YOLOv5小目标切图检测的思路与方法

目录

• 前言
• 思路：
• 一：切块
• 二：融合txt文件
• 三：原图显示
• 总结

前言

当我们在检测较大分辨率的图片时，对小目标的检测效果一直是较差的，所以就有了下面几种方法：

• 将图片压缩成大尺寸进行训练（ 想法：没显存，搞不来）
• 添加小检测头（想法：P5模型还有点用，P6模型完全没用）
• 添加一些检测模型和玄学机制（想法：你要是写论文就去看看知*吧，只需要在最后面加一句：已达到工业检测要求）
• 切图检测（想法：比较耗时，过程也比较繁琐，可以尝试）

切图检测

思路：

• 将原图切成你想要的数量
• 将切成的小图进行训练，得到模型
• 将你需要检测的图片切成小图，用模型检测，并得到每张图目标位置的信息，保存在对应图片的txt文件
• 将所有txt文件融合，得到1个txt文件，并在原图上显示

一：切块

# -*- coding:utf-8 -*-
import os
import matplotlib.pyplot as plt
import cv2
import numpy as np
 
 
def divide_img(img_path, img_name, save_path):
    imgg = img_path + img_name
    img = cv2.imread(imgg)
    #   img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
    h = img.shape[0]
    w = img.shape[1]
    n = int(np.floor(h * 1.0 / 1000)) + 1
    m = int(np.floor(w * 1.0 / 1000)) + 1
    print('h={},w={},n={},m={}'.format(h, w, n, m))
    dis_h = int(np.floor(h / n))
    dis_w = int(np.floor(w / m))
    num = 0
    for i in range(n):
        for j in range(m):
            num += 1
            print('i,j={}{}'.format(i, j))
            sub = img[dis_h * i:dis_h * (i + 1), dis_w * j:dis_w * (j + 1), :]
            cv2.imwrite(save_path + '{}_{}.bmp'.format(name, num), sub)
 
 
if __name__ == '__main__':
 
    img_path = r'G:\1/'
    save_path = r'开发者_Go入门G:\3/'
    img_list = os.listdir(img_path)
    for name in img_list:
        divide_img(img_path, name, save_path)

使用模型检测后得到：

二：融合txt文件

import os
from cv2 import cv2
 
# 保存所有图片的宽高
# todo: img_info={'name': [w_h, child_w_h, mix_row_w_h， mix_col_w_h]}
img_info = {}
all_info = {}
 
 
# 初始化img_info
def init(big_images_path, mix_percent, rows, cols):
    image_names = os.listdir(big_images_path)
    for img_name in image_names:
        big_path = big_images_path + '\\' + img_name
        # print(big_path)
        img = cv2.imread(big_path)
        size = img.shape[0:2]
        w = size[1]
        h = size[0]
        child_width = int(w) // cols
        child_height = int(h) // rows
 
        mix_row_width = int(child_width * mix_percent * 2)
        mix_row_height = child_height
 
        mix_col_width = child_width
        mix_col_height = int(child_height * mix_percent * 2)
        # 根据img保存wphp和h
        img_info[img_name.split('.')[0]] = [w, h, child_width, child_height, mix_row_width, mix_row_height,
                                            mix_col_width, mix_col_height]
 
 
# 读取所有检测出来的 小图片的label
def get_label_info(labels_path, mix_percent, rows, cols):
    labels = os.listdir(labels_path)
    for label in labels:
        # print(label)
        # todo: type: 0正常, 1row, 2col
        # 判断该label属于哪一张图片
        cur_label_belong = label.split('_')[0]
        cur_big_width = img_info[cur_label_belong][0]
        cur_big_height = img_info[cur_label_belong][1]
        # 融合区域距离边界的一小部分宽高
        cur_row_width_step = img_info[cur_label_belong][2] * (1 - mix_percent)
        cur_col_height_step = img_info[cur_label_belong][3] * (1 - mix_percent)
        # 文件名给予数据
        # child_type = []
        # child_num = []
        # label内容给予数据
        child_class_index = []
        child_x = []
        child_y = []
        child_width = []
        child_height = []
 
        type = -1
        num = -1
        class_index = -1
        x = 0.0
        y = 0.0
        width = 0.0
        height = 0.0
 
        # print(f'{label}')
        # 读取所有需要的数据
        f = open(labels_path + '\\' + label, 'r')
        lines = f.read()
        # print(lines)
        f.close()
        contents = lines.split('\n')[:-1]
        # print(contents)
        for content in contents:
            content = content.split(' ')
            # print(content)
            class_index = int(content[0])
            x = float(content[1])
       编程客栈     y = float(content[2])
            width = float(content[3])
            height = float(content[4])
            pass
            # print(class_index, x, y, width, height)
            assert class_index != -1 or x != -1.0 or y != -1.0 or width != -1.0 or height != -1.0, \
                f'class_index:{class_index}, x:{x}, y:{y}, width:{width}, height:{height}'
            # 转换成 数据 坐标, 并根据不同的num进行处理
            num = label.split('_')[-1].split('.')[0]  # 图片尾号 命名： xxxx_x.jpg  xxxx_mix_row_xx.jpg xxxx_mix_col_xx.jpg
            cur_img_width = 0
            cur_img_height = 0
            distance_x = 0
            distance_y = 0
            small_image_width = img_info[cur_label_belong][2]
            small_image_height = img_info[cur_label_belong][3]
            if label.www.devze.comfind('mix_row') != -1:
                # type = 1.
                distance_x = int(num) % (cols-1)
                distance_y = int(num) // (rows-1)
                cur_img_width = img_info[cur_label_belong][4]
                cur_img_height = img_info[cur_label_belong][5]
                # row x 加上step
                x = x * cur_img_width + cur_row_width_step + distance_x * small_image_width
                y = y * cur_img_height + distance_y * cur_img_height
            elif label.find('mix_col') != -1:
                # type = 2
                distance_x = int(num) % cols
                distance_y = int(num) // rows
                cur_img_width = img_info[cur_label_belong][6]
                cur_img_height = img_info[cur_label_belong][7]
                # col y 加上step
                print(f'x:{x}, y:{y}, cur_img_width:{cur_img_width}, cur_img_height:{cur_img_height}')
                x = x * cur_img_width + distance_x * cur_img_width
                y = y * cur_img_height + cur_col_height_step + distance_y * small_image_height
                print(f'x:{x}, y:{y}, height:{cur_col_height_step}')
            else:
                # type = 0
                distance_x = int(num) % cols
                distance_y = int(num) // rows
                cur_img_width = img_info[cur_label_belong][2]
                cur_img_height = img_info[cur_label_belong][3]
                # 小图片内， 无需加上 step
                x = x * cur_img_width + distance_x * cur_img_width
                y = y * cur_img_height + distance_y * cur_img_height
            assert cur_img_width != 0 or cur_img_height != 0 or distance_x != 0 or distance_y != 0, \
                f'cur_img_width:{cur_img_width}, cur_img_height:{cur_img_height}, distance_x:{distance_x}, distance_y:{distance_y}'
            assert x < cur_big_width and y < cur_big_height, f'{label}, {content}\nw:{cur_big_width}, h:{cur_big_height}, x:{x}, y:{y}'
            width = width * cur_img_width
            height = height * cur_img_height
            assert x != 0.0 or y != 0.0 or width != 0.0 or height != 0.0, f'x:{x}, y:{y}, width:{width}, height:{height}'
            # child_type.append(type)
            # child_num.append(num)
            child_class_index.append(class_index)
            child_x.append(x)
            child_y.append(y)
            child_width.append(width)
            child_height.append(height)
        # todo: 所有信息 根据 cur_label_belong 存储在all_info中
        for index, x, y, width, height in zip(child_class_index, child_x, child_y, child_width, child_height):
            if cur_label_belong not in all_info:
                all_info[cur_label_belong] = [[index, x, y, width, height]]
            else:
                all_info[cur_label_belong].append([index, x, y, width, height])
        child_class_index.clear()
        child_x.clear()
        child_y.clear()
        child_width.clear()
        child_height.clear()
 
 
# print((all_info['0342']))
# todo: 转成 yolo 格式， 保存
def save_yolo_label(yolo_labels_path):
    for key in all_info:
        # img_path = r'G:\Unity\code_project\other_project\data\joint\big_images' + '\\' + key + '.JPG'
        # img = cv2.imread(img_path)
        yolo_label_path = yolo_labels_path + '\\' + key + '.txt'
        cur_big_width = img_info[key][0]
        cur_big_height = img_info[key][1]
        content = ''
        i = 0
        for index, x, y, width, height in all_info[key]:
            # print(all_info[key][i])
            x = x / cur_big_width
            y = y / cur_big_height
            width = width / cur_big_width
            height = height / cur_big_height
            assert x < 1.0 and y < 1.0 and width < 1.0 and height < 1.0, f'{key} {i}\n{all_info[key][i]}\nx:{x}, y:{y}, width:{width}, height:{height}'
            content += f'{index} {x} {y} {width} {height}\n'
            i += 1
        with open(yolo_label_path, 'w') as f:
            f.write(content)
 
 
def joint_main(big_images_path=r'G:\3',
               labels_path=r'G:\5',
               yolo_labels_path=r'G:\6',
               mix_percent=0.2,
               rows=4,
               cols=4):
    print(f'融合图片, 原图片路径：{big_images_path}\n小图检测的txt结果路径：{labels_path}\n数据融合后txt结果路径：{yolo_labels_path}')
    init(big_images_path, mix_percent, rows, cols)
    get_label_info(labels_path, mix_percent, rows, cols)
    save_yolo_label(yolo_labels_path)
 
joint_main()

三：原图显示

# -*- coding: utf-8 -*-
import os
from PIL import Image
from PIL import ImageDraw, ImageFont
from cv2 import cv2
 
 
def draw_images(images_dir, txt_dir, box_dir, font_type_path):
    font = ImageFont.truetype(font_type_path, 50)
    if not os.path.exists(box_dir):
        os.makedirs(box_dir)
    # num = 0
 
    # 设置颜色
    all_colors = [http://www.devze.com'red', 'green', 'yellow', 'blue', 'pink', 'black', 'skyblue', 'brown', 'orange', 'purple', 'gray',
                  'lightpink', 'gold', 'brown', 'black']
    colors = {}
 
    for file in os.listdir(txt_dir):
        print(file)
        image = os.path.splitext(file)[0].replace('XML', 'bmp') + '.bmp'
        # 转换成cv2读取，防止图片载入错误
        img = cv2.imread(images_dir + '/' + image)
        TURN = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img = Image.fromarray(TURN)
        # img.show()
 
        if img.mode == "P":
            img = img.convert('RGB')
 
        w, h = img.size
        tag_path = txt_dir + '/' + file
        with open(tag_path) as f:
            for line in f:
                line_parts = line.splandroidit(' ')
                # 根据不同的 label 保存颜色
                if line_parts[0] not in colors.keys():
                    colors[line_parts[0]] = all_colors[len(colors.keys())]
                color = colors[line_parts[0]]
 
                draw = ImageDraw.Draw(img)
                x = (float(line_parts[1]) - 0.5 * float(line_parts[3])) * w
                y = (float(line_parts[2]) - 0.5 * float(line_parts[4])) * h
                xx = (float(line_parts[1]) + 0.5 * float(line_parts[3])) * w
                yy = (float(line_parts[2]) + 0.5 * float(line_parts[4])) * h
                draw.rectangle([x - 10, y - 10, xx, yy], fill=None, outline=color, width=5)
                # num += 1
            del draw
            img.save(box_dir + '/' + image)
        # print(file, num)
    # print(colors)
 
 
def draw_main(box_dir=r'G:\5',
              txt_dir=r'G:\6',
              image_source_dir=r'G:\3'):
    font_type_path = 'C:/Windows/Fonts/simsun.ttc'
    print(f'标注框, 数据来源: {txt_dir}\n 被标注图片: {image_source_dir}\n 结果保存路径: {box_dir}')
    draw_images(image_source_dir, txt_dir, box_dir, font_type_path)
 
 
draw_main()

效果对比：（上YOLOv5检测，下YOLOv5+切图检测）

参考：

https://blog.csdn.net/qq_43622870/article/details/124984295

总结

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