AI服务替换自有云模型识别

Controller/AiController.py

@@ -5,20 +5,25 @@ import os
 import threading
 import time
 
+import numpy as np
+from PIL import Image
 from django.views.generic.base import View
 
 from AnsjerPush.config import BASE_DIR
 from Model.models import UidPushModel, AiService, Device_Info, VodHlsTag, VodHlsTagType
-from Object.AiImageObject import ImageProcessingObject
+from Object.AiEngineObject import AiEngine
 from Object.ETkObject import ETkObject
 from Object.ResponseObject import ResponseObject
 from Object.enums.MessageTypeEnum import MessageTypeEnum
 from Object.utils import LocalDateTimeUtil
-from Object.utils.AmazonRekognitionUtil import AmazonRekognitionUtil
 from Service.CommonService import CommonService
 from Service.EquipmentInfoService import EquipmentInfoService
 from Service.PushService import PushObject
 
+LOGGING = logging.getLogger('info')
+CLOUD_BASED_AI_URL = '34.192.147.108:8001'
+MODEL_NAME = 'AI_5obj_pdcpv_detect_yolov5_pipeline'
+
 
 class AiView(View):
     def get(self, request, *args, **kwargs):
@@ -105,30 +110,34 @@ class AiView(View):
                 with open(file_path, 'wb') as f:
                     f.write(val)
                     f.close()
-
-            image_size = 0  # 每张小图片的大小，等于0是按原图大小进行合并
-            image_row = 1  # 合并成一张图后，一行有几个小图
-            image_processing_obj = ImageProcessingObject(dir_path, image_size, image_row)
-            image_processing_obj.merge_images()
+            ai_view = AiView()
+            ai_results = ai_view.image_aI_recognition(file_path_list, 0.45, 0.45, 50)
+            if not ai_results:
+                return response.json(0)
+            event_type = ai_view.get_cloud_recognition_tag(ai_results)
+            if event_type == 0:
+                return response.json(0)
+            # image_size = 0  # 每张小图片的大小，等于0是按原图大小进行合并
+            # image_row = 1  # 合并成一张图后，一行有几个小图
+            # image_processing_obj = ImageProcessingObject(dir_path, image_size, image_row)
+            # image_processing_obj.merge_images()
 
             # 获取识别结果
-            aws_rekognition = AmazonRekognitionUtil()
-            with open(dir_path + '.jpg', 'rb') as f:
-                rekognition_res = aws_rekognition.detect_labels(f.read())
+            # aws_rekognition = AmazonRekognitionUtil()
+            # with open(dir_path + '.jpg', 'rb') as f:
+            #     rekognition_res = aws_rekognition.detect_labels(f.read())
 
-            if not rekognition_res:
-                return response.json(0)
+            # if not rekognition_res:
+            #     return response.json(0)
 
-            label_dict = image_processing_obj.handle_rekognition_res(detect_group, rekognition_res)
-            if not label_dict['label_list']:
-                # 需要删除图片
-                # photo.close()
-                # self.del_path(os.path.join(BASE_DIR, 'static/ai/' + uid))
-                return response.json(0)
-
-            event_type = label_dict['event_type']
-            label_str = ','.join(label_dict['label_list'])
-            new_bounding_box_dict = label_dict['new_bounding_box_dict']
+            # label_dict = image_processing_obj.handle_rekognition_res(detect_group, rekognition_res)
+            # if not label_dict['label_list']:
+            #     # 需要删除图片
+            #     # photo.close()
+            #     # self.del_path(os.path.join(BASE_DIR, 'static/ai/' + uid))
+            #     return response.json(0)
+            label_str = ''  # ','.join(label_dict['label_list'])
+            new_bounding_box_dict = ''  # label_dict['new_bounding_box_dict']
 
             # 上传缩略图到s3
             file_dict = {}
@@ -213,6 +222,70 @@ class AiView(View):
             }
             return response.json(48, data)
 
+    @classmethod
+    def image_aI_recognition(cls, input_name_arr, nms_threshold, confidence, client_timeout):
+        """
+        自有图片云模型识别
+        :param input_name_arr: 推理图片地址名
+        :param nms_threshold: nms置信度
+        :param confidence: 目标置信度（一般只用调整这个）
+        :param client_timeout: 超时时间（秒为单位）
+        :return: results 推理结果
+        """
+        try:
+            t = time.time()
+            # 建立长连接
+            ai = AiEngine(url=CLOUD_BASED_AI_URL)
+            # 检查连通性、推理服务器状态
+            if ai.health:
+                LOGGING.info('健康状况通过')
+            # 设定模型
+            if ai.set_model(MODEL_NAME):
+                LOGGING.info('设置模型通过')
+            # 推理张数(一次最多推理128张！)
+            # 图片名称（这里可以改成内存）注意改完之后要检查input_tmp的【类型(type)、尺寸(shape)】是否和之前的一致
+            # 输入尺寸固定640wx360h，如需变动可以联系我们，我们这边做resize会快
+            input_name_arr = np.array(list(map(np.array, map(Image.open, input_name_arr))))
+            # 推理
+            results = ai.yolo_infer(input_name_arr, nms_threshold, confidence, client_timeout)
+            # 推理完请关闭长连接
+            ai.close()
+            LOGGING.info(f'coast:{time.time() - t:.4f}s')
+            # 报错返回
+            if results == 'e_timeout':
+                raise Exception('推理超时')
+            elif results == 'e_no_model':
+                raise Exception('没有设置模型')
+            LOGGING.info('云上模型推理结果：{}'.format(results))
+            return results
+        except Exception as e:
+            LOGGING.info('云模型AI识别失败,errLine:{}, errMsg:{}'.format(e.__traceback__.tb_lineno, repr(e)))
+            return {}
+
+    @classmethod
+    def get_cloud_recognition_tag(cls, results):
+        """
+        根据推理结果
+        返回组合类型，0则推理失败，1：人，2：车，3：宠物，4：包裹
+        """
+        if not results:
+            return 0
+        # 返回字典，可以复制到绘制python脚本中查看结果
+        tag_list = []
+        event_dict = {1: 1, 0: 2, 3: 2, 2: 3, 4: 4}
+        for k, v in results.items():
+            if len(v) > 0:
+                for item in v:
+                    tag_list.append(event_dict.get(item['classID'], 0))
+        if tag_list:
+            tag_list = set(tag_list)
+            event_type = ''
+            for val in tag_list:
+                event_type += str(val)
+            return int(event_type)
+        else:
+            return 0
+
     @classmethod
     def save_cloud_ai_tag(cls, uid, event_time, types):
         """

Object/AiEngineObject.py

@@ -0,0 +1,92 @@
+# -*- encoding: utf-8 -*-
+"""
+@File    : AiEngine.py
+@Time    : 2023/2/28 15:15
+@Author  : stephen
+@Email   : zhangdongming@asj6.wecom.work
+@Software: PyCharm
+"""
+import ast
+import time
+
+import numpy as np
+import tritonclient.grpc as grpcclient
+
+
+class AiEngine:
+    def __init__(self, **kwargs):
+
+        # create grpc conncet
+        try:
+            self.triton_client = grpcclient.InferenceServerClient(
+                url=kwargs.get('url'),
+                verbose=False,
+                ssl=False,
+                root_certificates=None,
+                private_key=None,
+                certificate_chain=None)
+            conncet = True
+        except Exception as e:
+            conncet = False
+        self.health = True
+        health_check = [
+            conncet,
+            self.triton_client.is_server_live(),
+            self.triton_client.is_server_ready()
+        ]
+        if False in health_check:
+            print("health_check failed:" + health_check)
+            self.health = False
+        self.model_name = None
+
+    def set_model(self, model_name):
+        if self.triton_client.is_model_ready(model_name):
+            self.model_name = model_name
+            return True
+        else:
+            return False
+
+    def get_model_name(self, model_name):
+        return self.model_name
+
+    def yolo_infer(self, img_arr, nms_threshold, confidence, client_timeout):
+        if self.model_name == 'AI_5obj_pdcpv_detect_yolov5_pipeline':
+            pic_num_sum = len(img_arr)
+            nms_threshold = np.array([[nms_threshold]] * pic_num_sum, dtype=np.float32)
+            confidence = np.array([[confidence]] * pic_num_sum, dtype=np.float32)
+            inputs = [
+                grpcclient.InferInput('img1', [pic_num_sum, 360, 640, 3], "UINT8"),
+                grpcclient.InferInput('nms_threshold', [pic_num_sum, 1], "FP32"),
+                grpcclient.InferInput('confidence', [pic_num_sum, 1], "FP32")
+            ]
+            outputs = [grpcclient.InferRequestedOutput('OUTPUT0')]
+
+            inputs[0].set_data_from_numpy(img_arr)
+            inputs[1].set_data_from_numpy(nms_threshold)
+            inputs[2].set_data_from_numpy(confidence)
+
+            try:
+                results = self.triton_client.infer(model_name=self.model_name, inputs=inputs, outputs=outputs,
+                                                   client_timeout=client_timeout)
+            except Exception as e:
+                return 'e_timeout'
+            result_str = results.as_numpy('OUTPUT0')[0].decode("UTF-8")
+            results = ast.literal_eval(result_str)
+            return results
+        else:
+            return 'e_no_model'
+
+    def close(self):
+        return self.triton_client.close()
+
+
+def func():
+    print('func start')
+    time.sleep(1)
+    print('func end')
+
+
+if __name__ == "__main__":
+    t = time.time()
+    func()
+    print(f'coast:{time.time() - t:.4f}s')