initial commit for aarti

.idea/.gitignore

+# Default ignored files
+/shelf/
+/workspace.xml

.idea/aart_ppe_detection.iml

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="PyDocumentationSettings">
+    <option name="format" value="PLAIN" />
+    <option name="myDocStringFormat" value="Plain" />
+  </component>
+</module>
\ No newline at end of file

.idea/inspectionProfiles/profiles_settings.xml

+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file

.idea/misc.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (aart_ppe_detection)" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file

.idea/modules.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/aart_ppe_detection.iml" filepath="$PROJECT_DIR$/.idea/aart_ppe_detection.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file

.idea/vcs.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>
\ No newline at end of file

Dockerfile

@@ -5,15 +5,16 @@ RUN apt-get install tzdata vim -y
 RUN apt-get update && apt-get install tzdata ffmpeg libsm6 libxext6  -y
 
 RUN pip3 install --upgrade pip
-# RUN pip3 install matplotlib>=3.2.2 tensorboard>=2.4.1 numpy>=1.18.5 opencv-python>=4.1.2 Pillow>=7.1.2 PyYAML>=5.3.1 requests>=2.23.0 scipy>=1.4.1 torch>=1.7.0 torchvision>=0.8.1 tqdm>=4.41.0 pandas seaborn expiringdict minio cachetools
-# RUN pip3 install pymongo Cython paho-mqtt==1.5.0 scikit-learn==0.22.2
+RUN pip3 install matplotlib>=3.2.2 tensorboard>=2.4.1 numpy>=1.18.5 opencv-python>=4.1.2 Pillow>=7.1.2 PyYAML>=5.3.1 requests>=2.23.0 scipy>=1.4.1 torch>=1.7.0 torchvision>=0.8.1 tqdm>=4.41.0 pandas seaborn expiringdict minio cachetools
+RUN pip3 install pymongo Cython paho-mqtt==1.5.0 scikit-learn==0.22.2
 
 RUN pip3 install absl-py==1.3.0 asttokens==2.2.1 backcall==0.2.0 cachetools==5.2.0 certifi==2022.12.7 charset-normalizer==2.1.1 colorama==0.4.6 contourpy==1.0.6
 RUN pip3 install cycler==0.11.0 Cython==0.29.32 decorator==5.1.1 dnspython==2.2.1 executing==1.2.0 expiringdict==1.2.2 fonttools==4.38.0
 RUN pip3 install google-auth==2.15.0 google-auth-oauthlib==0.4.6
 RUN pip3 install grpcio==1.51.1 idna==3.4 importlib-metadata==5.1.0 ipython jedi==0.18.2 kiwisolver==1.4.4 Markdown==3.4.1 MarkupSafe==2.1.1 matplotlib matplotlib-inline==0.1.6 minio==7.1.12 numpy>=1.18.5
 RUN pip3 install oauthlib==3.2.2 opencv-python==4.6.0.66 packaging==22.0 paho-mqtt==1.6.1 pandas parso==0.8.3 pickleshare==0.7.5 Pillow==9.3.0 prompt-toolkit==3.0.36 protobuf==3.20.3 pure-eval==0.2.2 pyasn1==0.4.8 pyasn1-modules==0.2.8 Pygments==2.13.0 pymongo==4.3.3 pyparsing==3.0.9 python-dateutil==2.8.2 pytz==2022.6 PyYAML==6.0 requests==2.28.1 requests-oauthlib==1.3.1 rsa==4.9 scipy torch==1.9.0 torchvision==0.10.0 tqdm==4.64.1 traitlets==5.7.0 typing_extensions==4.4.0 urllib3==1.26.13 wcwidth==0.2.5 Werkzeug==2.2.2 zipp==3.11.0
-RUN pip3 install seaborn
+RUN pip3 install seaborn psycopg2
+
 
 ADD . /app
 ADD Arial.ttf /root/.config/Ultralytics/

app.py

 import os
 # os.environ["config"]="{\"TZ\": \"Asia/Kolkata\", \"MONGO_URI\": \"mongodb://svc-ilens:svc2345@192.168.3.220:21017\", \"MONGO_DATABASE\": \"ilens_wps\", \"MONGO_COLLECTION\": \"janusDeployment\", \"MONGO_KEY\": \"deploymentId\", \"MONGO_VALUE\": \"rahul_12345\", \"MONGO_COLL\": \"serviceConfiguration\", \"MONGO_DB\": \"ilens_wps\"}"
 # os.environ["config"]="{\"TZ\": \"Asia/Kolkata\", \"MONGO_URI\": \"mongodb://admin:iLens$HPCLv605@10.5.2.91:2717\", \"MONGO_DATABASE\": \"ilens_ai\", \"MONGO_COLLECTION\": \"janusDeployment\", \"MONGO_KEY\": \"deploymentId\", \"MONGO_VALUE\": \"hpcl_cctv_ppe\", \"MONGO_COLL\": \"serviceConfiguration\", \"MONGO_DB\": \"ilens_ai\"}"
-# os.environ["config"]="{\"TZ\": \"Asia/Kolkata\", \"MONGO_URI\": \"mongodb://admin:iLens$1234@192.168.3.181:2717/admin\", \"MONGO_DATABASE\": \"ilens_ai\", \"MONGO_COLLECTION\": \"janusDeployment\", \"MONGO_KEY\": \"deploymentId\", \"MONGO_VALUE\": \"aarti_ppe\", \"MONGO_COLL\": \"serviceConfiguration\", \"MONGO_DB\": \"ilens_ai\"}"
+os.environ["config"]="{\"TZ\": \"Asia/Kolkata\", \"MONGO_URI\": \"mongodb://admin:iLens$1234@192.168.3.181:2717/admin\", \"MONGO_DATABASE\": \"ilens_ai\", \"MONGO_COLLECTION\": \"janusDeployment\", \"MONGO_KEY\": \"deploymentId\", \"MONGO_VALUE\": \"aarti_ppe\", \"MONGO_COLL\": \"serviceConfiguration\", \"MONGO_DB\": \"ilens_ai\"}"
 from edge_engine.edge_processor import ExecutePipeline
 from edge_engine.edge_processor import Pubs
 from scripts import Ppe

edge_engine/edge_processor.py

@@ -77,6 +77,7 @@ class ExecutePipeline:
         self.model = model
 
     def run_model(self):
+
         if EDGE_CONFIG["inputConf"]["sourceType"].lower() in ["rtsp", "usbcam"]:
             logger.info("Selected input stream as Direct cv input")
             self.threadedVideoStream = ThreadedVideoStream(stream_config=EDGE_CONFIG["inputConf"])
@@ -84,6 +85,7 @@ class ExecutePipeline:
             self.frameProcessor = FrameProcessor(stream=self.threadedVideoStream,
                                                  model=self.model)
         elif EDGE_CONFIG["inputConf"]["sourceType"].lower() == "videofile":
+
             self.fileVideoStream = FileVideoStream(stream_config=EDGE_CONFIG["inputConf"])
             self.fileVideoStream.start()
             self.frameProcessor = FrameProcessor(stream=self.fileVideoStream, model=self.model)

edge_engine/streamio/frameProcessor.py

@@ -6,17 +6,45 @@ class FrameProcessor:
     def __init__(self, stream, model):
         self.model = model
         self.stream = stream
+        self.old_video_capture_count = 0
         logger.info("Setting up frame processor !!")
+        self.count = 0
+        self.skip_frame_every = 30  # 1 does not skip any frame (n-1 frames get skipped)
+
 
     def run_model(self):
-        while self.stream.stream.isOpened():
+        count = 0
+        while self.stream.running():
             try:
+
+                # video_capture_count = self.stream.get_frame_count()
+                # if(video_capture_count != self.old_video_capture_count):
+                #
+                #     self.old_video_capture_count = video_capture_count
+                #     count = 0
+                # if(video_capture_count == 1):
+                #     count = 0
+
+                count += 1
                 logger.debug("Getting frame mask_model")
-                frame = self.stream.read()
-                logger.debug("Running mask_model")
-                data = {"frame": frame, "frameId": "{}".format(uuid4()), "deviceId": "{}".format(DEVICE_ID)}
-                self.model.predict(data)
-                logger.debug("publishing mask_model output")
+                frame, count_1 = self.stream.read()
+
+                if frame is not None and self.count % self.skip_frame_every == 0:
+                    fid = uuid4()
+                    data = {
+                        "frame": frame,
+                        "frameId": self.count,
+                        "deviceId": "{}".format(DEVICE_ID),
+                    }
+
+                    self.model.predict(data)
+                self.count += 1
+                # logger.debug("Running mask_model")
+                # data = {"frame": frame, "frameId": "{}".format(uuid4()), "deviceId": "{}".format(DEVICE_ID), "frame_id": count}
+                # self.model.predict(data)
+                # logger.debug("publishing mask_model output")
+                if(self.count == 3227):
+                    self.count = 0
             except Exception as e:
                 logger.error(e)
                 logger.error(traceback.format_exc())
\ No newline at end of file

edge_engine/streamio/videostream/filevideostream.py

@@ -14,6 +14,7 @@ else:
 
 class FileVideoStream:
     def __init__(self,stream_config, transform=None):
+        self.count = 0
         # initialize the file video stream along with the boolean
         # used to indicate if the thread should be stopped or not
         self.transform = transform
@@ -21,6 +22,8 @@ class FileVideoStream:
         # initialize the queue used to store frames read from
         # the video file
         self.build_pipeline()
+        self.count_dict = {"count": 0}
+
 
 
     def start(self):
@@ -29,6 +32,7 @@ class FileVideoStream:
         return self
 
     def build_cv_obj(self):
+        # self.count += 1
         self.stream = cv2.VideoCapture(self.stream_config["uri"])
         self.stopped = False
 
@@ -43,6 +47,8 @@ class FileVideoStream:
         self.thread.daemon = True
 
 
+
+
     def is_opened(self):
         return self.stream.isOpened()
 
@@ -58,12 +64,17 @@ class FileVideoStream:
             if not self.Q.full():
                 # read the next frame from the file
                 (grabbed, frame) = self.stream.read()
+                self.count += 1
+                self.count_dict["count"] = self.count
 
                 # if the `grabbed` boolean is `False`, then we have
                 # reached the end of the video file
                 if grabbed is False or frame is None:
                     #self.stopped = True
+                    self.count = 0
+                    self.count_dict["count"] = self.count
                     self.build_cv_obj()
+
                     continue
 
                 # if there are transforms to be done, might as well
@@ -88,9 +99,14 @@ class FileVideoStream:
 
         self.stream.release()
 
+
     def read(self):
         # return next frame in the queue
-        return self.Q.get()
+        return self.Q.get(), self.count_dict
+    def get_frame_count(self):
+
+        # return next frame in the queue
+        return self.count
 
     # Insufficient to have consumer use while(more()) which does
     # not take into account if the producer has reached end of

scripts/utils/infocenter.py

@@ -40,16 +40,17 @@ class MongoLogger:
         try:
             input_data = {
                 "eventId": str(uuid1()).split('-')[0],
-                "cameraId": data['deviceId'],
+                "cameraId": "4",
                 "cameraName": self.camera_mapping_json.get(data['deviceId'], "Thermal Camera"),
                 "timestamp": datetime.now(),
                 "frame": data['frame'],
-                "eventtype": "Intrusion Detection",
+                "eventtype": data["message"],
                 "bg_color": data["bg_color"],
                 "font_color": data["font_color"],
-                "intrusion_message": data["message"],
+                "intrusion_message": data["event_type"],
                 "alert_sound": data["alert_sound"],
-                "logged_activity": data["activity"]}
+                "logged_activity": data["activity"],
+                "frame_id": data["frame_id"]}
             if os.environ.get('app') is not None:
                 input_data['app'] = os.environ.get('app')
 

yolov5/detect.py

@@ -36,7 +36,7 @@ def run(model1, # model path or triton URL
         data=ROOT / 'data/coco128.yaml',  # dataset.yaml path
         imgsz=(640, 640),  # inference size (height, width)
         conf_thres=0.2,  # confidence threshold
-        iou_thres=0.45,  # NMS IOU threshold
+        iou_thres=0.15,  # NMS IOU threshold
         max_det=1000,  # maximum detections per image
         device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
         view_img=False,  # show results
@@ -59,7 +59,7 @@ def run(model1, # model path or triton URL
         dnn=False,  # use OpenCV DNN for ONNX inference
         vid_stride=1,  # video frame-rate stride
 ):
-    class_list = {0:"person", 1:"Air Breathing Mask"}
+    class_list = {0:"person", 1:"Safety helmet", 2:"Hand gloves", 3:"coverall suit", 4:"Air Breathing Mask"}
     # source = str(source)
     # save_img = not nosave and not source.endswith('.txt')  # save inference images
     # is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)