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train_crop.py
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train_crop.py
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# coding:utf-8
# 把DOTA数据集进行切割，生成600*600大小的图片和xml文件（hbb和obb都ok！）
import os
import scipy.misc as misc
from xml.dom.minidom import Document
import numpy as np
import copy, cv2
def save_to_xml(save_path, im_width, im_height, objects_axis, label_name, name, hbb=True):
im_depth = 0
object_num = len(objects_axis)
doc = Document()
annotation = doc.createElement('annotation')
doc.appendChild(annotation)
folder = doc.createElement('folder')
folder_name = doc.createTextNode('VOC2007')
folder.appendChild(folder_name)
annotation.appendChild(folder)
filename = doc.createElement('filename')
filename_name = doc.createTextNode(name)
filename.appendChild(filename_name)
annotation.appendChild(filename)
source = doc.createElement('source')
annotation.appendChild(source)
database = doc.createElement('database')
database.appendChild(doc.createTextNode('The VOC2007 Database'))
source.appendChild(database)
annotation_s = doc.createElement('annotation')
annotation_s.appendChild(doc.createTextNode('PASCAL VOC2007'))
source.appendChild(annotation_s)
image = doc.createElement('image')
image.appendChild(doc.createTextNode('flickr'))
source.appendChild(image)
flickrid = doc.createElement('flickrid')
flickrid.appendChild(doc.createTextNode('322409915'))
source.appendChild(flickrid)
owner = doc.createElement('owner')
annotation.appendChild(owner)
flickrid_o = doc.createElement('flickrid')
flickrid_o.appendChild(doc.createTextNode('knautia'))
owner.appendChild(flickrid_o)
name_o = doc.createElement('name')
name_o.appendChild(doc.createTextNode('yang'))
owner.appendChild(name_o)
size = doc.createElement('size')
annotation.appendChild(size)
width = doc.createElement('width')
width.appendChild(doc.createTextNode(str(im_width)))
height = doc.createElement('height')
height.appendChild(doc.createTextNode(str(im_height)))
depth = doc.createElement('depth')
depth.appendChild(doc.createTextNode(str(im_depth)))
size.appendChild(width)
size.appendChild(height)
size.appendChild(depth)
segmented = doc.createElement('segmented')
segmented.appendChild(doc.createTextNode('0'))
annotation.appendChild(segmented)
for i in range(object_num):
objects = doc.createElement('object')
annotation.appendChild(objects)
object_name = doc.createElement('name')
object_name.appendChild(doc.createTextNode(label_name[int(objects_axis[i][-1])]))
objects.appendChild(object_name)
pose = doc.createElement('pose')
pose.appendChild(doc.createTextNode('Unspecified'))
objects.appendChild(pose)
truncated = doc.createElement('truncated')
truncated.appendChild(doc.createTextNode('1'))
objects.appendChild(truncated)
difficult = doc.createElement('difficult')
difficult.appendChild(doc.createTextNode('0'))
objects.appendChild(difficult)
bndbox = doc.createElement('bndbox')
objects.appendChild(bndbox)
if hbb:
x0 = doc.createElement('xmin')
x0.appendChild(doc.createTextNode(str((objects_axis[i][0]))))
bndbox.appendChild(x0)
y0 = doc.createElement('ymin')
y0.appendChild(doc.createTextNode(str((objects_axis[i][1]))))
bndbox.appendChild(y0)
x1 = doc.createElement('xmax')
x1.appendChild(doc.createTextNode(str((objects_axis[i][2]))))
bndbox.appendChild(x1)
y1 = doc.createElement('ymax')
y1.appendChild(doc.createTextNode(str((objects_axis[i][5]))))
bndbox.appendChild(y1)
else:
x0 = doc.createElement('x0')
x0.appendChild(doc.createTextNode(str((objects_axis[i][0]))))
bndbox.appendChild(x0)
y0 = doc.createElement('y0')
y0.appendChild(doc.createTextNode(str((objects_axis[i][1]))))
bndbox.appendChild(y0)
x1 = doc.createElement('x1')
x1.appendChild(doc.createTextNode(str((objects_axis[i][2]))))
bndbox.appendChild(x1)
y1 = doc.createElement('y1')
y1.appendChild(doc.createTextNode(str((objects_axis[i][3]))))
bndbox.appendChild(y1)
x2 = doc.createElement('x2')
x2.appendChild(doc.createTextNode(str((objects_axis[i][4]))))
bndbox.appendChild(x2)
y2 = doc.createElement('y2')
y2.appendChild(doc.createTextNode(str((objects_axis[i][5]))))
bndbox.appendChild(y2)
x3 = doc.createElement('x3')
x3.appendChild(doc.createTextNode(str((objects_axis[i][6]))))
bndbox.appendChild(x3)
y3 = doc.createElement('y3')
y3.appendChild(doc.createTextNode(str((objects_axis[i][7]))))
bndbox.appendChild(y3)
f = open(save_path,'w')
f.write(doc.toprettyxml(indent = ''))
f.close()
class_list = ['plane', 'baseball-diamond', 'bridge', 'ground-track-field',
'small-vehicle', 'large-vehicle', 'ship',
'tennis-court', 'basketball-court',
'storage-tank', 'soccer-ball-field',
'roundabout', 'harbor',
'swimming-pool', 'helicopter']
def format_label(txt_list):
format_data = []
for i in txt_list[2:]:
format_data.append(
[int(xy) for xy in i.split(' ')[:8]] + [class_list.index(i.split(' ')[8])]
# {'x0': int(i.split(' ')[0]),
# 'x1': int(i.split(' ')[2]),
# 'x2': int(i.split(' ')[4]),
# 'x3': int(i.split(' ')[6]),
# 'y1': int(i.split(' ')[1]),
# 'y2': int(i.split(' ')[3]),
# 'y3': int(i.split(' ')[5]),
# 'y4': int(i.split(' ')[7]),
# 'class': class_list.index(i.split(' ')[8]) if i.split(' ')[8] in class_list else 0,
# 'difficulty': int(i.split(' ')[9])}
)
if i.split(' ')[8] not in class_list :
print ('warning found a new label :', i.split(' ')[8])
exit()
return np.array(format_data)
def clip_image(file_idx, image, boxes_all, width, height):
# print ('image shape', image.shape)
if len(boxes_all) > 0:
shape = image.shape
for start_h in range(0, shape[0], 256):
for start_w in range(0, shape[1], 256):
boxes = copy.deepcopy(boxes_all)
box = np.zeros_like(boxes_all)
start_h_new = start_h
start_w_new = start_w
if start_h + height > shape[0]:
start_h_new = shape[0] - height
if start_w + width > shape[1]:
start_w_new = shape[1] - width
top_left_row = max(start_h_new, 0)
top_left_col = max(start_w_new, 0)
bottom_right_row = min(start_h + height, shape[0])
bottom_right_col = min(start_w + width, shape[1])
subImage = image[top_left_row:bottom_right_row, top_left_col: bottom_right_col]
box[:, 0] = boxes[:, 0] - top_left_col
box[:, 2] = boxes[:, 2] - top_left_col
box[:, 4] = boxes[:, 4] - top_left_col
box[:, 6] = boxes[:, 6] - top_left_col
box[:, 1] = boxes[:, 1] - top_left_row
box[:, 3] = boxes[:, 3] - top_left_row
box[:, 5] = boxes[:, 5] - top_left_row
box[:, 7] = boxes[:, 7] - top_left_row
box[:, 8] = boxes[:, 8]
center_y = 0.25*(box[:, 1] + box[:, 3] + box[:, 5] + box[:, 7])
center_x = 0.25*(box[:, 0] + box[:, 2] + box[:, 4] + box[:, 6])
# print('center_y', center_y)
# print('center_x', center_x)
# print ('boxes', boxes)
# print ('boxes_all', boxes_all)
# print ('top_left_col', top_left_col, 'top_left_row', top_left_row)
cond1 = np.intersect1d(np.where(center_y[:]>=0 )[0], np.where(center_x[:]>=0 )[0])
cond2 = np.intersect1d(np.where(center_y[:] <= (bottom_right_row - top_left_row))[0],
np.where(center_x[:] <= (bottom_right_col - top_left_col))[0])
idx = np.intersect1d(cond1, cond2)
# idx = np.where(center_y[:]>=0 and center_x[:]>=0 and center_y[:] <= (bottom_right_row - top_left_row) and center_x[:] <= (bottom_right_col - top_left_col))[0]
# save_path, im_width, im_height, objects_axis, label_name
if len(idx) > 0:
name="%s_%04d_%04d.png" % (file_idx, top_left_row, top_left_col)
print(name)
xml = os.path.join(save_dir, 'labeltxt', "%s_%04d_%04d.xml" % (file_idx, top_left_row, top_left_col))
save_to_xml(xml, subImage.shape[1], subImage.shape[0], box[idx, :], class_list, str(name))
# print ('save xml : ', xml)
if subImage.shape[0] > 5 and subImage.shape[1] >5:
img = os.path.join(save_dir, 'images', "%s_%04d_%04d.png" % (file_idx, top_left_row, top_left_col))
cv2.imwrite(img, subImage)
print ('class_list', len(class_list))
raw_data = 'D:/datasets/DOTA/train/'
raw_images_dir = os.path.join(raw_data, 'images')
raw_label_dir = os.path.join(raw_data, 'labelTxt')
save_dir = 'D:/datasets/DOTA_clip/train/'
images = [i for i in os.listdir(raw_images_dir) if 'png' in i]
labels = [i for i in os.listdir(raw_label_dir) if 'txt' in i]
print ('find image', len(images))
print ('find label', len(labels))
min_length = 1e10
max_length = 1
for idx, img in enumerate(images):
# img = 'P1524.png'
print (idx, 'read image', img)
img_data = misc.imread(os.path.join(raw_images_dir, img))
# if len(img_data.shape) == 2:
# img_data = img_data[:, :, np.newaxis]
# print ('find gray image')
txt_data = open(os.path.join(raw_label_dir, img.replace('png', 'txt')), 'r').readlines()
# print (idx, len(format_label(txt_data)), img_data.shape)
# if max(img_data.shape[:2]) > max_length:
# max_length = max(img_data.shape[:2])
# if min(img_data.shape[:2]) < min_length:
# min_length = min(img_data.shape[:2])
# if idx % 50 ==0:
# print (idx, len(format_label(txt_data)), img_data.shape)
# print (idx, 'min_length', min_length, 'max_length', max_length)
box = format_label(txt_data)
clip_image(img.strip('.png'), img_data, box, 600, 600)
# rm train/images/* && rm train/labeltxt/*