Times are progressing, yolo is progressing, and I am still taking steps. I did a little bit of detect and pose in v8 and recorded it. I still want to complain about why this model is placed deep in this file, as shown in the picture.
The following tutorials should only be used to directly apply yolov8 without modification. I used to work in v7 environment, directly
pip install ultralytics
1. detect
Create a new dataset under the detect folder to put pictures (jpg) and labels in yolo format (txt). The training set and test set are directly divided, and then create a new data.yaml, as shown in the figure, and put your own path and category.
Put a detection box json into yolo code, change the category and folder path
import os
import json
import numpy as np
# 类和索引
CLASSES=["fish"]
def convert(size,box):
'''''
input:
size:(width,height);
box:(x1,x2,y1,y2)
output:
(x,y,w,h)
'''
dw=1./size[0]
dh=1./size[1]
x=(box[0]+box[1])/2.0
y=(box[2]+box[3])/2.0
w=box[1]-box[0]
h=box[3]-box[2]
x=x*dw
w=w*dw
y=y*dh
h=h*dh
return (x,y,w,h)
# json -> txt
def json2txt(path_json,path_txt):
# print(path_json,"r")
with open(path_json,"r") as path_json:
jsonx=json.load(path_json)
width=int(jsonx["imageWidth"]) # 原图的宽
height=int(jsonx["imageHeight"]) # 原图的高
with open(path_txt,"w+") as ftxt:
# 遍历每一个bbox对象
for shape in jsonx["shapes"]:
obj_cls=str(shape["label"]) # 获取类别
cls_id=CLASSES.index(obj_cls) # 获取类别索引
points=np.array(shape["points"]) # 获取(x1,y1,x2,y2)
x1=int(points[0][0])
y1=int(points[0][1])
x2=int(points[1][0])
y2=int(points[1][1])
# (左上角,右下角) -> (中心点,宽高) 归一化
bb=convert((width,height),(x1,x2,y1,y2))
ftxt.write(str(cls_id)+" "+" ".join([str(a) for a in bb])+"\n")
if __name__=="__main__":
# json文件夹
dir_json="C:\\Users\\ASUS\\Desktop\\111\\"
# txt文件夹
dir_txt="C:\\Users\\ASUS\\Desktop\\222\\"
if not os.path.exists(dir_txt):
os.makedirs(dir_txt)
# 得到所有json文件
list_json=os.listdir(dir_json)
# 遍历每一个json文件,转成txt文件
for cnt,json_name in enumerate(list_json):
print("cnt=%d,name=%s"%(cnt,json_name))
path_txt=dir_txt+json_name.replace(".json",".txt")
path_json = dir_json + json_name
print("path_json\t",path_json)
print("path_txt\t",path_txt)
# (x1,y1,x2,y2)->(x,y,w,h)
json2txt(path_json,path_txt)
When you're ready, just input it directly into the terminal. But if you want to change something, for example, if you want a category not to be output during prediction, just output the box. He can't change it, because the ultra package has been integrated and encapsulated. Seriously, if you want to make improvements on this model, you have to remove it and then work on it.
#训练的代码
yolo task=detect mode=train model=yolov8s.yaml data=D:/DATA/ultralytics-main/ultralytics/models/yolo/detect/data.yaml epochs=200 batch=128
# 预测的代码
yolo task=detect mode=predict model=D:/DATA/ultralytics-main/weights/best.pt source=D:/DATA/ultralytics-main/ultralytics/models/yolo/detect/dataset/images/val device=cpu2. pose
The pose data set is a little different from the previous one. When marking key points first, you must first use a rectangle to frame the target, and then mark the key points in this rectangle. You must ensure that the number of points in each photo is The same means that 1234 must correspond, each point is marked in order, and the total number needs to be the same. 3 can be blocked, but it can also be marked, and then just make this point invisible. Finally, we get a .json file, and then we need to convert it into a .txt file. 2 means visible and 0 means invisible. The code for the transfer is below, it works for me.
Then just follow the same command as above.
# 关键点检测json转txt
import os
import json
import shutil
import time
import numpy as np
from tqdm import tqdm
Dataset_root = 'C:/Users/ASUS/Desktop/strong121/labels/' # 转化的json文件地址
# 框的类别
bbox_class =["fish"]
# 关键点的类别，有多少类就写多少
keypoint_class = ['1', '2', '3','4', '5', '6', '7', '8', '9', '10', '11', '12',
'13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23',
'24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34',
'35', '36', '37', '38', '39', '40', '41', '42', '43', '44']
os.chdir(Dataset_root)
def process_single_json(labelme_path, save_folder='C:/Users/ASUS/Desktop/no/'):
with open(labelme_path, 'r', encoding='utf-8') as f:
labelme = json.load(f)
img_width = labelme['imageWidth'] # 图像宽度
img_height = labelme['imageHeight'] # 图像高度
# 生成 YOLO 格式的 txt 文件
suffix = labelme_path.split('.')[-2]
yolo_txt_path = suffix + '.txt'
with open(yolo_txt_path, 'w', encoding='utf-8') as f:
for each_ann in labelme['shapes']: # 遍历每个标注
if each_ann['shape_type'] == 'rectangle': # 每个框，在 txt 里写一行
yolo_str = ''
## 框的信息
# 框的类别 ID
bbox_class_id = bbox_class.index(each_ann['label'])
# print(bbox_class_id)
yolo_str += '{} '.format(bbox_class_id)
# 左上角和右下角的 XY 像素坐标
bbox_top_left_x = int(min(each_ann['points'][0][0], each_ann['points'][1][0]))
bbox_bottom_right_x = int(max(each_ann['points'][0][0], each_ann['points'][1][0]))
bbox_top_left_y = int(min(each_ann['points'][0][1], each_ann['points'][1][1]))
bbox_bottom_right_y = int(max(each_ann['points'][0][1], each_ann['points'][1][1]))
# 框中心点的 XY 像素坐标
bbox_center_x = int((bbox_top_left_x + bbox_bottom_right_x) / 2)
bbox_center_y = int((bbox_top_left_y + bbox_bottom_right_y) / 2)
# 框宽度
bbox_width = bbox_bottom_right_x - bbox_top_left_x
# 框高度
bbox_height = bbox_bottom_right_y - bbox_top_left_y
# 框中心点归一化坐标
bbox_center_x_norm = bbox_center_x / img_width
bbox_center_y_norm = bbox_center_y / img_height
# 框归一化宽度
bbox_width_norm = bbox_width / img_width
# 框归一化高度
bbox_height_norm = bbox_height / img_height
yolo_str += '{:.5f} {:.5f} {:.5f} {:.5f} '.format(bbox_center_x_norm, bbox_center_y_norm,
bbox_width_norm, bbox_height_norm)
# print(yolo_str)
# print("**********************")
# time.sleep(90000)
## 找到该框中所有关键点，存在字典 bbox_keypoints_dict 中
bbox_keypoints_dict = {}
for each_ann in labelme['shapes']: # 遍历所有标注
if each_ann['shape_type'] == 'point': # 筛选出关键点标注
# 关键点XY坐标、类别
x = int(each_ann['points'][0][0])
y = int(each_ann['points'][0][1])
label = each_ann['label']
if (x > bbox_top_left_x) & (x < bbox_bottom_right_x) & (y < bbox_bottom_right_y) & (
y > bbox_top_left_y): # 筛选出在该个体框中的关键点
bbox_keypoints_dict[label] = [x, y]
## 把关键点按顺序排好
for each_class in keypoint_class: # 遍历每一类关键点
if each_class in bbox_keypoints_dict:
keypoint_x_norm = bbox_keypoints_dict[each_class][0] / img_width
keypoint_y_norm = bbox_keypoints_dict[each_class][1] / img_height
yolo_str += '{:.5f} {:.5f} {} '.format(keypoint_x_norm, keypoint_y_norm,
2) # 2-可见不遮挡 1-遮挡 0-没有点
else: # 不存在的点，一律为0
yolo_str += '0 0 0 '
# 写入 txt 文件中
f.write(yolo_str + '\n')
shutil.move(yolo_txt_path, save_folder)
print('{} --> {} 转换完成'.format(labelme_path, yolo_txt_path))
save_folder = 'C:/Users/ASUS/Desktop/no' # 转换后的训练集标注文件至目录
for labelme_path in os.listdir(Dataset_root):
# try:
process_single_json(Dataset_root + labelme_path, save_folder=save_folder)
# except:
# print('******有误******', labelme_path)
print('YOLO格式的txt标注文件已保存至 ', save_folder)