import pytest import numpy as np from pathlib import Path from boxmot.utils import WEIGHTS import time import subprocess from numpy.testing import assert_allclose from boxmot import ( StrongSort, BotSort, DeepOcSort, OcSort, ByteTrack, ImprAssocTrack, get_tracker_config, create_tracker, ) from tests.test_config import MOTION_ONLY_TRACKING_NAMES, MOTION_N_APPEARANCE_TRACKING_NAMES @pytest.mark.parametrize("tracker_type", MOTION_ONLY_TRACKING_NAMES) def test_motion_tracker_update_time(tracker_type): tracker_conf = get_tracker_config(tracker_type) tracker = create_tracker( tracker_type=tracker_type, tracker_config=tracker_conf, reid_weights=WEIGHTS / 'mobilenetv2_x1_4_dukemtmcreid.pt', device='cpu', half=False, per_class=False ) rgb = np.random.randint(0, 255, size=(640, 640, 3), dtype=np.uint8) det = np.array([[144, 212, 578, 480, 0.82, 0], [425, 281, 576, 472, 0.56, 65]]) n_runs = 100 # Warm-up iteration to ensure initialization overhead is not measured tracker.update(det, rgb) start = time.perf_counter() for _ in range(n_runs): tracker.update(det, rgb) end = time.perf_counter() elapsed_time_per_iteration = (end - start) / n_runs fps = 1.0 / elapsed_time_per_iteration # Print FPS for each tracker type print(f"Tracker type: {tracker_type} - FPS: {fps:.2f}") result = subprocess.run( "cat /proc/cpuinfo | grep 'model name' | head -1", shell=True, capture_output=True, text=True ) print(result.stdout.strip()) max_allowed_time = 0.005 # maximum allowed time per iteration in seconds assert elapsed_time_per_iteration < max_allowed_time, ( f"Tracking algorithm's processing time per iteration ({elapsed_time_per_iteration:.6f}s) " f"exceeds the allowed limit of {max_allowed_time}s." ) @pytest.mark.parametrize("tracker_type", MOTION_N_APPEARANCE_TRACKING_NAMES) def test_motion_n_appearance_tracker_update_time(tracker_type): tracker_conf = get_tracker_config(tracker_type) tracker = create_tracker( tracker_type=tracker_type, tracker_config=tracker_conf, reid_weights=WEIGHTS / 'mobilenetv2_x1_4_dukemtmcreid.pt', device='cpu', half=False, per_class=False ) rgb = np.random.randint(0, 255, size=(640, 640, 3), dtype=np.uint8) det = np.array([[144, 212, 578, 480, 0.82, 0], [425, 281, 576, 472, 0.56, 65]]) n_runs = 100 # Warm-up iteration to avoid initialization overhead in timing tracker.update(det, rgb) start = time.perf_counter() for _ in range(n_runs): tracker.update(det, rgb) end = time.perf_counter() elapsed_time_per_iteration = (end - start) / n_runs fps = 1.0 / elapsed_time_per_iteration # Print FPS for each tracker type print(f"Tracker type: {tracker_type} - FPS: {fps:.2f}") max_allowed_time = 6 # maximum allowed time per iteration in seconds assert elapsed_time_per_iteration < max_allowed_time, ( f"Tracking algorithm's processing time per iteration ({elapsed_time_per_iteration:.4f}s) " f"exceeds the allowed limit of {max_allowed_time}s." )