一、概述
本文是针对目标检测的应用，接着目标检测(28条消息) YOLOv5训练自己的数据集_ONEPIECE_00的博客-CSDN博客及目标定位(28条消息) 目标检测及目标定位_ONEPIECE_00的博客-CSDN博客的针对检测后的结果判断座位上是否还有人。在目标定位的基础上修改代码，主要是增加对多个图片按顺序进行检测，并按结果一定逻辑判断其结果，以及增加定时的功能。主要就是在检测的结果的基础上，对结果的处理与应用，然后我分别以检测的detec.py文件、目标定位的site_pro.py、检测结果处理的pro.py文件进行讲解。
二、代码详解
1、目标检测
首先是对图片进行检测的detect.py进行修改，需要增加改变图片路径的端口，以及重新写main主函数，保证路径修改后能被执行。具体代码如下：
#写一个函数改变路径
def changes(new="yolov5-master/data/JPEGImages/01.jpg"):
global new_
new_=new
#把路径改为一个变量，便于修改
def parse_opt():
parser.add_argument('--source', type=str, default=new_, help='file/dir/URL/glob, 0 for webcam')
#将改变路径的函数与主函数写入同一函数中，保证函数实现时是用的同一路径
def main_(opt):
print(colorstr('detect: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
check_requirements(exclude=('tensorboard', 'thop'))
return run(**vars(opt))
#形参sou为路径
def main(sou):
changes(sou)
opt = parse_opt()
return main_(opt)
?完整detec.py代码，最后输出一定要用return输出，否则后面调用得到的是空。最好复制原来的代码重新建立一个detec2.py
import argparse
import sys
import time
from pathlib import Path
import cv2
import numpy as np
import torch
import torch.backends.cudnn as cudnn
FILE = Path(__file__).absolute()
sys.path.append(FILE.parents[0].as_posix()) # add yolov5/ to path
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, colorstr, is_ascii, non_max_suppression, \
apply_classifier, scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box
from utils.plots import Annotator, colors
from utils.torch_utils import select_device, load_classifier, time_sync
@torch.no_grad()
def run(weights='yolov5s.pt', # model.pt path(s)
source='data/images', # file/dir/URL/glob, 0 for webcam
imgsz=[640,640], # inference size (pixels)
conf_thres=0.25, # confidence threshold
iou_thres=0.45, # NMS IOU threshold
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
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='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
):
save_img = not nosave and not source.endswith('.txt') # save inference images保留推理的照片
webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
('rtsp://', 'rtmp://', 'http://', 'https://'))
# Directories目录
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
# Initialize初始化
set_logging()
device = select_device(device)
half &= device.type != 'cpu' # half precision only supported on CUDA
# Load model加载模型
w = weights[0] if isinstance(weights, list) else weights
classify, suffix = False, Path(w).suffix.lower()
pt, onnx, tflite, pb, saved_model = (suffix == x for x in ['.pt', '.onnx', '.tflite', '.pb', '']) # backend
stride, names = 64, [f'class{i}' for i in range(1000)] # assign defaults
if pt:
model = attempt_load(weights, map_location=device) # load FP32 model
stride = int(model.stride.max()) # model stride
names = model.module.names if hasattr(model, 'module') else model.names # get class names
if half:
model.half() # to FP16
if classify: # second-stage classifier
modelc = load_classifier(name='resnet50', n=2) # initialize
modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval()
elif onnx:
check_requirements(('onnx', 'onnxruntime'))
import onnxruntime
session = onnxruntime.InferenceSession(w, None)
else: # TensorFlow models
check_requirements(('tensorflow>=2.4.1',))
import tensorflow as tf
if pb: # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
def wrap_frozen_graph(gd, inputs, outputs):
x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped import
return x.prune(tf.nest.map_structure(x.graph.as_graph_element, inputs),
tf.nest.map_structure(x.graph.as_graph_element, outputs))
graph_def = tf.Graph().as_graph_def()
graph_def.ParseFromString(open(w, 'rb').read())
frozen_func = wrap_frozen_graph(gd=graph_def, inputs="x:0", outputs="Identity:0")
elif saved_model:
model = tf.keras.models.load_model(w)
elif tflite:
interpreter = tf.lite.Interpreter(model_path=w) # load TFLite model
interpreter.allocate_tensors() # allocate
input_details = interpreter.get_input_details() # inputs
output_details = interpreter.get_output_details() # outputs
int8 = input_details[0]['dtype'] == np.uint8 # is TFLite quantized uint8 model
imgsz = check_img_size(imgsz, s=stride) # check image size
ascii = is_ascii(names) # names are ascii (use PIL for UTF-8)
# Dataloader
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:
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
bs = 1 # batch_size
vid_path, vid_writer = [None] * bs, [None] * bs
# Run inference
if pt and device.type != 'cpu':
model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.parameters()))) # run once
t0 = time.time()
for path, img, im0s, vid_cap in dataset:
if onnx:
img = img.astype('float32')
else:
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img = img / 255.0 # 0 - 255 to 0.0 - 1.0
if len(img.shape) == 3:
img = img[None] # expand for batch dim
# Inference
t1 = time_sync()
if pt:
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
pred = model(img, augment=augment, visualize=visualize)[0]
elif onnx:
pred = torch.tensor(session.run([session.get_outputs()[0].name], {session.get_inputs()[0].name: img}))
else: # tensorflow model (tflite, pb, saved_model)
imn = img.permute(0, 2, 3, 1).cpu().numpy() # image in numpy
if pb:
pred = frozen_func(x=tf.constant(imn)).numpy()
elif saved_model:
pred = model(imn, training=False).numpy()
elif tflite:
if int8:
scale, zero_point = input_details[0]['quantization']
imn = (imn / scale + zero_point).astype(np.uint8) # de-scale
interpreter.set_tensor(input_details[0]['index'], imn)
interpreter.invoke()
pred = interpreter.get_tensor(output_details[0]['index'])
if int8:
scale, zero_point = output_details[0]['quantization']
pred = (pred.astype(np.float32) - zero_point) * scale # re-scale
pred[..., 0] *= imgsz[1] # x
pred[..., 1] *= imgsz[0] # y
pred[..., 2] *= imgsz[1] # w
pred[..., 3] *= imgsz[0] # h
pred = torch.tensor(pred)
# NMS
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
t2 = time_sync()
# Second-stage classifier (optional)
if classify:
pred = apply_classifier(pred, modelc, img, im0s)
# Process predictions
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
else:
p, s, im0, frame = path, '', im0s.copy(), 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
imc = im0.copy() if save_crop else im0 # for save_crop
annotator = Annotator(im0, line_width=line_thickness, pil=not ascii)
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 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 save_crop or view_img: # Add bbox to image
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)
# Print time (inference + NMS)
print(f'{s}Done. ({t2 - t1:.3f}s)')
# Stream results
im0 = annotator.result()
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[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 += '.mp4'
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
vid_writer[i].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 {colorstr('bold', save_dir)}{s}")
if update:
strip_optimizer(weights) # update model (to fix SourceChangeWarning)
print(f'Done. ({time.time() - t0:.3f}s)')
#添加
from site_pro import site
return site(source,pred,names)
def changes(new="yolov5-master/data/JPEGImages/01.jpg"):
global new_
new_=new
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='/content/gdrive/MyDrive/yolov5-master/runs/train/use_1/weights/best.pt', help='model.pt path(s)')
parser.add_argument('--source', type=str, default=new_, help='file/dir/URL/glob, 0 for webcam')
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640,640], help='inference size h,w')
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: --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('--visualize', action='store_true', help='visualize features')
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')
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')
opt = parser.parse_args()
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
return opt
def main_(opt):
print(colorstr('detect: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
check_requirements(exclude=('tensorboard', 'thop'))
return run(**vars(opt))
def main(sou):
changes(sou)
opt = parse_opt()
return main_(opt)?2.目标定位
这一步主要就只是将输出改为一个列表包含各个识别出来物体结果的形式，并以return输出，输出结果的形式为[（x,y,概率,识别出的类型,区域（方便计算，暂时分为左右两片区域））]。
[[0.5844, 0.6292, 0.8585752, 'person', 'left'], [0.6292, 0.4757, 0.8243118, 'computer', 'left'], [0.4219, 0.4757, 0.6576152, 'cup', 'right']]?完整代码:
import os
from PIL import Image
def site(source,pred,names):
img=Image.open(source)
x1,x2=img.size
s2=[]
for i1 in pred:
s=[]
for i2 in i1.data.cpu().numpy():
s1=[]
s=list(i2)
#获取中心的(x,y)坐标
x=s[0]=float(round((s[0]+s[2])/x1/2,4))
y=s[1]=float(round((s[1]+s[3])/x2/2,4))
#位置判断
if x>0.5:
w='left'
elif x<=0.5:
w='right'
s1.append(x)
s1.append(y)
s1.append(s[4])
s1.append(names[int(s[5])])
if s[4]<0.6:
break
s1.append(w)
s2.append(s1)
return s23.座位余量分析
我的目的是通过目标检测判断出位置余量的情况，有无人、有杂物长期无人存在、有人三种情况，首先是对这种情况进行判断，需要一定时间间隔内的该场景的多张图像，我采用通过按顺序调取一个文件夹的多张照片来模拟这种情景。
#情景模拟，取文件夹内图片的文件名
imagelist=os.listdir("/content/gdrive/MyDrive/yolov5-master/requirement")
print(imagelist)?然后通过调用图片路径对图片进行目标检测并保存结果，按一定的逻辑进行计算，如累加后超过一定值进行判断，然后可能根据不同的情况还需要设定一定的时间间隔，如有人就按正常情况，无人就缩短对下一张照片进行识别的时间间隔。
#定时函数
def clock_time(x):
a1=time.time()
while True:
a2=time.time()
if a2-a1>x:
break
for i1 in imagelist:
s=t.main("/content/gdrive/MyDrive/yolov5-master/requirement/"+i1)
x=0
#对识别的结果进行处理
for i2 in range(len(s)):
#right、left分别计算
if s[i2]==[]:
x=2
elif s[i2][3]=='person':
x=0
elif s[i2][3] in ['computer', 'person', 'phone', 'tablet phone', 'cup', 'bag', 'bag2', 'books']:
x=1
rt[s[i2][4]]=rt.get(s[i2][4])+x
#对有人、无人、有杂物分别采取不同发间隔时间
if x==0:
clock_time(1)
elif x==1:
clock_time(2)
elif x==2:
clock_time(3)?最后根据得到的结果，判断不同区域内的情况。
if rt['right']>1:
print("right无人")
else:
print("right有人")
if rt['left']>1:
print('left无人')
else:
print('left有人')?完整代码：
import time
import detect_2 as t
import os
#定时函数
def clock_time(x):
a1=time.time()
while True:
a2=time.time()
if a2-a1>x:
break
def time_():
imagelist=os.listdir("/content/gdrive/MyDrive/yolov5-master/requirement")
print(imagelist)
rt={'right':0,'left':0}
#对文件图片内进行按顺序进行检测
for i1 in imagelist:
#print(i1)
s=t.main("/content/gdrive/MyDrive/yolov5-master/requirement/"+i1)
#print(s)
#print(len(s))
x=0
#对识别的结果进行处理
for i2 in range(len(s)):
#right、left分别计算
if s[i2]==[]:
x=2
elif s[i2][3]=='person':
x=0
elif s[i2][3] in ['computer', 'person', 'phone', 'tablet phone', 'cup', 'bag', 'bag2', 'books']:
x=1
rt[s[i2][4]]=rt.get(s[i2][4])+x
#对有人、无人、有杂物分别采取不同发间隔时间
if x==0:
clock_time(1)
elif x==1:
clock_time(2)
elif x==2:
clock_time(3)
if rt['right']>1:
print("right无人")
else:
print("right有人")
if rt['left']>1:
print('left无人')
else:
print('left有人')
time_()
?三、总结
总的来说就是对目标检测的一种应用，用来分析判断一个区域内物体存在的情况，代码也比较简单，我这里写的也比较简陋，具体使用还需要真对自身情况再改善。最后在附上该代码的结果
?