another recipe

作者分享了从加州返回英国后的几款美食制作经历,包括黑无花果辣椒意大利面、扁面包配烤无花果及蓝纹奶酪、以及一款采用荞麦粉制作的独特饼干。这些食谱不仅展示了无花果的多种使用方式,还体现了作者对于食材搭配的独特见解。
It’s been a while since we got back to cold, wet England – and still seem to be on Pacific time, already missing the sun (and people :)) in California. We enjoyed lovely food throughout the stay, but not being able to cook for more than two weeks was a bit like torture for me – even more so when you come across so much lovely, fresh produce and you know you can’t cook it!

I’ll write about our trip later on, but here are some things I rustled up when we got back – I luckily got hold of the last figs of the season.

Black fig and chilli tagliatelli – a very simple pasta dish, from this recipe. I normally find creamy sauce too rich, but this was lovely. The heat from the grilled chillis and the sweetness of the figs work really well. I used green figs instead of black ones but it was equally delicious.

Flatbread with oven-dried figs, caramelised onions and blue cheese – a classic combination, I really like the rustic flavours. Roasting figs and slowly caramelising onions beforehand gives a lovely concentrated taste – they go wonderfully well with the walnuts and blue cheese. I used Italian ‘00’ flour instead of bread (strong) flour this time and it lent it a light, fluffy texture which complemented the topping nicely. Recipe from here (PDF link).

This is another recipe ‘with a twist’ from this Japanese patissier. The tuille biscuits are made with buckwheat flour – and the sandwiched custard cream has fig jam in it. These tuille biscuits are much crunchier and have more bite than the usual variety – and I think it works well with the textures/flavours from the figs. I used a shop bought jam for this, but you can always make it yourself (a good sounding recipe in the above PDF link) or you can have roasted (or even fresh) figs instead.
File "D:\roms\src\backend\recipe-optimizer\recipe_optimizer\apps\model\train.py", line 140, in filter_by_model_parameter LOG.warning( Message: 'parameter MinEPDTime step chuck not in std df' Arguments: () [2025-07-28 14:56:16,333] WARNING in train: parameter MinEPDTime step chuck not in std df File "D:\roms\Python39\lib\site-packages\waitress\runner.py", line 246, in run _serve(app, **kw) File "D:\roms\Python39\lib\site-packages\waitress\__init__.py", line 19, in serve server.run() File "D:\roms\Python39\lib\site-packages\waitress\server.py", line 331, in run self.task_dispatcher.shutdown() File "D:\roms\Python39\lib\site-packages\waitress\task.py", line 127, in shutdown --- Logging error --- self.thread_exit_cv.wait(0.1) File "D:\roms\Python39\lib\threading.py", line 316, in wait gotit = waiter.acquire(True, timeout) KeyboardInterrupt Traceback (most recent call last): File "D:\roms\Python39\lib\site-packages\pandas\core\indexes\base.py", line 3805, in get_loc return self._engine.get_loc(casted_key) File "index.pyx", line 167, in pandas._libs.index.IndexEngine.get_loc File "index.pyx", line 196, in pandas._libs.index.IndexEngine.get_loc File "pandas\\_libs\\hashtable_class_helper.pxi", line 7081, in pandas._libs.hashtable.PyObjectHashTable.get_item File "pandas\\_libs\\hashtable_class_helper.pxi", line 7089, in pandas._libs.hashtable.PyObjectHashTable.get_item KeyError: 'chuck' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "D:\roms\src\backend\recipe-optimizer\recipe_optimizer\apps\model\train.py", line 131, in filter_by_model_parameter self.std_dataframe.loc[parameter, step_name] is np.nan File "D:\roms\Python39\lib\site-packages\pandas\core\indexing.py", line 1183, in __getitem__ return self.obj._get_value(*key, takeable=self._takeable) File "D:\roms\Python39\lib\site-packages\pandas\core\frame.py", line 4214, in _get_value series = self._get_item_cache(col) File "D:\roms\Python39\lib\site-packages\pandas\core\frame.py", line 4638, in _get_item_cache loc = self.columns.get_loc(item) File "D:\roms\Python39\lib\site-packages\pandas\core\indexes\base.py", line 3812, in get_loc raise KeyError(key) from err KeyError: 'chuck' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "D:\roms\Python39\lib\logging\handlers.py", line 74, in emit self.doRollover() File "D:\roms\Python39\lib\logging\handlers.py", line 177, in doRollover self.rotate(self.baseFilename, dfn) File "D:\roms\Python39\lib\logging\handlers.py", line 115, in rotate os.rename(source, dest) PermissionError: [WinError 32] 另一个程序正在使用此文件,进程无法访问。: 'D:\\roms\\src\\backend\\recipe-optimizer\\logs\\roms.log' -> 'D:\\roms\\src\\backend\\recipe-optimizer\\logs\\roms.log.1' Call stack: File "D:\roms\Python39\lib\threading.py", line 930, in _bootstrap self._bootstrap_inner() File "D:\roms\Python39\lib\threading.py", line 973, in _bootstrap_inner self.run() File "D:\roms\Python39\lib\threading.py", line 910, in run self._target(*self._args, **self._kwargs) File "D:\roms\Python39\lib\site-packages\waitress\task.py", line 83, in handler_thread task.service() File "D:\roms\Python39\lib\site-packages\waitress\channel.py", line 430, in service task.service() File "D:\roms\Python39\lib\site-packages\waitress\task.py", line 167, in service self.execute() File "D:\roms\Python39\lib\site-packages\waitress\task.py", line 435, in execute app_iter = self.channel.server.application(environ, start_response) File "D:\roms\Python39\lib\site-packages\waitress\proxy_headers.py", line 64, in translate_proxy_headers return app(environ, start_response) File "D:\roms\Python39\lib\site-packages\flask\app.py", line 1536, in __call__ return self.wsgi_app(environ, start_response) File "D:\roms\Python39\lib\site-packages\flask\app.py", line 1511, in wsgi_app response = self.full_dispatch_request() File "D:\roms\Python39\lib\site-packages\flask\app.py", line 917, in full_dispatch_request rv = self.dispatch_request() File "D:\roms\Python39\lib\site-packages\flask\app.py", line 902, in dispatch_request return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) # type: ignore[no-any-return] File "D:\roms\Python39\lib\site-packages\webargs\core.py", line 657, in wrapper return func(*args, **kwargs) File "D:\roms\Python39\lib\site-packages\flask_smorest\arguments.py", line 83, in wrapper return func(*f_args, **f_kwargs) File "D:\roms\Python39\lib\site-packages\flask_jwt_extended\view_decorators.py", line 170, in decorator return current_app.ensure_sync(fn)(*args, **kwargs) File "D:\roms\src\backend\recipe-optimizer\recipe_optimizer\apps\model\api.py", line 95, in model_call data = model_service.call(model_id, **args) File "D:\roms\src\backend\recipe-optimizer\recipe_optimizer\apps\model\service.py", line 70, in call base_recipe_data = recipe_service.build_calculate_recipe_data(recipe, model) File "D:\roms\src\backend\recipe-optimizer\recipe_optimizer\apps\recipe\service\recipe.py", line 221, in build_calculate_recipe_data train_data = train.build_train_recipe_dict() File "D:\roms\src\backend\recipe-optimizer\recipe_optimizer\apps\model\train.py", line 101, in build_train_recipe_dict recipe_dict = { File "D:\roms\src\backend\recipe-optimizer\recipe_optimizer\apps\model\train.py", line 102, in <dictcomp> recipe.name: self.filter_by_model_parameter(recipe.content) File "D:\roms\src\backend\recipe-optimizer\recipe_optimizer\apps\model\train.py", line 140, in filter_by_model_parameter LOG.warning( Message: 'parameter MaxEPDTime step chuck not in std df' Arguments: () [2025-07-28 14:56:16,348] WARNING in train: parameter MaxEPDTime step chuck not in std df
07-29
本指南详细阐述基于Python编程语言结合OpenCV计算机视觉库构建实时眼部状态分析系统的技术流程。该系统能够准确识别眼部区域,并对眨眼动作与持续闭眼状态进行判别。OpenCV作为功能强大的图像处理工具库,配合Python简洁的语法特性与丰富的第三方模块支持,为开发此类视觉应用提供了理想环境。 在环境配置阶段,除基础Python运行环境外,还需安装OpenCV核心模块与dlib机器学习库。dlib库内置的HOG(方向梯度直方图)特征检测算法在面部特征定位方面表现卓越。 技术实现包含以下关键环节: - 面部区域检测:采用预训练的Haar级联分类器或HOG特征检测器完成初始人脸定位,为后续眼部分析建立基础坐标系 - 眼部精确定位:基于已识别的人脸区域,运用dlib提供的面部特征点预测模型准确标定双眼位置坐标 - 眼睑轮廓分析:通过OpenCV的轮廓提取算法精确勾勒眼睑边缘形态,为状态判别提供几何特征依据 - 眨眼动作识别:通过连续帧序列分析眼睑开合度变化,建立动态阈值模型判断瞬时闭合动作 - 持续闭眼检测:设定更严格的状态持续时间与闭合程度双重标准,准确识别长时间闭眼行为 - 实时处理架构:构建视频流处理管线,通过帧捕获、特征分析、状态判断的循环流程实现实时监控 完整的技术文档应包含模块化代码实现、依赖库安装指引、参数调优指南及常见问题解决方案。示例代码需具备完整的错误处理机制与性能优化建议,涵盖图像预处理、光照补偿等实际应用中的关键技术点。 掌握该技术体系不仅有助于深入理解计算机视觉原理,更为疲劳驾驶预警、医疗监护等实际应用场景提供了可靠的技术基础。后续优化方向可包括多模态特征融合、深度学习模型集成等进阶研究领域。 资源来源于网络分享,仅用于学习交流使用,请勿用于商业,如有侵权请联系我删除!
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