114 lines
4.7 KiB
Python
114 lines
4.7 KiB
Python
# evaluate.py
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import torch
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from PIL import Image
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import json
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import os
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import argparse
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import config
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from models.rord import RoRD
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from utils.data_utils import get_transform
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from data.ic_dataset import ICLayoutDataset
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# (已修改) 导入新的匹配函数
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from match import match_template_multiscale
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def compute_iou(box1, box2):
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x1, y1, w1, h1 = box1['x'], box1['y'], box1['width'], box1['height']
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x2, y2, w2, h2 = box2['x'], box2['y'], box2['width'], box2['height']
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inter_x1, inter_y1 = max(x1, x2), max(y1, y2)
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inter_x2, inter_y2 = min(x1 + w1, x2 + w2), min(y1 + h1, y2 + h2)
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inter_area = max(0, inter_x2 - inter_x1) * max(0, inter_y2 - inter_y1)
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union_area = w1 * h1 + w2 * h2 - inter_area
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return inter_area / union_area if union_area > 0 else 0
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# --- (已修改) 评估函数 ---
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def evaluate(model, val_dataset_dir, val_annotations_dir, template_dir):
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model.eval()
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all_tp, all_fp, all_fn = 0, 0, 0
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# 只需要一个统一的 transform 给匹配函数内部使用
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transform = get_transform()
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template_paths = [os.path.join(template_dir, f) for f in os.listdir(template_dir) if f.endswith('.png')]
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layout_image_names = [f for f in os.listdir(val_dataset_dir) if f.endswith('.png')]
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# (已修改) 循环遍历验证集中的每个版图文件
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for layout_name in layout_image_names:
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print(f"\n正在评估版图: {layout_name}")
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layout_path = os.path.join(val_dataset_dir, layout_name)
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annotation_path = os.path.join(val_annotations_dir, layout_name.replace('.png', '.json'))
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# 加载原始PIL图像，以支持滑动窗口
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layout_image = Image.open(layout_path).convert('L')
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# 加载标注信息
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if not os.path.exists(annotation_path):
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continue
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with open(annotation_path, 'r') as f:
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annotation = json.load(f)
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# 按模板对真实标注进行分组
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gt_by_template = {os.path.basename(box['template']): [] for box in annotation.get('boxes', [])}
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for box in annotation.get('boxes', []):
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gt_by_template[os.path.basename(box['template'])].append(box)
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# 遍历每个模板，在当前版图上进行匹配
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for template_path in template_paths:
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template_name = os.path.basename(template_path)
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template_image = Image.open(template_path).convert('L')
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# (已修改) 调用新的多尺度匹配函数
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detected = match_template_multiscale(model, layout_image, template_image, transform)
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gt_boxes = gt_by_template.get(template_name, [])
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# 计算 TP, FP, FN (这部分逻辑不变)
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matched_gt = [False] * len(gt_boxes)
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tp = 0
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if len(detected) > 0:
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for det_box in detected:
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best_iou = 0
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best_gt_idx = -1
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for i, gt_box in enumerate(gt_boxes):
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if matched_gt[i]: continue
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iou = compute_iou(det_box, gt_box)
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if iou > best_iou:
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best_iou, best_gt_idx = iou, i
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if best_iou > config.IOU_THRESHOLD:
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if not matched_gt[best_gt_idx]:
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tp += 1
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matched_gt[best_gt_idx] = True
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fp = len(detected) - tp
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fn = len(gt_boxes) - tp
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all_tp += tp
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all_fp += fp
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all_fn += fn
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# 计算最终指标
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precision = all_tp / (all_tp + all_fp) if (all_tp + all_fp) > 0 else 0
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recall = all_tp / (all_tp + all_fn) if (all_tp + all_fn) > 0 else 0
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f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
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return {'precision': precision, 'recall': recall, 'f1': f1}
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="评估 RoRD 模型性能")
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parser.add_argument('--model_path', type=str, default=config.MODEL_PATH)
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parser.add_argument('--val_dir', type=str, default=config.VAL_IMG_DIR)
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parser.add_argument('--annotations_dir', type=str, default=config.VAL_ANN_DIR)
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parser.add_argument('--templates_dir', type=str, default=config.TEMPLATE_DIR)
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args = parser.parse_args()
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model = RoRD().cuda()
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model.load_state_dict(torch.load(args.model_path))
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# (已修改) 不再需要预加载数据集，直接传入路径
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results = evaluate(model, args.val_dir, args.annotations_dir, args.templates_dir)
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print("\n--- 评估结果 ---")
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print(f" 精确率 (Precision): {results['precision']:.4f}")
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print(f" 召回率 (Recall): {results['recall']:.4f}")
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print(f" F1 分数 (F1 Score): {results['f1']:.4f}") |