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Python+Yolov5人脸口罩识别
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前言
Yolov5比较Yolov4,Yolov3等其他识别框架，速度快，代码结构简单，识别效率高，对硬件要求比较低。这篇博客针对Python+Yolov5人脸口罩识别编写代码，代码整洁，规则，易读。 学习与应用推荐首选。
文章目录
一、所需工具软件
二、使用步骤
1. 引入库
2. 识别图像特征
3. 识别参数定义
4. 运行结果
三、在线协助
一、所需工具软件
1. Python3.6以上
2. Pycharm代码编辑器
3. Torch,OpenCV库
二、使用步骤
1.引入库
代码如下（示例）：
import cv2
import torch
from numpy import random
from models.experimental import attempt_load
from utils.datasets import LoadStreams,LoadImages
from utils.general import check_img_size,check_requirements,check_imshow,non_max_suppression,apply_classifier,\
scale_coords,xyxy2xywh,strip_optimizer,set_logging,increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device,load_classifier,time_synchronized2.识别图像特征
代码如下（示例）：
def detect(save_img=False):
source,weights,view_img,save_txt,imgsz = opt.source,opt.weights,opt.view_img,opt.save_txt,opt.img_size
webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
('rtsp://','rtmp://','http://'))
# Directories
save_dir = Path(increment_path(Path(opt.project) / opt.name,exist_ok=opt.exist_ok)) # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True,exist_ok=True) # make dir
# Initialize
set_logging()
device = select_device(opt.device)
half = device.type != 'cpu' # half precision only supported on CUDA
# Load model
model = attempt_load(weights,map_location=device) # load FP32 model
stride = int(model.stride.max()) # model stride
imgsz = check_img_size(imgsz,s=stride) # check img_size
if half:
model.half() # to FP16
# Second-stage classifier
classify = False
if classify:
modelc = load_classifier(name='resnet101',n=2) # initialize
modelc.load_state_dict(torch.load('weights/resnet101.pt',map_location=device)['model']).to(device).eval()
# Set Dataloader
vid_path,vid_writer = None,None
if webcam:
view_img = check_imshow()
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source,img_size=imgsz,stride=stride)
else:
save_img = True
dataset = LoadImages(source,stride=stride)
# Get names and colors
names = model.module.names if hasattr(model,'module') else model.names
colors = [[random.randint(0,255) for _ in range(3)] for _ in names]
# Run inference
if device.type != 'cpu':
model(torch.zeros(1,3,imgsz,imgsz).to(device).type_as(next(model.parameters()))) # run once
t0 = time.time()
for path,img,im0s,vid_cap in dataset:
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
t1 = time_synchronized()
pred = model(img,augment=opt.augment)[0]
# Apply NMS
pred = non_max_suppression(pred,opt.conf_thres,opt.iou_thres,classes=opt.classes,agnostic=opt.agnostic_nms)
t2 = time_synchronized()
# Apply Classifier
if classify:
pred = apply_classifier(pred,modelc,im0s)
# Process detections
for i,det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p,s,im0,frame = path[i],'%g: ' % i,im0s[i].copy(),dataset.count
else:
p,frame = path,'',getattr(dataset,'frame',0)
p = Path(p) # to Path
save_path = str(save_dir / p.name) # img.jpg
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1,1,0]] # normalization gain whwh
if len(det):
# Rescale boxes from img_size to im0 size
det[:,:4] = scale_coords(img.shape[2:],det[:,:4],im0.shape).round()
# Write results
for *xyxy,conf,cls in reversed(det):
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1,4)) / gn).view(-1).tolist() # normalized xywh
line = (cls,*xywh,conf) if opt.save_conf else (cls,*xywh) # label format
with open(txt_path + '.txt','a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if save_img or view_img: # Add bbox to image
label = f'{names[int(cls)]} {conf:.2f}'
plot_one_box(xyxy,label=label,color=colors[int(cls)],line_thickness=3)
# Print time (inference + NMS)
print(f'{s}Done. ({t2 - t1:.3f}s)')
# Save results (image with detections)
if save_img:
if dataset.mode == 'image':
cv2.imwrite(save_path,im0)
else: # 'video'
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer,cv2.VideoWriter):
vid_writer.release() # release previous video writer
fourcc = 'mp4v' # output video codec
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
vid_writer = cv2.VideoWriter(save_path,cv2.VideoWriter_fourcc(*fourcc),fps,(w,h))
vid_writer.write(im0)
if save_txt or save_img:
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
print(f"Results saved to {save_dir}{s}")
print(f'Done. ({time.time() - t0:.3f}s)')
print(opt)
check_requirements()
with torch.no_grad():
if opt.update: # update all models (to fix SourceChangeWarning)
for opt.weights in ['yolov5s.pt','yolov5m.pt','yolov5l.pt','yolov5x.pt']:
detect()
strip_optimizer(opt.weights)
else:
detect()该处使用的url网络请求的数据。
3.识别参数定义：
代码如下（示例）：
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights',nargs='+',type=str,default='yolov5_best_road_crack_recog.pt',help='model.pt path(s)')
parser.add_argument('--img-size',type=int,default=640,help='inference size (pixels)')
parser.add_argument('--conf-thres',type=float,default=0.25,help='object confidence threshold')
parser.add_argument('--iou-thres',default=0.45,help='IOU threshold for NMS')
parser.add_argument('--view-img',action='store_true',help='display results')
parser.add_argument('--save-txt',help='save results to *.txt')
parser.add_argument('--classes',default='0',help='filter by class: --class 0,or --class 0 2 3')
parser.add_argument('--agnostic-nms',help='class-agnostic NMS')
parser.add_argument('--augment',help='augmented inference')
parser.add_argument('--update',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',help='existing project/name ok,do not increment')
opt = parser.parse_args()
print(opt)
check_requirements()
with torch.no_grad():
if opt.update: # update all models (to fix SourceChangeWarning)
for opt.weights in ['yolov5s.pt','yolov5x.pt']:
detect()
strip_optimizer(opt.weights)
else:
detect()4.运行结果如下：
三、在线协助：
如需安装运行环境或远程调试，见文章底部微信名片，由专业技术人员远程协助！
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