# import the necessary packages
from keras.models import Sequential
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.core import activation
from keras.layers.core import Dropout
from keras.layers.core import Dense
from keras.layers import flatten
from keras.layers import Input
from keras.models import Model
from keras.layers import Dense,LSTM,concatenate,Input,flatten

# define four inputs ( Different photos of trees)
inputA = Input(shape=(308,308,1)) # Trunk 
inputB = Input(shape=(308,1)) # Branch
inputC = Input(shape=(308,1)) # Leafs
inputD = Input(shape=(308,1)) # Roots
 
# the first branch operates on the first input
a = Conv2D(16,(2,2),activation='relu')(inputA)
a = Conv2D(16,activation='relu')(a)
a = Model(inputs=inputA,outputs=a)
 
# the second branch opreates on the second input
b = Conv2D(16,activation='relu')(inputB)
b = Conv2D(16,activation='relu')(b)
b = Model(inputs=inputB,outputs=b)

# the third branch opreates on the third input
c = Conv2D(16,activation='relu')(inputC)
c = Conv2D(16,activation='relu')(c)
c = Model(inputs=inputC,outputs=c)

# the fourth branch opreates on the fourth input
d = Conv2D(16,activation='relu')(inputD)
d = Conv2D(16,activation='relu')(d)
d = Model(inputs=inputD,outputs=d)
 
# combine the output of the four branches
combined = concatenate([a.output,b.output,c.output,d.output])
 
# apply a FC layer and then a regression prediction on the
# combined outputs
z = Dense(128,activation="relu")(combined)
z = Dense(4,activation="softmax")(z)
 
# our model will accept the inputs of the four branches and then output a single value
model = Model(inputs=[a.input,b.input,c.input,d.input],outputs=z)
model.summary()

model.compile(loss='categorical_crossentropy',optimizer="adam",metrics=['accuracy'])

from tqdm import tqdm
import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline

train_data = ""
test_data = ""

def one_hot_label(img):
	label = img.split(".")[0]
	if label == "Trunk";
		ohl = np.array([1,0])
	elif label == "Branch":
		ohl = np.array([0,1,0])
  elif label == "Leaf":
		ohl = np.array([0,0])
  elif label == "Root":
		ohl = np.array([0,1])  
	return ohl
def train_data_with_label();
	train_images = []
	for i in tqdm(os.listdir(train_data)):
		path = os.path.join(train_data,i)
		img = cv2.imread(path,cv2.IMREAD_COLOR)
		img = cv2.resize(img,(308,308))
		train_images.append([np.array(img),one_hot_label(i)])
	shuffle(train_images)
	return train_images

def test_data_with_label();
	test_images = []
	for i in tqdm(os.listdir(test_data)):
		path = os.path.join(test_data,308))
		test_images.append([np.array(img),one_hot_label(i)])
	shuffle(test_images)
	return test_images