evaluate.py

"""
학습된 모델 성능 평가 스크립트
- Validation 데이터로 상세 평가
- 클래스별 성능 분석
- 혼동 행렬, PR 곡선 등 시각화
"""

from ultralytics import YOLO
from pathlib import Path
import json
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from sklearn.metrics import classification_report, confusion_matrix


def evaluate_model(model_path, data_yaml='dataset_split/data.yaml', save_dir='evaluation_results'):
    """
    모델 상세 평가

    Args:
        model_path: 학습된 모델 경로 (.pt 파일)
        data_yaml: 데이터셋 설정 파일
        save_dir: 결과 저장 디렉토리
    """

    print("=" * 70)
    print("모델 성능 평가")
    print("=" * 70)

    # 모델 로드
    print(f"\n모델 로드: {model_path}")
    model = YOLO(model_path)

    # 저장 디렉토리 생성
    save_path = Path(save_dir)
    save_path.mkdir(exist_ok=True)

    # ========================================
    # 1. 전체 Validation 평가
    # ========================================
    print("\n" + "=" * 70)
    print("1. Validation 데이터셋 평가")
    print("=" * 70)

    metrics = model.val(
        data=data_yaml,
        split='val',
        save_json=True,
        save_hybrid=True,
        conf=0.001,
        iou=0.6,
        max_det=300,
        plots=True,
    )

    # 전체 성능 출력
    print("\n📊 전체 성능 지표:")
    print(f"  mAP50     : {metrics.box.map50:.4f}  (50% IoU에서 정확도)")
    print(f"  mAP50-95  : {metrics.box.map:.4f}  (50-95% IoU 평균)")
    print(f"  Precision : {metrics.box.mp:.4f}  (정밀도)")
    print(f"  Recall    : {metrics.box.mr:.4f}  (재현율)")

    # 클래스별 성능
    print("\n📋 클래스별 성능:")
    print(f"{'Class':<15} {'mAP50':>8} {'mAP50-95':>10} {'Precision':>10} {'Recall':>8}")
    print("-" * 65)

    class_names = ['Plastic', 'Vinyl', 'Can', 'Glass', 'Paper']

    for i, name in enumerate(class_names):
        if i < len(metrics.box.ap50):
            map50 = metrics.box.ap50[i]
            map50_95 = metrics.box.ap[i]
            precision = metrics.box.p[i] if i < len(metrics.box.p) else 0
            recall = metrics.box.r[i] if i < len(metrics.box.r) else 0

            print(f"{name:<15} {map50:>8.4f} {map50_95:>10.4f} {precision:>10.4f} {recall:>8.4f}")

    # ========================================
    # 2. 상세 분석 - 예측 결과 수집
    # ========================================
    print("\n" + "=" * 70)
    print("2. 상세 분석 - Validation 이미지 예측")
    print("=" * 70)

    # Validation 이미지 경로 읽기
    val_txt = Path('dataset_split/val.txt')
    if val_txt.exists():
        with open(val_txt, 'r') as f:
            val_images = [line.strip() for line in f.readlines()]
    else:
        # val.txt가 없으면 직접 찾기
        val_images_dir = Path('dataset_split/images/val')
        val_images = list(val_images_dir.glob('**/*.[jJ][pP][gG]'))

    print(f"Validation 이미지 수: {len(val_images)}")

    # 예측 결과 수집
    all_true_labels = []
    all_pred_labels = []
    all_confidences = []

    print("예측 진행 중...")
    for img_path in val_images[:100]:  # 일부만 샘플링 (시간 절약)
        # 예측
        results = model.predict(img_path, verbose=False, conf=0.25)

        # Ground Truth 레이블 읽기
        label_path = str(img_path).replace('/images/', '/labels/').replace('\\images\\', '\\labels\\')
        label_path = label_path.replace('.jpg', '.txt').replace('.JPG', '.txt')

        if Path(label_path).exists():
            with open(label_path, 'r') as f:
                for line in f:
                    parts = line.strip().split()
                    if len(parts) >= 5:
                        true_class = int(parts[0])
                        all_true_labels.append(true_class)

        # 예측 결과
        for r in results:
            for box in r.boxes:
                pred_class = int(box.cls[0])
                conf = float(box.conf[0])
                all_pred_labels.append(pred_class)
                all_confidences.append(conf)

    # ========================================
    # 3. 혼동 행렬 (Confusion Matrix)
    # ========================================
    print("\n" + "=" * 70)
    print("3. 혼동 행렬 생성")
    print("=" * 70)

    if len(all_true_labels) > 0 and len(all_pred_labels) > 0:
        # 길이 맞추기
        min_len = min(len(all_true_labels), len(all_pred_labels))
        all_true_labels = all_true_labels[:min_len]
        all_pred_labels = all_pred_labels[:min_len]

        # Confusion Matrix
        cm = confusion_matrix(all_true_labels, all_pred_labels, labels=range(5))

        # 시각화
        plt.figure(figsize=(10, 8))
        sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
                    xticklabels=class_names,
                    yticklabels=class_names)
        plt.title('Confusion Matrix', fontsize=16, fontweight='bold')
        plt.ylabel('True Label', fontsize=12)
        plt.xlabel('Predicted Label', fontsize=12)
        plt.tight_layout()

        cm_path = save_path / 'confusion_matrix_detailed.png'
        plt.savefig(cm_path, dpi=300, bbox_inches='tight')
        print(f"✅ 혼동 행렬 저장: {cm_path}")
        plt.close()

        # Classification Report
        print("\n📊 Classification Report:")
        report = classification_report(
            all_true_labels,
            all_pred_labels,
            target_names=class_names,
            digits=4
        )
        print(report)

        # 리포트 저장
        with open(save_path / 'classification_report.txt', 'w') as f:
            f.write(report)

    # ========================================
    # 4. 성능 분석 그래프
    # ========================================
    print("\n" + "=" * 70)
    print("4. 성능 분석 그래프 생성")
    print("=" * 70)

    # 클래스별 mAP 비교
    fig, axes = plt.subplots(2, 2, figsize=(14, 10))

    # (1) 클래스별 mAP50
    ax1 = axes[0, 0]
    map50_values = [metrics.box.ap50[i] if i < len(metrics.box.ap50) else 0
                    for i in range(5)]
    colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#FFA07A', '#98D8C8']
    bars1 = ax1.bar(class_names, map50_values, color=colors, alpha=0.7, edgecolor='black')
    ax1.set_ylabel('mAP50', fontsize=12, fontweight='bold')
    ax1.set_title('클래스별 mAP50', fontsize=14, fontweight='bold')
    ax1.set_ylim(0, 1)
    ax1.grid(axis='y', alpha=0.3)

    # 값 표시
    for bar in bars1:
        height = bar.get_height()
        ax1.text(bar.get_x() + bar.get_width()/2., height,
                f'{height:.3f}', ha='center', va='bottom', fontsize=10)

    # (2) 클래스별 mAP50-95
    ax2 = axes[0, 1]
    map50_95_values = [metrics.box.ap[i] if i < len(metrics.box.ap) else 0
                       for i in range(5)]
    bars2 = ax2.bar(class_names, map50_95_values, color=colors, alpha=0.7, edgecolor='black')
    ax2.set_ylabel('mAP50-95', fontsize=12, fontweight='bold')
    ax2.set_title('클래스별 mAP50-95', fontsize=14, fontweight='bold')
    ax2.set_ylim(0, 1)
    ax2.grid(axis='y', alpha=0.3)

    for bar in bars2:
        height = bar.get_height()
        ax2.text(bar.get_x() + bar.get_width()/2., height,
                f'{height:.3f}', ha='center', va='bottom', fontsize=10)

    # (3) Precision vs Recall
    ax3 = axes[1, 0]
    precision_values = [metrics.box.p[i] if i < len(metrics.box.p) else 0
                       for i in range(5)]
    recall_values = [metrics.box.r[i] if i < len(metrics.box.r) else 0
                    for i in range(5)]

    x = np.arange(len(class_names))
    width = 0.35
    ax3.bar(x - width/2, precision_values, width, label='Precision',
            color='skyblue', alpha=0.8, edgecolor='black')
    ax3.bar(x + width/2, recall_values, width, label='Recall',
            color='lightcoral', alpha=0.8, edgecolor='black')
    ax3.set_ylabel('Score', fontsize=12, fontweight='bold')
    ax3.set_title('Precision vs Recall', fontsize=14, fontweight='bold')
    ax3.set_xticks(x)
    ax3.set_xticklabels(class_names)
    ax3.legend()
    ax3.set_ylim(0, 1)
    ax3.grid(axis='y', alpha=0.3)

    # (4) F1-Score
    ax4 = axes[1, 1]
    f1_scores = [2 * (p * r) / (p + r) if (p + r) > 0 else 0
                 for p, r in zip(precision_values, recall_values)]
    bars4 = ax4.bar(class_names, f1_scores, color=colors, alpha=0.7, edgecolor='black')
    ax4.set_ylabel('F1-Score', fontsize=12, fontweight='bold')
    ax4.set_title('클래스별 F1-Score', fontsize=14, fontweight='bold')
    ax4.set_ylim(0, 1)
    ax4.grid(axis='y', alpha=0.3)

    for bar in bars4:
        height = bar.get_height()
        ax4.text(bar.get_x() + bar.get_width()/2., height,
                f'{height:.3f}', ha='center', va='bottom', fontsize=10)

    plt.tight_layout()
    performance_path = save_path / 'performance_analysis.png'
    plt.savefig(performance_path, dpi=300, bbox_inches='tight')
    print(f"✅ 성능 분석 그래프 저장: {performance_path}")
    plt.close()

    # ========================================
    # 5. 결과 JSON 저장
    # ========================================
    results_dict = {
        'overall': {
            'mAP50': float(metrics.box.map50),
            'mAP50_95': float(metrics.box.map),
            'precision': float(metrics.box.mp),
            'recall': float(metrics.box.mr),
        },
        'per_class': {}
    }

    for i, name in enumerate(class_names):
        if i < len(metrics.box.ap50):
            results_dict['per_class'][name] = {
                'mAP50': float(metrics.box.ap50[i]),
                'mAP50_95': float(metrics.box.ap[i]),
                'precision': float(metrics.box.p[i]) if i < len(metrics.box.p) else 0,
                'recall': float(metrics.box.r[i]) if i < len(metrics.box.r) else 0,
                'f1_score': f1_scores[i],
            }

    json_path = save_path / 'evaluation_results.json'
    with open(json_path, 'w', encoding='utf-8') as f:
        json.dump(results_dict, f, indent=2, ensure_ascii=False)

    print(f"✅ 결과 JSON 저장: {json_path}")

    # ========================================
    # 최종 요약
    # ========================================
    print("\n" + "=" * 70)
    print("평가 완료!")
    print("=" * 70)
    print(f"\n저장 위치: {save_path.absolute()}")
    print("\n생성된 파일:")
    print(f"  - confusion_matrix_detailed.png")
    print(f"  - performance_analysis.png")
    print(f"  - classification_report.txt")
    print(f"  - evaluation_results.json")

    return metrics, results_dict


if __name__ == '__main__':
    import sys

    # 모델 경로 입력
    if len(sys.argv) > 1:
        model_path = sys.argv[1]
    else:
        # 기본값: 최신 학습 모델
        default_path = 'waste_classification/yolov8n_5class/weights/best.pt'

        print(f"모델 경로를 입력하세요 (엔터: {default_path}):")
        user_input = input().strip()
        model_path = user_input if user_input else default_path

    # 모델 존재 확인
    if not Path(model_path).exists():
        print(f"❌ 모델 파일을 찾을 수 없습니다: {model_path}")
        sys.exit(1)

    # 평가 실행
    evaluate_model(model_path)