1107 intersection traffic meter fix (#1117)

Changed Y8 to Y11, got rid of DS-Yolo, added bitrates, unified Dockerfile.

Author: bwsw

samples/intersection_traffic_meter/README.md

@@ -1,6 +1,6 @@
 # Intersection traffic meter demo
 
-**NB**: The demo uses **YOLOV8** model which takes up to **10-15 minutes** to compile to TensorRT engine. The first launch may take a decent time.
+**NB**: The demo uses **YOLOV11** model which takes up to **10-15 minutes** to compile to TensorRT engine. The first launch may take a decent time.
 
 The pipeline detects when cars, trucks or buses cross a city intersection delimited by user-configured polygon and the direction of the crossing. The crossing events are attached to individual tracks and are counted for each video source and polygon edge separately; the counters are displayed on the frame. The crossing events are also stored with Graphite and displayed on a Grafana dashboard.
 
@@ -10,13 +10,9 @@ Preview:
 
 Tested on platforms:
 
-- Nvidia Turing
+- Nvidia Turing, Ampere
 - Nvidia Jetson Orin family
 
-Demonstrated operational modes:
-
-- real-time processing: RTSP streams (multiple sources at once);
-
 Demonstrated adapters:
 - Video loop adapter;
 - Always-ON RTSP sink adapter;

samples/intersection_traffic_meter/docker-compose.l4t.yml

@@ -21,7 +21,9 @@ services:
     privileged: true
     build:
       context: .
-      dockerfile: docker/Dockerfile.l4t
+      dockerfile: docker/Dockerfile
+      args:
+        IMAGE: ghcr.io/insight-platform/savant-deepstream-l4t:latest
     volumes:
       - zmq_sockets:/tmp/zmq-sockets
       - ../../cache:/cache
@@ -52,6 +54,7 @@ services:
       - SOURCE_ID=leeds
       - STUB_FILE_LOCATION=/stub_imgs/smpte100_1920x1080.jpeg
       - DEV_MODE=True
+      - ENCODER_BITRATE=8000000
     command: python -m adapters.ds.sinks.always_on_rtsp
 
   graphite:

samples/intersection_traffic_meter/docker-compose.x86.yml

@@ -9,8 +9,8 @@ services:
   module:
     privileged: true
     build:
-      context: .
-      dockerfile: docker/Dockerfile.x86
+      args:
+        IMAGE: ghcr.io/insight-platform/savant-deepstream:latest
     extends:
       file: docker-compose.l4t.yml
       service: module

samples/intersection_traffic_meter/docker/Dockerfile

@@ -0,0 +1,3 @@
+ARG IMAGE
+FROM ${IMAGE}
+RUN python -m pip install statsd~=4.0

samples/intersection_traffic_meter/docker/Dockerfile.l4t

@@ -1,11 +1,11 @@
 name: intersection_traffic_meter
 
 parameters:
-  frame:
-    width: 1920
-    height: 1080
   output_frame:
     codec: ${oc.env:CODEC, 'h264'}
+    h264_encoder_params:
+      bitrate: 8000000
+      profile: Baseline
   draw_func:
     module: samples.intersection_traffic_meter.overlay
     class_name: Overlay
@@ -15,6 +15,22 @@ parameters:
   send_stats: True
   batch_size: 1
 
+  default_yolo_converter:
+    module: savant.converter.yolo
+    class_name: TensorToBBoxConverter
+    kwargs:
+      confidence_threshold: 0.25
+      nms_iou_threshold: 0.45
+      top_k: 300
+
+  default_yolo_selector:
+    module: savant.selector.detector
+    class_name: MinMaxSizeBBoxSelector
+    kwargs:
+      min_width: 30
+      min_height: 30
+
+
 pipeline:
 
   elements:
@@ -26,61 +42,47 @@ pipeline:
         config_path: ${oc.env:PROJECT_PATH}/samples/intersection_traffic_meter/polygon_config.yml
 
     - element: nvinfer@detector
-      name: yolov8m
+      name: yolov11n
       model:
         remote:
-          url: s3://savant-data/models/yolov8m_f630b10/yolov8m_f630b10.zip
-          checksum_url: s3://savant-data/models/yolov8m_f630b10/yolov8m_f630b10.md5
+          url: s3://savant-data/models/yolo11n/yolo11n.zip
+          checksum_url: s3://savant-data/models/yolo11n/yolo11n.md5
           parameters:
-            endpoint: https://eu-central-1.linodeobjects.com
+            endpoint: https://eu-central-1.linodeobjects.com              
         format: onnx
-        model_file: yolov8m.onnx
-        config_file: config_infer_primary_yoloV8.txt
-        # max GPU RAM used to build the engine, 6GB by default
-        # set lower than total GPU RAM available on your hardware
+        model_file: yolo11n.onnx
+        batch_size: ${parameters.batch_size}
         workspace_size: 6144
+        input:
+          shape: [3, 640, 640]
+          scale_factor: 0.0039215697906911373
+          maintain_aspect_ratio: true
+          symmetric_padding: true
         output:
+          layer_names: [output0]
+          num_detected_classes: 80  # required for YOLOv11
+          converter: ${parameters.default_yolo_converter}
           objects:
             # COCO bicycle
             - class_id: 1
               label: ${parameters.detected_object_label}
-              selector:
-                module: savant.selector.detector
-                class_name: BBoxSelector
-                kwargs:
-                  confidence_threshold: 0.2
+              selector: ${parameters.default_yolo_selector}
             # COCO car
             - class_id: 2
               label: ${parameters.detected_object_label}
-              selector:
-                module: savant.selector.detector
-                class_name: BBoxSelector
-                kwargs:
-                  confidence_threshold: 0.2
+              selector: ${parameters.default_yolo_selector}
             # COCO motorcycle
             - class_id: 3
               label: ${parameters.detected_object_label}
-              selector:
-                module: savant.selector.detector
-                class_name: BBoxSelector
-                kwargs:
-                  confidence_threshold: 0.2
+              selector: ${parameters.default_yolo_selector}
             # COCO bus
             - class_id: 5
               label: ${parameters.detected_object_label}
-              selector:
-                module: savant.selector.detector
-                class_name: BBoxSelector
-                kwargs:
-                  confidence_threshold: 0.2
+              selector: ${parameters.default_yolo_selector}
             # COCO truck
             - class_id: 7
               label: ${parameters.detected_object_label}
-              selector:
-                module: savant.selector.detector
-                class_name: BBoxSelector
-                kwargs:
-                  confidence_threshold: 0.2
+              selector: ${parameters.default_yolo_selector}
 
     - element: nvtracker
       properties: