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簡化合併

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satori
2024-11-19 15:18:41 +08:00
parent d7161c7df1
commit ef0ef48b87
1 changed files with 27 additions and 74 deletions
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101
pp.py
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@@ -1,4 +1,4 @@
#!/usr/bin/env python3
#!/usr/bin/env python3.10
import os
import io
@@ -94,41 +94,22 @@ def process_images(conn):
if isinstance(image, Image.Image):
image = np.array(image)
print('---------------------', id, content)
en = EN.ocr(image, cls=True)[0]
ch = CH.ocr(image, cls=True)[0]
jp = JP.ocr(image, cls=True)[0]
kr = KR.ocr(image, cls=True)[0]
ru = RU.ocr(image, cls=True)[0]
en = en if en is not None else []
ch = ch if ch is not None else []
jp = jp if jp is not None else []
kr = kr if kr is not None else []
ru = ru if ru is not None else []
# 執行提取文字
en = EN.ocr(image, cls=True)[0] or []
ch = CH.ocr(image, cls=True)[0] or []
jp = JP.ocr(image, cls=True)[0] or []
kr = KR.ocr(image, cls=True)[0] or []
ru = RU.ocr(image, cls=True)[0] or []
# 排除字符长度小于2的行
jp = [x for x in jp if len(x[1][0]) > 1]
kr = [x for x in kr if len(x[1][0]) > 1]
ch = [x for x in ch if len(x[1][0]) > 1]
en = [x for x in en if len(x[1][0]) > 1]
ru = [x for x in ru if len(x[1][0]) > 1]
# 排除纯数字的行
jp = [x for x in jp if not x[1][0].isdigit()]
kr = [x for x in kr if not x[1][0].isdigit()]
ch = [x for x in ch if not x[1][0].isdigit()]
en = [x for x in en if not x[1][0].isdigit()]
ru = [x for x in ru if not x[1][0].isdigit()]
# 排除置信度小于 0.8 的行
jp = [x for x in jp if x[1][1] > 0.8]
kr = [x for x in kr if x[1][1] > 0.8]
ch = [x for x in ch if x[1][1] > 0.8]
en = [x for x in en if x[1][1] > 0.8]
ru = [x for x in ru if x[1][1] > 0.8]
# 排除字符长度小于2的行, 排除纯数字的行, 排除置信度小于 0.8 的行
jp = [x for x in jp if len(x[1][0]) > 1 and not x[1][0].isdigit() and x[1][1] > 0.8]
kr = [x for x in kr if len(x[1][0]) > 1 and not x[1][0].isdigit() and x[1][1] > 0.8]
ch = [x for x in ch if len(x[1][0]) > 1 and not x[1][0].isdigit() and x[1][1] > 0.8]
en = [x for x in en if len(x[1][0]) > 1 and not x[1][0].isdigit() and x[1][1] > 0.8]
ru = [x for x in ru if len(x[1][0]) > 1 and not x[1][0].isdigit() and x[1][1] > 0.8]
print(f'置信度大于 0.8 的行: jp {len(jp)} kr {len(kr)} ch {len(ch)} en {len(en)} ru {len(ru)}')
# 去除字符串中包含的数字和标点(不作计数)
jp_ex = [[x[0], (x[1][0].translate(str.maketrans('', '', '0123456789.,，。！？：；“”‘’\'\"')), x[1][1])] for x in jp]
kr_ex = [[x[0], (x[1][0].translate(str.maketrans('', '', '0123456789.,，。！？：；“”‘’\'\"')), x[1][1])] for x in kr]
@@ -136,53 +117,25 @@ def process_images(conn):
en_ex = [[x[0], (x[1][0].translate(str.maketrans('', '', '0123456789.,，。！？：；“”‘’\'\"')), x[1][1])] for x in en]
ru_ex = [[x[0], (x[1][0].translate(str.maketrans('', '', '0123456789.,，。！？：；“”‘’\'\"')), x[1][1])] for x in ru]
# 计算置信度平均数
jpx = np.mean([x[1][1] for x in jp_ex]) if len(jp_ex) > 0 else 0
krx = np.mean([x[1][1] for x in kr_ex]) if len(kr_ex) > 0 else 0
chx = np.mean([x[1][1] for x in ch_ex]) if len(ch_ex) > 0 else 0
enx = np.mean([x[1][1] for x in en_ex]) if len(en_ex) > 0 else 0
rux = np.mean([x[1][1] for x in ru_ex]) if len(ru_ex) > 0 else 0
# 计算置信度平均值 x 计算总字数
jpx = (np.mean([x[1][1] for x in jp_ex]) if jp_ex else 0) * len(''.join([x[1][0] for x in jp_ex]))
krx = (np.mean([x[1][1] for x in kr_ex]) if kr_ex else 0) * len(''.join([x[1][0] for x in kr_ex]))
chx = (np.mean([x[1][1] for x in ch_ex]) if ch_ex else 0) * len(''.join([x[1][0] for x in ch_ex]))
enx = (np.mean([x[1][1] for x in en_ex]) if en_ex else 0) * len(''.join([x[1][0] for x in en_ex]))
rux = (np.mean([x[1][1] for x in ru_ex]) if ru_ex else 0) * len(''.join([x[1][0] for x in ru_ex]))
# 计算总字数
jpt = len(''.join([x[1][0] for x in jp_ex]))
krt = len(''.join([x[1][0] for x in kr_ex]))
cht = len(''.join([x[1][0] for x in ch_ex]))
ent = len(''.join([x[1][0] for x in en_ex]))
rut = len(''.join([x[1][0] for x in ru_ex]))
# 找出置信度最高的语言, 结构化存储
confidences = {'jp': jpx, 'kr': krx, 'ch': chx, 'en': enx, 'ru': rux}
max_confidence_language = max(confidences, key=confidences.get)
languages = {'en': en, 'ch': ch, 'jp': jp, 'kr': kr, 'ru': ru}
data = [{'text': text[0], 'confidence': text[1], 'coordinate': coord} for coord, text in languages[max_confidence_language]]
print("data:", data)
# 计算总字数 x 置信度平均数
jpx = jpx * jpt
krx = krx * krt
chx = chx * cht
enx = enx * ent
rux = rux * rut
print('jp', jpx)
print('kr', krx)
print('ch', chx)
print('en', enx)
print('ru', rux)
# 创建一个新的字典，其中键是浮点数（置信度），值是语言
confidence_dict = {jpx: 'jp', krx: 'kr', chx: 'ch', enx: 'en', rux: 'ru'}
# 找出置信度最高的语言
max = np.max([jpx, krx, chx, enx, rux])
max_confidence_language = confidence_dict[max]
# 结构化存储
data = []
# 使用置信度最高的语言作为键来访问字典
all = {'en': en, 'ch': ch, 'jp': jp, 'kr': kr, 'ru': ru}
for 坐标, 文本 in all[max_confidence_language]:
print(max_confidence_language, 坐标, 文本)
data.append({'text': 文本[0], 'confidence': 文本[1], 'coordinate': 坐标 })
# 转换为字符串存储到索引库
obj = { "_id": str(id), "text": ' '.join([x['text'] for x in data]) }
print("转换为字符串存储到索引库:", obj)
res = requests.put(zinc_url, headers=headers, data=json.dumps(obj), proxies={'http': '', 'https': ''})
print("\033[1;32m{}\033[0m".format(id) if json.loads(res.text)['message'] == 'ok' else id, text)
print("\033[1;32m{}\033[0m".format(id) if json.loads(res.text)['message'] == 'ok' else obj["id"], obj["text"])
# 转换为 JSON 存储到数据库
with conn.cursor() as cursor: