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 supervision as sv
            import roboflow
            
            roboflow.login()
            rf = roboflow.Roboflow()
            
            # replace with the boot project you choose above
            project = rf.workspace("sindhu").project("car_dent_scratch_detection-1")
            boot_model = project.version(9).model
            
            results = boot_model.predict("boot.jpg").json()
            boots = sv.Detections.from_roboflow(results)
            
            # print number of boots
            print(len(boots))

Guide: How to Count Boots in a Zone

With a bit more code, you can count the number of boot present in a specific zone of your image or video.

The following code snippet counts the number of boot present in each frame in a video.

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.

Read our blog post on counting objects in a zone

import numpy as np
            import supervision as sv
            import roboflow
            
            SOURCE_VIDEO_PATH = "boot.mp4"
            TARGET_VIDEO_PATH = "boot_out.mp4"
            
            # use https://roboflow.github.io/polygonzone/ to get the points for your shape
            polygon = np.array([
                # draw 50x50 box in top left corner
                [0, 0],
                [50, 0],
                [50, 50],
                [0, 50]
            ])
            
            roboflow.login()
            rf = roboflow.Roboflow()
            
            # replace with the boot project you choose above
            project = rf.workspace("sindhu").project("car_dent_scratch_detection-1")
            boot_model = project.version(9).model
            
            # create BYTETracker instance
            boot_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 PolygonZone instance
            zone = sv.PolygonZone(polygon=polygon, frame_resolution_wh=(video_info.width, video_info.height))
            
            # create box annotator
            box_annotator = sv.BoxAnnotator(thickness=4, text_thickness=4, text_scale=2)
            
            colors = sv.ColorPalette.default()
            
            # create instance of BoxAnnotator
            zone_annotator = sv.PolygonZoneAnnotator(thickness=4, text_thickness=4, text_scale=2, zone=zone, color=colors.colors[0])
            
            # 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 = boot_model.predict(frame).json()
                boots = sv.Detections.from_roboflow(results)
            
                # show boot detections in real time
                print(boots)
            
                # tracking boot detections
                boots = boot_tracker.update_with_detections(boots)
            
                annotated_frame = box_annotator.annotate(scene=frame, detections=boots)
                annotated_frame = zone_annotator.annotate(scene=annotated_frame)
            
                # return frame with box and line annotated result
                return annotated_frame
            
            # process the whole video
            sv.process_video(
                source_path = SOURCE_VIDEO_PATH,
                target_path = TARGET_VIDEO_PATH,
                callback=callback
            )

Guide: How to Track Boots Crossing a Line

You can count how many boots have crossed a line using the supervision LineCounter method.

The following code snippet counts the number of boots that cross a line in a video.

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 = "boot.mp4"
            TARGET_VIDEO_PATH = "boot_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 boot project you choose above
            project = rf.workspace("sindhu").project("car_dent_scratch_detection-1")
            boot_model = project.version(9).model
            
            # create BYTETracker instance
            boot_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 = boot_model.predict(frame).json()
                boots = sv.Detections.from_roboflow(results)
            
                # show boot detections in real time
                print(boots)
            
                # tracking boot detections
                boots = boot_tracker.update_with_detections(boots)
                annotated_frame = trace_annotator.annotate(
                    scene=frame.copy(),
                    detections=boots
                )
                annotated_frame=box_annotator.annotate(
                    scene=annotated_frame,
                    detections=boots
                )
            
                # update line counter
                line_zone.trigger(boots)
            
                # 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
            )