如何使用python过滤列表，删除最低概率值

Question

我有一个我想检测文本的图像，我正在使用easyocr检测文本。OCR给出输出边界框值和概率，如输出图像所示。我想删除小于0.4的检测到的任何文本的概率，我如何更改该概率

图1

图2

results元素给出了第一个文本和第二个文本“AA”的输出概率，如图所示。我想删除检测到的概率最低的文本

图像1的输出

图像2的输出

要求

pip安装pytesseract

pip安装easyocr

使用python main.py-i image1.jpg运行代码

# import the necessary packages
from pytesseract import Output
import pytesseract
import argparse
import cv2
from matplotlib import pyplot as plt
import numpy as np
import os
import easyocr
from PIL import ImageDraw, Image



def remove_lines(image):
    result = image.copy()
    gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
    thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

    # Remove horizontal lines
    horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (40,1))
    remove_horizontal = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)
    cnts = cv2.findContours(remove_horizontal, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    cnts = cnts[0] if len(cnts) == 2 else cnts[1]
    for c in cnts:
        cv2.drawContours(result, [c], -1, (255,255,255), 5)


    # Remove vertical lines
    vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,40))
    remove_vertical = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, vertical_kernel, iterations=2)
    cnts = cv2.findContours(remove_vertical, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    cnts = cnts[0] if len(cnts) == 2 else cnts[1]
    for c in cnts:
        cv2.drawContours(result, [c], -1, (255,255,255), 5)

    plt.imshow(result)
    plt.show()

    return result



# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
    help="path to input image to be OCR'd")
ap.add_argument("-c", "--min-conf", type=int, default=0,
    help="mininum confidence value to filter weak text detection")
args = vars(ap.parse_args())


reader = easyocr.Reader(['ch_sim','en']) # need to run only once to load model into memory



# load the input image, convert it from BGR to RGB channel ordering,
# and use Tesseract to localize each area of text in the input image
image = cv2.imread(args["image"])
# image = remove_lines(image)

results = reader.readtext(image)


print(results)

Answer 1

results=[([[5, 0],[233, 0],[233, 15],[5, 15]],' Ꮎ ]TC T III3 UᎪCU 3', 0.011015821004953916),
 ([[241, 0], [390, 0], [390, 15], [241, 15] ] , 'ᎠA[ [ C 0lᎢ', 0.0023567583563770737),
 ([[2, 16], [46, 16], [46, 42], [2, 42]], 'MM', 0.9965182566504686),
 ([[98, 16], [140, 16], [140, 46], [98, 46]], 'D', 0.9973547096148511),
 ([[182, 16], [220, 16],[220, 44], [182, 44]], 'Y', 0.9971791823074896),
 ([[24, 46], [62, 46], [62, 74], [24, 74]], '62', 0.9999828941291119),
 ([[94, 46], [130, 46], [130, 74], [94, 74]], '26', 0.9997197349619524),
 ([[180, 46], [242, 46], [242, 74], [180, 74]],'1970', 0.999931275844574)]

low_precision = []
for text in results:
    if text[2]<0.5: # precision here
        low_precision.append(text)
for i in low_precision:
    results.remove(i) # remove low precision
print(results)

结果:

[([[2, 16], [46, 16], [46, 42], [2, 42]], 'MM', 0.9965182566504686),
 ([[98, 16], [140, 16], [140, 46], [98, 46]], 'D', 0.9973547096148511),
 ([[182, 16], [220, 16], [220, 44], [182, 44]], 'Y', 0.9971791823074896),
 ([[24, 46], [62, 46], [62, 74], [24, 74]], '62', 0.9999828941291119),
 ([[94, 46], [130, 46], [130, 74], [94, 74]], '26', 0.9997197349619524),
 ([[180, 46], [242, 46], [242, 74], [180, 74]], '1970', 0.999931275844574)]