注：.题中附录并没有给出苹果的标签集，所以需要我们自己通过前4问得到训练的标签集，采用的是yolov5 7.0 版本，该版本带分割功能
一：关于数据集的制作：
clc;
close all;
clear;
%-----这个是生成yolov5 数据集的--------
% 图像文件夹路径
folder_path = 'E:/新建文件夹/yatai/Attachment/Apple/';
% 图像文件列表
image_files = dir(fullfile(folder_path, '*.jpg')); % 假设所有图片都是jpg格式
% 解析文件名中的数字，并转换为数值类型
numbers = cellfun(@(x) sscanf(x, '%d.jpg'), {image_files.name});
% 根据解析出的数字对文件列表进行排序
[~, sorted_idx] = sort(numbers);
image_files = image_files(sorted_idx);
% 存储每张图片苹果数量的数组
apple_counts = zeros(length(image_files), 1);
% 存储每张图片的平均成熟度评分
average_red_intensity_ratio_per_image = zeros(length(image_files), 1);
% 确保输出文件夹存在
output_folder = 'E:\新建文件夹\yatai\Attachment\Attachment 2\APPlemasktxt';
if ~exist(output_folder, 'dir')
mkdir(output_folder);
end
% 存储每张图片的平均苹果质量评分
average_quality_scores_per_image = zeros(length(image_files), 1);
% 遍历每张图片
for i = 1: length(image_files) %2
% 读取图像
img = imread(fullfile(folder_path, image_files(i).name));
·······省略了部分代码
% 给分割的对象标记不同的标签
labelled_img = bwlabel(binary_img);
% figure;
% 在原始图像上绘制分割结果
% imshow(img);
% hold on;
colors=['b' 'g' 'r' 'c' 'm' 'y'];
for k = 1:length(unique(labelled_img)) - 1
boundary = bwboundaries(labelled_img == k);
for b = 1:length(boundary)
plot(boundary{b}(:,2), boundary{b}(:,1), colors(mod(k,length(colors))+1), 'LineWidth', 2);
end
end
% title('Segmented Apples');
% hold off;
% 计数分割后的苹果
number_of_apples = max(labelled_img(:));
disp(['Number of segmented apples: ', num2str(number_of_apples)]);
apple_counts(i) = number_of_apples;
% 打印当前图片的苹果数量
fprintf('Image %d (%s): %d apples detected.\n', i, image_files(i).name, number_of_apples);
%下面是制作分割的数据集
% 给分割的对象标记不同的标签
labelled_img = bwlabel(binary_img);
% 准备写入YOLOv5格式的分割轮廓点文件
% 根据图像文件名创建对应的txt文件名
baseFileName = sprintf('%d.txt', i);
txt_filename = fullfile(output_folder, baseFileName);
fileID = fopen(txt_filename, 'w');
% 确保文件已成功打开
if fileID == -1
error('Cannot open file %s for writing.', txt_filename);
end
% 获取图像尺寸
img_height = size(img, 1);
img_width = size(img, 2);
% 遍历每个苹果，写入轮廓点信息
for k = 1:max(labelled_img(:))
[B,~] = bwboundaries(labelled_img == k, 'noholes');
contours = B{1}; % 取第一组轮廓点
% 检查contours的尺寸
if size(contours, 2) == 2 % 确保contours有两列
% 转换为归一化坐标
contours_normalized = contours ./ [img_height, img_width];
% 写入文件
fprintf(fileID, '0 '); % 假设苹果的类别ID为0
for p = 1:size(contours_normalized, 1)
% fprintf('Plotting point at (%f, %f)\n', contours_normalized(p, 2), contours_normalized(p, 1)); % 调试信息
fprintf(fileID, '%f %f ', contours_normalized(p, 2), contours_normalized(p, 1));
end
fprintf(fileID, '\n');
else
warning('Contour for apple %d in image %d does not have correct dimensions.', k, i);
end
end
fclose(fileID);
end二：关于yolov5 7.0 的训练：
我的电脑是3080 训练了20轮测试，下面就是部分测试的结果
下面是关于数据集的划分代码
'''
Descripttion: split_img.py
version: 1.0
Author: UniDome
Date: 2022-04-20 16:28:45
LastEditors: UniDome
LastEditTime: 2022-04-20 16:39:56
'''
import os, shutil, random
from tqdm import tqdm
def split_img(img_path, label_path, split_list):
try: # 创建数据集文件夹
Data = 'E:/新建文件夹/yatai/Attachment/Attachment 2/output'
os.mkdir(Data)
train_img_dir = Data + '/images/train'
val_img_dir = Data + '/images/val'
test_img_dir = Data + '/images/test'
train_label_dir = Data + '/labels/train'
val_label_dir = Data + '/labels/val'
test_label_dir = Data + '/labels/test'
# 创建文件夹
os.makedirs(train_img_dir)
os.makedirs(train_label_dir)
os.makedirs(val_img_dir)
os.makedirs(val_label_dir)
os.makedirs(test_img_dir)
os.makedirs(test_label_dir)
except:
print('文件目录已存在')
train, val, test = split_list
all_img = os.listdir(img_path)
all_img_path = [os.path.join(img_path, img) for img in all_img]
# all_label = os.listdir(label_path)
# all_label_path = [os.path.join(label_path, label) for label in all_label]
train_img = random.sample(all_img_path, int(train * len(all_img_path)))
train_img_copy = [os.path.join(train_img_dir, img.split('\\')[-1]) for img in train_img]
train_label = [toLabelPath(img, label_path) for img in train_img]
train_label_copy = [os.path.join(train_label_dir, label.split('\\')[-1]) for label in train_label]
for i in tqdm(range(len(train_img)), desc='train ', ncols=80, unit='img'):
_copy(train_img[i], train_img_dir)
_copy(train_label[i], train_label_dir)
all_img_path.remove(train_img[i])
val_img = random.sample(all_img_path, int(val / (val + test) * len(all_img_path)))
val_label = [toLabelPath(img, label_path) for img in val_img]
for i in tqdm(range(len(val_img)), desc='val ', ncols=80, unit='img'):
_copy(val_img[i], val_img_dir)
_copy(val_label[i], val_label_dir)
all_img_path.remove(val_img[i])
test_img = all_img_path
test_label = [toLabelPath(img, label_path) for img in test_img]
for i in tqdm(range(len(test_img)), desc='test ', ncols=80, unit='img'):
_copy(test_img[i], test_img_dir)
_copy(test_label[i], test_label_dir)
def _copy(from_path, to_path):
shutil.copy(from_path, to_path)
def toLabelPath(img_path, label_path):
img = img_path.split('\\')[-1]
label = img.split('.jpg')[0] + '.txt'
return os.path.join(label_path, label)
def main():
img_path = r'E:\新建文件夹\yatai\Attachment\Apple'
label_path = r'E:\新建文件夹\yatai\Attachment\Attachment 2\APPlemasktxt'
split_list = [0.7, 0.2, 0.1] # 数据集划分比例[train:val:test]
split_img(img_path, label_path, split_list)
if __name__ == '__main__':
main()