import os
import cv2
from torch.utils.data import DataLoader, Dataset
from torch.utils.data import random_split
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision

class TreesDataset(Dataset):
    def __init__(self, data_links) -> None:
        self.X, self.Y = readData(data_links)

    def __len__(self):
        return len(self.X)

    def __getitem__(self, index):
        return (self.X[index], self.Y[index])


class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(3264, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = torch.flatten(x, 1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

def create_datalinks(root_dir):
    data_links = os.listdir(root_dir)
    data_links = [root_dir + "/" + x for x in data_links]
    return data_links

def preprocess(img):
    scale_percent = 10
    width = int(img.shape[1] * scale_percent / 100)
    height = int(img.shape[0] * scale_percent / 100)
    dim = (width, height)
    resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)
    resized = torchvision.transforms.functional.to_tensor(resized)
    return resized

def readData(data_links):
    x, y = [], []
    for link in data_links:
        img = cv2.imread(link, cv2.IMREAD_COLOR)
        img = preprocess(img)
        if("ground" in link):
            label = 1
        elif("AS12" in link):
            label = 0
        x.append(img)
        y.append(label)

    return x, y

links_3_plus_ground = create_datalinks("new_data/AS12_3") + create_datalinks("new_data/ground")

dataset = TreesDataset(links_3_plus_ground)

train_set, test_set = random_split(dataset, [300, 50], generator=torch.Generator().manual_seed(42))

trainloader = DataLoader(train_set, batch_size=10, shuffle=True, num_workers=2)
testloader = DataLoader(test_set, batch_size=10, shuffle=True, num_workers=2)

classes = ('tree', 'ground')
epochs_num = 15

net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

for epoch in range(epochs_num):

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 10 == 0:  
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 10))
            running_loss = 0.0

print('Finished Training')

correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy : %d %%' % (100 * correct / total))