"""延迟分析 - batch=1 优化后"""
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

import time
import numpy as np

from config.settings import get_settings
from core.preprocessor import ImagePreprocessor, BatchPreprocessor

settings = get_settings()
preprocessor = ImagePreprocessor(settings.inference)

img = np.random.randint(0, 255, (1080, 1920, 3), dtype=np.uint8)
roi_mock = type('ROI', (), {'x1': 300, 'y1': 100, 'x2': 1000, 'y2': 800, 'enabled': True, 'roi_type': 0})()

times_preprocess_single = []
times_preprocess_batch = []

for _ in range(100):
    # 1. preprocess_single
    start = time.perf_counter()
    cropped = preprocessor.preprocess_single(img, roi_mock)
    t = (time.perf_counter() - start) * 1000
    times_preprocess_single.append(t)
    
    # 2. preprocess_batch (batch=1)
    start = time.perf_counter()
    batch_data, _ = preprocessor._batch_preprocessor.preprocess_batch([cropped[0]])
    t = (time.perf_counter() - start) * 1000
    times_preprocess_batch.append(t)

print("延迟分析 (batch=1 优化后):")
print(f"  preprocess_single: {np.mean(times_preprocess_single):.2f}ms")
print(f"  preprocess_batch: {np.mean(times_preprocess_batch):.2f}ms")
print(f"  总预处理: {np.mean(times_preprocess_single) + np.mean(times_preprocess_batch):.2f}ms")
print()
print(f"TensorRT batch=1 推理: ~2.5ms")
print(f"TensorRT batch=4 推理: ~5.0ms")
print()
print("推算总延迟:")
print(f"  batch=1: {np.mean(times_preprocess_single) + np.mean(times_preprocess_batch):.2f} + 2.5 ≈ 8-12ms")
print(f"  batch=4: {np.mean(times_preprocess_single) + np.mean(times_preprocess_batch):.2f} + 5 ≈ 10-15ms")