import os
import sys

import matplotlib.pyplot as plt
from pandas.core.common import flatten

import torch
from torch import nn
from torch import optim
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms, models
import albumentations as A
from albumentations.pytorch import ToTensorV2

from tqdm import tqdm
import random
import cv2

sys.path.append('/workspace')
import dataset
import argparse

def parse_args():
    parser = argparse.ArgumentParser(description='MiSLAS training (Stage-2)')
    parser.add_argument('--input',
                        help='test image path',
                        required=True,
                        type=str)
    args = parser.parse_args()
    return args

classes = ('no_trunk', 'trunk')

test_transforms = A.Compose(
    [
        A.SmallestMaxSize(max_size=350),
        A.CenterCrop(height=256, width=256),
        A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
        ToTensorV2(),
    ]
)

def main():
    args = parse_args()
    assert os.path.exists(args.input)
    device = torch.device("cuda:3") if torch.cuda.is_available() else torch.device("cpu")

    model = models.resnet50(pretrained=True)
    model.fc = nn.Sequential(
        nn.Dropout(0.5),
        nn.Linear(model.fc.in_features, 2)
    )

    state_dict = torch.load('./result/best_model.pth')
    model.load_state_dict(state_dict)

    for _, p in model.named_parameters():
        p.requires_grad = False

    model.to(device)
    model.eval()

    test_transforms = A.Compose(
        [
            A.SmallestMaxSize(max_size=350),
            A.CenterCrop(height=256, width=256),
            A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
            ToTensorV2(),
        ]
    )

    image = cv2.imread(args.input)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image = test_transforms(image=image)["image"]
    image = torch.unsqueeze(image, 0).to(device)

    output = model(image)
    _, preds = output.max(1)

    input_cls = 'trunk' if 't_' in args.input else 'no_trunk'

    print("input: %s \n" %(input_cls))
    print("output: %s" %(classes[preds.item()]))

if __name__ == '__main__':
    main()