## Author: SHIVANK SHARMA
## Modified: Toshio Iwata
## Released under Apache 2.0 license 

import numpy as np
import pandas as pd
import os
import cv2
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from PIL import Image
from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import Adam
from sklearn.metrics import accuracy_score
np.random.seed(42)

from matplotlib import style
style.use('fivethirtyeight')

data_dir = './'
train_path = './train_1'
test_path = './'

# Resizing the images to 30x30x3
IMG_HEIGHT = 30
IMG_WIDTH = 30
channels = 3

NUM_CATEGORIES = len(os.listdir(train_path))
NUM_CATEGORIES

# Label Overview
### Modified for lisa
classes = { 0:'posi',
            1:'nega',
            2:'white', 
            3:'beju', 
            4:'black', 
            5:'wh2bk', 
            6:'bk2wh', 
            7:'wh_bk', 
            8:'bk_wh',
            9:'bumpy' }

###################################################
## for making models
###################################################
# モデルを作成
model1 = keras.Sequential(name="my_model1")
# model1を確認
print(model1)
model1.add(keras.layers.Conv2D(filters=16, kernel_size=(3,3), activation='relu', input_shape=(IMG_HEIGHT,IMG_WIDTH,channels)))


model2 = keras.Sequential(name="my_model2")
# model2を確認
print(model2)
model2.add(keras.layers.Conv2D(filters=32, kernel_size=(3,3), activation='relu', input_shape=(28,28,16)))
model2.add(keras.layers.MaxPool2D(pool_size=(2, 2)))


model3 = keras.Sequential(name="my_model3")
# model3を確認
print(model3)
model3.add(keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu', input_shape=(13,13,32)))


model4 = keras.Sequential(name="my_model4")
# model4を確認
print(model4)
model4.add(keras.layers.Conv2D(filters=128, kernel_size=(3,3), activation='relu', input_shape=(11,11,64)))
model4.add(keras.layers.MaxPool2D(pool_size=(2, 2)))
model4.add(keras.layers.Flatten())


model5 = keras.Sequential(name="my_model5")
# model5を確認
print(model5)
##model5.add(keras.layers.Dense(256, activation='relu', input_dim=2048))
model5.add(keras.layers.Dense(128, activation='relu', input_dim=2048))
model5.add(keras.layers.Dropout(rate=0.5))


model6 = keras.Sequential(name="my_model6")
# model6を確認
print(model6)
##model6.add(keras.layers.Dense(43, activation='softmax', input_dim=256))
### Midified for lisa
model6.add(keras.layers.Dense(10, activation='softmax', input_dim=128))

import csv

###################################################
## for read
###################################################
########################### lay1
lay1 = model1.layers[0]
tmpw = lay1.get_weights()

with open('w1.csv', mode='r') as f:
    reader = csv.reader(f)
    r = 0
    c = 0
    i = 0
    k = 0
    print("w1 shape : ", lay1.get_weights()[0].shape)
    for row in reader:
#        print(row)
        for k in range(0, lay1.get_weights()[0].shape[3]):
            tmpval = row[k] ### [0]縺ｫ驥阪∩菫よ焚
            tmpval = float(tmpval) * 256
            tmpval = int(tmpval)
            ### limitter 2023/11/08
            if tmpval > 255 :
                tmpval = 255 
            elif tmpval < -256 :
                tmpval = -256 
            tmpw[0][r][c][i][k] = tmpval ###row[k] ### [0]縺ｫ驥阪∩菫よ焚
        c += 1
        if(c == range(lay1.get_weights()[0].shape[1]).stop):
            c = 0
            r += 1
            if(r == range(lay1.get_weights()[0].shape[0]).stop):
                r = 0
                i += 1

with open('b1.csv', mode='r') as f:
    reader = csv.reader(f)
    print("b1 shape : ", lay1.get_weights()[1].shape)
    for row in reader:
#        print(row)
        for k in range(0, lay1.get_weights()[1].shape[0]):
            tmpval = row[k] ### [1]縺ｫ繝舌う繧｢繧ｹ
            tmpval = float(tmpval) * 256 * 256
            tmpval = int(tmpval)
            tmpw[1][k] = tmpval ###row[k] ### [1]縺ｫ繝舌う繧｢繧ｹ

lay1.set_weights(tmpw)
print("w1 read done")

########################### lay2
lay2 = model2.layers[1-1]
tmpw = lay2.get_weights()

with open('w2.csv', mode='r') as f:
    reader = csv.reader(f)
    r = 0
    c = 0
    i = 0
    k = 0
    print("w2 shape : ", lay2.get_weights()[0].shape)
    for row in reader:
#        print(row)
        for k in range(0, lay2.get_weights()[0].shape[3]):
            tmpval = row[k] ### [0]縺ｫ驥阪∩菫よ焚
            tmpval = float(tmpval) * 256
##            tmpval = int(tmpval)
            if tmpval > 48 :
                tmpval = 64
            elif tmpval >= 24 :
                tmpval = 32
            elif tmpval >= 8 :
                tmpval = 16
            elif tmpval >= 0 :
                tmpval = 4
            elif tmpval >= -8 :
                tmpval = -4
            elif tmpval >= -24 :
                tmpval = -16
            elif tmpval >= -48 :
                tmpval = -32
            else :
                tmpval = -64
            tmpw[0][r][c][i][k] = tmpval ###row[k] ### [0]縺ｫ驥阪∩菫よ焚
        c += 1
        if(c == range(lay2.get_weights()[0].shape[1]).stop):
            c = 0
            r += 1
            if(r == range(lay2.get_weights()[0].shape[0]).stop):
                r = 0
                i += 1

with open('b2.csv', mode='r') as f:
    reader = csv.reader(f)
    print("b2 shape : ", lay2.get_weights()[1].shape)
    for row in reader:
#        print(row)
        for k in range(0, lay2.get_weights()[1].shape[0]):
            tmpval = row[k] ### [1]縺ｫ繝舌う繧｢繧ｹ
            tmpval = float(tmpval) * 256 * 256
            tmpval = int(tmpval)
            tmpw[1][k] = tmpval ###row[k] ### [1]縺ｫ繝舌う繧｢繧ｹ

lay2.set_weights(tmpw)
print("w2 read done")

########################### lay3
lay3 = model3.layers[3-3]
tmpw = lay3.get_weights()

with open('w3.csv', mode='r') as f:
    reader = csv.reader(f)
    r = 0
    c = 0
    i = 0
    k = 0
    print("w3 shape : ", lay3.get_weights()[0].shape)
    for row in reader:
#        print(row)
        for k in range(0, lay3.get_weights()[0].shape[3]):
            tmpval = row[k] ### [0]縺ｫ驥阪∩菫よ焚
            tmpval = float(tmpval) * 256
##            tmpval = int(tmpval)
            if tmpval > 48 :
                tmpval = 64
            elif tmpval >= 0 :
                tmpval = 16
            elif tmpval >= -48 :
                tmpval = -16
            else :
                tmpval = -64
            tmpw[0][r][c][i][k] = tmpval ## row[k] ### [0]縺ｫ驥阪∩菫よ焚
        c += 1
        if(c == range(lay3.get_weights()[0].shape[1]).stop):
            c = 0
            r += 1
            if(r == range(lay3.get_weights()[0].shape[0]).stop):
                r = 0
                i += 1

with open('b3.csv', mode='r') as f:
    reader = csv.reader(f)
    print("b3 shape : ", lay3.get_weights()[1].shape)
    for row in reader:
#        print(row)
        for k in range(0, lay3.get_weights()[1].shape[0]):
            tmpval = row[k] ### [1]縺ｫ繝舌う繧｢繧ｹ
            tmpval = float(tmpval) * 256 * 256
            tmpval = int(tmpval)
            tmpw[1][k] = tmpval ## row[k] ### [1]縺ｫ繝舌う繧｢繧ｹ

lay3.set_weights(tmpw)
print("w3 read done")

########################### lay4
lay4 = model4.layers[4-4]
tmpw = lay4.get_weights()

with open('w4.csv', mode='r') as f:
    reader = csv.reader(f)
    r = 0
    c = 0
    i = 0
    k = 0
    print("w4 shape : ", lay4.get_weights()[0].shape)
    for row in reader:
#        print(row)
        for k in range(0, lay4.get_weights()[0].shape[3]):
            tmpval = row[k] ### [0]縺ｫ驥阪∩菫よ焚
            tmpval = float(tmpval) * 256
#            tmpval = int(tmpval)
            if tmpval > 48 :
                tmpval = 64
            elif tmpval >= 0 :
                tmpval = 16
            elif tmpval >= -48 :
                tmpval = -16
            else :
                tmpval = -64
            tmpw[0][r][c][i][k] = tmpval ## row[k] ### [0]縺ｫ驥阪∩菫よ焚
        c += 1
        if(c == range(lay4.get_weights()[0].shape[1]).stop):
            c = 0
            r += 1
            if(r == range(lay4.get_weights()[0].shape[0]).stop):
                r = 0
                i += 1

with open('b4.csv', mode='r') as f:
    reader = csv.reader(f)
    print("b4 shape : ", lay4.get_weights()[1].shape)
    for row in reader:
#        print(row)
        for k in range(0, lay4.get_weights()[1].shape[0]):
            tmpval = row[k] ### [1]縺ｫ繝舌う繧｢繧ｹ
            tmpval = float(tmpval) * 256 * 256
            tmpval = int(tmpval)
            tmpw[1][k] = tmpval ## row[k] ### [1]縺ｫ繝舌う繧｢繧ｹ

lay4.set_weights(tmpw)
print("w4 read done")

########################### lay5
lay5 = model5.layers[7-7]
tmpw = lay5.get_weights()

with open('w5.csv', mode='r') as f:
    reader = csv.reader(f)
    i = 0
    k = 0
    print("w5 shape : ", lay5.get_weights()[0].shape)
    for row in reader:
#        print(row)
        for k in range(0, lay5.get_weights()[0].shape[1]):
            tmpval = row[k] ### [0]縺ｫ驥阪∩菫よ焚
            tmpval = float(tmpval) * 256
#            tmpval = int(tmpval)
            if tmpval > 48 :
                tmpval = 64
            elif tmpval >= 0 :
                tmpval = 16
            elif tmpval >= -48 :
                tmpval = -16
            else :
                tmpval = -64
            tmpw[0][i][k] = tmpval ### row[k] ### [0]縺ｫ驥阪∩菫よ焚
        i += 1

with open('b5.csv', mode='r') as f:
    reader = csv.reader(f)
    print("b5 shape : ", lay5.get_weights()[1].shape)
    for row in reader:
#        print(row)
        for k in range(0, lay5.get_weights()[1].shape[0]):
            tmpval = row[k] ### [1]縺ｫ繝舌う繧｢繧ｹ
            tmpval = float(tmpval) * 256 * 256
            tmpval = int(tmpval)
            tmpw[1][k] = tmpval ## row[k] ### [1]縺ｫ繝舌う繧｢繧ｹ

lay5.set_weights(tmpw)
print("w5 read done")

########################### lay6
lay6 = model6.layers[9-9]
tmpw = lay6.get_weights()

with open('w6.csv', mode='r') as f:
    reader = csv.reader(f)
    i = 0
    k = 0
    print("w6 shape : ", lay6.get_weights()[0].shape)
    for row in reader:
#        print(row)
        for k in range(0, lay6.get_weights()[0].shape[1]):
            tmpval = row[k] ### [0]縺ｫ驥阪∩菫よ焚
            tmpval = float(tmpval) * 256
            tmpval = int(tmpval)
            ### limitter 2023/11/08
            if tmpval > 255 :
                tmpval = 255 
            elif tmpval < -256 :
                tmpval = -256 
#            if tmpval > 48 :
#                tmpval = 64
#            elif tmpval >= 0 :
#                tmpval = 16
#            elif tmpval >= -48 :
#                tmpval = -16
#            else :
#                tmpval = -64
            tmpw[0][i][k] = tmpval ### row[k] ### [0]縺ｫ驥阪∩菫よ焚
        i += 1

with open('b6.csv', mode='r') as f:
    reader = csv.reader(f)
    print("b6 shape : ", lay6.get_weights()[1].shape)
    for row in reader:
#        print(row)
        for k in range(0, lay6.get_weights()[1].shape[0]):
            tmpval = row[k] ### [1]縺ｫ繝舌う繧｢繧ｹ
            tmpval = float(tmpval) * 256 * 256
            tmpval = int(tmpval)
            tmpw[1][k] = tmpval ## row[k] ### [1]縺ｫ繝舌う繧｢繧ｹ


lay6.set_weights(tmpw)
print("w6 read done")

###################################################
## for write
###################################################

########################### lay1
with open("w1_int.csv", mode='w') as f:
    for i in range(0, lay1.get_weights()[0].shape[2]):
        for r in range(0, lay1.get_weights()[0].shape[0]):
            for c in range(0, lay1.get_weights()[0].shape[1]):
                for k in range(0, lay1.get_weights()[0].shape[3]):
                    tmpval = lay1.get_weights()[0][r][c][i][k] ### [0]に重み係数
                    f.write(str(tmpval))
                    f.write(', ')
                f.write('\n')

with open("b1_int.csv", mode='w') as f:
    for c in range(0, lay1.get_weights()[1].shape[0]):
        tmpval = lay1.get_weights()[1][c] ### [1]にバイアス
        f.write(str(tmpval))
        f.write(', ')

print("w1 write done")

########################### lay2
with open("w2_int.csv", mode='w') as f:
    for i in range(0, lay2.get_weights()[0].shape[2]):
        for r in range(0, lay2.get_weights()[0].shape[0]):
            for c in range(0, lay2.get_weights()[0].shape[1]):
                for k in range(0, lay2.get_weights()[0].shape[3]):
                    tmpval = lay2.get_weights()[0][r][c][i][k] ### [0]に重み係数
                    f.write(str(tmpval))
                    f.write(', ')
                f.write('\n')

with open("b2_int.csv", mode='w') as f:
    for c in range(0, lay2.get_weights()[1].shape[0]):
        tmpval = lay2.get_weights()[1][c] ### [1]にバイアス
        f.write(str(tmpval))
        f.write(', ')

print("w2 write done")

########################### lay3
with open("w3_int.csv", mode='w') as f:
    for i in range(0, lay3.get_weights()[0].shape[2]):
        for r in range(0, lay3.get_weights()[0].shape[0]):
            for c in range(0, lay3.get_weights()[0].shape[1]):
                for k in range(0, lay3.get_weights()[0].shape[3]):
                    tmpval = lay3.get_weights()[0][r][c][i][k] ### [0]に重み係数
                    f.write(str(tmpval))
                    f.write(', ')
                f.write('\n')

with open("b3_int.csv", mode='w') as f:
    for c in range(0, lay3.get_weights()[1].shape[0]):
        tmpval = lay3.get_weights()[1][c] ### [1]にバイアス
        f.write(str(tmpval))
        f.write(', ')

print("w3 write done")

########################### lay4
with open("w4_int.csv", mode='w') as f:
    for i in range(0, lay4.get_weights()[0].shape[2]):
        for r in range(0, lay4.get_weights()[0].shape[0]):
            for c in range(0, lay4.get_weights()[0].shape[1]):
                for k in range(0, lay4.get_weights()[0].shape[3]):
                    tmpval = lay4.get_weights()[0][r][c][i][k] ### [0]に重み係数
                    f.write(str(tmpval))
                    f.write(', ')
                f.write('\n')

with open("b4_int.csv", mode='w') as f:
    for c in range(0, lay4.get_weights()[1].shape[0]):
        tmpval = lay4.get_weights()[1][c] ### [1]にバイアス
        f.write(str(tmpval))
        f.write(', ')

print("w4 write done")

########################### lay5
with open("w5_int.csv", mode='w') as f:
    for r in range(0, lay5.get_weights()[0].shape[0]):
        for c in range(0, lay5.get_weights()[0].shape[1]):
            tmpval = lay5.get_weights()[0][r][c] ### [0]に重み係数
            f.write(str(tmpval))
            f.write(', ')
        f.write('\n')

with open("b5_int.csv", mode='w') as f:
    for c in range(0, lay5.get_weights()[1].shape[0]):
        tmpval = lay5.get_weights()[1][c] ### [1]にバイアス
        f.write(str(tmpval))
        f.write(', ')

print("w5 write done")

########################### lay6
with open("w6_int.csv", mode='w') as f:
    for r in range(0, lay6.get_weights()[0].shape[0]):
        for c in range(0, lay6.get_weights()[0].shape[1]):
            tmpval = lay6.get_weights()[0][r][c] ### [0]に重み係数
            f.write(str(tmpval))
            f.write(', ')
        f.write('\n')

with open("b6_int.csv", mode='w') as f:
    for c in range(0, lay6.get_weights()[1].shape[0]):
        tmpval = lay6.get_weights()[1][c] ### [1]にバイアス
        f.write(str(tmpval))
        f.write(', ')

print("w6 write done")

test = pd.read_csv(data_dir + '/TestCrack.csv') ### Modified for lisa

## Modified for Shiroume
def resize (image, size):
    h, w = image.shape[:2]
    aspect = w / h
    nh = nw = size
    if 1 >= aspect:
        nw = round(nh * aspect)
    else:
        nh = round(nw / aspect)
    resized = cv2.resize(image, dsize=(nw, nh))
    return resized

## Modified for Shiroume
def shiroume (image, size):
    resized = resize(image, size)

    h, w = resized.shape[:2]
    x = y = 0
    if h < w:
        y = (size - h) // 2
    else:
        x = (size - w) // 2
    resized = Image.fromarray(resized) #一旦PIL形式に変換
    canvas = Image.new(resized.mode, (size, size), (255, 255, 255)) # 黒埋めなら(0,0,0)にする
    canvas.paste(resized, (x, y))
    dst = np.array(canvas) #numpy(OpenCV)形式に戻す
    return dst

labels = test["ClassId"].values
imgs = test["Path"].values

data =[]

for img in imgs:
    try:
        image = cv2.imread(data_dir + '/' +img)

        ## Modified for Shiroume
        h, w = image.shape[:2]
        if h < w :
            val = w
        else :
            val = h
        ume_image = shiroume(image, val) #リサイズしたいサイズを指定

        image_fromarray = Image.fromarray(image, 'RGB') ## ume_image for shiroume
        resize_image = image_fromarray.resize((IMG_HEIGHT, IMG_WIDTH))
        data.append(np.array(resize_image))
    except:
        print("Error in " + img)
X_test = np.array(data)
####X_test = X_test/255
print("x_test shape : ", X_test.shape)

######### star prediction

lay1out = model1.predict(X_test) 
print("lay1out shape : ", lay1out.shape)
lay1out = lay1out / 256
 
lay2out = model2.predict(lay1out) 
print("lay2out shape : ", lay2out.shape)
lay2out = lay2out / 256

lay3out = model3.predict(lay2out) 
print("lay3out shape : ", lay3out.shape)
lay3out = lay3out / 256

lay4out = model4.predict(lay3out) 
print("lay4out shape : ", lay4out.shape)
lay4out = lay4out / 256

lay5out = model5.predict(lay4out) 
print("lay5out shape : ", lay5out.shape)
lay5out = lay5out / 256

lay6out = model6.predict(lay5out) 
pred = np.argmax(lay6out, axis=-1) 

#Accuracy with the test data
print('Test Data accuracy: ',accuracy_score(labels, pred)*100)
'''
errno = 0
plt.figure(figsize = (25, 25))
for i in range(1470):
    prediction = pred[i]
    actual = labels[i]
    if prediction != actual:
        print('prediction error ', i, ' : pred ', pred[i], ' : actual ', labels[i])
        plt.subplot(3, 5, errno + 1)
        plt.grid(False)
        plt.xticks([])
        plt.yticks([])
        plt.imshow(X_test[i])
        errno = errno + 1
'''

from sklearn.metrics import confusion_matrix
cf = confusion_matrix(labels, pred)

import seaborn as sns
df_cm = pd.DataFrame(cf, index = classes,  columns = classes)
plt.figure(figsize = (20,20))
sns.heatmap(df_cm, annot=True)

from sklearn.metrics import classification_report

print(classification_report(labels, pred))

plt.figure(figsize = (25, 25))

### Modified for lisa
### changed index of images to show up
for i in range(30):
    plt.subplot(6, 5, i + 1)
    plt.grid(False)
    plt.xticks([])
    plt.yticks([])
    plt.rcParams["font.size"] = 16
    if i%10 == 0 :
        rndval = int(i/10)
    elif i%10 == 1 :
        rndval = 2066 + int(i/10)
    elif i%10 == 2 :
        rndval = 4066 + 0 + int(i/10)
    elif i%10 == 3 :
        rndval = 4066 + 3 + int(i/10)
    elif i%10 == 4 :
        rndval = 4066 + 6 + int(i/10)
    elif i%10 == 5 :
        rndval = 4066 + 9 + int(i/10)
    elif i%10 == 6 :
        rndval = 4066 + 12 + int(i/10)
    elif i%10 == 7 :
        rndval = 4066 + 15 + int(i/10)
    elif i%10 == 8 :
        rndval = 4066 + 18 + int(i/10)
    elif i%10 == 9 :
        rndval = 4087 + int(i/10)
    prediction = pred[rndval]
    actual = labels[rndval]
    col = 'g'
    if prediction != actual:
        col = 'r'
    plt.xlabel('Actual={} || Pred={}'.format(actual, prediction), color = col)
    for m in range(0, X_test[rndval].shape[0]): 
        for n in range(0, X_test[rndval].shape[1]): 
            tmpg = X_test[rndval][m][n][0]
            tmpr = X_test[rndval][m][n][2]
            X_test[rndval][m][n][0] = tmpr
            X_test[rndval][m][n][2] = tmpg
    plt.imshow(X_test[rndval])
plt.show()