Yolov5目标检测之detect.py部分代码详解
# YOLOv5 by Ultralytics, GPL-3.0 license
"""
Run inference on images, videos, directories, streams, etc.
Usage - sources:
$ python path/to/detect.py --weights yolov5s.pt --source 0 # webcam
img.jpg # image
vid.mp4 # video
path/ # directory
path/*.jpg # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
Usage - formats:
$ python path/to/detect.py --weights yolov5s.pt # PyTorch
yolov5s.torchscript # TorchScript
yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
yolov5s.xml # OpenVINO
yolov5s.engine # TensorRT
yolov5s.mlmodel # CoreML (MacOS-only)
yolov5s_saved_model # TensorFlow SavedModel
yolov5s.pb # TensorFlow GraphDef
yolov5s.tflite # TensorFlow Lite
yolov5s_edgetpu.tflite # TensorFlow Edge TPU
"""
import argparse
import os
import sys
from pathlib import Path
from numpy import double
import torch
import torch.backends.cudnn as cudnn
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
from models.common import DetectMultiBackend
from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, time_sync
@torch.no_grad()
def run(
weights=ROOT / 'yolov5s.pt', # model.pt path(s)
source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam
data=ROOT / 'data/coco128.yaml', # dataset.yaml path
imgsz=(640, 640), # inference size (height, width)
conf_thres=0.25, # confidence threshold 置信度阈值
iou_thres=0.45, # NMS IOU threshold iou阈值
# 若预测框和真实框之间的iou值大于iou_threshold，
# 就表明此预测框具有和真实框的相同属性（如类别、是否前景）。
max_det=1000, # maximum detections per image
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
view_img=False, # show results
save_txt=False, # save results to *.txt是否将预测的框坐标以txt文件形式保存
save_conf=False, # save confidences in --save-txt labels
save_crop=False, # save cropped prediction boxes
nosave=False, # do not save images/videos
classes=None, # filter by class: --class 0, or --class 0 2 3
agnostic_nms=False, # class-agnostic NMS
augment=False, # augmented inference
visualize=False, # visualize features
update=False, # update all models
project=ROOT / 'runs/detect', # save results to project/name
name='exp', # save results to project/name
exist_ok=False, # existing project/name ok, do not increment
line_thickness=3, # bounding box thickness (pixels)
hide_labels=False, # hide labels
hide_conf=False, # hide confidences
half=False, # use FP16 half-precision inference
dnn=False, # use OpenCV DNN for ONNX inference
):
source = str(source)
save_img = not nosave and not source.endswith('.txt') # save inference images
#endswith() 方法用于判断字符串是否以指定后缀结尾
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
#startswith判断字符串是否以指定字符或子字符串开头
webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
#isnumeric检测字符串是否只有数字组成
if is_url and is_file:
source = check_file(source) # download
# Directories
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
# Load model
device = select_device(device)
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
#选择编译框架，选择。pt
stride, names, pt = model.stride, model.names, model.pt
imgsz = check_img_size(imgsz, s=stride) # check image size
# 确保输入图片的尺寸imgsz能整除stride=32 如果不能则调整为能被整除并返回
# Dataloader加载数据
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, auto=pt)
bs = len(dataset) # batch_size
else:
# 如果检测视频的时候想显示出来，可以在这里加一行view_img = True
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
bs = 1 # batch_size
vid_path, vid_writer = [None] * bs, [None] * bs
# Run inference
model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup
dt, seen = [0.0, 0.0, 0.0], 0
for path, im, im0s, vid_cap, s in dataset:
t1 = time_sync()
im = torch.from_numpy(im).to(device)
im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
#是否使用半精度
im /= 255 # 0 - 255 to 0.0 - 1.0
#归一化转到0-1
if len(im.shape) == 3:
im = im[None] # expand for batch dim
# 增加一个维度
t2 = time_sync()
dt[0] += t2 - t1
# Inference
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
pred = model(im, augment=augment, visualize=visualize)
t3 = time_sync()
dt[1] += t3 - t2
# NMS
"""
pred:前向传播的输出
conf_thres:置信度阈值
iou_thres:iou阈值
classes:是否只保留特定的类别
agnostic:进行nms是否也去除不同类别之间的框
经过nms之后,预测框格式:xywh-->xyxy(左上角右下角)
pred是一个列表list[torch.tensor],长度为batch_size
每一个torch.tensor的shape为(num_boxes, 6),内容为box+conf+cls
"""
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
dt[2] += time_sync() - t3
# Second-stage classifier (optional)
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
# Process predictions
# 对每一张图片作处理
for i, det in enumerate(pred): # per image
seen += 1
if webcam: # batch_size >= 1
p, im0, frame = path[i], im0s[i].copy(), dataset.count
s += f'{i}: '
else:
p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
# 但是大部分我们一般都是从LoadImages流读取本都文件中的照片或者视频 所以batch_size=1
# p: 当前图片/视频的绝对路径 如 F:\yolo_v5\yolov5-U\data\images\bus.jpg
# s: 输出信息 初始为 ''
# im0: 原始图片 letterbox + pad 之前的图片
# frame: 视频流
p = Path(p) # to Path
# 当前路径yolov5/data/images/
# 设置保存图片/视频的路径
save_path = str(save_dir / p.name) # im.jpg
# 设置保存框坐标txt文件的路径
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt
# 设置打印信息(图片长宽)
s += '%gx%g ' % im.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
imc = im0.copy() if save_crop else im0 # for save_crop
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
if len(det):
# Rescale boxes from img_size to im0 size
# 调整预测框的坐标：基于resize+pad的图片的坐标-->基于原size图片的坐标
# 此时坐标格式为xyxy
det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
# Print results打印检测到的类别数量
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
# 保存预测结果
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()
# 将xyxy(左上角+右下角)格式转为xywh(中心点+宽长)格式，
# 并除上w，h做归一化，转化为列表再保存
# normalized xywh
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
with open(txt_path + '.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
# # 在原图上画框 + 将预测到的目标剪切出来 保存成图片
# 保存在save_dir/crops下 在原图像画图或者保存结果
if save_img or save_crop or view_img: # Add bbox to image
# 在原图上画框
c = int(cls) # integer class
label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
annotator.box_label(xyxy, label, color=colors(c, True))
# if save_crop:
# save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
# Stream results
im0 = annotator.result()
if view_img:
#cv2.waitKey(1) # 1 millisecond
cv2.imshow(str(p), im0)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Save results (image with detections)
if save_img:
if dataset.mode == 'image':
cv2.imwrite(save_path, im0)
else: # 'video' or 'stream'
if vid_path[i] != save_path: # new video
vid_path[i] = save_path
if isinstance(vid_writer[i], cv2.VideoWriter):
vid_writer[i].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 = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
vid_writer[i].write(im0)
# Print time (inference-only)
LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
# Print results
t = tuple(x / seen * 1E3 for x in dt) # speeds per image
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
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 ''
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
if update:
strip_optimizer(weights) # update model (to fix SourceChangeWarning)
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='runs/train/exp7/weights/best.pt', help='model path(s)')
#parser.add_argument('--source', type=str, default='rtsp://admin:[email protected]:5035/main/av_stream', help='file/dir/URL/glob, 0 for webcam')
parser.add_argument('--source', type=str, default='0', help='file/dir/URL/glob, 0 for webcam')
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[480], help='inference size h,w')
"""
weights: 训练的权重路径,可以使用自己训练的权重,也可以使用官网提供的权重
默认官网的权重yolov5s.pt(yolov5n.pt/yolov5s.pt/yolov5m.pt/yolov5l.pt/yolov5x.pt/区别在于网络的宽度和深度以此增加)
source: 测试数据，可以是图片/视频路径，也可以是'0'(电脑自带摄像头),也可以是rtsp等视频流, 默认data/images
data: 配置数据文件路径, 包括image/label/classes等信息, 训练自己的文件, 需要作相应更改, 可以不用管
如果设置了只显示个别类别即使用了--classes = 0 或二者1, 2, 3等, 则需要设置该文件，数字和类别相对应才能只检测某一个类
imgsz: 网络输入图片大小, 默认的大小是640
conf-thres: 置信度阈值， 默认为0.25
iou-thres: 做nms的iou阈值, 默认为0.45
max-det: 保留的最大检测框数量, 每张图片中检测目标的个数最多为1000类
device: 设置设备CPU/CUDA, 可以不用设置
view-img: 是否展示预测之后的图片/视频, 默认False, --view-img 电脑界面出现图片或者视频检测结果
save-txt: 是否将预测的框坐标以txt文件形式保存, 默认False, 使用--save-txt 在路径runs/detect/exp*/labels/*.txt下生成每张图片预测的txt文件
save-conf: 是否将置信度conf也保存到txt中, 默认False
save-crop: 是否保存裁剪预测框图片, 默认为False, 使用--save-crop 在runs/detect/exp*/crop/剪切类别文件夹/ 路径下会保存每个接下来的目标
nosave: 不保存图片、视频, 要保存图片，不设置--nosave 在runs/detect/exp*/会出现预测的结果
classes: 设置只保留某一部分类别, 形如0或者0 2 3, 使用--classes = n, 则在路径runs/detect/exp*/下保存的图片为n所对应的类别, 此时需要设置data
agnostic-nms: 进行NMS去除不同类别之间的框, 默认False
augment: TTA测试时增强/多尺度预测, 可以提分
visualize: 是否可视化网络层输出特征
update: 如果为True,则对所有模型进行strip_optimizer操作,去除pt文件中的优化器等信息,默认为False
project: 保存测试日志的文件夹路径
name: 保存测试日志文件夹的名字, 所以最终是保存在project/name中
exist_ok: 是否重新创建日志文件, False时重新创建文件
line-thickness: 画框的线条粗细
hide-labels: 可视化时隐藏预测类别
hide-conf: 可视化时隐藏置信度
half: 是否使用F16精度推理, 半进度提高检测速度
dnn: 用OpenCV DNN预测
"""
parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img', action='store_true', help='show 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('--save-crop', action='store_true', help='save cropped prediction boxes')
parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 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('--visualize', action='store_true', help='visualize features')
parser.add_argument('--update', action='store_true', help='update all models')
parser.add_argument('--project', default=ROOT / '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')
parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
opt = parser.parse_args()
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
print_args(vars(opt))
return opt
def main(opt):
check_requirements(exclude=('tensorboard', 'thop'))
run(**vars(opt))
if __name__ == "__main__":
opt = parse_opt()
main(opt)
