yolov5-Lite介绍
这里项目链接查看，或者这里下载。
经过本人测试，与yolov5-7.0相比，训练好的权重文件大小大约是yolov5-7.0的0.3倍（yolov5-Lite——3.4M，yolov5-7.0——13M），置信度均在0.9之上。特别的，我之所以使用此Lite改进算法，是因为需要部署在智能小车上实现图像识别的功能，而小车上只有CPU，yolov5-7.0使用CPU计算的速度太慢了，一秒只能处理3张图像，距离功能的要求还差些，而Lite算法的权重参数减少了很多，速度也相应快了一些，部署在小车上，使用CPU计算的速度快了0.8倍，不算很多，但也算是勉强能使用了，每秒5/6张图片。
需求
算法自带检测图片、视频的detect.py脚本，但是拿来自己灵活的使用还是有许多问题，一般图像检测都是对实时性有要求，detect.py脚本是检测本地的图片视频。我修改一部分代码，将detect.py脚本写成一个api，直接调用函数，传入一个img数组对象，即可输出detections字典，包含各检测对象的类别、位置信息、置信度。
修改代码
原函数
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
save_img = not opt.nosave and not source.endswith('.txt') # save inference images
webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
('rtsp://', 'rtmp://', 'http://', 'https://'))
# 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:
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()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
# 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)')
# Stream results
if view_img:
cv2.imshow(str(p), im0)
cv2.waitKey(1) # 1 millisecond
# Save results (image with detections)
if save_img:
if dataset.mode == 'image':
cv2.imwrite(save_path, im0)
else: # 'video' or 'stream'
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release() # release previous video writer
if vid_cap: # video
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))
else: # stream
fps, w, h = 30, im0.shape[1], im0.shape[0]
save_path += '.mp4'
vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), 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)')
修改后的函数
class DETECT_API:
def __init__(self,opt):
weights,imgsz = opt.weights,opt.img_size
self.device = select_device(opt.device)
# device = device_ if torch.cuda.is_available() else 'cpu' # 设置代码执行的设备 cuda device, i.e. 0 or 0,1,2,3 or cpu
# self.device = device
self.half = self.device.type != 'cpu' # half precision only supported on CUDA
# self.imgsz = (imgsz, imgsz) # 输入图片的大小 默认640(pixels)
self.conf_thres = opt.conf_thres # object置信度阈值 默认0.25 用在nms中
self.iou_thres = opt.iou_thres # 做nms的iou阈值 默认0.45 用在nms中
# self.max_det = max_det # 每张图片最多的目标数量 用在nms中
self.classes = opt.classes # 在nms中是否是只保留某些特定的类 默认是None 就是所有类只要满足条件都可以保留 --class 0, or --class 0 2 3
self.agnostic_nms = opt.agnostic_nms # 进行nms是否也除去不同类别之间的框 默认False
self.augment = opt.augment # 预测是否也要采用数据增强 TTA 默认False
# self.visualize = False # 特征图可视化 默认FALSE
# self.half = False # 是否使用半精度 Float16 推理 可以缩短推理时间 但是默认是False
# self.dnn = False # 使用OpenCV DNN进行ONNX推理
# Load model
self.model = attempt_load(weights, map_location=self.device) # load FP32 model
if self.half:
self.model.half() # to FP16
if self.device.type != 'cpu':
self.model(torch.zeros(1, 3, imgsz, imgsz).to(self.device).type_as(next(self.model.parameters()))) # run once
self.stride = int(self.model.stride.max()) # model stride
self.imgsz = check_img_size(imgsz, s=self.stride) # check img_size
# Get names and colors
self.names = self.model.module.names if hasattr(self.model, 'module') else self.model.names
colors = [[random.randint(0, 255) for _ in range(3)] for _ in self.names]
def detect2(self,img):
'''
检测图像，输入图片数组
Args:
img: 图片数组
Returns:字典{'class': cls, 'conf': conf, 'position': xywh}
'''
# Set Dataloader
# dataset = LoadImages(img_path, img_size=self.imgsz, stride=self.stride)
# 用于存放结果
detections = []
s = ''
if True:
# print(path)
im0 = img*1
# Padded resize
img = letterbox(im0, self.imgsz, stride=self.stride)[0]
# Convert
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = ascontiguousarray(img) # np.ascontiguousarray(img)
img = torch.from_numpy(img).to(self.device)
img = img.half() if self.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 = self.model(img, augment=self.augment)[0]
# Apply NMS
pred = non_max_suppression(pred, self.conf_thres, self.iou_thres, classes=self.classes, agnostic=self.agnostic_nms)
t2 = time_synchronized()
# Process detections
for i, det in enumerate(pred): # detections per image
s = '%gx%g ' % img.shape[2:] # print string
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):
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4))).view(-1).tolist()
xywh = [round(x) for x in xywh]
xywh = [xywh[0] - xywh[2] // 2, xywh[1] - xywh[3] // 2, xywh[2],
xywh[3]] # 检测到目标位置，格式：（left，top，w，h）
cls = self.names[int(cls)]
conf = float(conf)
detections.append({'class': cls, 'conf': conf, 'position': xywh})
# 输出结果
for i in detections:
print(i)
# Print time (inference + NMS)
print(f'{s}Done. ({t2 - t1:.3f}s)')
return detections
将代码封装为一个类，先载入模型，之后就可以传入图像进行图像检测了。
未来更好测试，写了各简单的GUI。使用python自带的tkinter库实现。
# 界面设计
detect_state = False
top = tk.Tk()
top.title('YOLOV5-Lite Detect')
top['bg'] = 'white'
width = 300
height = 150
win_width = top.winfo_screenwidth()
win_height = top.winfo_screenheight()
center_place = str(int(win_width/2 - width/2))+'+'+str(int(win_height/2 - height/2))
top.geometry(str(width)+'x'+str(height)+'+'+center_place)
label = tk.Label(top,text='path')
label.pack(fill='both')
btn_img = tk.Button(top,text='选择图片',command=select_img)
btn_img.pack(fill='both')
btn_video = tk.Button(top,text='选择视频',command=select_video)
btn_video.pack(fill='both')
btn_detect = tk.Button(top,text='DETECT',command=mt_detect)
btn_detect.pack(fill='both')
top.mainloop()
完整代码（调用接口脚本）
import tkinter as tk
from tkinter import filedialog#用于打开文件 核心：filepath = filedialog.askopenfilename() #获得选择好的文件,单个文件
import argparse
import time
from pathlib import Path
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random, ascontiguousarray
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages, letterbox
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
class DETECT_API:
def __init__(self,opt):
weights,imgsz = opt.weights,opt.img_size
self.device = select_device(opt.device)
# device = device_ if torch.cuda.is_available() else 'cpu' # 设置代码执行的设备 cuda device, i.e. 0 or 0,1,2,3 or cpu
# self.device = device
self.half = self.device.type != 'cpu' # half precision only supported on CUDA
# self.imgsz = (imgsz, imgsz) # 输入图片的大小 默认640(pixels)
self.conf_thres = opt.conf_thres # object置信度阈值 默认0.25 用在nms中
self.iou_thres = opt.iou_thres # 做nms的iou阈值 默认0.45 用在nms中
# self.max_det = max_det # 每张图片最多的目标数量 用在nms中
self.classes = opt.classes # 在nms中是否是只保留某些特定的类 默认是None 就是所有类只要满足条件都可以保留 --class 0, or --class 0 2 3
self.agnostic_nms = opt.agnostic_nms # 进行nms是否也除去不同类别之间的框 默认False
self.augment = opt.augment # 预测是否也要采用数据增强 TTA 默认False
# self.visualize = False # 特征图可视化 默认FALSE
# self.half = False # 是否使用半精度 Float16 推理 可以缩短推理时间 但是默认是False
# self.dnn = False # 使用OpenCV DNN进行ONNX推理
# Load model
self.model = attempt_load(weights, map_location=self.device) # load FP32 model
if self.half:
self.model.half() # to FP16
if self.device.type != 'cpu':
self.model(torch.zeros(1, 3, imgsz, imgsz).to(self.device).type_as(next(self.model.parameters()))) # run once
self.stride = int(self.model.stride.max()) # model stride
self.imgsz = check_img_size(imgsz, s=self.stride) # check img_size
# Get names and colors
self.names = self.model.module.names if hasattr(self.model, 'module') else self.model.names
colors = [[random.randint(0, 255) for _ in range(3)] for _ in self.names]
def detect(self,img_path):
'''
检测图像，输入图片路径，不能输入视频路径
Args:
img_path: 图片路径
Returns:字典{'class': cls, 'conf': conf, 'position': xywh}
'''
# Set Dataloader
dataset = LoadImages(img_path, img_size=self.imgsz, stride=self.stride)
# 用于存放结果
detections = []
s = ''
for path, img, im0s, vid_cap in dataset:
print(path)
img = torch.from_numpy(img).to(self.device)
img = img.half() if self.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 = self.model(img, augment=self.augment)[0]
# Apply NMS
pred = non_max_suppression(pred, self.conf_thres, self.iou_thres, classes=self.classes, agnostic=self.agnostic_nms)
t2 = time_synchronized()
# Process detections
for i, det in enumerate(pred): # detections per image
s = '%gx%g ' % img.shape[2:] # print string
im0 = im0s
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):
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4))).view(-1).tolist()
xywh = [round(x) for x in xywh]
xywh = [xywh[0] - xywh[2] // 2, xywh[1] - xywh[3] // 2, xywh[2],
xywh[3]] # 检测到目标位置，格式：（left，top，w，h）
cls = self.names[int(cls)]
conf = float(conf)
detections.append({'class': cls, 'conf': conf, 'position': xywh})
# 输出结果
for i in detections:
print(i)
# Print time (inference + NMS)
print(f'{s}Done. ({t2 - t1:.3f}s)')
return detections
def detect2(self,img):
'''
检测图像，输入图片数组
Args:
img: 图片数组
Returns:字典{'class': cls, 'conf': conf, 'position': xywh}
'''
# Set Dataloader
# dataset = LoadImages(img_path, img_size=self.imgsz, stride=self.stride)
# 用于存放结果
detections = []
s = ''
if True:
# print(path)
im0 = img*1
# Padded resize
img = letterbox(im0, self.imgsz, stride=self.stride)[0]
# Convert
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = ascontiguousarray(img) # np.ascontiguousarray(img)
img = torch.from_numpy(img).to(self.device)
img = img.half() if self.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 = self.model(img, augment=self.augment)[0]
# Apply NMS
pred = non_max_suppression(pred, self.conf_thres, self.iou_thres, classes=self.classes, agnostic=self.agnostic_nms)
t2 = time_synchronized()
# Process detections
for i, det in enumerate(pred): # detections per image
s = '%gx%g ' % img.shape[2:] # print string
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):
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4))).view(-1).tolist()
xywh = [round(x) for x in xywh]
xywh = [xywh[0] - xywh[2] // 2, xywh[1] - xywh[3] // 2, xywh[2],
xywh[3]] # 检测到目标位置，格式：（left，top，w，h）
cls = self.names[int(cls)]
conf = float(conf)
detections.append({'class': cls, 'conf': conf, 'position': xywh})
# 输出结果
for i in detections:
print(i)
# Print time (inference + NMS)
print(f'{s}Done. ({t2 - t1:.3f}s)')
return detections
def select_img():
global detect_state
pass
filepath = filedialog.askopenfilename(title='选择图片',filetypes=[('图片', '*.jpg *.png'), ('All files', '*')])
label['text'] = filepath
detect_state = 1
def select_video():
global detect_state
pass
filepath = filedialog.askopenfilename(title='选择视频', filetypes=[('视频', '*.mp4'), ('All files', '*')])
label['text'] = filepath
detect_state = 2
def mt_detect():
global detect_state
pass
path = label['text']
print(path)
if not detect_state:
print('请选择图片或视频')
else:
# opt.source = path
if detect_state == 1:
show_img(path)
elif detect_state == 2:
show_video(path)
detect_state = 0
def show_img(img_path):
# # 传入图片路径
# detections = Detect.detect(img_path)
# print(detections)
img = cv2.imread(img_path)
t1 = time.time()
detections = Detect.detect2(img)
t2 = time.time()
for i in detections:
# print(i)
x, y, w, h = i['position']
img = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 3)
img = cv2.putText(img, "{} {}".format(i['class'], round(i['conf'], 4)), (x, y - 5),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 1,
cv2.LINE_AA)
img = cv2.putText(img, "{}s".format( round((t2 - t1), 3)),
(10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 1, cv2.LINE_AA)
cv2.imshow('yolov5-Lite img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
def show_video(video_path):
cap = cv2.VideoCapture(video_path)
while cap.isOpened():
ret, img = cap.read()
if ret:
pass
t1 = time.time()
detections = Detect.detect2(img)
t2 = time.time()
for i in detections:
# print(i)
x, y, w, h = i['position']
img = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 3)
img = cv2.putText(img, "{} {}".format(i['class'], round(i['conf'], 4)), (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 1, cv2.LINE_AA)
img = cv2.putText(img, "{}FPS - {}s".format(round(1/(t2-t1),2), round((t2-t1),3)), (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 1,cv2.LINE_AA)
cv2.imshow('yolov5-Lite img', img)
# cv2.waitKey(1000)
if cv2.waitKey(10) == ord('q'):
break
else:
break
cap.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='runs/train/exp9/weights/best.pt',
help='model.pt path(s)')
#parser.add_argument('--source', type=str,default='',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=0.5, help='IOU threshold for NMS')
parser.add_argument('--device', default='cpu', 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('--nosave', action='store_true', help='do not save images/videos')
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', 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(exclude=('pycocotools', 'thop'))
# 初始化模型
Detect = DETECT_API(opt)
print('初始化完成/n')
# 界面设计
detect_state = False
top = tk.Tk()
top.title('YOLOV5-Lite Detect')
top['bg'] = 'white'
width = 300
height = 150
win_width = top.winfo_screenwidth()
win_height = top.winfo_screenheight()
center_place = str(int(win_width/2 - width/2))+'+'+str(int(win_height/2 - height/2))
top.geometry(str(width)+'x'+str(height)+'+'+center_place)
label = tk.Label(top,text='path')
label.pack(fill='both')
btn_img = tk.Button(top,text='选择图片',command=select_img)
btn_img.pack(fill='both')
btn_video = tk.Button(top,text='选择视频',command=select_video)
btn_video.pack(fill='both')
btn_detect = tk.Button(top,text='DETECT',command=mt_detect)
btn_detect.pack(fill='both')
top.mainloop()
如何运行
首先，你需要配置yolov5-Lite算法的运行环境，使能够正确的训练模型。配置过程与yolov5-7.0一致，如果报错，检测对应的库的版本是否符合条件，一般不需要最新的库，库的版本不要太高。训练好模型权重之后，parser.add_argument('--weights', nargs='+', type=str, default='runs/train/exp9/weights/best.pt', help='model.pt path(s)')修改成自己的权重路径即可。
一些截图
红色警告是torch版本问题，可以忽略，暂时没发现有什么影响。
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最后
先到这吧，有问题可评论。文章来源地址https://www.toymoban.com/news/detail-764807.html
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