cluster4npu/tests/quick_fix_detection_issues.py

#!/usr/bin/env python3
"""
Quick fixes for detection result issues.
快速修復偵測結果問題的補丁程式。
"""

import sys
import os
sys.path.append(os.path.dirname(os.path.abspath(__file__)))

def apply_quick_fixes():
    """應用快速修復到檢測結果"""
    
    print("=== 快速修復偵測結果問題 ===")
    print()
    
    # 修復建議
    fixes = [
        {
            "issue": "過多的偵測結果 (100+ 物件)",
            "cause": "可能的原因：模型輸出格式不匹配、閾值太低、測試模式",
            "solutions": [
                "1. 提高信心閾值到 0.5-0.7",
                "2. 添加檢測數量限制",
                "3. 檢查是否在測試/調試模式",
                "4. 驗證模型輸出格式"
            ]
        },
        {
            "issue": "座標異常 (0,0 或負值)",
            "cause": "可能的原因：座標轉換錯誤、輸出格式不匹配",
            "solutions": [
                "1. 檢查座標轉換邏輯",
                "2. 驗證輸入圖片尺寸",
                "3. 確認模型輸出格式",
                "4. 添加座標有效性檢查"
            ]
        },
        {
            "issue": "LiveView 卡頓",
            "cause": "可能的原因：處理過多檢測結果導致渲染瓶頸",
            "solutions": [
                "1. 限制顯示的檢測數量",
                "2. 降低 FPS 或跳幀顯示",
                "3. 異步處理檢測結果",
                "4. 優化渲染代碼"
            ]
        }
    ]
    
    for fix in fixes:
        print(f"問題: {fix['issue']}")
        print(f"原因: {fix['cause']}")
        print("解決方案:")
        for solution in fix['solutions']:
            print(f"   {solution}")
        print()
    
    # 立即可用的代碼修復
    print("=== 立即可用的代碼修復 ===")
    print()
    
    print("1. 在 Multidongle.py 的 _process_yolo_generic 函數開頭添加:")
    print("""
# 緊急修復：限制檢測數量
MAX_DETECTIONS = 50
if len(boxes) > MAX_DETECTIONS:
    print(f"WARNING: Too many detections ({len(boxes)}), limiting to {MAX_DETECTIONS}")
    boxes = sorted(boxes, key=lambda x: x.score, reverse=True)[:MAX_DETECTIONS]
    """)
    
    print("\n2. 在創建 BoundingBox 之前添加驗證:")
    print("""
# 座標有效性檢查
if x1 < 0 or y1 < 0 or x1 >= x2 or y1 >= y2:
    continue  # 跳過無效的邊界框
if (x2 - x1) * (y2 - y1) < 4:  # 最小面積
    continue  # 跳過太小的框
if best_score > 2.0:  # 檢查異常分數
    continue  # 跳過異常分數
    """)
    
    print("\n3. 在 PostProcessorOptions 中設置更嚴格的參數:")
    print("""
postprocess_options = PostProcessorOptions(
    postprocess_type=PostProcessType.YOLO_V5,
    threshold=0.6,  # 提高閾值
    class_names=["person", "bicycle", "car", "motorbike", "aeroplane"],
    nms_threshold=0.4,
    max_detections_per_class=10  # 限制每類檢測數量
)
    """)
    
    print("\n4. 添加檢測結果統計和警告:")
    print("""
# 在函數結尾添加
class_counts = {}
for box in boxes:
    class_counts[box.class_name] = class_counts.get(box.class_name, 0) + 1

for class_name, count in class_counts.items():
    if count > 20:
        print(f"WARNING: Abnormally high count for {class_name}: {count}")
    """)

def create_emergency_filter():
    """創建緊急過濾函數"""
    
    filter_code = '''
def emergency_filter_detections(boxes, max_total=50, max_per_class=10):
    """緊急過濾檢測結果"""
    if len(boxes) <= max_total:
        return boxes
    
    # 按類別分組
    from collections import defaultdict
    class_groups = defaultdict(list)
    for box in boxes:
        class_groups[box.class_name].append(box)
    
    # 每類保留最高分數的檢測
    filtered = []
    for class_name, class_boxes in class_groups.items():
        class_boxes.sort(key=lambda x: x.score, reverse=True)
        keep_count = min(len(class_boxes), max_per_class)
        filtered.extend(class_boxes[:keep_count])
    
    # 總數限制
    if len(filtered) > max_total:
        filtered.sort(key=lambda x: x.score, reverse=True)
        filtered = filtered[:max_total]
    
    return filtered
'''
    
    with open("emergency_filter.py", "w", encoding="utf-8") as f:
        f.write(filter_code)
    
    print("緊急過濾函數已保存到 emergency_filter.py")

if __name__ == "__main__":
    apply_quick_fixes()
    create_emergency_filter()
    
    print("\n=== 下一步建議 ===")
    print("1. 檢查當前的後處理配置")
    print("2. 調整信心閾值和檢測限制")
    print("3. 使用 debug_detection_issues.py 分析結果")
    print("4. 考慮使用 improved_yolo_postprocessing.py 中的改進版本")
    print("5. 如果問題持續，請檢查模型文件和配置")