Ultralytics YOLOv8?是由?Ultralytics?開(kāi)發(fā)的一個(gè)前沿的?SOTA?模型。它在以前成功的?YOLO?版本基礎(chǔ)上，引入了新的功能和改進(jìn)，進(jìn)一步提升了其性能和靈活性。YOLOv8?基于快速、準(zhǔn)確和易于使用的設(shè)計(jì)理念，使其成為廣泛的目標(biāo)檢測(cè)、圖像分割和圖像分類(lèi)任務(wù)的絕佳選擇。YOLOv5 自從 2020 年發(fā)布以來(lái)，一直是沒(méi)有論文的。而 YOLOv8（YOLOv5團(tuán)隊(duì)）這次首次承認(rèn)將先發(fā)布 arXiv 版本的論文（目前還在火速撰寫(xiě)中）。
1.環(huán)境安裝
YOLOv8 代碼鏈接：
GitHub - ultralytics/ultralytics: YOLOv8 ?? in PyTorch > ONNX > CoreML > TFLite
yolov8是一個(gè)工程性的代碼，訓(xùn)練、測(cè)試和配置都封裝得很便捷，下載源碼打開(kāi)后運(yùn)行以下命令安裝需要的環(huán)境依賴(lài)
pip install -r requirements.txt通過(guò)pip list命令查看torch版本，需要注意系統(tǒng)默認(rèn)安裝最新的cpu版的pytorch，需要自己安裝GPU版的PyTorch:?Previous PyTorch Versions | PyTorch
?環(huán)境安裝好后運(yùn)行v8文件夾的predict.py測(cè)試一下效果，控制臺(tái)沒(méi)有報(bào)錯(cuò)且顯示帶+cu11x的torch版本和GPU型號(hào)，說(shuō)明環(huán)境安裝沒(méi)問(wèn)題。
2. 制作數(shù)據(jù)集
yolov8提供的有已經(jīng)制作好的完整的數(shù)據(jù)集，運(yùn)行相關(guān)腳本可以直接下載現(xiàn)成的數(shù)據(jù)集。
本教程以coco-128數(shù)據(jù)集為例，在coco數(shù)據(jù)集80個(gè)類(lèi)基礎(chǔ)上，再訓(xùn)練一個(gè)自己的類(lèi)（飲水機(jī)）。首先，使用labelme標(biāo)注，標(biāo)注好后，用以下labelme2yoloseg.py代碼生成yolo數(shù)據(jù)集格式?。
'''
Created on Nov 2, 2022
@author: LULU LI
'''
import logging
import os
import string
import sys
import argparse
import shutil
import math
from collections import OrderedDict
import json
import cv2
import PIL.Image
from sklearn.model_selection import train_test_split
from labelme import utils
label_idx_map = {'water_dispenser':80}
class Labelme2YOLO(object):
def __init__(self, json_dir):
self._json_dir = json_dir
self._label_id_map = label_idx_map
def _make_train_val_dir(self):
self._label_dir_path = os.path.join(self._json_dir,
'YOLODataset/labels/')
self._image_dir_path = os.path.join(self._json_dir,
'YOLODataset/images/')
for yolo_path in (os.path.join(self._label_dir_path + 'train/'),
os.path.join(self._label_dir_path + 'val/'),
os.path.join(self._image_dir_path + 'train/'),
os.path.join(self._image_dir_path + 'val/')):
if os.path.exists(yolo_path):
shutil.rmtree(yolo_path)
os.makedirs(yolo_path)
def _get_label_id_map(self, json_dir):
# label_set = set()
#
# for file_name in os.listdir(json_dir):
# if file_name.endswith('json'):
# json_path = os.path.join(json_dir, file_name)
# data = json.load(open(json_path))
# for shape in data['shapes']:
# label_set.add(shape['label'].rstrip(string.digits).rstrip( '_' ).rstrip(string.digits))
return [(label, label_id) for label, label_id in label_idx_map]
def _train_test_split(self, folders, json_names, val_size):
if len(folders) > 0 and 'train' in folders and 'val' in folders:
train_folder = os.path.join(self._json_dir, 'train/')
train_json_names = [train_sample_name + '.json' \
for train_sample_name in os.listdir(train_folder) \
if os.path.isdir(os.path.join(train_folder, train_sample_name))]
val_folder = os.path.join(self._json_dir, 'val/')
val_json_names = [val_sample_name + '.json' \
for val_sample_name in os.listdir(val_folder) \
if os.path.isdir(os.path.join(val_folder, val_sample_name))]
return train_json_names, val_json_names
train_idxs, val_idxs = train_test_split(range(len(json_names)),
test_size=val_size)
train_json_names = [json_names[train_idx] for train_idx in train_idxs]
val_json_names = [json_names[val_idx] for val_idx in val_idxs]
return train_json_names, val_json_names
def convert(self, val_size):
json_names = [file_name for file_name in os.listdir(self._json_dir) \
if os.path.isfile(os.path.join(self._json_dir, file_name)) and \
file_name.endswith('.json')]
folders = [file_name for file_name in os.listdir(self._json_dir) \
if os.path.isdir(os.path.join(self._json_dir, file_name))]
train_json_names, val_json_names = self._train_test_split(folders, json_names, val_size)
self._make_train_val_dir()
# convert labelme object to yolo format object, and save them to files
# also get image from labelme json file and save them under images folder
for target_dir, json_names in zip(('train/', 'val/'),
(train_json_names, val_json_names)):
for json_name in json_names:
json_path = os.path.join(self._json_dir, json_name)
json_data = json.load(open(json_path))
print('Converting %s for %s ...' % (json_name, target_dir.replace('/', '')))
img_path = self._save_yolo_image(json_data,
json_name,
self._image_dir_path,
target_dir)
yolo_obj_list = self._get_yolo_object_list(json_data, img_path)
self._save_yolo_label(json_name,
self._label_dir_path,
target_dir,
yolo_obj_list)
print('Generating dataset.yaml file ...')
self._save_dataset_yaml()
def convert_one(self, json_name):
json_path = os.path.join(self._json_dir, json_name)
json_data = json.load(open(json_path))
print('Converting %s ...' % json_name)
img_path = self._save_yolo_image(json_data, json_name,
self._json_dir, '')
yolo_obj_list = self._get_yolo_object_list(json_data, img_path)
self._save_yolo_label(json_name, self._json_dir,
'', yolo_obj_list)
def _get_yolo_object_list(self, json_data, img_path):
yolo_obj_list = []
img_h, img_w, _ = cv2.imread(img_path).shape
for shape in json_data['shapes']:
# labelme circle shape is different from others
# it only has 2 points, 1st is circle center, 2nd is drag end point
try:
if shape['shape_type'] == 'circle':
yolo_obj = self._get_circle_shape_yolo_object(shape, img_h, img_w)
elif shape['shape_type'] == 'polygon': # lll
yolo_obj = self._get_polygon_shape_yolo_object(shape, img_h, img_w)
yolo_obj_list.append(yolo_obj)
elif shape['shape_type'] == 'rectangle':
yolo_obj = self._get_other_shape_yolo_object(shape, img_h, img_w)
except Exception as e:
logging.Logger(e)
return yolo_obj_list
def _get_circle_shape_yolo_object(self, shape, img_h, img_w):
obj_center_x, obj_center_y = shape['points'][0]
radius = math.sqrt((obj_center_x - shape['points'][1][0]) ** 2 +
(obj_center_y - shape['points'][1][1]) ** 2)
obj_w = 2 * radius
obj_h = 2 * radius
yolo_center_x = round(float(obj_center_x / img_w), 6)
yolo_center_y = round(float(obj_center_y / img_h), 6)
yolo_w = round(float(obj_w / img_w), 6)
yolo_h = round(float(obj_h / img_h), 6)
label_id = self._label_id_map[shape['label'].rstrip(string.digits).rstrip( '_' ).rstrip(string.digits)]
return label_id, yolo_center_x, yolo_center_y, yolo_w, yolo_h
def _get_other_shape_yolo_object(self, shape, img_h, img_w):
def __get_object_desc(obj_port_list):
__get_dist = lambda int_list: max(int_list) - min(int_list)
x_lists = [port[0] for port in obj_port_list]
y_lists = [port[1] for port in obj_port_list]
return min(x_lists), __get_dist(x_lists), min(y_lists), __get_dist(y_lists)
obj_x_min, obj_w, obj_y_min, obj_h = __get_object_desc(shape['points'])
yolo_center_x = round(float((obj_x_min + obj_w / 2.0) / img_w), 6)
yolo_center_y = round(float((obj_y_min + obj_h / 2.0) / img_h), 6)
yolo_w = round(float(obj_w / img_w), 6)
yolo_h = round(float(obj_h / img_h), 6)
label_id = self._label_id_map[shape['label'].rstrip(string.digits).rstrip( '_' ).rstrip(string.digits)]
return label_id, yolo_center_x, yolo_center_y, yolo_w, yolo_h
# compute polygon points # add by lll
def _get_polygon_shape_yolo_object(self, shape, img_h, img_w):
def __get_points_list(obj_port_list):
x_lists = [port[0] for port in obj_port_list]
y_lists = [port[1] for port in obj_port_list]
return x_lists, y_lists
label_id_polygon_points = []
label_id = self._label_id_map[shape['label'].rstrip(string.digits).rstrip( '_' ).rstrip(string.digits)]
label_id_polygon_points.append(label_id)
x_lists, y_lists = __get_points_list(shape['points'])
for x_point, y_point in zip(x_lists, y_lists):
yolo_x = round(float(x_point / img_w), 6)
label_id_polygon_points.append(yolo_x)
yolo_y = round(float(y_point / img_h), 6)
label_id_polygon_points.append(yolo_y)
return tuple(label_id_polygon_points)
def _save_yolo_label(self, json_name, label_dir_path, target_dir, yolo_obj_list):
txt_path = os.path.join(label_dir_path,
target_dir,
json_name.replace('.json', '.txt'))
with open(txt_path, 'w+') as f: # lll
for yolo_obj_idx, yolo_obj in enumerate(yolo_obj_list):
if len(yolo_obj) > 5: # lll
for point in yolo_obj:
point_line = '%s ' % point
f.write(point_line)
f.write('\n')
else:
yolo_obj_line = '%s %s %s %s %s\n' % yolo_obj \
if yolo_obj_idx + 1 != len(yolo_obj_list) else \
'%s %s %s %s %s' % yolo_obj
f.write(yolo_obj_line)
def _save_yolo_image(self, json_data, json_name, image_dir_path, target_dir):
img_name = json_name.replace('.json', '.png')
img_path = os.path.join(image_dir_path, target_dir, img_name)
I = PIL.Image.open(os.path.join(os.path.join(image_dir_path,"../../"),json_data['imagePath']))
I.save(img_path)
# if not os.path.exists(img_path):
# img = utils.img_b64_to_arr(json_data['imageData'])
# PIL.Image.fromarray(img).save(img_path)
return img_path
def _save_dataset_yaml(self):
yaml_path = os.path.join(self._json_dir, 'YOLODataset/', 'dataset.yaml')
with open(yaml_path, 'w+') as yaml_file:
yaml_file.write('train: %s\n' % \
os.path.join(self._image_dir_path, 'train/'))
yaml_file.write('val: %s\n\n' % \
os.path.join(self._image_dir_path, 'val/'))
yaml_file.write('nc: %i\n\n' % len(self._label_id_map))
names_str = ''
for label, _ in self._label_id_map.items():
names_str += "'%s', " % label
names_str = names_str.rstrip(', ')
yaml_file.write('names: [%s]' % names_str)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--json_dir', type=str, default='E:/datasets/background/training_data/segment',
help='Please input the path of the labelme json files.')
parser.add_argument('--val_size', type=float, nargs='?', default=0.1,
help='Please input the validation dataset size, for example 0.1 ')
parser.add_argument('--json_name', type=str, nargs='?', default=None,
help='If you put json name, it would convert only one json file to YOLO.')
args = parser.parse_args(sys.argv[1:])
convertor = Labelme2YOLO(args.json_dir)
if args.json_name is None:
convertor.convert(val_size=args.val_size)
else:
convertor.convert_one(args.json_name)轉(zhuǎn)化成功后YOLODataset下會(huì)生成images和labels兩個(gè)目錄，分別是圖像和標(biāo)簽。
3.修改配置
3.1 數(shù)據(jù)集配置
復(fù)制一份coco128-seg.yaml，作為自己的配置文件，將train和val路徑修改為圖片images下訓(xùn)練集和驗(yàn)證集路徑，不用指定label路徑，讀取數(shù)據(jù)集的時(shí)候label路徑是將圖片路徑中的‘images’替換成‘labels’獲取的
，
?3.2 修改類(lèi)別數(shù)
3.3 修改default配置
根據(jù)自己實(shí)際需要修改，我這里只修改訓(xùn)練輪數(shù)和batch-size,其他的用的默認(rèn)的
4.訓(xùn)練?
設(shè)置好配置文件和模型對(duì)應(yīng)的路徑，即可進(jìn)行訓(xùn)練，控制臺(tái)打印訓(xùn)練進(jìn)度。通過(guò)tensorboard可以查看具體訓(xùn)練效果，命令行輸入：tensorboard --logdir .\runs\segment\，瀏覽器打開(kāi)輸出的鏈接。
?
?文章來(lái)源地址http://www.zghlxwxcb.cn/news/detail-443505.html
?5.測(cè)試
將需要測(cè)試的圖片或者視頻放在assets目錄下，指定好權(quán)重和assets路徑
測(cè)試效果如下
?
?文章來(lái)源：http://www.zghlxwxcb.cn/news/detail-443505.html
?
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