import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]=""
import sys
import gc
import time
import cv2
import random
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from tqdm import tqdm
from random_eraser import get_random_eraser
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
datagen = ImageDataGenerator(
rotation_range=20, #旋转
width_shift_range=0.1, #水平位置平移
# height_shift_range=0.2, #上下位置平移
shear_range=0.5, #错切变换,让所有点的x坐标(或者y坐标)保持不变,而对应的y坐标(或者x坐标)则按比例发生平移
zoom_range=[0.9,0.9], # 单方向缩放,当一个数值时两个方向等比例缩放,参数为list时长宽不同程度缩放。参数大于0小于1时,执行的是放大操作,当参数大于1时,执行的是缩小操作。
channel_shift_range = 40, #偏移通道数值,改变图片颜色,越大颜色越深
horizontal_flip=True, #水平翻转,垂直翻转vertical_flip
fill_mode='nearest', #操作导致图像缺失时填充方式。“constant”、“nearest”(默认)、“reflect”和“wrap”
preprocessing_function = get_random_eraser(p=0.7,v_l=0,v_h=255,s_l=0.01,s_h=0.03,r_1=1,r_2=1.5,pixel_level=True)
)
# train_generator = datagen.flow_from_directory(
# 'base/Images/',
# save_to_dir = 'base/fake/',
# batch_size=1
# )
# for i in range(5):
# train_generator.next()
# !
# df_train = pd.read_csv('base/Annotations/label.csv', header=None)
# df_train.columns = ['image_id', 'class', 'label']
# classes = ['collar_design_labels', 'neckline_design_labels', 'skirt_length_labels',
# 'sleeve_length_labels', 'neck_design_labels', 'coat_length_labels', 'lapel_design_labels',
# 'pant_length_labels']
# !
# classes = ['collar_design_labels']
# !
# for i in range(len(classes)):
# gc.enable()
# # 单个分类
# cur_class = classes[i]
# df_load = df_train[(df_train['class'] == cur_class)].copy()
# df_load.reset_index(inplace=True)
# del df_load['index']
# # print(cur_class)
# # 加载数据和label
# n = len(df_load)
# # n_class = len(df_load['label'][0])
# # width = 256
# # X = np.zeros((n,width, width, 3), dtype=np.uint8)
# # y = np.zeros((n, n_class), dtype=np.uint8)
# print(f'starting load trainset {cur_class} {n}')
# sys.stdout.flush()
# for i in tqdm(range(n)):
# # tmp_label = df_load['label'][i]
# img = load_img('base/{0}'.format(df_load['image_id'][i]))
# x = img_to_array(img)
# x = x.reshape((1,) + x.shape)
# m=0
# for batch in datagen.flow(x,batch_size=1):
# # plt.imshow(array_to_img(batch[0]))
# # print(batch)
# array_to_img(batch[0]).save(f'base/fake/{format(df_load["image_id"][i])}-{m}.jpg')
# m+=1
# if m>3:
# break
# gc.collect()
# !
img = load_img('base/Images/collar_design_labels/2f639f11de22076ead5fe1258eae024d.jpg')
plt.figure()
plt.imshow(img)
x = img_to_array(img)
x = x.reshape((1,) + x.shape)
i = 0
for batch in datagen.flow(x,batch_size=5):
plt.figure()
plt.imshow(array_to_img(batch[0]))
# print(len(batch))
i += 1
if i >0:
break
gen = ImageDataGenerator(horizontal_flip = True,
vertical_flip = True,
width_shift_range = 0.1,
height_shift_range = 0.1,
zoom_range = 0.1,
rotation_range = 40)
def gen_flow_for_two_inputs(X1, X2, y):
genX1 = gen.flow(X1,y, batch_size=batch_size,seed=666)
genX2 = gen.flow(X1,X2, batch_size=batch_size,seed=666)
while True:
X1i = genX1.next()
X2i = genX2.next()
yield [X1i[0], X2i[1]], X1i[1]
generator = ImageDataGenerator(rotation_range=5.,
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True,
vertical_flip=True)
def generate_data_generator(generator, X, Y1, Y2):
genX = generator.flow(X, seed=7)
genY1 = generator.flow(Y1, seed=7)
while True:
Xi = genX.next()
Yi1 = genY1.next()
Yi2 = function(Y2)
yield Xi, [Yi1, Yi2]
model.fit_generator(generate_data_generator(generator, X, Y1, Y2),
epochs=epochs)
def batch_generator(generator,X,Y):
Xgen = generator.flow(X)
while True:
yield Xgen.next(),Y
h = model.fit_generator(batch_generator(datagen, X_all, y_all),
steps_per_epoch=len(X_all)//32+1,
epochs=80,workers=3,
callbacks=[EarlyStopping(patience=3), checkpointer,ReduceLROnPlateau(monitor='val_loss',factor=0.5,patience=1)],
validation_data=(X_val,y_val))