import glob
import os.path
from os.path import isdir

from PIL import Image
import torch
from torchvision import transforms
from torchvision.transforms import Resize

from .utils import load_weight
from .model import Model


# CONSTANT
MEAN = [0.485, 0.456, 0.406]
STD = [0.229, 0.224, 0.225]
SIZE = [224, 224]
CLASSES = {0: "non-occluded",
           1: "occluded"}


def test_image(opt):
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model = Model(opt.model, 2, False).to(device)
    model = load_weight(model, opt.weight)
    model.eval()

    # transform data
    transform = transforms.Compose([
            transforms.Resize(SIZE),
            transforms.ToTensor(),
            transforms.Normalize(MEAN, STD)
    ])

    # Image
    if isdir(opt.image):
        imgs = glob.glob(os.path.join(opt.image,"*.png"))
        for path in imgs:
            img = Image.open(path).convert("RGB")
            img = transform(img).to(device)
            output = model(img.unsqueeze(0))
            output = torch.softmax(output, 1)
            prob, pred = torch.max(output, 1)

            print("Image {} is {} - {:.2f} %".format(
                path, CLASSES[pred.item()], prob.item() * 100
            ))
    else:
        img = Image.open(opt.image).convert("RGB")
        img = transform(img).to(device)
        output = model(img.unsqueeze(0))
        output = torch.softmax(output, 1)
        prob, pred = torch.max(output, 1)

        print("Image {} is {} - {:.2f} %".format(
            opt.image, CLASSES[pred.item()], prob.item() * 100
        ))

def test_node(image:torch.Tensor,length=10,thres=95,model_name="convnext_tiny"):
    weight_dic = {
        "convnext_tiny":"best_convnext_tiny.pth",
        "convnext_base":"best_convnext_base.pth"
    }
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model = Model(model_name, 2, False).to(device)
    weight = os.path.join(os.path.dirname(os.path.abspath(__file__)), "model", weight_dic[model_name])
    model = load_weight(model, weight)
    model.eval()
    image = image.permute(0,3,1,2)
    torch_resize = Resize([224, 224])
    output = model(torch_resize(image).to(device))
    output = torch.softmax(output, 1)
    prob, pred = torch.max(output, 1)
    probs, preds = [round(i.item() * 100,2) for i in prob], [CLASSES[i.item()] for i in pred]
    print("Image is {} - {} %".format(
        preds, probs
    ))
    nums = []
    for idx, a,b in zip(range(len(probs)),preds,probs):
        if a=="non-occluded" and b > thres:
            nums.append(idx)
    start = -1
    end = -1
    period = []
    for idx in range(len(nums)):
        if idx == 0:
            start = nums[idx]
            end = nums[idx]
        else:
            if nums[idx] == end + 1:
                end = nums[idx]
            else:
                if end - start + 1 >= length:
                    period.append([start, end])
                start = nums[idx]
                end = nums[idx]
    if end - start + 1 >= length:
        period.append([start, end])
    temp_period = []
    for i in period:
        a = i[0]
        while a+length-1 <= i[1]:
            temp_period.append([a,a+length-1])
            a = a+length
    return (image.permute(0,2,3,1), image.permute(0,2,3,1)[nums,:,:,:], str(preds), str(probs), str(nums), temp_period)



if __name__ == "__main__":
    from argparse import ArgumentParser

    parser = ArgumentParser()
    parser.add_argument("--model", type=str, help="Model name")
    parser.add_argument("--weight", type=str, help="Weight path (.pth)")
    parser.add_argument("--image", type=str, help="Image path")
    args = parser.parse_args()

    test_image(args)