破解网站后台密码有人做吗,微信小视频网站开发,网站 百度地图,沈阳推广平台哪个好1.YOLOv8图像分割支持的数据格式#xff1a; (1).用于训练YOLOv8分割模型的数据集标签格式如下: 1).每幅图像对应一个文本文件#xff1a;数据集中的每幅图像都有一个与图像文件同名的对应文本文件#xff0c;扩展名为.txt; 2).文本文件中每个目标(object)占一行… 1.YOLOv8图像分割支持的数据格式 (1).用于训练YOLOv8分割模型的数据集标签格式如下: 1).每幅图像对应一个文本文件数据集中的每幅图像都有一个与图像文件同名的对应文本文件扩展名为.txt; 2).文本文件中每个目标(object)占一行文本文件中的每一行对应图像中的一个目标实例 3).每行目标信息如下所示之间用空格分隔 A.目标类别索引整数例如0代表person1代表car等等; B.目标边界坐标mask区域周围的边界坐标归一化为[0, 1];
class-index x1 y1 x2 y2 ... xn yn 注每行的长度不必相等每个分隔label必须至少有3对xy点 (2).数据集YAML格式Ultralytics框架使用YAML文件格式来定义用于训练分隔模型的数据集和模型配置如下面测试数据集melon中melon_seg.yaml内容如下 在网上下载了60多幅包含西瓜和冬瓜的图像组成melon数据集
path: ../datasets/melon_seg # dataset root dir
train: images/train # train images (relative to path)
val: images/val # val images (relative to path)
test: # test images (optional)# Classes
names:0: watermelon1: wintermelon 2.使用半自动标注工具 EISeg 对数据集melon进行标注 (1).从 PaddleSeg 中下载通用场景的图像标注高精度模型static_hrnet18_ocr64_cocolvis.zip (2).标注前先按照下面操作设置好 1).选中JSON保存取消COCO保存; 2).选中自动保存; 3).取消灰度保存. 3.编写Python脚本将EISeg生成的json文件转换成YOLOv8 segment支持的txt文件
import os
import json
import argparse
import colorama
import random
import shutil
import cv2# supported image formats
img_formats (.bmp, .jpeg, .jpg, .png, .webp)def parse_args():parser argparse.ArgumentParser(descriptionjson(EISeg) to txt(YOLOv8))parser.add_argument(--dir, requiredTrue, typestr, helpimages directory, all json files are in the label directory, and generated txt files are also in the label directory)parser.add_argument(--labels, requiredTrue, typestr, helptxt file that hold indexes and labels, one label per line, for example: face 0)parser.add_argument(--val_size, default0.2, typefloat, helpthe proportion of the validation set to the overall dataset:[0., 0.5])parser.add_argument(--name, requiredTrue, typestr, helpthe name of the dataset)args parser.parse_args()return argsdef get_labels_index(name):labels {} # key,valuewith open(name, r) as file:for line in file:# print(line:, line)key_value []for v in line.split( ):# print(v:, v)key_value.append(v.replace(\n, )) # remove line breaks(\n) at the end of the lineif len(key_value) ! 2:print(colorama.Fore.RED Error: each line should have only two values(key value):, len(key_value))continuelabels[key_value[0]] key_value[1]with open(name, r) as file:line_num len(file.readlines())if line_num ! len(labels):print(colorama.Fore.RED Error: there may be duplicate lables:, line_num, len(labels))return labelsdef get_json_files(dir):jsons []for x in os.listdir(dir/label):if x.endswith(.json):jsons.append(x)return jsonsdef parse_json(name_json, name_image):img cv2.imread(name_image)if img is None:print(colorama.Fore.RED Error: unable to load image:, name_image)raiseheight, width img.shape[:2]with open(name_json, r) as file:data json.load(file)objects[]for i in range(0, len(data)):object []object.append(data[i][name])object.append(data[i][points])objects.append(object)return width, height, objectsdef write_to_txt(name_json, width, height, objects, labels):name_txt name_json[:-len(.json)] .txt# print(name txt:, name_txt)with open(name_txt, w) as file:for obj in objects: # 0: name; 1: pointsif len(obj[1]) 3:print(colorama.Fore.RED Error: must be at least 3 pairs:, len(obj[1]), name_json)raiseif obj[0] not in labels:print(colorama.Fore.RED Error: unsupported label:, obj[0], labels)raisestring for pt in obj[1]:string string str(round(pt[0] / width, 6)) str(round(pt[1] / height, 6))string labels[obj[0]] string \rfile.write(string)def json_to_txt(dir, jsons, labels):for json in jsons:name_json dir /label/ jsonname_image for format in img_formats:file dir / json[:-len(.json)] formatif os.path.isfile(file):name_image filebreakif not name_image:print(colorama.Fore.RED Error: required image does not exist:, json[:-len(.json)])raise# print(name image:, name_image)width, height, objects parse_json(name_json, name_image)# print(fwidth: {width}; height: {height}; objects: {objects})write_to_txt(name_json, width, height, objects, labels)def get_random_sequence(length, val_size):numbers list(range(0, length))val_sequence random.sample(numbers, int(length*val_size))# print(val_sequence:, val_sequence)train_sequence [x for x in numbers if x not in val_sequence]# print(train_sequence:, train_sequence)return train_sequence, val_sequencedef get_files_number(dir):count 0for file in os.listdir(dir):if os.path.isfile(os.path.join(dir, file)):count 1return countdef split_train_val(dir, jsons, name, val_size):if val_size 0.5 or val_size 0.01:print(colorama.Fore.RED Error: the interval for val_size should be:[0.01, 0.5]:, val_size)raisedst_dir_images_train datasets/ name /images/traindst_dir_images_val datasets/ name /images/valdst_dir_labels_train datasets/ name /labels/traindst_dir_labels_val datasets/ name /labels/valtry:os.makedirs(dst_dir_images_train) #, exist_okTrueos.makedirs(dst_dir_images_val)os.makedirs(dst_dir_labels_train)os.makedirs(dst_dir_labels_val)except OSError as e:print(colorama.Fore.RED Error: cannot create directory:, e.strerror)raise# print(jsons:, jsons)train_sequence, val_sequence get_random_sequence(len(jsons), val_size)for index in train_sequence:for format in img_formats:file dir / jsons[index][:-len(.json)] format# print(file:, file)if os.path.isfile(file):shutil.copy(file, dst_dir_images_train)breakfile dir /label/ jsons[index][:-len(.json)] .txtif os.path.isfile(file):shutil.copy(file, dst_dir_labels_train)for index in val_sequence:for format in img_formats:file dir / jsons[index][:-len(.json)] formatif os.path.isfile(file):shutil.copy(file, dst_dir_images_val)breakfile dir /label/ jsons[index][:-len(.json)] .txtif os.path.isfile(file):shutil.copy(file, dst_dir_labels_val)num_images_train get_files_number(dst_dir_images_train)num_images_val get_files_number(dst_dir_images_val)num_labels_train get_files_number(dst_dir_labels_train)num_labels_val get_files_number(dst_dir_labels_val)if num_images_train num_images_val ! len(jsons) or num_labels_train num_labels_val ! len(jsons):print(colorama.Fore.RED Error: the number of files is inconsistent:, num_images_train, num_images_val, num_labels_train, num_labels_val, len(jsons))raisedef generate_yaml_file(labels, name):path os.path.join(datasets, name, name.yaml)# print(path:, path)with open(path, w) as file:file.write(path: ../datasets/%s # dataset root dir\n % name)file.write(train: images/train # train images (relative to path)\n)file.write(val: images/val # val images (relative to path)\n)file.write(test: # test images (optional)\n\n)file.write(# Classes\n)file.write(names:\n)for key, value in labels.items():# print(fkey: {key}; value: {value})file.write( %d: %s\n % (int(value), key))if __name__ __main__:colorama.init()args parse_args()# 1. parse JSON file and write it to a TXT filelabels get_labels_index(args.labels)# print(labels:, labels)jsons get_json_files(args.dir)# print(fjsons: {jsons}; number: {len(jsons)})json_to_txt(args.dir, jsons, labels)# 2. split the datasetsplit_train_val(args.dir, jsons, args.name, args.val_size)# 3. generate a YAML filegenerate_yaml_file(labels, args.name)print(colorama.Fore.GREEN execution completed ) 以上脚本包含3个功能 1).将json文件转换成txt文件 2).将数据集随机拆分成训练集和测试集 3).产生需要的yaml文件 4.编写Python脚本进行train:
import argparse
import colorama
from ultralytics import YOLOdef parse_args():parser argparse.ArgumentParser(descriptionYOLOv8 train)parser.add_argument(--yaml, requiredTrue, typestr, helpyaml file)parser.add_argument(--epochs, requiredTrue, typeint, helpnumber of training)parser.add_argument(--task, requiredTrue, typestr, choices[detect, segment], helpspecify what kind of task)args parser.parse_args()return argsdef train(task, yaml, epochs):if task detect:model YOLO(yolov8n.pt) # load a pretrained modelelif task segment:model YOLO(yolov8n-seg.pt) # load a pretrained modelelse:print(colorama.Fore.RED Error: unsupported task:, task)raiseresults model.train(datayaml, epochsepochs, imgsz640) # train the modelmetrics model.val() # Itll automatically evaluate the data you trained, no arguments needed, dataset and settings rememberedmodel.export(formatonnx) #, dynamicTrue) # export the model, cannot specify dynamicTrue, opencv does not support# model.export(formatonnx, opset12, simplifyTrue, dynamicFalse, imgsz640)model.export(formattorchscript) # libtorchif __name__ __main__:colorama.init()args parse_args()train(args.task, args.yaml, args.epochs)print(colorama.Fore.GREEN execution completed ) 执行结果如下图所示会生成best.pt、best.onnx、best.torchscript 5.生成的best.onnx使用Netron进行可视化结果如下图所示 说明 1).输入images: float32[1,3,640,640] :与YOLOv8 detect一致大小为3通道640*640 2).输出包括2层output0和output1 A.output0: float32[1,38,8400] a.8400模型预测的所有box的数量与YOLOv8 detect一致 b.38: 每个框给出38个值4xc, yc, width, height2class, confidences32mask weights B.output1: float32[1,32,160,160] :最终mask大小是160*160;output1中的masks实际上只是原型masks并不代表最终masks。为了得到某个box的最终mask你可以将每个mask与其对应的mask weight相乘然后将所有这些乘积相加。此外你可以在box上应用NMS以获得具有特定置信度阈值的box子集 6.编写Python脚本实现predict
import colorama
import argparse
from ultralytics import YOLO
import osdef parse_args():parser argparse.ArgumentParser(descriptionYOLOv8 predict)parser.add_argument(--model, requiredTrue, typestr, helpmodel file)parser.add_argument(--dir_images, requiredTrue, typestr, helpdirectory of test images)parser.add_argument(--dir_result, requiredTrue, typestr, helpdirectory where the image results are saved)args parser.parse_args()return argsdef get_images(dir):# supported image formatsimg_formats (.bmp, .jpeg, .jpg, .png, .webp)images []for file in os.listdir(dir):if os.path.isfile(os.path.join(dir, file)):# print(file)_, extension os.path.splitext(file)for format in img_formats:if format extension.lower():images.append(file)breakreturn imagesdef predict(model, dir_images, dir_result):model YOLO(model) # load an modelmodel.info() # display model informationimages get_images(dir_images)# print(images:, images)os.makedirs(dir_result) #, exist_okTrue)for image in images:results model.predict(dir_images/image)for result in results:# print(result)result.save(dir_result/image)if __name__ __main__:colorama.init()args parse_args()predict(args.model, args.dir_images, args.dir_result)print(colorama.Fore.GREEN execution completed ) 执行结果如下图所示 其中一幅图像的分割结果如下图所示以下是epochs设置为100时生成的best.pt的结果 GitHubhttps://github.com/fengbingchun/NN_Test
