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:

  1. 将图片压缩成大尺寸进行训练( 想法:没显存,搞不来)
  2. 添加小检测头(想法:P5模型还有点用,P6模型完全没用)
  3. 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)
  4. 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:

  1. 将原图切成你想要的数量
  2. 将切成的小图进行训练,得到模型
  3. 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
  4. 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 osimport matplotlib.pyplot as pltimport cv2import numpy as npdef 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'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 osfrom 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_infodef 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w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labeldef 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])            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.find('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 osfrom PIL import Imagefrom PIL import ImageDraw, ImageFontfrom cv2 import cv2def 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 = ['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.split(' ')                # 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:(左YOLOv5检测,右YOLOv5+切图检测)

参考:

https://blog.csdn.net/qq_43622870/article/details/124984295?ops_request_misc=&request_id=&biz_id=102&utm_term=yolov5%E5%B0%8F%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-0-124984295.142^v68^control,201^v4^add_ask,213^v2^t3_control2&spm=1018.2226.3001.4187