Adding python node hub

nodes_hub/opencv-plot/plot.py

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+import os
+from dataclasses import dataclass
+
+import cv2
+import numpy as np
+
+from dora import Node
+
+CI = os.environ.get("CI")
+
+IMAGE_WIDTH = int(os.getenv("IMAGE_WIDTH", "640"))
+IMAGE_HEIGHT = int(os.getenv("IMAGE_HEIGHT", "480"))
+
+FONT = cv2.FONT_HERSHEY_SIMPLEX
+
+
+@dataclass
+class Plotter:
+    frame: np.array = np.array([])
+    bboxes: np.array = np.array([[]])
+    conf: np.array = np.array([])
+    label: np.array = np.array([])
+
+
+if __name__ == "__main__":
+    plotter = Plotter()
+    node = Node()
+    for event in node:
+        event_type = event["type"]
+        if event_type == "INPUT":
+            if event["id"] == "image":
+                frame = event["value"].to_numpy()
+                frame = frame.reshape((IMAGE_HEIGHT, IMAGE_WIDTH, 3)).copy()
+                plotter.frame = frame
+
+            elif event["id"] == "bbox":
+                bboxes = event["value"][0]["bbox"].values.to_numpy()
+                conf = event["value"][0]["conf"].values.to_numpy()
+                label = event["value"][0]["names"].values.to_pylist()
+                plotter.bboxes = np.reshape(bboxes, (-1, 4))
+                plotter.conf = conf
+                plotter.label = label
+                continue
+
+            for bbox in zip(plotter.bboxes, plotter.conf, plotter.label):
+                [
+                    [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,
+                    f"{label}, {confidence:0.2f}",
+                    (int(max_x) - 120, int(max_y) - 10),
+                    FONT,
+                    0.5,
+                    (0, 255, 0),
+                    2,
+                    1,
+                )
+
+            if CI != "true":
+                cv2.imshow("frame", plotter.frame)
+                if cv2.waitKey(1) & 0xFF == ord("q"):
+                    break

nodes_hub/opencv-plot/requirements.txt

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+numpy<2.0.0
+opencv-python

nodes_hub/opencv-video-capture/requirements.txt

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+numpy<2.0.0
+pyarrow
+opencv-python

nodes_hub/opencv-video-capture/video_capture.py

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+import os
+import time
+
+import cv2
+import numpy as np
+import pyarrow as pa
+
+from dora import Node
+
+CAM_INDEX = int(os.getenv("CAM_INDEX", "0"))
+IMAGE_WIDTH = int(os.getenv("IMAGE_WIDTH", "640"))
+IMAGE_HEIGHT = int(os.getenv("IMAGE_HEIGHT", "480"))
+MAX_DURATION = int(os.getenv("DURATION", "20"))
+FONT = cv2.FONT_HERSHEY_SIMPLEX
+
+
+start = time.time()
+
+
+if __name__ == "__main__":
+
+    video_capture = cv2.VideoCapture(CAM_INDEX)
+    node = Node()
+
+    while time.time() - start < MAX_DURATION:
+        event = node.next()
+        if event is None:
+            break
+        if event is not None:
+            event_type = event["type"]
+            if event_type == "INPUT":
+                ret, frame = video_capture.read()
+
+                # Fail to read camera
+                if not ret:
+                    frame = np.zeros((IMAGE_HEIGHT, IMAGE_WIDTH, 3), dtype=np.uint8)
+                    cv2.putText(
+                        frame,
+                        "No Webcam was found at index %d" % (CAM_INDEX),
+                        (int(30), int(30)),
+                        FONT,
+                        0.75,
+                        (255, 255, 255),
+                        2,
+                        1,
+                    )
+
+                node.send_output(
+                    "image",
+                    pa.array(frame.ravel()),
+                    event["metadata"],
+                )

nodes_hub/ultralytics-yolo/requirements.txt

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+numpy<2.0.0
+pyarrow
+ultralytics

nodes_hub/ultralytics-yolo/yolo.py

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+## Imports
+import os
+
+import numpy as np
+import pyarrow as pa
+from ultralytics import YOLO
+
+from dora import Node
+
+## OS Environment variable
+IMAGE_WIDTH = int(os.getenv("IMAGE_WIDTH", "640"))
+IMAGE_HEIGHT = int(os.getenv("IMAGE_HEIGHT", "480"))
+MODEL = os.getenv("MODEL", "yolov8n.pt")
+
+if __name__ == "__main__":
+
+    model = YOLO(MODEL)
+
+    node = Node("object_detection")
+
+    for event in node:
+        event_type = event["type"]
+        if event_type == "INPUT":
+            event_id = event["id"]
+            if event_id == "image":
+                frame = (
+                    event["value"].to_numpy().reshape((IMAGE_HEIGHT, IMAGE_WIDTH, 3))
+                )
+                frame = frame[:, :, ::-1]  # OpenCV image (BGR to RGB)
+                results = model(frame, verbose=False)  # includes NMS
+                # Process results
+                bboxes = np.array(results[0].boxes.xyxy.cpu())
+                conf = np.array(results[0].boxes.conf.cpu())
+                labels = np.array(results[0].boxes.cls.cpu())
+                names = [model.names.get(label) for label in labels]
+
+                node.send_output(
+                    "bbox",
+                    pa.array(
+                        [
+                            {
+                                "bbox": bboxes.ravel(),
+                                "conf": conf,
+                                "labels": labels,
+                                "names": names,
+                            }
+                        ]
+                    ),
+                    event["metadata"],
+                )