摔倒检测+yolov5_le2i fall detection dataset

数据集来源

开始使用目标检测检测人体，再通过宽高比值变化来判断是否摔倒，该方法在密集场景不管用，所以想着找一个摔倒数据集来进行检测是否摔倒，附上数据集来源：
中国华录杯——定向算法赛（人体摔倒姿态识别）这个训练集数据集我没有找到，官网下载的也只是未标注的测试集
Le2i Fall detection Dataset
• Annotations

• 标签格式为txt格式
• 标签前两行为跌倒帧数的起始与终止点，也存在没有这两行的文件
• 之后一行就是六个字段，帧编号， XX，人物bbx

Le2i Fall detection Dataset 原文下载速度不敢恭维，附上 百度云下载链接, 提取码：qghr
中国华录杯——定向算法赛（人体摔倒姿态识别）测试集，无标注，自己找的一些算法打的标注，还算过得去，需要自取百度云 百度云下载链接 , 提取码：asdh
以上两个数据集要是侵权了会删除

数据的处理

对Le2i Fall detection Dataset数据转换成voc格式的xml文件：

1. 数据集解压后如下图，不知道有两个场景是没有标注文件的，号称假摔，所以我把他删了
   
   
2. 解压后运行如下代码

# coding=utf-8
"""
@Time: 2021/3/9 10:23
@IDE: PyCharm
@Author: chengzhang
@File: processVideo.py
"""
import os
from tqdm import tqdm
import cv2
from xml.dom.minidom import Document

count = 0


def process(fileDir):
    dataset = [os.path.join(fileDir, i) for i in os.listdir(fileDir)]

    for dir in dataset:
        Annotation = os.path.join(dir, "Annotation_files")
        Annotations = [os.path.join(Annotation, i) for i in os.listdir(Annotation)]
        for ann in tqdm(Annotations):
            txt = open(ann).readlines()
            if len(txt[0].split(',')) < 2:
                videoPath = ann.replace('Annotation_files', 'Videos').replace('.txt', '.avi')
                start, end = int(txt[0].replace('\n', '')), int(txt[1].replace('\n', ''))
                video(videoPath, txt, start, end)


def video(videoPath, txt, start, end):
    global count
    vs = cv2.VideoCapture(videoPath)
    ind = 1
    while True:
        success, frame = vs.read()
        if frame is None:
            break
        if (ind + 8) >= start and ind <= end:
            Jpg = '{:06d}.jpg'.format(count)
            outPutJpg = os.path.join(save, Jpg)
            outPutXML = outPutJpg.replace('jpg', 'xml')
            cv2.imwrite(outPutJpg, frame)
            count += 1
            imgShape = frame.shape
            info = txt[ind].replace('\n', '').split(',')
            _, _, xmin, ymin, xmax, ymax = info
            bboxsInfo = [[int(xmin), int(ymin), int(xmax), int(ymax)]]
            bbox2xml(bboxsInfo, imgShape, Jpg, outPutXML)
        ind += 1

def bbox2xml(bboxsInfo, imgShape, outPutJpg, outPutXML):
    # for boxInfo in bboxsInfo:
    xmlBuilder = Document()
    annotation = xmlBuilder.createElement("annotation")  # 创建annotation标签
    xmlBuilder.appendChild(annotation)
    Pheight, Pwidth, Pdepth = imgShape

    folder = xmlBuilder.createElement("folder")  # folder标签
    folderContent = xmlBuilder.createTextNode("VOC2007")
    folder.appendChild(folderContent)
    annotation.appendChild(folder)

    filename = xmlBuilder.createElement("filename")  # filename标签
    filenameContent = xmlBuilder.createTextNode(outPutJpg)
    filename.appendChild(filenameContent)
    annotation.appendChild(filename)

    size = xmlBuilder.createElement("size")  # size标签
    width = xmlBuilder.createElement("width")  # size子标签width
    widthContent = xmlBuilder.createTextNode(str(Pwidth))
    width.appendChild(widthContent)
    size.appendChild(width)
    height = xmlBuilder.createElement("height")  # size子标签height
    heightContent = xmlBuilder.createTextNode(str(Pheight))
    height.appendChild(heightContent)
    size.appendChild(height)
    depth = xmlBuilder.createElement("depth")  # size子标签depth
    depthContent = xmlBuilder.createTextNode(str(Pdepth))
    depth.appendChild(depthContent)
    size.appendChild(depth)
    annotation.appendChild(size)

    for bbox in bboxsInfo:
        label = "fall"
        box_xmin, box_ymin, box_xmax, box_ymax = bbox

        object = xmlBuilder.createElement("object")
        picname = xmlBuilder.createElement("name")
        nameContent = xmlBuilder.createTextNode(label)
        picname.appendChild(nameContent)
        object.appendChild(picname)
        pose = xmlBuilder.createElement("pose")
        poseContent = xmlBuilder.createTextNode("Unspecified")
        pose.appendChild(poseContent)
        object.appendChild(pose)
        truncated = xmlBuilder.createElement("truncated")
        truncatedContent = xmlBuilder.createTextNode("0")
        truncated.appendChild(truncatedContent)
        object.appendChild(truncated)
        difficult = xmlBuilder.createElement("difficult")
        difficultContent = xmlBuilder.createTextNode("0")
        difficult.appendChild(difficultContent)
        object.appendChild(difficult)

        bndbox = xmlBuilder.createElement("bndbox")
        xmin = xmlBuilder.createElement("xmin")
        xminContent = xmlBuilder.createTextNode(str(box_xmin))
        xmin.appendChild(xminContent)
        bndbox.appendChild(xmin)
        ymin = xmlBuilder.createElement("ymin")
        yminContent = xmlBuilder.createTextNode(str(box_ymin))
        ymin.appendChild(yminContent)
        bndbox.appendChild(ymin)
        xmax = xmlBuilder.createElement("xmax")
        xmaxContent = xmlBuilder.createTextNode(str(box_xmax))
        xmax.appendChild(xmaxContent)
        bndbox.appendChild(xmax)
        ymax = xmlBuilder.createElement("ymax")
        ymaxContent = xmlBuilder.createTextNode(str(box_ymax))
        ymax.appendChild(ymaxContent)
        bndbox.appendChild(ymax)
        object.appendChild(bndbox)

        annotation.appendChild(object)
    f = open(outPutXML, 'w')
    xmlBuilder.writexml(f, indent='\t', newl='\n', addindent='\t', encoding='utf-8')

    f.close()


if __name__ == '__main__':
    fileDir = r"E:\数据集\FallDataset\dataset"  # 源数据集路径
    save = r"E:\数据集\FallDataset\FallDataset"  # 保存voc格式路径
    process(fileDir)
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
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134

模型训练

1. 使用U神的yolov5，链接
2. 将voc格式的数据集转换成yolov5所需要的格式，数据格式如下：其中txt标注为下面代码生成的归一化数据
   

# coding=utf-8
"""
@Time: 2020/9/17 9:07
@IDE: PyCharm
@Author: chengzhang
@File: voc2v5.py
"""
import os
import glob
from tqdm import tqdm
import xml.etree.ElementTree as ET
import shutil
import argparse
from random import shuffle
import cv2


def check_x_y_w_h(norm_x, norm_y, norm_w, norm_h):
    norm_x = 0.00001 if norm_x <= 0 else norm_x
    norm_x = 0.99999 if norm_x >= 1 else norm_x
    norm_y = 0.00001 if norm_y <= 0 else norm_y
    norm_y = 0.99999 if norm_y >= 1 else norm_y
    norm_w = 0.00001 if norm_w <= 0 else norm_w
    norm_w = 0.99999 if norm_w >= 1 else norm_w
    norm_h = 0.00001 if norm_h <= 0 else norm_h
    norm_h = 0.99999 if norm_h >= 1 else norm_h
    return norm_x, norm_y, norm_w, norm_h


def convert(size, box):
    dw = 1. / (size[0])
    dh = 1. / (size[1])
    x = (box[0] + box[1]) / 2.0 - 1
    y = (box[2] + box[3]) / 2.0 - 1
    w = box[1] - box[0]
    h = box[3] - box[2]
    x = x * dw
    w = w * dw
    y = y * dh
    h = h * dh
    x, y, w, h = check_x_y_w_h(x, y, w, h)
    return (x, y, w, h)


def write_txt(train_xml, dataset):
    with open(dataset, "w", encoding="utf-8") as f:
        for xml in tqdm(train_xml):

            f.write(xml.replace('.xml', '.jpg') + '\n')
            target_label_txt = xml.replace('.xml', '.txt')
            img = cv2.imread(xml.replace('.xml', '.jpg'))
            h, w, c = img.shape
            # 输入文件xml
            in_file = open(xml)
            # 输出label txt
            out_file = open(target_label_txt, 'w')
            tree = ET.parse(in_file)
            root = tree.getroot()
            for obj in root.iter('object'):
                try:
                    cls = obj.find('name').text
                except:
                    continue

                if cls not in classes:
                    continue
                xmlbox = obj.find('bndbox')
                b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
                     float(xmlbox.find('ymax').text))
                bb = convert((w, h), b)
                out_file.write(str(0) + " " + " ".join([str(a) for a in bb]) + '\n')


def check_dir(dir):
    try:
        shutil.rmtree(dir)
    except:
        os.makedirs(dir)
    if not os.path.exists(dir):
        os.makedirs(dir)


def parse_annotation(img_dir):
    all_imgs = []
    all_xmls = []
    for tmpdir in img_dir:
        for home, dirs, files in os.walk(tmpdir):
            for filename in files:
                # 文件名列表，包含完整路径
                if filename.endswith((".jpg", ".jpeg", ".png")):
                    filePath = os.path.join(home, filename)
                    all_imgs.append(filePath)
                elif filename.endswith(".xml"):
                    filePath = os.path.join(home, filename)
                    all_xmls.append(filePath)

    return all_xmls


if __name__ == '__main__':
    classes = ['fall']

    parser = argparse.ArgumentParser()
    parser.add_argument("--train_txt", default="/data/share/imageAlgorithm/zhangcheng/code/yolov5/data/train_fall.txt")
    parser.add_argument("--test_txt", default="/data/share/imageAlgorithm/zhangcheng/code/yolov5/data/val_fall.txt")

    flags = parser.parse_args()

    imgPathList = [
        "/data/share/imageAlgorithm/zhangcheng/dataset/falldataset/FallDataset/",
        "/data/share/imageAlgorithm/zhangcheng/dataset/falldataset/FallDataset_A/"
    ]

    dataset = parse_annotation(imgPathList)
    shuffle(dataset)
    num = len(dataset)
    trainNum = int(0.9 * num)

    trainList = dataset[: trainNum]
    testList = dataset[trainNum:]

    write_txt(trainList, flags.train_txt)
    write_txt(testList, flags.test_txt)
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
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123

3. 根据上面处理的数据进行训练

CUDA_VISIBLE_DEVICES=4,5,6,7 nohup python train.py --img 640 --batch 16 --epochs 600 --data ./data/fallDown.yaml --weights ./weights/yolov5l.pt > train_fall.log 2>&1 &
1

4. 训练曲线如下：
   30个epoch结果