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add scale robust way

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Jiao77
2025-06-09 01:49:13 +08:00
parent 7cc1a5b8d2
commit 98f6709768
4 changed files with 254 additions and 110 deletions
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22
config.py
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@@ -3,29 +3,27 @@
# --- 训练参数 ---
LEARNING_RATE = 1e-4
BATCH_SIZE = 4
NUM_EPOCHS = 20 # 增加了训练轮数
NUM_EPOCHS = 20
PATCH_SIZE = 256
# (新增) 训练时尺度抖动范围
SCALE_JITTER_RANGE = (0.7, 1.5)
# --- 匹配与评估参数 ---
# 关键点检测的置信度阈值
KEYPOINT_THRESHOLD = 0.5
# RANSAC 重投影误差阈值（像素）
RANSAC_REPROJ_THRESHOLD = 5.0
# RANSAC 判定为有效匹配所需的最小内点数
MIN_INLIERS = 15 # 适当提高以增加匹配的可靠性
# IoU (Intersection over Union) 阈值，用于评估
MIN_INLIERS = 15
IOU_THRESHOLD = 0.5
# (新增) 推理时模板匹配的图像金字塔尺度
PYRAMID_SCALES = [0.75, 1.0, 1.5]
# (新增) 推理时处理大版图的滑动窗口参数
INFERENCE_WINDOW_SIZE = 1024
INFERENCE_STRIDE = 768 # 小于INFERENCE_WINDOW_SIZE以保证重叠
# --- 文件路径 ---
# 训练数据目录
# (路径保持不变, 请根据您的环境修改)
LAYOUT_DIR = 'path/to/layouts'
# 模型保存目录
SAVE_DIR = 'path/to/save'
# 验证集图像目录
VAL_IMG_DIR = 'path/to/val/images'
# 验证集标注目录
VAL_ANN_DIR = 'path/to/val/annotations'
# 模板图像目录
TEMPLATE_DIR = 'path/to/templates'
# 默认加载的模型路径
MODEL_PATH = 'path/to/save/model_final.pth'

82
evaluate.py
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@@ -10,7 +10,8 @@ import config
from models.rord import RoRD
from utils.data_utils import get_transform
from data.ic_dataset import ICLayoutDataset
from match import match_template_to_layout
# (已修改) 导入新的匹配函数
from match import match_template_multiscale
def compute_iou(box1, box2):
x1, y1, w1, h1 = box1['x'], box1['y'], box1['width'], box1['height']
@@ -21,45 +22,73 @@ def compute_iou(box1, box2):
union_area = w1 * h1 + w2 * h2 - inter_area
return inter_area / union_area if union_area > 0 else 0
def evaluate(model, val_dataset, template_dir):
# --- (已修改) 评估函数 ---
def evaluate(model, val_dataset_dir, val_annotations_dir, template_dir):
model.eval()
all_tp, all_fp, all_fn = 0, 0, 0
# 只需要一个统一的 transform 给匹配函数内部使用
transform = get_transform()
template_paths = [os.path.join(template_dir, f) for f in os.listdir(template_dir) if f.endswith('.png')]
layout_image_names = [f for f in os.listdir(val_dataset_dir) if f.endswith('.png')]
for layout_tensor, annotation in val_dataset:
layout_tensor = layout_tensor.unsqueeze(0).cuda()
gt_by_template = {box['template']: [] for box in annotation.get('boxes', [])}
# (已修改) 循环遍历验证集中的每个版图文件
for layout_name in layout_image_names:
print(f"\n正在评估版图: {layout_name}")
layout_path = os.path.join(val_dataset_dir, layout_name)
annotation_path = os.path.join(val_annotations_dir, layout_name.replace('.png', '.json'))
# 加载原始PIL图像，以支持滑动窗口
layout_image = Image.open(layout_path).convert('L')
# 加载标注信息
if not os.path.exists(annotation_path):
continue
with open(annotation_path, 'r') as f:
annotation = json.load(f)
# 按模板对真实标注进行分组
gt_by_template = {os.path.basename(box['template']): [] for box in annotation.get('boxes', [])}
for box in annotation.get('boxes', []):
gt_by_template[box['template']].append(box)
gt_by_template[os.path.basename(box['template'])].append(box)
# 遍历每个模板，在当前版图上进行匹配
for template_path in template_paths:
template_name = os.path.basename(template_path)
template_tensor = transform(Image.open(template_path).convert('L')).unsqueeze(0).cuda()
template_image = Image.open(template_path).convert('L')
# (已修改) 调用新的多尺度匹配函数
detected = match_template_multiscale(model, layout_image, template_image, transform)
detected = match_template_to_layout(model, layout_tensor, template_tensor)
gt_boxes = gt_by_template.get(template_name, [])
# 计算 TP, FP, FN (这部分逻辑不变)
matched_gt = [False] * len(gt_boxes)
tp = 0
for det_box in detected:
best_iou = 0
best_gt_idx = -1
for i, gt_box in enumerate(gt_boxes):
if matched_gt[i]: continue
iou = compute_iou(det_box, gt_box)
if iou > best_iou:
best_iou, best_gt_idx = iou, i
if best_iou > config.IOU_THRESHOLD:
tp += 1
matched_gt[best_gt_idx] = True
if len(detected) > 0:
for det_box in detected:
best_iou = 0
best_gt_idx = -1
for i, gt_box in enumerate(gt_boxes):
if matched_gt[i]: continue
iou = compute_iou(det_box, gt_box)
if iou > best_iou:
best_iou, best_gt_idx = iou, i
if best_iou > config.IOU_THRESHOLD:
if not matched_gt[best_gt_idx]:
tp += 1
matched_gt[best_gt_idx] = True
all_tp += tp
all_fp += len(detected) - tp
all_fn += len(gt_boxes) - tp
fp = len(detected) - tp
fn = len(gt_boxes) - tp
all_tp += tp
all_fp += fp
all_fn += fn
# 计算最终指标
precision = all_tp / (all_tp + all_fp) if (all_tp + all_fp) > 0 else 0
recall = all_tp / (all_tp + all_fn) if (all_tp + all_fn) > 0 else 0
f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
@@ -75,10 +104,11 @@ if __name__ == "__main__":
model = RoRD().cuda()
model.load_state_dict(torch.load(args.model_path))
val_dataset = ICLayoutDataset(args.val_dir, args.annotations_dir, get_transform())
results = evaluate(model, val_dataset, args.templates_dir)
print("评估结果：")
# (已修改) 不再需要预加载数据集，直接传入路径
results = evaluate(model, args.val_dir, args.annotations_dir, args.templates_dir)
print("\n--- 评估结果 ---")
print(f" 精确率 (Precision): {results['precision']:.4f}")
print(f" 召回率 (Recall): {results['recall']:.4f}")
print(f" F1 分数 (F1 Score): {results['f1']:.4f}")

199
match.py
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@@ -12,69 +12,174 @@ import config
from models.rord import RoRD
from utils.data_utils import get_transform
def extract_keypoints_and_descriptors(model, image, kp_thresh):
# --- 特征提取函数 (基本无变动) ---
def extract_keypoints_and_descriptors(model, image_tensor, kp_thresh):
with torch.no_grad():
detection_map, desc = model(image)
binary_map = (detection_map > kp_thresh).float()
coords = torch.nonzero(binary_map[0, 0]).float()
keypoints_input = coords[:, [1, 0]] * 8.0 # Stride of descriptor is 8
detection_map, desc = model(image_tensor)
device = detection_map.device
binary_map = (detection_map > kp_thresh).squeeze(0).squeeze(0)
coords = torch.nonzero(binary_map).float() # y, x
if len(coords) == 0:
return torch.tensor([], device=device), torch.tensor([], device=device)
descriptors = F.grid_sample(desc, coords.flip(1).view(1, -1, 1, 2) / torch.tensor([(desc.shape[3]-1)/2, (desc.shape[2]-1)/2], device=desc.device) - 1, align_corners=True).squeeze().T
descriptors = F.normalize(descriptors, p=2, dim=1)
return keypoints_input, descriptors
# 描述子采样
coords_for_grid = coords.flip(1).view(1, -1, 1, 2) # N, 2 -> 1, N, 1, 2 (x,y)
# 归一化到 [-1, 1]
coords_for_grid = coords_for_grid / torch.tensor([(desc.shape[3]-1)/2, (desc.shape[2]-1)/2], device=device) - 1
descriptors = F.grid_sample(desc, coords_for_grid, align_corners=True).squeeze().T
descriptors = F.normalize(descriptors, p=2, dim=1)
# 将关键点坐标从特征图尺度转换回图像尺度
# VGG到relu4_3的下采样率为8
keypoints = coords.flip(1) * 8.0 # x, y
return keypoints, descriptors
# --- (新增) 滑动窗口特征提取函数 ---
def extract_features_sliding_window(model, large_image, transform):
"""
使用滑动窗口从大图上提取所有关键点和描述子
"""
print("使用滑动窗口提取大版图特征...")
device = next(model.parameters()).device
W, H = large_image.size
window_size = config.INFERENCE_WINDOW_SIZE
stride = config.INFERENCE_STRIDE
all_kps = []
all_descs = []
for y in range(0, H, stride):
for x in range(0, W, stride):
# 确保窗口不越界
x_end = min(x + window_size, W)
y_end = min(y + window_size, H)
# 裁剪窗口
patch = large_image.crop((x, y, x_end, y_end))
# 预处理
patch_tensor = transform(patch).unsqueeze(0).to(device)
# 提取特征
kps, descs = extract_keypoints_and_descriptors(model, patch_tensor, config.KEYPOINT_THRESHOLD)
if len(kps) > 0:
# 将局部坐标转换为全局坐标
kps[:, 0] += x
kps[:, 1] += y
all_kps.append(kps)
all_descs.append(descs)
if not all_kps:
return torch.tensor([], device=device), torch.tensor([], device=device)
print(f"大版图特征提取完毕，共找到 {sum(len(k) for k in all_kps)} 个关键点。")
return torch.cat(all_kps, dim=0), torch.cat(all_descs, dim=0)
# --- 互近邻匹配 (无变动) ---
def mutual_nearest_neighbor(descs1, descs2):
if len(descs1) == 0 or len(descs2) == 0:
return torch.empty((0, 2), dtype=torch.int64)
sim = descs1 @ descs2.T
nn12 = torch.max(sim, dim=1)
nn21 = torch.max(sim, dim=0)
ids1 = torch.arange(0, sim.shape[0], device=sim.device)
mask = (ids1 == nn21.indices[nn12.indices])
matches = torch.stack([ids1[mask], nn12.indices[mask]], dim=1)
return matches.cpu().numpy()
return matches
def match_template_to_layout(model, layout_image, template_image):
layout_kps, layout_descs = extract_keypoints_and_descriptors(model, layout_image, config.KEYPOINT_THRESHOLD)
template_kps, template_descs = extract_keypoints_and_descriptors(model, template_image, config.KEYPOINT_THRESHOLD)
# --- (已修改) 多尺度、多实例匹配主函数 ---
def match_template_multiscale(model, layout_image, template_image, transform):
"""
在不同尺度下搜索模板，并检测多个实例
"""
# 1. 对大版图使用滑动窗口提取全部特征
layout_kps, layout_descs = extract_features_sliding_window(model, layout_image, transform)
if len(layout_kps) < config.MIN_INLIERS:
print("从大版图中提取的关键点过少，无法进行匹配。")
return []
active_layout_mask = torch.ones(len(layout_kps), dtype=bool, device=layout_kps.device)
found_instances = []
active_layout_mask = torch.ones(len(layout_kps), dtype=bool, device=layout_kps.device)
# 2. 多实例迭代检测
while True:
current_indices = torch.nonzero(active_layout_mask).squeeze(1)
if len(current_indices) < config.MIN_INLIERS:
current_active_indices = torch.nonzero(active_layout_mask).squeeze(1)
# 如果剩余活动关键点过少，则停止
if len(current_active_indices) < config.MIN_INLIERS:
break
current_layout_kps, current_layout_descs = layout_kps[current_indices], layout_descs[current_indices]
matches = mutual_nearest_neighbor(template_descs, current_layout_descs)
current_layout_kps = layout_kps[current_active_indices]
current_layout_descs = layout_descs[current_active_indices]
if len(matches) < 4: break
best_match_info = {'inliers': 0, 'H': None, 'src_pts': None, 'dst_pts': None, 'mask': None}
src_pts = template_kps[matches[:, 0]].cpu().numpy()
dst_pts = current_layout_kps[matches[:, 1]].cpu().numpy()
# 3. 图像金字塔：遍历模板的每个尺度
print("在新尺度下搜索模板...")
for scale in config.PYRAMID_SCALES:
W, H = template_image.size
new_W, new_H = int(W * scale), int(H * scale)
# 缩放模板
scaled_template = template_image.resize((new_W, new_H), Image.LANCZOS)
template_tensor = transform(scaled_template).unsqueeze(0).to(layout_kps.device)
# 提取缩放后模板的特征
template_kps, template_descs = extract_keypoints_and_descriptors(model, template_tensor, config.KEYPOINT_THRESHOLD)
if len(template_kps) < 4: continue
H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, config.RANSAC_REPROJ_THRESHOLD)
if H is None or mask.sum() < config.MIN_INLIERS:
# 匹配当前尺度的模板和活动状态的版图特征
matches = mutual_nearest_neighbor(template_descs, current_layout_descs)
if len(matches) < 4: continue
# RANSAC
# 注意：模板关键点坐标需要还原到原始尺寸，才能计算正确的H
src_pts = template_kps[matches[:, 0]].cpu().numpy() / scale
dst_pts_indices = current_active_indices[matches[:, 1]]
dst_pts = layout_kps[dst_pts_indices].cpu().numpy()
H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, config.RANSAC_REPROJ_THRESHOLD)
if H is not None and mask.sum() > best_match_info['inliers']:
best_match_info = {'inliers': mask.sum(), 'H': H, 'mask': mask, 'scale': scale, 'dst_pts': dst_pts}
# 4. 如果在所有尺度中找到了最佳匹配，则记录并屏蔽
if best_match_info['inliers'] > config.MIN_INLIERS:
print(f"找到一个匹配实例！内点数: {best_match_info['inliers']}, 使用的模板尺度: {best_match_info['scale']:.2f}x")
inlier_mask = best_match_info['mask'].ravel().astype(bool)
inlier_layout_kps = best_match_info['dst_pts'][inlier_mask]
x_min, y_min = inlier_layout_kps.min(axis=0)
x_max, y_max = inlier_layout_kps.max(axis=0)
instance = {'x': int(x_min), 'y': int(y_min), 'width': int(x_max - x_min), 'height': int(y_max - y_min), 'homography': best_match_info['H']}
found_instances.append(instance)
# 屏蔽已匹配区域的关键点，以便检测下一个实例
kp_x, kp_y = layout_kps[:, 0], layout_kps[:, 1]
region_mask = (kp_x >= x_min) & (kp_x <= x_max) & (kp_y >= y_min) & (kp_y <= y_max)
active_layout_mask[region_mask] = False
print(f"剩余活动关键点: {active_layout_mask.sum()}")
else:
# 如果在所有尺度下都找不到好的匹配，则结束搜索
print("在所有尺度下均未找到新的匹配实例，搜索结束。")
break
inlier_mask = mask.ravel().astype(bool)
# 区域屏蔽逻辑
inlier_layout_kps = dst_pts[inlier_mask]
x_min, y_min = inlier_layout_kps.min(axis=0)
x_max, y_max = inlier_layout_kps.max(axis=0)
instance = {'x': int(x_min), 'y': int(y_min), 'width': int(x_max - x_min), 'height': int(y_max - y_min), 'homography': H}
found_instances.append(instance)
kp_x, kp_y = layout_kps[:, 0], layout_kps[:, 1]
region_mask = (kp_x >= x_min) & (kp_x <= x_max) & (kp_y >= y_min) & (kp_y <= y_max)
active_layout_mask[region_mask] = False
print(f"找到实例，内点数: {mask.sum()}。剩余活动关键点: {active_layout_mask.sum()}")
return found_instances
def visualize_matches(layout_path, template_path, bboxes, output_path):
def visualize_matches(layout_path, bboxes, output_path):
layout_img = cv2.imread(layout_path)
for i, bbox in enumerate(bboxes):
x, y, w, h = bbox['x'], bbox['y'], bbox['width'], bbox['height']
@@ -83,8 +188,9 @@ def visualize_matches(layout_path, template_path, bboxes, output_path):
cv2.imwrite(output_path, layout_img)
print(f"可视化结果已保存至: {output_path}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="使用 RoRD 进行模板匹配")
parser = argparse.ArgumentParser(description="使用 RoRD 进行多尺度模板匹配")
parser.add_argument('--model_path', type=str, default=config.MODEL_PATH)
parser.add_argument('--layout', type=str, required=True)
parser.add_argument('--template', type=str, required=True)
@@ -96,13 +202,14 @@ if __name__ == "__main__":
model.load_state_dict(torch.load(args.model_path))
model.eval()
layout_tensor = transform(Image.open(args.layout).convert('L')).unsqueeze(0).cuda()
template_tensor = transform(Image.open(args.template).convert('L')).unsqueeze(0).cuda()
detected_bboxes = match_template_to_layout(model, layout_tensor, template_tensor)
layout_image = Image.open(args.layout).convert('L')
template_image = Image.open(args.template).convert('L')
detected_bboxes = match_template_multiscale(model, layout_image, template_image, transform)
print("\n检测到的边界框:")
for bbox in detected_bboxes:
print(bbox)
if args.output:
visualize_matches(args.layout, args.template, detected_bboxes, args.output)
visualize_matches(args.layout, detected_bboxes, args.output)

61
train.py
View File

@@ -15,13 +15,14 @@ import config
from models.rord import RoRD
from utils.data_utils import get_transform
# --- 训练专用数据集类 ---
# --- (已修改) 训练专用数据集类 ---
class ICLayoutTrainingDataset(Dataset):
def __init__(self, image_dir, patch_size=256, transform=None):
def __init__(self, image_dir, patch_size=256, transform=None, scale_range=(1.0, 1.0)):
self.image_dir = image_dir
self.image_paths = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith('.png')]
self.patch_size = patch_size
self.transform = transform
self.scale_range = scale_range # 新增尺度范围参数
def __len__(self):
return len(self.image_paths)
@@ -29,14 +30,30 @@ class ICLayoutTrainingDataset(Dataset):
def __getitem__(self, index):
img_path = self.image_paths[index]
image = Image.open(img_path).convert('L')
W, H = image.size
x = np.random.randint(0, W - self.patch_size + 1)
y = np.random.randint(0, H - self.patch_size + 1)
patch = image.crop((x, y, x + self.patch_size, y + self.patch_size))
# --- 新增：尺度抖动数据增强 ---
# 1. 随机选择一个缩放比例
scale = np.random.uniform(self.scale_range[0], self.scale_range[1])
# 2. 根据缩放比例计算需要从原图裁剪的尺寸
crop_size = int(self.patch_size / scale)
# 确保裁剪尺寸不超过图像边界
if crop_size > min(W, H):
crop_size = min(W, H)
# 3. 随机裁剪
x = np.random.randint(0, W - crop_size + 1)
y = np.random.randint(0, H - crop_size + 1)
patch = image.crop((x, y, x + crop_size, y + crop_size))
# 4. 将裁剪出的图像块缩放回标准的 patch_size
patch = patch.resize((self.patch_size, self.patch_size), Image.LANCZOS)
# --- 尺度抖动结束 ---
patch_np = np.array(patch)
# 实现8个方向的离散几何变换
# 实现8个方向的离散几何变换 (这部分逻辑不变)
theta_deg = np.random.choice([0, 90, 180, 270])
is_mirrored = np.random.choice([True, False])
cx, cy = self.patch_size / 2.0, self.patch_size / 2.0
@@ -59,10 +76,10 @@ class ICLayoutTrainingDataset(Dataset):
patch = self.transform(patch)
transformed_patch = self.transform(transformed_patch)
H_tensor = torch.from_numpy(H[:2, :]).float() # 通常损失函数需要2x3的仿射矩阵
H_tensor = torch.from_numpy(H[:2, :]).float()
return patch, transformed_patch, H_tensor
# --- 特征图变换与损失函数 ---
# --- 特征图变换与损失函数 (无变动) ---
def warp_feature_map(feature_map, H_inv):
B, C, H, W = feature_map.size()
grid = F.affine_grid(H_inv, feature_map.size(), align_corners=False).to(feature_map.device)
@@ -77,34 +94,29 @@ def compute_detection_loss(det_original, det_rotated, H):
def compute_description_loss(desc_original, desc_rotated, H, margin=1.0):
B, C, H_feat, W_feat = desc_original.size()
num_samples = 100
# 随机采样锚点坐标
coords = torch.rand(B, num_samples, 2, device=desc_original.device) * 2 - 1 # [-1, 1]
# 提取锚点描述子
coords = torch.rand(B, num_samples, 2, device=desc_original.device) * 2 - 1
anchor = F.grid_sample(desc_original, coords.unsqueeze(1), align_corners=False).squeeze(2).transpose(1, 2)
# 计算正样本坐标
coords_hom = torch.cat([coords, torch.ones(B, num_samples, 1, device=coords.device)], dim=2)
M_inv = torch.inverse(torch.cat([H, torch.tensor([0.0, 0.0, 1.0]).view(1, 1, 3).repeat(H.shape[0], 1, 1)], dim=1))
coords_transformed = (coords_hom @ M_inv.transpose(1, 2))[:, :, :2]
# 提取正样本描述子
positive = F.grid_sample(desc_rotated, coords_transformed.unsqueeze(1), align_corners=False).squeeze(2).transpose(1, 2)
# 随机采样负样本
neg_coords = torch.rand(B, num_samples, 2, device=desc_original.device) * 2 - 1
negative = F.grid_sample(desc_rotated, neg_coords.unsqueeze(1), align_corners=False).squeeze(2).transpose(1, 2)
triplet_loss = nn.TripletMarginLoss(margin=margin, p=2)
return triplet_loss(anchor, positive, negative)
# --- 主函数与命令行接口 ---
# --- (已修改) 主函数与命令行接口 ---
def main(args):
print("--- 开始训练 RoRD 模型 ---")
print(f"训练参数: Epochs={args.epochs}, Batch Size={args.batch_size}, LR={args.lr}")
transform = get_transform()
dataset = ICLayoutTrainingDataset(args.data_dir, patch_size=config.PATCH_SIZE, transform=transform)
# 在数据集初始化时传入尺度抖动范围
dataset = ICLayoutTrainingDataset(
args.data_dir,
patch_size=config.PATCH_SIZE,
transform=transform,
scale_range=config.SCALE_JITTER_RANGE
)
dataloader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True, num_workers=4)
model = RoRD().cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
@@ -116,14 +128,11 @@ def main(args):
original, rotated, H = original.cuda(), rotated.cuda(), H.cuda()
det_original, desc_original = model(original)
det_rotated, desc_rotated = model(rotated)
loss = compute_detection_loss(det_original, det_rotated, H) + compute_description_loss(desc_original, desc_rotated, H)
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss_val += loss.item()
print(f"--- Epoch {epoch+1} 完成, 平均 Loss: {total_loss_val / len(dataloader):.4f} ---")
if not os.path.exists(args.save_dir):