bev-project/visualize_results.py

#!/usr/bin/env python3
"""
可视化nuScenes推理结果的简化脚本
直接使用pickle文件生成可视化
"""

import pickle
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
import argparse


def visualize_detection_bev(results, idx=0, output_path='detection_bev.png'):
    """可视化3D检测结果（BEV视图）"""
    fig, ax = plt.subplots(figsize=(12, 12))
    
    result = results[idx]
    
    if 'boxes_3d' in result:
        boxes = result['boxes_3d']
        scores = result['scores_3d']  
        labels = result['labels_3d']
        
        # 类别名称
        class_names = [
            'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
            'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
        ]
        
        colors = plt.cm.tab10(np.linspace(0, 1, 10))
        
        for box, score, label in zip(boxes, scores, labels):
            if score < 0.3:
                continue
            
            # 提取中心和尺寸
            center = box[:2]
            size = box[3:5]
            yaw = box[6]
            
            # 绘制框
            l, w = size[0], size[1]
            corners = np.array([
                [-l/2, -w/2], [l/2, -w/2], 
                [l/2, w/2], [-l/2, w/2], [-l/2, -w/2]
            ])
            
            rot = np.array([
                [np.cos(yaw), -np.sin(yaw)],
                [np.sin(yaw), np.cos(yaw)]
            ])
            corners = corners @ rot.T + center
            
            color = colors[label % 10]
            ax.plot(corners[:, 0], corners[:, 1], color=color, linewidth=2)
            ax.scatter(center[0], center[1], color=color, s=50)
            
            # 标签
            class_name = class_names[label] if label < len(class_names) else f'cls_{label}'
            ax.text(center[0], center[1], f'{class_name}\n{score:.2f}',
                   fontsize=8, color='white',
                   bbox=dict(boxstyle='round', facecolor=color, alpha=0.7))
    
    ax.set_xlim(-50, 50)
    ax.set_ylim(-50, 50)
    ax.set_aspect('equal')
    ax.grid(True, alpha=0.3)
    ax.set_xlabel('X (meters)', fontsize=14)
    ax.set_ylabel('Y (meters)', fontsize=14)
    ax.set_title('3D Detection Results (BEV View)', fontsize=16)
    
    plt.tight_layout()
    plt.savefig(output_path, dpi=150)
    plt.close()
    
    print(f"✅ 已保存: {output_path}")


def visualize_segmentation(results, idx=0, output_path='segmentation.png'):
    """可视化BEV分割结果"""
    fig, ax = plt.subplots(figsize=(10, 10))
    
    result = results[idx]
    
    if 'seg_pred' in result:
        seg_pred = result['seg_pred']  # (C, H, W)
        
        # 创建彩色图
        h, w = seg_pred.shape[1:]
        color_map = np.zeros((h, w, 3), dtype=np.uint8)
        
        colors = {
            0: [128, 64, 128],    # drivable_area
            1: [244, 35, 232],    # ped_crossing
            2: [70, 70, 70],      # walkway
            3: [220, 20, 60],     # stop_line
            4: [157, 234, 50],    # carpark_area
            5: [255, 255, 0],     # divider
        }
        
        class_names = [
            'drivable_area', 'ped_crossing', 'walkway',
            'stop_line', 'carpark_area', 'divider'
        ]
        
        for idx, name in enumerate(class_names):
            if idx < seg_pred.shape[0]:
                mask = seg_pred[idx] > 0.5
                color_map[mask] = colors[idx]
        
        ax.imshow(color_map)
        ax.set_title('BEV Segmentation', fontsize=16)
        ax.axis('off')
        
        # 图例
        from matplotlib.patches import Patch
        legend_elements = [
            Patch(facecolor=np.array(colors[i])/255, label=class_names[i])
            for i in range(len(class_names))
        ]
        ax.legend(handles=legend_elements, loc='upper right')
    
    plt.tight_layout()
    plt.savefig(output_path, dpi=150)
    plt.close()
    
    print(f"✅ 已保存: {output_path}")


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--results', default='results.pkl', help='结果pickle文件')
    parser.add_argument('--output-dir', default='visualizations', help='输出目录')
    parser.add_argument('--samples', type=int, default=5, help='可视化样本数')
    args = parser.parse_args()
    
    print(f"\n{'='*80}")
    print("可视化nuScenes推理结果")
    print(f"{'='*80}\n")
    
    # 创建输出目录
    Path(args.output_dir).mkdir(exist_ok=True)
    
    # 加载结果
    print(f"加载结果: {args.results}")
    with open(args.results, 'rb') as f:
        results = pickle.load(f)
    
    print(f"结果数量: {len(results)}")
    print(f"可视化前 {min(args.samples, len(results))} 个样本\n")
    
    # 可视化
    for i in range(min(args.samples, len(results))):
        print(f"处理样本 {i+1}/{min(args.samples, len(results))}...")
        
        det_path = Path(args.output_dir) / f"sample_{i:04d}_detection.png"
        seg_path = Path(args.output_dir) / f"sample_{i:04d}_segmentation.png"
        
        visualize_detection_bev(results, i, str(det_path))
        visualize_segmentation(results, i, str(seg_path))
    
    print(f"\n{'='*80}")
    print(f"✅ 完成！结果保存在: {args.output_dir}/")
    print(f"{'='*80}\n")


if __name__ == '__main__':
    main()