def aug_labelme(filepath, jsonpath, augs=None, num=0, yamlFilePath=''):
"""
augs: ['flip','noise','affine','rotate','...']
"""
default_augs = ['noise', 'rotation', 'trans', 'flip', 'zoom']
if augs is None:
augs = ['noise', 'rotation', 'trans', 'flip', 'zoom']
# elif not isinstance(augs,list):
else:
if not isinstance(augs, list):
try:
augs = list(str(augs))
except:
raise ValueError(
"parameter:aug's type is wrong. expect a string or list,got {}"
.format(str(type(augs))))
# else:
augs = list(set(augs).intersection(set(default_augs)))
if len(augs) > 0 and augs is not None:
pass
else:
logger.warning(
'augumentation method is not supported.using default augumentation method.'
)
augs = ['noise', 'rotation', 'trans', 'flip', 'zoom']
# l = np.random.randint(2,size=len(augs)).tolist()
if 'flip' in augs:
augs.remove('flip')
augs.append('flip')
l = np.random.randint(2, size=len(augs))
if np.sum(l) == 0:
l[0] = 1
l[l != 1] = 1
l = l.tolist()
# l = [0, 0, 0, 1, 0] # for test
p = list(zip(augs, l))
img = filepath
# processedImg = jsonpath
if os.path.exists(yamlFilePath):
processedImg = processorWithLabel(jsonpath, yamlFilePath, flag=True)
else:
processedImg = jsonpath
# print("======================={}".format(np.max(processedImg)))
for i in p:
# if i[0]!='flip':
if i[1] == 1:
if i[0] == 'noise':
n = imgNoise(img,
processedImg,
flag=False,
labelFile=yamlFilePath)
tmp = n['noise']
img, processedImg = tmp.oriImg, tmp.processedImg
del n, tmp
elif i[0] == 'rotation':
angle = random.randint(-45, 45)
r = imgRotation(img,
processedImg,
flag=False,
angle=angle,
labelFile=yamlFilePath)
tmp = r['rotation']
img, processedImg = tmp.oriImg, tmp.processedImg
del r, tmp
elif i[0] == 'trans':
t = imgTranslation(img,
processedImg,
flag=False,
labelFile=yamlFilePath)
tmp = t['trans']
img, processedImg = tmp.oriImg, tmp.processedImg
del t, tmp
elif i[0] == 'zoom':
zoomFactor = random.uniform(0.8, 1.8)
# print("==========2============={}".format(np.max(processedImg)))
z = imgZoom(img,
processedImg,
zoomFactor,
flag=False,
labelFile='')
# print(type(z))
tmp = z['zoom']
img, processedImg = tmp.oriImg, tmp.processedImg
del z, tmp
elif i[0] == 'flip':
imgList = []
processedImgList = []
f = imgFlip(img,
processedImg,
flag=False,
labelFile=yamlFilePath)
tmp = f['h_v']
imgList.append(tmp.oriImg)
processedImgList.append(tmp.processedImg)
tmp = f['h']
imgList.append(tmp.oriImg)
processedImgList.append(tmp.processedImg)
tmp = f['v']
imgList.append(tmp.oriImg)
processedImgList.append(tmp.processedImg)
img, processedImg = imgList, processedImgList
del tmp, f, imgList, processedImgList
parent_path = os.path.dirname(filepath)
if os.path.exists(parent_path + os.sep + 'jsons_'):
pass
else:
os.makedirs(parent_path + os.sep + 'jsons_')
# fileName = jsonpath.split(os.sep)[-1].replace(".json", '')
(_, fileName) = os.path.split(jsonpath)
fileName = fileName.replace(".json", '')
# io.imsave('D:\\testALg\\mask2json\\mask2json\\static\\jsons_\\12.png',processedImg*255)
if isinstance(img, np.ndarray):
io.imsave(
parent_path + os.sep + 'jsons_' + os.sep + fileName +
'_{}_assumble.jpg'.format(num), img)
assumbleJson = getMultiShapes(parent_path + os.sep + 'jsons_' +
os.sep + fileName +
'_{}_assumble.jpg'.format(num),
processedImg,
flag=True,
labelYamlPath=yamlFilePath)
saveJsonPath = parent_path + os.sep + 'jsons_' + os.sep + fileName + '_{}_assumble.json'.format(
num)
with open(saveJsonPath, 'w') as f:
f.write(assumbleJson)
print("Done!")
print("see here {}".format(parent_path + os.sep + 'jsons_'))
elif isinstance(img, list):
for i in range(0, len(img)):
io.imsave(
parent_path + os.sep + 'jsons_' + os.sep + fileName +
'_{}_assumble{}.jpg'.format(num, i), img[i])
assumbleJson = getMultiShapes(parent_path + os.sep + 'jsons_' +
os.sep + fileName +
'_{}_assumble{}.jpg'.format(num, i),
processedImg[i],
flag=True,
labelYamlPath=yamlFilePath)
saveJsonPath = parent_path + os.sep + 'jsons_' + os.sep + fileName + '_{}_assumble{}.json'.format(
num, i)
with open(saveJsonPath, 'w') as f:
f.write(assumbleJson)
print("Done!")
print("see here {}".format(parent_path + os.sep + 'jsons_'))
def aug_labelimg(filepath, xmlpath, augs=None, num=0, labelpath=''):
default_augs = ['noise', 'rotation', 'trans', 'flip', 'zoom']
if augs is None:
augs = ['noise', 'rotation', 'trans', 'flip', 'zoom']
# elif not isinstance(augs,list):
else:
if not isinstance(augs, list):
try:
augs = list(str(augs))
except:
raise ValueError(
"parameter:aug's type is wrong. expect a string or list,got {}"
.format(str(type(augs))))
# else:
augs = list(set(augs).intersection(set(default_augs)))
if len(augs) > 0 and augs is not None:
pass
else:
logger.warning(
'augumentation method is not supported.using default augumentation method.'
)
augs = ['noise', 'rotation', 'trans', 'flip', 'zoom']
# l = np.random.randint(2,size=len(augs)).tolist()
if 'flip' in augs:
augs.remove('flip')
augs.append('flip')
l = np.random.randint(2, size=len(augs))
if np.sum(l) == 0:
l[0] = 1
l[l != 1] = 1
l = l.tolist()
# print(l)
p = list(zip(augs, l))
img = filepath
# processedImg = xmlpath
jsonpath = x2jConvert_pascal(xmlpath, filepath)
# processedImg = processorWithLabel(jsonpath, labelpath, flag=True)
if os.path.exists(labelpath):
processedImg = processorWithLabel(jsonpath, labelpath, flag=True)
else:
processedImg = jsonpath
# os.remove(jsonpath)
for i in p:
if i[1] == 1:
if i[0] == 'noise':
n = imgNoise(img, processedImg, flag=False)
tmp = n['noise']
img, processedImg = tmp.oriImg, tmp.processedImg
del n, tmp
if i[0] == 'rotation':
angle = random.randint(-10, 10)
r = imgRotation(img, processedImg, flag=False, angle=angle)
tmp = r['rotation']
img, processedImg = tmp.oriImg, tmp.processedImg
del r, tmp
elif i[0] == 'trans':
t = imgTranslation(img, processedImg, flag=False, factor=0.1)
tmp = t['trans']
img, processedImg = tmp.oriImg, tmp.processedImg
del t, tmp
elif i[0] == 'zoom':
zoomFactor = random.uniform(0.8, 1.8)
z = imgZoom(img, processedImg, zoomFactor, flag=False)
tmp = z['zoom']
img, processedImg = tmp.oriImg, tmp.processedImg
del z, tmp
elif i[0] == 'flip':
imgList = []
processedImgList = []
f = imgFlip(img, processedImg, flag=False)
tmp = f['h_v']
imgList.append(tmp.oriImg)
processedImgList.append(tmp.processedImg)
tmp = f['h']
imgList.append(tmp.oriImg)
processedImgList.append(tmp.processedImg)
tmp = f['v']
imgList.append(tmp.oriImg)
processedImgList.append(tmp.processedImg)
img, processedImg = imgList, processedImgList
del tmp, f, imgList, processedImgList
parent_path = os.path.dirname(filepath)
if os.path.exists(parent_path + os.sep + 'xmls_'):
pass
else:
os.makedirs(parent_path + os.sep + 'xmls_')
# fileName = jsonpath.split(os.sep)[-1].replace(".json", '')
(_, fileName) = os.path.split(jsonpath)
fileName = fileName.replace(".json", '')
if isinstance(img, np.ndarray):
io.imsave(
parent_path + os.sep + 'xmls_' + os.sep + fileName +
'_{}_assumble.jpg'.format(num), img)
assumbleJson = getMultiShapes(parent_path + os.sep + 'xmls_' + os.sep +
fileName +
'_{}_assumble.jpg'.format(num),
processedImg,
flag=True,
labelYamlPath=labelpath)
saveJsonPath = parent_path + os.sep + 'xmls_' + os.sep + fileName + '_{}_assumble.json'.format(
num)
with open(saveJsonPath, 'w') as f:
f.write(assumbleJson)
j2xConvert(saveJsonPath)
# os.remove(saveJsonPath)
print("Done!")
print("see here {}".format(parent_path + os.sep + 'xmls_'))
# print("see here {}".format(saveJsonPath))
elif isinstance(img, list):
for i in range(0, len(img)):
io.imsave(
parent_path + os.sep + 'xmls_' + os.sep + fileName +
'_{}_assumble{}.jpg'.format(num, i), img[i])
assumbleJson = getMultiShapes(parent_path + os.sep + 'xmls_' +
os.sep + fileName +
'_{}_assumble{}.jpg'.format(num, i),
processedImg[i],
flag=True,
labelYamlPath=labelpath)
saveJsonPath = parent_path + os.sep + 'xmls_' + os.sep + fileName + '_{}_assumble{}.json'.format(
num, i)
with open(saveJsonPath, 'w') as f:
f.write(assumbleJson)
j2xConvert(saveJsonPath)
os.remove(saveJsonPath)
print("Done!")
print("see here {}".format(parent_path + os.sep + 'xmls_'))
def imgTranslation(oriImg: str,
oriLabel: str,
flag=True,
labelFile='',
factor=0.5):
"""
image translation
factor : Translation factor
"""
if isinstance(oriImg, str):
if os.path.exists(oriImg):
img = io.imread(oriImg)
else:
raise FileNotFoundError('Original image not found')
elif isinstance(oriImg, np.ndarray):
img = oriImg
else:
logger.error('input {} type error'.format(imgFlip))
return
imgShape = img.shape
if isinstance(oriLabel, str):
if os.path.exists(labelFile):
mask = processorWithLabel(oriLabel, labelFile, flag=True)
else:
mask = processor(oriLabel, flag=True)
elif isinstance(oriLabel, np.ndarray):
mask = oriLabel
else:
raise TypeError(
"input parameter 'oriLabel' type {} is not supported".format(
type(oriLabel)))
trans_h = random.randint(0, int(factor * imgShape[1]))
trans_v = random.randint(0, int(factor * imgShape[0]))
trans_mat = np.float32([[1, 0, trans_h], [0, 1, trans_v]])
transImg = cv2.warpAffine(img, trans_mat, (imgShape[1], imgShape[0]))
transMask = cv2.warpAffine(mask, trans_mat, (imgShape[1], imgShape[0]))
if flag:
parent_path = os.path.dirname(oriLabel)
if os.path.exists(parent_path + os.sep + 'jsons_'):
pass
else:
os.makedirs(parent_path + os.sep + 'jsons_')
fileName = oriLabel.split(os.sep)[-1].replace('.json', '')
io.imsave(
parent_path + os.sep + 'jsons_' + os.sep + fileName +
'_translation.jpg', transImg)
transMask_j = getMultiShapes(parent_path + os.sep + 'jsons_' + os.sep +
fileName + '_translation.jpg',
transMask,
flag=True,
labelYamlPath='')
saveJsonPath = parent_path + os.sep + 'jsons_' + os.sep + fileName + '_translation.json'
if transMask_j is not None:
with open(saveJsonPath, 'w') as f:
f.write(transMask_j)
else:
pass
else:
d = dict()
d['trans'] = Ori_Pro(transImg, transMask)
return d
def imgRotation(oriImg: str,
oriLabel: str,
angle=30,
scale=1,
flag=True,
labelFile=''):
"""
旋转
"""
logger.warning(
'rotation may cause salt-and-pepper noise. in order to solve this issue, small objects may be missing!'
)
if isinstance(oriImg, str):
if os.path.exists(oriImg):
img = io.imread(oriImg)
else:
raise FileNotFoundError('Original image not found')
elif isinstance(oriImg, np.ndarray):
img = oriImg
else:
logger.error('input {} type error'.format(imgFlip))
return
imgShape = img.shape
if isinstance(oriLabel, str):
if os.path.exists(labelFile):
mask = processorWithLabel(oriLabel, labelFile, flag=True)
else:
mask = processor(oriLabel, flag=True)
elif isinstance(oriLabel, np.ndarray):
mask = oriLabel
else:
raise TypeError(
"input parameter 'oriLabel' type {} is not supported".format(
type(oriLabel)))
center = (0.5 * imgShape[1], 0.5 * imgShape[0])
mat = cv2.getRotationMatrix2D(center, angle, scale)
affedImg = cv2.warpAffine(img, mat, (imgShape[1], imgShape[0]))
affedMask = cv2.warpAffine(mask, mat, (imgShape[1], imgShape[0]))
if flag:
parent_path = os.path.dirname(oriLabel)
if os.path.exists(parent_path + os.sep + 'jsons_'):
pass
else:
os.makedirs(parent_path + os.sep + 'jsons_')
fileName = oriLabel.split(os.sep)[-1].replace('.json', '')
io.imsave(
parent_path + os.sep + 'jsons_' + os.sep + fileName +
'_rotation.jpg', affedImg)
affedMask_j = getMultiShapes(parent_path + os.sep + 'jsons_' + os.sep +
fileName + '_rotation.jpg',
affedMask,
flag=True,
labelYamlPath='')
saveJsonPath = parent_path + os.sep + 'jsons_' + os.sep + fileName + '_rotation.json'
if affedMask_j is not None:
with open(saveJsonPath, 'w') as f:
f.write(affedMask_j)
else:
pass
else:
d = dict()
d['rotation'] = Ori_Pro(affedImg, affedMask)
return d
def imgNoise(oriImg: str, oriLabel: str, flag=True, labelFile=''):
"""
see skimage.util.random_noise
"""
noise_type = ['gaussian', 'poisson', 's&p', 'speckle']
l = np.random.randint(2, size=len(noise_type)).tolist()
# print(l)
p = list(zip(noise_type, l))
if isinstance(oriImg, str):
if os.path.exists(oriImg):
img = io.imread(oriImg)
else:
raise FileNotFoundError('Original image not found')
elif isinstance(oriImg, np.ndarray):
img = oriImg
else:
logger.error('input type error')
return
for i in p:
if i[1] != 0:
img = snoise.random_noise(img, mode=i[0])
img = np.array(img * 255).astype(np.uint8)
if flag:
# parent_path = os.path.dirname(oriImg)
(parent_path, file_path) = os.path.split(oriImg)
if os.path.exists(parent_path + os.sep + 'jsons_'):
pass
else:
os.makedirs(parent_path + os.sep + 'jsons_')
# fileName = oriImg.split(os.sep)[-1].replace('.json', '')
fileName = os.path.splitext(file_path)[0]
io.imsave(
parent_path + os.sep + 'jsons_' + os.sep + fileName + '_noise.jpg',
img)
try:
if isinstance(oriLabel, str):
shutil.copyfile(
oriLabel, parent_path + os.sep + 'jsons_' + os.sep +
fileName + '_noise.json')
base64code = imgEncode(parent_path + os.sep + 'jsons_' +
os.sep + fileName + '_noise.jpg')
with open(
parent_path + os.sep + 'jsons_' + os.sep + fileName +
'_noise.json', 'r') as f:
load_dict = json.load(f)
load_dict['imageData'] = base64code
with open(
parent_path + os.sep + 'jsons_' + os.sep + fileName +
'_noise.json', 'w') as f:
# json.dump(load_dict,parent_path+os.sep+'jsons_'+os.sep+fileName+'_noise.json')
f.write(json.dumps(load_dict))
elif isinstance(oriLabel, np.ndarray):
"""
labeled file can be an Image
"""
noisedMask_j = getMultiShapes(parent_path + os.sep + 'jsons_' +
os.sep + fileName + '_noise.jpg',
oriLabel,
flag=True,
labelYamlPath=labelFile)
with open(
parent_path + os.sep + 'jsons_' + os.sep + fileName +
'_noise.json', 'w') as f:
f.write(json.dumps(noisedMask_j))
except Exception:
print(traceback.format_exc())
else:
d = dict()
# mask = processor(oriLabel,flag=True)
if isinstance(oriLabel, str):
if os.path.exists(labelFile):
mask = processorWithLabel(oriLabel, labelFile, flag=True)
else:
mask = processor(oriLabel, flag=True)
elif isinstance(oriLabel, np.ndarray):
mask = oriLabel
else:
raise TypeError(
"input parameter 'oriLabel' type {} is not supported".format(
type(oriLabel)))
# print('======================')
# print(np.max(mask))
# print('======================')
d['noise'] = Ori_Pro(img, mask)
return d
def imgFlip(oriImg: str,
oriLabel: str,
flip_list=[1, 0, -1],
flag=True,
labelFile=''):
"""
flipList: flip type. see cv2.flip :
1: 水平翻转 \n
0: 垂直翻转 \n
-1: 同时翻转 \n
>>> import cv2
>>> help(cv2.flip)
"""
if isinstance(oriImg, str):
if os.path.exists(oriImg):
img = io.imread(oriImg)
else:
raise FileNotFoundError('Original image not found')
elif isinstance(oriImg, np.ndarray):
img = oriImg
else:
logger.error('input {} type error'.format(imgFlip))
return
try:
if len(flip_list) > 1 and (1 in flip_list or 0 in flip_list
or -1 in flip_list):
if isinstance(oriLabel, str):
if os.path.exists(labelFile):
mask = processorWithLabel(oriLabel, labelFile, flag=True)
else:
mask = processor(oriLabel, flag=True)
elif isinstance(oriLabel, np.ndarray):
mask = oriLabel
else:
raise TypeError(
"input parameter 'oriLabel' type is not supported")
# print(type(mask))
h_ori = cv2.flip(img, 1)
v_ori = cv2.flip(img, 0)
h_v_ori = cv2.flip(img, -1)
h_mask = cv2.flip(mask, 1) if 1 in flip_list else None
v_mask = cv2.flip(mask, 0) if 0 in flip_list else None
h_v_mask = cv2.flip(mask, -1) if -1 in flip_list else None
"""
maybe dict or zip is better :)
"""
if flag:
parent_path = os.path.dirname(oriLabel)
if os.path.exists(parent_path + os.sep + 'jsons_'):
pass
else:
os.makedirs(parent_path + os.sep + 'jsons_')
fileName = oriLabel.split(os.sep)[-1].replace('.json', '')
io.imsave(
parent_path + os.sep + 'jsons_' + os.sep + fileName +
'_h.jpg', h_ori) if 1 in flip_list else do_nothing()
io.imsave(
parent_path + os.sep + 'jsons_' + os.sep + fileName +
'_v.jpg', v_ori) if 0 in flip_list else do_nothing()
io.imsave(
parent_path + os.sep + 'jsons_' + os.sep + fileName +
'_h_v.jpg', h_v_ori) if -1 in flip_list else do_nothing()
h_j = getMultiShapes(
parent_path + os.sep + 'jsons_' + os.sep + fileName +
'_h.jpg',
h_mask,
flag=True,
labelYamlPath='') if h_mask is not None else None
# h_j = getMultiShapes(parent_path+os.sep+'jsons_'+os.sep+fileName+'_h.jpg',h_mask,flag=True,labelYamlPath='D:\\testALg\\mask2json\\mask2json\\multi_objs_json\\info.yaml')
v_j = getMultiShapes(
parent_path + os.sep + 'jsons_' + os.sep + fileName +
'_v.jpg',
v_mask,
flag=True,
labelYamlPath='') if v_mask is not None else None
h_v_j = getMultiShapes(
parent_path + os.sep + 'jsons_' + os.sep + fileName +
'_h_v.jpg',
h_v_mask,
flag=True,
labelYamlPath='') if h_v_mask is not None else None
for saveJsonPath in [
parent_path + os.sep + 'jsons_' + os.sep + fileName +
'_h.json', parent_path + os.sep + 'jsons_' + os.sep +
fileName + '_v.json', parent_path + os.sep + 'jsons_' +
os.sep + fileName + '_H_V.json'
]:
# if saveJsonPath is not None:
# print(saveJsonPath)
if saveJsonPath.endswith('_h.json'):
if h_j is not None:
with open(saveJsonPath, 'w') as f:
f.write(h_j)
else:
pass
elif saveJsonPath.endswith('_v.json'):
if v_j is not None:
with open(saveJsonPath, 'w') as f:
f.write(v_j)
else:
pass
elif saveJsonPath.endswith('_H_V.json'):
if h_v_j is not None:
with open(saveJsonPath, 'w') as f:
f.write(h_v_j)
else:
pass
rmQ.rm(saveJsonPath) if os.path.exists(
saveJsonPath) else do_nothing()
return ""
else:
d = dict()
d['h'] = Ori_Pro(h_ori, h_mask)
d['v'] = Ori_Pro(v_ori, v_mask)
d['h_v'] = Ori_Pro(h_v_ori, h_v_mask)
return d
else:
logger.warning("<===== param:flip_list is not valid =====>")
except Exception:
# print(e)
print(traceback.format_exc())
def imgZoom(oriImg: str,
oriLabel: str,
size: float = 0,
flag=True,
labelFile=''):
"""
size : The zoom factor along the axes, default 0.8~1.8
"""
# pass
if isinstance(oriImg, str):
if os.path.exists(oriImg):
img = io.imread(oriImg)
else:
raise FileNotFoundError('Original image not found')
elif isinstance(oriImg, np.ndarray):
img = oriImg
else:
logger.error('input {} type {} error'.format('oriImg', type(oriImg)))
return
try:
size = float(size)
# if size == 0.0:
# raise ValueError('zoom factor cannot be zero')
except:
logger.warning('input {} type error ,got {}.'.format(
'size', type(size)))
size = random.uniform(0.8, 1.8)
size = round(size, 2)
if size <= 0 or size == 1:
size = random.uniform(0.8, 1.8)
size = round(size, 2)
resOri = _getZoomedImg(img, size)
if isinstance(oriLabel, str):
if os.path.exists(labelFile):
# print(True)
mask = processorWithLabel(oriLabel, labelFile, flag=True)
else:
mask = processor(oriLabel, flag=True)
elif isinstance(oriLabel, np.ndarray):
mask = oriLabel
else:
raise TypeError(
"input parameter 'oriLabel' type {} is not supported".format(
type(oriLabel)))
maskImgs = _splitImg(mask)
# print('========='+str(len(maskImgs)))
resMask = np.zeros((resOri.shape[0], resOri.shape[1]))
if len(maskImgs) > 0:
for m in maskImgs:
# print(np.max(m.img))
res = _getZoomedImg(m.img, size)
res[res > 0] = m.clasId
res[res != m.clasId] = 0
resMask += res
# io.imsave('D:\\testALg\\mask2json\\mask2json\\static\\jsons_\\{}.png'.format(np.max(m.img)),resMask*255)
resMask = resMask.astype(np.uint8)
# io.imsave('D:\\testALg\\mask2json\\mask2json\\static\\jsons_\\12.png',resMask*255)
# print(np.max(resMask))
if np.max(resMask) == 0:
logger.warning(
'After zoom,none ROIs detected! Zoomfactor={} maybe too large.'.
format(size))
# return
if flag:
parent_path = os.path.dirname(oriLabel)
if os.path.exists(parent_path + os.sep + 'jsons_'):
pass
else:
os.makedirs(parent_path + os.sep + 'jsons_')
fileName = oriLabel.split(os.sep)[-1].replace('.json', '')
io.imsave(
parent_path + os.sep + 'jsons_' + os.sep + fileName + '_zoom.jpg',
resOri)
zoomedMask_j = getMultiShapes(parent_path + os.sep + 'jsons_' +
os.sep + fileName + '_zoom.jpg',
resMask,
flag=True,
labelYamlPath=labelFile)
saveJsonPath = parent_path + os.sep + 'jsons_' + os.sep + fileName + '_zoom.json'
if zoomedMask_j is not None:
with open(saveJsonPath, 'w') as f:
f.write(zoomedMask_j)
logger.info('Done! See {} .'.format(
saveJsonPath)) if LOGFlag == 'True' else entity.do_nothing()
else:
pass
else:
d = dict()
# print(resOri.shape)
# io.imsave('D:\\testALg\\mask2json\\mask2json\\static\\jsons_\\13.png',resMask*255)
d['zoom'] = Ori_Pro(resOri, resMask)
return d