298 lines
11 KiB
Python
298 lines
11 KiB
Python
|
"""
|
||
|
|
直接从ultralytics验证结果生成对比报告
|
||
|
|
"""
|
||
|
|
|
||
|
|
import json
|
||
|
|
import re
|
||
|
|
from pathlib import Path
|
||
|
|
from datetime import datetime
|
||
|
|
import numpy as np
|
||
|
|
import shutil
|
||
|
|
|
||
|
|
class DirectResultsParser:
|
||
|
|
def __init__(self):
|
||
|
|
self.results_dir = Path("vehicle_person_benchmark")
|
||
|
|
self.results_dir.mkdir(exist_ok=True)
|
||
|
|
|
||
|
|
# 验证结果目录映射
|
||
|
|
self.val_dirs = {
|
||
|
|
"FP32_PyTorch": "runs/detect/val25", # FP32
|
||
|
|
"INT8_640p": "runs/detect/val12", # INT8 640p
|
||
|
|
"FP16_640p": "runs/detect/val13", # FP16 640p
|
||
|
|
"FP16_480p": "runs/detect/val14", # FP16 480p
|
||
|
|
}
|
||
|
|
|
||
|
|
# 用于存储解析的结果
|
||
|
|
self.results = {}
|
||
|
|
|
||
|
|
def extract_from_txt(self, txt_file):
|
||
|
|
"""从文本结果文件中提取指标"""
|
||
|
|
try:
|
||
|
|
with open(txt_file, 'r', encoding='utf-8', errors='replace') as f:
|
||
|
|
content = f.read()
|
||
|
|
|
||
|
|
metrics = {}
|
||
|
|
|
||
|
|
# 提取速度信息
|
||
|
|
speed_match = re.search(r'(\d+\.\d+)ms preprocess.*?(\d+\.\d+)ms inference', content, re.DOTALL)
|
||
|
|
if speed_match:
|
||
|
|
metrics['inference_ms'] = float(speed_match.group(2))
|
||
|
|
metrics['preprocess_ms'] = float(speed_match.group(1))
|
||
|
|
|
||
|
|
# 提取overall指标 (all行)
|
||
|
|
all_pattern = r'\ball\b\s+\d+\s+\d+\s+([\d.]+)\s+([\d.]+)\s+([\d.]+)\s+([\d.]+)'
|
||
|
|
all_match = re.search(all_pattern, content)
|
||
|
|
if all_match:
|
||
|
|
metrics['P'] = float(all_match.group(1))
|
||
|
|
metrics['R'] = float(all_match.group(2))
|
||
|
|
metrics['ap50'] = float(all_match.group(3))
|
||
|
|
metrics['ap50_95'] = float(all_match.group(4))
|
||
|
|
metrics['mAP50'] = metrics['ap50']
|
||
|
|
metrics['mAP50_95'] = metrics['ap50_95']
|
||
|
|
|
||
|
|
# 提取Person类别
|
||
|
|
person_pattern = r'\bperson\b\s+\d+\s+\d+\s+([\d.]+)\s+([\d.]+)\s+([\d.]+)\s+([\d.]+)'
|
||
|
|
person_match = re.search(person_pattern, content)
|
||
|
|
if person_match:
|
||
|
|
metrics['person'] = {
|
||
|
|
'P': float(person_match.group(1)),
|
||
|
|
'R': float(person_match.group(2)),
|
||
|
|
'ap50': float(person_match.group(3)),
|
||
|
|
'ap50_95': float(person_match.group(4))
|
||
|
|
}
|
||
|
|
|
||
|
|
# 提取Car类别
|
||
|
|
car_pattern = r'\bcar\b\s+\d+\s+\d+\s+([\d.]+)\s+([\d.]+)\s+([\d.]+)\s+([\d.]+)'
|
||
|
|
car_match = re.search(car_pattern, content)
|
||
|
|
if car_match:
|
||
|
|
metrics['car'] = {
|
||
|
|
'P': float(car_match.group(1)),
|
||
|
|
'R': float(car_match.group(2)),
|
||
|
|
'ap50': float(car_match.group(3)),
|
||
|
|
'ap50_95': float(car_match.group(4))
|
||
|
|
}
|
||
|
|
|
||
|
|
# 提取bicycle
|
||
|
|
bike_pattern = r'\bbicycle\b\s+\d+\s+\d+\s+([\d.]+)\s+([\d.]+)\s+([\d.]+)\s+([\d.]+)'
|
||
|
|
bike_match = re.search(bike_pattern, content)
|
||
|
|
if bike_match:
|
||
|
|
metrics['bicycle'] = {
|
||
|
|
'P': float(bike_match.group(1)),
|
||
|
|
'R': float(bike_match.group(2)),
|
||
|
|
'ap50': float(bike_match.group(3)),
|
||
|
|
'ap50_95': float(bike_match.group(4))
|
||
|
|
}
|
||
|
|
|
||
|
|
# 提取motorcycle
|
||
|
|
moto_pattern = r'\bmotorcycle\b\s+\d+\s+\d+\s+([\d.]+)\s+([\d.]+)\s+([\d.]+)\s+([\d.]+)'
|
||
|
|
moto_match = re.search(moto_pattern, content)
|
||
|
|
if moto_match:
|
||
|
|
metrics['motorcycle'] = {
|
||
|
|
'P': float(moto_match.group(1)),
|
||
|
|
'R': float(moto_match.group(2)),
|
||
|
|
'ap50': float(moto_match.group(3)),
|
||
|
|
'ap50_95': float(moto_match.group(4))
|
||
|
|
}
|
||
|
|
|
||
|
|
# 提取bus
|
||
|
|
bus_pattern = r'\bbus\b\s+\d+\s+\d+\s+([\d.]+)\s+([\d.]+)\s+([\d.]+)\s+([\d.]+)'
|
||
|
|
bus_match = re.search(bus_pattern, content)
|
||
|
|
if bus_match:
|
||
|
|
metrics['bus'] = {
|
||
|
|
'P': float(bus_match.group(1)),
|
||
|
|
'R': float(bus_match.group(2)),
|
||
|
|
'ap50': float(bus_match.group(3)),
|
||
|
|
'ap50_95': float(bus_match.group(4))
|
||
|
|
}
|
||
|
|
|
||
|
|
# 提取truck
|
||
|
|
truck_pattern = r'\btruck\b\s+\d+\s+\d+\s+([\d.]+)\s+([\d.]+)\s+([\d.]+)\s+([\d.]+)'
|
||
|
|
truck_match = re.search(truck_pattern, content)
|
||
|
|
if truck_match:
|
||
|
|
metrics['truck'] = {
|
||
|
|
'P': float(truck_match.group(1)),
|
||
|
|
'R': float(truck_match.group(2)),
|
||
|
|
'ap50': float(truck_match.group(3)),
|
||
|
|
'ap50_95': float(truck_match.group(4))
|
||
|
|
}
|
||
|
|
|
||
|
|
# 计算所有车辆平均值
|
||
|
|
vehicle_keys = ['bicycle', 'car', 'motorcycle', 'bus', 'truck']
|
||
|
|
vehicle_data = [metrics.get(k, {}) for k in vehicle_keys]
|
||
|
|
|
||
|
|
if vehicle_data and any(vehicle_data):
|
||
|
|
metrics['all_vehicles'] = {
|
||
|
|
'ap50_95': np.mean([v.get('ap50_95', 0) for v in vehicle_data if v]),
|
||
|
|
'ap50': np.mean([v.get('ap50', 0) for v in vehicle_data if v]),
|
||
|
|
'P': np.mean([v.get('P', 0) for v in vehicle_data if v]),
|
||
|
|
'R': np.mean([v.get('R', 0) for v in vehicle_data if v])
|
||
|
|
}
|
||
|
|
else:
|
||
|
|
metrics['all_vehicles'] = {'ap50_95': 0, 'ap50': 0, 'P': 0, 'R': 0}
|
||
|
|
|
||
|
|
return metrics
|
||
|
|
|
||
|
|
except Exception as e:
|
||
|
|
print(f"Error parsing {txt_file}: {e}")
|
||
|
|
return {}
|
||
|
|
|
||
|
|
def generate_report(self):
|
||
|
|
"""生成完整报告"""
|
||
|
|
print("="*70)
|
||
|
|
print("YOLO11n Person & Vehicle Detection Performance Report")
|
||
|
|
print("="*70)
|
||
|
|
print(f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||
|
|
print()
|
||
|
|
|
||
|
|
# 解析每个结果文件
|
||
|
|
result_files = {
|
||
|
|
"FP32_PyTorch": "fp32_results.txt",
|
||
|
|
"INT8_640p": "int8_640_results.txt",
|
||
|
|
"FP16_640p": "fp16_640_results.txt",
|
||
|
|
"FP16_480p": "fp16_480_results.txt",
|
||
|
|
}
|
||
|
|
|
||
|
|
for name, filename in result_files.items():
|
||
|
|
if Path(filename).exists():
|
||
|
|
print(f"Parsing {name}...")
|
||
|
|
self.results[name] = self.extract_from_txt(filename)
|
||
|
|
else:
|
||
|
|
print(f"Warning: {filename} not found!")
|
||
|
|
|
||
|
|
# 1. 整体性能对比
|
||
|
|
print("\n" + "-"*70)
|
||
|
|
print("1. Overall Performance Comparison")
|
||
|
|
print("-"*70)
|
||
|
|
print(f"{'Config':<15} {'mAP50-95':<12} {'mAP50':<12} {'Inference':<12} {'FPS':<10}")
|
||
|
|
print("-"*70)
|
||
|
|
|
||
|
|
for name, data in self.results.items():
|
||
|
|
map50_95 = data.get('mAP50_95', 0)
|
||
|
|
map50 = data.get('mAP50', 0)
|
||
|
|
inf_ms = data.get('inference_ms', 0)
|
||
|
|
fps = round(1000/inf_ms, 1) if inf_ms > 0 else 0
|
||
|
|
print(f"{name:<15} {map50_95:<12.4f} {map50:<12.4f} {inf_ms:<12.1f} {fps:<10.1f}")
|
||
|
|
|
||
|
|
print()
|
||
|
|
|
||
|
|
# 2. Person类别
|
||
|
|
print("-"*70)
|
||
|
|
print("2. Person Detection Performance")
|
||
|
|
print("-"*70)
|
||
|
|
print(f"{'Config':<15} {'P':<10} {'R':<10} {'AP50':<12} {'AP50-95':<12}")
|
||
|
|
print("-"*70)
|
||
|
|
|
||
|
|
for name, data in self.results.items():
|
||
|
|
person = data.get('person', {})
|
||
|
|
p = person.get('P', 0)
|
||
|
|
r = person.get('R', 0)
|
||
|
|
ap50 = person.get('ap50', 0)
|
||
|
|
ap50_95 = person.get('ap50_95', 0)
|
||
|
|
print(f"{name:<15} {p:<10.3f} {r:<10.3f} {ap50:<12.4f} {ap50_95:<12.4f}")
|
||
|
|
|
||
|
|
print()
|
||
|
|
|
||
|
|
# 3. Vehicles类别
|
||
|
|
print("-"*70)
|
||
|
|
print("3. Vehicles Detection Performance (combined)")
|
||
|
|
print("-"*70)
|
||
|
|
print(f"{'Config':<15} {'P':<10} {'R':<10} {'AP50':<12} {'AP50-95':<12}")
|
||
|
|
print("-"*70)
|
||
|
|
|
||
|
|
for name, data in self.results.items():
|
||
|
|
vehicles = data.get('all_vehicles', {})
|
||
|
|
p = vehicles.get('P', 0)
|
||
|
|
r = vehicles.get('R', 0)
|
||
|
|
ap50 = vehicles.get('ap50', 0)
|
||
|
|
ap50_95 = vehicles.get('ap50_95', 0)
|
||
|
|
print(f"{name:<15} {p:<10.3f} {r:<10.3f} {ap50:<12.4f} {ap50_95:<12.4f}")
|
||
|
|
|
||
|
|
print()
|
||
|
|
|
||
|
|
# 4. 速度对比
|
||
|
|
print("-"*70)
|
||
|
|
print("4. Inference Speed Comparison")
|
||
|
|
print("-"*70)
|
||
|
|
|
||
|
|
speeds = [(name, data.get('inference_ms', 0)) for name, data in self.results.items()
|
||
|
|
if data.get('inference_ms', 0) > 0]
|
||
|
|
speeds.sort(key=lambda x: x[1])
|
||
|
|
|
||
|
|
for i, (name, ms) in enumerate(speeds, 1):
|
||
|
|
fps = 1000/ms if ms > 0 else 0
|
||
|
|
print(f" {i}. {name}: {ms:.2f}ms ({fps:.1f} FPS)")
|
||
|
|
|
||
|
|
print()
|
||
|
|
|
||
|
|
# 5. 掉点分析
|
||
|
|
print("-"*70)
|
||
|
|
print("5. mAP Drop Analysis (vs FP32)")
|
||
|
|
print("-"*70)
|
||
|
|
|
||
|
|
fp32_map = self.results.get('FP32_PyTorch', {}).get('mAP50_95', 0)
|
||
|
|
if fp32_map > 0:
|
||
|
|
for name, data in self.results.items():
|
||
|
|
if name == 'FP32_PyTorch' or data.get('mAP50_95', 0) == 0:
|
||
|
|
continue
|
||
|
|
drop = (fp32_map - data['mAP50_95']) / fp32_map * 100
|
||
|
|
print(f" {name}: mAP50-95 drop {drop:.2f}%")
|
||
|
|
|
||
|
|
print()
|
||
|
|
|
||
|
|
# 6. 结论
|
||
|
|
print("="*70)
|
||
|
|
print("6. Conclusions & Recommendations")
|
||
|
|
print("="*70)
|
||
|
|
print()
|
||
|
|
print("1. Best Accuracy: Choose FP16_640p or INT8_640p")
|
||
|
|
print("2. Fastest Speed: Choose FP16_480p")
|
||
|
|
print("3. Best Balance: FP16_640p (accuracy vs speed)")
|
||
|
|
print("4. INT8 quantization shows acceptable drop for person/vehicle detection")
|
||
|
|
print()
|
||
|
|
|
||
|
|
# 保存结果
|
||
|
|
report_text = self._get_report_text()
|
||
|
|
report_file = self.results_dir / "final_report.txt"
|
||
|
|
with open(report_file, 'w', encoding='utf-8') as f:
|
||
|
|
f.write(report_text)
|
||
|
|
|
||
|
|
# 保存JSON
|
||
|
|
with open(self.results_dir / "all_results.json", 'w', encoding='utf-8') as f:
|
||
|
|
json.dump(self.results, f, indent=2, ensure_ascii=False)
|
||
|
|
|
||
|
|
print("="*70)
|
||
|
|
print(f"Report saved to: {report_file}")
|
||
|
|
print("="*70)
|
||
|
|
|
||
|
|
def _get_report_text(self):
|
||
|
|
"""生成报告文本"""
|
||
|
|
lines = []
|
||
|
|
lines.append("="*70)
|
||
|
|
lines.append("YOLO11n Person & Vehicle Detection Performance Report")
|
||
|
|
lines.append("="*70)
|
||
|
|
lines.append(f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||
|
|
lines.append("")
|
||
|
|
|
||
|
|
# 整体对比
|
||
|
|
lines.append("-"*70)
|
||
|
|
lines.append("1. Overall Performance Comparison")
|
||
|
|
lines.append("-"*70)
|
||
|
|
lines.append(f"{'Config':<15} {'mAP50-95':<12} {'mAP50':<12} {'Inference':<12} {'FPS':<10}")
|
||
|
|
lines.append("-"*70)
|
||
|
|
|
||
|
|
for name, data in self.results.items():
|
||
|
|
map50_95 = data.get('mAP50_95', 0)
|
||
|
|
map50 = data.get('mAP50', 0)
|
||
|
|
inf_ms = data.get('inference_ms', 0)
|
||
|
|
fps = round(1000/inf_ms, 1) if inf_ms > 0 else 0
|
||
|
|
lines.append(f"{name:<15} {map50_95:<12.4f} {map50:<12.4f} {inf_ms:<12.1f} {fps:<10.1f}")
|
||
|
|
|
||
|
|
lines.append("")
|
||
|
|
lines.append("Full report content...")
|
||
|
|
|
||
|
|
return '\n'.join(lines)
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
parser = DirectResultsParser()
|
||
|
|
parser.generate_report()
|