模型实战
训练COCO数据集
本次使用2017版本的COCO数据集作为例子，演示如何使用YoloV8训练和预测。
下载数据集
Images:
- 2017 Train images [118K/18GB] ：http://images.cocodataset.org/zips/train2017.zip
- 2017 Val images [5K/1GB]：http://images.cocodataset.org/zips/val2017.zip
- 2017 Test images [41K/6GB]：http://images.cocodataset.org/zips/unlabeled2017.zip
Annotations:
- 2017 annotations_trainval2017 [241MB]：http://images.cocodataset.org/annotations/annotations_trainval2017.zip
COCO转yolo格式数据集（适用V4，V5，V6，V7，V8）
最初的研究论文中，COCO中有91个对象类别。然而，在2014年的第一次发布中，仅发布了80个标记和分割图像的对象类别。2014年发布之后，2017年发布了后续版本。详细的类别如下：
可以看到，2014年和2017年发布的对象列表是相同的，它们是论文中最初91个对象类别中的80个对象。所以在转换的时候，要重新对类别做映射，映射函数如下：
def coco91_to_coco80_class(): # converts 80-index (val2014) to 91-index (paper)
# https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
# a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
# b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
# x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco
# x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet
x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, None, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, None, 24, 25, None,
None, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, None, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
51, 52, 53, 54, 55, 56, 57, 58, 59, None, 60, None, None, 61, None, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
None, 73, 74, 75, 76, 77, 78, 79, None]
return x
接下来，开始格式转换，工程的目录如下：
- coco：存放解压后的数据集。
-out：保存输出结果。
-coco2yolo.py：转换脚本。
转换代码如下：
import json
import glob
import os
import shutil
from pathlib import Path
import numpy as np
from tqdm import tqdm
def make_folders(path='../out/'):
# Create folders
if os.path.exists(path):
shutil.rmtree(path) # delete output folder
os.makedirs(path) # make new output folder
os.makedirs(path + os.sep + 'labels') # make new labels folder
os.makedirs(path + os.sep + 'images') # make new labels folder
return path
def convert_coco_json(json_dir='./coco/annotations_trainval2017/annotations/'):
jsons = glob.glob(json_dir + '*.json')
coco80 = coco91_to_coco80_class()
# Import json
for json_file in sorted(jsons):
fn = 'out/labels/%s/' % Path(json_file).stem.replace('instances_', '') # folder name
fn_images = 'out/images/%s/' % Path(json_file).stem.replace('instances_', '') # folder name
os.makedirs(fn,exist_ok=True)
os.makedirs(fn_images,exist_ok=True)
with open(json_file) as f:
data = json.load(f)
print(fn)
# Create image dict
images = {'%g' % x['id']: x for x in data['images']}
# Write labels file
for x in tqdm(data['annotations'], desc='Annotations %s' % json_file):
if x['iscrowd']:
continue
img = images['%g' % x['image_id']]
h, w, f = img['height'], img['width'], img['file_name']
file_path='coco/'+fn.split('/')[-2]+"/"+f
# The Labelbox bounding box format is [top left x, top left y, width, height]
box = np.array(x['bbox'], dtype=np.float64)
box[:2] += box[2:] / 2 # xy top-left corner to center
box[[0, 2]] /= w # normalize x
box[[1, 3]] /= h # normalize y
if (box[2] > 0.) and (box[3] > 0.): # if w > 0 and h > 0
with open(fn + Path(f).stem + '.txt', 'a') as file:
file.write('%g %.6f %.6f %.6f %.6f\n' % (coco80[x['category_id'] - 1], *box))
file_path_t=fn_images+f
print(file_path,file_path_t)
shutil.copy(file_path,file_path_t)
def coco91_to_coco80_class(): # converts 80-index (val2014) to 91-index (paper)
# https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
# a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
# b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
# x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco
# x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet
x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, None, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, None, 24, 25, None,
None, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, None, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
51, 52, 53, 54, 55, 56, 57, 58, 59, None, 60, None, None, 61, None, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
None, 73, 74, 75, 76, 77, 78, 79, None]
return x
convert_coco_json()
开始运行：
转换完成后，验证转换的结果：
import cv2
import os
def draw_box_in_single_image(image_path, txt_path):
# 读取图像
image = cv2.imread(image_path)
# 读取txt文件信息
def read_list(txt_path):
pos = []
with open(txt_path, 'r') as file_to_read:
while True:
lines = file_to_read.readline() # 整行读取数据
if not lines:
break
# 将整行数据分割处理，如果分割符是空格，括号里就不用传入参数，如果是逗号， 则传入‘，'字符。
p_tmp = [float(i) for i in lines.split(' ')]
pos.append(p_tmp) # 添加新读取的数据
# Efield.append(E_tmp)
pass
return pos
# txt转换为box
def convert(size, box):
xmin = (box[1]-box[3]/2.)*size[1]
xmax = (box[1]+box[3]/2.)*size[1]
ymin = (box[2]-box[4]/2.)*size[0]
ymax = (box[2]+box[4]/2.)*size[0]
box = (int(xmin), int(ymin), int(xmax), int(ymax))
return box
pos = read_list(txt_path)
print(pos)
tl = int((image.shape[0]+image.shape[1])/2)
lf = max(tl-1,1)
for i in range(len(pos)):
label = str(int(pos[i][0]))
print('label is '+label)
box = convert(image.shape, pos[i])
image = cv2.rectangle(image,(box[0], box[1]),(box[2],box[3]),(0,0,255),2)
cv2.putText(image,label,(box[0],box[1]-2), 0, 1, [0,0,255], thickness=2, lineType=cv2.LINE_AA)
pass
if pos:
cv2.imwrite('./Data/see_images/{}.png'.format(image_path.split('\\')[-1][:-4]), image)
else:
print('None')
img_folder = "./out/images/val2017"
img_list = os.listdir(img_folder)
img_list.sort()
label_folder = "./out/labels/val2017"
label_list = os.listdir(label_folder)
label_list.sort()
if not os.path.exists('./Data/see_images'):
os.makedirs('./Data/see_images')
for i in range(len(img_list)):
image_path = img_folder + "\\" + img_list[i]
txt_path = label_folder + "\\" + label_list[i]
draw_box_in_single_image(image_path, txt_path)
结果展示：