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 meat project you choose above
project = rf.workspace("osmando").project("ecommerce-photography")
meat_model = project.version(10).model

results = meat_model.predict("meat.jpg").json()
meat = sv.Detections.from_roboflow(results)

# print number of meat
print(len(meat))

How to Count Meat in a Zone

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

The following code snippet counts the number of meat 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 = "meat.mp4"
TARGET_VIDEO_PATH = "meat_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 meat project you choose above
project = rf.workspace("osmando").project("ecommerce-photography")
meat_model = project.version(10).model

# create BYTETracker instance
meat_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 = meat_model.predict(frame).json()
    meat = sv.Detections.from_roboflow(results)

    # show meat detections in real time
    print(meat)

    # tracking meat detections
    meat = meat_tracker.update_with_detections(meat)

    annotated_frame = box_annotator.annotate(scene=frame, detections=meat)
    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
)

How to Track Meat Crossing a Line

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

The following code snippet counts the number of meat 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 = "meat.mp4"
TARGET_VIDEO_PATH = "meat_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 meat project you choose above
project = rf.workspace("osmando").project("ecommerce-photography")
meat_model = project.version(10).model

# create BYTETracker instance
meat_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 = meat_model.predict(frame).json()
    meat = sv.Detections.from_roboflow(results)

    # show meat detections in real time
    print(meat)

    # tracking meat detections
    meat = meat_tracker.update_with_detections(meat)
    annotated_frame = trace_annotator.annotate(
        scene=frame.copy(),
        detections=meat
    )
    annotated_frame=box_annotator.annotate(
        scene=annotated_frame,
        detections=meat
    )

    # update line counter
    line_zone.trigger(meat)

    # 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
)