YOLOv5：通过真实结果的txt文件与预测结果的txt文件进行结果评估

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YOLOv5：通过真实结果的txt文件与预测结果的txt文件进行结果评估

前言
前提条件
相关介绍
项目结构
YOLOv5：通过真实结果的txt文件与预测结果的txt文件进行结果评估
- val_txt.py
- 输出结果
参考

前言

由于本人水平有限，难免出现错漏，敬请批评改正。
更多精彩内容，可点击进入YOLO系列专栏、自然语言处理
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YOLOv7训练自己的数据集（口罩检测）
YOLOv8训练自己的数据集（足球检测）
YOLOv5：TensorRT加速YOLOv5模型推理
YOLOv5：IoU、GIoU、DIoU、CIoU、EIoU
玩转Jetson Nano（五）：TensorRT加速YOLOv5目标检测
YOLOv5：添加SE、CBAM、CoordAtt、ECA注意力机制
YOLOv5：yolov5s.yaml配置文件解读、增加小目标检测层
Python将COCO格式实例分割数据集转换为YOLO格式实例分割数据集
YOLOv5：使用7.0版本训练自己的实例分割模型（车辆、行人、路标、车道线等实例分割）
使用Kaggle GPU资源免费体验Stable Diffusion开源项目

前提条件

熟悉Python

项目结构

YOLOv5：通过真实结果的txt文件与预测结果的txt文件进行结果评估

问题描述：直接通过真实结果的YOLO格式的txt标注文件与预测结果的YOLO格式的txt标注文件进行结果评估，免去原本val.py中的模型预测。
数据集

images：图片数据所在的文件夹。
labels：真实框结果txt文件所在的文件夹。
results：预测框结果txt文件所在的文件夹。

val_txt.py

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
import argparse # 解析命令行参数模块
import json # 实现字典列表和JSON字符串之间的相互解析
import os # 与操作系统进行交互的模块 包含文件路径操作和解析
import sys # sys系统模块 包含了与Python解释器和它的环境有关的函数
from pathlib import Path # Path将str转换为Path对象 使字符串路径易于操作的模块
from threading import Thread # 线程操作模块import numpy as np
import torch
from tqdm import tqdmFILE = Path(__file__).resolve() # # FILE = WindowsPath './yolov5-6.1/val.py'
ROOT = FILE.parents[0]  # YOLOv5 root directory
if str(ROOT) not in sys.path:sys.path.append(str(ROOT))  # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relativefrom modelsmon import DetectMultiBackend
from utils.callbacks import Callbacks
from utils.datasets import create_dataloader
from utils.general import (LOGGER, box_iou, check_dataset, check_img_size, check_requirements, check_yaml,coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args,scale_coords, xywh2xyxy, xyxy2xywh)
from utils.metrics import ConfusionMatrix, ap_per_class
from utils.plots import output_to_target, plot_images, plot_val_study
from utils.torch_utils import select_device, time_syncimport cv2def save_one_txt(predn, save_conf, shape, file):# Save one txt resultgn = torch.tensor(shape)[[1, 0, 1, 0]]  # normalization gain whwhfor *xyxy, conf, cls in predn.tolist():xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywhline = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label formatwith open(file, 'a') as f:f.write(('%g ' * len(line)).rstrip() % line + '\n')def process_batch(detections, labels, iouv):"""Return correct predictions matrix. Both sets of boxes are in (x1, y1, x2, y2) format.Arguments:detections (Array[N, 6]), x1, y1, x2, y2, conf, classlabels (Array[M, 5]), class, x1, y1, x2, y2Returns:correct (Array[N, 10]), for 10 IoU levels"""correct = torch.zeros(detections.shape[0], iouv.shape[0], dtype=torch.bool, device=iouv.device)iou = box_iou(labels[:, 1:], detections[:, :4])x = torch.where((iou >= iouv[0]) & (labels[:, 0:1] == detections[:, 5]))  # IoU above threshold and classes matchif x[0].shape[0]:matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()  # [label, detection, iou]if x[0].shape[0] > 1:matches = matches[matches[:, 2].argsort()[::-1]]matches = matches[np.unique(matches[:, 1], return_index=True)[1]]# matches = matches[matches[:, 2].argsort()[::-1]]matches = matches[np.unique(matches[:, 0], return_index=True)[1]]matches = torch.Tensor(matches).to(iouv.device)correct[matches[:, 1].long()] = matches[:, 2:3] >= iouvreturn correct@torch.no_grad()
def run(data,weights=None,  # model.pt path(s)batch_size=32,  # batch sizeimgsz=640,  # inference size (pixels)conf_thres=0.001,  # confidence thresholdiou_thres=0.6,  # NMS IoU thresholdtask='val',  # train, val, test, speed or studydevice='',  # cuda device, i.e. 0 or 0,1,2,3 or cpuworkers=8,  # max dataloader workers (per RANK in DDP mode)single_cls=False,  # treat as single-class datasetaugment=False,  # augmented inferenceverbose=False,  # verbose outputsave_txt=False,  # save results to *.txtsave_hybrid=False,  # save label+prediction hybrid results to *.txtsave_conf=False,  # save confidences in --save-txt labelssave_json=False,  # save a COCO-JSON results fileproject=ROOT / 'runs/val',  # save to project/namename='exp',  # save to project/nameexist_ok=False,  # existing project/name ok, do not incrementhalf=True,  # use FP16 half-precision inferencednn=False,  # use OpenCV DNN for ONNX inferencemodel=None,dataloader=None,save_dir=Path(''),plots=True,callbacks=Callbacks(),compute_loss=None,):""":params data: 数据集配置文件地址 包含数据集的路径、类别个数、类名、下载地址等信息 train.py时传入data_dict:params weights: 模型的权重文件地址 运行train.py=None 运行test.py=默认weights/yolov5s.pt:params batch_size: 前向传播的批次大小 运行test.py传入默认32 运行train.py则传入batch_size // WORLD_SIZE * 2:params imgsz: 输入网络的图片分辨率 运行test.py传入默认640 运行train.py则传入imgsz_test:params conf_thres: object置信度阈值 默认0.25:params iou_thres: 进行NMS时IOU的阈值 默认0.6:params task: 设置测试的类型 有train, val, test, speed or study几种 默认val:params device: 测试的设备:params single_cls: 数据集是否只用一个类别 运行test.py传入默认False 运行train.py则传入single_cls:params augment: 测试是否使用TTA Test Time Augment 默认False:params verbose: 是否打印出每个类别的mAP 运行test.py传入默认Fasle 运行train.py则传入nc < 50 and final_epoch:params save_txt: 是否以txt文件的形式保存模型预测框的坐标 默认True:params save_hybrid: 是否save label+prediction hybrid results to *.txt  默认False:params save_conf: 是否保存预测每个目标的置信度到预测tx文件中 默认True:params save_json: 是否按照coco的json格式保存预测框，并且使用cocoapi做评估（需要同样coco的json格式的标签）运行test.py传入默认Fasle 运行train.py则传入is_coco and final_epoch(一般也是False):params project: 测试保存的源文件 默认runs/test:params name: 测试保存的文件地址 默认exp  保存在runs/test/exp下:params exist_ok: 是否存在当前文件 默认False 一般是 no exist-ok 连用  所以一般都要重新创建文件夹:params half: 是否使用半精度推理 FP16 half-precision inference 默认False:params model: 模型 如果执行test.py就为None 如果执行train.py就会传入ema.ema(ema模型):params dataloader: 数据加载器 如果执行test.py就为None 如果执行train.py就会传入testloader:params save_dir: 文件保存路径 如果执行test.py就为‘’ 如果执行train.py就会传入save_dir(runs/train/expn):params plots: 是否可视化 运行test.py传入默认True 运行train.py则传入plots and final_epoch:params wandb_logger: 网页可视化 类似于tensorboard 运行test.py传入默认None 运行train.py则传入wandb_logger(train):params compute_loss: 损失函数 运行test.py传入默认None 运行train.py则传入compute_loss(train):return (Precision, Recall, map@0.5, map@0.5:0.95, box_loss, obj_loss, cls_loss)"""# Initialize/load model and set device# 初始化模型并选择相应的计算设备# 判断是否是训练时调用run函数(执行train.py脚本), 如果是就使用训练时的设备 一般都是traintraining = Falsedevice = select_device(device, batch_size=batch_size)# Directories# 生成save_dir文件路径  run\val\expsave_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run# make dir run\val\exp\labels(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dirnc = NUMER_OF_CLASSES  # number of classes# 计算mAP相关参数# 设置iou阈值 从0.5-0.95取10个(0.05间隔)   iou vector for mAP@0.5:0.95# iouv: [0.50000, 0.55000, 0.60000, 0.65000, 0.70000, 0.75000, 0.80000, 0.85000, 0.90000, 0.95000]iouv = torch.linspace(0.5, 0.95, 10).to(device)  # iou vector for mAP@0.5:0.95niou = iouv.numel() # # mAP@0.5:0.95 iou个数=10个# 初始化一些测试需要的参数seen = 0 # 初始化测试的图片的数量# 初始化混淆矩阵confusion_matrix = ConfusionMatrix(nc=nc) # 获取数据集所有类别的类名names = NAMES# 设置tqdm进度条的显示信息s = ('%20s' + '%11s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')# 初始化p, r, f1, mp, mr, map50, map指标和时间t0, t1, t2dt, p, r, f1, mp, mr, map50, map = [0.0, 0.0, 0.0], 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0# 初始化测试集的损失loss = torch.zeros(3, device=device)# 初始化json文件中的字典 统计信息 ap等jdict, stats, ap, ap_class = [], [], [], []# pbar = tqdm(dataloader, desc=s, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')  # progress bartarget_datas = read_target_datas()pbar = tqdm(target_datas, desc=s, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')  # progress barpred_datas = read_pred_datas()# ======开始验证 ========for batch_i, (im, targets, paths, shapes) in enumerate(pbar):# 预处理图片和target# im = im.to(device, non_blocking=True)targets = targets.to(device)height,width = shapes[0]# targets[:, 2:] *= torch.Tensor([620, 425, 620, 425]).to(device)  # to pixelstargets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device)  # to pixelsout = [pred_datas[batch_i]]for si, pred in enumerate(out): # pred :[xmin,ymin,xmax,ymax,score,class_label]# 统计每张图片的真实框、预测框信息# 获取第si张图片的gt标签信息 包括class, x, y, w, h    target[:, 0]为标签属于哪张图片的编号labels = targets[targets[:, 0] == si, 1:] # [:, class+xywh]nl = len(labels) # 第si张图片的gt个数# 获取标签类别tcls = labels[:, 0].tolist() if nl else []  # target classpath, shape = Path(paths[si]), shapes[si][0] # 第si张图片的地址# 统计测试图片数量 +1seen += 1# 如果预测为空，则添加空的信息到stats里if len(pred) == 0:if nl:stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))continuepredn = pred.clone() # Evaluateif nl:tbox = xywh2xyxy(labels[:, 1:5])  # target boxeslabelsn = torch.cat((labels[:, 0:1], tbox), 1)  # native-space labelscorrect = process_batch(predn, labelsn, iouv)if plots:confusion_matrix.process_batch(predn, labelsn)else:correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool)stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))  # (correct, conf, pcls, tcls)# Save/log# 保存预测信息到txt文件  runs\test\exp7\labels\image_name.txtif save_txt:save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / (path.stem + '.txt'))# Compute metricsstats = [np.concatenate(x, 0) for x in zip(*stats)]  # to numpyif len(stats) and stats[0].any():tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)ap50, ap = ap[:, 0], ap.mean(1)  # AP@0.5, AP@0.5:0.95mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()nt = np.bincount(stats[3].astype(np.int64), minlength=nc)  # number of targets per classelse:nt = torch.zeros(1)# Print resultspf = '%20s' + '%11i' * 2 + '%11.3g' * 4  # print formatLOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map))# Print results per classif (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):for i, c in enumerate(ap_class):LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))# Print speedst = tuple(x / seen * 1E3 for x in dt)  # speeds per imageif not training:# shape = (batch_size, 3, imgsz, imgsz)# LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image.' % t)# Plotsif plots:confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))callbacks.run('on_val_end')def parse_opt():'''opt参数详解data: 数据集配置文件地址 包含数据集的路径、类别个数、类名、下载地址等信息weights: 模型的权重文件地址 weights/yolov5s.ptbatch_size: 前向传播的批次大小 默认32imgsz: 输入网络的图片分辨率 默认640conf-thres: object置信度阈值 默认0.001iou-thres: 进行NMS时IOU的阈值 默认0.6task: 设置测试的类型 有train, val, test, speed or study几种 默认valdevice: 测试的设备single-cls: 数据集是否只用一个类别 默认Falseaugment: 测试是否使用TTA Test Time Augment 默认Falseverbose: 是否打印出每个类别的mAP 默认False下面三个参数是auto-labelling(有点像RNN中的teaching forcing)相关参数详见: 下面解释是作者原话save-txt: traditional auto-labellingsave-hybrid: save hybrid autolabels, combining existing labels with new predictions before NMS (existing predictions given confidence=1.0 before NMS.save-conf: add confidences to any of the above commandssave-json: 是否按照coco的json格式保存预测框，并且使用cocoapi做评估（需要同样coco的json格式的标签） 默认Falseproject: 测试保存的源文件 默认runs/testname: 测试保存的文件地址 默认exp  保存在runs/test/exp下exist-ok: 是否存在当前文件 默认False 一般是 no exist-ok 连用  所以一般都要重新创建文件夹half: 是否使用半精度推理 默认False'''parser = argparse.ArgumentParser()parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model.pt path(s)')# parser.add_argument('--batch-size', type=int, default=32, help='batch size')parser.add_argument('--batch-size', type=int, default=1, help='batch size')parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold')parser.add_argument('--iou-thres', type=float, default=0.6, help='NMS IoU threshold')parser.add_argument('--task', default='val', help='train, val, test, speed or study')parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')parser.add_argument('--augment', action='store_true', help='augmented inference')parser.add_argument('--verbose', action='store_true', help='report mAP by class')parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file')parser.add_argument('--project', default=ROOT / 'runs/val', help='save to project/name')parser.add_argument('--name', default='exp', help='save to project/name')parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')opt = parser.parse_args()opt.data = check_yaml(opt.data)  # check YAMLopt.save_json |= opt.data.endswith('coco.yaml')opt.save_txt |= opt.save_hybridprint_args(FILE.stem, opt)return optdef main(opt):# 检测requirements文件中需要的包是否安装好了check_requirements(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))# 如果task in ['train', 'val', 'test']就正常测试 训练集/验证集/测试集if opt.task in ('train', 'val', 'test'):  # run normallyif opt.conf_thres > 0.001:  # .info(f'WARNING: confidence threshold {opt.conf_thres} >> 0.001 will produce invalid mAP values.')run(**vars(opt))def read_target_datas():'''datas = [[torch.Tensor([cv2.imread('../datasets/coco128/images/train2017/000000000034.jpg')]),torch.Tensor([[ 0.00000, 22.00000,  0.34621,  0.49361,  0.68942,  0.84632]]),('/data/mytest/datasets/coco128/images/train2017/000000000034.jpg',),[cv2.imread('../datasets/coco128/imagesNAME/train2017/000000000034.jpg').shape],]] # [(im, targets, paths, shapes),...]'''img_dir = IMG_DIRlabel_dir = TARGET_LABEL_DIRtxt_name_list = [i for i in os.listdir(label_dir) if i.endswith('.txt')]target_datas = []for txt_name in txt_name_list:image_path = os.path.join(img_dir,txt_name[:-4]+'.jpg') txt_path = os.path.join(label_dir,txt_name)img = cv2.imread(image_path)h,w = img.shape[:2]with open(txt_path, 'r') as t:lines = t.readlines()targets = []for line in lines:content = line.split(' ')label = int(content[0])norm_x_center = float(content[1])norm_y_center = float(content[2])norm_width = float(content[3])norm_height = float(content[4])targets.append([0.0, label, norm_x_center, norm_y_center, norm_width, norm_height])target_datas.append([torch.Tensor([img]),torch.Tensor(targets),(image_path,),((h,w),(h,w))])return target_datasdef read_pred_datas():'''datas = [[torch.Tensor([cv2.imread('../datasets/coco128/images/train2017/000000000034.jpg')]),torch.Tensor([[ 0.00000, 22.00000,  0.34621,  0.49361,  0.68942,  0.84632]]),('/data/mytest/datasets/coco128/images/train2017/000000000034.jpg',),[cv2.imread('../datasets/coco128/images/train2017/000000000034.jpg').shape],]] # [(im, targets, paths, shapes),...]'''img_dir = IMG_DIRlabel_dir = PRED_LABEL_DIRtxt_name_list = [i for i in os.listdir(label_dir) if i.endswith('.txt')]pred_datas = []for txt_name in txt_name_list:image_path = os.path.join(img_dir,txt_name[:-4]+'.jpg') txt_path = os.path.join(label_dir,txt_name)img = cv2.imread(image_path)h,w = img.shape[:2]with open(txt_path, 'r') as t:lines = t.readlines()preds = []for line in lines:content = line.split(' ')label = int(content[0])norm_x_center = float(content[1])norm_y_center = float(content[2])norm_width = float(content[3])norm_height = float(content[4])try:score = float(content[5])except Exception as e:score = float(0.99)xmin = (norm_x_center - norm_width / 2) * wymin = (norm_y_center - norm_height / 2) * hxmax = (norm_x_center + norm_width / 2) * wymax = (norm_y_center + norm_height / 2) * hpreds.append([xmin, ymin, xmax, ymax, score, label])# pred_datas.append([torch.Tensor([img]),torch.Tensor(preds),(image_path,),((h,w),(h,w))])pred_datas.append(torch.Tensor(preds))return pred_datasif __name__ == "__main__":# 全局定义数据集图片路径，真实框的txt文件夹路径，预测框的txt文件夹路径IMG_DIR = r'.\datasets\images'TARGET_LABEL_DIR = r'.\datasets\labels'PRED_LABEL_DIR = r'.\datasets\results'# class2nameNAMES = {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich',49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}# number of classesNUMER_OF_CLASSES = len(NAMES)opt = parse_opt()main(opt)

输出结果

参考

[1] .git

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