app.py

import time
import edgeiq

"""
Use object detection to detect objects in the frame in realtime. The
types of objects detected can be changed by selecting different models.

To change the computer vision model, follow this guide:
https://dashboard.alwaysai.co/docs/application_development/changing_the_model.html

To change the engine and accelerator, follow this guide:
https://dashboard.alwaysai.co/docs/application_development/changing_the_engine_and_accelerator.html
"""


def main():
    obj_detect = edgeiq.ObjectDetection(
            "alwaysai/vehicle_license_mobilenet_ssd_nano")
    obj_detect.load(engine=edgeiq.Engine.TENSOR_RT)

    print("Loaded model:\n{}\n".format(obj_detect.model_id))
    print("Engine: {}".format(obj_detect.engine))
    print("Accelerator: {}\n".format(obj_detect.accelerator))
    print("Labels:\n{}\n".format(obj_detect.labels))

    tracker = edgeiq.CentroidTracker(deregister_frames=5)
    fps = edgeiq.FPS()

    try:
        with edgeiq.WebcamVideoStream(cam=0) as video_stream, \
            edgeiq.Streamer() as streamer:

            # Allow Webcam to warm up
            time.sleep(2.0)
            fps.start()

            # loop detection
            while True:
                frame = video_stream.read()
                predictions = []

                results = obj_detect.detect_objects(
                                        frame,
                                        confidence_level=.5
                                    )

                # Generate text to display on streamer
                text = ["Model: {}".format(obj_detect.model_id)]
                text.append(
                        "Inference time: {:1.3f} s".format(
                            results.duration
                            )
                        )
                text.append("Objects:")

                # Update tracker results with the new predictions
                objects = tracker.update(results.predictions)

                if len(objects) == 0:
                    text.append("no predictions")
                else:
                    # Create a new prediction list
                    for (object_id, prediction) in objects.items():
                        text.append("{}_{}: {:2.2f}%".format(
                                            prediction.label,
                                            object_id,
                                            prediction.confidence * 100
                                        )
                                    )
                        new_label = '{} {}'.format(
                                            prediction.label,
                                            object_id
                                        )
                        prediction.label = '{} {}'.format(
                                            new_label.split(" ")[0],
                                            object_id
                                        )
                        predictions.append(prediction)

                # Mark up the image and update text
                frame = edgeiq.markup_image(
                            frame, predictions,
                            show_labels=True,
                            show_confidences=False,
                            colors=obj_detect.colors,
                            line_thickness=4,
                            font_size=1,
                            font_thickness=4
                        )

                streamer.send_data(frame, text)

                fps.update()

                if streamer.check_exit():
                    break

    finally:
        fps.stop()
        streamer.close()
        print("elapsed time: {:.2f}".format(fps.get_elapsed_seconds()))
        print("approx. FPS: {:.2f}".format(fps.compute_fps()))

        print("Program Ending")


if __name__ == "__main__":
    main()