YOLOV5训练自己的数据集|目标检测及目标定位图像处理|数据分析|python

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。
【YOLOV5训练自己的数据集|目标检测及目标定位】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。

# YOLOv5by 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 Pathimport cv2 import numpy as np import torch import torch.backends.cudnn as cudnnFILE = Path(__file__).absolute() sys.path.append(FILE.parents[0].as_posix())# add yolov5/ to pathfrom 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 optdef 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。

文章图片

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。

文章图片

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)?