训练完一个YOLOV5模型后，可以使用模型快速在已有XML标注文件中添加新类别，下面是在已有XML标注文件中添加新类别的具体脚本：文章来源：https://www.toymoban.com/news/detail-609908.html
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
import argparse
import os
import sys
from pathlib import Path
import cv2
import torch
import torch.backends.cudnn as cudnn
import glob
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
from models.common import DetectMultiBackend
from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,
increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, time_sync
import xml.etree.ElementTree as et
@torch.no_grad()
def run(weights=ROOT / 'weights/yolov5x.pt', # model.pt path(s)
source='/home/image/imgs_xml', # file/dir/URL/glob, 0 for webcam
data=ROOT / 'data/coco128.yaml', # dataset.yaml path
imgsz=(640, 640), # inference size (height, width)
conf_thres=0.4, # confidence threshold
iou_thres=0.2, # NMS IOU threshold
max_det=1000, # maximum detections per image
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
view_img=False, # show results
save_txt=False, # save results to *.txt
save_conf=False, # save confidences in --save-txt labels
save_crop=False, # save cropped prediction boxes
nosave=False, # do not save images/videos
classes=None, # filter by class: --class 0, or --class 0 2 3
agnostic_nms=False, # class-agnostic NMS
augment=False, # augmented inference
visualize=False, # visualize features
update=False, # update all models
project=ROOT / 'runs/detect', # save results to project/name
name='exp', # save results to project/name
exist_ok=False, # existing project/name ok, do not increment
line_thickness=3, # bounding box thickness (pixels)
hide_labels=False, # hide labels
hide_conf=False, # hide confidences
half=False, # use FP16 half-precision inference
dnn=False, # use OpenCV DNN for ONNX inference
):
source = str(source)
# Directories
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
# Load model
device = select_device(device)
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data)
stride, names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engine
imgsz = check_img_size(imgsz, s=stride) # check image size
# Half
half &= (pt or jit or onnx or engine) and device.type != 'cpu' # FP16 supported on limited backends with CUDA
if pt or jit:
model.model.half() if half else model.model.float()
index = 0
image_list = glob.glob(os.path.join(source, "*.jpg"))
for image_path in image_list:
dataset = LoadImages(image_path, img_size=imgsz, stride=stride, auto=pt)
bs = 1 # batch_size
# vid_path, vid_writer = [None] * bs, [None] * bs
# Run inference
model.warmup(imgsz=(1 if pt else bs, 3, *imgsz), half=half) # warmup
dt, seen = [0.0, 0.0, 0.0], 0
for path, im, im0s, vid_cap, s in dataset:
t1 = time_sync()
im = torch.from_numpy(im).to(device)
im = im.half() if half else im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0
if len(im.shape) == 3:
im = im[None] # expand for batch dim
t2 = time_sync()
dt[0] += t2 - t1
# Inference
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
pred = model(im, augment=augment, visualize=visualize)
t3 = time_sync()
dt[1] += t3 - t2
# NMS
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
dt[2] += time_sync() - t3
for i, det in enumerate(pred): # per image
p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
p = Path(p) # to Path
# save_path = os.path.join(source, p.name) # im.jpg
tree_ = et.ElementTree()
# tree_.parse(image_path.replace(".jpg", ".xml").replace("JPEGImages","Annotations"))
tree_.parse(image_path.replace(".jpg", ".xml"))
root = tree_.getroot()
print("length det : ",len(det))
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
# Write results
for *xyxy, conf, cls in reversed(det):
# print(xyxy)
c = int(cls) # integer class
if c == 0:
object = et.SubElement(root, "object")
name = et.SubElement(object, "name")
name.text = "person"
pose = et.SubElement(object, "pose")
pose.text = "Unspecified"
truncated = et.SubElement(object, "truncated")
truncated.text = "0"
difficult = et.SubElement(object, "difficult")
difficult.text = "0"
occluded = et.SubElement(object, "occluded")
occluded.text = "0"
bndbox = et.SubElement(object, "bndbox")
xmin = et.SubElement(bndbox, "xmin")
xmin.text = str(int(xyxy[0]))
ymin = et.SubElement(bndbox, "ymin")
ymin.text = str(int(xyxy[1]))
xmax = et.SubElement(bndbox, "xmax")
xmax.text = str(int(xyxy[2]))
ymax = et.SubElement(bndbox, "ymax")
ymax.text = str(int(xyxy[3]))
pretty_xml(root, ' ', '\n')
tree = et.ElementTree(root)
tree.write(image_path.replace(".jpg", ".xml"), encoding="utf-8")
# tree.write(image_path.replace(".jpg", ".xml").replace("JPEGImages","Annotations"), encoding="utf-8")
print(image_path, index)
index += 1
def pretty_xml(element, indent, newline, level=0): # elemnt为传进来的Elment类，参数indent用于缩进，newline用于换行
if element: # 判断element是否有子元素
if (element.text is None) or element.text.isspace(): # 如果element的text没有内容
element.text = newline + indent * (level + 1)
else:
element.text = newline + indent * (level + 1) + element.text.strip() + newline + indent * (level + 1)
# else: # 此处两行如果把注释去掉，Element的text也会另起一行
# element.text = newline + indent * (level + 1) + element.text.strip() + newline + indent * level
temp = list(element) # 将element转成list
for subelement in temp:
if temp.index(subelement) < (len(temp) - 1): # 如果不是list的最后一个元素，说明下一个行是同级别元素的起始，缩进应一致
subelement.tail = newline + indent * (level + 1)
else: # 如果是list的最后一个元素， 说明下一行是母元素的结束，缩进应该少一个
subelement.tail = newline + indent * level
pretty_xml(subelement, indent, newline, level=level + 1) # 对子元素进行递归操作
if __name__ == "__main__":
run()
以上代码需要修改run函数中的：weights为yolov5模型路径，source为图片数据和xml标注文件所在文件夹，修改的xml也在source路径下。亲测可用！文章来源地址https://www.toymoban.com/news/detail-609908.html
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