import time #line:2
import numpy as np #line:3
import cv2 #line:4
import matplotlib .pyplot as plt #line:5
from keras .preprocessing .image import ImageDataGenerator #line:7
from keras .models import model_from_json #line:8
np .set_printoptions (suppress =True )#line:10
rename =True #line:20
names =['Elegant','Romantic','Ethnic','Country','Active','Mannish','Futurism','Sophisticated']#line:22
order =[5 ,6 ,7 ,0 ,1 ,2 ,3 ,4 ]#line:25
rotation_range =2 #line:28
width_shift_range =0.02 #line:29
height_shift_range =0.02 #line:30
channel_shift_range =40.0 #line:31
shear_range =0.02 #line:32
zoom_range =[1.0 ,1.1 ]#line:33
horizontal_flip =True #line:34
vertical_flip =False #line:35
batch_size =1 #line:38
average_num =10 #line:41
img_save =False #line:44
g_size =3 #line:47
def show (OOOO0O000O0OO0O0O ,name ='_'):#line:51
    plt .figure (figsize =(8 ,8 ))#line:52
    if np .max (OOOO0O000O0OO0O0O )>1 :#line:53
        OOOO0O000O0OO0O0O =np .array (OOOO0O000O0OO0O0O ,dtype =int )#line:54
        plt .imshow (OOOO0O000O0OO0O0O ,vmin =0 ,vmax =255 )#line:55
    else :#line:56
        plt .imshow (OOOO0O000O0OO0O0O ,vmin =0 ,vmax =1 )#line:57
    plt .gray ()#line:58
    if img_save :#line:59
        plt .savefig (name +'.png')#line:60
    else :#line:61
        plt .show ()#line:62
    plt .close ()#line:63
class tool_nadera :#line:80
    def __init__ (O0O0OOO0OO0OO00O0 ,O000OO0000O00O0O0 ,O00OOO0000000O00O ,verbose =1 ):#line:81
        O0O0OOO0OO0OO00O0 .verbose =verbose #line:82
        if O0O0OOO0OO0OO00O0 .verbose >0 :#line:89
            print ('Loading nadera model...',end ='')#line:90
            O0000O0O0OOOOOOOO =time .time ()#line:91
        O0O0O0000O00O0OOO =open (O000OO0000O00O0O0 ,'r')#line:92
        OO000000000O0O0O0 =O0O0O0000O00O0OOO .read ()#line:93
        O0O0O0000O00O0OOO .close ()#line:94
        if O0O0OOO0OO0OO00O0 .verbose >0 :#line:95
            O0OO0OOOOOO000OOO =time .time ()#line:96
            print ('Done.({}s)'.format (round (O0OO0OOOOOO000OOO -O0000O0O0OOOOOOOO ,2 )))#line:97
        if O0O0OOO0OO0OO00O0 .verbose >0 :#line:99
            print ('Loading nadera weights...',end ='')#line:100
            O0000O0O0OOOOOOOO =time .time ()#line:101
        O0O0OOO0OO0OO00O0 .model =model_from_json (OO000000000O0O0O0 )#line:103
        O0O0OOO0OO0OO00O0 .model .load_weights (O00OOO0000000O00O )#line:104
        O0O0OOO0OO0OO00O0 .model .trainable =False #line:105
        if O0O0OOO0OO0OO00O0 .verbose >0 :#line:108
            O0OO0OOOOOO000OOO =time .time ()#line:109
            print ('Done.({}s)'.format (round (O0OO0OOOOOO000OOO -O0000O0O0OOOOOOOO ,2 )))#line:110
        class O000000O00O00OOO0 (ImageDataGenerator ):#line:116
            def __init__ (OO0O00000000OO000 ,*O00OO0000OO00O0O0 ,**OOO0OOO0OOO000O0O ):#line:117
                super ().__init__ (*O00OO0000OO00O0O0 ,**OOO0OOO0OOO000O0O )#line:118
            def make_line (OOO0000OOO00O000O ,O000OOOO0O0OOOO0O ):#line:120
                OOO000000000OOO00 =cv2 .cvtColor (O000OOOO0O0OOOO0O ,cv2 .COLOR_RGB2GRAY )#line:122
                OOO000000000OOO00 =np .uint8 (OOO000000000OOO00 )#line:123
                OO0000OOO00OOOO00 =cv2 .Canny (OOO000000000OOO00 ,threshold1 =50 ,threshold2 =200 )#line:124
                OO0000OOO00OOOO00 =OO0000OOO00OOOO00 .reshape ((512 ,256 ,1 ))#line:125
                return OO0000OOO00OOOO00 #line:126
            def make_beta (O0000O0OO0O0O0OOO ,OOO0OOO000OOOO000 ):#line:128
                OOOOOO000O0OOOOOO =cv2 .GaussianBlur (OOO0OOO000OOOO000 ,(9 ,9 ),0 )#line:130
                O0O000O0OOOOO000O =np .sum (OOOOOO000O0OOOOOO ,axis =2 )#line:132
                O0O000O0OOOOO000O [O0O000O0OOOOO000O <252 *3 ]=255 #line:133
                O0O000O0OOOOO000O [O0O000O0OOOOO000O >=252 *3 ]=0 #line:134
                O0O000000O00OO00O =np .ones ((5 ,5 ),np .uint8 )#line:136
                O0O000O0OOOOO000O =cv2 .erode (O0O000O0OOOOO000O ,O0O000000O00OO00O ,iterations =1 )#line:137
                O0O000O0OOOOO000O =O0O000O0OOOOO000O .reshape ((512 ,256 ,1 ))#line:142
                return O0O000O0OOOOO000O #line:143
            def make_blur (OOO00OO00OOO0O0OO ,O0OOO0OOO0O0OO0O0 ):#line:145
                OOO0OO0O0O0O000OO =cv2 .GaussianBlur (O0OOO0OOO0O0OO0O0 ,(51 ,51 ),0 )#line:147
                return OOO0OO0O0O0O000OO #line:148
            def flow (OO00OOO000O000O0O ,*O0OO0O0OOO00O0O0O ,**OO0O0OOOO0O00000O ):#line:150
                O000OO00O00O0OOOO =super ().flow (*O0OO0O0OOO00O0O0O ,**OO0O0OOOO0O00000O )#line:151
                O0000O0000O00O000 =np .zeros ((batch_size ,512 ,256 ,1 ))#line:153
                O000O000O00OOOOO0 =np .zeros ((batch_size ,512 ,256 ,1 ))#line:154
                OO0O000O0O0OO0O00 =np .zeros ((batch_size ,512 ,256 ,3 ))#line:155
                O0O0O0OOO0OO0O00O =np .zeros ((batch_size ,8 ))#line:156
                while True :#line:158
                    OOOO0OOOO0OOO0OO0 ,OOOOOOOOO000OOO0O =next (O000OO00O00O0OOOO )#line:159
                    for O00O0000O0000O00O ,O0O000O0OO0OOO00O in enumerate (OOOO0OOOO0OOO0OO0 ):#line:162
                        O000O000O00OOOOO0 [O00O0000O0000O00O ]=OO00OOO000O000O0O .make_beta (O0O000O0OO0OOO00O )/255.0 #line:164
                        OOO00O0OOOO0OOOO0 =O000O000O00OOOOO0 [O00O0000O0000O00O ].reshape (O000O000O00OOOOO0 [O00O0000O0000O00O ].shape [:2 ])#line:165
                        O0O00000OOOOO0OOO =np .random .uniform (-channel_shift_range ,channel_shift_range )#line:168
                        O0O000O0OO0OOO00O =np .clip (O0O000O0OO0OOO00O +O0O00000OOOOO0OOO ,0 ,255 )#line:169
                        O0O000O0OO0OOO00O [:,:,0 ][OOO00O0OOOO0OOOO0 ==0 ]=255 #line:172
                        O0O000O0OO0OOO00O [:,:,1 ][OOO00O0OOOO0OOOO0 ==0 ]=255 #line:173
                        O0O000O0OO0OOO00O [:,:,2 ][OOO00O0OOOO0OOOO0 ==0 ]=255 #line:174
                        O0000O0000O00O000 [O00O0000O0000O00O ]=OO00OOO000O000O0O .make_line (O0O000O0OO0OOO00O )/255.0 #line:176
                        OO0O000O0O0OO0O00 [O00O0000O0000O00O ]=OO00OOO000O000O0O .make_blur (O0O000O0OO0OOO00O )/255.0 #line:177
                        O0O0O0OOO0OO0O00O [O00O0000O0000O00O ]=OOOOOOOOO000OOO0O [O00O0000O0000O00O ]#line:178
                    yield [O0000O0000O00O000 ,O000O000O00OOOOO0 ,OO0O000O0O0OO0O00 ],O0O0O0OOO0OO0O00O #line:180
        O0O0OOO0OO0OO00O0 .MIDG =O000000O00O00OOO0 (rescale =1.0 ,rotation_range =rotation_range ,width_shift_range =width_shift_range ,height_shift_range =height_shift_range ,shear_range =shear_range ,zoom_range =zoom_range ,horizontal_flip =horizontal_flip ,vertical_flip =vertical_flip ,)#line:192
    def do_nadera (OOOO0O0OOOOO0OO00 ,OOO0000O00O0OOOOO ,mode =''):#line:196
        if OOOO0O0OOOOO0OO00 .verbose >0 :#line:198
            OO0O0OO00OO000000 =time .time ()#line:199
        OO00OO000OO0000O0 =np .array ([OOO0000O00O0OOOOO ])#line:202
        OOO00OOO0O000000O =[[0 for OOO00O0O000OO0OO0 in range (8 )]for O0000OOO0O0O0O0OO in range (len (OO00OO000OO0000O0 ))]#line:206
        OOO00OOO0O000000O =np .array (OOO00OOO0O000000O )#line:207
        '''
        #======================================
        # 生成器の確認
        #======================================
        #生成器に1枚だけ入れる
        gen_test = self.MIDG.flow(np.array([x_test[0]]), np.array([y_train[0]]), batch_size=batch_size)
        
        #5*5で生成して確認
        gen_ims_line = []
        gen_ims_beta = []
        gen_ims_blur = []
        for i in range(g_size**2):
            x_tmp, y_tmp = next(gen_test)
            gen_ims_line.append(deepcopy(x_tmp[0][0].reshape((512, 256))))
            gen_ims_beta.append(deepcopy(x_tmp[1][0].reshape((512, 256))))
            gen_ims_blur.append(deepcopy(x_tmp[2][0]))
            #print(y_tmp[0])
        
        stacks_line = []
        for i in range(g_size):
            stack = np.concatenate(gen_ims_line[g_size*i:g_size*(i + 1)], axis=1)
            stacks_line.append(stack)
        stacks_line = np.concatenate(stacks_line, axis=0)
        show(stacks_line, name='stacks_line')
        
        stacks_beta = []
        for i in range(g_size):
            stack = np.concatenate(gen_ims_beta[g_size*i:g_size*(i + 1)], axis=1)
            stacks_beta.append(stack)
        stacks_beta = np.concatenate(stacks_beta, axis=0)
        show(stacks_beta, name='stacks_beta')
        
        stacks_blur = []
        for i in range(g_size):
            stack = np.concatenate(gen_ims_blur[g_size*i:g_size*(i + 1)], axis=1)
            stacks_blur.append(stack)
        stacks_blur = np.concatenate(stacks_blur, axis=0)
        show(stacks_blur, name='stacks_blur')
        '''#line:248
        for OOO0O000O0000OO0O in range (len (OO00OO000OO0000O0 [:])):#line:252
            OO0OO00O0O000000O =OOOO0O0OOOOO0OO00 .MIDG .flow (np .array ([OO00OO000OO0000O0 [OOO0O000O0000OO0O ]]),np .array ([OOO00OOO0O000000O [OOO0O000O0000OO0O ]]),batch_size =batch_size )#line:255
            if np .min (OO00OO000OO0000O0 [OOO0O000O0000OO0O ])==255 :#line:268
                O000000O00O0O0O0O =np .array ([np .zeros (len (names ))],float )#line:269
            else :#line:270
                O000000O00O0O0O0O =OOOO0O0OOOOO0OO00 .model .predict_generator (OO0OO00O0O000000O ,steps =average_num ,use_multiprocessing =False ,workers =1 )#line:271
            O000000O00O0O0O0O [O000000O00O0O0O0O <0.0 ]=0.0 #line:282
            O000000O00O0O0O0O [O000000O00O0O0O0O >0.7 ]=0.7 #line:283
            O000000O00O0O0O0O *=(100.0 /70.0 )#line:284
            OOOO0OOOOOO0OOOO0 =np .mean (O000000O00O0O0O0O ,axis =0 )#line:288
            O00OOO00O00O00O00 =np .std (O000000O00O0O0O0O ,axis =0 )#line:289
            """
            meanは0.0-1.0の８つの値
            """#line:294
            O0O0O0O0O00O0O00O =np .array (names )#line:296
            if rename :#line:298
                O0O0O0O0O00O0O00O =O0O0O0O0O00O0O00O [order ]#line:299
                OOOO0OOOOOO0OOOO0 =OOOO0OOOOOO0OOOO0 [order ]#line:300
                O00OOO00O00O00O00 =O00OOO00O00O00O00 [order ]#line:301
            if OOOO0O0OOOOO0OO00 .verbose >0 :#line:303
                OOOO0O00O0OOOOOOO =time .time ()#line:304
                print ('nadera end.({}s)'.format (round (OOOO0O00O0OOOOOOO -OO0O0OO00OO000000 ,2 )))#line:305
            if mode =='values':#line:308
                return O0O0O0O0O00O0O00O ,OOOO0OOOOOO0OOOO0 ,O00OOO00O00O00O00 #line:309
            else :#line:310
                OOOO0O0O0OO0O0O0O =np .argmax (OOOO0OOOOOO0OOOO0 )#line:311
                return O0O0O0O0O00O0O00O [OOOO0O0O0OO0O0O0O ],OOOO0OOOOOO0OOOO0 [OOOO0O0O0OO0O0O0O ],O00OOO00O00O00O00 [OOOO0O0O0OO0O0O0O ]#line:312
if __name__ =='__main__':#line:318
    pass #line:320
