基于目标检测结果的特定类别图像与XML文件快速分析方法

### 1. 提取特定类别的图像和XML文件

为了从大量数据中提取特定类别的图像和对应的XML标注文件，可以按照以下步骤操作：

```python
import os
from shutil import copy
from lxml import etree

# 定义源文件夹和目标文件夹路径
origin_folder = "/Data02/decoded_det_seg/det_img/val"
dst_folder = "/home/xxx/datasets/val_sub_2"
class_sub = 'traffic_lights'

# 创建目标文件夹
os.makedirs(dst_folder, exist_ok=True)
os.makedirs(os.path.join(dst_folder, class_sub, 'images'), exist_ok=True)
os.makedirs(os.path.join(dst_folder, class_sub, 'xmls'), exist_ok=True)

# 解析XML文件并提取特定类别的图像和XML
for file in os.listdir(os.path.join(origin_folder, 'xmls_15')):
img_file = file.replace('xml', 'jpg')
src_xml_path = os.path.join(origin_folder, 'xmls_15', file)
src_img_path = os.path.join(origin_folder, 'images', img_file)
dst_xml_path = os.path.join(dst_folder, class_sub, 'xmls', file)
dst_img_path = os.path.join(dst_folder, class_sub, 'images', img_file)

# 解析XML文件
with open(src_xml_path) as fid:
xml_str = fid.read().encode('utf-8')
xml = etree.fromstring(xml_str)
data = parse_xml_to_dict(xml)['annotation']

if 'object' in data.keys():
for obj in data['object']:
if obj['name'] == class_sub:
copy(src_img_path, dst_img_path)
copy(src_xml_path, dst_xml_path)
break

# XML解析函数
def parse_xml_to_dict(xml):
"""将XML文件解析成字典形式
Args:
xml: 使用lxml.etree解析的XML树
Returns:
包含XML内容的Python字典
"""
if len(xml) == 0:
return {xml.tag: xml.text}
result = {}
for child in xml:
child_result = parse_xml_to_dict(child)
if child.tag != 'object':
result[child.tag] = child_result[child.tag]
else:
if child.tag not in result:
result[child.tag] = []
result[child.tag].append(child_result[child.tag])
return {xml.tag: result}
```

### 2. 分析特定类别的预测结果

在提取了特定类别的图像和XML文件后，可以进一步分析模型对该类别的预测结果。以下是代码示例：

```python
target_idx = class_names.index('traffic_lights')
all_box = []

for label, dets in enumerate(detections):
if label != target_idx:
continue
for bbox in dets:
score = bbox[-1]
if score > score_thresh:
x0, y0, x1, y1 = [int(i) for i in bbox[:4]]
all_box.append([label, x0, y0, x1, y1, score])
```

### 3. 可视化特定类别的标注框

为了更直观地展示特定类别的标注框，可以在图像上叠加这些框。以下是代码示例：

```python
import cv2 as cv

for file in os.listdir(os.path.join(origin_folder, 'xml_tmp')):
file_path = os.path.join(origin_folder, 'xml_tmp', file)
img_path = os.path.join(origin_folder, 'img_tmp', file.replace('xml', 'jpg'))
img = cv.imread(img_path)

with open(file_path) as fid:
xml_str = fid.read().encode('utf-8')
xml = etree.fromstring(xml_str)
data = parse_xml_to_dict(xml)['annotation']

if 'object' in data.keys():
for obj in data['object']:
if obj['name'] == class_sub:
xmin = int(float(obj['bndbox']['xmin']))
xmax = int(float(obj['bndbox']['xmax']))
ymin = int(float(obj['bndbox']['ymin']))
ymax = int(float(obj['bndbox']['ymax']))
cv.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 1)

cv.imwrite(img_path, img)
```

如图所示，预测结果用黄色框表示，而XML标注的真实边界框用红色框表示。