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import json, sys, random
import numpy as np
import tensorflow as tf
import tensorflow.keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Activation
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.optimizers import SGD

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# download dataset from json object
f = open(r'./ships-in-satellite-imagery/shipsnet.json') #download at http://vipnas.buitrongan.com:5000/sharing/Gd1gOIi9A
dataset = json.load(f)
f.close()

input_data = np.array(dataset['data']).astype('uint8')
output_data = np.array(dataset['labels']).astype('uint8')

input_data.shape

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# output encoding
y = tensorflow.keras.utils.to_categorical(output_data, 2)

# shuffle all indexes
indexes = np.arange(2800)
np.random.shuffle(indexes)

X_train = X[indexes].transpose([0,2,3,1])
y_train = y[indexes]

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physical_devices = tf.config.experimental.list_physical_devices('GPU') 
for physical_device in physical_devices: 
    tf.config.experimental.set_memory_growth(physical_device, True)

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model = Sequential()

model.add(Conv2D(32, (3, 3), padding='same', input_shape=(80, 80, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2))) #40x40
model.add(Dropout(0.25))

model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2))) #20x20
model.add(Dropout(0.25))

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# optimization setup
sgd = SGD(lr=0.01, momentum=0.9, nesterov=True)
model.compile(
    loss='categorical_crossentropy',
    optimizer=sgd,
    metrics=['accuracy'])

# training
model.fit(
    X_train, 

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image = Image.open('./ships-in-satellite-imagery/scenes/scenes/sfbay_1.png')
pix = image.load()

n_spectrum = 3
width = image.size[0]
height = image.size[1]

# creat vector
picture_vector = []
for chanel in range(n_spectrum):

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def cutting(x, y):
    area_study = np.arange(3*80*80).reshape(3, 80, 80)
    for i in range(80):
        for j in range(80):
            area_study[0][i][j] = picture_tensor[0][y+i][x+j]
            area_study[1][i][j] = picture_tensor[1][y+i][x+j]
            area_study[2][i][j] = picture_tensor[2][y+i][x+j]
    area_study = area_study.reshape([-1, 3, 80, 80])
    area_study = area_study.transpose([0,2,3,1])
    area_study = area_study / 255

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step = 10; coordinates = []
for y in range(int((height-(80-step))/step)):
    for x in range(int((width-(80-step))/step) ):
        area = cutting(x*step, y*step)
        result = model.predict(area)
        if result[0][1] > 0.90 and not_near(x*step,y*step, 88, coordinates):
            coordinates.append([[x*step, y*step], result])
            print(result)
            plt.imshow(area[0])
            plt.show()

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for e in coordinates:
    show_ship(e[0][0], e[0][1], e[1][0][1])
   
picture_tensor = picture_tensor.transpose(1,2,0)
picture_tensor.shape

plt.figure(1, figsize = (15, 30))

plt.subplot(3,1,1)
plt.imshow(picture_tensor)