程序示例精选
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_synchronized
2.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: