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
 
,            #旋轉
        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,             #錯切變換，讓所有點的x座標(或者y座標)保持不變，而對應的y座標(或者x座標)則按比例發生平移
        zoom_range=[0.9,0.9],        # 單方向縮放，當一個數值時兩個方向等比例縮放，引數為list時長寬不同程度縮放。引數大於0小於1時，執行的是放大操作，當引數大於1時，執行的是縮小操作。
        channel_shift_range = 40
 
,   #偏移通道數值，改變圖片顏色，越大顏色越深
        horizontal_flip=True,       #水平翻轉，垂直翻轉vertical_flip
        fill_mode='nearest',         #操作導致影象缺失時填充方式。“constant”、“nearest”（預設）、“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()

# #     單個分類
#     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)

# #     載入資料和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

#多輸入，設定隨機種子
# Define the image transformations here
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)

# Here is the function that merges our two generators
# We use the exact same generator with the same random seed for both the y and angle arrays
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()
            #Assert arrays are equal - this was for peace of mind, but slows down training
            #np.testing.assert_array_equal(X1i[0],X2i[0])
            yield [X1i[0], X2i[1]], X1i[1]

#手動構造，直接輸出多label
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))