latest/how_to/filter_detections/

[giscus[bot]]

latest/how_to/filter_detections/

A set of easy-to-use utilities that will come in handy in any computer vision project.

https://supervision.roboflow.com/latest/how_to/filter_detections/

[Pencil77]

can i use it to filter the predictions in video?

[onuralpszr]

Yes you can filter videos as well.

[casperdias]

Hi can you help me I have this code:

from ultralytics import YOLO
import supervision as sv
import numpy as np
import cv2

# Replace 'rtsp://your_rtsp_stream_url' with your actual RTSP stream URL
rtsp_url = 'rtsp_url'

# Create a VideoCapture object
cap = cv2.VideoCapture(rtsp_url)
MODEL = "yolov8n.pt"
model = YOLO(MODEL)
model.fuse()

selected_classes = [0]

video_info_url = sv.VideoInfo(width=1920, height=1080, fps=15)
# settings
LINE_START = sv.Point(974, 757)
LINE_END = sv.Point(1450, 843)

# Check if the camera opened successfully
if not cap.isOpened():
    print("Error: Couldn't open the RTSP stream.")
    exit()

# create BYTETracker instance
byte_tracker = sv.ByteTrack(track_thresh=0.25, track_buffer=30, match_thresh=0.8, frame_rate=25)

# create VideoInfo instance
video_info = sv.VideoInfo.from_video_path(rtsp_url)

# create LineZone instance, it is previously called LineCounter class
triggering_anchors = [sv.Position.BOTTOM_LEFT, sv.Position.BOTTOM_RIGHT]
line_zone = sv.LineZone(start=LINE_START, end=LINE_END, triggering_anchors=triggering_anchors)

# create instance of BoxAnnotator
box_annotator = sv.BoxAnnotator(thickness=2, text_thickness=2, text_scale=1)

# create instance of TraceAnnotator
trace_annotator = sv.TraceAnnotator(thickness=2, trace_length=50)

# create LineZoneAnnotator instance, it is previously called LineCounterAnnotator class
line_zone_annotator = sv.LineZoneAnnotator(thickness=1, text_thickness=1, text_scale=0.5)

with sv.VideoSink(target_path='target_video.mp4', video_info=video_info_url) as sink:    
    while True:
        try:
            ret, frame_ori = cap.read()

            if not ret:
                print("Error: Couldn't read frame.")
                raise Exception("Error: Couldn't read frame.")

            results = model(frame_ori, verbose=False)[0]
            detections = sv.Detections.from_ultralytics(results)
            # only consider class id from selected_classes define above
            detections = detections[np.isin(detections.class_id, selected_classes)]
            # tracking detections
            detections = byte_tracker.update_with_detections(detections)
            labels = [
                f"#{tracker_id} {model.model.names[class_id]} {confidence:0.2f}"
                for confidence, class_id, tracker_id
                in zip(detections.confidence, detections.class_id, detections.tracker_id)
            ]
            annotated_frame = trace_annotator.annotate(
                scene=frame_ori.copy(),
                detections=detections
            )
            annotated_frame=box_annotator.annotate(
                scene=annotated_frame,
                detections=detections,
                labels=labels)

            # update line counter
            results_line = line_zone.trigger(detections)

            # Trigger Orang Masuk dan Keluar
            if results_line[0].size > 0:
                if results_line[0][0] == True:
                    print("Ada Orang Masuk")
                    with sv.ImageSink(target_dir_path="result") as sink:
                        for xyxy in detections.xyxy:
                            cropped_image = sv.crop_image(image=frame_ori, xyxy=xyxy)
                            sink.save_image(image=cropped_image)

            if results_line[1].size > 0:
                if results_line[1][0] == True:
                    print("Ada Orang Keluar")

            cv2.imshow('RTSP Stream', line_zone_annotator.annotate(annotated_frame, line_counter=line_zone))

            sink.write_frame(frame=line_zone_annotator.annotate(annotated_frame, line_counter=line_zone))

            if cv2.waitKey(1) & 0xFF == ord('q'):
                break

        except Exception as e:
            print("Error occurred:", e)
            cap = cv2.VideoCapture(rtsp_url)

# Release the VideoCapture and close the window
cap.release()
cv2.destroyAllWindows()

When someone enters, the code should cut the image and continue, but instead it creates an error occurred: 'ImageSink' object has no attribute 'write_frame' and makes it not respond.

Whats wrong with this code??

[kelvindbk]

The problem is in this line

with sv.ImageSink(target_dir_path="result") as sink:

this overrides the previous sink variable

with sv.VideoSink(target_path='target_video.mp4', video_info=video_info_url) as sink:

[codin-son]

hello, how can we filter using IOU? im using inferencepipeline for the detections, i want to make the iou can be modify by the user but it seem like the iou parameter for inferencepipeline init will not change when user modify the iou value

[SkalskiP]

Hi @codin-son 👋🏻 If you are unhappy with results coming straight from InferencePipeline try using sv.Detections.with_nms. This will allow you to use Non-Max Suppression - an algorithm that leverages Intersection over Union (IoU) to filter detections.