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import numpy as np
import cv2
import imageio

def simplest_color_balance(img, percent):
    out_channels = []
    channels = cv2.split(img)
    for channel in channels:
        total_pixels = img.shape[0] * img.shape[1]
        low_val, high_val = np.percentile(channel, [percent, 100 - percent])

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import os
import xml.etree.ElementTree as ET
import csv

# Set the paths for the input and output directories
voc_path = '/home/Yanwei_Liu/Datasets/PASCALRAW/annotations/'
train_img_path = '/home/Yanwei_Liu/Datasets/PASCALRAW/images/train/'
val_img_path = '/home/Yanwei_Liu/Datasets/PASCALRAW/images/val/'

train_file_path = '/home/Yanwei_Liu/Datasets/PASCALRAW/trainval/train.txt'

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(Pdb) pdb.set_trace = lambda: None  # This replaces the set_trace() function!
(Pdb) continue

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"""
Implementation of IQA metrics in PyTorch, including PSNR, SSIM, LPIPS, NIQE, and LOE.
"""
import torch
import torch.nn as nn
import torchvision.models as models
import torchvision.transforms.functional as F
from torch.nn.functional import conv2d
from IQA_pytorch import SSIM, LPIPSvgg

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import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error
from FinMind.data import DataLoader

dl = DataLoader()
stock_data = dl.taiwan_stock_daily(
    stock_id='2330', start_date='2010-01-01', end_date='2022-12-20'

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# https://stackoverflow.com/questions/41974615/how-do-i-calculate-pdf-probability-density-function-in-python
from scipy.stats import norm
print(norm.cdf(x, mean, std))

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# https://stackoverflow.com/a/46672301/13369757
import pandas as pd
pd.set_option('display.float_format', '{:.2f}'.format)

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import scrapetube
import random
import pandas as pd

AllVideoList = []
videos = scrapetube.get_channel("UCJIfeSCssxSC_Dhc5s7woww") # channel ID

titleList = []
urlList = []
while True:

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from bayes_opt import BayesianOptimization
from bayes_opt.logger import JSONLogger
from bayes_opt.event import Events

logger = JSONLogger(path="./tau_logs.json")


pbounds = {'tau1': (0.1, 0.2), 'tau2': (0.3, 0.4),
           'tau3': (0.5, 0.6), 'tau4': (0.7, 0.8),
           }

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# ref: https://blog.csdn.net/MachineLearner/article/details/104587288
def plot_decision_boundary(pred_func, X, y, figure=None):
    """Plot a decision boundary"""

    if figure is None:  # If no figure is given, create a new one
        plt.figure()
    # Set min and max values and give it some padding
    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
    h = 0.01