Fix typos, CI and README

examples/python-dataflow/dataflow.yml

@@ -3,7 +3,8 @@ nodes:
     build: pip install ../../node-hub/opencv-video-capture
     path: opencv-video-capture
     inputs:
-      tick: plot/tick
+      tick: dora/timer/millis/16
+      stop: plot/end
     outputs:
       - image
       - text
@@ -17,7 +18,6 @@ nodes:
     path: opencv-plot
     inputs:
       image: camera/image
-      tick: 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
       text: camera/text
     outputs:
-      - tick
+      - end

examples/python-dataflow/dataflow_dynamic.yml

@@ -3,7 +3,8 @@ nodes:
     build: pip install ../../node-hub/opencv-video-capture
     path: opencv-video-capture
     inputs:
-      tick: plot/tick
+      tick: dora/timer/millis/16
+      stop: plot/end
     outputs:
       - image
     env:
@@ -16,6 +17,5 @@ nodes:
     path: dynamic
     inputs:
       image: camera/image
-      tick: 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
     outputs:
-      - tick
+      - end

examples/python-dataflow/dataflow_yolo.yml

@@ -3,7 +3,8 @@ nodes:
     build: pip install ../../node-hub/opencv-video-capture
     path: opencv-video-capture
     inputs:
-      tick: plot/tick
+      tick: dora/timer/millis/16
+      stop: plot/end
     outputs:
       - image
     env:
@@ -21,7 +22,7 @@ nodes:
     outputs:
       - bbox
     env:
-      MODEL: yolov5n.pt
+      MODEL: yolov8n.pt
 
   - id: plot
     build: pip install ../../node-hub/opencv-plot
@@ -31,8 +32,5 @@ 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
     outputs:
-      - tick
+      - end

node-hub/opencv-plot/README.md

@@ -13,12 +13,8 @@ This node is used to plot a text and a list of bbox on a base image (ideal for o
       # bbox: Arrow array of bbox
       # text: Arrow array of size 1 containing the text to be plotted
 
-      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
+      - end
 
     env:
       PLOT_WIDTH: 640 # optional, default is image input width
@@ -26,13 +22,11 @@ This node is used to plot a text and a list of bbox on a base image (ideal for o
 ```
 
 # Inputs
-- 
-- `tick`: empty Arrow array to trigger the capture
 
 - `image`: Arrow array containing the base image
 
 ```python
-image = {
+image: {
     "width": np.uint32,
     "height": np.uint32,
     "channels": np.uint8,
@@ -50,29 +44,33 @@ decoded_image = {
 
 ```
 
+- `bbox`: an arrow array containing the bounding boxes, confidence scores, and class names of the detected objects
+
+```Python
+
+bbox: {
+    "bbox": np.array,  # flattened array of bounding boxes
+    "conf": np.array,  # flat array of confidence scores
+    "names": np.array,  # flat array of class names
+}
+
+encoded_bbox = pa.array([bbox])
+
+decoded_bbox = {
+    "bbox": encoded_bbox[0]["bbox"].values.to_numpy().reshape(-1, 3),
+    "conf": encoded_bbox[0]["conf"].values.to_numpy(),
+    "names": encoded_bbox[0]["names"].values.to_numpy(zero_copy_only=False),
+}
+```
+
 - `text`: Arrow array containing the text to be plotted
 
 ```python
-text = {
-    "text": str,
-    "font_scale": np.float32,
-    "color": (np.uint8, np.uint8, np.uint8),
-    "thickness": np.uint32,
-    "position": (np.uint32, np.uint32)
-}
+text: str
 
 encoded_text = pa.array([text])
 
-decoded_text = {
-    "text": encoded_text[0]["text"].as_py(),
-    "font_scale": np.float32(encoded_text[0]["font_scale"].as_py()),
-    "color": (np.uint8(encoded_text[0]["color"].as_py()[0]),
-              np.uint8(encoded_text[0]["color"].as_py()[1]),
-              np.uint8(encoded_text[0]["color"].as_py()[2])),
-    "thickness": np.uint32(encoded_text[0]["thickness"].as_py()),
-    "position": (np.uint32(encoded_text[0]["position"].as_py()[0]),
-                 np.uint32(encoded_text[0]["position"].as_py()[1]))
-}
+decoded_text = encoded_text[0].as_py()
 ``` 
 
 ## License

node-hub/opencv-plot/main.py

@@ -7,6 +7,8 @@ import pyarrow as pa
 
 from dora import Node
 
+RUNNER_CI = True if os.getenv("CI", False) == "true" else False
+
 
 class Plot:
     frame: np.array = np.array([])
@@ -23,7 +25,7 @@ class Plot:
     height: np.uint32 = None
 
 
-def plot_frame(plot, ci_enabled):
+def plot_frame(plot):
     for bbox in zip(plot.bboxes["bbox"], plot.bboxes["conf"], plot.bboxes["names"]):
         [
             [min_x, min_y, max_x, max_y],
@@ -63,15 +65,10 @@ def plot_frame(plot, ci_enabled):
     if plot.width is not None and plot.height is not None:
         plot.frame = cv2.resize(plot.frame, (plot.width, plot.height))
 
-    if not ci_enabled:
+    if not RUNNER_CI:
         if len(plot.frame.shape) >= 3:
             cv2.imshow("Dora Node: opencv-plot", plot.frame)
 
-            if cv2.waitKey(1) & 0xFF == ord('q'):
-                return True
-
-    return False
-
 
 def main():
     # Handle dynamic nodes, ask for the name of the node in the dataflow, and the same values as the ENV variables.
@@ -96,11 +93,6 @@ def main():
         if isinstance(plot_height, str) and plot_height.isnumeric():
             plot_height = int(plot_height)
 
-    # check if the code is running in a CI environment (e.g. GitHub Actions) (parse to bool)
-    ci_enabled = os.getenv("CI", False)
-    if ci_enabled == "true":
-        ci_enabled = True
-
     node = Node(args.name)  # provide the name to connect to the dataflow if dynamic node
     plot = Plot()
 
@@ -115,17 +107,7 @@ def main():
         if event_type == "INPUT":
             event_id = event["id"]
 
-            if event_id == "tick":
-                if ci_enabled:
-                    break
-
-                node.send_output(
-                    "tick",
-                    pa.array([]),
-                    event["metadata"]
-                )
-
-            elif event_id == "image":
+            if event_id == "image":
                 arrow_image = event["value"][0]
                 image = {
                     "width": np.uint32(arrow_image["width"].as_py()),
@@ -136,29 +118,45 @@ def main():
 
                 plot.frame = np.reshape(image["data"], (image["height"], image["width"], image["channels"]))
 
-                if plot_frame(plot, ci_enabled):
-                    break
+                plot_frame(plot)
+                if not RUNNER_CI:
+                    if cv2.waitKey(1) & 0xFF == ord("q"):
+                        break
+                else:
+                    break  # break the loop for CI
 
             elif event_id == "bbox":
                 arrow_bbox = event["value"][0]
                 plot.bboxes = {
                     "bbox": arrow_bbox["bbox"].values.to_numpy().reshape(-1, 4),
                     "conf": arrow_bbox["conf"].values.to_numpy(),
-                    "names": arrow_bbox["names"].values.to_pylist(),
+                    "names": arrow_bbox["names"].values.to_numpy(zero_copy_only=False),
                 }
 
-                if plot_frame(plot, ci_enabled):
-                    break
-
+                plot_frame(plot)
+                if not RUNNER_CI:
+                    if cv2.waitKey(1) & 0xFF == ord("q"):
+                        break
+                else:
+                    break  # break the loop for CI
             elif event_id == "text":
                 plot.text = event["value"][0].as_py()
 
-                if plot_frame(plot, ci_enabled):
-                    break
+                plot_frame(plot)
+                if not RUNNER_CI:
+                    if cv2.waitKey(1) & 0xFF == ord("q"):
+                        break
+                else:
+                    break  # break the loop for CI
 
         elif event_type == "ERROR":
             raise Exception(event["error"])
 
+    node.send_output(
+        "end",
+        pa.array([0], type=pa.uint8())
+    )
+
 
 if __name__ == "__main__":
     main()

node-hub/opencv-video-capture/README.md

@@ -10,7 +10,7 @@ This node is used to capture video from a camera using OpenCV.
     path: opencv-video-capture
     inputs:
       tick: dora/timer/millis/16 # try to capture at 60fps
-
+      # stop: some stop signal from another node 
     outputs:
       - image: # the captured image
 
@@ -31,7 +31,7 @@ This node is used to capture video from a camera using OpenCV.
 
 ```Python
 
-image = {
+image: {
     "width": np.uint32,
     "height": np.uint32,
     "channels": np.uint8,

node-hub/opencv-video-capture/main.py

@@ -58,7 +58,7 @@ def main():
                     frame = np.zeros((480, 640, 3), dtype=np.uint8)
                     cv2.putText(
                         frame,
-                        f'Error: Could not read frame from camera at path {video_capture_path}.',
+                        f'Error: no frame for camera at path {video_capture_path}.',
                         (int(30), int(30)),
                         cv2.FONT_HERSHEY_SIMPLEX,
                         0.50,
@@ -84,6 +84,10 @@ def main():
                     event["metadata"]
                 )
 
+            if event_id == "stop":
+                video_capture.release()
+                break
+
         elif event_type == "ERROR":
             raise Exception(event["error"])
 

node-hub/ultralytics-yolo/README.md

@@ -22,7 +22,7 @@ This node is used to detect objects in images using YOLOv8.
 - `image`: Arrow array containing the base image
 
 ```python
-image = {
+image: {
     "width": np.uint32,
     "height": np.uint32,
     "channels": np.uint8,
@@ -46,7 +46,7 @@ decoded_image = {
 
 ```Python
 
-bbox = {
+bbox: {
     "bbox": np.array,  # flattened array of bounding boxes
     "conf": np.array,  # flat array of confidence scores
     "names": np.array,  # flat array of class names
@@ -57,7 +57,7 @@ encoded_bbox = pa.array([bbox])
 decoded_bbox = {
     "bbox": encoded_bbox[0]["bbox"].values.to_numpy().reshape(-1, 3),
     "conf": encoded_bbox[0]["conf"].values.to_numpy(),
-    "names": encoded_bbox[0]["names"].values.to_pylist(),
+    "names": encoded_bbox[0]["names"].values.to_numpy(zero_copy_only=False),
 }
 ```
 

node-hub/ultralytics-yolo/main.py

@@ -41,7 +41,7 @@ def main():
                     "data": arrow_image["data"].values.to_numpy().astype(np.uint8)
                 }
 
-                frame = image["data"].reshape((image["height"], image["width"], 3))
+                frame = image["data"].reshape((image["height"], image["width"], image["channels"]))
 
                 frame = frame[:, :, ::-1]  # OpenCV image (BGR to RGB)
                 results = model(frame, verbose=False)  # includes NMS