## 錯誤 報錯錯誤信息`OSError: [WinError 126] 找不到指定的模塊。 Error loading` ```ts (tinywan-modelscope) D:\AI\python>python resty_easyocr.py Traceback (most recent call last): File "D:\AI\python\resty_easyocr.py", line 1, in <module> import easyocr File "D:\anaconda3\envs\tinywan-modelscope\lib\site-packages\easyocr\__init__.py", line 1, in <module> from .easyocr import Reader File "D:\anaconda3\envs\tinywan-modelscope\lib\site-packages\easyocr\easyocr.py", line 3, in <module> from .recognition import get_recognizer, get_text File "D:\anaconda3\envs\tinywan-modelscope\lib\site-packages\easyocr\recognition.py", line 2, in <module> import torch File "D:\anaconda3\envs\tinywan-modelscope\lib\site-packages\torch\__init__.py", line 148, in <module> raise err OSError: [WinError 126] 找不到指定的模塊。 Error loading "D:\anaconda3\envs\tinywan-modelscope\lib\site-packages\torch\lib\fbgemm.dll" or one of its dependencies. ``` 這個錯誤信息表明在嘗試加載 `fbgemm.dll`（Facebook的GEMM（通用矩陣乘法）庫的一個優化版本，專為CPU上的深度學習操作而設計）或其依賴項時出現了問題。這個庫是PyTorch（一個廣泛使用的深度學習庫）的一部分，特別是在使用Intel CPU并啟用特定優化時。 使用conda安裝或更新PyTorch的示例命令 ```ts conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch ``` ## 識別 腳本`resty_easyocr.py` ```python import easyocr import sys def extract_text_from_image(image_path): """ 從給定的圖片路徑中提取文本。 參數: image_path (str): 圖片文件的路徑。 返回: str: 提取的文本，每行文本之間用換行符分隔。 """ # 初始化EasyOCR，指定需要識別的語言，這里是中國簡體（ch_sim）和英文（en） reader = easyocr.Reader(['ch_sim', 'en']) # 使用readtext方法從圖片中讀取文本 results = reader.readtext(image_path) # 初始化一個空字符串用于存儲提取的文本 text = "" # 遍歷識別結果 for result in results: # 每個result是一個元組，其中result[0]是邊界框信息，result[1]是識別到的文本 # 這里我們只需要文本部分，并將其添加到text字符串中，每個文本后添加一個換行符 text += result[1] + "\n" # 返回最終提取的文本 return text if __name__ == "__main__": # 檢查是否提供了命令行參數（圖片路徑） if len(sys.argv) != 2: print("Usage: python script.py <image_path>") sys.exit(1) # 非零退出碼表示錯誤 # 從命令行參數中獲取圖片路徑 image_path = sys.argv[1] # 調用函數提取文本 text = extract_text_from_image(image_path) # 打印提取的文本 print(text) ```  需要識別的圖片  執行命令識別結果 ``` (tinywan-modelscope) D:\AI\python>python resty_easyocr.py .\demo.png Neither CUDA nor MPS are available - defaulting to CPU. Note: This module is much faster with a GPU. D:\anaconda3\envs\tinywan-modelscope\lib\site-packages\easyocr\detection.py:78: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. net.load_state_dict(copyStateDict(torch.load(trained_model, map_location=device))) D:\anaconda3\envs\tinywan-modelscope\lib\site-packages\easyocr\recognition.py:169: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. state_dict = torch.load(model_path, map_location=device) Casbin實戰教程 ABAC模型策略設計研發 ```  ## phpy Docker鏡像 ```ts docker pull phpswoole/phpy:1.0.5 ``` 通過`docker scout quickview`查看鏡像供應鏈