一、概述
本文是关于目标检测后根据物体的坐标来确定物体所处的区域，适用于需要根据物体在图像中的位置来分别判断的情况，而且对应的是YOLOv5模型。YOLOv5目标检测的内容可以看看我之前的一篇文章YOLOv5训练自己的数据集_ONEPIECE_00的博客-CSDN博客
本文采用的目标定位的方法，其实就是根据物体检测后得到的数据，比如(x,y,w,h)的坐标，检测结果，以及检测的准确度，然后判断出物体所在的位置。我采用的是重新写一个py文件，放入我的位判断位置的函数，然后再从YOLOv5的detect.py中去调取我的函数，这样比较方便后期的修改。我写的函数中三个形参分别对应的是输入图片的路径source、预测的结果pred、以及标签label包含的数据（是一个列表形式）names，也分别对应detect.py文件中的参数。然后在写py文件的时候要注意命名，因为YOLOv5官方项目文件中包含很多py文件，容易重名。
二、代码详解
下面是YOLOv5中完整detect.py文件，然后我就根据我的三个输入的形参来分别描述。
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Run inference on images, videos, directories, streams, etc.
Usage:
$ python path/to/detect.py --source path/to/img.jpg --weights yolov5s.pt --img 640
"""
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)')
from site_pro import site
site(source,pred,names)
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='/content/gdrive/MyDrive/yolov5-master/data/JPEGImages/01.jpg', 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'))
run(**vars(opt))
if __name__ == "__main__":
opt = parse_opt()
main(opt)1.source（被检测图片的路径）
之所以要将这个图片的路径来作为一个输入，是因为我碰到了一个问题，就是不同的照片，像素值不同，然后得到的坐标也有很大的差异。我是通过得到图片的大小，然后再分别用对应的坐标去除，得到以比例形式存在的坐标。
def site(source,pred,names):
#d得到图片的大小
img=Image.open(source)
x1,x2=img.size
#可以通过print查看具体大小
#print(x1,x2) 这个基本上就是该参数的全部作用。
2.pred(预测的结果)
pred是包含的预测的结果，对应(x,y,w,h,识别准确度,物体的类别)，其中物体的类别他是用索引对应标签来表示的。下面的图片就是pred内数据的形式，它一个列表，然后保存的一个tensor(张量)形式的数据。
由于pred是一个张量在一个列表中的形式，然后就涉及到一个张量的转化，下面是一个关于张量（tensor）性质的简述。
这是张量转化为数组的具体方法，然后具体使用的话还是最好再转化为list（列表形式）
numpy=tensor.numpy()下面就是我的tensor数组化，再列表化投入使用的过程。如果想看到中间的变化过程，可以加print()测试一下。由于pred中坐标(x,y,w,h)中，(x,y)是表示的左上角点的坐标,而(w,h)是代表右下角点的坐标，然后通过求和计算得到其中心点的坐标来参与判断。而且可以根据识别准确度的大小来判断是否采用该数据。需要注意的是将tensor数据转化为numpy型时，如果使用了gpu，生成的数据属于gpu tensor型，需要先转化numpy=gputensor.data.cpu().numpy()。
for i1 in pred:
s=[]
#转化为数组，并迭代
#for i2 in i1.data.cpu().numpy() #使用gpu时
for i2 in i1.numpy():
s1=[]
#列表化
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 and y<0.5:
w="2 site"
elif x<0.5 and y>0.5:
w="3 site"
elif x>0.5 and y>0.5:
w="4 site"
else:
w="1 site"
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)
print(s1)3.names(标签label)
names是一个包含你的标签的列表(如下图，这是我的label内容)，然后可以通过pred中的最后一个数据，就是对应的索引来得到检测出的物体的类型。
['computer', 'person', 'phone', 'tablet phone', 'cup', 'bag', 'bag2', 'books']4.总结
以上就是我大概的思路以及部分代码，下面是我最后的输出形式,可以根据自己的需求改变。
#(x,y,识别的准确度,,检测出的物体类型,自己设置的位置区域)
[0.5844, 0.6292, 0.8585756, 'person', '4 site']
[0.6292, 0.4757, 0.82431185, 'computer', '1 site']
[0.4219, 0.4757, 0.6576148, 'cup', '2 site']最后附上完整代码，以及如何从detec.py文件中调用函数，需要注意的是函数的调用要写detect。py中run函数的最后，具体可以看我发出的detect.py代码。
#site_pro 是我的py文件名，site是函数名
from site_pro import site
site(source,pred,names)#函数完整代码
import os
from PIL import Image
def site(source,pred,names):
img=Image.open(source)
x1,x2=img.size
print(x1)
print(x2)
print(img.size)
for i1 in pred:
s=[]
#如果使用gpu训练自己数据的话，需要先将gpu tensor转化
#for i2 in i1.data.cpu().numpy()
for i2 in i1.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 and y<0.5:
w="2 site"
elif x<0.5 and y>0.5:
w="3 site"
elif x>0.5 and y>0.5:
w="4 site"
else:
w="1 site"
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)
print(s1)
版权声明：本文为CSDN博主「ONEPIECE..」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/ONEPIECE_00/article/details/120589325
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