From e8c048e5aa16189f0659fc7d8b6a3112b3305cbf Mon Sep 17 00:00:00 2001
From: piqxznopb <1207967813@qq.com>
Date: Mon, 20 Jan 2025 15:07:17 +0800
Subject: [PATCH] =?UTF-8?q?=E6=B7=BB=E5=8A=A0Model=5Fset.py=E6=96=87?=
 =?UTF-8?q?=E4=BB=B6?=
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---
 Models_set.py | 100 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 100 insertions(+)
 create mode 100644 Models_set.py

diff --git a/Models_set.py b/Models_set.py
new file mode 100644
index 0000000..ee2b19a
--- /dev/null
+++ b/Models_set.py
@@ -0,0 +1,100 @@
+import requests
+import torch
+from PIL import Image
+from transformers import pipeline
+from paddleocr import PaddleOCR
+from transformers import BlipProcessor, BlipForConditionalGeneration
+
+
+"""事实证明除中文外，英语转成其他语言更难"""
+language_pairs = {
+    "en-zh": "Helsinki-NLP/opus-mt-en-zh",  # 1:英语到中文
+    "zh-en": "Helsinki-NLP/opus-mt-zh-en",  # 2:中文到英语
+    "en-fr": "Helsinki-NLP/opus-mt-en-fr",  # 3:英语到法语
+    "fr-en": "Helsinki-NLP/opus-mt-fr-en",  # 4:法语到英语
+    "en-es": "Helsinki-NLP/opus-mt-en-es",  # 5:英语到西班牙语
+    "es-en": "Helsinki-NLP/opus-mt-es-en",  # 6:西班牙语到英语
+    "en-de": "Helsinki-NLP/opus-mt-en-de",  # 7:英语到德语
+    "de-en": "Helsinki-NLP/opus-mt-de-en",  # 8:德语到英语
+    "en-ru": "Helsinki-NLP/opus-mt-en-ru",  # 9:英语到俄语
+    "ru-en": "Helsinki-NLP/opus-mt-ru-en",  # 10:俄语到英语
+    "zh-fr": "Helsinki-NLP/opus-mt-zh-fr",  # 11:中文到法语
+    "fr-zh": "Helsinki-NLP/opus-mt-fr-zh",  # 12:法语到中文
+}
+
+flag = int(input('请选择要进行的模式（文字识别请输入1，图片理解请输入2）：'))
+pairs = 1  # 默认为英转中
+# image_path_identify = '测试图片/img_12.jpg'  # 用来文字识别的图片所在的文件路径
+# image_path_understand = r"测试图片/birds-6968147_640.jpg"  # 用来进行图片理解的文件路径
+while True:
+    if flag == 1:
+        image_path_identify = input("请输入测试图片本地路径：")  # 用来文字识别的图片所在的文件路径
+        if torch.cuda.is_available():
+            # 使用 GPU
+            ocr = PaddleOCR(use_angle_cls=True, lang='ch', use_gpu=True)
+            print("使用 GPU 进行 OCR")
+        else:
+            # 使用 CPU
+            ocr = PaddleOCR(use_angle_cls=True, lang='ch', use_gpu=False)
+            print("使用 CPU 进行 OCR")
+        flag = 0
+        result = ocr.ocr(image_path_identify, cls=True)
+
+        # 提取纯文字内容
+        extracted_text = [res[1][0] for res in result[0]]
+
+        """-----------------------------------------这是文字识别的输出--------------------------------------------------"""
+        print("文字识别结果如下：")
+        for text in extracted_text:
+            print(text)
+
+    if flag == 2:
+        image_path_understand = input("请输入测试图片本地路径：")  # 用来进行图片理解的文件路径
+        # 图片理解模块
+        processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+        model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+
+        image_path = image_path_understand  # 本地图片路径
+        image = Image.open(image_path)
+
+        # 处理图片
+        inputs = processor(images=image, return_tensors="pt")
+        output = model.generate(**inputs)
+
+        # 解码生成的描述
+        description = processor.decode(output[0], skip_special_tokens=True)
+        print(description)
+
+        if pairs == 1:
+            language_pair = "en-zh"
+        if pairs == 2:
+            language_pair = "zh-en"
+        if pairs == 3:
+            language_pair = "en-fr"
+        if pairs == 4:
+            language_pair = "fr-en"
+        if pairs == 5:
+            language_pair = "en-es"
+        if pairs == 6:
+            language_pair = "es-en"
+        if pairs == 7:
+            language_pair = "en-de"
+        if pairs == 8:
+            language_pair = "de-en"
+        if pairs == 9:
+            language_pair = "en-ru"
+        if pairs == 10:
+            language_pair = "ru-en"
+        if pairs == 11:
+            language_pair = "zh-fr"
+        if pairs == 12:
+            language_pair = "fr-zh"
+        translator = pipeline("translation", model=language_pairs[language_pair])
+
+        # 翻译文本
+        result = translator(description, max_length=40)
+        translation_text = result[0]['translation_text']
+        print(translation_text)
+    flag_break = input("还要继续吗，退出请按1再回车:")
+    if flag_break == '1':
+        break
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