stream_context_create解决file_ge…

本文介绍了如何在PHP程序中使用file_get_contents函数时遇到超时问题的解决方法,包括增加超时时间限制和失败时重试三次。同时展示了如何通过自定义函数实现POST请求,结合康盛的RC4加密解密算法,构建了一个安全性较高的WebService。

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stream_context_create作用:
创建并返回一个文本数据流并应用各种选项,可用于fopen(),file_get_contents()等过程的超时设置、代理服务器、请求方式、头信息设置的特殊过程。
函数原型:resource stream_context_create ([ array $options [, array $params ]] )

在使用file_get_contents函数的时候,经常会出现超时的情况,在这里要通过查看一下错误提示,看看是哪种错误,比较常见的是读取超时,这种情况大家可以通过一些方法来尽量的避免或者解决。这里就简单介绍两种:

一、增加超时的时间限制

这里需要注意:set_time_limit只是设置你的PHP程序的超时时间,而不是file_get_contents函数读取URL的超时时间。一开始以为set_time_limit也能影响到file_get_contents,后来经测试,是无效的。真正的修改file_get_contents延时可以用resource $context的timeout参数:
  $opts = array(
    'http'=>array(
    'method'=>"GET",
    'timeout'=>60,
  )
);
//创建数据流上下文
$context = stream_context_create($opts);

$html =file_get_contents('http://blog.sina.com/mirze', false, $context);

//fopen输出文件指针处的所有剩余数据:
//fpassthru($fp); //fclose()前使用

二、一次有延时的话那就多试几次

有时候失败是因为网络等因素造成,没有解决办法,但是可以修改程序,失败时重试几次,仍然失败就放弃,因为file_get_contents()如果失败将返回 FALSE,所以可以下面这样编写代码:
$cnt=0;
while($cnt < 3 && ($str=@file_get_contents('http://blog.sina.com/mirze'))===FALSE) $cnt++;

以上方法对付超时已经OK了。

那么Post呢?细心点有人发现了'method'=>"GET", 对!是不是能设置成post呢?百度找了下相关资料,还真可以!而且有人写出了山寨版的post传值函数,如下:

function Post($url, $post = null){
     $context = array();

     if (is_array($post)) {
         ksort($post);

         $context['http'] = array (
             'timeout'=>60,
             'method' => 'POST',
             'content' => http_build_query($post, '', '&'),
         );
     }
     return file_get_contents($url, false, stream_context_create($context));
}

$data = array(
     'name' => 'test',
     'email' => 'test@gmail.com',
     'submit' => 'submit',
);
echo Post('http://www.ej38.com', $data);

OK , 上面函数完美了,既解决了超时控制又解决了Post传值。再配合康盛的改良版RC4加密解密算法,做一个安全性很高的webservice就简单多了。

参考:http://hi.baidu.com/lssbing/blog/item/9a2dcb0f1183a1266059f38d.html
+--------------------------------------------------------------------------------------------------------+ | npu-smi 25.0.rc1.1 Version: 25.0.rc1.1 | +-------------------------------+-----------------+------------------------------------------------------+ | NPU Name | Health | Power(W) Temp(C) Hugepages-Usage(page) | | Chip Device | Bus-Id | AICore(%) Memory-Usage(MB) | +===============================+=================+======================================================+ | 2 310P3 | OK | NA 49 24 / 24 | | 0 0 | 0000:01:00.0 | 0 1706 / 44216 | +-------------------------------+-----------------+------------------------------------------------------+ | 2 310P3 | OK | NA 47 0 / 0 | | 1 1 | 0000:01:00.0 | 0 1367 / 43757 | +===============================+=================+======================================================+ | 3 310P3 | OK | NA 52 0 / 0 | | 0 2 | 0000:03:00.0 | 0 1484 / 44216 | +-------------------------------+-----------------+------------------------------------------------------+ | 3 310P3 | OK | NA 49 0 / 0 | | 1 3 | 0000:03:00.0 | 0 1456 / 43757 | +===============================+=================+======================================================+ | 5 310P3 | OK | NA 49 0 / 0 | | 0 4 | 0000:81:00.0 | 0 1599 / 44216 | +-------------------------------+-----------------+------------------------------------------------------+ | 5 310P3 | OK | NA 47 0 / 0 | | 1 5 | 0000:81:00.0 | 0 1341 / 43757 | +===============================+=================+======================================================+ | 6 310P3 | OK | NA 51 0 / 0 | | 0 6 | 0000:83:00.0 | 0 1505 / 44216 | +-------------------------------+-----------------+------------------------------------------------------+ | 6 310P3 | OK | NA 50 0 / 0 | | 1 7 | 0000:83:00.0 | 0 1433 / 43757 | +===============================+=================+======================================================+ +-------------------------------+-----------------+------------------------------------------------------+ | NPU Chip | Process id | Process name | Process memory(MB) | +===============================+=================+======================================================+ | 2 0 | 2450404 | python | 98 | | 2 0 | 2526327 | python | 99 | | 2 0 | 2522713 | python | 98 | +===============================+=================+======================================================+ | No running processes found in NPU 3 | +===============================+=================+======================================================+ | No running processes found in NPU 5 | +===============================+=================+======================================================+ | No running processes found in NPU 6 | +===============================+=================+======================================================+ 这是当前npu的使用状态,现在需要怎么办,代码中有错误吗,还是代码衔接问题 import os import io import cv2 import time import logging import numpy as np from PIL import Image from fastapi import FastAPI, File, UploadFile, Request from fastapi.responses import JSONResponse from fastapi.staticfiles import StaticFiles from pydantic import BaseModel from typing import Optional, List, Dict, Any from datetime import datetime # 华为昇腾推理API import acl # 配置日志 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) app = FastAPI(title="Ascend-OM Object Detection API") RESULT_DIR = 'results' os.makedirs(RESULT_DIR, exist_ok=True) app.mount("/gqj_check/img", StaticFiles(directory=RESULT_DIR), name="results") CUSTOM_NAMES = { "A_安全帽": "安全帽", "B_安全带": "安全带", "C_绝缘靴": "绝缘靴", "D_绝缘手套": "绝缘手套", "E_验电器": "验电器", "F_工具包": "工具包", "G_钳子": "钳子", "H_力矩扳手": "力矩扳手", "I_钢丝刷": "钢丝刷", "J_头灯": "头灯", "K_力矩头": "力矩头", "L_防护旗": "防护旗", "M_扳手": "扳手", "N_螺丝刀": "螺丝刀", "O_脚扣": "脚扣", "P_水平尺": "水平尺", "Q_手锤子": "手锤子" } class ImageUrl(BaseModel): image_url: str class DetectionItem(BaseModel): class_name: str count: int confidence: float class ApiResponse(BaseModel): code: int msg: str request_time: str end_time: str total_time: str status: Optional[str] data: List[DetectionItem] inference_time: Optional[str] image_url: Optional[str] source_url: Optional[str] # --------- 昇腾 NPU 推理相关 --------- class AscendModel: def __init__(self, om_path): logger.info("初始化ACL环境...") self.acl_init() self.context, self.stream = None, None self.model_id, self.model_desc = None, None self.input_size = (960, 960) # 需与模型输入一致 self.input_data = None self.output_data = None logger.info("加载模型...") self.load_model(om_path) logger.info("模型加载完成") def acl_init(self): """初始化ACL环境""" ret = acl.init() if ret != 0: raise Exception(f"ACL初始化失败,错误码: {ret}") def load_model(self, om_path): """加载OM模型并准备输入输出缓冲区""" if not os.path.exists(om_path): raise FileNotFoundError(f"模型文件不存在: {om_path}") # 创建上下文和流 self.context = acl.rt.create_context(0) self.stream = acl.rt.create_stream() # 加载模型 self.model_id = acl.mdl.load_from_file(om_path) self.model_desc = acl.mdl.create_desc(self.model_id) # 准备输入输出缓冲区 self._prepare_buffers() def _prepare_buffers(self): """准备模型输入输出缓冲区""" # 获取输入描述 input_num = acl.mdl.get_num_inputs(self.model_desc) input_desc = acl.mdl.get_input_desc(self.model_desc, 0) input_size = acl.mdl.get_desc_size(input_desc) self.input_data = acl.rt.malloc(input_size, acl.rt.mem_type_device) # 获取输出描述 output_num = acl.mdl.get_num_outputs(self.model_desc) self.output_data = [] for i in range(output_num): output_desc = acl.mdl.get_output_desc(self.model_desc, i) output_size = acl.mdl.get_desc_size(output_desc) output_buf = acl.rt.malloc(output_size, acl.rt.mem_type_device) self.output_data.append(output_buf) def preprocess(self, image: Image.Image): """图像预处理""" img = image.resize(self.input_size) img = np.array(img) if img.shape[2] == 4: img = img[:, :, :3] # 去除alpha通道 img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) img = img.astype(np.float32) / 255.0 img = np.transpose(img, (2, 0, 1)) img = np.expand_dims(img, 0) return img def infer(self, img_np: np.ndarray): """执行模型推理""" logger.info("开始推理...") start_time = time.time() # 将数据拷贝到设备 img_flat = img_np.flatten() acl.rt.memcpy(self.input_data, img_flat.nbytes, acl.util.numpy_to_ptr(img_flat), img_flat.nbytes, acl.rt.memcpy_host_to_device) # 执行推理 inputs = [self.input_data] outputs = self.output_data ret = acl.mdl.execute(self.model_id, inputs, outputs) if ret != 0: raise Exception(f"推理执行失败,错误码: {ret}") # 处理输出(根据实际模型输出格式调整) # 这里假设输出是检测框数组 [class_id, conf, x1, y1, x2, y2] output_buffer = outputs[0] output_size = acl.mdl.get_desc_size(acl.mdl.get_output_desc(self.model_desc, 0)) host_output = np.zeros(output_size // 4, dtype=np.float32) # 假设是float32类型 acl.rt.memcpy(acl.util.numpy_to_ptr(host_output), output_size, output_buffer, output_size, acl.rt.memcpy_device_to_host) # 解析输出为检测框格式 dets = [] num_dets = int(host_output[0]) # 假设第一个元素是检测框数量 for i in range(num_dets): base_idx = 1 + i * 6 if base_idx + 5 >= len(host_output): break class_id = int(host_output[base_idx]) conf = host_output[base_idx + 1] x1, y1, x2, y2 = host_output[base_idx+2:base_idx+6] dets.append([class_id, conf, x1, y1, x2, y2]) inference_time = time.time() - start_time logger.info(f"推理完成,耗时: {inference_time:.3f}秒") return dets def __del__(self): """资源释放""" logger.info("释放资源...") if hasattr(self, 'model_id') and self.model_id: acl.mdl.unload(self.model_id) if hasattr(self, 'model_desc') and self.model_desc: acl.mdl.destroy_desc(self.model_desc) if hasattr(self, 'input_data') and self.input_data: acl.rt.free(self.input_data) if hasattr(self, 'output_data') and self.output_data: for buf in self.output_data: acl.rt.free(buf) if hasattr(self, 'stream') and self.stream: acl.rt.destroy_stream(self.stream) if hasattr(self, 'context') and self.context: acl.rt.destroy_context(self.context) acl.finalize() # --------- FastAPI 业务逻辑 --------- model = None # 延迟初始化,通过命令行参数加载 def process_image(image: Image.Image, request_time: datetime, original_filename: Optional[str] = None): """处理图像并返回检测结果""" timestamp = request_time.strftime("%Y%m%d_%H%M%S_%f") # 生成文件名 if original_filename: base_name = os.path.basename(original_filename) name, ext = os.path.splitext(base_name) original_filename = f"{name}_{timestamp}{ext}" else: original_filename = f"original_{timestamp}.jpg" original_path = os.path.join(RESULT_DIR, original_filename) result_filename = f"detection_{timestamp}.jpg" result_path = os.path.join(RESULT_DIR, result_filename) # 保存原图 image.save(original_path) logger.info(f"原图保存至: {original_path}") # 推理过程 logger.info("开始预处理和推理...") start_inference_time = time.time() img_np = model.preprocess(image) results = model.infer(img_np) end_inference_time = time.time() inference_time = end_inference_time - start_inference_time logger.info(f"推理耗时: {inference_time:.3f} 秒") # 解析推理结果 class_counts = {} detected_objects = [] opencv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) height, width = opencv_image.shape[:2] for det in results: class_id, conf, x1, y1, x2, y2 = det # 过滤低置信度结果 if conf < 0.5: continue # 检查类别ID有效性 if 0 <= class_id < len(CUSTOM_NAMES): class_name = list(CUSTOM_NAMES.values())[int(class_id)] else: class_name = f"未知类别({class_id})" logger.warning(f"检测到未知类别ID: {class_id}") # 更新计数 class_counts[class_name] = class_counts.get(class_name, 0) + 1 detected_objects.append({"class_name": class_name, "confidence": conf}) # 绘制检测框(确保坐标在有效范围内) x1 = max(0, min(int(x1), width)) y1 = max(0, min(int(y1), height)) x2 = max(0, min(int(x2), width)) y2 = max(0, min(int(y2), height)) cv2.rectangle(opencv_image, (x1, y1), (x2, y2), (0,255,0), 2) cv2.putText(opencv_image, f"{class_name}:{conf:.2f}", (x1, max(10, y1-10)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0,255,0), 2) # 保存结果图 cv2.imwrite(result_path, opencv_image) logger.info(f"结果图保存至: {result_path}") # 构建返回结果 detection_result = [] for class_name, count in class_counts.items(): class_detections = [d for d in detected_objects if d["class_name"] == class_name] max_confidence = max([d["confidence"] for d in class_detections]) if class_detections else 0 detection_result.append({ "class_name": class_name, # 与DetectionItem保持一致 "count": count, "confidence": round(max_confidence, 4) }) return { "detection_result": detection_result, "inference_time": inference_time, "original_path": original_path, "result_path": result_path } @app.post("/gqj_check/localfile") async def detect_objects(request: Request, file: UploadFile = File(...)): request_time = datetime.now() if model is None: end_time = datetime.now() return JSONResponse( content={"code": 500, "msg": "模型未加载", "request_time": str(request_time), "end_time": str(end_time), "total_time": "0", "data": []}, status_code=500 ) try: contents = await file.read() image = Image.open(io.BytesIO(contents)) result = process_image(image, request_time, file.filename) end_time = datetime.now() base_url = str(request.base_url) result_filename = os.path.basename(result["result_path"]) debug_image_url = f"{base_url}gqj_check/img/{result_filename}" response_data = { "code": 200, "msg": "成功", "request_time": str(request_time), "end_time": str(end_time), "total_time": f"{(end_time-request_time).total_seconds():.3f}秒", "status": "success", "data": result["detection_result"], "inference_time": f"{result['inference_time']:.3f}", # 与模型保持一致 "debug_image_url": debug_image_url } logger.info("检测完成,返回结果") return JSONResponse(content=response_data) except Exception as e: end_time = datetime.now() logger.error(f"检测异常: {str(e)}", exc_info=True) return JSONResponse( content={"code": 500, "msg": str(e), "request_time": str(request_time), "end_time": str(end_time), "total_time": "0", "data": []}, status_code=500 ) if __name__ == "__main__": import argparse import uvicorn parser = argparse.ArgumentParser() parser.add_argument('--model_path', type=str, required=True, help='OM模型路径') parser.add_argument('--port', type=int, default=8000) parser.add_argument('--input_size', type=int, nargs=2, default=(960, 960), help='模型输入尺寸') args = parser.parse_args() logger.info(f"正在加载模型: {args.model_path},服务端口: {args.port}") model = AscendModel(args.model_path) model.input_size = tuple(args.input_size) # 设置输入尺寸 logger.info(f"模型加载完成,输入尺寸: {model.input_size},服务启动中...") uvicorn.run(app, host="0.0.0.0", port=args.port)
最新发布
07-31
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