yolov5实战-利用yolov5实现口罩佩戴检测算法并转换瑞芯微模型

一、参考文献

利用yolov5实现口罩佩戴检测算法(非常详细)
目标检测—数据集格式转化及训练集和验证集划分

二、下载数据集

在这附上博主用的口罩数据集链接：https://pan.baidu.com/s/1Gud8jemSCdjG00TYA74WpQ
提取码：sv74

下载之后是mask.zip，解压之后是有两个文件夹images和labels，images是图片这里大概8000张图片，这里的lables已经是txt（yolo的训练标签就是txt），而一般的标签都是xml格式。标签:0:no-mask,1:mask

三、转换数据集

这呢，推荐大家去看炮哥的这篇博客，目标检测—数据集格式转化及训练集和验证集划分
这里因为博主用的数据集因为标签已经是txt格式了，但我先将txt转xml格式，再用代码直接将xml格式转为yolo(txt)格式并划分训练集和测试集。(这里不直接用txt的格式直接划分，炮哥是这样解释的，txt划分后放入训练会出错)
本例子中我的做法将区别于他们所有人的做法，我是先用炮哥的代码把yolo的txt转换为xml；然后将所有的images和labels放入一个img的文件夹，然后用自己的代码划分训练集和验证集。

1、新建文件夹

在/data/下新建voc_data文件夹，在voc_data文件夹下新建Annotations，JPEGImages，YOLO

• Annotations：存放转换之后的xml标注
• JPEGImages：将解压后的images中的图片全部拷贝到此
• YOLO：将解压后的txt文件全部拷贝到次

2、将txt的标注转换为xml

在data目录下新建yolo_to_voc.py，注意main方法中的路径

from xml.dom.minidom import Document
import os
import cv2
 
# 参考链接：https://blog.csdn.net/didiaopao/article/details/120022845
 
# def makexml(txtPath, xmlPath, picPath):  # txt所在文件夹路径，xml文件保存路径，图片所在文件夹路径
def makexml(picPath, txtPath, xmlPath):  # txt所在文件夹路径，xml文件保存路径，图片所在文件夹路径
    """此函数用于将yolo格式txt标注文件转换为voc格式xml标注文件
    在自己的标注图片文件夹下建三个子文件夹，分别命名为picture、txt、xml
    """
    dic = {'0': "no-mask",  # 创建字典用来对类型进行转换
           '1': "mask",  # 此处的字典要与自己的classes.txt文件中的类对应，且顺序要一致
           }
    files = os.listdir(txtPath)
    for i, name in enumerate(files):
        xmlBuilder = Document()
        annotation = xmlBuilder.createElement("annotation")  # 创建annotation标签
        xmlBuilder.appendChild(annotation)
        txtFile = open(txtPath + name)
        print("文件：",txtPath + name)
        txtList = txtFile.readlines()
        img = cv2.imread(picPath + name[0:-4] + ".jpg")
        Pheight, Pwidth, Pdepth = img.shape
 
        folder = xmlBuilder.createElement("folder")  # folder标签
        foldercontent = xmlBuilder.createTextNode("driving_annotation_dataset")
        folder.appendChild(foldercontent)
        annotation.appendChild(folder)  # folder标签结束
 
        filename = xmlBuilder.createElement("filename")  # filename标签
        filenamecontent = xmlBuilder.createTextNode(name[0:-4] + ".jpg")
        filename.appendChild(filenamecontent)
        annotation.appendChild(filename)  # filename标签结束
 
        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)  # size子标签width结束
 
        height = xmlBuilder.createElement("height")  # size子标签height
        heightcontent = xmlBuilder.createTextNode(str(Pheight))
        height.appendChild(heightcontent)
        size.appendChild(height)  # size子标签height结束
 
        depth = xmlBuilder.createElement("depth")  # size子标签depth
        depthcontent = xmlBuilder.createTextNode(str(Pdepth))
        depth.appendChild(depthcontent)
        size.appendChild(depth)  # size子标签depth结束
 
        annotation.appendChild(size)  # size标签结束
 
        for j in txtList:
            oneline = j.strip().split(" ")
            object = xmlBuilder.createElement("object")  # object 标签
            picname = xmlBuilder.createElement("name")  # name标签
            namecontent = xmlBuilder.createTextNode(dic[oneline[0]])
            picname.appendChild(namecontent)
            object.appendChild(picname)  # name标签结束
 
            pose = xmlBuilder.createElement("pose")  # pose标签
            posecontent = xmlBuilder.createTextNode("Unspecified")
            pose.appendChild(posecontent)
            object.appendChild(pose)  # pose标签结束
 
            truncated = xmlBuilder.createElement("truncated")  # truncated标签
            truncatedContent = xmlBuilder.createTextNode("0")
            truncated.appendChild(truncatedContent)
            object.appendChild(truncated)  # truncated标签结束
 
            difficult = xmlBuilder.createElement("difficult")  # difficult标签
            difficultcontent = xmlBuilder.createTextNode("0")
            difficult.appendChild(difficultcontent)
            object.appendChild(difficult)  # difficult标签结束
 
            bndbox = xmlBuilder.createElement("bndbox")  # bndbox标签
            xmin = xmlBuilder.createElement("xmin")  # xmin标签
            mathData = int(((float(oneline[1])) * Pwidth + 1) - (float(oneline[3])) * 0.5 * Pwidth)
            xminContent = xmlBuilder.createTextNode(str(mathData))
            xmin.appendChild(xminContent)
            bndbox.appendChild(xmin)  # xmin标签结束
 
            ymin = xmlBuilder.createElement("ymin")  # ymin标签
            mathData = int(((float(oneline[2])) * Pheight + 1) - (float(oneline[4])) * 0.5 * Pheight)
            yminContent = xmlBuilder.createTextNode(str(mathData))
            ymin.appendChild(yminContent)
            bndbox.appendChild(ymin)  # ymin标签结束
 
            xmax = xmlBuilder.createElement("xmax")  # xmax标签
            mathData = int(((float(oneline[1])) * Pwidth + 1) + (float(oneline[3])) * 0.5 * Pwidth)
            xmaxContent = xmlBuilder.createTextNode(str(mathData))
            xmax.appendChild(xmaxContent)
            bndbox.appendChild(xmax)  # xmax标签结束
 
            ymax = xmlBuilder.createElement("ymax")  # ymax标签
            mathData = int(((float(oneline[2])) * Pheight + 1) + (float(oneline[4])) * 0.5 * Pheight)
            ymaxContent = xmlBuilder.createTextNode(str(mathData))
            ymax.appendChild(ymaxContent)
            bndbox.appendChild(ymax)  # ymax标签结束
 
            object.appendChild(bndbox)  # bndbox标签结束
 
            annotation.appendChild(object)  # object标签结束
 
        f = open(xmlPath + name[0:-4] + ".xml", 'w')
        xmlBuilder.writexml(f, indent='\t', newl='\n', addindent='\t', encoding='utf-8')
        f.close()
 
if __name__ == "__main__":
    # picPath = "VOCdevkit/VOC2007/JPEGImages/"  # 图片所在文件夹路径，后面的/一定要带上
    # txtPath = "VOCdevkit/VOC2007/YOLO/"  # txt所在文件夹路径，后面的/一定要带上
    # xmlPath = "VOCdevkit/VOC2007/Annotations/"  # xml文件保存路径，后面的/一定要带上
    picPath = "voc_data/JPEGImages/"  # 图片所在文件夹路径，后面的/一定要带上
    txtPath = "voc_data/YOLO/"  # txt所在文件夹路径，后面的/一定要带上
    xmlPath = "voc_data/Annotations/"  # xml文件保存路径，后面的/一定要带上
    makexml(picPath, txtPath, xmlPath)
 

执行转换

python yolo_to_voc.py

转换之后在data/voc_data/Annotations中就都是xml了

注意数据集中有个labels是错误的，类型是none，需要删除否则程序会报错，可以在代码的21行加上print(“文件：”,txtPath + name)，找到具体哪个文件有问题。

3、划分训练集和验证集

将voc_data/Annotations和JPEGImages都拷贝到data/img目录下

cp -rp data/voc_data/Annotations/* data/img/
cp -rp data/voc_data/JPEGImages/* data/img/

拷贝划分程序process-date文件夹到data目录下

注意修改create_all.py中的类别，然后执行

python create_all.py

本代码会自动划分训练集和验证集，并把错误的图片或者标注文件筛选出来

四、训练

我们使用yolov5l.pt的预训练模型来训练

1、准备mask.yml

在data目录下新建mask.yml。指定路径和类别

2、修改models/yolov5l.yaml

修改识别类型

3、新建train_mask.py

cp train.py train_mask.py

重点修改如下

epochs设置训练300轮
batch-size设置10，可以根据gpu的性能设置，默认是16

4、训练

python train_mask.py

5、生成的模型

五、推理验证

1、新建detect_mask.py

cp detect.py detect_mask.py

修改如下配置

2、推理图片

python detect_mask.py --source data/test/

3、推理视频

下载行人视频

lux https://www.bilibili.com/video/BV1Q54y1L74D

推理

python detect_mask.py --source D:\ai\mask.mp4

六、转换瑞芯微

1、导出中间模型

修改models/yolo.py

修改注释，切记导出完之后改回去，否则推理，训练都会报错

导出

cp export.py export_mask.py

修改如下

python export_mask.py

会在best.pt目录下生成best.torchscript.pt

2、中间模型转换瑞芯微模型

rk工具路径：

/cnn/rknn/rknn-toolkit-master/examples/pytorch/yolov5

拷贝中间模型

将best.torchscript.pt拷贝到rk工具路径中

mv best.torchscript.pt /cnn/rknn/rknn-toolkit-master/examples/pytorch/yolov5/mask.torchscript.pt

新建convert_mask.py

代码如下，重点

• PT_MODEL = ‘mask.torchscript.pt’
• RKNN_MODEL = ‘mask.rknn’

import os
import numpy as np
import cv2
from rknn.api import RKNN


PT_MODEL = 'mask.torchscript.pt'
RKNN_MODEL = 'mask.rknn'

IMG_PATH = 'bus.jpg'
DATASET = './dataset.txt'

# QUANTIZE_ON = False
QUANTIZE_ON = True

BOX_THRESH = 0.5
NMS_THRESH = 0.6
IMG_SIZE = 640

CLASSES = ("person")

def sigmoid(x):
    return 1 / (1 + np.exp(-x))

def xywh2xyxy(x):
    # Convert [x, y, w, h] to [x1, y1, x2, y2]
    y = np.copy(x)
    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
    return y

def process(input, mask, anchors):

    anchors = [anchors[i] for i in mask]
    grid_h, grid_w = map(int, input.shape[0:2])

    box_confidence = sigmoid(input[..., 4])
    box_confidence = np.expand_dims(box_confidence, axis=-1)

    box_class_probs = sigmoid(input[..., 5:])

    box_xy = sigmoid(input[..., :2])*2 - 0.5

    col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w)
    row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h)
    col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
    row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
    grid = np.concatenate((col, row), axis=-1)
    box_xy += grid
    box_xy *= int(IMG_SIZE/grid_h)

    box_wh = pow(sigmoid(input[..., 2:4])*2, 2)
    box_wh = box_wh * anchors

    box = np.concatenate((box_xy, box_wh), axis=-1)

    return box, box_confidence, box_class_probs

def filter_boxes(boxes, box_confidences, box_class_probs):
    """Filter boxes with box threshold. It's a bit different with origin yolov5 post process!

    # Arguments
        boxes: ndarray, boxes of objects.
        box_confidences: ndarray, confidences of objects.
        box_class_probs: ndarray, class_probs of objects.

    # Returns
        boxes: ndarray, filtered boxes.
        classes: ndarray, classes for boxes.
        scores: ndarray, scores for boxes.
    """
    box_classes = np.argmax(box_class_probs, axis=-1)
    box_class_scores = np.max(box_class_probs, axis=-1)
    pos = np.where(box_confidences[...,0] >= BOX_THRESH)


    boxes = boxes[pos]
    classes = box_classes[pos]
    scores = box_class_scores[pos]

    return boxes, classes, scores

def nms_boxes(boxes, scores):
    """Suppress non-maximal boxes.

    # Arguments
        boxes: ndarray, boxes of objects.
        scores: ndarray, scores of objects.

    # Returns
        keep: ndarray, index of effective boxes.
    """
    x = boxes[:, 0]
    y = boxes[:, 1]
    w = boxes[:, 2] - boxes[:, 0]
    h = boxes[:, 3] - boxes[:, 1]

    areas = w * h
    order = scores.argsort()[::-1]

    keep = []
    while order.size > 0:
        i = order[0]
        keep.append(i)

        xx1 = np.maximum(x[i], x[order[1:]])
        yy1 = np.maximum(y[i], y[order[1:]])
        xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])
        yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])

        w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)
        h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)
        inter = w1 * h1

        ovr = inter / (areas[i] + areas[order[1:]] - inter)
        inds = np.where(ovr <= NMS_THRESH)[0]
        order = order[inds + 1]
    keep = np.array(keep)
    return keep


def yolov5_post_process(input_data):
    masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
    anchors = [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
              [59, 119], [116, 90], [156, 198], [373, 326]]

    boxes, classes, scores = [], [], []
    for input,mask in zip(input_data, masks):
        b, c, s = process(input, mask, anchors)
        b, c, s = filter_boxes(b, c, s)
        boxes.append(b)
        classes.append(c)
        scores.append(s)

    boxes = np.concatenate(boxes)
    boxes = xywh2xyxy(boxes)
    classes = np.concatenate(classes)
    scores = np.concatenate(scores)

    nboxes, nclasses, nscores = [], [], []
    for c in set(classes):
        inds = np.where(classes == c)
        b = boxes[inds]
        c = classes[inds]
        s = scores[inds]

        keep = nms_boxes(b, s)

        nboxes.append(b[keep])
        nclasses.append(c[keep])
        nscores.append(s[keep])

    if not nclasses and not nscores:
        return None, None, None

    boxes = np.concatenate(nboxes)
    classes = np.concatenate(nclasses)
    scores = np.concatenate(nscores)

    return boxes, classes, scores

def draw(image, boxes, scores, classes):
    """Draw the boxes on the image.

    # Argument:
        image: original image.
        boxes: ndarray, boxes of objects.
        classes: ndarray, classes of objects.
        scores: ndarray, scores of objects.
        all_classes: all classes name.
    """
    for box, score, cl in zip(boxes, scores, classes):
        top, left, right, bottom = box
        print('class: {}, score: {}'.format(CLASSES[cl], score))
        print('box coordinate left,top,right,down: [{}, {}, {}, {}]'.format(top, left, right, bottom))
        top = int(top)
        left = int(left)
        right = int(right)
        bottom = int(bottom)

        cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2)
        cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),
                    (top, left - 6),
                    cv2.FONT_HERSHEY_SIMPLEX,
                    0.6, (0, 0, 255), 2)


def letterbox(im, new_shape=(640, 640), color=(0, 0, 0)):
    # Resize and pad image while meeting stride-multiple constraints
    shape = im.shape[:2]  # current shape [height, width]
    if isinstance(new_shape, int):
        new_shape = (new_shape, new_shape)

    # Scale ratio (new / old)
    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])

    # Compute padding
    ratio = r, r  # width, height ratios
    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding

    dw /= 2  # divide padding into 2 sides
    dh /= 2

    if shape[::-1] != new_unpad:  # resize
        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
    return im, ratio, (dw, dh)



if __name__ == '__main__':

    # Create RKNN object
    rknn = RKNN(verbose=False)

    if not os.path.exists(PT_MODEL):
        print('model not exist')
        exit(-1)
    _force_builtin_perm = False
    # pre-process config
    print('--> Config model')
    rknn.config(
                reorder_channel='2 1 0',
                mean_values=[[0, 0, 0]],
                std_values=[[255, 255, 255]],
                optimization_level=3,
                # target_platform = 'rk1808',
                target_platform='rv1126',
                quantize_input_node= QUANTIZE_ON,
                output_optimize=1,
                force_builtin_perm=_force_builtin_perm)
    print('done')

    # Load ONNX model
    print('--> Loading model')
    ret = rknn.load_pytorch(model=PT_MODEL, input_size_list=[[3,640, 640]])
    if ret != 0:
        print('Load yolov5 failed!')
        exit(ret)
    print('done')

    # Build model
    print('--> Building model')
    ret = rknn.build(do_quantization=QUANTIZE_ON, dataset=DATASET, pre_compile=True)
    if ret != 0:
        print('Build yolov5 failed!')
        exit(ret)
    print('done')

    # Export RKNN model
    print('--> Export RKNN model')
    ret = rknn.export_rknn(RKNN_MODEL)
    if ret != 0:
        print('Export yolov5rknn failed!')
        exit(ret)
    print('done')

    # # exit()
    # # init runtime environment
    # print('--> Init runtime environment')
    # # ret = rknn.init_runtime()
    # ret = rknn.init_runtime('rv1109', device_id='1109')
    # # ret = rknn.init_runtime('rk1808', device_id='1808')
    # if ret != 0:
    #     print('Init runtime environment failed')
    #     exit(ret)
    # print('done')

    # # Set inputs
    # img = cv2.imread(IMG_PATH)
    # img, ratio, (dw, dh) = letterbox(img, new_shape=(IMG_SIZE, IMG_SIZE))
    # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    # # Inference
    # print('--> Running model')
    # outputs = rknn.inference(inputs=[img], inputs_pass_through=[0 if not _force_builtin_perm else 1])

    # # post process
    # input0_data = outputs[0]
    # input1_data = outputs[1]
    # input2_data = outputs[2]

    # input0_data = input0_data.reshape([3,-1]+list(input0_data.shape[-2:]))
    # input1_data = input1_data.reshape([3,-1]+list(input1_data.shape[-2:]))
    # input2_data = input2_data.reshape([3,-1]+list(input2_data.shape[-2:]))

    # input_data = list()
    # input_data.append(np.transpose(input0_data, (2, 3, 0, 1)))
    # input_data.append(np.transpose(input1_data, (2, 3, 0, 1)))
    # input_data.append(np.transpose(input2_data, (2, 3, 0, 1)))

    # boxes, classes, scores = yolov5_post_process(input_data)

    # img_1 = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
    # if boxes is not None:
    #     draw(img_1, boxes, scores, classes)
    # cv2.imshow("post process result", img_1)
    # cv2.waitKeyEx(0)

    rknn.release()

3、执行转换

python convert_mask.py

则会在当前目录下生成mask.rknn