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概述
做无人机视角下目标检测项目，希望利用VisDrone做预训练模型。模型用的是Ultralytics他们家的YOLO系列。需要将原数据集转化为YOLO格式，幸运的是，在 ../ultralytics/cfg/datasets/VisDrone.yaml 中有相关代码可直接使用，也可以简要修改，如提取指定目标等；此外，VisDrone-DET的分辨率在2000x1500左右，直接resize不利于小目标的检测，需要进行裁剪。
功能函数
格式转化并提取其中的部分目标（以车辆目标为例）
思路介绍
参考ultralytics官方转化代码，需要注意的是，VisDrone-DET论文中包括ignored regions, pedestrian, people, bicycle, car, van, truck, tricycle, awning-tricycle, bus, motor共11类，而ultralytics利用判断语句
if row[4] == '0': # VisDrone 'ignored regions' class 0
continue
cls = int(row[5]) - 1
跳过了 ignore regions 标签，同理，在此处修改为，也可以跳过class 1行人和class 2人群的标注（吐槽，我一直不明白为什么要分成 pedestrian 和 people 两类），但同时后面的 cls 编码也要同步修改
if row[4] == '0' or row[5] == '1' or row[5] == '2': # skip VisDrone class 0 'ignored regions' , class 1 'pedestrian' snd class 2 'people'
continue
# cls = int(row[5]) - 1
cls = int(row[5]) - 3
函数实现
def visdrone2yolo(dir):
from PIL import Image
from tqdm import tqdm
from pathlib import Path
def convert_box(size, box):
# Convert VisDrone box to YOLO xywh box
dw = 1. / size[0]
dh = 1. / size[1]
return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
labels_dir = os.path.join(dir, 'labels')
os.makedirs(labels_dir, exist_ok=True) # make labels directory
anno_dir = os.path.join(dir, 'annotations')
pbar = tqdm(Path(anno_dir).glob('*.txt'),
# desc=f'Converting {dir}'
)
for f in pbar:
image_dir = os.path.join(dir, 'images', f.name)
img_size = Image.open(Path(image_dir).with_suffix('.jpg')).size
lines = []
with open(f, 'r') as file: # read annotation.txt
for row in [x.split(',') for x in file.read().strip().splitlines()]:
# if row[4] == '0': # VisDrone 'ignored regions' class 0
if row[4] == '0' or row[5] == '1' or row[5] == '2': # skip VisDrone class 0 'ignored regions' , class 1 'pedestrian' snd class 2 'people'
continue
# cls = int(row[5]) - 1
cls = int(row[5]) - 3
box = convert_box(img_size, tuple(map(int, row[:4])))
lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
fl.writelines(lines) # write label.txt
可视化检查
思路介绍
用于可视化检查转换后的yolo格式后，框是否对应，debug用，也可以其他yolo格式数据可视化（写论文用）。
函数实现
def draw_yolo_boxes(img_dir, label_dir, class_names, output_dir=None):
import cv2
if output_dir and not os.path.exists(output_dir):
os.makedirs(output_dir)
for label_file in os.listdir(label_dir):
if not label_file.endswith('.txt'):
continue
img_file = os.path.join(img_dir, os.path.splitext(label_file)[0] + '.jpg')
img = cv2.imread(img_file)
if img is None:
print(f"Image {img_file} not found.")
continue
height, width, _ = img.shape
label_path = os.path.join(label_dir, label_file)
with open(label_path, 'r') as f:
for line in f.readlines():
parts = line.strip().split()
class_id = int(parts[0])
x_center = float(parts[1])
y_center = float(parts[2])
bbox_width = float(parts[3])
bbox_height = float(parts[4])
# Convert YOLO format to OpenCV format
x_min = int((x_center - bbox_width / 2) * width)
y_min = int((y_center - bbox_height / 2) * height)
x_max = int((x_center + bbox_width / 2) * width)
y_max = int((y_center + bbox_height / 2) * height)
# Draw rectangle
cv2.rectangle(img, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2)
# Draw label
if class_id < 0 or class_id >= len(class_names):
print(f"label:{label_file} class_id:{class_id} ERROR!")
continue
label = f"{class_names[class_id]}"
label_size, base_line = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
top = max(y_min, label_size[1])
cv2.rectangle(img, (x_min, top - label_size[1]), (x_min + label_size[0], top + base_line), (255, 255, 255), cv2.FILLED)
cv2.putText(img, label, (x_min, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1)
if output_dir:
output_file = os.path.join(output_dir, os.path.basename(img_file))
cv2.imwrite(output_file, img)
else:
cv2.imshow('Image', img)
# 每张图展示时间
cv2.waitKey(1000)
cv2.destroyAllWindows()
图片切分
思路介绍
读标签，根据裁剪的大小和重叠区域计算边界框的坐标，需要注意的是，如果边界框在裁剪区域外，则忽略该边界框。输出的文件名格式为{原始文件名}_{x}_{y}表示在原切割前图片从左上角作为第一张图片切割的坐标，如image1_1_0表示被裁剪后的左上角第二个区域。
代码实现
import os
import cv2
import math
def load_labels(label_path):
with open(label_path, 'r') as file:
lines = file.readlines()
labels = []
for line in lines:
parts = line.strip().split()
label = {
'class': int(parts[0]),
'x_center': float(parts[1]),
'y_center': float(parts[2]),
'width': float(parts[3]),
'height': float(parts[4])
}
labels.append(label)
return labels
def save_labels(label_path, labels):
with open(label_path, 'w') as file:
for label in labels:
file.write(f"{label['class']} {label['x_center']} {label['y_center']} {label['width']} {label['height']}\n")
def crop_image_and_labels(image, labels, crop_x, crop_y, crop_w, crop_h):
cropped_image = image[crop_y:crop_y+crop_h, crop_x:crop_x+crop_w]
img_h, img_w = image.shape[:2]
cropped_labels = []
for label in labels:
x_center = label['x_center'] * img_w
y_center = label['y_center'] * img_h
width = label['width'] * img_w
height = label['height'] * img_h
new_x_center = x_center - crop_x
new_y_center = y_center - crop_y
if 0 <= new_x_center <= crop_w and 0 <= new_y_center <= crop_h:
new_x_center /= crop_w
new_y_center /= crop_h
new_width = width / crop_w
new_height = height / crop_h
cropped_labels.append({
'class': label['class'],
'x_center': new_x_center,
'y_center': new_y_center,
'width': new_width,
'height': new_height
})
return cropped_image, cropped_labels
def process_dataset(image_dir, label_dir, output_image_dir, output_label_dir, crop_size, overlap):
if not os.path.exists(output_image_dir):
os.makedirs(output_image_dir)
if not os.path.exists(output_label_dir):
os.makedirs(output_label_dir)
crop_w, crop_h = crop_size
overlap_w, overlap_h = int(crop_w * overlap), int(crop_h * overlap)
for image_filename in os.listdir(image_dir):
if image_filename.endswith(('.jpg', '.jpeg', '.png')):
image_path = os.path.join(image_dir, image_filename)
label_path = os.path.join(label_dir, os.path.splitext(image_filename)[0] + '.txt')
image = cv2.imread(image_path)
labels = load_labels(label_path)
img_h, img_w = image.shape[:2]
x_steps = math.ceil((img_w - crop_w) / (crop_w - overlap_w)) + 1
y_steps = math.ceil((img_h - crop_h) / (crop_h - overlap_h)) + 1
for y in range(y_steps):
for x in range(x_steps):
crop_x = x * (crop_w - overlap_w)
crop_y = y * (crop_h - overlap_h)
crop_x = min(crop_x, img_w - crop_w)
crop_y = min(crop_y, img_h - crop_h)
cropped_image, cropped_labels = crop_image_and_labels(image, labels, crop_x, crop_y, crop_w, crop_h)
output_image_filename = f"{os.path.splitext(image_filename)[0]}_{x}_{y}.jpg"
output_image_path = os.path.join(output_image_dir, output_image_filename)
output_label_filename = f"{os.path.splitext(image_filename)[0]}_{x}_{y}.txt"
output_label_path = os.path.join(output_label_dir, output_label_filename)
cv2.imwrite(output_image_path, cropped_image)
save_labels(output_label_path, cropped_labels)
代码执行
定义输入文件树
VisDrone-DET
├─ VisDrone2019-DET-dev
│ ├─ annotations
│ │ └─ *.txt
│ └─ images
│ └─ *.jpg
├─ VisDrone2019-DET-train
│ ├─ annotations
│ │ └─ *.txt
│ └─ images
│ └─ *.jpg
└─ VisDrone2019-DET-val
│ ├─ annotations
│ │ └─ *.txt
│ └─ images
│ └─ *.jpg
└─ VisDrone_Progress.py
YOLO格式转化和可视化执行代码VisDrone_Progress.py为：
# def visdrone2yolo(dir):
# ...
# def draw_yolo_boxes(img_dir, label_dir, class_names,
# ...
# def get_file_names(directory):
# ...
# def compare_directories(dir_a, dir_b):
# ...
if __name__ == "__main__":
# 搞不明白路径就直接绝对路径就行
dir = '../VisDrone-DET'
# 若前面保留 pedestrian 和 people
# class_names = ['pedestrian', 'people', 'bicycle',
# 'car', 'van', 'trunck',
# 'tricycle', 'awning-tricycle',
# 'bus', 'motor']
# 若不保留 pedestrian 和 people
class_names = [ 'bicycle',
'car', 'van', 'trunck',
'tricycle', 'awning-tricycle',
'bus', 'motor']
for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
# YOLO 格式转化
visdrone2yolo(os.path.join(dir,d))
# 标签可视化(仅检查时取消注释)
# draw_yolo_boxes(os.path.join(dir, d, 'images'), os.path.join(dir, d, 'labels'), class_names)
转换后文件树如下，此时yolo格式标注被保存在labels文件夹下：
VisDrone-DET
├─ VisDrone2019-DET-dev
│ ├─ annotations
│ │ └─ *.txt
│ ├─ images
│ │ └─ *.jpg
│ └─ labels
│ └─ *.txt
├─ VisDrone2019-DET-train
│ ├─ annotations
│ │ └─ *.txt
│ ├─ images
│ │ └─ *.jpg
│ └─ labels
│ └─ *.txt
└─ VisDrone2019-DET-val
├─ annotations
│ └─ *.txt
├─ images
│ └─ *.jpg
└─ labels
└─ *.txt
数据集裁剪执行代码Split.py为：
import os
import cv2
import math
# def load_labels(label_path):
# ...
# def save_labels(label_path, labels):
# ...
# def crop_image_and_labels(image, labels, crop_x, crop_y, crop_w, crop_h):
# ...
# def process_dataset(image_dir, label_dir, output_image_dir, output_label_dir, crop_size, overlap):
# ...
if __name__ == "__main__":
# 配置裁剪参数和路径
crop_size = (720, 720) # 裁剪区域的宽和高
overlap = 0.2 # 裁剪区域之间的重叠百分比
data_root = '../VisDrone-DETdataset/'
for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
image_dir = os.path.join(data_root, d, 'images')
label_dir = os.path.join(data_root, d, 'labels')
output_images_dir = os.path.join(data_root, d, 'images_split')
output_labels_dir = os.path.join(data_root, d, 'labels_split')
process_dataset(image_dir, label_dir, output_image_dir, output_label_dir, crop_size, overlap)
print('{d} split')
# 标签可视化(仅检查时取消注释, 引入可视化函数)
# draw_yolo_boxes(output_images_dir, output_labels_dir, class_names)
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