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import gradio as gr
import numpy as np

# Global variable to store the first click coordinates
start_point = None

def draw_bounding_box(image: np.ndarray, event: gr.SelectData) -> tuple:
    """
    Handle click events to draw a bounding box on the image.
    

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import sys

# Function to print a formatted section header
def print_header(msg):
    print("\n" + "=" * len(msg))
    print(msg)
    print("=" * len(msg))

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Package                       Version
----------------------------- -----------
absl-py                       2.1.0
accelerate                    0.30.0
addict                        2.4.0
aiofiles                      23.2.1
aiohttp                       3.9.5
aiosignal                     1.3.1
aliyun-python-sdk-core        2.15.1
aliyun-python-sdk-kms         2.16.3

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import colorlog
import logging

# 创建日志记录器
logger = colorlog.getLogger(__name__)

# 设置日志级别和格式
logger.setLevel(logging.DEBUG)
formatter = colorlog.ColoredFormatter(
    "%(log_color)s%(levelname)-8s%(reset)s %(log_color)s%(asctime)s | %(blue)s%(message)s",

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n00004475,organism, being
n00005787,benthos
n00006024,heterotroph
n00006484,cell
n00007846,person, individual, someone, somebody, mortal, soul
n00015388,animal, animate_being, beast, brute, creature, fauna
n00017222,plant, flora, plant_life
n00021265,food, nutrient
n00021939,artifact, artefact
n00120010,hop

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"""
# Anomaly Simulation for MVTecAD 
# Download describable textures dataset
wget https://www.robots.ox.ac.uk/~vgg/data/dtd/download/dtd-r1.0.1.tar.gz
# Download MVTec anomaly detection dataset
wget https://www.mydrive.ch/shares/38536/3830184030e49fe74747669442f0f282/download/420938113-1629952094/mvtec_anomaly_detection.tar.xz
"""

import os
import random

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import math
import os
import random
from itertools import product

import numpy as np
from imgaug import augmenters as iaa
from PIL import Image

IMG_SIZE = 600

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import numpy as np

def voc_ap(rec, prec):
    # correct AP calculation
    # first append sentinel values at the end
    mrec = np.concatenate(([0.], rec, [1.]))  #[0.  0.0666, 0.1333, 0.4   , 0.4666,  1.] 
    mpre = np.concatenate(([0.], prec, [0.])) #[0.  1.,     0.6666, 0.4285, 0.3043,  0.]
    for i in range(mpre.size - 1, 0, -1):
        mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])  #[1.     1.     0.6666 0.4285 0.3043 0.    ]

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import torch

checkpoint = torch.load("resnet18.pth.tar")
state_dict = checkpoint["state_dict"]

from collections import OrderedDict

new_state_dict = OrderedDict()
for k, v in state_dict.items():
    name = k[7:]  # remove 'module.' of dataparallel