git clone https://github.com/xingyizhou/CenterNet.git
依据readme进行操作
1.google云获得权重文件
2.下载源码DCNv2 https://github.com/CharlesShang/DCNv2.git
依据readme编译
3. 需要把 ./torch/nn/functional.py bn=False (具体哪个不太清楚,但有)
我训练的是车尾/喷漆车牌/正常车牌检测
在data下建立文件夹路径如图
carHail/image_and_xml:保存所有图片的xml文件;
carHail/annotations:保存有文件xml_json.py生成的json文件;
carHail/images:保存所有图片的原图;
xml_json.py 文件为:
# -*- coding: utf-8 -*-
# @Time : 2020/1/19 下午1:59
# @Author : shenyingying
# @Email : [email protected]
# @File : xml_json.py
# @Software: PyCharm
import xml.etree.ElementTree as ET
import os
import json
coco = dict()
coco['images'] = []
coco['type'] = 'instances'
coco['annotations'] = []
coco['categories'] = []
category_set = dict()
image_set = set()
category_item_id = -1
image_id = 20180000000
annotation_id = 0
def addCatItem(name):
global category_item_id
category_item = dict()
category_item['supercategory'] = 'none'
category_item_id += 1
category_item['id'] = category_item_id
category_item['name'] = name
coco['categories'].append(category_item)
category_set[name] = category_item_id
return category_item_id
def addImgItem(file_name, size):
global image_id
if file_name is None:
raise Exception('Could not find filename tag in xml file.')
if size['width'] is None:
raise Exception('Could not find width tag in xml file.')
if size['height'] is None:
raise Exception('Could not find height tag in xml file.')
image_id += 1
image_item = dict()
image_item['id'] = image_id
image_item['file_name'] = file_name
image_item['width'] = size['width']
image_item['height'] = size['height']
coco['images'].append(image_item)
image_set.add(file_name)
return image_id
def addAnnoItem(object_name, image_id, category_id, bbox):
global annotation_id
annotation_item = dict()
annotation_item['segmentation'] = []
seg = []
# bbox[] is x,y,w,h
# left_top
seg.append(bbox[0])
seg.append(bbox[1])
# left_bottom
seg.append(bbox[0])
seg.append(bbox[1] + bbox[3])
# right_bottom
seg.append(bbox[0] + bbox[2])
seg.append(bbox[1] + bbox[3])
# right_top
seg.append(bbox[0] + bbox[2])
seg.append(bbox[1])
annotation_item['segmentation'].append(seg)
annotation_item['area'] = bbox[2] * bbox[3]
annotation_item['iscrowd'] = 0
annotation_item['ignore'] = 0
annotation_item['image_id'] = image_id
annotation_item['bbox'] = bbox
annotation_item['category_id'] = category_id
annotation_id += 1
annotation_item['id'] = annotation_id
coco['annotations'].append(annotation_item)
def parseXmlFiles(xml_path):
for f in os.listdir(xml_path):
if not f.endswith('.xml'):
continue
bndbox = dict()
size = dict()
current_image_id = None
current_category_id = None
file_name = None
size['width'] = None
size['height'] = None
size['depth'] = None
xml_file = os.path.join(xml_path, f)
print(xml_file)
tree = ET.parse(xml_file)
root = tree.getroot()
if root.tag != 'annotation':
raise Exception('pascal voc xml root element should be annotation, rather than {}'.format(root.tag))
# elem is , , ,
for elem in root:
current_parent = elem.tag
current_sub = None
object_name = None
if elem.tag == 'folder':
continue
if elem.tag == 'filename':
file_name = elem.text
if file_name in category_set:
raise Exception('file_name duplicated')
# add img item only after parse tag
elif current_image_id is None and file_name is not None and size['width'] is not None:
if file_name not in image_set:
current_image_id = addImgItem(file_name, size)
print('add image with {} and {}'.format(file_name, size))
else:
raise Exception('duplicated image: {}'.format(file_name))
# subelem is , , , ,
for subelem in elem:
bndbox['xmin'] = None
bndbox['xmax'] = None
bndbox['ymin'] = None
bndbox['ymax'] = None
current_sub = subelem.tag
if current_parent == 'object' and subelem.tag == 'name':
object_name = subelem.text
if object_name not in category_set:
current_category_id = addCatItem(object_name)
else:
current_category_id = category_set[object_name]
elif current_parent == 'size':
if size[subelem.tag] is not None:
raise Exception('xml structure broken at size tag.')
size[subelem.tag] = int(subelem.text)
# option is , , , , when subelem is
for option in subelem:
if current_sub == 'bndbox':
if bndbox[option.tag] is not None:
raise Exception('xml structure corrupted at bndbox tag.')
bndbox[option.tag] = int(option.text)
# only after parse the
if bndbox['xmin'] is not None:
if object_name is None:
raise Exception('xml structure broken at bndbox tag')
if current_image_id is None:
raise Exception('xml structure broken at bndbox tag')
if current_category_id is None:
raise Exception('xml structure broken at bndbox tag')
bbox = []
# x
bbox.append(bndbox['xmin'])
# y
bbox.append(bndbox['ymin'])
# w
bbox.append(bndbox['xmax'] - bndbox['xmin'])
# h
bbox.append(bndbox['ymax'] - bndbox['ymin'])
print('add annotation with {},{},{},{}'.format(object_name, current_image_id, current_category_id,
bbox))
addAnnoItem(object_name, current_image_id, current_category_id, bbox)
if __name__ == '__main__':
xml_path = '/data_1/project/Detection/CenterNet/data/carHail/val/Annotations' # 这是xml文件所在的地址
json_file = '/data_1/project/Detection/CenterNet/data/carHail/val.json' # 这是你要生成的json文件
parseXmlFiles(xml_path) # 只需要改动这两个参数就行了
json.dump(coco, open(json_file, 'w'))
# -*- coding: utf-8 -*-
# @Time : 2020/1/19 下午2:38
# @Author : shenyingying
# @Email : [email protected]
# @File : compute_mean_std.py
# @Software: PyCharm
import cv2, os, argparse
import numpy as np
from tqdm import tqdm
def main():
dirs = r'/data_1/project/Detection/CenterNet/data/carHail/images' # 修改你自己的图片路径
img_file_names = os.listdir(dirs)
m_list, s_list = [], []
for img_filename in tqdm(img_file_names):
img = cv2.imread(dirs + '/' + img_filename)
img = img / 255.0
m, s = cv2.meanStdDev(img)
m_list.append(m.reshape((3,)))
s_list.append(s.reshape((3,)))
m_array = np.array(m_list)
s_array = np.array(s_list)
m = m_array.mean(axis=0, keepdims=True)
s = s_array.mean(axis=0, keepdims=True)
print("mean = ", m[0][::-1])
print("std = ", s[0][::-1])
if __name__ == '__main__':
main()
不加载权重的训练
python main.py ctdet --exp_id carHail --batch_size 4 --lr 1.25e-4
加载权重的训练
python main.py ctdet --exp_id carHail --batch_size 4 --lr 1.25e-4 --load_model ../models/ctdet_dla_2x.pth
多卡训练
python main.py ctdet --exp_id carHail --batch_size 4 --lr 1.25e-4 --gpus 0,1,2 --load_model ../models/ctdet_dla_2x.pth
断点恢复
python main.py ctdet --exp_id carHail --batch_size 4 --lr 1.25e-4 --gpus 0,1,2 --load_model ../models/ctdet_dla_2x.pth --resume
训练完成后,在./exp/ctdet/food/文件夹下会出现一堆文件;
其中,model_last是最后一次epoch的模型;model_best是val最好的模型
运行demo文件检查下训练的模型,–demo设置你要预测的图片/图片文件夹/视频所在的路径;
原始预测:
python demo.py ctdet --demo ../data/carHail/images --loda_model exp/ctdet/carHail/model_best.pt
带数据增强的预测:
python demo.py ctdet --demo ../data/carHail/images --loda_model exp/ctdet/carHail/model_best.pth --flip_test
多尺度预测
python demo.py ctdet --demo ../data/carHail/images --loda_model exp/ctdet/carHail/model_best.pth --test_scales 0.5,0.75,1.0,1.25,1.5
注意,如果多尺度预测报错,一般就是你自己没有编译nms。
编译方法:到 path/to/CenterNet/src/lib/externels 目录下,运行:
python setup.py build_ext --inplace
python test.py ctdet --exp_id carHail --not_prefetch_test ctdet --loda_model exp/ctdet/carHail/model_best.pth