增加cvat反向上传
This commit is contained in:
琉璃月光
122
YOLO—detect/annotations.xml
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122
YOLO—detect/annotations.xml
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<?xml version="1.0" encoding="utf-8"?>
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<annotations>
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<version>1.1</version>
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<meta>
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<task>
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<id>258</id>
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<name>12.161</name>
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<size>27</size>
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<mode>annotation</mode>
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<overlap>0</overlap>
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<bugtracker></bugtracker>
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<created>2025-12-16 02:07:59.378104+00:00</created>
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<updated>2025-12-16 02:14:18.382442+00:00</updated>
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<subset>default</subset>
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<start_frame>0</start_frame>
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<stop_frame>26</stop_frame>
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<frame_filter></frame_filter>
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<segments>
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<segment>
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<id>176</id>
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<start>0</start>
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<stop>26</stop>
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<url>http://www.xj-robot.com:9000/api/jobs/176</url>
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</segment>
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</segments>
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<owner>
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<username>huangxin</username>
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<email>2193534909@qq.com</email>
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</owner>
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<assignee></assignee>
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<labels>
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<label>
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<name>hole</name>
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<color>#335b66</color>
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<type>any</type>
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<attributes>
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</attributes>
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</label>
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<label>
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<name>crack</name>
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<color>#974b72</color>
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<type>any</type>
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<attributes>
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</attributes>
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</label>
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</labels>
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</task>
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<dumped>2025-12-16 02:14:39.603909+00:00</dumped>
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</meta>
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<image id="0" name="1.jpg" width="1279" height="1706">
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<box label="crack" source="manual" occluded="0" xtl="360.20" ytl="567.60" xbr="1210.70" ybr="985.80" z_order="0">
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</box>
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</image>
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<image id="1" name="2.jpg" width="1279" height="1706">
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<box label="hole" source="manual" occluded="0" xtl="350.15" ytl="229.28" xbr="481.67" ybr="420.92" z_order="0">
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</box>
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<box label="hole" source="manual" occluded="0" xtl="419.68" ytl="724.60" xbr="942.00" ybr="1046.50" z_order="0">
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</box>
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</image>
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<image id="2" name="3.jpg" width="1706" height="1279">
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<box label="hole" source="manual" occluded="0" xtl="960.40" ytl="391.70" xbr="1326.90" ybr="515.40" z_order="0">
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</box>
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</image>
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<image id="3" name="4.jpg" width="1706" height="1279">
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<box label="hole" source="manual" occluded="0" xtl="427.10" ytl="314.70" xbr="1401.10" ybr="1074.20" z_order="0">
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</box>
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</image>
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<image id="4" name="微信图片_20251216095823_205.jpg" width="1279" height="1706">
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</image>
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<image id="5" name="微信图片_20251216095823_206.jpg" width="1706" height="1279">
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</image>
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<image id="6" name="微信图片_20251216095823_207.jpg" width="1279" height="1706">
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</image>
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<image id="7" name="微信图片_20251216095823_208.jpg" width="1706" height="1279">
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</image>
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<image id="8" name="微信图片_20251216095823_209.jpg" width="1706" height="1279">
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</image>
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<image id="9" name="微信图片_20251216095823_210.jpg" width="1279" height="1706">
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</image>
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<image id="10" name="微信图片_20251216095823_211.jpg" width="1279" height="1706">
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</image>
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<image id="11" name="微信图片_20251216095823_212.jpg" width="1279" height="1706">
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</image>
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<image id="12" name="微信图片_20251216095823_213.jpg" width="1279" height="1706">
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</image>
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<image id="13" name="微信图片_20251216095823_214.jpg" width="1279" height="1706">
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<box label="hole" source="manual" occluded="0" xtl="352.04" ytl="150.90" xbr="910.70" ybr="1274.50" z_order="0">
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</box>
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</image>
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<image id="14" name="微信图片_20251216095823_215.jpg" width="1279" height="1706">
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</image>
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<image id="15" name="微信图片_20251216095823_216.jpg" width="1706" height="1279">
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</image>
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<image id="16" name="微信图片_20251216095823_217.jpg" width="1279" height="1706">
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</image>
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<image id="17" name="微信图片_20251216095823_218.jpg" width="1279" height="1706">
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</image>
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<image id="18" name="微信图片_20251216095823_219.jpg" width="1706" height="1279">
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<box label="crack" source="manual" occluded="0" xtl="1121.12" ytl="110.31" xbr="1207.51" ybr="489.69" z_order="0">
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</box>
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</image>
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<image id="19" name="微信图片_20251216095823_220.jpg" width="1279" height="1706">
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</image>
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<image id="20" name="微信图片_20251216095823_221.jpg" width="1279" height="1706">
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</image>
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<image id="21" name="微信图片_20251216095823_222.jpg" width="1279" height="1706">
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<box label="crack" source="manual" occluded="0" xtl="927.58" ytl="905.66" xbr="1091.67" ybr="999.61" z_order="0">
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</box>
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</image>
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<image id="22" name="微信图片_20251216095823_223.jpg" width="1279" height="1706">
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</image>
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<image id="23" name="微信图片_20251216095823_224.jpg" width="1279" height="1706">
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</image>
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<image id="24" name="微信图片_20251216095823_225.jpg" width="1279" height="1706">
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</image>
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<image id="25" name="微信图片_20251216095823_226.jpg" width="1279" height="1706">
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</image>
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<image id="26" name="微信图片_20251216095823_227.jpg" width="1279" height="1706">
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<box label="hole" source="manual" occluded="0" xtl="605.10" ytl="583.04" xbr="744.10" ybr="678.24" z_order="0">
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</box>
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</image>
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</annotations>
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100
YOLO—detect/cvattodetect.py
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100
YOLO—detect/cvattodetect.py
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import os
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import xml.etree.ElementTree as ET
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from pathlib import Path
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def cvat_to_yolo_detect(xml_path, output_dir, class_name_to_id=None):
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"""
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将 CVAT 导出的 XML（目标检测模式）转换为 YOLO Detect 格式
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Args:
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xml_path (str): CVAT 导出的 XML 文件路径
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output_dir (str): 输出 .txt 标注文件的目录
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class_name_to_id (dict, optional): 类别名到 ID 的映射。
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如果为 None，则自动从 XML 的 <labels> 中按顺序分配（0,1,2...）
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"""
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output_dir = Path(output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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tree = ET.parse(xml_path)
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root = tree.getroot()
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# 自动提取类别顺序（如果未提供映射）
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if class_name_to_id is None:
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class_name_to_id = {}
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labels_elem = root.find(".//labels")
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if labels_elem is not None:
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for idx, label in enumerate(labels_elem.findall("label")):
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name = label.find("name").text
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class_name_to_id[name] = idx
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else:
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print("⚠️ 未找到 <labels>，请手动提供 class_name_to_id")
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print(f"类别映射: {class_name_to_id}")
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# 遍历所有 <image>
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for image in root.findall("image"):
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img_name = image.get("name")
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width = int(image.get("width"))
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height = int(image.get("height"))
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# 构建 .txt 文件名（去掉扩展名）
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stem = Path(img_name).stem
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txt_path = output_dir / f"{stem}.txt"
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boxes = []
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for box in image.findall("box"):
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label = box.get("label")
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if label not in class_name_to_id:
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print(f"⚠️ 未知类别 '{label}'，跳过（图片: {img_name}）")
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continue
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class_id = class_name_to_id[label]
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xtl = float(box.get("xtl"))
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ytl = float(box.get("ytl"))
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xbr = float(box.get("xbr"))
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ybr = float(box.get("ybr"))
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# 转为 YOLO 格式（归一化）
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x_center = (xtl + xbr) / 2 / width
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y_center = (ytl + ybr) / 2 / height
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w = (xbr - xtl) / width
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h = (ybr - ytl) / height
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# 限制在 [0,1]（防止因标注误差越界）
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x_center = max(0.0, min(1.0, x_center))
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y_center = max(0.0, min(1.0, y_center))
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w = max(0.0, min(1.0, w))
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h = max(0.0, min(1.0, h))
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boxes.append(f"{class_id} {x_center:.6f} {y_center:.6f} {w:.6f} {h:.6f}")
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# 写入 .txt 文件（即使无框也创建空文件）
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with open(txt_path, "w") as f:
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f.write("\n".join(boxes))
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print(f"✅ {img_name} → {len(boxes)} 个目标")
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print(f"\n🎉 转换完成！YOLO 标注已保存至: {output_dir}")
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if __name__ == "__main__":
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# ====== 配置区 ======
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XML_PATH = "annotations.xml" # 替换为你的 CVAT XML 路径
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OUTPUT_LABELS_DIR = "labels" # 输出的 YOLO .txt 目录
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# 方式1：自动从 XML 提取类别（推荐）
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CLASS_MAP = None
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# 方式2：手动指定（确保与训练时一致）
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# CLASS_MAP = {
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# "hole": 0,
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# "crack": 1
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# }
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# ====== 执行转换 ======
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cvat_to_yolo_detect(
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xml_path=XML_PATH,
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output_dir=OUTPUT_LABELS_DIR,
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class_name_to_id=CLASS_MAP
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)
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@ -98,8 +98,8 @@ if __name__ == "__main__":
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# 修改为你自己的路径
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# 修改为你自己的路径
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#TRAIN_DIR = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/cls-new/19cc/train"
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#TRAIN_DIR = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/cls-new/19cc/train"
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#VAL_DIR = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/cls-new/19cc/val"
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#VAL_DIR = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/cls-new/19cc/val"
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TRAIN_DIR = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/11c/train"
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TRAIN_DIR = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/point2/train"
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VAL_DIR = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/11c/val"
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VAL_DIR = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/point2/val1"
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split_train_to_val(
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split_train_to_val(
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train_dir=TRAIN_DIR,
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train_dir=TRAIN_DIR,
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val_dir=VAL_DIR,
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val_dir=VAL_DIR,
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109
tool/divid_val1.py
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109
tool/divid_val1.py
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import os
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import shutil
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import random
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from pathlib import Path
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def split_train_to_val(train_dir, val_dir, ratio=0.1, seed=42):
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"""
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从 train_dir 随机抽取 ratio 比例的 **带标签图像** 到 val_dir。
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自动判断是分类结构（有子文件夹）还是平铺结构（无子文件夹）。
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Args:
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train_dir (str): 训练集路径
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val_dir (str): 验证集路径（会自动创建）
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ratio (float): 抽取比例，如 0.1 表示 10%
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seed (int): 随机种子，保证可复现
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"""
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train_path = Path(train_dir)
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val_path = Path(val_dir)
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if not train_path.exists():
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raise FileNotFoundError(f"训练目录不存在: {train_path}")
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# 设置随机种子
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random.seed(seed)
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# 获取所有一级子项
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items = [p for p in train_path.iterdir()]
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# 判断是否为分类结构：所有子项都是目录且非空
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is_classification = all(p.is_dir() for p in items) and len(items) > 0
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# 定义图像扩展名
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IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.gif'}
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if is_classification:
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print("📁 检测到分类结构（含类别子文件夹）")
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for class_dir in items:
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class_name = class_dir.name
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src_class_dir = train_path / class_name
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dst_class_dir = val_path / class_name
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dst_class_dir.mkdir(parents=True, exist_ok=True)
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# 只找图像文件
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image_files = [
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f for f in src_class_dir.iterdir()
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if f.is_file() and f.suffix.lower() in IMG_EXTENSIONS
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]
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if not image_files:
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print(f" ⚠️ 类别 '{class_name}' 中无图像文件，跳过")
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continue
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num_val = max(1, int(len(image_files) * ratio))
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val_images = random.sample(image_files, num_val)
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for img in val_images:
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# 移动图像
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shutil.move(str(img), str(dst_class_dir / img.name))
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# 移动同名 .txt 标签
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txt_file = img.with_suffix('.txt')
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if txt_file.exists():
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shutil.move(str(txt_file), str(dst_class_dir / txt_file.name))
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print(f" ✅ 类别 '{class_name}': {len(val_images)} 张图像已移至 val")
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else:
|
||||||
|
print("📄 检测到平铺结构（YOLO格式：图像 + 同名 .txt 标签）")
|
||||||
|
val_path.mkdir(parents=True, exist_ok=True)
|
||||||
|
|
||||||
|
# 只收集图像文件（作为采样单元）
|
||||||
|
image_files = [
|
||||||
|
f for f in train_path.iterdir()
|
||||||
|
if f.is_file() and f.suffix.lower() in IMG_EXTENSIONS
|
||||||
|
]
|
||||||
|
|
||||||
|
if not image_files:
|
||||||
|
print("⚠️ 训练目录中未找到任何图像文件（支持格式: jpg, png 等）")
|
||||||
|
return
|
||||||
|
|
||||||
|
# 随机抽取图像
|
||||||
|
num_val = max(1, int(len(image_files) * ratio))
|
||||||
|
val_images = random.sample(image_files, num_val)
|
||||||
|
|
||||||
|
# 移动选中的图像及其标签
|
||||||
|
for img in val_images:
|
||||||
|
# 移动图像
|
||||||
|
shutil.move(str(img), str(val_path / img.name))
|
||||||
|
# 移动同名 .txt
|
||||||
|
txt_file = img.with_suffix('.txt')
|
||||||
|
if txt_file.exists():
|
||||||
|
shutil.move(str(txt_file), str(val_path / txt_file.name))
|
||||||
|
|
||||||
|
print(f"✅ 平铺结构: 已移动 {len(val_images)} 张图像及其标签到 {val_path}")
|
||||||
|
|
||||||
|
print(f"\n🎉 分割完成！验证集已保存至: {val_path}")
|
||||||
|
|
||||||
|
|
||||||
|
# ======================
|
||||||
|
# 使用示例
|
||||||
|
# ======================
|
||||||
|
if __name__ == "__main__":
|
||||||
|
TRAIN_DIR = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/point2/train"
|
||||||
|
VAL_DIR = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/point2/val1"
|
||||||
|
split_train_to_val(
|
||||||
|
train_dir=TRAIN_DIR,
|
||||||
|
val_dir=VAL_DIR,
|
||||||
|
ratio=0.1,
|
||||||
|
seed=42
|
||||||
|
)
|
||||||
117
tool/save_bigangle.py
Normal file
117
tool/save_bigangle.py
Normal file
@ -0,0 +1,117 @@
|
|||||||
|
import cv2
|
||||||
|
import os
|
||||||
|
import shutil
|
||||||
|
import numpy as np
|
||||||
|
from ultralytics import YOLO
|
||||||
|
|
||||||
|
IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff', '.webp'}
|
||||||
|
|
||||||
|
|
||||||
|
def process_obb_images(
|
||||||
|
model_path,
|
||||||
|
image_dir,
|
||||||
|
large_angle_output_dir="./large_angle_images", # 新增：大角度图像保存目录
|
||||||
|
conf_thresh=0.15,
|
||||||
|
imgsz=640,
|
||||||
|
angle_threshold_deg=10.0 # 夹角阈值：超过即视为“大角度”
|
||||||
|
):
|
||||||
|
"""
|
||||||
|
批量处理 OBB 图像，若任意两个目标的主方向夹角 > angle_threshold_deg，
|
||||||
|
则将原图移动到 large_angle_output_dir。
|
||||||
|
"""
|
||||||
|
os.makedirs(large_angle_output_dir, exist_ok=True)
|
||||||
|
results_dict = {}
|
||||||
|
|
||||||
|
print("加载 YOLO 模型...")
|
||||||
|
model = YOLO(model_path)
|
||||||
|
print("✅ 模型加载完成")
|
||||||
|
|
||||||
|
# 获取图像文件
|
||||||
|
image_files = [f for f in os.listdir(image_dir) if os.path.splitext(f.lower())[1] in IMG_EXTENSIONS]
|
||||||
|
if not image_files:
|
||||||
|
print(f"❌ 未找到图像文件：{image_dir}")
|
||||||
|
return results_dict
|
||||||
|
|
||||||
|
print(f"发现 {len(image_files)} 张图像待处理")
|
||||||
|
|
||||||
|
for img_filename in image_files:
|
||||||
|
img_path = os.path.join(image_dir, img_filename)
|
||||||
|
print(f"\n正在处理：{img_filename}")
|
||||||
|
|
||||||
|
img = cv2.imread(img_path)
|
||||||
|
if img is None:
|
||||||
|
print(f"❌ 跳过：无法读取图像 {img_path}")
|
||||||
|
continue
|
||||||
|
|
||||||
|
# 推理 OBB
|
||||||
|
results = model(img, save=False, imgsz=imgsz, conf=conf_thresh, mode='obb')
|
||||||
|
result = results[0]
|
||||||
|
|
||||||
|
# 提取旋转角
|
||||||
|
boxes = result.obb
|
||||||
|
angles_deg = []
|
||||||
|
has_large_angle = False # 标记是否有大角度的目标
|
||||||
|
if boxes is None or len(boxes) == 0:
|
||||||
|
print("❌ 该图像中未检测到任何目标")
|
||||||
|
else:
|
||||||
|
for i, box in enumerate(boxes):
|
||||||
|
cls = int(box.cls.cpu().numpy()[0])
|
||||||
|
conf = box.conf.cpu().numpy()[0]
|
||||||
|
cx, cy, w, h, r_rad = box.xywhr.cpu().numpy()[0]
|
||||||
|
direction = r_rad if w >= h else r_rad + np.pi / 2
|
||||||
|
direction = direction % np.pi
|
||||||
|
angle_deg = np.degrees(direction)
|
||||||
|
angles_deg.append(angle_deg)
|
||||||
|
print(f" Box {i + 1}: Class={cls}, Conf={conf:.3f}, 主方向={angle_deg:.2f}°")
|
||||||
|
|
||||||
|
# 计算两两夹角
|
||||||
|
pairwise_angles_deg = []
|
||||||
|
if len(angles_deg) >= 2:
|
||||||
|
for i in range(len(angles_deg)):
|
||||||
|
for j in range(i + 1, len(angles_deg)):
|
||||||
|
diff_rad = abs(np.radians(angles_deg[i]) - np.radians(angles_deg[j]))
|
||||||
|
min_diff_rad = min(diff_rad, np.pi - diff_rad)
|
||||||
|
angle_diff_deg = np.degrees(min_diff_rad)
|
||||||
|
pairwise_angles_deg.append(angle_diff_deg)
|
||||||
|
print(f" Box {i + 1} 与 Box {j + 1} 夹角: {angle_diff_deg:.2f}°")
|
||||||
|
if angle_diff_deg > angle_threshold_deg:
|
||||||
|
has_large_angle = True
|
||||||
|
elif len(angles_deg) == 1:
|
||||||
|
print(" 仅检测到一个目标，无法计算夹角")
|
||||||
|
|
||||||
|
# 保存结果
|
||||||
|
results_dict[img_filename] = {
|
||||||
|
"angles_deg": angles_deg,
|
||||||
|
"pairwise_angles_deg": pairwise_angles_deg,
|
||||||
|
"has_large_angle": has_large_angle
|
||||||
|
}
|
||||||
|
|
||||||
|
# 如果存在大角度，移动原图到新文件夹
|
||||||
|
if has_large_angle:
|
||||||
|
print(f"发现夹角 > {angle_threshold_deg}°，移动原图到大角度文件夹")
|
||||||
|
shutil.move(img_path, os.path.join(large_angle_output_dir, img_filename))
|
||||||
|
|
||||||
|
print(f"\n所有图像处理完成！大角度图像已移动至: {large_angle_output_dir}")
|
||||||
|
return results_dict
|
||||||
|
|
||||||
|
|
||||||
|
# ------------------- 测试调用 -------------------
|
||||||
|
if __name__ == "__main__":
|
||||||
|
MODEL_PATH = r'/home/hx/yolo/ultralytics_yolo11-main/runs/train/exp_obb_new3/weights/best.pt'
|
||||||
|
IMAGE_SOURCE_DIR = r"/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/ready"
|
||||||
|
LARGE_ANGLE_DIR = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/large_angle_images" # 新增输出目录
|
||||||
|
|
||||||
|
results = process_obb_images(
|
||||||
|
model_path=MODEL_PATH,
|
||||||
|
image_dir=IMAGE_SOURCE_DIR,
|
||||||
|
large_angle_output_dir=LARGE_ANGLE_DIR,
|
||||||
|
conf_thresh=0.15,
|
||||||
|
imgsz=640,
|
||||||
|
angle_threshold_deg=10.0
|
||||||
|
)
|
||||||
|
|
||||||
|
# 可选：打印大角度图像
|
||||||
|
large_angle_imgs = [name for name, info in results.items() if info["has_large_angle"]]
|
||||||
|
print(f"\n共 {len(large_angle_imgs)} 张图像包含 >10° 的夹角：")
|
||||||
|
for name in large_angle_imgs:
|
||||||
|
print(f" - {name}")
|
||||||
19
ultralytics_yolo11-main/train_xiantiao_detect.py
Normal file
19
ultralytics_yolo11-main/train_xiantiao_detect.py
Normal file
@ -0,0 +1,19 @@
|
|||||||
|
from ultralytics import YOLO
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
#model = YOLO(r'/home/hx/yolo/ultralytics_yolo11-main/ultralytics/cfg/models/11/yolo11-obb.yaml')
|
||||||
|
model = YOLO(r'/home/hx/yolo/ultralytics_yolo11-main/ultralytics/cfg/models/11/yolo11.yaml')
|
||||||
|
results = model.train(
|
||||||
|
data='xiantiaodata.yaml',
|
||||||
|
epochs=2000,
|
||||||
|
imgsz=640,
|
||||||
|
batch=4,
|
||||||
|
workers=10,
|
||||||
|
device='0',
|
||||||
|
project='runs/train',
|
||||||
|
name='exp_detect',
|
||||||
|
exist_ok=False,
|
||||||
|
optimizer='AdamW',
|
||||||
|
lr0=0.0005,
|
||||||
|
patience=0,
|
||||||
|
)
|
||||||
@ -2,7 +2,7 @@
|
|||||||
# YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
# YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||||
|
|
||||||
# Parameters
|
# Parameters
|
||||||
nc: 1 # number of classes
|
nc: 2 # number of classes
|
||||||
scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'
|
scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'
|
||||||
# [depth, width, max_channels]
|
# [depth, width, max_channels]
|
||||||
n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
|
n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
|
||||||
|
|||||||
7
ultralytics_yolo11-main/xiantiaodata.yaml
Normal file
7
ultralytics_yolo11-main/xiantiaodata.yaml
Normal file
@ -0,0 +1,7 @@
|
|||||||
|
path: /home/hx/开发/ML_xiantiao/class_xiantiao_pc/test_image # 数据集所在路径
|
||||||
|
train: train # 数据集路径下的train.txt
|
||||||
|
val: val # 数据集路径下的val.txt
|
||||||
|
test: test # 数据集路径下的test.txt
|
||||||
|
|
||||||
|
nc: 2
|
||||||
|
names: ['hole','crack']
|
||||||
File diff suppressed because it is too large
Load Diff
@ -3,7 +3,7 @@ import os
|
|||||||
|
|
||||||
# =================== 配置 ===================
|
# =================== 配置 ===================
|
||||||
xml_file = 'annotations.xml' # 你的 CVAT XML 文件路径
|
xml_file = 'annotations.xml' # 你的 CVAT XML 文件路径
|
||||||
images_dir = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/20251208' # 图像文件夹（用于读取宽高）
|
images_dir = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/20251209' # 图像文件夹（用于读取宽高）
|
||||||
output_dir = 'labels_keypoints' # 输出 YOLO 标签目录
|
output_dir = 'labels_keypoints' # 输出 YOLO 标签目录
|
||||||
os.makedirs(output_dir, exist_ok=True)
|
os.makedirs(output_dir, exist_ok=True)
|
||||||
|
|
||||||
|
|||||||
@ -1,4 +1,5 @@
|
|||||||
import os
|
import os
|
||||||
|
import shutil
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
import cv2
|
import cv2
|
||||||
from ultralytics import YOLO
|
from ultralytics import YOLO
|
||||||
@ -6,15 +7,15 @@ from ultralytics import YOLO
|
|||||||
# ---------------------------
|
# ---------------------------
|
||||||
# 配置路径（请按需修改）
|
# 配置路径（请按需修改）
|
||||||
# ---------------------------
|
# ---------------------------
|
||||||
MODEL_PATH = "gaiban.pt" # 你的二分类模型
|
MODEL_PATH = "xialiao.pt" # 你的二分类模型
|
||||||
INPUT_FOLDER = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/12.2" # 输入图像文件夹
|
INPUT_FOLDER = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/ready" # 输入图像文件夹
|
||||||
OUTPUT_ROOT = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/12.2.2" # 输出根目录（会生成 合格/不合格 子文件夹）
|
OUTPUT_ROOT = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/ready/result" # 输出根目录
|
||||||
|
|
||||||
# 类别映射（必须与训练时的 data.yaml 顺序一致）
|
# 类别映射（必须与训练时的 data.yaml 顺序一致）
|
||||||
CLASS_NAMES = {0: "不合格", 1: "合格"}
|
CLASS_NAMES = {0: "不合格", 1: "合格"}
|
||||||
|
|
||||||
# ---------------------------
|
# ---------------------------
|
||||||
# 批量推理函数
|
# 批量推理函数（移动原图）
|
||||||
# ---------------------------
|
# ---------------------------
|
||||||
def batch_classify(model_path, input_folder, output_root):
|
def batch_classify(model_path, input_folder, output_root):
|
||||||
# 加载模型
|
# 加载模型
|
||||||
@ -36,27 +37,32 @@ def batch_classify(model_path, input_folder, output_root):
|
|||||||
if img_path.suffix.lower() not in IMG_EXTS:
|
if img_path.suffix.lower() not in IMG_EXTS:
|
||||||
continue
|
continue
|
||||||
|
|
||||||
# 读取图像
|
# 读取图像（用于推理）
|
||||||
img = cv2.imread(str(img_path))
|
img = cv2.imread(str(img_path))
|
||||||
if img is None:
|
if img is None:
|
||||||
print(f"❌ 无法读取: {img_path}")
|
print(f"❌ 无法读取图像（可能已损坏或被占用）: {img_path}")
|
||||||
continue
|
continue
|
||||||
|
|
||||||
# 推理（整图）
|
# 推理（整图分类）
|
||||||
results = model(img)
|
results = model(img)
|
||||||
probs = results[0].probs.data.cpu().numpy()
|
probs = results[0].probs.data.cpu().numpy()
|
||||||
pred_class_id = int(probs.argmax())
|
pred_class_id = int(probs.argmax())
|
||||||
pred_label = CLASS_NAMES[pred_class_id]
|
pred_label = CLASS_NAMES[pred_class_id]
|
||||||
confidence = float(probs[pred_class_id])
|
confidence = float(probs[pred_class_id])
|
||||||
|
|
||||||
# 保存原图到对应文件夹
|
# ⚠️ 关键修改：移动原图（不是复制）
|
||||||
dst = output_root / pred_label / img_path.name
|
dst = output_root / pred_label / img_path.name
|
||||||
cv2.imwrite(str(dst), img)
|
try:
|
||||||
|
shutil.move(str(img_path), str(dst))
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 移动失败 {img_path} → {dst}: {e}")
|
||||||
|
continue
|
||||||
|
|
||||||
print(f"✅ {img_path.name} → {pred_label} ({confidence:.2f})")
|
print(f"✅ {img_path.name} → {pred_label} ({confidence:.2f})")
|
||||||
processed += 1
|
processed += 1
|
||||||
|
|
||||||
print(f"\n🎉 共处理 {processed} 张图像，结果已保存至: {output_root}")
|
print(f"\n🎉 共处理并移动 {processed} 张图像，结果已保存至: {output_root}")
|
||||||
|
|
||||||
|
|
||||||
# ---------------------------
|
# ---------------------------
|
||||||
# 运行入口
|
# 运行入口
|
||||||
|
|||||||
152
推理图片反向上传CVAT/detect/trans_obbtocvat.py
Normal file
152
推理图片反向上传CVAT/detect/trans_obbtocvat.py
Normal file
@ -0,0 +1,152 @@
|
|||||||
|
# yolo_detect_to_cvat.py
|
||||||
|
import os
|
||||||
|
import xml.etree.ElementTree as ET
|
||||||
|
from pathlib import Path
|
||||||
|
import cv2
|
||||||
|
|
||||||
|
IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff', '.webp'}
|
||||||
|
|
||||||
|
|
||||||
|
def yolo_detect_to_cvat_xml(label_dir, image_dir, class_id_to_name, output_xml):
|
||||||
|
"""
|
||||||
|
将 YOLO Detect 格式的标签（class cx cy w h）转换为 CVAT XML 格式。
|
||||||
|
"""
|
||||||
|
|
||||||
|
label_dir = Path(label_dir)
|
||||||
|
image_dir = Path(image_dir)
|
||||||
|
|
||||||
|
# ======== 构建基本 XML 结构 ========
|
||||||
|
root = ET.Element("annotations")
|
||||||
|
ET.SubElement(root, "version").text = "1.1"
|
||||||
|
|
||||||
|
meta = ET.SubElement(root, "meta")
|
||||||
|
task = ET.SubElement(meta, "task")
|
||||||
|
|
||||||
|
txt_files = sorted([f for f in label_dir.glob("*.txt")])
|
||||||
|
total = len(txt_files)
|
||||||
|
|
||||||
|
ET.SubElement(task, "id").text = "1"
|
||||||
|
ET.SubElement(task, "name").text = "yolo_detect_import"
|
||||||
|
ET.SubElement(task, "size").text = str(total)
|
||||||
|
ET.SubElement(task, "mode").text = "annotation"
|
||||||
|
ET.SubElement(task, "overlap").text = "0"
|
||||||
|
ET.SubElement(task, "bugtracker").text = ""
|
||||||
|
ET.SubElement(task, "created").text = ""
|
||||||
|
ET.SubElement(task, "updated").text = ""
|
||||||
|
ET.SubElement(task, "subset").text = "default"
|
||||||
|
ET.SubElement(task, "start_frame").text = "0"
|
||||||
|
ET.SubElement(task, "stop_frame").text = str(total - 1)
|
||||||
|
ET.SubElement(task, "frame_filter").text = ""
|
||||||
|
|
||||||
|
# labels
|
||||||
|
labels_elem = ET.SubElement(task, "labels")
|
||||||
|
for name in class_id_to_name.values():
|
||||||
|
lab = ET.SubElement(labels_elem, "label")
|
||||||
|
ET.SubElement(lab, "name").text = name
|
||||||
|
ET.SubElement(lab, "color").text = "#ffffff"
|
||||||
|
ET.SubElement(lab, "type").text = "any"
|
||||||
|
ET.SubElement(lab, "attributes")
|
||||||
|
|
||||||
|
ET.SubElement(meta, "dumped").text = ""
|
||||||
|
|
||||||
|
# ======== 处理每张图片 ========
|
||||||
|
for idx, txt_file in enumerate(txt_files):
|
||||||
|
stem = txt_file.stem
|
||||||
|
|
||||||
|
# 自动匹配图像文件（支持多种扩展名）
|
||||||
|
img_path = None
|
||||||
|
for ext in IMG_EXTENSIONS:
|
||||||
|
p = image_dir / f"{stem}{ext}"
|
||||||
|
if p.exists():
|
||||||
|
img_path = p
|
||||||
|
break
|
||||||
|
p = image_dir / f"{stem.upper()}{ext}"
|
||||||
|
if p.exists():
|
||||||
|
img_path = p
|
||||||
|
break
|
||||||
|
|
||||||
|
if img_path is None:
|
||||||
|
print(f"⚠ 找不到对应图像: {stem}")
|
||||||
|
continue
|
||||||
|
|
||||||
|
# 获取图像尺寸（用于反归一化）
|
||||||
|
img = cv2.imread(str(img_path))
|
||||||
|
if img is None:
|
||||||
|
print(f"⚠ 无法读取图像: {img_path}，跳过")
|
||||||
|
H, W = 1080, 1920 # fallback
|
||||||
|
else:
|
||||||
|
H, W = img.shape[:2]
|
||||||
|
|
||||||
|
# 创建 <image> 节点
|
||||||
|
image_elem = ET.SubElement(root, "image", {
|
||||||
|
"id": str(idx),
|
||||||
|
"name": img_path.name,
|
||||||
|
"width": str(W),
|
||||||
|
"height": str(H)
|
||||||
|
})
|
||||||
|
|
||||||
|
# 读取 YOLO Detect 标签
|
||||||
|
with open(txt_file, "r") as f:
|
||||||
|
for line in f:
|
||||||
|
line = line.strip()
|
||||||
|
if not line:
|
||||||
|
continue
|
||||||
|
parts = line.split()
|
||||||
|
if len(parts) != 5:
|
||||||
|
print(f"⚠ 标签格式错误（应为5列）: {line} in {txt_file}")
|
||||||
|
continue
|
||||||
|
|
||||||
|
cls_id = int(parts[0])
|
||||||
|
cx, cy, bw, bh = map(float, parts[1:])
|
||||||
|
|
||||||
|
# 反归一化
|
||||||
|
cx_abs = cx * W
|
||||||
|
cy_abs = cy * H
|
||||||
|
w_abs = bw * W
|
||||||
|
h_abs = bh * H
|
||||||
|
|
||||||
|
# 计算左上和右下
|
||||||
|
xtl = cx_abs - w_abs / 2
|
||||||
|
ytl = cy_abs - h_abs / 2
|
||||||
|
xbr = cx_abs + w_abs / 2
|
||||||
|
ybr = cy_abs + h_abs / 2
|
||||||
|
|
||||||
|
# 边界裁剪（防止越界）
|
||||||
|
xtl = max(0, min(W, xtl))
|
||||||
|
ytl = max(0, min(H, ytl))
|
||||||
|
xbr = max(0, min(W, xbr))
|
||||||
|
ybr = max(0, min(H, ybr))
|
||||||
|
|
||||||
|
# 添加 box（无 rotation 字段！）
|
||||||
|
ET.SubElement(image_elem, "box", {
|
||||||
|
"label": class_id_to_name.get(cls_id, f"class_{cls_id}"),
|
||||||
|
"source": "manual",
|
||||||
|
"occluded": "0",
|
||||||
|
"xtl": f"{xtl:.2f}",
|
||||||
|
"ytl": f"{ytl:.2f}",
|
||||||
|
"xbr": f"{xbr:.2f}",
|
||||||
|
"ybr": f"{ybr:.2f}",
|
||||||
|
"z_order": "0"
|
||||||
|
})
|
||||||
|
|
||||||
|
print(f"✔ 处理 {img_path.name}")
|
||||||
|
|
||||||
|
# 保存 XML
|
||||||
|
tree = ET.ElementTree(root)
|
||||||
|
tree.write(output_xml, encoding="utf-8", xml_declaration=True)
|
||||||
|
print(f"\n✅ 已生成 CVAT XML 文件: {output_xml}")
|
||||||
|
|
||||||
|
|
||||||
|
# ------------------- 主函数 -------------------
|
||||||
|
if __name__ == "__main__":
|
||||||
|
CLASS_MAP = {
|
||||||
|
0: "hole",
|
||||||
|
1: "crack"
|
||||||
|
}
|
||||||
|
|
||||||
|
yolo_detect_to_cvat_xml(
|
||||||
|
label_dir="/home/hx/yolo/推理图片反向上传CVAT/detect/inference_results/labels",
|
||||||
|
image_dir="/home/hx/开发/ML_xiantiao/class_xiantiao_pc/test_image/train",
|
||||||
|
class_id_to_name=CLASS_MAP,
|
||||||
|
output_xml="detect_annotations.xml"
|
||||||
|
)
|
||||||
135
推理图片反向上传CVAT/detect/tuili_save_txt_f.py
Normal file
135
推理图片反向上传CVAT/detect/tuili_save_txt_f.py
Normal file
@ -0,0 +1,135 @@
|
|||||||
|
import os
|
||||||
|
import cv2
|
||||||
|
from pathlib import Path
|
||||||
|
from ultralytics import YOLO
|
||||||
|
|
||||||
|
IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff', '.webp'}
|
||||||
|
|
||||||
|
|
||||||
|
class ObjectDetector:
|
||||||
|
"""封装 YOLO 目标检测模型"""
|
||||||
|
|
||||||
|
def __init__(self, model_path):
|
||||||
|
if not os.path.exists(model_path):
|
||||||
|
raise FileNotFoundError(f"模型文件不存在: {model_path}")
|
||||||
|
self.model = YOLO(model_path)
|
||||||
|
print(f"[INFO] 成功加载 YOLO 目标检测模型: {model_path}")
|
||||||
|
|
||||||
|
def detect(self, img_np, conf_threshold=0.0):
|
||||||
|
"""返回所有置信度 >= conf_threshold 的检测结果"""
|
||||||
|
results = self.model.predict(img_np, conf=conf_threshold, verbose=False)
|
||||||
|
detections = []
|
||||||
|
for result in results:
|
||||||
|
boxes = result.boxes.cpu().numpy()
|
||||||
|
for box in boxes:
|
||||||
|
detection_info = {
|
||||||
|
'bbox_xyxy': box.xyxy[0], # [x1, y1, x2, y2]
|
||||||
|
'confidence': float(box.conf.item()),
|
||||||
|
'class_id': int(box.cls.item())
|
||||||
|
}
|
||||||
|
detections.append(detection_info)
|
||||||
|
return detections
|
||||||
|
|
||||||
|
|
||||||
|
def save_yolo_detect_labels_from_folder(
|
||||||
|
model_path,
|
||||||
|
image_dir,
|
||||||
|
output_dir,
|
||||||
|
conf_threshold=0.5,
|
||||||
|
label_map={0: "hole", 1: "crack"} # 可选，仅用于日志
|
||||||
|
):
|
||||||
|
"""
|
||||||
|
对 image_dir 中所有图像进行 YOLO Detect 推理，
|
||||||
|
每个类别保留最高置信度框，保存为 YOLO 格式的 .txt 标签文件。
|
||||||
|
|
||||||
|
YOLO 格式: <class_id> <cx_norm> <cy_norm> <w_norm> <h_norm>
|
||||||
|
"""
|
||||||
|
image_dir = Path(image_dir)
|
||||||
|
output_dir = Path(output_dir)
|
||||||
|
labels_dir = output_dir / "labels"
|
||||||
|
labels_dir.mkdir(parents=True, exist_ok=True)
|
||||||
|
|
||||||
|
# 获取图像列表
|
||||||
|
image_files = [
|
||||||
|
f for f in sorted(os.listdir(image_dir))
|
||||||
|
if os.path.splitext(f.lower())[1] in IMG_EXTENSIONS
|
||||||
|
]
|
||||||
|
if not image_files:
|
||||||
|
print(f"❌ 未在 {image_dir} 中找到支持的图像文件")
|
||||||
|
return
|
||||||
|
|
||||||
|
print(f"共找到 {len(image_files)} 张图像，开始推理...")
|
||||||
|
detector = ObjectDetector(model_path)
|
||||||
|
|
||||||
|
for img_filename in image_files:
|
||||||
|
img_path = image_dir / img_filename
|
||||||
|
stem = Path(img_filename).stem
|
||||||
|
txt_path = labels_dir / f"{stem}.txt"
|
||||||
|
|
||||||
|
# 读图
|
||||||
|
img = cv2.imread(str(img_path))
|
||||||
|
if img is None:
|
||||||
|
print(f"⚠️ 跳过无效图像: {img_path}")
|
||||||
|
txt_path.write_text("") # 写空文件
|
||||||
|
continue
|
||||||
|
|
||||||
|
H, W = img.shape[:2]
|
||||||
|
|
||||||
|
# 推理（获取所有 ≥ conf_threshold 的框）
|
||||||
|
all_detections = detector.detect(img, conf_threshold=conf_threshold)
|
||||||
|
|
||||||
|
# 按类别保留最高置信度框
|
||||||
|
best_per_class = {}
|
||||||
|
for det in all_detections:
|
||||||
|
cls_id = det['class_id']
|
||||||
|
if cls_id not in best_per_class or det['confidence'] > best_per_class[cls_id]['confidence']:
|
||||||
|
best_per_class[cls_id] = det
|
||||||
|
|
||||||
|
top_detections = list(best_per_class.values())
|
||||||
|
|
||||||
|
# 转为 YOLO 格式并写入
|
||||||
|
lines = []
|
||||||
|
for det in top_detections:
|
||||||
|
x1, y1, x2, y2 = det['bbox_xyxy']
|
||||||
|
cx = (x1 + x2) / 2.0
|
||||||
|
cy = (y1 + y2) / 2.0
|
||||||
|
bw = x2 - x1
|
||||||
|
bh = y2 - y1
|
||||||
|
|
||||||
|
# 归一化
|
||||||
|
cx_norm = cx / W
|
||||||
|
cy_norm = cy / H
|
||||||
|
w_norm = bw / W
|
||||||
|
h_norm = bh / H
|
||||||
|
|
||||||
|
# 限制在 [0, 1]
|
||||||
|
cx_norm = max(0.0, min(1.0, cx_norm))
|
||||||
|
cy_norm = max(0.0, min(1.0, cy_norm))
|
||||||
|
w_norm = max(0.0, min(1.0, w_norm))
|
||||||
|
h_norm = max(0.0, min(1.0, h_norm))
|
||||||
|
|
||||||
|
line = f"{det['class_id']} {cx_norm:.6f} {cy_norm:.6f} {w_norm:.6f} {h_norm:.6f}"
|
||||||
|
lines.append(line)
|
||||||
|
|
||||||
|
# 写入标签文件
|
||||||
|
with open(txt_path, "w") as f:
|
||||||
|
if lines:
|
||||||
|
f.write("\n".join(lines) + "\n")
|
||||||
|
|
||||||
|
print(f"✅ {img_filename} -> {len(lines)} 个检测框已保存")
|
||||||
|
|
||||||
|
print(f"\n🎉 全部完成！标签文件保存在: {labels_dir}")
|
||||||
|
|
||||||
|
|
||||||
|
# ------------------- 主函数调用 -------------------
|
||||||
|
if __name__ == "__main__":
|
||||||
|
MODEL_PATH = "/home/hx/yolo/ultralytics_yolo11-main/runs/train/exp_detect/weights/best.pt"
|
||||||
|
IMAGE_DIR = "/home/hx/开发/ML_xiantiao/class_xiantiao_pc/test_image/train"
|
||||||
|
OUTPUT_DIR = "./inference_results"
|
||||||
|
|
||||||
|
save_yolo_detect_labels_from_folder(
|
||||||
|
model_path=MODEL_PATH,
|
||||||
|
image_dir=IMAGE_DIR,
|
||||||
|
output_dir=OUTPUT_DIR,
|
||||||
|
conf_threshold=0.5
|
||||||
|
)
|
||||||
@ -5,7 +5,7 @@ import os
|
|||||||
|
|
||||||
# ====================== 用户配置 ======================
|
# ====================== 用户配置 ======================
|
||||||
MODEL_PATH = 'point.pt'
|
MODEL_PATH = 'point.pt'
|
||||||
IMAGE_SOURCE_DIR = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/20251208'
|
IMAGE_SOURCE_DIR = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/20251212'
|
||||||
OUTPUT_DIR = './keypoints_txt'
|
OUTPUT_DIR = './keypoints_txt'
|
||||||
|
|
||||||
IMG_EXTENSIONS = {'.png', '.jpg', '.jpeg', '.bmp', '.tiff', '.tif', '.webp'}
|
IMG_EXTENSIONS = {'.png', '.jpg', '.jpeg', '.bmp', '.tiff', '.tif', '.webp'}
|
||||||
|
|||||||
@ -11,7 +11,7 @@ labels_dir = "keypoints_txt"
|
|||||||
output_xml = "annotations_cvat.xml"
|
output_xml = "annotations_cvat.xml"
|
||||||
|
|
||||||
# 图片目录（用于 width/height）
|
# 图片目录（用于 width/height）
|
||||||
images_dir = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/20251208"
|
images_dir = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/20251212"
|
||||||
|
|
||||||
# 类别映射
|
# 类别映射
|
||||||
class_mapping_reverse = {
|
class_mapping_reverse = {
|
||||||
|
|||||||
Reference in New Issue
Block a user