@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")
A manufacturing engineer wants to detect belt misalignment and overheating before failure. codeproject blue iris
Introduction In the rapidly evolving landscape of the Industrial Internet of Things (IIoT) and Edge Artificial Intelligence, developers face a common bottleneck: how to process, analyze, and act upon massive streams of sensor data without choking the central cloud or breaking the bank. Enter CodeProject Blue Iris —an open-source, modular framework designed specifically for intelligent video analytics, multi-sensor fusion, and edge-based automation. @pipeline
For Docker users:
# 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() For Docker users: # Blue Iris Python pipeline
pipeline.add_source(camera) pipeline.add_node(thermal_check) pipeline.run()
# On Ubuntu 22.04 / Debian 12 git clone https://github.com/CodeProject/BlueIris.git cd BlueIris mkdir build && cd build cmake .. -DCMAKE_BUILD_TYPE=Release make -j$(nproc) sudo make install blue_iris_cli --input /dev/video0 --output ./detections --model yolov8n