YOLOv5小目标切图检测
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'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])
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 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 = ['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