSize) +
'.npy')
train_target = np.load('saved_data/trainExtra_target.npy')
else:
train_data = np.load('saved_data/train' + str(imgSize) + '.npy')
train_target = np.load('saved_data/train_target.npy')
x_train, x_val_train, y_train, y_val_train = train_test_split(
train_data,
train_target,
test_size=percentTrainForValidation,
random_state=17)
print(
"\nTraining Set shape (num Instances, RGB chanels, width, height): " +
str(x_train.shape) + "\nTraining labels: " + str(y_train.shape) +
"\nValidating set shape: " + str(x_val_train.shape) +
"\nValidating set labels: " + str(y_val_train.shape) + "\n" +
SEPARATOR)
print("\nMaking data augmentation...\n" + SEPARATOR)
datagen = da.prepareDataAugmentation(train_data=train_data)
currentDate = datetime.today()
timeStamp = currentDate.strftime("%d-%m-%Y_%H-%M")
print("\nCreating model...\n" + SEPARATOR)
if (loadPreviousModel):
model = load_model(pathToPreviousModel)
print("Loaded model from: " + pathToPreviousModel)
model.summary()
else:
if (hiperParamOpt):
print("\nHyperparameter optimization...\n" + SEPARATOR)
model = KerasClassifier(build_fn=create_model,
epochs=NumEpoch,
batch_size=batchSize,
validation_split=percentTrainForValidation)
grid_result = hiperParametersOptimization(model, x_train, y_train)
# summarize results
print("Best score: %f using parameters %s" %
(grid_result.best_score_, grid_result.best_params_))
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
print("%f (%f) with: %r" % (mean, stdev, param))
grid_result.best_estimator_.model.save(
"saved_data/GridCV_Best_estimator" + timeStamp + ".h5")
model = grid_result
else:
model = create_model()
if (saveNetArchImage):
if (hiperParamOpt):
plot_model(grid_result.best_estimator,
to_file='saved_data/model_' + timeStamp + '.png')
else:
plot_model(model, to_file='saved_data/model_' + timeStamp + '.png')
if (onlyEvaluate):
print("\nEvaluating Model...\n" + SEPARATOR)
evaluateModel(model, x_val_train, y_val_train)
else:
if hiperParamOpt is False:
fitKerasModel(datagen, model, timeStamp, x_train, x_val_train,
y_train, y_val_train)
makePrediction(model, timeStamp)
@author: ladvien
This script finds subimages in an image, extracts them, and saves
them to a file.
"""
import os
import sys
image_tools_path = "/home/ladvien/deep_arcane/"
sys.path.append(image_tools_path)
from image_utils import ImageUtils
iu = ImageUtils()
#############
# Parameters
#############
input_path = "/home/ladvien/deep_arcane/images/0_raw/0_scraped"
output_path = "/home/ladvien/deep_arcane/images/0_raw/1_extracted"
minimum_size = 30
dry_run = False
#############
# Extract
#############
@author: ladvien """ import cv2 import sys import os import matplotlib.pyplot as plt image_tools_path = "/home/ladvien/deep_arcane/" sys.path.append(image_tools_path) from image_utils import ImageUtils iu = ImageUtils() ############# # Parameters ############# input_path = "/home/ladvien/deep_arcane/images/0_raw/3_all_white/" output_path = "/home/ladvien/deep_arcane/images/0_raw/4_no_empty/" threshold = 240 ############# # Extract ############# if not os.path.exists(output_path):
""" Created on Wed Nov 11 19:00:54 2020 @author: ladvien """ import cv2 import sys import os image_tools_path = "/home/ladvien/deep_arcane/" sys.path.append(image_tools_path) from image_utils import ImageUtils iu = ImageUtils() ############# # Parameters ############# input_path = "/home/ladvien/denoising_vae/data/raw/" output_path = "/home/ladvien/denoising_vae/data/extracted/" threshold = 240 minimum_size = 30 target_size = (128, 128) dry_run = False #############
yield mpimpg.imread(image_path)
return load_image(images)
def processVideo(video_input, video_output):
print("* Processing video %s to %s" % (video_input, video_output))
input_video_clip = VideoFileClip(video_input)
clip = input_video_clip.fl_image(process_image)
clip.write_videofile(video_output, audio=False)
print("* Done")
if __name__ == "__main__":
utils = ImageUtils()
parser = argparse.ArgumentParser()
parser.add_argument("-v",
action="store_true",
dest="process_video",
default=False)
parser.add_argument("--input",
action="store",
dest="src_video",
default="project_video.mp4")
args = parser.parse_args()
utils.compute_perspective_transform_for_points(src_points, dst_points)
load_image = load_jpg_images_from("camera_cal")
def create_feature_extractor():
if (prepareData):
imgUtils = ImageUtils(imgSize, useAditional=useAditional, keepAspectRatio=keepAspectRatio,
useKaggleData=useKaggleData)
imgUtils.dataPreparation()()
K.set_image_data_format('channels_first')
K.set_floatx('float32')
np.random.seed(RDM)
print("\nLoading train data...\n" + SEPARATOR)
if (keepAspectRatio):
if (useAditional):
train_data = np.load('saved_data/trainExtra' + str(imgSize) + '_OrigAspectRatio.npy')
train_target = np.load('saved_data/trainExtra_target.npy')
else:
train_data = np.load('saved_data/train' + str(imgSize) + '_OrigAspectRatio.npy')
train_target = np.load('saved_data/train_target.npy')
else:
if (useAditional):
train_data = np.load('saved_data/trainExtra' + str(imgSize) + '.npy')
train_target = np.load('saved_data/trainExtra_target.npy')
else:
train_data = np.load('saved_data/train' + str(imgSize) + '.npy')
train_target = np.load('saved_data/train_target.npy')
x_train, x_val_train, y_train, y_val_train = train_test_split(
train_data, train_target, test_size=percentTrainForValidation,
random_state=RDM)
print("\nLoading test data...\n" + SEPARATOR)
if (keepAspectRatio):
test_data = np.load('saved_data/test' + str(imgSize) + '_OrigAspectRatio.npy')
else:
test_data = np.load('saved_data/test' + str(imgSize) + '.npy')
if (dataAugmentation):
print("\nMaking data augmentation...\n" + SEPARATOR)
datagen = da.prepareDataAugmentation(train_data=train_data)
print("\nCreating model...\n" + SEPARATOR)
if (loadPreviousModel):
baseModel = load_model(pathToPreviousModel)
print("Loaded model from: " + pathToPreviousModel)
if (ftModel == "VGG16"):
model = Model(input=baseModel.input, outputs=baseModel.get_layer("block5_pool").output)
elif (ftModel == "IV3"):
model = Model(input=baseModel.input, outputs=baseModel.get_layer("mixed10").output)
else:
if (ftModel == "VGG16"):
# loading VGG16 model weights
model = VGG16(weights='imagenet', include_top=False, input_shape=(3, imgSize, imgSize))
elif (ftModel == "IV3"):
model = InceptionV3(weights='imagenet', include_top=False, input_shape=(3, imgSize, imgSize))
# Extracting features from the train dataset using the VGG16 pre-trained model
print("\nGenerating features...\n" + SEPARATOR)
print("\nTraining features...\n")
if (dataAugmentation):
# predict_generator(self, generator, steps, max_q_size=10, workers=1, pickle_safe=False, verbose=1)
# TODO dar mas imagenes7
batches = 0
features_train = []
train_labels = []
for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=batchSize, shuffle=True):
features_batch = model.predict_on_batch(x_batch)
features_train.append(features_batch)
train_labels.append(y_batch)
batches += 1
print("Batches: " + str(batches) + '/' + str(len(x_train)))
if batches >= len(x_train):
# we need to break the loop by hand because
# the generator loops indefinitely
break
print("\nValidation features...\n")
batches = 0
features_valid = []
valid_labels = []
for x_batch, y_batch in datagen.flow(x_val_train, y_val_train, batch_size=batchSize, shuffle=True):
features_batch = model.predict_on_batch(x_batch)
features_valid.append(features_batch)
valid_labels.append(y_batch)
batches += 1
print("Batches: " + str(batches) + '/' + str(len(x_val_train)))
if batches >= len(x_val_train) // batchSize:
# we need to break the loop by hand because
# the generator loops indefinitely
break
print("\nTest features...\n")
features_test = model.predict(test_data, batch_size=batchSize, verbose=1)
else:
features_train = model.predict(x_train, batch_size=batchSize, verbose=1)
print("\nValidation features...\n")
features_valid = model.predict(x_val_train, batch_size=batchSize, verbose=1)
print("\nTest features...\n")
features_test = model.predict(test_data, batch_size=batchSize, verbose=1)
if (dataAugmentation):
if (useAditional):
if (keepAspectRatio):
np.save('saved_data/feaExt_DATrain' + str(imgSize) + '.npy', features_train,
allow_pickle=True, fix_imports=True)
np.save('saved_data/feaExt_DATrain' + str(imgSize) + '_target.npy', train_labels,
allow_pickle=True, fix_imports=True)
np.save('saved_data/feaExt_DAValid' + str(imgSize) + '.npy', features_valid,
allow_pickle=True, fix_imports=True)
np.save('saved_data/feaExt_DAValid' + str(imgSize) + '_target.npy', valid_labels,
allow_pickle=True, fix_imports=True)
np.save('saved_data/feaExt_test' + str(imgSize) + '.npy', features_test,
allow_pickle=True, fix_imports=True)
else:
np.save('saved_data/feaExt_DA_NAR_Train' + str(imgSize) + '.npy', features_train,
allow_pickle=True, fix_imports=True)
np.save('saved_data/feaExt_DA_NAR_Train' + str(imgSize) + '_target.npy', train_labels,
allow_pickle=True, fix_imports=True)
np.save('saved_data/feaExt_DA_NAR_Valid' + str(imgSize) + '.npy', features_valid,
allow_pickle=True, fix_imports=True)
np.save('saved_data/feaExt_DA_NAR_Valid' + str(imgSize) + '_target.npy', valid_labels,
allow_pickle=True, fix_imports=True)
np.save('saved_data/feaExt__NAR_test' + str(imgSize) + '.npy', features_test,
allow_pickle=True, fix_imports=True)
else:
if (keepAspectRatio):
np.save('saved_data/fea_DATrain' + str(imgSize) + '.npy', features_train,
allow_pickle=True, fix_imports=True)
np.save('saved_data/fea_DATrain' + str(imgSize) + '_target.npy', train_labels,
allow_pickle=True, fix_imports=True)
np.save('saved_data/fea_DAValid' + str(imgSize) + '.npy', features_valid,
allow_pickle=True, fix_imports=True)
np.save('saved_data/fea_DAValid' + str(imgSize) + '_target.npy', valid_labels,
allow_pickle=True, fix_imports=True)
np.save('saved_data/fea_test' + str(imgSize) + '.npy', features_test,
allow_pickle=True, fix_imports=True)
else:
np.save('saved_data/fea_DA_NAR_Train' + str(imgSize) + '.npy', features_train,
allow_pickle=True, fix_imports=True)
np.save('saved_data/fea_DA_NAR_Train' + str(imgSize) + '_target.npy', train_labels,
allow_pickle=True, fix_imports=True)
np.save('saved_data/fea_DA_NAR_Valid' + str(imgSize) + '.npy', features_valid,
allow_pickle=True, fix_imports=True)
np.save('saved_data/fea_DA_NAR_Valid' + str(imgSize) + '_target.npy', valid_labels,
allow_pickle=True, fix_imports=True)
np.save('saved_data/fea__NAR_test' + str(imgSize) + '.npy', features_test,
allow_pickle=True, fix_imports=True)
else:
if (useAditional):
if (keepAspectRatio):
np.save('saved_data/feaExt_Train' + str(imgSize) + '.npy', features_train,
allow_pickle=True, fix_imports=True)
np.save('saved_data/feaExt_Train' + str(imgSize) + '_target.npy', y_train,
allow_pickle=True, fix_imports=True)
np.save('saved_data/feaExt_Valid' + str(imgSize) + '.npy', features_valid,
allow_pickle=True, fix_imports=True)
np.save('saved_data/feaExt_Valid' + str(imgSize) + '_target.npy', y_val_train,
allow_pickle=True, fix_imports=True)
np.save('saved_data/feaExt_test' + str(imgSize) + '.npy', features_test,
allow_pickle=True, fix_imports=True)
else:
np.save('saved_data/feaExt_NAR_Train' + str(imgSize) + '.npy', features_train,
allow_pickle=True, fix_imports=True)
np.save('saved_data/feaExt_NAR_Train' + str(imgSize) + '_target.npy', y_train,
allow_pickle=True, fix_imports=True)
np.save('saved_data/feaExt_NAR_Valid' + str(imgSize) + '.npy', features_valid,
allow_pickle=True, fix_imports=True)
np.save('saved_data/feaExt_NAR_Valid' + str(imgSize) + '_target.npy', y_val_train,
allow_pickle=True, fix_imports=True)
np.save('saved_data/feaExt_NAR_test' + str(imgSize) + '.npy', features_test,
allow_pickle=True, fix_imports=True)
else:
if (keepAspectRatio):
np.save('saved_data/fea_Train' + str(imgSize) + '.npy', features_train,
allow_pickle=True, fix_imports=True)
np.save('saved_data/fea_Train' + str(imgSize) + '_target.npy', y_train,
allow_pickle=True, fix_imports=True)
np.save('saved_data/fea_Valid' + str(imgSize) + '.npy', features_valid,
allow_pickle=True, fix_imports=True)
np.save('saved_data/fea_Valid' + str(imgSize) + '_target.npy', y_val_train,
allow_pickle=True, fix_imports=True)
np.save('saved_data/fea_test' + str(imgSize) + '.npy', features_test,
allow_pickle=True, fix_imports=True)
else:
np.save('saved_data/fea_NAR_Train' + str(imgSize) + '.npy', features_train,
allow_pickle=True, fix_imports=True)
np.save('saved_data/fea_NAR_Train' + str(imgSize) + '_target.npy', y_train,
allow_pickle=True, fix_imports=True)
np.save('saved_data/fea_NAR_Valid' + str(imgSize) + '.npy', features_valid,
allow_pickle=True, fix_imports=True)
np.save('saved_data/fea_NAR_Valid' + str(imgSize) + '_target.npy', y_val_train,
allow_pickle=True, fix_imports=True)
np.save('saved_data/fea_NAR_test' + str(imgSize) + '.npy', features_test,
allow_pickle=True, fix_imports=True)
def main():
if (prepareData):
imgUtils = ImageUtils(imgSize, useAditional=useAditional, keepAspectRatio=keepAspectRatio,
useKaggleData=useKaggleData)
imgUtils.dataPreparationOVA()
K.set_image_data_format('channels_first')
K.set_floatx('float32')
np.random.seed(17)
print("\nLoading train data...\n" + SEPARATOR)
train_target = []
if (keepAspectRatio):
if (useAditional):
train_data = np.load('saved_data/trainExtra' + str(imgSize) + '_OrigAspectRatio.npy')
train_target.append(np.load('saved_data/trainExtraOVA1_target.npy'))
train_target.append(np.load('saved_data/trainExtraOVA2_target.npy'))
train_target.append(np.load('saved_data/trainExtraOVA3_target.npy'))
else:
train_data = np.load('saved_data/train' + str(imgSize) + '_OrigAspectRatio.npy')
train_target.append(np.load('saved_data/train_targetOVA1.npy'))
train_target.append(np.load('saved_data/train_targetOVA2.npy'))
train_target.append(np.load('saved_data/train_targetOVA3.npy'))
else:
if (useAditional):
train_data = np.load('saved_data/trainExtra' + str(imgSize) + '.npy')
train_target.append(np.load('saved_data/trainExtraOVA1_target.npy'))
train_target.append(np.load('saved_data/trainExtraOVA2_target.npy'))
train_target.append(np.load('saved_data/trainExtraOVA3_target.npy'))
else:
train_data = np.load('saved_data/train' + str(imgSize) + '.npy')
train_target.append(np.load('saved_data/train_targetOVA1.npy'))
train_target.append(np.load('saved_data/train_targetOVA2.npy'))
train_target.append(np.load('saved_data/train_targetOVA3.npy'))
print("\nMaking data augmentation...\n" + SEPARATOR)
datagen = da.prepareDataAugmentation(train_data=train_data)
model = []
currentDate = datetime.today()
timeStamp = currentDate.strftime("%d-%m-%Y_%H-%M")
for i in range(len(train_target)):
x_train, x_val_train, y_train, y_val_train = train_test_split(
train_data, train_target[i], test_size=percentTrainForValidation,
random_state=17)
print("\nCreating model " + str(i + 1) + "...\n" + SEPARATOR)
if (loadPreviousModel):
model.append(load_model(pathToPreviousModel[i]))
print("Loaded model from: " + pathToPreviousModel[i])
model[i].summary()
else:
model.append(create_model())
print("\nTraining Set shape (num Instances, RGB chanels, width, height): " + str(
x_train.shape) + "\nTraining labels: " + str(y_train.shape) + "\nValidating set shape: " + str(
x_val_train.shape) + "\nValidating set labels: " + str(
y_val_train.shape) + "\n" + SEPARATOR)
if (saveNetArchImage):
plot_model(model[i], to_file='saved_data/model_' + timeStamp + '.png')
if (onlyEvaluate):
print("\nEvaluating Model " + str(i + 1) + "...\n" + SEPARATOR)
evaluateModel(model[i], x_val_train, y_val_train)
else:
print("\nFitting model " + str(i + 1) + "...\n" + SEPARATOR)
checkPoint = ModelCheckpoint(
"saved_data/OVA_model" + str(i + 1) + "_ep{epoch:02d}_" + timeStamp + ".hdf5",
save_best_only=True)
model[i].fit_generator(datagen.flow(x_train, y_train, batch_size=batchSize,
shuffle=True),
steps_per_epoch=10, epochs=NumEpoch,
validation_data=(x_val_train, y_val_train),
callbacks=[checkPoint]) # , verbose=2)
print("\nLoading test data...\n" + SEPARATOR)
if (keepAspectRatio):
test_data = np.load('saved_data/test' + str(imgSize) + '_OrigAspectRatio.npy')
test_id = np.load('saved_data/test_id.npy')
else:
test_data = np.load('saved_data/test' + str(imgSize) + '.npy')
test_id = np.load('saved_data/test_id.npy')
pred = []
for i in range(len(model)):
print("\nPredicting with model " + str(i + 1) + "...\n" + SEPARATOR)
pred.append(model[i].predict_proba(test_data))
predictions = np.transpose(np.vstack((pred[0][:, 1], pred[1][:, 1], pred[2][:, 1])))
df = pd.DataFrame(predictions,
columns=['Type_1', 'Type_2', 'Type_3'])
df['image_name'] = test_id
df.to_csv("../submission/OVA_" + timeStamp + ".csv", index=False)
# -*- coding: utf-8 -*- """ Created on Mon Nov 2 01:56:16 2020 @author: ladvien """ import os import sys import cv2 import matplotlib.pyplot as plt image_tools_path = "/home/ladvien/deep_arcane/" sys.path.append(image_tools_path) from image_utils import ImageUtils iu = ImageUtils() ############# # Parameters ############# input_path = "/home/ladvien/deep_arcane/images/0_raw/1_extracted" output_path = "/home/ladvien/deep_arcane/images/0_raw/2_black_and_white/" threshold = 120 samples = 10 ############# # Extract #############
"""
import sys
import os
import cv2
import numpy as np
from random import randint
import matplotlib.pyplot as plt
image_tools_path = "/home/ladvien/deep_arcane/"
sys.path.append(image_tools_path)
from image_utils import ImageUtils
iu = ImageUtils()
def noisy(noise_typ, image):
if noise_typ == "gauss":
row, col, ch = image.shape
mean = 0
var = 0.1
sigma = var**0.5
gauss = np.random.normal(mean, sigma, (row, col, ch))
gauss = gauss.reshape(row, col, ch)
noisy = image + gauss
return noisy
elif noise_typ == "s&p":
row, col, ch = image.shape
s_vs_p = 0.5
# Test num 001. Marek
# <https://www.kaggle.com/marek3000/test-num-001/code/>
from image_utils import ImageUtils
imgSize = 10
useAditional = True
keepAspectRatio = True
useKaggleData = True
if __name__ == '__main__':
imgUtils = ImageUtils(imgSize, useAditional, keepAspectRatio, useKaggleData)
imgUtils.dataPreparation()
args = parser.parse_args()
data_directory = args.data_directory
num_classes = args.num_classes
gpu = args.gpu
save_dir = args.save_dir
architecture = args.architecture
optimizer = args.optimizer
loss_function = args.loss_function
learning_rate = args.learning_rate
drop_out = args.drop_out
epochs = args.epochs
print_every = args.print_every
min_accuracy = args.min_accuracy
image_utils = ImageUtils(data_directory)
model_utils = ModelUtils(gpu)
print('\nBuilding model...\n')
model = model_utils.build_model(architecture,
num_classes,
dropout_prob=drop_out)
data_loaders = image_utils.create_data_loaders()
optimizer = model_utils.create_optimizer(model,
optimizer,
learning_rate=learning_rate)
criterion = model_utils.loss_function(loss_function)
print('\nTraining network...\n')
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
with tf.Session(config=config) as sess:
# 保证存放model的文件夹存在
if not os.path.exists(training_options['model_path']):
os.mkdir(training_options['model_path'])
# 定义神经网络
net, train = model.build_network(training_options=training_options, image=image,
drop_rate=training_options['drop_rate'], labels=labels)
# 获得saver对象，可以保存model以及读取model
saver = tf.train.Saver()
saver.restore(sess, training_options['model_path'] + training_options['model_name'])
# 初始化 imageUtils类，获得所有训练，测试数据
imageUtils = ImageUtils()
test_data = imageUtils.test_data
test_label = imageUtils.test_label
total_result = np.zeros((200, 2))
for i in range(200):
result = sess.run([net['digit1'], net['digit2'], net['digit3'], net['digit4']],
feed_dict={image: imageUtils.trainstion_data(test_data, start=i * 1000,end=i * 1000 + 1000)})
result = code_utils.batch_out_transition(result)
predicted = [result[0][index] + result[1][index] + result[2][index] + result[3][index]
for index in range(len(result[0]))]
label = code_utils.batch_out_transition(test_label[i * 1000:i * 1000 + 1000])
four_right_count = np.count_nonzero([predicted[index] == label[index] for index in range(len(predicted))])
one_right_count = np.count_nonzero(
[predicted[index][s_index] == label[index][s_index] for index in range(len(predicted)) for s_index in
range(len(predicted[index]))])
total_result[i, 0] = four_right_count / 1000 * 100
with tf.Session(config=config) as sess:
# 保证存放model的文件夹存在
if not os.path.exists(training_options['model_path']):
os.mkdir(training_options['model_path'])
# 定义神经网络
net, train = model.build_network(training_options=training_options,
image=image,
drop_rate=training_options['drop_rate'],
labels=labels)
# 获得saver对象，可以保存model以及读取model
saver = tf.train.Saver()
saver.restore(
sess, training_options['model_path'] + training_options['model_name'])
# 初始化 imageUtils类，获得所有训练，测试数据
imageUtils = ImageUtils()
test_data = imageUtils.test_data
test_label = imageUtils.test_label
total_result = np.zeros((200, 2))
for i in range(200):
result = sess.run(
[net['digit1'], net['digit2'], net['digit3'], net['digit4']],
feed_dict={
image:
imageUtils.trainstion_data(test_data,
start=i * 1000,
end=i * 1000 + 1000)
})
result = code_utils.batch_out_transition(result)
predicted = [
result[0][index] + result[1][index] + result[2][index] +