simplifying examples to use node hub

Cargo.toml

@@ -30,8 +30,8 @@ members = [
     "libraries/shared-memory-server",
     "libraries/extensions/download",
     "libraries/extensions/telemetry/*",
-    "tool_nodes/dora-record",
-    "tool_nodes/dora-rerun",
+    "nodes_hub/dora-record",
+    "nodes_hub/dora-rerun",
     "libraries/extensions/ros2-bridge",
     "libraries/extensions/ros2-bridge/msg-gen",
     "libraries/extensions/ros2-bridge/python",

examples/python-dataflow/dataflow.yml

@@ -1,25 +1,25 @@
 nodes:
   - id: webcam
-    custom:
-      source: ./webcam.py
-      inputs:
-        tick:
-          source: dora/timer/millis/50
-          queue_size: 1000
-      outputs:
-        - image
+    build: pip install -r ../../nodes_hub/opencv-video-capture/requirements.txt
+    path: ../../nodes_hub/opencv-video-capture/video_capture.py
+    inputs:
+      tick: dora/timer/millis/50
+    outputs:
+      - image
+    env:
+      DURATION: 100
 
   - id: object_detection
-    custom:
-      source: ./object_detection.py
-      inputs:
-        image: webcam/image
-      outputs:
-        - bbox
+    build: pip install -r ../../nodes_hub/ultralytics-yolo/requirements.txt
+    path: ../../nodes_hub/ultralytics-yolo/yolo.py
+    inputs:
+      image: webcam/image
+    outputs:
+      - bbox
 
   - id: plot
-    custom:
-      source: ./plot.py
-      inputs:
-        image: webcam/image
-        bbox: object_detection/bbox
+    build: pip install -r ../../nodes_hub/opencv-plot/requirements.txt
+    path: ../../nodes_hub/opencv-plot/plot.py
+    inputs:
+      image: webcam/image
+      bbox: object_detection/bbox

examples/python-dataflow/dataflow_dynamic.yml

@@ -1,16 +1,15 @@
 nodes:
   - id: webcam
-    custom:
-      source: ./webcam.py
-      inputs:
-        tick:
-          source: dora/timer/millis/50
-          queue_size: 1000
-      outputs:
-        - image
+    build: pip install -r ../../nodes_hub/opencv-webcam/requirements.txt
+    path: ../../nodes_hub/opencv-webcam/webcam.py
+    inputs:
+      tick:
+        source: dora/timer/millis/50
+        queue_size: 1000
+    outputs:
+      - image
 
   - id: plot
-    custom:
-      source: dynamic
-      inputs:
-        image: webcam/image
+    path: dynamic
+    inputs:
+      image: webcam/image

examples/python-dataflow/example.py

@@ -1,5 +0,0 @@
-from dora import Node
-
-node = Node("plot")
-
-event = node.next()

examples/python-dataflow/object_detection.py

@@ -1,40 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-import cv2
-import numpy as np
-from ultralytics import YOLO
-
-from dora import Node
-import pyarrow as pa
-
-model = YOLO("yolov8n.pt")
-
-node = Node()
-
-for event in node:
-    event_type = event["type"]
-    if event_type == "INPUT":
-        event_id = event["id"]
-        if event_id == "image":
-            print("[object detection] received image input")
-            frame = event["value"].to_numpy()
-            frame = cv2.imdecode(frame, -1)
-            frame = frame[:, :, ::-1]  # OpenCV image (BGR to RGB)
-            results = model(frame)  # includes NMS
-            # Process results
-            boxes = np.array(results[0].boxes.xyxy.cpu())
-            conf = np.array(results[0].boxes.conf.cpu())
-            label = np.array(results[0].boxes.cls.cpu())
-            # concatenate them together
-            arrays = np.concatenate((boxes, conf[:, None], label[:, None]), axis=1)
-
-            node.send_output("bbox", pa.array(arrays.ravel()), event["metadata"])
-        else:
-            print("[object detection] ignoring unexpected input:", event_id)
-    elif event_type == "STOP":
-        print("[object detection] received stop")
-    elif event_type == "ERROR":
-        print("[object detection] error: ", event["error"])
-    else:
-        print("[object detection] received unexpected event:", event_type)

examples/python-dataflow/plot.py

@@ -1,96 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-import os
-from dora import Node
-from dora import DoraStatus
-
-import cv2
-import numpy as np
-from utils import LABELS
-
-CI = os.environ.get("CI")
-
-font = cv2.FONT_HERSHEY_SIMPLEX
-
-
-class Plotter:
-    """
-    Plot image and bounding box
-    """
-
-    def __init__(self):
-        self.image = []
-        self.bboxs = []
-
-    def on_input(
-        self,
-        dora_input,
-    ) -> DoraStatus:
-        """
-        Put image and bounding box on cv2 window.
-
-        Args:
-            dora_input["id"] (str): Id of the dora_input declared in the yaml configuration
-            dora_input["value"] (arrow array): message of the dora_input
-        """
-        if dora_input["id"] == "image":
-            frame = dora_input["value"].to_numpy()
-            frame = cv2.imdecode(frame, -1)
-            self.image = frame
-
-        elif dora_input["id"] == "bbox" and len(self.image) != 0:
-            bboxs = dora_input["value"].to_numpy()
-            self.bboxs = np.reshape(bboxs, (-1, 6))
-        for bbox in self.bboxs:
-            [
-                min_x,
-                min_y,
-                max_x,
-                max_y,
-                confidence,
-                label,
-            ] = bbox
-            cv2.rectangle(
-                self.image,
-                (int(min_x), int(min_y)),
-                (int(max_x), int(max_y)),
-                (0, 255, 0),
-                2,
-            )
-
-            cv2.putText(
-                self.image,
-                LABELS[int(label)] + f", {confidence:0.2f}",
-                (int(max_x), int(max_y)),
-                font,
-                0.75,
-                (0, 255, 0),
-                2,
-                1,
-            )
-
-        if CI != "true":
-            cv2.imshow("frame", self.image)
-            if cv2.waitKey(1) & 0xFF == ord("q"):
-                return DoraStatus.STOP
-
-        return DoraStatus.CONTINUE
-
-
-plotter = Plotter()
-node = Node()
-
-for event in node:
-    event_type = event["type"]
-    if event_type == "INPUT":
-        status = plotter.on_input(event)
-        if status == DoraStatus.CONTINUE:
-            pass
-        elif status == DoraStatus.STOP:
-            print("plotter returned stop status")
-            break
-    elif event_type == "STOP":
-        print("received stop")
-    else:
-        print("received unexpected event:", event_type)

examples/python-dataflow/plot_dynamic.py

@@ -1,97 +1,71 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
 import os
-from dora import Node
-from dora import DoraStatus
+from dataclasses import dataclass
 
 import cv2
 import numpy as np
-from utils import LABELS
-
-CI = os.environ.get("CI")
-
-font = cv2.FONT_HERSHEY_SIMPLEX
-
-
-class Plotter:
-    """
-    Plot image and bounding box
-    """
 
-    def __init__(self):
-        self.image = []
-        self.bboxs = []
-
-    def on_input(
-        self,
-        dora_input,
-    ) -> DoraStatus:
-        """
-        Put image and bounding box on cv2 window.
-
-        Args:
-            dora_input["id"] (str): Id of the dora_input declared in the yaml configuration
-            dora_input["value"] (arrow array): message of the dora_input
-        """
-        if dora_input["id"] == "image":
-            frame = dora_input["value"].to_numpy()
-            frame = cv2.imdecode(frame, -1)
-            self.image = frame
-
-        elif dora_input["id"] == "bbox" and len(self.image) != 0:
-            bboxs = dora_input["value"].to_numpy()
-            self.bboxs = np.reshape(bboxs, (-1, 6))
-        for bbox in self.bboxs:
-            [
-                min_x,
-                min_y,
-                max_x,
-                max_y,
-                confidence,
-                label,
-            ] = bbox
-            cv2.rectangle(
-                self.image,
-                (int(min_x), int(min_y)),
-                (int(max_x), int(max_y)),
-                (0, 255, 0),
-                2,
-            )
-
-            cv2.putText(
-                self.image,
-                LABELS[int(label)] + f", {confidence:0.2f}",
-                (int(max_x), int(max_y)),
-                font,
-                0.75,
-                (0, 255, 0),
-                2,
-                1,
-            )
-
-        if CI != "true":
-            cv2.imshow("frame", self.image)
-            if cv2.waitKey(1) & 0xFF == ord("q"):
-                return DoraStatus.STOP
+from dora import Node
 
-        return DoraStatus.CONTINUE
+CI = os.environ.get("CI")
 
+IMAGE_WIDTH = int(os.getenv("IMAGE_WIDTH", "640"))
+IMAGE_HEIGHT = int(os.getenv("IMAGE_HEIGHT", "480"))
 
-plotter = Plotter()
+FONT = cv2.FONT_HERSHEY_SIMPLEX
 
-node = Node("plot")
 
-for event in node:
-    event_type = event["type"]
-    if event_type == "INPUT":
-        status = plotter.on_input(event)
-        if status == DoraStatus.CONTINUE:
-            pass
-        elif status == DoraStatus.STOP:
-            print("plotter returned stop status")
-            break
-    elif event_type == "STOP":
-        print("received stop")
-    else:
-        print("received unexpected event:", event_type)
+@dataclass
+class Plotter:
+    frame: np.array = np.array([])
+    bboxes: np.array = np.array([])
+
+
+if __name__ == "__main__":
+    plotter = Plotter()
+    node = Node("plot")
+
+    for event in node:
+        event_type = event["type"]
+        if event_type == "INPUT":
+            if event["id"] == "image":
+                frame = event["value"].to_numpy()
+                frame = (
+                    event["value"].to_numpy().reshape((IMAGE_HEIGHT, IMAGE_WIDTH, 3))
+                )
+                plotter.frame = frame
+
+            elif event["id"] == "bbox" and len(plotter.frame) != 0:
+                bboxs = event["value"].to_numpy()
+                plotter.bboxes = np.reshape(bboxs, (-1, 6))
+            for bbox in plotter.bboxs:
+                [
+                    min_x,
+                    min_y,
+                    max_x,
+                    max_y,
+                    confidence,
+                    label,
+                ] = bbox
+                cv2.rectangle(
+                    plotter.frame,
+                    (int(min_x), int(min_y)),
+                    (int(max_x), int(max_y)),
+                    (0, 255, 0),
+                    2,
+                )
+
+                cv2.putText(
+                    plotter.frame,
+                    LABELS[int(label)] + f", {confidence:0.2f}",
+                    (int(max_x), int(max_y)),
+                    FONT,
+                    0.75,
+                    (0, 255, 0),
+                    2,
+                    1,
+                )
+
+            if CI != "true":
+                cv2.imshow("frame", plotter.frame)
+                if cv2.waitKey(1) & 0xFF == ord("q"):
+                    break

examples/python-dataflow/requirements.txt

@@ -1,47 +0,0 @@
-# YOLOv5 requirements
-# Usage: pip install -r requirements.txt
-
-# Base ----------------------------------------
-ultralytics
-gitpython
-ipython  # interactive notebook
-matplotlib>=3.2.2
-numpy<2.0.0 # See: https://github.com/opencv/opencv-python/issues/997
-opencv-python>=4.1.1
-Pillow>=7.1.2
-psutil  # system resources
-PyYAML>=5.3.1
-requests>=2.23.0
-scipy>=1.4.1
-thop>=0.1.1  # FLOPs computation
-torch  # see https://pytorch.org/get-started/locally (recommended)
-torchvision
-tqdm>=4.64.0
-
-# Logging -------------------------------------
-tensorboard>=2.4.1
-# wandb
-# clearml
-
-# Plotting ------------------------------------
-pandas>=1.1.4
-seaborn>=0.11.0
-
-# Export --------------------------------------
-# coremltools>=5.2  # CoreML export
-# onnx>=1.9.0  # ONNX export
-# onnx-simplifier>=0.4.1  # ONNX simplifier
-# nvidia-pyindex  # TensorRT export
-# nvidia-tensorrt  # TensorRT export
-# scikit-learn==0.19.2  # CoreML quantization
-# tensorflow>=2.4.1  # TFLite export (or tensorflow-cpu, tensorflow-aarch64)
-# tensorflowjs>=3.9.0  # TF.js export
-# openvino-dev  # OpenVINO export
-
-# Extras --------------------------------------
-# albumentations>=1.0.3
-# pycocotools>=2.0  # COCO mAP
-# roboflow
-
-opencv-python>=4.1.1
-maturin

examples/python-dataflow/run.rs

@@ -50,21 +50,6 @@ async fn main() -> eyre::Result<()> {
         );
     }
 
-    run(
-        get_python_path().context("Could not get pip binary")?,
-        &["-m", "pip", "install", "--upgrade", "pip"],
-        None,
-    )
-    .await
-    .context("failed to install pip")?;
-    run(
-        get_pip_path().context("Could not get pip binary")?,
-        &["install", "-r", "requirements.txt"],
-        None,
-    )
-    .await
-    .context("pip install failed")?;
-
     run(
         "maturin",
         &["develop"],

examples/python-dataflow/utils.py

@@ -1,82 +0,0 @@
-LABELS = [
-    "ABC",
-    "bicycle",
-    "car",
-    "motorcycle",
-    "airplane",
-    "bus",
-    "train",
-    "truck",
-    "boat",
-    "traffic light",
-    "fire hydrant",
-    "stop sign",
-    "parking meter",
-    "bench",
-    "bird",
-    "cat",
-    "dog",
-    "horse",
-    "sheep",
-    "cow",
-    "elephant",
-    "bear",
-    "zebra",
-    "giraffe",
-    "backpack",
-    "umbrella",
-    "handbag",
-    "tie",
-    "suitcase",
-    "frisbee",
-    "skis",
-    "snowboard",
-    "sports ball",
-    "kite",
-    "baseball bat",
-    "baseball glove",
-    "skateboard",
-    "surfboard",
-    "tennis racket",
-    "bottle",
-    "wine glass",
-    "cup",
-    "fork",
-    "knife",
-    "spoon",
-    "bowl",
-    "banana",
-    "apple",
-    "sandwich",
-    "orange",
-    "broccoli",
-    "carrot",
-    "hot dog",
-    "pizza",
-    "donut",
-    "cake",
-    "chair",
-    "couch",
-    "potted plant",
-    "bed",
-    "dining table",
-    "toilet",
-    "tv",
-    "laptop",
-    "mouse",
-    "remote",
-    "keyboard",
-    "cell phone",
-    "microwave",
-    "oven",
-    "toaster",
-    "sink",
-    "refrigerator",
-    "book",
-    "clock",
-    "vase",
-    "scissors",
-    "teddy bear",
-    "hair drier",
-    "toothbrush",
-]

examples/python-dataflow/webcam.py

@@ -1,52 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-import os
-import time
-import numpy as np
-import cv2
-
-from dora import Node
-
-node = Node()
-
-CAMERA_INDEX = int(os.getenv("CAMERA_INDEX", 0))
-CAMERA_WIDTH = 640
-CAMERA_HEIGHT = 480
-video_capture = cv2.VideoCapture(CAMERA_INDEX)
-font = cv2.FONT_HERSHEY_SIMPLEX
-
-start = time.time()
-
-# Run for 20 seconds
-while time.time() - start < 10:
-    # Wait next dora_input
-    event = node.next()
-    event_type = event["type"]
-    if event_type == "INPUT":
-        ret, frame = video_capture.read()
-        if not ret:
-            frame = np.zeros((CAMERA_HEIGHT, CAMERA_WIDTH, 3), dtype=np.uint8)
-            cv2.putText(
-                frame,
-                "No Webcam was found at index %d" % (CAMERA_INDEX),
-                (int(30), int(30)),
-                font,
-                0.75,
-                (255, 255, 255),
-                2,
-                1,
-            )
-        node.send_output(
-            "image",
-            cv2.imencode(".jpg", frame)[1].tobytes(),
-            event["metadata"],
-        )
-    elif event_type == "STOP":
-        print("received stop")
-        break
-    else:
-        print("received unexpected event:", event_type)
-        break
-
-video_capture.release()