in real time video missing some frames and missing counting yolov8 and byte track code with supervision

[theekshana1987]

Search before asking

• I have searched the Supervision issues and found no similar feature requests.

Question

in following code i used open cv and supervision with byte track for tracking and counting when with video it is working correctly and giving good out put. when i work with real time it is missing some frame and video writer also giving very speed video due to that. Please check code running you can see it. please help me really appreciate. I am really struck

https://www.youtube.com/watch?v=OS5qI9YBkfk I use above video ..

Additional

from typing import List
import supervision
import cv2
from supervision.draw.color import ColorPalette
from supervision.geometry.dataclasses import Point
from supervision.video.dataclasses import VideoInfo
from supervision.video.source import get_video_frames_generator
from supervision.video.sink import VideoSink
from supervision.notebook.utils import show_frame_in_notebook
from supervision.tools.detections import Detections, BoxAnnotator
from supervision.tools.line_counter import LineCounter, LineCounterAnnotator
from yolox.tracker.byte_tracker import BYTETracker, STrack
from onemetric.cv.utils.iou import box_iou_batch
from dataclasses import dataclass
import numpy as np
from ultralytics import YOLO
from tqdm.notebook import tqdm
import time
import os
import random
import sys

LINE_START = Point(630, 300)
LINE_END = Point(50, 300)
model = YOLO("best_yolov8.pt")
CLASS_NAMES_DICT = model.model.names
# class_ids of interest - car, motorcycle, bus and truck
CLASS_ID = [0]

fps = 1/30
width = 1280
height = 1280
video_codec = cv2.VideoWriter_fourcc("D", "I", "V", "X")

@dataclass(frozen=True)
class BYTETrackerArgs:
    track_thresh: float = 0.25
    track_buffer: int = 30
    match_thresh: float = 0.8
    aspect_ratio_thresh: float = 3.0
    min_box_area: float = 1.0
    mot20: bool = False

# converts Detections into format that can be consumed by match_detections_with_tracks function
def detections2boxes(detections: Detections) -> np.ndarray:
    return np.hstack((
        detections.xyxy,
        detections.confidence[:, np.newaxis]
    ))


# converts List[STrack] into format that can be consumed by match_detections_with_tracks function
def tracks2boxes(tracks:List[STrack]) -> np.ndarray:
    return np.array([
        track.tlbr
        for track
        in tracks
    ], dtype=float)


# matches our bounding boxes with predictions
def match_detections_with_tracks(
    detections: Detections, 
    tracks: List[STrack]
) -> Detections:
    if not np.any(detections.xyxy) or len(tracks) == 0:
        return np.empty((0,))

    tracks_boxes = tracks2boxes(tracks=tracks)
    iou = box_iou_batch(tracks_boxes, detections.xyxy)
    track2detection = np.argmax(iou, axis=1)
    
    tracker_ids = [None] * len(detections)
    
    for tracker_index, detection_index in enumerate(track2detection):
        if iou[tracker_index, detection_index] != 0:
            tracker_ids[detection_index] = tracks[tracker_index].track_id

    return tracker_ids

byte_tracker = BYTETracker(BYTETrackerArgs())
# create VideoInfo instance
name = random.randint(0, 1000)
print(name)
if os.path.isdir(str(name)) is False:
    name = random.randint(0, 1000)
    name = str(name)

name = os.path.join(os.getcwd(), str(name))
print("ALl logs saved in dir:", name)
os.mkdir(name)
#video_info = VideoInfo.from_video_path("cocnrec.mp4")
# create frame generator
#generator = get_video_frames_generator("cocnrec.mp4")

# create LineCounter instance
line_counter = LineCounter(start=LINE_START, end=LINE_END)
# create instance of BoxAnnotator and LineCounterAnnotator
box_annotator = BoxAnnotator(color=ColorPalette(), thickness=4, text_thickness=4, text_scale=2)
line_annotator = LineCounterAnnotator(thickness=4, text_thickness=4, text_scale=2)


#cap = cv2.VideoCapture("cocnrec.mp4")
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
ret = cap.set(3, 864)
ret = cap.set(4, 480)
cur_dir = os.path.dirname(os.path.abspath(sys.argv[0]))
#fps_MS = int(fps * 1000)


start = time.time()
video_file_count = 1
video_file = os.path.join(name, str(video_file_count) + ".avi")
print("Capture video saved location : {}".format(video_file))

video_writer = cv2.VideoWriter(
    video_file, video_codec, fps, (int(cap.get(3)), int(cap.get(4)))
)

# open target video file
#with VideoSink("test.mp4", video_info) as sink:
    # loop over video frames
    #for frame in tqdm(generator, total=video_info.total_frames):
while cap.isOpened():
    start_time = time.time()
    ret, frame = cap.read()
    if ret == True:
        # model prediction on single frame and conversion to supervision Detections
        results = model(frame)
        detections = Detections(
            xyxy=results[0].boxes.xyxy.cpu().numpy(),
            confidence=results[0].boxes.conf.cpu().numpy(),
            class_id=results[0].boxes.cls.cpu().numpy().astype(int)
        )
        # filtering out detections with unwanted classes
        mask = np.array([class_id in CLASS_ID for class_id in detections.class_id], dtype=bool)
        detections.filter(mask=mask, inplace=True)
        # tracking detections
        tracks = byte_tracker.update(
            output_results=detections2boxes(detections=detections),
            img_info=frame.shape,
            img_size=frame.shape
        )
        tracker_id = match_detections_with_tracks(detections=detections, tracks=tracks)
        detections.tracker_id = np.array(tracker_id)
        # filtering out detections without trackers
        mask = np.array([tracker_id is not None for tracker_id in detections.tracker_id], dtype=bool)
        detections.filter(mask=mask, inplace=True)
        # format custom labels
        labels = [
            f"#{tracker_id} {CLASS_NAMES_DICT[class_id]} {confidence:0.2f}"
            for _, confidence, class_id, tracker_id
            in detections
        ]
        # updating line counter
        line_counter.update(detections=detections)
        # annotate and display frame
        frame = box_annotator.annotate(frame=frame, detections=detections, labels=labels)
        line_annotator.annotate(frame=frame, line_counter=line_counter)
        cv2.imshow("frame", frame)
        if time.time() - start > 1000:
            start = time.time()
            video_file_count += 1
            video_file = os.path.join(name, str(video_file_count) + ".avi")
            video_writer = cv2.VideoWriter(
                video_file, video_codec, fps, (int(cap.get(3)), int(cap.get(4)))
            )
            # No sleeping! We don't want to sleep, we want to write
            # time.sleep(10)

        # Write the frame to the current video writer
        video_writer.write(frame)
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    else:
        break
cap.release()
cv2.destroyAllWindows()

[github-actions[bot]]

Hello there, thank you for opening an Issue ! 🙏🏻 The team was notified and they will get back to you asap.

[SkalskiP]

Let me move it to our QA section.

[SkalskiP]

@theekshana1987 did you try to run a much simpler version of the code? Just capture the frame from a web cam and save it to a video file?

[theekshana1987]

yes it is worked perfectly..

import numpy as np
import cv2
import time
import os
import random
import sys

fps = 30
width = 1280
height = 1280
video_codec = cv2.VideoWriter_fourcc("D", "I", "V", "X")

name = random.randint(0, 1000)
print(name)
if os.path.isdir(str(name)) is False:
name = random.randint(0, 1000)
name = str(name)

name = os.path.join(os.getcwd(), str(name))
print("ALl logs saved in dir:", name)
os.mkdir(name)