228 lines
9.3 KiB
Python
228 lines
9.3 KiB
Python
# match.py
|
||
|
||
import argparse
|
||
import os
|
||
from pathlib import Path
|
||
|
||
import cv2
|
||
import numpy as np
|
||
import torch
|
||
import torch.nn.functional as F
|
||
from PIL import Image
|
||
|
||
from models.rord import RoRD
|
||
from utils.config_loader import load_config, to_absolute_path
|
||
from utils.data_utils import get_transform
|
||
|
||
# --- 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_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)
|
||
|
||
# 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, matching_cfg):
|
||
"""
|
||
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 = int(matching_cfg.inference_window_size)
|
||
stride = int(matching_cfg.inference_stride)
|
||
keypoint_threshold = float(matching_cfg.keypoint_threshold)
|
||
|
||
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, 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
|
||
|
||
# --- (已修改) 多尺度、多实例匹配主函数 ---
|
||
def match_template_multiscale(model, layout_image, template_image, transform, matching_cfg):
|
||
"""
|
||
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, matching_cfg)
|
||
|
||
min_inliers = int(matching_cfg.min_inliers)
|
||
if len(layout_kps) < min_inliers:
|
||
print("从大版图中提取的关键点过少，无法进行匹配。")
|
||
return []
|
||
|
||
found_instances = []
|
||
active_layout_mask = torch.ones(len(layout_kps), dtype=bool, device=layout_kps.device)
|
||
pyramid_scales = [float(s) for s in matching_cfg.pyramid_scales]
|
||
keypoint_threshold = float(matching_cfg.keypoint_threshold)
|
||
ransac_threshold = float(matching_cfg.ransac_reproj_threshold)
|
||
|
||
# 2. 多实例迭代检测
|
||
while True:
|
||
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) < min_inliers:
|
||
break
|
||
|
||
current_layout_kps = layout_kps[current_active_indices]
|
||
current_layout_descs = layout_descs[current_active_indices]
|
||
|
||
best_match_info = {'inliers': 0, 'H': None, 'src_pts': None, 'dst_pts': None, 'mask': None}
|
||
|
||
# 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 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, keypoint_threshold)
|
||
|
||
if len(template_kps) < 4: continue
|
||
|
||
# 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, ransac_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'] > 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
|
||
|
||
return found_instances
|
||
|
||
|
||
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']
|
||
cv2.rectangle(layout_img, (x, y), (x + w, y + h), (0, 255, 0), 2)
|
||
cv2.putText(layout_img, f"Match {i+1}", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
|
||
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 进行多尺度模板匹配")
|
||
parser.add_argument('--config', type=str, default="configs/base_config.yaml", help="YAML 配置文件路径")
|
||
parser.add_argument('--model_path', type=str, default=None, help="模型权重路径，若未提供则使用配置文件中的路径")
|
||
parser.add_argument('--layout', type=str, required=True)
|
||
parser.add_argument('--template', type=str, required=True)
|
||
parser.add_argument('--output', type=str)
|
||
args = parser.parse_args()
|
||
|
||
cfg = load_config(args.config)
|
||
config_dir = Path(args.config).resolve().parent
|
||
matching_cfg = cfg.matching
|
||
model_path = args.model_path or str(to_absolute_path(cfg.paths.model_path, config_dir))
|
||
|
||
transform = get_transform()
|
||
model = RoRD().cuda()
|
||
model.load_state_dict(torch.load(model_path))
|
||
model.eval()
|
||
|
||
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, matching_cfg)
|
||
|
||
print("\n检测到的边界框:")
|
||
for bbox in detected_bboxes:
|
||
print(bbox)
|
||
|
||
if args.output:
|
||
visualize_matches(args.layout, detected_bboxes, args.output) |