yolo v3 pytorch版 训练自己的数据集
Yolo v3入坑指南
Yolo v3比Frcnn好调试多了……就是数据集准备比较麻烦……
但是好Debug，linux和win10差别不大……
代码链接（cpu版本）：Yolo v3
这个代码……作者说的太草率了……data怎么准备都没说清……好歹issue里面有大神解答，给了傻瓜版教程，运行他的几个脚本就好了，data文件夹就准备好啦！
data文件准备，按照这个数据集准备
虽然这个作者是用它来训练coco数据集，但是data整个是个四不像……不用json不用xml用txt……所以训练自己的比较麻烦……
准备好data，还有修改config/yolov3.cfg文件。
参考链接：修改cfg
打开yolov3.cfg文件后，搜索yolo，共有三处yolo，下面以一处的修改作为示例。
[convolutional] #紧挨着[yolo]上面的[convolutional]
size=1
stride=1
pad=1
filters=21 #filters=3*(你的class种类数+5）
activation=linear
[yolo]
mask = 6,7,8
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=2 #修改classes
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=0 #显存大的写1 反之0
除此之外，cfg中的其他参数可以参考这个链接来进行修改，比如可以修改一些数据增强的参数，如果想要接着上次训练的weight继续训练，就参考这个链接进行微调（但是我使用的代码不支持clear操作，只能使用第二种方法）。
然后开始训练吧！个人感觉yolov3学的效果不是很好，frcnn训练了十轮能达到的效果，yolov3可能要80轮左右，开始前几十轮mAP很低就多加几轮试试，issue里面提到这个代码训练100轮，coco也达不到作者所说的mAP……所以……慎重……男票用这个帮我把自己的数据集跑到了92%左右的mAP，效果还是很好的。
准备数据集的那个代码有些bug调不出来，所以还是使用第一个的代码吧。
记录一些命令
1.查看tensorboard
在pycharm里面打开命令行（我总是忘，还是记录一下吧）代码中右键open in termianl
tensorboard --logdir=./logs --host=127.0.0.1 --port=6006
2.测试图片
运行detect.py
3.记录一下用过的脚本之 voc的xml转化为coco的json
import os
import json
import xml.etree.ElementTree as ET
import numpy as np
import cv2
def _isArrayLike(obj):
return hasattr(obj, '__iter__') and hasattr(obj, '__len__')
class voc2coco:
def __init__(self, devkit_path=None, year=None):
# self.classes = ('__background__',
# 'aeroplane', 'bicycle', 'bird', 'boat',
# 'bottle', 'bus', 'car', 'cat', 'chair',
# 'cow', 'diningtable', 'dog', 'horse',
# 'motorbike', 'person', 'pottedplant',
# 'sheep', 'sofa', 'train', 'tvmonitor')
self.classes = ('none',
'll', 'rr') #写你自己的class
self.num_classes = len(self.classes)
assert 'VOCdevkit' in devkit_path, 'VOC地址不存在: {}'.format(devkit_path)
self.data_path = os.path.join(devkit_path, 'VOC' + year)
self.annotaions_path = os.path.join(self.data_path, 'Annotations')
self.image_set_path = os.path.join(self.data_path, 'ImageSets')
self.year = year
self.categories_to_ids_map = self._get_categories_to_ids_map()
self.categories_msg = self._categories_msg_generator()
def _load_annotation(self, ids=[]):
ids = ids if _isArrayLike(ids) else [ids]
image_msg = []
annotation_msg = []
annotation_id = 1
for index in ids:
filename = '{:0>6}'.format(index)
json_file = os.path.join(self.data_path, 'Segmentation_json', filename + '.json')
num=0
if os.path.exists(json_file):
img_file = os.path.join(self.data_path, 'JPEGImages', filename + '.jpg')
im = cv2.imread(img_file)
width = im.shape[1]
height = im.shape[0]
seg_data = json.load(open(json_file, 'r'))
assert type(seg_data) == type(dict()), 'annotation file format {} not supported'.format(type(seg_data))
for shape in seg_data['shapes']:
seg_msg = []
for point in shape['points']:
seg_msg += point
one_ann_msg = {"segmentation": [seg_msg],
"area": self._area_computer(shape['points']),
"iscrowd": 0,
"image_id": int(index),
"bbox": self._points_to_mbr(shape['points']),
"category_id": self.categories_to_ids_map[shape['label']],
"id": annotation_id,
"ignore": 0
}
annotation_msg.append(one_ann_msg)
annotation_id += 1
else:
xml_file = os.path.join(self.annotaions_path, filename + '.xml')
tree = ET.parse(xml_file)
size = tree.find('size')
objs = tree.findall('object')
width = size.find('width').text
height = size.find('height').text
for obj in objs:
bndbox = obj.find('bndbox')
[xmin, xmax, ymin, ymax] \
= [int(bndbox.find('xmin').text) - 1, int(bndbox.find('xmax').text),
int(bndbox.find('ymin').text) - 1, int(bndbox.find('ymax').text)]
if xmin < 0:
xmin = 0
if ymin < 0:
ymin = 0
bbox = [xmin, xmax, ymin, ymax]
one_ann_msg = {"segmentation": self._bbox_to_mask(bbox),
"area": self._bbox_area_computer(bbox),
"iscrowd": 0,
"image_id": int(num),
"bbox": [xmin, ymin, xmax - xmin, ymax - ymin],
"category_id": self.categories_to_ids_map[obj.find('name').text],
"id": annotation_id,
"ignore": 0
}
annotation_msg.append(one_ann_msg)
annotation_id += 1
one_image_msg = {"file_name": filename + ".jpg",
"height": int(height),
"width": int(width),
"id": int(num)
}
image_msg.append(one_image_msg)
num=num+1
return image_msg, annotation_msg
def _bbox_to_mask(self, bbox):
assert len(bbox) == 4, 'Wrong bndbox!'
mask = [bbox[0], bbox[2], bbox[0], bbox[3], bbox[1], bbox[3], bbox[1], bbox[2]]
return [mask]
def _bbox_area_computer(self, bbox):
width = bbox[1] - bbox[0]
height = bbox[3] - bbox[2]
return width * height
def _save_json_file(self, filename=None, data=None):
json_path = os.path.join(self.data_path, 'cocoformatJson')
assert filename is not None, 'lack filename'
if os.path.exists(json_path) == False:
os.mkdir(json_path)
if not filename.endswith('.json'):
filename += '.json'
assert type(data) == type(dict()), 'data format {} not supported'.format(type(data))
with open(os.path.join(json_path, filename), 'w') as f:
f.write(json.dumps(data))
def _get_categories_to_ids_map(self):
return dict(zip(self.classes, range(self.num_classes)))
def _get_all_indexs(self):
ids = []
for root, dirs, files in os.walk(self.annotaions_path, topdown=False):
for f in files:
if str(f).endswith('.xml'):
id = int(str(f).strip('.xml'))
ids.append(id)
assert ids is not None, 'There is none xml file in {}'.format(self.annotaions_path)
return ids
def _get_indexs_by_image_set(self, image_set=None):
if image_set is None:
return self._get_all_indexs()
else:
image_set_path = os.path.join(self.image_set_path, 'Main', image_set + '.txt')
assert os.path.exists(image_set_path), 'Path does not exist: {}'.format(image_set_path)
with open(image_set_path) as f:
ids = [x.strip() for x in f.readlines()]
return ids
def _points_to_mbr(self, points):
assert _isArrayLike(points), 'Points should be array like!'
x = [point[0] for point in points]
y = [point[1] for point in points]
assert len(x) == len(y), 'Wrong point quantity'
xmin, xmax, ymin, ymax = min(x), max(x), min(y), max(y)
height = ymax - ymin
width = xmax - xmin
return [xmin, ymin, width, height]
def _categories_msg_generator(self):
categories_msg = []
for category in self.classes:
if category == 'none':
continue
one_categories_msg = {"supercategory": "none",
"id": self.categories_to_ids_map[category],
"name": category
}
categories_msg.append(one_categories_msg)
return categories_msg
def _area_computer(self, points):
assert _isArrayLike(points), 'Points should be array like!'
tmp_contour = []
for point in points:
tmp_contour.append([point])
contour = np.array(tmp_contour, dtype=np.int32)
area = cv2.contourArea(contour)
return area
def voc_to_coco_converter(self):
img_sets = ['trainval', 'test']
for img_set in img_sets:
ids = self._get_indexs_by_image_set(img_set)
img_msg, ann_msg = self._load_annotation(ids)
result_json = {"images": img_msg,
"type": "instances",
"annotations": ann_msg,
"categories": self.categories_msg}
self._save_json_file('voc_' + self.year + '_' + img_set, result_json)
def demo():
# 转换pascal地址是'./VOC2007/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt'
converter = voc2coco('D:\\Coding\\python\\data_myself\\VOCdevkit2007', '2007')
converter.voc_to_coco_converter()
if __name__ == "__main__":
demo()
这个是别人写的，但是我找不到原作者了……尴尬……侵删！