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转载：https://blog.csdn.net/qq_58355216/article/details/128318604
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