def DOTA2COCO(srcpath, destfile):
imageparent = os.path.join(srcpath, 'images')
labelparent = os.path.join(srcpath, 'labelTxt')
data_dict = {}
info = {
'contributor': 'captain group',
'data_created': '2019',
'description': 'This is 1.5 version of DOTA dataset.',
'url': 'http://captain.whu.edu.cn/DOTAweb/',
'version': '1.5',
'year': 2019
}
data_dict['info'] = info
data_dict['images'] = []
data_dict['categories'] = []
data_dict['annotations'] = []
for idex, name in enumerate(wordname_16):
single_cat = {'id': idex + 1, 'name': name, 'supercategory': name}
data_dict['categories'].append(single_cat)
inst_count = 1
image_id = 1
with open(destfile, 'w') as f_out:
filenames = util.GetFileFromThisRootDir(labelparent)
for file in filenames:
basename = util.custombasename(file)
# image_id = int(basename[1:])
imagepath = os.path.join(imageparent, basename + '.png')
img = cv2.imread(imagepath)
height, width, c = img.shape
single_image = {}
single_image['file_name'] = basename + '.png'
single_image['id'] = image_id
single_image['width'] = width
single_image['height'] = height
data_dict['images'].append(single_image)
# annotations
objects = util.parse_dota_poly2(file)
for obj in objects:
single_obj = {}
single_obj['area'] = obj['area']
single_obj['category_id'] = wordname_16.index(obj['name']) + 1
single_obj['segmentation'] = []
single_obj['segmentation'].append(obj['poly'])
single_obj['iscrowd'] = 0
xmin, ymin, xmax, ymax = min(obj['poly'][0::2]), min(obj['poly'][1::2]), \
max(obj['poly'][0::2]), max(obj['poly'][1::2])
width, height = xmax - xmin, ymax - ymin
single_obj['bbox'] = xmin, ymin, width, height
single_obj['image_id'] = image_id
data_dict['annotations'].append(single_obj)
single_obj['id'] = inst_count
inst_count = inst_count + 1
image_id = image_id + 1
json.dump(data_dict, f_out)
def DOTA2COCOTrain(srcpath, destfile, cls_names, difficult='2'):
# set difficult to filter '2', '1', or do not filter, set '-1'
imageparent = os.path.join(srcpath, 'images')
labelparent = os.path.join(srcpath, 'labelTxt-v1.0')
data_dict = {}
data_dict['images'] = []
data_dict['categories'] = []
data_dict['annotations'] = []
for idex, name in enumerate(cls_names):
single_cat = {'id': idex + 1, 'name': name, 'supercategory': name}
data_dict['categories'].append(single_cat)
inst_count = 1
image_id = 1
with open(destfile, 'w') as f_out:
filenames = util.GetFileFromThisRootDir(labelparent)
for file in filenames:
basename = util.custombasename(file)
# image_id = int(basename[1:])
imagepath = os.path.join(imageparent, basename + '.png')
img = Image.open(imagepath)
height = img.height
width = img.width
# img = cv2.imread(imagepath)
# height, width, c = img.shape
single_image = {}
single_image['file_name'] = basename + '.png'
single_image['id'] = image_id
single_image['width'] = width
single_image['height'] = height
data_dict['images'].append(single_image)
# annotations
objects = util.parse_dota_poly2(file)
for obj in objects:
if obj['difficult'] == difficult:
print('difficult: ', difficult)
continue
single_obj = {}
single_obj['area'] = obj['area']
single_obj['category_id'] = cls_names.index(obj['name']) + 1
single_obj['segmentation'] = []
single_obj['segmentation'].append(obj['poly'])
single_obj['iscrowd'] = 0
xmin, ymin, xmax, ymax = min(obj['poly'][0::2]), min(obj['poly'][1::2]), \
max(obj['poly'][0::2]), max(obj['poly'][1::2])
width, height = xmax - xmin, ymax - ymin
single_obj['bbox'] = xmin, ymin, width, height
single_obj['image_id'] = image_id
data_dict['annotations'].append(single_obj)
single_obj['id'] = inst_count
inst_count = inst_count + 1
image_id = image_id + 1
json.dump(data_dict, f_out)
def SplitSingle(self, name, rate, extent):
"""
split a single image and ground truth
:param name: image name
:param rate: the resize scale for the image
:param extent: the image format
:return:
"""
try:
img = cv2.imread(os.path.join(self.imagepath, name + extent))
print('img name:', name)
except:
print('img name:', name)
if np.shape(img) == ():
return
fullname = os.path.join(self.labelpath, name + '.txt')
objects = util.parse_dota_poly2(fullname)
for obj in objects:
obj['poly'] = list(map(lambda x: rate * x, obj['poly']))
#obj['poly'] = list(map(lambda x: ([2 * y for y in x]), obj['poly']))
if (rate != 1):
resizeimg = cv2.resize(img,
None,
fx=rate,
fy=rate,
interpolation=cv2.INTER_CUBIC)
else:
resizeimg = img
outbasename = name + '__' + str(rate) + '__'
weight = np.shape(resizeimg)[1]
height = np.shape(resizeimg)[0]
# if (max(weight, height) < self.subsize):
# return
left, up = 0, 0
while (left < weight):
if (left + self.subsize >= weight):
left = max(weight - self.subsize, 0)
up = 0
while (up < height):
if (up + self.subsize >= height):
up = max(height - self.subsize, 0)
right = min(left + self.subsize, weight - 1)
down = min(up + self.subsize, height - 1)
subimgname = outbasename + str(left) + '___' + str(up)
# self.f_sub.write(name + ' ' + subimgname + ' ' + str(left) + ' ' + str(up) + '\n')
self.savepatches(resizeimg, objects, subimgname, left, up,
right, down)
if (up + self.subsize >= height):
break
else:
up = up + self.slide
if (left + self.subsize >= weight):
break
else:
left = left + self.slide
def SplitSingle(self, name, rate, extent):
"""
split a single image and ground truth 把一张原图和GT分割成子图
:param name: image name
:param rate: the resize scale for the image
:param extent: the image format
:return:
"""
img = cv2.imread(os.path.join(self.imagepath,
name + extent)) # 加载原图 imread 不能有中文路径
if np.shape(img) == ():
return
fullname = os.path.join(self.labelpath, name + '.txt')
objects = util.parse_dota_poly2(fullname)
for obj in objects:
obj['poly'] = list(map(lambda x: rate * x, obj['poly']))
# obj['poly'] = list(map(lambda x: ([2 * y for y in x]), obj['poly']))
if (rate != 1): # 如果输入的图不为1，则对原图进行缩放处理
resizeimg = cv2.resize(img,
None,
fx=rate,
fy=rate,
interpolation=cv2.INTER_CUBIC)
else:
resizeimg = img
outbasename = name + '__' + str(rate) + '__' # eg:P0003__2__
weight = np.shape(resizeimg)[1] #获取宽和高
height = np.shape(resizeimg)[0]
left, up = 0, 0 # 这个循环是先宽后高的，要注意一下
while (left < weight):
if (left + self.subsize >=
weight): # 如果右边界大于图片宽度，就把图左定义为刚好可以放下最后一个子图或者子图大于原图情况下取0
left = max(weight - self.subsize, 0)
up = 0
while (up < height):
if (up + self.subsize >= height):
up = max(height - self.subsize, 0) #不能超过上边
right = min(left + self.subsize, weight - 1)
down = min(up + self.subsize, height - 1)
subimgname = outbasename + str(left) + '___' + str(
up) # eg:P0003__2__0__0
# self.f_sub.write(name + ' ' + subimgname + ' ' + str(left) + ' ' + str(up) + '\n')
self.savepatches(resizeimg, objects, subimgname, left, up,
right, down)
if (up + self.subsize >= height):
break
else:
up = up + self.slide #窗口滑动
if (left + self.subsize >= weight):
break
else:
left = left + self.slide # 窗口滑动
def transfer_process(self, original_dir, target_dir):
inst_count = len(self.annotations) + 1
image_id = len(self.images) + 1
for file_name in self.file_list:
original_img_path = os.path.join(original_dir, 'images',
file_name + '.png')
target_img_path = os.path.join(target_dir, 'images',
file_name + '.png')
shutil.copyfile(original_img_path, target_img_path)
original_anno_path = os.path.join(original_dir, 'labelTxt',
file_name + '.txt')
target_anno_path = os.path.join(target_dir, 'labelTxt',
file_name + '.txt')
shutil.copyfile(original_anno_path, target_anno_path)
objects = util.parse_dota_poly2(target_anno_path)
if not len(objects):
continue
for obj in objects:
single_obj = {}
single_obj['area'] = obj['area']
single_obj['category_id'] = wordname_18.index(obj['name'])
single_obj['segmentation'] = []
single_obj['segmentation'].append(obj['poly'])
single_obj['iscrowd'] = 0
xmin, ymin, xmax, ymax = min(obj['poly'][0::2]), min(obj['poly'][1::2]), \
max(obj['poly'][0::2]), max(obj['poly'][1::2])
width, height = xmax - xmin, ymax - ymin
single_obj['bbox'] = xmin, ymin, width, height
single_obj['image_id'] = image_id
single_obj['id'] = inst_count
self.annotations.append(single_obj)
inst_count = inst_count + 1
# images
# basename = util.custombasename(file)
# # image_id = int(basename[1:])
#
# imagepath = os.path.join(imageparent, basename + '.png')
img = cv2.imread(target_img_path)
height, width, c = img.shape
single_image = {}
single_image['file_name'] = file_name + '.png'
single_image['id'] = image_id
single_image['width'] = width
single_image['height'] = height
self.images.append(single_image)
image_id = image_id + 1
def rotate_single_run(name, srcpath, dstpath):
"""
only support 0, 90, 180, 270 now
:param img:
:param boxes:
:param angle:
:return:
"""
src_imgpath = os.path.join(srcpath, 'images')
dst_imgpath = os.path.join(dstpath, 'images')
src_labelTxt = os.path.join(srcpath, 'labelTxt')
dst_labelTxt = os.path.join(dstpath, 'labelTxt')
objs = util.parse_dota_poly2(os.path.join(src_labelTxt, name + '.txt'))
img = cv2.imread(os.path.join(src_imgpath, name + '.png'))
angle = [np.pi / 2, np.pi, np.pi / 2 * 3]
img_90 = np.rot90(img, 1)
img_180 = np.rot90(img, 2)
img_270 = np.rot90(img, 3)
# cv2.imwrite(os.path.join(dst_imgpath, name + '_90.png'), img_90)
# cv2.imwrite(os.path.join(dst_imgpath, name + '_180.png'), img_180)
# cv2.imwrite(os.path.join(dst_imgpath, name + '_270.png'), img_270)
h, w, c = img.shape
print('h:', h, 'w:', w, 'c:', c)
angles = [np.pi / 2, np.pi, np.pi / 2 * 3]
rotate_90 = rotate_matrix(np.pi / 2)
rotate_180 = rotate_matrix(np.pi)
rotate_270 = rotate_matrix(np.pi / 2 * 3)
rotate_90_polys = []
rotate_180_polys = []
rotate_270_polys = []
for obj in objs:
poly = np.array(obj['poly'])
poly = np.reshape(poly, newshape=(2, 4), order='F')
centered_poly = poly - np.array([[w / 2.], [h / 2.]])
rotated_poly_90 = np.matmul(rotate_90, centered_poly) + np.array(
[[h / 2.], [w / 2.]])
rotated_poly_180 = np.matmul(rotate_180, centered_poly) + np.array(
[[w / 2.], [h / 2.]])
rotated_poly_270 = np.matmul(rotate_270, centered_poly) + np.array(
[[h / 2.], [w / 2.]])
rotate_90_polys.append(
np.reshape(rotated_poly_90, newshape=(8), order='F'))
rotate_180_polys.append(
np.reshape(rotated_poly_180, newshape=(8), order='F'))
rotate_270_polys.append(
np.reshape(rotated_poly_270, newshape=(8), order='F'))
with open(os.path.join(dst_labelTxt, name + '_90.txt'), 'w') as f_out:
for index, poly in enumerate(rotate_90_polys):
cls = objs[index]['name']
diff = objs[index]['difficult']
outline = ' '.join(map(str, list(poly))) + ' ' + cls + ' ' + diff
f_out.write(outline + '\n')
with open(os.path.join(dst_labelTxt, name + '_180.txt'), 'w') as f_out:
for index, poly in enumerate(rotate_180_polys):
cls = objs[index]['name']
diff = objs[index]['difficult']
outline = ' '.join(map(str, list(poly))) + ' ' + cls + ' ' + diff
f_out.write(outline + '\n')
with open(os.path.join(dst_labelTxt, name + '_270.txt'), 'w') as f_out:
for index, poly in enumerate(rotate_270_polys):
cls = objs[index]['name']
diff = objs[index]['difficult']
outline = ' '.join(map(str, list(poly))) + ' ' + cls + ' ' + diff
f_out.write(outline + '\n')
def HRSC2COCOTrain(srcpath, destfile, cls_names, train_set_file):
imageparent = os.path.join(srcpath, 'images')
labelparent = os.path.join(srcpath, 'labelTxt_L1')
data_dict = {}
info = { 'contributor': 'Jian Ding',
'data_created': '2019',
'description': 'This is the L1 of HRSC',
'url': 'sss',
'version': '1.0',
'year': 2019}
data_dict['info'] = info
data_dict['images'] = []
data_dict['categories'] = []
data_dict['annotations'] = []
for idex, name in enumerate(cls_names):
single_cat = {'id': idex + 1, 'name': name, 'supercategory': name}
data_dict['categories'].append(single_cat)
inst_count = 1
image_id = 1
with open(destfile, 'w') as f_out:
# filenames = util.GetFileFromThisRootDir(labelparent)
with open(train_set_file, 'r') as f_in:
lines = f_in.readlines()
filenames = [os.path.join(labelparent, x.strip()) + '.txt' for x in lines]
for file in filenames:
basename = util.custombasename(file)
# image_id = int(basename[1:])
imagepath = os.path.join(imageparent, basename + '.bmp')
img = Image.open(imagepath)
height = img.height
width = img.width
print('height: ', height)
print('width: ', width)
single_image = {}
single_image['file_name'] = basename + '.bmp'
single_image['id'] = image_id
single_image['width'] = width
single_image['height'] = height
data_dict['images'].append(single_image)
# annotations
objects = util.parse_dota_poly2(file)
for obj in objects:
single_obj = {}
single_obj['area'] = obj['area']
single_obj['category_id'] = cls_names.index(obj['name']) + 1
single_obj['segmentation'] = []
single_obj['segmentation'].append(obj['poly'])
single_obj['iscrowd'] = 0
xmin, ymin, xmax, ymax = min(obj['poly'][0::2]), min(obj['poly'][1::2]), \
max(obj['poly'][0::2]), max(obj['poly'][1::2])
width, height = xmax - xmin, ymax - ymin
single_obj['bbox'] = xmin, ymin, width, height
single_obj['image_id'] = image_id
data_dict['annotations'].append(single_obj)
single_obj['id'] = inst_count
inst_count = inst_count + 1
image_id = image_id + 1
json.dump(data_dict, f_out)
def SplitSingle(self, name, rate, extent):
"""
split a single image and ground truth
:param name: image name
:param rate: the resize scale for the image
:param extent: the image format
:return:
"""
img = cv2.imread(os.path.join(self.imagepath, name + extent))
if np.shape(img) == ():
return
fullname = os.path.join(self.labelpath, name + '.txt')
objects = util.parse_dota_poly2(fullname)
for obj in objects:
obj['poly'] = list(map(lambda x: rate * x, obj['poly']))
#obj['poly'] = list(map(lambda x: ([2 * y for y in x]), obj['poly']))
if (rate != 1):
resizeimg = cv2.resize(img,
None,
fx=rate,
fy=rate,
interpolation=cv2.INTER_CUBIC)
else:
resizeimg = img
# img.shape:(H,W,C) e.g. (600,1000,3)
if resizeimg.shape[0] < self.subsize_h:
# top bottom left right
resizeimg = cv2.copyMakeBorder(resizeimg,
0,
self.subsize_h - resizeimg.shape[0],
0,
0,
cv2.BORDER_CONSTANT,
value=0)
if resizeimg.shape[1] < self.subsize_w:
resizeimg = cv2.copyMakeBorder(resizeimg,
0,
0,
0,
self.subsize_w - resizeimg.shape[1],
cv2.BORDER_CONSTANT,
value=0)
outbasename = name + '__' + str(rate) + '__'
weight = np.shape(resizeimg)[1]
height = np.shape(resizeimg)[0]
left, up = 0, 0
while (left < weight):
if (left + self.subsize_w >= weight):
left = max(weight - self.subsize_w, 0)
up = 0
while (up < height):
if (up + self.subsize_h >= height):
up = max(height - self.subsize_h, 0)
right = min(left + self.subsize_w, weight - 1)
down = min(up + self.subsize_h, height - 1)
subimgname = outbasename + str(left) + '___' + str(up)
# self.f_sub.write(name + ' ' + subimgname + ' ' + str(left) + ' ' + str(up) + '\n')
self.savepatches(resizeimg, objects, subimgname, left, up,
right, down)
if (up + self.subsize_h >= height):
break
else:
up = up + self.slide_h
if (left + self.subsize_w >= weight):
break
else:
left = left + self.slide_w
def DOTA2RBOXES(srcpath, destfile):
imageparent = os.path.join(srcpath, 'images')
labelparent = os.path.join(srcpath, 'labelTxt')
data_dict = {}
info = {
'contributor': 'captain group',
'data_created': '2019',
'description': 'Object detection for aerial pictures.',
'url': 'http://rscup.bjxintong.com.cn/#/theme/2',
'version': 'preliminary contest',
'year': 2019
}
data_dict['info'] = info
data_dict['images'] = []
data_dict['categories'] = []
data_dict['annotations'] = []
for idex, name in enumerate(wordname_18):
if name == '__background__':
continue
single_cat = {'id': idex, 'name': name, 'supercategory': name}
data_dict['categories'].append(single_cat)
data_dict['annotations'] = []
inst_count = 1
image_id = 1
with open(destfile, 'w') as f_out:
filenames = util.GetFileFromThisRootDir(labelparent)
for file in filenames:
# annotations
objects = util.parse_dota_poly2(file)
if not len(objects):
continue
for obj in objects:
single_obj = {}
single_obj['area'] = obj['area']
single_obj['category_id'] = wordname_18.index(obj['name']) + 1
single_obj['segmentation'] = []
single_obj['segmentation'].append(obj['poly'])
single_obj['iscrowd'] = 0
single_obj['image_id'] = image_id
# print(obj['poly'])
single_obj['bbox'], single_obj['urbox'], single_obj[
'orbox'] = BOXES_cpt(obj['poly'])
data_dict['annotations'].append(single_obj)
single_obj['id'] = inst_count
inst_count = inst_count + 1
# break
# images
basename = util.custombasename(file)
# image_id = int(basename[1:])
imagepath = os.path.join(imageparent, basename + '.png')
# print('name:', imagepath)
img = cv2.imread(imagepath)
height, width, c = img.shape
single_image = {}
single_image['file_name'] = basename + '.png'
single_image['id'] = image_id
single_image['width'] = width
single_image['height'] = height
data_dict['images'].append(single_image)
image_id += 1
# break
json.dump(data_dict, f_out)
def DOTA2COCO(srcpath, destfile):
imageparent = os.path.join(srcpath, 'images')
labelparent = os.path.join(srcpath, 'labelTxt')
data_dict = {}
info = {
'contributor': 'captain group',
'data_created': '2019',
'description': 'Object detection for aerial pictures.',
'url': 'http://rscup.bjxintong.com.cn/#/theme/2',
'version': 'preliminary contest',
'year': 2019
}
data_dict['info'] = info
data_dict['images'] = []
data_dict['categories'] = []
data_dict['annotations'] = []
for idex, name in enumerate(wordname_18):
if name == '__background__':
continue
single_cat = {'id': idex, 'name': name, 'supercategory': name}
data_dict['categories'].append(single_cat)
inst_count = 1
image_id = 1
# file_list = []
with open(destfile, 'w') as f_out:
filenames = util.GetFileFromThisRootDir(labelparent)
for file in filenames:
# annotations
objects = util.parse_dota_poly2(file)
if not len(objects):
continue
for obj in objects:
single_obj = {}
single_obj['area'] = obj['area']
single_obj['category_id'] = wordname_18.index(obj['name'])
single_obj['segmentation'] = []
# print('1', obj['poly'])
# poly = [[float(obj['poly'][0]), float(obj['poly'][1])]]
# poly = [float(obj['poly'][0]), float(obj['poly'][1]), float(obj['poly'][2]),
# float(obj['poly'][3]), float(obj['poly'][4]), float(obj['poly'][5]),
# float(obj['poly'][6]), float(obj['poly'][7]) ]
# print('2', poly)
single_obj['segmentation'].append(obj['poly'])
single_obj['iscrowd'] = 0
xmin, ymin, xmax, ymax = min(obj['poly'][0::2]), min(obj['poly'][1::2]), \
max(obj['poly'][0::2]), max(obj['poly'][1::2])
width, height = xmax - xmin, ymax - ymin
single_obj['bbox'] = xmin, ymin, width, height
single_obj['image_id'] = image_id
data_dict['annotations'].append(single_obj)
single_obj['id'] = inst_count
inst_count = inst_count + 1
# images
basename = util.custombasename(file)
# image_id = int(basename[1:])
imagepath = os.path.join(imageparent, basename + '.png')
img = cv2.imread(imagepath)
height, width, c = img.shape
single_image = {}
single_image['file_name'] = basename + '.png'
single_image['id'] = image_id
single_image['width'] = width
single_image['height'] = height
data_dict['images'].append(single_image)
image_id = image_id + 1
# file_list.append(basename + '.png')
# if image_id>=200:
# break
print('img:{}, ins:{}'.format(image_id - 1, inst_count - 1))
json.dump(data_dict, f_out)
def DOTA2COCO(srcpath, dest_dir):
multi_mkdir(dest_dir)
labelparent = os.path.join(srcpath, 'labelTxt')
inst_count = 0
image_id = 0
categories_dict = {}
data_dict = {}
for category in wordname_18:
if category == '__background__':
continue
categories_dict[category] = collections.OrderedDict({'T': 0, 'S': 0, 'M': 0, 'L': 0, 'total': 0})
data_dict[category] = []
categories_dict['all'] = collections.OrderedDict({'T': 0, 'S': 0, 'M': 0, 'L': 0, 'total': 0})
# with open(destfile, 'w') as f_out:
filenames = util.GetFileFromThisRootDir(labelparent)
for file in filenames:
image_id = image_id + 1
objects = util.parse_dota_poly2(file)
if not len(objects):
continue
basename = util.custombasename(file)
# data_dict[basename] = []
for obj in objects:
inst_count = inst_count + 1
single_obj = {}
single_obj['filename'] = basename
if obj['area'] <16*16:
categories_dict[obj['name']]['T'] += 1
elif obj['area']<32*32:
categories_dict[obj['name']]['S'] += 1
elif obj['area']<96*96:
categories_dict[obj['name']]['M'] += 1
else:
categories_dict[obj['name']]['L'] += 1
xmin, ymin, xmax, ymax = min(obj['poly'][0::2]), min(obj['poly'][1::2]), \
max(obj['poly'][0::2]), max(obj['poly'][1::2])
single_obj['bbox'] = dots4ToRec8([xmin, ymin, xmax, ymax])
data_dict[obj['name']].append(single_obj)
# data_dict['annotations']
# single_obj['id'] = inst_count
# if image_id>=2:
# break
# print(data_dict)
if 'val' in dest_dir:
print('val/name:'.ljust(24), 'T:'.ljust(24), 'S:'.ljust(24), 'M:'.ljust(24), 'L:'.ljust(24))
else:
print('train/name:'.ljust(24), 'T:'.ljust(24), 'S:'.ljust(24), 'M:'.ljust(24), 'L:'.ljust(24))
for category in wordname_18:
if category == '__background__':
continue
categories_dict[category]['total'] = categories_dict[category]['T'] + categories_dict[category]['S'] \
+ categories_dict[category]['M'] + categories_dict[category]['L']
txt_file_path = os.path.join(dest_dir, category + '.txt')
with open(txt_file_path, "w") as save_f:
# print(category, len(data_dict[category]))
for category_ins in data_dict[category]:
# line = '{}'.format(category_ins['bbox']).replace(',', '')
line = '{} {} {}'.format(category_ins['filename'], 1.0, category_ins['bbox'])
line = line.replace('(', '').replace(',', '').replace(')', '')
save_f.writelines(line + '\n')
save_f.close()
print('{}'.format(category).ljust(24), '{}'.format(100* categories_dict[category]['T']/categories_dict[category]['total']).ljust(24),
'{}'.format(100* categories_dict[category]['S'] / categories_dict[category]['total']).ljust(24),
'{}'.format(100* categories_dict[category]['M'] / categories_dict[category]['total']).ljust(24),
'{}'.format(100* categories_dict[category]['L'] / categories_dict[category]['total']).ljust(24),
'{}'.format(categories_dict[category]['total']))
# print(line)
# break
categories_dict['all']['T'] += categories_dict[category]['T']
categories_dict['all']['S'] += categories_dict[category]['S']
categories_dict['all']['M'] += categories_dict[category]['M']
categories_dict['all']['L'] += categories_dict[category]['L']
categories_dict['all']['total'] += categories_dict[category]['total']
# break
print('{}'.format('all').ljust(24),
'{}'.format(100 * categories_dict['all']['T'] / categories_dict['all']['total']).ljust(24),
'{}'.format(100 * categories_dict['all']['S'] / categories_dict['all']['total']).ljust(24),
'{}'.format(100 * categories_dict['all']['M'] / categories_dict['all']['total']).ljust(24),
'{}'.format(100 * categories_dict['all']['L'] / categories_dict['all']['total']).ljust(24),
'{}'.format(categories_dict['all']['total']))
print('img:{}, ins:{}'.format(image_id, inst_count))
def minicategory2COCO(srcpath, destfile):
imageparent = os.path.join(srcpath, 'images')
labelparent = os.path.join(srcpath, 'labelTxt')
data_dict = {}
info = {
'contributor': 'captain group',
'data_created': '2019',
'description': 'Object detection for aerial pictures.',
'url': 'http://rscup.bjxintong.com.cn/#/theme/2',
'version': 'preliminary contest',
'year': 2019
}
data_dict['info'] = info
data_dict['images'] = []
data_dict['categories'] = []
data_dict['annotations'] = []
for idex, name in enumerate(mininame_3):
if name == '__background__':
continue
single_cat = {'id': idex, 'name': name, 'supercategory': name}
data_dict['categories'].append(single_cat)
inst_count = 1
image_id = 1
with open(destfile, 'w') as f_out:
filenames = util.GetFileFromThisRootDir(labelparent)
for file in filenames:
# annotations
objects = util.parse_dota_poly2(file)
if not len(objects):
continue
box_num = 0
for obj in objects:
if obj['name'] not in mininame_3:
continue
box_num += 1
single_obj = {}
single_obj['area'] = obj['area']
single_obj['category_id'] = mininame_3.index(obj['name'])
single_obj['segmentation'] = []
single_obj['segmentation'].append(obj['poly'])
single_obj['iscrowd'] = 0
xmin, ymin, xmax, ymax = min(obj['poly'][0::2]), min(obj['poly'][1::2]), \
max(obj['poly'][0::2]), max(obj['poly'][1::2])
width, height = xmax - xmin, ymax - ymin
single_obj['bbox'] = xmin, ymin, width, height
single_obj['image_id'] = image_id
data_dict['annotations'].append(single_obj)
single_obj['id'] = inst_count
inst_count = inst_count + 1
if not box_num:
continue
# images
basename = util.custombasename(file)
# image_id = int(basename[1:])
imagepath = os.path.join(imageparent, basename + '.png')
img = cv2.imread(imagepath)
height, width, c = img.shape
single_image = {}
single_image['file_name'] = basename + '.png'
single_image['id'] = image_id
single_image['width'] = width
single_image['height'] = height
data_dict['images'].append(single_image)
image_id = image_id + 1
# print(data_dict)
# break
json.dump(data_dict, f_out)
def ICDAR2COCOTrain(srcpath, destfile, cls_names):
imageparent = os.path.join(srcpath, 'images')
labelparent = os.path.join(srcpath, 'labels')
data_dict = {}
info = {
'contributor': 'ming71',
'data_created': '2020',
'description': 'ICDAR2015 dataset',
'url': 'sss',
'version': '1.0',
'year': 2020
}
data_dict['info'] = info
data_dict['images'] = []
data_dict['categories'] = []
data_dict['annotations'] = []
for idex, name in enumerate(cls_names):
single_cat = {'id': idex + 1, 'name': name, 'supercategory': name}
data_dict['categories'].append(single_cat)
inst_count = 1
image_id = 1
with open(destfile, 'w') as f_out:
# filenames = util.GetFileFromThisRootDir(labelparent)
filenames = os.listdir(labelparent)
for file in filenames:
basename = util.custombasename(file)
import ipdb
ipdb.set_trace()
# image_id = int(basename[1:])
imagepath = os.path.join(imageparent, basename + '.jpg')
img = Image.open(imagepath)
height = img.height
width = img.width
print('height: ', height)
print('width: ', width)
single_image = {}
single_image['file_name'] = basename + '.jpg'
single_image['id'] = image_id
single_image['width'] = width
single_image['height'] = height
data_dict['images'].append(single_image)
# annotations
objects = util.parse_dota_poly2(file)
for obj in objects:
single_obj = {}
single_obj['area'] = obj['area']
single_obj['category_id'] = cls_names.index(obj['name']) + 1
single_obj['segmentation'] = []
single_obj['segmentation'].append(obj['poly'])
single_obj['iscrowd'] = 0
xmin, ymin, xmax, ymax = min(obj['poly'][0::2]), min(obj['poly'][1::2]), \
max(obj['poly'][0::2]), max(obj['poly'][1::2])
width, height = xmax - xmin, ymax - ymin
single_obj['bbox'] = xmin, ymin, width, height
single_obj['image_id'] = image_id
data_dict['annotations'].append(single_obj)
single_obj['id'] = inst_count
inst_count = inst_count + 1
image_id = image_id + 1
json.dump(data_dict, f_out)