YOLOv8 segment介绍

      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person，1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(description="json(EISeg) to txt(YOLOv8)")parser.add_argument("--dir", required=True, type=str, help="images directory, all json files are in the label directory, and generated txt files are also in the label directory")parser.add_argument("--labels", required=True, type=str, help="txt file that hold indexes and labels, one label per line, for example: face 0")parser.add_argument("--val_size", default=0.2, type=float, help="the proportion of the validation set to the overall dataset:[0., 0.5]")parser.add_argument("--name", required=True, type=str, help="the 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 + "\r"file.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(f"width: {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/train"dst_dir_images_val = "datasets/" + name + "/images/val"dst_dir_labels_train = "datasets/" + name + "/labels/train"dst_dir_labels_val = "datasets/" + name + "/labels/val"try:os.makedirs(dst_dir_images_train) #, exist_ok=Trueos.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")] + ".txt"if 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")] + ".txt"if 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(f"key: {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(f"jsons: {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(description="YOLOv8 train")parser.add_argument("--yaml", required=True, type=str, help="yaml file")parser.add_argument("--epochs", required=True, type=int, help="number of training")parser.add_argument("--task", required=True, type=str, choices=["detect", "segment"], help="specify 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(data=yaml, epochs=epochs, imgsz=640) # train the modelmetrics = model.val() # It'll automatically evaluate the data you trained, no arguments needed, dataset and settings rememberedmodel.export(format="onnx") #, dynamic=True) # export the model, cannot specify dynamic=True, opencv does not support# model.export(format="onnx", opset=12, simplify=True, dynamic=False, imgsz=640)model.export(format="torchscript") # 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：4：xc, yc, width, height；2：class, confidences；32：mask 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(description="YOLOv8 predict")parser.add_argument("--model", required=True, type=str, help="model file")parser.add_argument("--dir_images", required=True, type=str, help="directory of test images")parser.add_argument("--dir_result", required=True, type=str, help="directory 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_ok=True)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

      GitHub：https://github.com/fengbingchun/NN_Test