Separate a simple dataflow for CI (without yolo) and a yolo dataflow

.github/workflows/ci.yml

@@ -321,7 +321,7 @@ jobs:
           dora stop --name ci-python-test  --grace-duration 5s
           dora build ../examples/python-dataflow/dataflow_dynamic.yml
           dora start ../examples/python-dataflow/dataflow_dynamic.yml --name ci-python-dynamic --detach
-          ultralytics-yolo --name object-detection
+          opencv-plot --name plot
           sleep 5
           dora stop --name ci-python-dynamic  --grace-duration 5s
           dora destroy

examples/python-dataflow/README.md

@@ -7,13 +7,10 @@ This examples shows how to create and connect dora nodes in Python.
 The [`dataflow.yml`](./dataflow.yml) defines a simple dataflow graph with the following three nodes:
 
 - a webcam node, that connects to your webcam and feed the dataflow with webcam frame as jpeg compressed bytearray.
-- an object detection node, that apply Yolo v5 on the webcam image. The model is imported from Pytorch Hub. The output
-  is the bounding box of each object detected, the confidence and the associated label. You can have more info
-  here: https://pytorch.org/hub/ultralytics_yolov5/
-- a window plotting node, that will retrieve the webcam image and the Yolov5 bounding box and join the two together.
+- a window plotting node, that will retrieve the webcam image and plot it.
 
 The same dataflow is implemented for a `dynamic-node` in [`dataflow_dynamic.yml`](./dataflow_dynamic.yml). It contains
-the same nodes as the previous dataflow, but the object detection node is a dynamic node. See the next section for more
+the same nodes as the previous dataflow, but the plot node is a dynamic node. See the next section for more
 information on how to start such a dataflow.
 
 ## Getting started
@@ -40,5 +37,5 @@ dora start ./dataflow.yml (or dora start ./dataflow_dynamic.yml)
 
 ```bash
 # activate your virtual environment in another terminal
-python ultralytics-yolo --name object-detection --model yolov5n.pt
+python opencv-plot --name plot
 ```

examples/python-dataflow/dataflow.yml

@@ -13,19 +13,6 @@ nodes:
       IMAGE_WIDTH: 640
       IMAGE_HEIGHT: 480
 
-  - id: object-detection
-    build: pip install ../../node-hub/ultralytics-yolo
-    path: ultralytics-yolo
-    inputs:
-      image:
-        source: camera/image
-        queue_size: 1
-
-    outputs:
-      - bbox
-    env:
-      MODEL: yolov5n.pt
-
   - id: plot
     build: pip install ../../node-hub/opencv-plot
     path: opencv-plot
@@ -34,8 +21,6 @@ nodes:
         source: camera/image
         queue_size: 1
 
-      bbox: object-detection/bbox
-
       tick:
         source: dora/timer/millis/16 # this node display a window, so it's better to deflect the timer, so when the window is closed, the ticks are not sent anymore in the graph
         queue_size: 1

examples/python-dataflow/dataflow_dynamic.yml

@@ -13,29 +13,14 @@ nodes:
       IMAGE_WIDTH: 640
       IMAGE_HEIGHT: 480
 
-  - id: object-detection
-    build: pip install ../../node-hub/ultralytics-yolo
-    path: dynamic
-    inputs:
-      image:
-        source: camera/image
-        queue_size: 1
-
-    outputs:
-      - bbox
-    env:
-      MODEL: yolov5n.pt
-
   - id: plot
     build: pip install ../../node-hub/opencv-plot
-    path: opencv-plot
+    path: dynamic
     inputs:
       image:
         source: camera/image
         queue_size: 1
 
-      bbox: object-detection/bbox
-
       tick:
         source: dora/timer/millis/16 # this node display a window, so it's better to deflect the timer, so when the window is closed, the ticks are not sent anymore in the graph
         queue_size: 1

examples/python-dataflow/dataflow_yolo.yml

@@ -0,0 +1,44 @@
+nodes:
+  - id: camera
+    build: pip install ../../node-hub/opencv-video-capture
+    path: opencv-video-capture
+    inputs:
+      tick: plot/tick
+
+    outputs:
+      - image
+
+    env:
+      CAPTURE_PATH: 0
+      IMAGE_WIDTH: 640
+      IMAGE_HEIGHT: 480
+
+  - id: object-detection
+    build: pip install ../../node-hub/ultralytics-yolo
+    path: ultralytics-yolo
+    inputs:
+      image:
+        source: camera/image
+        queue_size: 1
+
+    outputs:
+      - bbox
+    env:
+      MODEL: yolov5n.pt
+
+  - id: plot
+    build: pip install ../../node-hub/opencv-plot
+    path: opencv-plot
+    inputs:
+      image:
+        source: camera/image
+        queue_size: 1
+
+      bbox: object-detection/bbox
+
+      tick:
+        source: dora/timer/millis/16 # this node display a window, so it's better to deflect the timer, so when the window is closed, the ticks are not sent anymore in the graph
+        queue_size: 1
+
+    outputs:
+      - tick

examples/python-dataflow/run.rs

@@ -50,13 +50,9 @@ async fn main() -> eyre::Result<()> {
         );
     }
 
-    run(
-        "pip",
-        &["install", "maturin"],
-        Some (venv),
-    )
-    .await
-    .context("pip install maturin failed")?;
+    run("pip", &["install", "maturin"], Some(venv))
+        .await
+        .context("pip install maturin failed")?;
 
     run(
         "maturin",