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# https://stackoverflow.com/a/45925049/13369757
import matplotlib.pyplot as plt 
import numpy as np

fig, host = plt.subplots(figsize=(12,10)) # (width, height) in inches
    
par1 = host.twinx()
par2 = host.twinx()

host.set_xlabel("Threshold")

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import numpy as np
import torch
import pickle
from torch.utils.data import Dataset, DataLoader

# 提取特徵
def extract(loader):
    featureExtractor.eval()
    features = None
    with torch.no_grad():

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import os
import shutil

fileListingFile = "missclassified_file_path.txt"
outputDir = "./"

# sample path in txt: /root/notebooks/XXXXX/datasets/XXXXXXXX.jpg

with open(fileListingFile, "r") as file:
    fileNames = [file.strip() for file in file.readlines()]

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batch_iterator = iter(dataloader)

images, targets = next(batch_iterator)
try:
	images, targets = next(batch_iterator)
except StopIteration:
	batch_iterator = iter(data_loader)
	images, targets = next(batch_iterator)

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# https://github.com/rajatkoner08/oodformer/blob/master/networks/resnet.py
'''ResNet in PyTorch.
BasicBlock and Bottleneck module is from the original ResNet paper:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
    Deep Residual Learning for Image Recognition. arXiv:1512.03385
PreActBlock and PreActBottleneck module is from the later paper:
[2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
    Identity Mappings in Deep Residual Networks. arXiv:1603.05027
Original code is from https://github.com/kuangliu/pytorch-cifar/blob/master/models/resnet.py
'''

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from torchvision.utils import save_image

features, imgs, labels, image_path = get_features_trained_weight(model, transform_dataset)

img1 = imgs[1] #第1張圖片
img1 = torch.from_numpy(img1) #格式轉換
save_image(img1, 'img1.png')  #保存成'img1.png'

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def get_features_trained_weight(model, transform_dataset):
    if torch.cuda.is_available():
        device = 'cuda'
    else:
        device = 'cpu'
    if isinstance(model,torch.nn.DataParallel):
        model = model.module

    model.eval()
    model.to(device)

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# thop(simple)
from thop import profile
model = mobilenetv3_large(width_mult=args.width_mult, num_classes=2)
input = torch.randn(1, 3, 224, 224)
macs, params = profile(model, inputs=(input, ), verbose=False)
print("%.2f | %.2f" % (params / (1000 ** 2), macs / (1000 ** 3)))

# torch info(detailed)
from torchinfo import summary
model = mobilenetv3_large(width_mult=args.width_mult, num_classes=2)

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# https://www.jianshu.com/p/9a9000d226b6
# https://stackoverflow.com/questions/64420379/is-it-possible-to-add-own-function-in-transform-compose-in-pytorch

import cv2
import torchvision.transforms as transforms

class hisEqulColor(object):
    def __call__(self, img):
        img = cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)   # PyTorch的Transform要求PIL格式的圖片，若要使用OpenCV則需先進行轉換
        ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB)

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# 針對每個元件計算其Overkill rate和Leakage rate
# 目前只適用於batch size = 1的情形
# 用來產生overkill和leakage數值的dataframe
import pandas as pd
import torch

test_df_mapping2_label = test_df.copy()                                          #複製一份要mapping到2個label的testing資料
test_df_mapping2_label.loc[test_df_mapping2_label['class'] == 0, 'class'] = 0    #將大於1的label轉成1
test_df_mapping2_label.loc[test_df_mapping2_label['class'] == 1, 'class'] = 1
test_df_mapping2_label.loc[test_df_mapping2_label['class'] == 2, 'class'] = 1