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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_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, img_size=imgsz, 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, img, 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, s, im0, frame = path,
'', im0s, 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, 0, 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, im0, 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',
type=
float, default=
0.45,
help=
'IOU threshold for NMS')
-
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(
'--classes', nargs=
'+',
type=
int, default=
'0',
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',
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()
-
-
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()
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！
转载：https://blog.csdn.net/alicema1111/article/details/128324661
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