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@pipeline.node def thermal_check(frame): # Assume frame is thermal image resized = cv2.resize(frame, (224, 224)) interpreter.invoke() anomaly_score = interpreter.get_output() if anomaly_score > 0.8: bi.trigger_alert("Conveyor Bearing Overheating") bi.mqtt_publish("factory/belt/alert", "thermal_anomaly")
For Docker users:
A manufacturing engineer wants to detect belt misalignment and overheating before failure.
# Blue Iris Python pipeline snippet import blue_iris as bi import cv2 from tensorflow import lite interpreter = lite.Interpreter(model_path="thermal_anomaly.tflite") Define pipeline camera = bi.Camera("rtsp://192.168.1.100/stream") pipeline = bi.Pipeline()
@pipeline.node def thermal_check(frame): # Assume frame is thermal image resized = cv2.resize(frame, (224, 224)) interpreter.invoke() anomaly_score = interpreter.get_output() if anomaly_score > 0.8: bi.trigger_alert("Conveyor Bearing Overheating") bi.mqtt_publish("factory/belt/alert", "thermal_anomaly")
For Docker users:
A manufacturing engineer wants to detect belt misalignment and overheating before failure.
# Blue Iris Python pipeline snippet import blue_iris as bi import cv2 from tensorflow import lite interpreter = lite.Interpreter(model_path="thermal_anomaly.tflite") Define pipeline camera = bi.Camera("rtsp://192.168.1.100/stream") pipeline = bi.Pipeline()
