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detect_test.py
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detect_test.py
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import argparse
import time
from pathlib import Path
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, 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
def detect(save_img=False):
# 获取输出文件夹,输入源,权重,参数等参数
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) # 获取设备
# 如果设备为gpu,使用Float16
half = device.type != 'cpu' # half precision only supported on CUDA
# Load model
# 加载Float32模型,确保用户设定的输入图片分辨率能整除32(如不能则调整为能整除并返回)
model = attempt_load(weights, map_location=device) # load FP32 model
imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size
if half:
# 设置Float16
model.half() # to FP16
# Second-stage classifier
# 设置第二次分类,默认不使用
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
# 通过不同的输入源来设置不同的数据加载方式
vid_path, vid_writer = None, None
if webcam:
view_img = True
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz)
else:
save_img = True
# 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
# view_img = True
dataset = LoadImages(source, img_size=imgsz)
# Get names and colors
# 获取类别名字
names = model.module.names if hasattr(model, 'module') else model.names
# 设置画框的颜色
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
# Run inference
t0 = time.time()
# 进行一次前向推理,测试程序是否正常
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
_ = model(img.half() if half else img) if device.type != 'cpu' else None # run once
"""
path 图片/视频路径
img 进行resize+pad之后的图片
img0 原size图片
cap 当读取图片时为None,读取视频时为视频源
"""
for path, img, im0s, vid_cap in dataset:
img = torch.from_numpy(img).to(device)
# 图片也设置为Float16
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
# 没有batch_size的话则在最前面添加一个轴
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
t1 = time_synchronized()
"""
前向传播 返回pred的shape是(1, num_boxes, 5+num_class)
h,w为传入网络图片的长和宽,注意dataset在检测时使用了矩形推理,所以这里h不一定等于w
num_boxes = h/32 * w/32 + h/16 * w/16 + h/8 * w/8
pred[..., 0:4]为预测框坐标
预测框坐标为xywh(中心点+宽长)格式
pred[..., 4]为objectness置信度
pred[..., 5:-1]为分类结果
"""
pred = model(img, augment=opt.augment)[0]
# Apply 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, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
t2 = time_synchronized()
# Apply Classifier
# 添加二次分类,默认不使用
if classify:
pred = apply_classifier(pred, modelc, img, im0s)
# 裁剪区域的标签,自加
roi_num = 0
# Process detections
# 对每一张图片作处理
for i, det in enumerate(pred): # detections per image
# 如果输入源是webcam,则batch_size不为1,取出dataset中的一张图片
if webcam: # batch_size >= 1
p, s, im0 = Path(path[i]), '%g: ' % i, im0s[i].copy()
else:
p, s, im0 = Path(path), '', im0s
# 设置保存图片/视频的路径
save_path = str(save_dir / p.name)
# 设置保存框坐标txt文件的路径
txt_path = str(save_dir / 'labels' / p.stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '')
# 设置打印信息(图片长宽)
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
# 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(img.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 += '%g %ss, ' % (n, names[int(c)]) # add to string
# Write results
for *xyxy, conf, cls in reversed(det):
# 裁剪区域的标签,自加
roi_num += 1
if save_txt: # Write to file
# 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
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')
# 在原图上画框
if save_img or view_img: # Add bbox to image
label = '%s %.2f' % (names[int(cls)], conf)
# 将检测结果选取出来,自加
roi_l, roi_u, roi_r, roi_d = int(xyxy[0]), int(xyxy[1]), int(xyxy[2]), int(xyxy[3])
roi = im0[roi_u:roi_d, roi_l:roi_r]
cv2.imwrite(save_path[:-4] + '_' + str(roi_num) + '.jpg', roi)
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
# Print time (inference + NMS)
# 打印前向传播+nms时间
print('%sDone. (%.3fs)' % (s, t2 - t1))
# Stream results
# 如果设置展示,则show图片/视频
if view_img:
cv2.imshow(str(p), im0)
if cv2.waitKey(1) == ord('q'): # q to quit
raise StopIteration
# Save results (image with detections)
# 设置保存图片/视频
if save_img:
if dataset.mode == 'images':
cv2.imwrite(save_path, im0)
print(save_path)
else:
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:
print('Results saved to %s' % save_dir)
# 打开保存图片和txt的路径(好像只适用于MacOS系统)
# 打印总时间
print('Done. (%.3fs)' % (time.time() - t0))
if __name__ == '__main__':
"""
weights:训练的权重
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
output:网络预测之后的图片/视频的保存路径
img-size:网络输入图片大小
conf-thres:置信度阈值
iou-thres:做nms的iou阈值
device:设置设备
view-img:是否展示预测之后的图片/视频,默认False
save-txt:是否将预测的框坐标以txt文件形式保存,默认False
classes:设置只保留某一部分类别,形如0或者0 2 3
agnostic-nms:进行nms是否也去除不同类别之间的框,默认False
augment:推理的时候进行多尺度,翻转等操作(TTA)推理
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
"""
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='best.pt', help='model.pt path(s)')
parser.add_argument('--source', type=str, default='a_test/pic', 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=0.25, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
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='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('--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)
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()
# 去除pt文件中的优化器等信息
strip_optimizer(opt.weights)
else:
detect()