发布时间:2022-10-10 09:30
参考: https://blog.csdn.net/Shenpibaipao/article/details/111240711
数据集下载:https://www.cityscapes-dataset.com/downloads/
下载leftImg8bit_trainvaltest.zip
和 gtFine_trainvaltest.zip
,创建文件夹cityscape
,解压到cityscape
放成如下的格式:
─ cityscape
── getfine
── leftImg8bit
── 处理脚本.py
执行:
python 处理脚本.py
生成:
─ cityscape
── getfine
── leftImg8bit
── 处理脚本.py
── labels # 存放框图的标签
── images # yolov3+版本的脚本将会拷贝输出图片到这个位置
── yolo_train.txt # 训练样本的路径
── yolo_val.txt # 验证样本的路径
── yolo_test.txt # 测试样本的路径
处理脚本:
import json
import os
from pathlib import Path
import re
from tqdm import tqdm
import shutil
def convert_annotation(image_id, paths):
global label_map
def find_box(points): # 该函数用来找出xmin, xmax, ymin ,ymax 即bbox包围框
_x, _y = [float(pot[0]) for pot in points], [float(pot[1]) for pot in points]
return min(_x), max(_x), min(_y), max(_y)
def convert(size, bbox): # 转为中心坐标
# size: (原图宽, 原图长)
center_x, center_y = (bbox[0] + bbox[1]) / 2.0 - 1, (bbox[2] + bbox[3]) / 2.0 - 1
center_w, center_h = bbox[1] - bbox[0], bbox[3] - bbox[2]
return center_x / size[0], center_y / size[1], center_w / size[0], center_h / size[1]
final_label_path, final_output_path = paths
label_json_url = final_label_path / f'{image_id}_gtFine_polygons.json'
# 输出到 :final_output_path / f'{image_id}_leftImg8bit.txt'
load_dict = json.load(open(label_json_url, 'r')) # 图像的实例
output_cache = []
for obj in load_dict['objects']: # load_dict['objects'] -> 目标的几何框体
obj_label = obj['label'] # 目标的类型
if obj_label in ['out of roi', 'ego vehicle']: # 直接跳过这两种类型 注意测试集里只有这两种类型 跳过的话测试集合里将为空的标签
continue
if obj_label not in label_map.keys(): # 记录目标类型转为int值
label_map[obj_label] = len(label_map.keys()) # 标签从0开始
x, y, w, h = convert((load_dict['imgWidth'], load_dict['imgHeight']), find_box(obj['polygon'])) # 归一化为中心点
# yolo 标准格式:img.jpg -> img.txt
# 内容的类别 归一化后的中心点x坐标 归一化后的中心点y坐标 归一化后的目标框宽度w 归一化后的目标况高度h
output_cache.append(f'{label_map[obj_label]} {x} {y} {w} {h}\n')
with open(final_output_path / f'{image_id}_leftImg8bit.txt', 'w') as label_f: # 写出标签文件
label_f.writelines(output_cache)
def mkdir(url):
if not os.path.exists(url):
os.makedirs(url)
if __name__ == '__main__':
root_dir = Path(__file__).parent
image_dir = root_dir / 'leftImg8bit'
label_dir = root_dir / 'getfine'
image_output_root_dir = root_dir / 'images'
label_output_root_dir = root_dir / 'labels'
label_map = {} # 存放所有的类别标签 eg. {'car': 0, 'person': 1}
for _t_ in tqdm(os.listdir(image_dir)): # _t_ as ['train', 'test' 'val;]
type_files = [] # 存放一类的所有文件,如训练集所有文件
mkdir(image_output_root_dir / _t_), mkdir(label_output_root_dir / _t_)
for cities_name in os.listdir(image_dir / _t_):
_final_img_path = image_dir / _t_ / cities_name # root_dir / leftImg8bit / test / berlin
_final_label_path = label_dir / _t_ / cities_name # root_dir / getfine / test / berlin
# berlin_000000_000019_leftImg8bit.png -> berlin_000000_000019_gtFine_polygons.json
image_ids = list(map(lambda s: re.sub(r'_leftImg8bit\.png', '', s), os.listdir(_final_img_path)))
# print(names[:0]) -> berlin_000000_000019
for img_id in image_ids:
convert_annotation(img_id, [_final_label_path, label_output_root_dir / _t_]) # 转化标签
img_file = f'{img_id}_leftImg8bit.png'
shutil.copy(_final_img_path / img_file, f'images/{_t_}/{img_file}') # 复制移动图片
type_files.append(f'images/{_t_}/{img_id}_leftImg8bit.png\n')
with open(root_dir / f'yolo_{_t_}.txt', 'w') as f: # 记录训练样本等的具体内容
f.writelines(type_files)
with open(label_output_root_dir / 'classes.txt', 'w') as f: # 写出类别对应
for k, v in label_map.items():
f.write(f'{k}\n')
print([k for k in label_map.keys()], len([k for k in label_map.keys()]))
参考:https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data#11-create-datasetyaml
下载yolov5: https://github.com/ultralytics/yolov5
如下图并列放置cityscape
和yolov5
文件夹,cityscape
替代掉下图datasets
的位置
在yolov5/data
下创建cityscape.yaml
,内容如下:
# YOLOv5 by Ultralytics, GPL-3.0 license
# COCO 2017 dataset http://cocodataset.org by Microsoft
# Example usage: python train.py --data coco.yaml
# parent
# ├── yolov5
# └── datasets
# └── coco ← downloads here
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../cityscape # dataset root dir
train: images/train # train images (relative to 'path') 118287 images
val: images/val # val images (relative to 'path') 5000 images
test: images/test # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
# Classes
nc: 38 # number of classes
names: ['rectification border','sky','road','building','static','pole','train','ground','traffic light','traffic sign','sidewalk','terrain','bus','vegetation','car','fence','person','motorcycle','cargroup','parking','dynamic','persongroup','license plate','bicycle','rider','wall','bicyclegroup','truck','trailer','bridge','polegroup','ridergroup','guard rail','caravan','tunnel','truckgroup','rail track','motorcyclegroup'] # class names
开训!
python train.py --img 1280 --batch 16 --epochs 300 --data cityscape.yaml --weights yolov5l6.pt
续训
python train.py --img 1280 --batch 16 --epochs 300 --data cityscape.yaml --weights runs/train/exp8/last.pt
apt update
apt install libgl1-mesa-glx
apt update
apt install -y libglib2.0-0 libsm6 libxext6 libxrender-dev