To use the snippet below, you will need to run pip install roboflow supervision. Replace the project name and model name with any model trained on Universe, such as those listed above.

import numpy as np
            import supervision as sv
            import roboflow
            
            SOURCE_VIDEO_PATH = "moon.mp4"
            TARGET_VIDEO_PATH = "moon_out.mp4"
            
            # use https://roboflow.github.io/polygonzone/ to get the points for your line
            LINE_START = sv.Point(0, 300)
            LINE_END = sv.Point(800, 300)
            
            roboflow.login()
            rf = roboflow.Roboflow()
            
            # replace with the moon project you choose above
            project = rf.workspace("smcm-ai-class").project("phase-your-moon")
            moon_model = project.version(8).model
            
            # create BYTETracker instance
            moon_tracker = sv.ByteTrack(track_thresh=0.25, track_buffer=30, match_thresh=0.8, frame_rate=30)
            
            # create VideoInfo instance
            video_info = sv.VideoInfo.from_video_path(SOURCE_VIDEO_PATH)
            
            # create frame generator
            generator = sv.get_video_frames_generator(SOURCE_VIDEO_PATH)
            
            # create LineZone instance, it is previously called LineCounter class
            line_zone = sv.LineZone(start=LINE_START, end=LINE_END)
            
            # create instance of BoxAnnotator
            box_annotator = sv.BoxAnnotator(thickness=4, text_thickness=4, text_scale=2)
            
            # create instance of TraceAnnotator
            trace_annotator = sv.TraceAnnotator(thickness=4, trace_length=50)
            line_zone_annotator = sv.LineZoneAnnotator(thickness=4, text_thickness=4, text_scale=2)
            
            # define call back function to be used in video processing
            def callback(frame: np.ndarray, index:int) -> np.ndarray:
                # model prediction on single frame and conversion to supervision Detections
                results = moon_model.predict(frame).json()
                the_moon = sv.Detections.from_roboflow(results)
            
                # show moon detections in real time
                print(the_moon)
            
                # tracking moon detections
                the_moon = moon_tracker.update_with_detections(the_moon)
                annotated_frame = trace_annotator.annotate(
                    scene=frame.copy(),
                    detections=the_moon
                )
                annotated_frame=box_annotator.annotate(
                    scene=annotated_frame,
                    detections=the_moon
                )
            
                # update line counter
                line_zone.trigger(the_moon)
            
                # return frame with box and line annotated result
                return line_zone_annotator.annotate(annotated_frame, line_counter=line_zone)
            
            # process the whole video
            sv.process_video(
                source_path = SOURCE_VIDEO_PATH,
                target_path = TARGET_VIDEO_PATH,
                callback=callback
            )