eval.py

import argparse
import json

# from google.colab.patches import cv2_imshow
import math
import os
import time
from collections import Counter

import cv2
import matplotlib.pyplot as plt
import numpy as np

# from mmocr.utils.ocr import MMOCR
from PIL import Image, ImageDraw, ImageFont
from tools.minimum_hull import minimum_bounding_rectangle
from tqdm import tqdm
from vietocr.tool.config import Cfg
from vietocr.tool.predictor import Predictor


mapper = [
    ["hoangsa"],
    ["truongsa"],
    ["hoangsa", "truongsa"],
    ["hoang", "sa"],
    ["truong", "sa"],
    ["hoang", "sa", "truong", "sa"],
    ["hoàng", "sa", "trường", "sa"],
    ["hoang", "sa", "trương", "sa"],
    ["hoàng", "sa", "trương", "sa"],
    ["hoàng", "sa"],
    ["trường", "sa"],
    ["trương", "sa"],
    ["quan", "dao", "truong"],
    ["quan", "dao", "hoang"],
    ["paracel"],
    ["spratly"],
]


def check(word_bag):
    global mapper
    for cond in mapper:
        count_dict = Counter(cond)
        for word in word_bag:
            if word in count_dict:
                count_dict[word] -= 1
        f = 1
        for element, count in count_dict.items():
            if count > 0:
                f = 0
        if f == 1:
            return 1
    return 0


eval = False
if eval == True:
    ### POSITIVE GT
    path = "../Dataset/Vietnam_map/prediction/positive/vietnamese/predicted"
    files = os.listdir(path)
    pos = 0
    neg = 0
    for file in files:
        word_bag = []
        with open(path + "/" + file, "r") as file:
            lines = file.readlines()
        for i in range(len(lines)):
            lines[i] = lines[i].strip()
            word_bag.append(lines[i].split(",")[-1].lower())
        if check(word_bag) == 1:
            neg += 1
        else:
            pos += 1

    path = "../Dataset/Vietnam_map/prediction/positive/english/predicted"
    files = os.listdir(path)
    for file in files:
        word_bag = []
        with open(path + "/" + file, "r") as file:
            lines = file.readlines()
        for i in range(len(lines)):
            lines[i] = lines[i].strip()
            word_bag.append(lines[i].split(",")[-1].lower())
        if check(word_bag) == 1:
            neg += 1
        else:
            pos += 1
    TP = pos
    FN = neg

    ### NEGATIVE GT
    path = "../Dataset/Vietnam_map/prediction/negative/vietnamese/predicted"
    files = os.listdir(path)
    pos = 0
    neg = 0
    for file in files:
        word_bag = []
        with open(path + "/" + file, "r") as file:
            lines = file.readlines()
        for i in range(len(lines)):
            lines[i] = lines[i].strip()
            word_bag.append(lines[i].split(",")[-1].lower())
        if check(word_bag) == 1:
            neg += 1
        else:
            pos += 1

    path = "../Dataset/Vietnam_map/prediction/negative/english/predicted"
    files = os.listdir(path)
    for file in files:
        word_bag = []
        with open(path + "/" + file, "r") as file:
            lines = file.readlines()
        for i in range(len(lines)):
            lines[i] = lines[i].strip()
            word_bag.append(lines[i].split(",")[-1].lower())
        if check(word_bag) == 1:
            neg += 1
        else:
            pos += 1
    FP = pos
    TN = neg

    Precision = TP / (TP + FP)
    Recall = TP / (TP + FN)
    F1 = 2 * (Precision * Recall) / (Precision + Recall)

    print("Precision:", Precision)
    print("Recall:", Recall)
    print("F1:", F1)