def predict_preprocess(full_img, bbox):
# 対象領域を切り出す
x, y, w, h = bbox
target_img = full_img[y:y+h, x:x+w, :]
# 画像から特徴抽出
return MyAlgorithm.feature_extraction(target_img)
def read_data(datasetdir, lv):
alcon = AlconUtils(datasetdir)
# アノテーションの読み込み
fn = "target_lv" + lv + ".csv"
alcon.load_annotations_target(fn)
fn = "groundtruth_lv" + lv + ".csv"
alcon.load_annotations_ground(fn)
# KNNモデルの作成
dataset = {}
for bb_id, target in alcon.targets.items():
img_filename = alcon.get_filename_char(bb_id)
code = alcon.ground_truth[bb_id][0]
if code not in dataset:
dataset[code] = []
#if len(dataset[code]) == 100:
# continue
img = cv2.imread(img_filename)
feature = MyAlgorithm.feature_extraction(img)
dataset[code].append(feature)
labels = []
data = []
classes = sorted(dataset.keys())
for label, values in dataset.items():
labels += [classes.index(label)] * len(values)
data += values
data = np.asarray(data, dtype=np.float)
labels = np.asarray(labels, dtype=np.int)
return data, labels, classes
def main(datasetdir, lv):
start = time.time()
"""
評価コードのメイン
:param datasetdir データセットを格納したディレクトリへのパス
"""
# 初期化
print("Now evaluation...")
alcon = AlconUtils(datasetdir)
myalgorithm = MyAlgorithm(datasetdir)
# ターゲットの読み込み
fn = "target_lv" + lv + "_test_5.csv"
alcon.load_annotations_target(fn)
results = {}
prev = None
# １ターゲットずつ認識していくループ
for bb_id, target in alcon.targets.items():
img_file_id, *bb = target
img_filename = os.path.join(datasetdir, "images", img_file_id + ".jpg")
if prev != img_filename:
# ページ全体の画像
full_img = cv2.imread(img_filename)
prev = img_filename
# 認識処理（ここで各自のアルゴリズムを呼び出します）
result = myalgorithm.predict(full_img, bb)
# boundingbox id と紐付けて保存
results[bb_id] = result
# 評価
fnGround = "groundtruth_lv" + lv + "_test_5.csv"
alcon.load_annotations_ground(fnGround)
alcon.evaluation(results)
# ファイルに結果を書き込む
alcon.write_results(results)
elapsed_time = time.time() - start
print("elapsed_time:", elapsed_time, "[sec]")
def main(datasetdir,lv):
# 初期化
alcon = AlconUtils(datasetdir)
# アノテーションの読み込み
fn = "target_lv" + lv + "_samp_5.csv"
alcon.load_annotations_target(fn)
fn = "groundtruth_lv" + lv + "_samp_5.csv"
alcon.load_annotations_ground(fn)
# KNNモデルの作成
dataset = {}
for bb_id, target in alcon.targets.items():
img_filename = alcon.get_filename_char( bb_id )
code = alcon.ground_truth[bb_id][0]
if code not in dataset:
dataset[code] = []
# if len(dataset[code]) == 5:
# continue
img = cv2.imread( img_filename )
feature = MyAlgorithm.feature_extraction(img)
dataset[code].append(feature)
labels = []
data = []
classes = sorted(dataset.keys())
for label, values in dataset.items():
labels += [classes.index(label)] * len(values)
data += values
data = np.asarray(data, dtype=np.float)
labels = np.asarray(labels, dtype=np.int)
classifier = KNeighborsClassifier() #編集
classifier.fit(data, labels)
outputfile = "./model.pkl"
joblib.dump((classes, classifier), outputfile)
from alcon_utils import AlconUtils
import cv2
import os.path
import pickle
datasetdir = "../../dataset/"
annotation_name = "iiyama_0.1"
"""
評価コードのメイン
:param datasetdir データセットを格納したディレクトリへのパス
"""
# 初期化
alcon = AlconUtils(datasetdir)
myalgorithm = MyAlgorithm(datasetdir)
# ターゲットの読み込み
file_name_last = "_lv1_" + annotation_name + ".csv"
alcon.load_annotations_target("target" + file_name_last)
imgs = {}
results_pre = {}
# １ターゲットずつ認識していくループ
print("coco")
def predict_preprocess(full_img, bbox):
# 対象領域を切り出す
x, y, w, h = bbox
target_img = full_img[y:y+h, x:x+w, :]
def main(datasetdir, lv):
config = tensorflow.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.95
backend.tensorflow_backend.set_session(tensorflow.Session(config=config))
# 初期化
alcon = AlconUtils(datasetdir)
# アノテーションの読み込み
alcon.load_annotations_target("target_lv" + lv + ".csv")
alcon.load_annotations_ground("groundtruth_lv" + lv + ".csv")
dataset = {}
for bb_id, target in alcon.targets.items():
code = alcon.ground_truth[bb_id][0]
if code not in dataset:
dataset[code] = []
# if len(dataset[code]) == 10:
# continue
img_filename = alcon.get_filename_char(bb_id)
img = cv2.imread(img_filename)
feature = MyAlgorithm.feature_extraction(img)
dataset[code].append(feature)
labels = []
data = []
classes = sorted(dataset.keys())
for label, values in dataset.items():
labels += [classes.index(label)] * len(values)
data += values
batch_size = 128
num_classes = 46
epochs = 12
img_rows, img_cols = 32, 32
channel = 1
y_train = keras.utils.to_categorical(labels, num_classes)
classifier = Sequential()
classifier.add(
keras.layers.normalization.BatchNormalization(input_shape=(img_rows,
img_cols,
channel)))
classifier.add(Conv2D(32, (3, 3), activation='relu'))
classifier.add(Conv2D(64, (3, 3), activation='relu'))
classifier.add(MaxPooling2D(pool_size=(4, 4)))
classifier.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
classifier.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
classifier.add(Dropout(0.5))
classifier.add(Flatten())
classifier.add(Dense(128, activation='relu'))
classifier.add(Dropout(0.5))
classifier.add(Dense(num_classes, activation='softmax'))
classifier.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Nadam(),
metrics=['accuracy'])
classifier.summary()
numpy_data = numpy.asarray(data, dtype=numpy.float)
float_data = numpy_data.reshape(numpy_data.shape[0],img_rows,img_cols, channel )\
.astype('float32')
numpy_labels = numpy.asarray(labels, dtype=numpy.int)
y_train = keras.utils.to_categorical(numpy_labels, num_classes)
classifier.fit(float_data,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=None)
outputfile = "./model.pkl"
outputfile2 = "./model2.pkl"
joblib.dump(classes, outputfile)
classifier.save(outputfile2)
def main(datasetdir,lv):
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 1.0
K.tensorflow_backend.set_session(tf.Session(config=config))
# 初期化
alcon = AlconUtils(datasetdir)
# アノテーションの読み込み
fn = "target_lv" + lv + ".csv"
alcon.load_annotations_target(fn)
fn = "groundtruth_lv" + lv + ".csv"
alcon.load_annotations_ground(fn)
# KNNモデルの作成
dataset = {}
print(len(alcon.targets.items()))
for bb_id, target in alcon.targets.items():
img_filename = alcon.get_filename_char( bb_id )
code = alcon.ground_truth[bb_id][0]
if code not in dataset:
dataset[code] = []
if len(dataset[code]) == 100:
continue
img = cv2.imread( img_filename )
feature = MyAlgorithm.feature_extraction(img)
dataset[code].append(feature)
labels = []
data = []
classes = sorted(dataset.keys())
print(len(dataset.items()))
for label, values in dataset.items():
labels += [classes.index(label)] * len(values)
data += values
data = np.asarray(data, dtype=np.float)
labels = np.asarray(labels, dtype=np.int)
print(data.shape)
print(labels.shape)
# classifier = KNeighborsClassifier()
# classifier = RandomForestClassifier()
# classifier.fit(data, labels)
batch_size = 128
num_classes = 46
epochs = 12
img_rows, img_cols = 32, 32
# (x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = data
y_train = labels
x_train = x_train.reshape(x_train.shape[0],img_rows,img_cols,3)
# x_test = x_test.reshape(x_test.shape[0],img_rows,img_cols,1)
input_shape = (img_rows, img_cols, 3)
x_train = x_train.astype('float32')
# x_test = x_test.astype('float32')
# x_test /= 255
y_train = keras.utils.to_categorical(y_train, num_classes)
# y_test = keras.utils.to_categorical(y_test, num_classes)
classifier = Sequential()
classifier.add(Conv2D(32, kernel_size=(3,3), activation='relu', input_shape=input_shape,padding='same'))
classifier.add(Conv2D(64,(3,3),activation='relu',padding='same'))
classifier.add(MaxPooling2D(pool_size=(2,2)))
classifier.add(Dropout(0.25))
classifier.add(Flatten())
classifier.add(Dense(128,activation='relu'))
classifier.add(Dropout(0.5))
classifier.add(Dense(num_classes, activation='softmax'))
classifier.compile(loss=keras.losses.categorical_crossentropy,optimizer=keras.optimizers.Adadelta(),metrics=['accuracy'])
classifier.fit(x_train,y_train,batch_size=batch_size,epochs=epochs,verbose=1,validation_data=None)
# classifier.fit(data,labels)
outputfile = "./model.pkl"
outputfile2 = "./model2.pkl"
joblib.dump(classes, outputfile)
classifier.save(outputfile2)
def main(datasetdir, lv, length):
pr = cProfile.Profile()
pr.enable()
config = tensorflow.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.93
keras.backend.tensorflow_backend.set_session(
tensorflow.Session(config=config))
# 初期化
alcon = AlconUtils(datasetdir)
# アノテーションの読み込み
alcon.load_annotations_target("target_lv1_samp_0.9.csv")
alcon.load_annotations_ground("groundtruth_lv1_samp_0.9.csv")
dataset = {}
for bb_id, target in alcon.targets.items():
code = alcon.ground_truth[bb_id][0]
if code not in dataset:
dataset[code] = []
if len(dataset[code]) == int(length):
continue
img_filename = alcon.get_filename_char(bb_id)
img = cv2.imread(img_filename)
feature = MyAlgorithm.feature_extraction(img)
dataset[code].append(feature)
labels = []
data = []
classes = sorted(dataset.keys())
for label, values in dataset.items():
labels += [classes.index(label)] * len(values)
data += values
num_classes = 46
input_shape = (32, 32, 1) # img_rows img_cols channel
classifier = keras.models.Sequential()
classifier.add(
keras.layers.normalization.BatchNormalization(input_shape=input_shape))
classifier.add(Conv2D(32, kernel_size=(3, 3), activation='relu')) # 30*30
classifier.add(Conv2D(64, (3, 3), activation='relu')) # 28*28
classifier.add(Dropout(0.5))
classifier.add(MaxPooling2D(pool_size=(4, 4))) # 7*7
classifier.add(Flatten())
classifier.add(Dense(128, activation='relu'))
classifier.add(Dropout(0.5))
classifier.add(Dense(num_classes, activation='softmax'))
classifier.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Nadam(),
metrics=['accuracy'])
classifier.summary()
x_data = np.asarray(data).reshape(len(data), *input_shape)
y_train = keras.utils.to_categorical(labels, num_classes)
classifier.fit(x_data, y_train, batch_size=84, epochs=12)
joblib.dump(classes, "./model.pkl")
classifier.save("./model2.pkl")
pr.disable()
pstats.Stats(pr).sort_stats('tottime').print_stats(5)