目标检测yolo格式与labelme标注互相转换及其可视化

          yolo目标检测数据采用矩形框进行标注，其标注格式为[cls_id xp yp wp hp]，cls_id表示目标所属的类别序号。xp、yp表示目标中心点相对坐标，其中xp等于目标的绝对横坐标除以图像宽度，yp等于目标的绝对纵坐标除以图像高度。wp和hp表示目标的相对宽度和高度，其中wp等于目标的绝对宽度除以图像宽度，hp等于目标的绝对高度除以图像高度。每张图片的标注结果以txt文本文件存储，每一行[cls_id xp yp wp hp]表示一个目标。

cv_img=cv2.imdecode(np.fromfile(imagePath,dtype=np.uint8),flags=cv2.IMREAD_COLOR)

        labelme矩形目标的标注格式为[x1 y1 x2 y2]，表示目标的左上和右下的绝对坐标。

1 yolo转labelme 

        yolo转labelme标注程序如下所示：

# -*- coding: utf-8 -*-
"""
Created on Tue Mar 29 17:42:11 2022
@author: https://blog.csdn.net/suiyingy?type=blog
"""
import cv2
import os
import json
import shutil
import numpy as np
from pathlib import Path

id2cls = {0:'pig'}

def xyxy2labelme(labels, w, h, image_path, save_dir='res/'):
    save_dir = str(Path(save_dir)) + '/'
    if not os.path.exists(save_dir):
        os.makedirs(save_dir)
    label_dict = {}
    label_dict['version'] = '5.0.1'
    label_dict['flags'] = {}
    label_dict['imageData'] = None
    label_dict['imagePath'] = image_path
    label_dict['imageHeight'] = h
    label_dict['imageWidth'] = w
    label_dict['shapes'] = []
    for l in labels:
        tmp = {}
        tmp['label'] = id2cls[int(l[0])]
        tmp['points'] =[[l[1], l[2]], [l[3], l[4]]]
        tmp['group_id']= None
        tmp['shape_type'] = 'rectangle'
        tmp['flags'] = {}
        label_dict['shapes'].append(tmp)    
    fn = save_dir+image_path.rsplit('.', 1)[0]+'.json'
    with open(fn, 'w') as f:
        json.dump(label_dict, f)

def yolo2labelme(yolo_image_dir, yolo_label_dir, save_dir='res/'):
    yolo_image_dir = str(Path(yolo_image_dir)) + '/'
    yolo_label_dir = str(Path(yolo_label_dir)) + '/'
    save_dir = str(Path(save_dir)) + '/'
    image_files = os.listdir(yolo_image_dir)
    for iimgf, imgf in enumerate(image_files):
        print(iimgf+1, '/', len(image_files), imgf)
        fn = imgf.rsplit('.', 1)[0]
        shutil.copy(yolo_image_dir + imgf, save_dir + imgf)
        image = cv2.imread(yolo_image_dir + imgf)
        h,w = image.shape[:2]
        if not os.path.exists(yolo_label_dir + fn + '.txt'):
            continue
        labels = np.loadtxt(yolo_label_dir + fn + '.txt').reshape(-1, 5)
        if len(labels) < 1:
            continue
        labels[:,1::2] = w * labels[:, 1::2]
        labels[:,2::2] = h * labels[:, 2::2]
        labels_xyxy = np.zeros(labels.shape)
        labels_xyxy[:, 1] = np.clip(labels[:, 1] - labels[:, 3]/2, 0, w)
        labels_xyxy[:, 2] = np.clip(labels[:, 2] - labels[:, 4]/2, 0, h)
        labels_xyxy[:, 3] = np.clip(labels[:, 1] + labels[:, 3]/2, 0, w)
        labels_xyxy[:, 4] = np.clip(labels[:, 2] + labels[:, 4]/2, 0, h)
        xyxy2labelme(labels_xyxy, w, h, imgf, save_dir)
    print('Completed!')

if __name__ == '__main__':
    yolo_image_dir = r'H:\Data\pigs\images\train'
    yolo_label_dir = r'H:\Data\pigs\labels\train'
    save_dir = r'res/'
    yolo2labelme(yolo_image_dir, yolo_label_dir, save_dir)

 2 labelme转yolo

        labelme转yolo标注程序如下所示：

# -*- coding: utf-8 -*-
"""
Created on Tue Mar 29 17:42:11 2022
@author: https://blog.csdn.net/suiyingy?type=blog
"""
import cv2
import os
import json
import shutil
import numpy as np
from pathlib import Path
from glob import glob

id2cls = {0: 'pig'}
cls2id = {'pig': 0}

#支持中文路径
def cv_imread(filePath):
    cv_img=cv2.imdecode(np.fromfile(filePath,dtype=np.uint8),flags=cv2.IMREAD_COLOR)
    return cv_img

def labelme2yolo_single(label_file):
    anno= json.load(open(label_file, "r", encoding="utf-8"))
    shapes = anno['shapes']
    w0, h0 = anno['imageWidth'], anno['imageHeight']
    image_path = os.path.basename(anno['imagePath'])
    labels = []
    for s in shapes:
        pts = s['points']
        x1, y1 = pts[0]
        x2, y2 = pts[1]
        x = (x1 + x2) / 2 / w0 
        y = (y1 + y2) / 2 / h0
        w  = abs(x2 - x1) / w0
        h  = abs(y2 - y1) / h0
        cid = cls2id[s['label']]        
        labels.append([cid, x, y, w, h])
    return np.array(labels), image_path

def labelme2yolo(labelme_label_dir, save_dir='res/'):
    labelme_label_dir = str(Path(labelme_label_dir)) + '/'
    save_dir = str(Path(save_dir)) + '/'
    yolo_label_dir = save_dir + 'labels/'
    yolo_image_dir = save_dir + 'images/'
    if not os.path.exists(yolo_image_dir):
        os.makedirs(yolo_image_dir)
    if not os.path.exists(yolo_label_dir):
        os.makedirs(yolo_label_dir)

    json_files = glob(labelme_label_dir + '*.json')
    for ijf, jf in enumerate(json_files):
        print(ijf+1, '/', len(json_files), jf)
        filename = os.path.basename(jf).rsplit('.', 1)[0]
        labels, image_path = labelme2yolo_single(jf)
        if len(labels) > 0:
            np.savetxt(yolo_label_dir + filename + '.txt', labels)
            shutil.copy(labelme_label_dir + image_path, yolo_image_dir + image_path)
    print('Completed!')
    
if __name__ == '__main__':
    root_dir = r'D:\tmp\images'
    save_dir = r'res'
    labelme2yolo(root_dir, save_dir)

3 labelme标注可视化 

        labelme标注结果可视化程序如下所示：

# -*- coding: utf-8 -*-
"""
Created on Tue Mar 29 17:42:11 2022
@author: https://blog.csdn.net/suiyingy?type=blog
"""
import cv2
import os
import json
import shutil
import numpy as np
from pathlib import Path
from glob import glob


id2cls = {0: 'pig'}
cls2id = {'pig': 0}
id2color = {0: (0, 255, 0)}

#支持中文路径
def cv_imread(filePath):
    cv_img=cv2.imdecode(np.fromfile(filePath,dtype=np.uint8),flags=cv2.IMREAD_COLOR)
    return cv_img

def get_labelme_info(label_file):
    anno= json.load(open(label_file, "r", encoding="utf-8"))
    shapes = anno['shapes']
    image_path = os.path.basename(anno['imagePath'])
    labels = []
    for s in shapes:
        pts = s['points']
        x1, y1 = pts[0]
        x2, y2 = pts[1]
        color = id2color[cls2id[s['label']]]
        labels.append([color, x1, y1, x2, y2])
    return labels, image_path

def vis_labelme(labelme_label_dir, save_dir='res/'):
    labelme_label_dir = str(Path(labelme_label_dir)) + '/'
    save_dir = str(Path(save_dir)) + '/'
    if not os.path.exists(save_dir):
        os.makedirs(save_dir)
    json_files = glob(labelme_label_dir + '*.json')
    for ijf, jf in enumerate(json_files):
        print(ijf+1, '/', len(json_files), jf)
        filename = os.path.basename(jf).rsplit('.', 1)[0]
        labels, image_path = get_labelme_info(jf)
        image = cv_imread(labelme_label_dir + image_path)
        for label in labels:
            color = label[0]
            x1, y1, x2, y2 = label[1:]
            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
            cv2.rectangle(image, (x1, y1), (x2, y2), color, 3)
        #显示图片
        # cv2.imshow(filename, image)
        # cv2.waitKey(0)
        #支持中文路径，保存图片
        cv2.imencode(os.path.splitext(image_path)[-1], image)[1].tofile(save_dir + image_path)
    print('Completed!')
    
if __name__ == '__main__':
    root_dir = r'D:\tmp\images'
    save_dir = r'res'
    vis_labelme(root_dir, save_dir)

4 yolo标注可视化

        yolo格式数据可视化程序如下所示：

# -*- coding: utf-8 -*-
"""
Created on Tue Mar 29 17:42:11 2022
@author: https://blog.csdn.net/suiyingy?type=blog
"""
import cv2
import os
import numpy as np
from pathlib import Path

id2cls = {0: 'pig'}
cls2id = {'pig': 0}
id2color = {0: (0, 255, 0)}

#支持中文路径
def cv_imread(filePath):
    cv_img=cv2.imdecode(np.fromfile(filePath,dtype=np.uint8),flags=cv2.IMREAD_COLOR)
    return cv_img
    
def vis_yolo(yolo_image_dir, yolo_label_dir, save_dir='res/'):
    yolo_image_dir = str(Path(yolo_image_dir)) + '/'
    yolo_label_dir = str(Path(yolo_label_dir)) + '/'
    save_dir = str(Path(save_dir)) + '/'
    if not os.path.exists(save_dir):
        os.makedirs(save_dir)
    image_files = os.listdir(yolo_image_dir)
    for iimgf, imgf in enumerate(image_files):
        print(iimgf+1, '/', len(image_files), imgf)
        fn = imgf.rsplit('.', 1)[0]
        image = cv_imread(yolo_image_dir + imgf)
        h,w = image.shape[:2]
        if not os.path.exists(yolo_label_dir + fn + '.txt'):
            continue
        labels = np.loadtxt(yolo_label_dir + fn + '.txt').reshape(-1, 5)
        if len(labels) > 0:
            labels[:,1::2] = w * labels[:, 1::2]
            labels[:,2::2] = h * labels[:, 2::2]
            labels_xyxy = np.zeros(labels.shape)
            labels_xyxy[:, 1] = np.clip(labels[:, 1] - labels[:, 3]/2, 0, w)
            labels_xyxy[:, 2] = np.clip(labels[:, 2] - labels[:, 4]/2, 0, h)
            labels_xyxy[:, 3] = np.clip(labels[:, 1] + labels[:, 3]/2, 0, w)
            labels_xyxy[:, 4] = np.clip(labels[:, 2] + labels[:, 4]/2, 0, h)
            for label in labels_xyxy:
                color = id2color[int(label[0])]
                x1, y1, x2, y2 = label[1:]
                x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
                cv2.rectangle(image, (x1, y1), (x2, y2), color, 3)
        cv2.imencode(os.path.splitext(imgf)[-1], image)[1].tofile(save_dir + imgf)
    print('Completed!')
        
if __name__ == '__main__':
    yolo_image_dir = r'H:\Data\pigs\images\train'
    yolo_label_dir = r'H:\Data\pigs\labels\train'
    save_dir = r'res1/'
    vis_yolo(yolo_image_dir, yolo_label_dir, save_dir)