import torch
import cv2
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
import torch.optim as optim
from PIL import Image

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])


# def imshow(img):
#     img = img / 2 + 0.5
#     npimg = img.numpy()
#     plt.imshow(np.transpose(npimg, (1, 2, 0)))
#     plt.show()


# dataiter = iter(trainloader)
# images, labels = dataiter.next()

# # show images
# imshow(torchvision.utils.make_grid(images))
# # print labels
# print(' '.join('%5s' % classes[labels[j]] for j in range(4)))


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__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(16 * 71 * 71, 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 = x.view(x.size(0), 16 * 71 * 71)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x



def train():
    trainset = torchvision.datasets.ImageFolder(
        root='./resources/zbior_uczacy', transform=transform)
    trainloader = torch.utils.data.DataLoader(
        trainset, batch_size=1, shuffle=True, num_workers=2)

    classes = ('glass', 'metal', 'paper', 'plastic')
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

    for epoch in range(10):  # loop over the dataset multiple times
        print("siema")
        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            # get the inputs; data is a list of [inputs, labels]
            inputs, labels = data

            # zero the parameter gradients
            optimizer.zero_grad()

            # forward + backward + optimize
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            # print statistics
            running_loss += loss.item()
            if i:    # print every 2000 mini-batches
                print('[%d, %5d] loss: %.3f' %
                      (epoch + 1, i + 1, running_loss))
                running_loss = 0.0
                print("kyrw")

    print('Finished Training')
    PATH = './wytrenowaned.pth'
    torch.save(net.state_dict(), PATH)


def predict(img_path):
    net = Net()
    PATH = './wytrenowaned.pth'
    img = Image.open(img_path)
    pil_to_tensor = transforms.ToTensor()(img).unsqueeze_(0)
    if(pil_to_tensor.shape[1] == 1):
        print(img_path)
    classes = ('glass', 'metal', 'paper', 'plastic')
    # testset = torchvision.datasets.ImageFolder(
    #     root='./resources/smieci', transform=transform)
    # testloader = torch.utils.data.DataLoader(
    #     testset, batch_size=4, shuffle=True, num_workers=2)
    # dataiter = iter(testloader)
    # images, labels = dataiter.next()


# print images
# imshow(torchvision.utils.make_grid(images))
    # print('GroundTruth: ', ' '.join('%5s' %
    #                                 classes[labels[j]] for j in range(4)))
    # print('---')
    # print(images)
    # print('---')
    net.load_state_dict(torch.load(PATH))
    outputs = net(pil_to_tensor)
    return classes[torch.max(outputs, 1)[1]]

    # print(classes[torch.max(outputs, 1)[1]])
    # print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]
    #                               for j in range(1)))

# 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 of the network on the test images: %d %%' % (
#     100 * correct / total))

# class_correct = list(0. for i in range(4))
# class_total = list(0. for i in range(4))
# with torch.no_grad():
#     for data in testloader:
#         images, labels = data
#         outputs = net(images)
#         _, predicted = torch.max(outputs, 1)
#         c = (predicted == labels).squeeze()
#         for i in range(3):
#             label = labels[i]
#             print(labels)
#             class_correct[label] += c[i].item()
#             class_total[label] += 1


# for i in range(4):
#     print('Accuracy of %5s : %2d %%' % (
#         classes[i], 100 * class_correct[i] / class_total[i]))
# train()