目标检测及目标定位_ONEPIECE_00的博客

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。

for i1 in pred:
    s=[]
    #转化为数组，并迭代
    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=[]
    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)

​