Showing 1 - 50 of 196

Top Crisp Packet Computer Vision Models

The models below have been fine-tuned for various crisp packet detection tasks. You can try out each model in your browser, or test an edge deployment solution (i.e. to an NVIDIA Jetson). You can use the datasets associated with the models below as a starting point for building your own crisp packet detection model.

At the bottom of this page, we have guides on how to count crisp packets in images and videos.

Multi Object

2161 images1 model

bus car cat dog fork fracture helicopter human knife monkey product spoon text truck van 0 Amblance American_Football Apple Artichoke

Object Detection Model snap

LitterLocator - Home Project

by The Workspace Workspace

5349 images1 model

Showing 1 - 50 of 196

1 2 3 4

Guide: How to Count Crisp Packets with Computer Vision

With a model hosted on Roboflow like the ones above and the open source supervision Python package, you can count crisp packets in your images and videos.

The following code snippet counts the number of crisp packets present in an image.

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 crisp packet project you choose above
            project = rf.workspace("trash-dataset-for-oriented-bounded-box").project("trash-detection-1fjjc")
            crisp_packet_model = project.version(14).model
            
            results = crisp_packet_model.predict("crisp_packet.jpg").json()
            crisp_packets = sv.Detections.from_roboflow(results)
            
            # print number of crisp packets
            print(len(crisp_packets))

Guide: How to Count Crisp Packets in a Zone

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

The following code snippet counts the number of crisp packet 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 = "crisp_packet.mp4"
            TARGET_VIDEO_PATH = "crisp_packet_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 crisp packet project you choose above
            project = rf.workspace("trash-dataset-for-oriented-bounded-box").project("trash-detection-1fjjc")
            crisp_packet_model = project.version(14).model
            
            # create BYTETracker instance
            crisp_packet_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 = crisp_packet_model.predict(frame).json()
                crisp_packets = sv.Detections.from_roboflow(results)
            
                # show crisp packet detections in real time
                print(crisp_packets)
            
                # tracking crisp packet detections
                crisp_packets = crisp_packet_tracker.update_with_detections(crisp_packets)
            
                annotated_frame = box_annotator.annotate(scene=frame, detections=crisp_packets)
                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 Crisp Packets Crossing a Line

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

The following code snippet counts the number of crisp packets 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 = "crisp_packet.mp4"
            TARGET_VIDEO_PATH = "crisp_packet_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 crisp packet project you choose above
            project = rf.workspace("trash-dataset-for-oriented-bounded-box").project("trash-detection-1fjjc")
            crisp_packet_model = project.version(14).model
            
            # create BYTETracker instance
            crisp_packet_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 = crisp_packet_model.predict(frame).json()
                crisp_packets = sv.Detections.from_roboflow(results)
            
                # show crisp packet detections in real time
                print(crisp_packets)
            
                # tracking crisp packet detections
                crisp_packets = crisp_packet_tracker.update_with_detections(crisp_packets)
                annotated_frame = trace_annotator.annotate(
                    scene=frame.copy(),
                    detections=crisp_packets
                )
                annotated_frame=box_annotator.annotate(
                    scene=annotated_frame,
                    detections=crisp_packets
                )
            
                # update line counter
                line_zone.trigger(crisp_packets)
            
                # 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
            )