增加yolov5 烟火1.0检测 解决work为2的问题 解决PIL图像截断Base64剩余几个字节读不到而报错的问题

serverless/common/yolov5_firesmoke_1.0/nuclio/readme.txt

@@ -0,0 +1,5 @@
+模型来自firesmokeversion3 的数据训练
+best.pt
+yolov5 v3.0 版本
+yolov5s 模型
+目前2023年2月15日yolov5的最新代码支持该模型的推理，不用切换到v3.0的代码推理，但是训练必须要用v3.0的代码

serverless/pytorch/ultralytics/yolov5/nuclio/function.yaml

@@ -109,7 +109,7 @@ spec:
 
   triggers:
     myHttpTrigger:
-      maxWorkers: 2
+      maxWorkers: 1
       kind: 'http'
       workerAvailabilityTimeoutMilliseconds: 10000
       attributes:

serverless/pytorch/ultralytics/yolov5/nuclio/main.py

@@ -4,6 +4,9 @@ from PIL import Image
 import io
 import torch
 
+from PIL import ImageFile
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
 def init_context(context):
     context.logger.info("Init context...  0%")
 
@@ -17,7 +20,7 @@ def handler(context, event):
     context.logger.info("Run yolo-v5 model")
     data = event.body
     buf = io.BytesIO(base64.b64decode(data["image"]))
-    threshold = float(data.get("threshold", 0.5))
+    threshold = float(data.get("threshold", 0.3))
     context.user_data.model.conf = threshold
     image = Image.open(buf)
     yolo_results_json = context.user_data.model(image).pandas().xyxy[0].to_dict(orient='records')

serverless/pytorch/ultralytics/yolov5_firesmoke_1.0/nuclio/function.yaml

@@ -0,0 +1,45 @@
+metadata:
+  name: ultralytics-yolov5_firesmoke_1.0
+  namespace: cvat
+  annotations:
+    name: YOLO v5_firesmoke_1.0
+    type: detector
+    framework: pytorch
+    spec: |
+      [
+        { "id": 0, "name": "fire" },
+        { "id": 1, "name": "smoke"}
+      ]
+
+spec:
+  description: YOLO v5 via pytorch hub_firesmoke_1.0
+  runtime: 'python:3.6'
+  handler: main:handler
+  eventTimeout: 30s
+  build:
+    image: cvat.ultralytics-yolov5_firesmoke_1.0
+    baseImage: ultralytics/yolov5:latest-cpu
+
+    directives:
+      preCopy:
+        - kind: USER
+          value: root
+        - kind: RUN
+          value: apt update && apt install --no-install-recommends -y libglib2.0-0
+        - kind: WORKDIR
+          value: /opt/nuclio
+
+  triggers:
+    myHttpTrigger:
+      maxWorkers: 1
+      kind: 'http'
+      workerAvailabilityTimeoutMilliseconds: 10000
+      attributes:
+        maxRequestBodySize: 33554432 # 32MB
+
+  platform:
+    attributes:
+      restartPolicy:
+        name: always
+        maximumRetryCount: 3
+      mountMode: volume

serverless/pytorch/ultralytics/yolov5_firesmoke_1.0/nuclio/main.py

@@ -0,0 +1,43 @@
+import json
+import base64
+from PIL import Image
+import io
+import torch
+
+from PIL import ImageFile
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+def init_context(context):
+    context.logger.info("Init context...  0%")
+
+    # Read the DL model
+    model = torch.hub.load('ultralytics/yolov5', 'custom', path='/opt/nuclio/common/yolov5_firesmoke_1.0/nuclio/model.pt') # or yolov5m, yolov5l, yolov5x, custom
+    context.user_data.model = model
+
+    context.logger.info("Init context...100%")
+
+def handler(context, event):
+    context.logger.info("Run yolo-v5_firesmoke_1.0 model")
+    data = event.body
+    buf = io.BytesIO(base64.b64decode(data["image"]))
+    threshold = float(data.get("threshold", 0.3))
+    context.user_data.model.conf = threshold
+    image = Image.open(buf)
+    yolo_results_json = context.user_data.model(image).pandas().xyxy[0].to_dict(orient='records')
+
+    encoded_results = []
+    for result in yolo_results_json:
+        encoded_results.append({
+            'confidence': result['confidence'],
+            'label': result['name'],
+            'points': [
+                result['xmin'],
+                result['ymin'],
+                result['xmax'],
+                result['ymax']
+            ],
+            'type': 'rectangle'
+        })
+
+    return context.Response(body=json.dumps(encoded_results), headers={},
+        content_type='application/json', status_code=200)