数据标注费时费力，又费钱！深谙其苦的我开发了这个项目。（如果想快速使用请看前半部分） 大家好，我是大家的好朋友~ cv调包侠，深度学习算法攻城狮（实习僧）一枚， 下面我将诚心地发布一个自己的原创：Auto_maker!
他能干什么？
大家可以特别方便地通过我的Auto_maker 实现目标检测数据集的实时制作，包括：10分钟完成 真实数据采集，自动标注，转换，增强，并且可以直接进行yolov3， yolov4 ，yolov5，efficientdet等，并且可以直接导出成onnx，并使用openvino和tensorRT加速；除了检测以外，还支持分类算法，可以一分钟完成图片智能分类欢迎star~
同时他具有高精度，高实时性，高效率，他是人工标注的10倍以上效率，并且精度可控~
cv调包侠录制了一个视频讲解~大家也可以通过这篇文章得到更多的了解！
注意：为了更好的效果，您在开始使用后，先按下s键开始标注，按下b键开始保存，期间如果有不准确的地方，您有几个机会去调整boxes：1 再按下s键，重新描框即可；2您可以通过test_img 重新筛选删除图片。
github：https://github.com/CVUsers/Auto_maker
项目比较简单，cv调包侠不到半小时就把代码下面开始讲解项目结构和代码。剩下的就是在完善逻辑，优化用户体验了，改了许久，大家可以方便地使用，简单地体验~
在我们运行Auto_maker 前，需要安装opencv的库：opencv-contrib-python 库
pip install opencv-contrib-python
然后运行 get_images.py 就能看到实时图像，再按下"s"键就可以用鼠标绘制目标框，绘制完后回车一下~
然后按下“b”键就会看到控制台输出开始保存的提示~
然后我们可以左右上下地平移物体，如果内外前后地移动了物体后，追踪框若是发生了偏移，那么就请再按一下“s”重新标注一下~会继续保存图片到images文件夹中，同时也会生成xml到Annotations文件夹中。
就这样，大家不断地平移，切换场景，并且打开mix_up模式，会获得更多，更丰富的图片，这样对我们训练的模型也会更准确和更泛化。
尤其是目标检测中的多尺度问题，需要我们丰富数据及其标注框在图片中的相对大小来解决，我在mix_up 的同时使用了随机等比例缩放，获得更多样式~。
parser = argparse.ArgumentParser('Auto_maker')
parser.add_argument('-t', "--tracker", type=str, default='csrt', help='choose opencv tracker methods')
parser.add_argument('-i', '--source_path', type=str, default='0',
help='0 or 1 is your capture, or use video.mp4 or use path_dir like: ./images/')
parser.add_argument('--show', default=True, action='store_true', help='mix_up picture show')
parser.add_argument('--mix', default='./mix_img/', action='store_true',
help='default:False is do not use mix_up method, and use ./mix_up to mix_up')
parser.add_argument('--Scaling_probability', default=[0.6, 1.4], action='store_true',
help='The probability of scaling your boxes')
classes_list = ['UsingPhone', 'LikePhone'] # 类别名称 不建议使用"_"命名
obj_name = classes_list[0] # 此次标注的类别名称
args = parser.parse_args()
counter, flag = 0, 0
path = "images/"
test_path = 'test_img/'
OPENCV_OBJECT_TRACKERS = { # OPENCV_OBJECT_TRACKERS 默认使用csrt
"csrt": cv2.TrackerCSRT_create,
"kcf": cv2.TrackerKCF_create,
"boosting": cv2.TrackerBoosting_create,
"mil": cv2.TrackerMIL_create,
"tld": cv2.TrackerKCF_create,
"medianflow": cv2.TrackerMedianFlow_create,
"mosse": cv2.TrackerMOSSE_create
}像这样，tracker可以切换成：csrt， kcf，boosting等方式，这是opencv中的追踪算法，csrt是较准的，同时你也可以使用deepsort 进行跟踪，或者使用自己训练好的一个模型，进行其他大量数据的预训练。
--source_path 我们可以切换为0 ：使用内置相机，切换为1：使用外界相机； 切换为图片路径：images/ 下的图片，可以这样标注~，切换为视频路径：demo.mp4 来标注视频帧，注意一个视频帧数很多，大家可以修改程序中的cv2.waitkey()来改善。
--show 就是显示我们的mix_up 的图片~
--mix 是我们使用mix_up 并且使用随机等比例缩放的路径,如果default = False,就是不使用mix_up 做增强，如果使用，就将mix_img 的路径放入：./mix_img/
--Scaling_probability 就是缩放比例的区间。
classes_list ：我们将所有的类别写进来，并
影像分类中，使用简单的固定ROI方式，在运行maker_classification.py 后我们可以轻松地按下s键保存图片~图片就会根据main中的类别名称保存到data/train/类别名/下面，如果是测试集，就在main中修改为test模式
parser.add_argument('--dtype', type=str, default='pause', help='your label')
parser.add_argument('--train_test', type=str, default='test', help='train/test')--dtype:类别 修改成自己的数据类别，就会在data/train/下面生成这样的文件夹，里面存放这个类别的图片
--train_test 现在收集的数据是训练集还是测试集。切换train或者test会分别保存到train或者test文件夹下。
github：https://github.com/CVUsers/Auto_maker
github图片
数据标注部分
自动标注过程
数据效果图
标注文件集
模型已放在github上，轻量级模型，这两天会更新更高精度模型~
模型已放在github上，轻量级模型，这两天会更新更高精度模型~
我们现在得到了所有的图片/标注文件（同名），那么就可以开始训练了，训练过程很简单，我们只需要转换一下数据：
运行voc_label.py 数据转换成YOLO格式：通过这个脚本，你可以在labels文件夹中生成归一化后的标签，同时生成一份训练集：train.txt 和测试集test.txt
我们只需要修改classes：类别即可。
import os
import xml.etree.ElementTree as ET
import os
import cv2
import random
classes = ['UsingPhone', 'LikePhone']
def convert(size, box):
print(size, box)
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)
def convert_annotation(image_id):
print(image_id)
in_file = open(r'./Annotations/%s' % (image_id), 'rb') # 读取xml文件路径
out_file = open('./labels/%s.txt' % (image_id.split('.')[0]), 'w') # 需要保存的txt格式文件路径
tree = ET.parse(in_file)
root = tree.getroot()
size = root.find('size')
w = int(size.find('width').text)
h = int(size.find('height').text)
if w == 0 and h == 0:
img = cv2.imread('./images/' +image_id.replace('xml', 'jpg'))
w, h = img.shape[1], img.shape[0]
for obj in root.iter('object'):
cls = obj.find('name').text
if cls not in classes:
print('*******************************'*2, cls)
break
cls_id = classes.index(cls)
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(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
image_ids_train = os.listdir('./Annotations') # 读取xml文件名索引
for image_id in image_ids_train:
print(image_id)
convert_annotation(image_id)
trainval_percent = 0.1 # 可自行进行调节
train_percent = 1
xmlfilepath = './labels'
total_xml = os.listdir(xmlfilepath)
num = len(total_xml)
list = range(num)
tv = int(num * trainval_percent)
tr = int(tv * train_percent)
trainval = random.sample(list, tv)
train = random.sample(trainval, tr)
ftest = open('./test.txt', 'w')
ftrain = open('./train.txt', 'w')
for i in list:
name = total_xml[i] + '\n'
if i in trainval:
if i in train:
ftest.write('../images/' + name.replace('txt', 'jpg'))
else:
ftrain.write('../images/' + name.replace('txt', 'jpg'))
ftrain.close()
ftest.close()下面YOLOv5 和V3 一样，我们只需要修改yolov5/data/voc.yaml即可（v3 和v4 若是使用darknet也是差不多哦~）：
train: ../train.txt # 16551 images
val: ../test.txt # 4952 images
# number of classes
nc: 2
# class names
names: ['UsingPhone', 'LikePhone']在训练此yolov5 前，请安装pytorch1.6 以上，如果你使用的是torch1.5以下，那么请区clone yolov5 的第二个版本以下，或者你使用yolov3~
然后修改train.py 的这个部分
parser = argparse.ArgumentParser()
parser.add_argument('--weights', type=str, default='weights/yolov5s.pt', help='initial weights path')
parser.add_argument('--cfg', type=str, default='models/yolov5s.yaml', help='model.yaml path')
parser.add_argument('--data', type=str, default='data/voc.yaml', help='data.yaml path')
parser.add_argument('--hyp', type=str, default='data/hyp.scratch.yaml', help='hyperparameters path')
parser.add_argument('--epochs', type=int, default=300)
parser.add_argument('--batch-size', type=int, default=10, help='total batch size for all GPUs')
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')
parser.add_argument('--rect', action='store_true', help='rectangular training')
parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
parser.add_argument('--notest', action='store_true', help='only test final epoch')
parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
parser.add_argument('--log-imgs', type=int, default=16, help='number of images for W&B logging, max 100')
parser.add_argument('--log-artifacts', action='store_true', help='log artifacts, i.e. final trained model')
parser.add_argument('--workers', type=int, default=0, help='maximum number of dataloader workers')
parser.add_argument('--project', default='runs/train', help='save to project/name')
parser.add_argument('--name', default='exp', help='save to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
opt = parser.parse_args()--weights 预训练模型路径
--cfg 网络结构路径
--data voc.yaml路径
训练效果图可以在runs 下查看result.txt
或者使用tensorboard查看，我们到yolov5或者3 路径下执行tensorboard --logdir=runs
mAP和precision 和recall 如下，我的模型只训练了70次~ 我接下来会使用4w张图片训练完，来查看准确率，并且实际体验效果，然后我会放在我的github和公众号：70次效果也不错~
这是损失~
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default=r'D:\cvuser\Auto_maker\yolov5\runs\train\exp7\weights\best.pt', help='model.pt path(s)')
parser.add_argument('--source', type=str, default='0', help='source') # file/folder, 0 for webcam
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.45, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img', action='store_true', help='display results')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
parser.add_argument('--augment', action='store_true', default=True, help='augmented inference')
parser.add_argument('--update', action='store_true', help='update all models')
parser.add_argument('--project', default='runs/detect', help='save results to project/name')
parser.add_argument('--name', default='exp', help='save results to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
opt = parser.parse_args()我们把--weights 改成模型的绝对路径即可~
并且打开--augment 为True
在目标检测数据标注代码：get_images.py中：
parser = argparse.ArgumentParser('Auto_maker')
parser.add_argument('-t', "--tracker", type=str, default='csrt', help='choose opencv tracker methods')
parser.add_argument('-i', '--source_path', type=str, default='0',
help='0 or 1 is your capture, or use video.mp4 or use path_dir like: ./images/')
parser.add_argument('--show', default=True, action='store_true', help='mix_up picture show')
parser.add_argument('--mix', default='./mix_img/', action='store_true',
help='default:False is do not use mix_up method, and use ./mix_up to mix_up')
parser.add_argument('--Scaling_probability', default=[0.6, 1.4], action='store_true',
help='The probability of scaling your boxes')
classes_list = ['UsingPhone', 'LikePhone'] # 类别名称 不建议使用"_"命名
obj_name = classes_list[0] # 此次标注的类别名称
args = parser.parse_args()
counter, flag = 0, 0
path = "images/"
test_path = 'test_img/'
OPENCV_OBJECT_TRACKERS = { # OPENCV_OBJECT_TRACKERS 默认使用csrt
"csrt": cv2.TrackerCSRT_create,
"kcf": cv2.TrackerKCF_create,
"boosting": cv2.TrackerBoosting_create,
"mil": cv2.TrackerMIL_create,
"tld": cv2.TrackerKCF_create,
"medianflow": cv2.TrackerMedianFlow_create,
"mosse": cv2.TrackerMOSSE_create
}
if os.path.isdir(args.source_path): # 图片文件夹自动标注
run_on_images(args.source_path, mix=args.mix)
elif os.path.isfile(args.source_path): # 标注一个视频文件
run_on_video(args.source_path, mix=args.mix)
elif '0' in args.source_path or '1' in args.source_path: # 实时标注 （建议使用实际使用时的相机录制~）
run_on_video(int(args.source_path), mix=args.mix)程序入口，判断传入的是文件夹还是视频还是相机路径，做出相应响应。
run_on_video 函数
saveimg = False
mix_img = False
wd = getcwd()
tracker = OPENCV_OBJECT_TRACKERS[args.tracker]() # 定义追踪器
intBB = None
vs = cv2.VideoCapture(source)
while True:
frame = vs.read()
frame = frame[1]
frame = cv2.resize(frame, (640, 480))
frame = cv2.flip(frame, 3)
frame1 = frame.copy()
(H, W) = frame.shape[:2]
if frame is None:
break
if intBB is not None:
(success, box) = tracker.update(frame)
if success:
(x, y, w, h) = [int(v) for v in box]
if mix:
Scaling_probability = random.randint(args.Scaling_probability[0] * 10,
args.Scaling_probability[1] * 10) / 10
try:
mix_frame = cv2.resize(frame1[y:y + h, x:x + w],
(int(w * Scaling_probability), int(h * Scaling_probability)))
w_, h_ = int(w * Scaling_probability), int(h * Scaling_probability)
mix_img = mix_roi_img(mix, mix_frame, x, y, w_, h_)
if saveimg:
saveROIImg(frame, frame1, x, y, x + w_, y + h_, obj_name, flag=True, mix=mix_img)
except:
pass
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
if saveimg:
saveROIImg(frame, frame1, x, y, x + w, y + h, obj_name)
cv2.imshow('frame', frame)
key = cv2.waitKey(50) & 0xFF
if key == ord('s'):
print('class is:', obj_name)
tracker = OPENCV_OBJECT_TRACKERS[args.tracker]()
tracker1 = OPENCV_OBJECT_TRACKERS[args.tracker]()
intBB = None
intBB = cv2.selectROI('frame', frame, fromCenter=False, showCrosshair=True)
tracker.init(frame, intBB)
elif key == ord('b'):
saveimg = True
elif key == 27:
cv2.destroyAllWindows()
vs.release()
break先通过tracker = args.tracker () 定义追踪器，然后显示实时视频，监听鼠标，若为“s” 那么启动追踪器，并获取关键区域roi。同时初始化追踪器。然后获取roi的bounding box位置
intBB = cv2.selectROI('frame', frame, fromCenter=False, showCrosshair=True)
tracker.init(frame, intBB)在mix_up 中：
def mix_roi_img(mix, img, x, y, w, h): # 使用mix_up贴图
global counter
if os.path.isdir(mix):
i = random.choice(os.listdir(mix))
img_back = cv2.imread(os.path.join(mix, i))
try:
img_back = cv2.resize(img_back, (640, 480))
except:
print(f'{os.path.join(mix, i)} connot open it!')
rows, cols, channels = img.shape # rows，cols最后一定要是前景图片的，后面遍历图片需要用到
center = [x, y] # 在新背景图片中的位置
for i in range(cols):
for j in range(rows):
# if dilate[i, j] == 0:
if center[0] + i < 640 and center[1] + j < 480:
img_back[center[1] + j, center[0] + i] = img[j, i] # 此处替换颜色，为BGR通道
cv2.imshow(f'mix_{i}', img_back)
cv2.waitKey(30)
counter += 1
if counter % 20 == 0:
cv2.destroyAllWindows()
return img_back默认640x480 的图片大小，大家可以修改，然后把图片进行随机贴合。大家可以在这里做更多的贴图算法优化，我这里就是像素点的转换，还有其他方法，比如边缘检测，将需要的部分留下，不需要的部分用原mix_up 的图片替换~
# 定义一个创建一级分支object的函数
def create_object(root, xi, yi, xa, ya, obj_name): # 参数依次，树根，xmin，ymin，xmax，ymax
_object = ET.SubElement(root, 'object') # 创建一级分支object
name = ET.SubElement(_object, 'name') # 创建二级分支
name.text = str(obj_name)
pose = ET.SubElement(_object, 'pose')
pose.text = 'Unspecified'
truncated = ET.SubElement(_object, 'truncated')
truncated.text = '0'
difficult = ET.SubElement(_object, 'difficult')
difficult.text = '0'
bndbox = ET.SubElement(_object, 'bndbox') # 创建bndbox
xmin = ET.SubElement(bndbox, 'xmin')
xmin.text = '%s' % xi
ymin = ET.SubElement(bndbox, 'ymin')
ymin.text = '%s' % yi
xmax = ET.SubElement(bndbox, 'xmax')
xmax.text = '%s' % xa
ymax = ET.SubElement(bndbox, 'ymax')
ymax.text = '%s' % ya
# 创建xml文件的函数
def create_tree(image_name, imgdir, h, w):
global annotation
annotation = ET.Element('annotation') # 创建树根annotation
folder = ET.SubElement(annotation, 'folder') # 创建一级分支folder
folder.text = (imgdir) # 添加folder标签内容
filename = ET.SubElement(annotation, 'filename') # 创建一级分支filename
filename.text = image_name
path = ET.SubElement(annotation, 'path') # 创建一级分支path
path.text = getcwd() + '\{}\{}'.format(imgdir, image_name) # 用于返回当前工作目录
source = ET.SubElement(annotation, 'source') # 创建一级分支source
database = ET.SubElement(source, 'database') # 创建source下的二级分支database
database.text = 'Unknown'
size = ET.SubElement(annotation, 'size') # 创建一级分支size
width = ET.SubElement(size, 'width') # 创建size下的二级分支图像的宽、高及depth
width.text = str(w)
height = ET.SubElement(size, 'height')
height.text = str(h)
depth = ET.SubElement(size, 'depth')
depth.text = '3'
segmented = ET.SubElement(annotation, 'segmented') # 创建一级分支segmented
segmented.text = '0'这边是xml树的构建。
def saveROIImg(frame, img, xmin, ymin, xmax, ymax, obj_name, flag=False, mix=False): # 保存图片和xml
global counter, saveimg
name = find_max_name(obj_name, mix)
H, W = frame.shape[0], frame.shape[-2]
name += 1
if flag:
print("Saving image:", name, xmin, ymin, xmax, ymax)
cv2.imwrite(path + f'mix_{obj_name}_' + str(name) + ".jpg", mix)
cv2.rectangle(mix, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
cv2.imwrite(test_path + f'mix_{obj_name}_' + str(name) + ".jpg", mix)
create_tree(f'mix_{obj_name}_' + str(name) + '.jpg ', 'images', H, W)
create_object(annotation, xmin, ymin, xmax, ymax, obj_name)
cv2.waitKey(180)
tree = ET.ElementTree(annotation)
tree.write('.\Annotations\{}.xml'.format(f'mix_{obj_name}_' + str(name)))
return
print("Saving image:", name, xmin, ymin, xmax, ymax)
cv2.imwrite(path + f'{obj_name}_' + str(name) + ".jpg", img)
cv2.imwrite(test_path + f'{obj_name}_' + str(name) + ".jpg", frame)
cv2.imshow('images', img)
create_tree(f'{obj_name}_' + str(name) + '.jpg ', 'images', H, W)
create_object(annotation, xmin, ymin, xmax, ymax, obj_name)
cv2.waitKey(50)
tree = ET.ElementTree(annotation)
tree.write('.\Annotations\{}.xml'.format(f'{obj_name}_' + str(name)))保存模式，选择是否保存mix_up的图片。
maker_classification.py
import argparse
import win32api
import win32con
import cv2 as cv
import os
import numpy as np
save_path = 'data'
def get_roi(frame, x1, x2, y1, y2):
dst = frame[y1+2:y2, x1+2:x2]
cv.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), thickness=1)
return dst
def get_data(dtype):
max = 0
for i in os.listdir('data/{}/{}'.format(args.train_test, dtype)):
if int(i.split('_')[2].split('.')[0]) > max:
max = int(i.split('_')[2].split('.')[0])
return max + 1
def main():
if not os.path.isdir('./data/'):
os.makedirs('./data/')
if not os.path.isdir('./data/train/'):
os.makedirs('./data/train/')
if not os.path.isdir('./data/test/'):
os.makedirs('./data/test/')
if not os.path.isdir('./data/train/{}'.format(args.dtype)):
os.makedirs('./data/train/{}'.format(args.dtype))
if not os.path.isdir('./data/test/{}'.format(args.dtype)):
os.makedirs('./data/test/{}'.format(args.dtype))
m_0 = get_data(args.dtype)
capture = cv.VideoCapture(0)
while True:
ret, frame = capture.read()
roi = get_roi(frame, 100, 350, 100, 350)
k = cv.waitKey(20)
if k == 27: # 按下ESC退出
break
elif k == ord('s'): # 按下'A'会保存当前图片到指定目录下
cv.imwrite("{}/{}/{}/{}.jpg".format(save_path, args.train_test, args.dtype, m_0), roi)
m_0 += 1
# flip_image = cv.flip(skin, 1) # 这里用到的是水平翻转，因为后面的参数是一
# cv.imwrite("E:\\aiFile\\picture\\gesture_data\\0\\%s.jpg" % m_0, flip_image)
# m_0 += 1
print('正在保存0-roi图片,本次图片数量:', m_0)
cv.imshow("roi", roi)
cv.imshow("frame", frame)
c = cv.waitKey(20)
if c == 27:
break
cv.waitKey(0)
capture.release()
cv.destroyAllWindows()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--dtype', type=str, default='pause', help='your label')
parser.add_argument('--train_test', type=str, default='test', help='train/test')
args = parser.parse_args()
main()maker_by_Guss.py
import cv2
import imutils
import numpy as np
import argparse
import os
bg = None
def run_avg(image, aWeight):
global bg
if bg is None:
bg = image.copy().astype('float')
return
cv2.accumulateWeighted(image, bg, aWeight)
def segment(image, threshold=25):
global bg
diff = cv2.absdiff(bg.astype('uint8'), image)
thresholded = cv2.threshold(diff,
threshold,
255,
cv2.THRESH_BINARY)[1]
(cnts, _) = cv2.findContours(thresholded.copy(),
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
if len(cnts) == 0:
return
else:
segmented = max(cnts, key=cv2.contourArea)
return (thresholded, segmented)
def main(dtype):
aWeight = 0.5
camera = cv2.VideoCapture(0)
top, right, bottom, left = 90, 380, 285, 590
num_frames = 0
thresholded = None
count = 0
while(True):
(grabbed, frame) = camera.read()
if grabbed:
frame = imutils.resize(frame, width=700)
frame = cv2.flip(frame, 1)
clone = frame.copy()
(height, width) = frame.shape[:2]
roi = frame[top:bottom, right:left]
gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)
if num_frames < 30:
run_avg(gray, aWeight)
else:
hand = segment(gray)
if hand is not None:
(thresholded, segmented) = hand
cv2.drawContours(
clone, [segmented + (right, top)], -1, (0, 0, 255))
cv2.rectangle(clone, (left, top), (right, bottom), (0, 255, 0), 2)
num_frames += 1
cv2.imshow('Video Feed', clone)
if not thresholded is None:
cv2.imshow('Thesholded', thresholded)
keypress = cv2.waitKey(1) & 0xFF
if keypress == ord('q'):
break
if keypress == ord('s'):
if not os.path.isdir('./data/'):
os.makedirs('./data/')
if not os.path.isdir('./data/train/'):
os.makedirs('./data/train/')
if not os.path.isdir('./data/test/'):
os.makedirs('./data/test/')
if not os.path.isdir('./data/train/{}'.format(args.dtype)):
os.makedirs('./data/train/{}'.format(args.dtype))
if not os.path.isdir('./data/test/{}'.format(args.dtype)):
os.makedirs('./data/test/{}'.format(args.dtype))
cv2.imwrite('data/{}/saved_v2_{:04}.jpg'.format(dtype, count), thresholded)
count += 1
print(count, 'saved.')
else:
camera.release()
break
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--dtype', type=str, default='pause', help='your label')
args = parser.parse_args()
main(args.dtype)
cv2.destroyAllWindows()使用高斯边缘消除后保存，适用于特征鲜明物体。
class Net(nn.Module):
def __init__(self, num_classes=2):
super().__init__()
self.net = mobilenet_v2(pretrained=True) # backbone + neck + head
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.logit = nn.Linear(1280, len(args.classes)) # [bs, 1280] -> [bs, classes]
def forward(self, x): # [bs,3,224,224]
x = self.net.features(x) # [bs, 1280, 7, 7] 224//32
x = self.avg_pool(x) # [bs, 1280, 1, 1]
x = x.view(x.size(0), -1) # [bs, 1280]
# x = torch.reshape()
x = self.logit(x)
return x定义网络和主干网络
def run(images_list, val_list):
train_dataset = GestureDataset(images_list)
train_dataloader = DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0,
)
val_dataset = GestureDataset(val_list)
val_dataloader = DataLoader(
val_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0
)
model = Net()
model.cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=1.5e-4)
loss_fn = nn.CrossEntropyLoss()
best_score = float("inf") # 0XFFFFFFF
best_acc = 0.
for epoch in range(args.epochs):
print('Epoch:', epoch)
train_one(train_dataloader, model, optimizer, loss_fn, None)
scores = val_one(val_dataloader, model, loss_fn)
if scores['loss'] <= best_score:
best_score = scores['loss']
print('*****best_loss:', best_score, 'acc:', best_acc)
if scores['accuracy'] >= best_acc:
best_acc = scores['accuracy']
print('*******save best*******', epoch)
torch.save(model.state_dict(), "ckpt/model.pth")训练部分
class GestureDataset(Dataset):
def __init__(self, images_list, transformers=None):
self.images_list = images_list # 3000
self.transformers = transformers
def __len__(self):
return len(self.images_list)
def normalize(self, image):
image = np.transpose(image, (2, 0, 1)) # [3,224,224]
mean = [0.485, 0.56, 0.06]
std = [0.229, 0.224, 0.225]
image = image.astype(np.float32) / 255 # [0,1]
image -= np.array(mean).reshape((3,1,1))
image /= np.array(std).reshape((3,1,1))
# image[0] -= mean # [-0.5, 0.5]
# image /=std # []
return image
def __getitem__(self, index: int):
image_size = 224
name:str = self.images_list[index]
image_name = name
image = np.array(Image.open(image_name)) # uint8 [0-255]
image = cv2.resize(image, (image_size,image_size))
label_str = args.classes.index(name.split("\\")[-2])
label = int(label_str)
result = {
"image": self.normalize(image),
"label": label
}
return result数据增强和数据读取
detect.py
import argparse
import torch
import cv2
import os
from PIL import Image
from torchvision import transforms
import torch, torch.nn as nn, torch.nn.functional as F
from torchvision.models.mobilenet import mobilenet_v2
import time
device = torch.device('cuda')
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
transform = transforms.Compose([
transforms.Resize(224),
transforms.ToTensor(),
transforms.Normalize(mean=mean,
std=std)
])
class Net(nn.Module):
def __init__(self, num_classes=2):
super().__init__()
self.net = mobilenet_v2(pretrained=True) # backbone + neck + head
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.logit = nn.Linear(1280, len(args.classes)) # [bs, 1280] -> [bs, classes]
def forward(self, x): # [bs,3,224,224]
x = self.net.features(x) # [bs, 1280, 7, 7] 224//32
x = self.avg_pool(x) # [bs, 1280, 1, 1]
x = x.view(x.size(0), -1) # [bs, 1280]
# x = torch.reshape()
x = self.logit(x)
return x
def predict():
# net = torch.load('./ckpt/model.pth')
# net = net.cuda()
net = Net()
net.load_state_dict(torch.load(args.model))
net = net.cuda()
net.eval()
# net.to("cuda")
# net.to(torch.device("cuda:0"))
torch.no_grad()
return net
def run(img):
img = Image.fromarray(img[:, :, ::-1])
# img = Image.open(img_path)
img = transform(img).unsqueeze(0)
img_ = img.to(device)
outputs = net(img_)
outputs = torch.softmax(outputs, dim=1)
score, predicted = torch.max(outputs, 1)
return score[0].item(), predicted[0].item()
def get_roi(frame, x1, x2, y1, y2):
dst = frame[y1:y2, x1:x2]
cv2.rectangle(frame, (x1 -2, y1-2), (x2+4, y2+4), (0, 0, 255), thickness=2)
return dst
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--classes', type=str, default=['pause', 'hand'], help='your label')
parser.add_argument('--source', type=int, default=0, help='your label')
parser.add_argument('--model', type=str, default='./ckpt/model.pth', help='your label')
parser.add_argument('--threshold', type=str, default='0.9', help='your label')
args = parser.parse_args()
net = predict()
video = cv2.VideoCapture(args.source)
while True:
time1 = time.time()
ret, img = video.read()
img_copy = img
roi = get_roi(img, 100, 324, 100, 324)
# cv2.rectangle(img_copy, (95, 95), (328, 328), (0, 0, 255), thickness=1)
if ret:
cv2.imshow('img', roi)
score, name = run(roi)
name = args.classes[name]
if float(score) >= float(args.threshold):
cv2.putText(img_copy, str(name + ' '+str(round(score, 2))), (90, 90), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (255, 255, 255), 2)
cv2.imshow('frame', img_copy)
time2 = time.time()
print("Inference Time:", round(time2 - time1, 3))
cv2.waitKey(5)效果图：
mobilenet-v2 模型导出onnx部分
import torch,onnx,collections
import torch.nn as nn
from torchvision.models.mobilenet import mobilenet_v2
class Net(nn.Module):
def __init__(self, num_classes=3):
super().__init__()
self.net = mobilenet_v2(pretrained=True) # backbone + neck + head
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.logit = nn.Linear(1280, num_classes) # [bs, 1280] -> [bs, classes]
def forward(self, x): # [bs,3,224,224]
x = self.net.features(x) # [bs, 1280, 7, 7] 224//32
x = self.avg_pool(x) # [bs, 1280, 1, 1]
x = x.view(x.size(0), -1) # [bs, 1280]
# x = torch.reshape()
x = self.logit(x)
return x
print('notice !!!! ----> use python3 run this script!!! \n')
INPUT_DICT = 'ckpt\model.pth'
OUT_ONNX = 'ckpt\cls_model.onnx'
x = torch.randn(1, 3, 224, 224)
input_names = ["input"]
out_names = ["output"]
net = Net()
xmodel= torch.load(INPUT_DICT, map_location=torch.device('cuda'))
net.load_state_dict(xmodel)
net.eval()
torch.onnx.export(net, x, OUT_ONNX, export_params=True, training=False, input_names=input_names, output_names=out_names)
print('please run: python3 -m onnxsim test.onnx test_sim.onnx\n')
print('convert done!\n')yolov3 和yolov5 导出和openvino推理代码见：
yolov5 使用TensorRT推理代码见：
这是cv调包侠的原创项目，没有参考过任何人，当然后来也看过网上用类似的方法实现了，但是我的更完整，完善，可移植性高，并且自带数据增强。
数据增强中，massic我没有使用，因为大多数模型自带massic，如果经过两次massic会更小，误检上会有问题。
但是不是越多增强越好~，比如我们没有使用翻转，因为模型自带翻转，以及hsv通道的增强，旋转和亮度，我们无需管，还有其他的数据正确策略，比如cut-mix ，等等，欢迎fork我的项目，并且完善~让项目更加简单，更高效：
github：https://github.com/CVUsers/Auto_maker
项目已发布：github：https://github.com/CVUsers/Auto_maker
附带4w张玩手机数据：关注公众号回复：玩手机
公众号：DeepAi 视界
二维码：
我们还可以标注什么数据?
答：绝大多数voc，coco数据，例如：猫狗，人，车，各种物体，但是过小的物体慎用~
作者 ：cv调包侠 本科大三 深度学习算法攻城狮实习僧 上海第二工业大学