def test():
img = "D:\\testALg\mask2json\mask2json\static\\1-2cvt.png"
oriImg = "D:\\testALg\mask2json\mask2json\static\\1-2cvt.jpg"
imgShape = cv2.imread(img).shape
region = getShape.process(img)
# print(type(region))
# print(region.shape)
points = []
shapes = []
for i in range(0, region.shape[0]):
print(region[i][0])
points.append(region[i][0].tolist())
obj = dict()
obj['version'] = '4.2.9'
obj['flags'] = {}
shape = dict()
shape['label'] = 'weld' #whatever other label
shape['points'] = points
shape['group_id'] = 'null'
shape['shape_type'] = 'polygon'
shape['flags'] = {}
shapes.append(shape)
obj['shapes'] = shapes
obj['imagePath'] = oriImg.split(os.sep)[-1]
obj['imageData'] = str(img2base64.imgEncode(oriImg))
obj['imageHeight'] = imgShape[0]
obj['imageWidth'] = imgShape[1]
j = json.dumps(obj, sort_keys=True, indent=4)
print(j)
with open('D:\\testALg\mask2json\mask2json\static\\1-2cvt.json', 'w') as f:
f.write(j)
rmQ.rm('D:\\testALg\mask2json\mask2json\static\\1-2cvt.json')
def x2jConvert_pascal(xmlpath, originImgPath, flag=True):
# pass
if not os.path.exists(xmlpath) or not os.path.exists(originImgPath):
logger.error('file not exist')
return
base64Code = imgEncode(originImgPath)
shapes = getPolygonPascal(xmlpath)
(fatherPath, filename_ext) = os.path.split(originImgPath)
(filename, _) = os.path.splitext(filename_ext)
ob = dict()
ob['imageData'] = base64Code
ob['flags'] = {}
ob['version'] = labelmeVersion
ob['imagePath'] = filename_ext
img = io.imread(originImgPath)
imgShape = img.shape
del img
ob['imageHeight'] = imgShape[0]
ob['imageWidth'] = imgShape[1]
ob['shapes'] = shapes
if flag:
with open(fatherPath + os.sep + filename + '_p.json',
'w',
encoding='utf-8') as f:
j = json.dumps(ob, sort_keys=True, indent=4)
f.write(j)
logger.info('save to path {}'.format(fatherPath + os.sep + filename +
'_p.json'))
return fatherPath + os.sep + filename + '_p.json'
else:
return json.dumps(ob, sort_keys=True, indent=4)
def getMultiShapes(oriImgPath,
labelPath,
savePath='',
labelYamlPath='',
flag=False,
areaThresh=500):
"""
oriImgPath : for change img to base64 \n
labelPath : after fcn/unet or other machine learning objects outlining , the generated label img
or labelme labeled imgs(after json files converted to mask files) \n
savePath : json file save path \n
labelYamlPath : after json files converted to mask files. if doesn't have this file,should have a labeled img.
but the classes should change by yourself(labelme 4.2.9 has a bug,when change the label there will be an error.
) \n
"""
# print('-==================')
# print(oriImgPath)
# print(labelPath)
# print(savePath)
# print(labelYamlPath)
# print('-==================')
if isinstance(labelPath, str):
if os.path.exists(labelPath):
label_img = io.imread(labelPath)
else:
raise FileNotFoundError('mask/labeled image not found')
else:
# img = oriImg
label_img = labelPath
# print(np.max(label_img))
if np.max(label_img) > 127:
# print('too many classes! \n maybe binary?')
label_img[label_img > 127] = 255
label_img[label_img != 255] = 0
label_img = label_img / 255
labelShape = label_img.shape
labels = readYmal(labelYamlPath, label_img)
# print(list(labels))
shapes = []
obj = dict()
obj['version'] = labelme_version
obj['flags'] = {}
for la in list(labels):
if la[1] > 0:
# print(la[0])
img = copy.deepcopy(label_img) # img = label_img.copy()
img = img.astype(np.uint8)
img[img == la[1]] = 255
img[img != 255] = 0
region = process(img.astype(np.uint8))
if isinstance(region, np.ndarray):
points = []
for i in range(0, region.shape[0]):
# print(region[i][0])
points.append(region[i][0].tolist())
shape = dict()
shape['label'] = la[0]
shape['points'] = points
shape['group_id'] = 'null'
shape['shape_type'] = 'polygon'
shape['flags'] = {}
shapes.append(shape)
elif isinstance(region, list):
# print(len(region))
for subregion in region:
points = []
for i in range(0, subregion.shape[0]):
points.append(subregion[i][0].tolist())
shape = dict()
shape['label'] = la[0]
shape['points'] = points
shape['group_id'] = 'null'
shape['shape_type'] = 'polygon'
shape['flags'] = {}
shapes.append(shape)
# print(len(shapes))
obj['shapes'] = shapes
# print(shapes)
(_, imgname) = os.path.split(oriImgPath)
obj['imagePath'] = imgname
# print(obj['imagePath'])
obj['imageData'] = str(imgEncode(oriImgPath))
obj['imageHeight'] = labelShape[0]
obj['imageWidth'] = labelShape[1]
j = json.dumps(obj, sort_keys=True, indent=4)
# print(j)
if not flag:
saveJsonPath = savePath + os.sep + obj['imagePath'][:-4] + '.json'
# print(saveJsonPath)
with open(saveJsonPath, 'w') as f:
f.write(j)
rmQ.rm(saveJsonPath)
else:
return j
def test():
# BASE_DIR = os.path.abspath(os.curdir)
BASE_DIR = os.path.abspath(os.path.dirname(os.getcwd()))
"""
do not use cv2.imread to load the label img. there is a bug
"""
# oriImgPath = 'D:\\testALg\\mask2json\\mask2json\\static\\multi_objs_sameclass.jpg'
# label_img = io.imread('D:\\testALg\\mask2json\\mask2json\\multi_objs_sameclass_json\\label.png')
oriImgPath = BASE_DIR + '/static/multi_objs.jpg'
label_img = io.imread(BASE_DIR + '/static/multi_objs_json/label.png')
labelShape = label_img.shape
labels = readYmal(BASE_DIR + '/static/multi_objs_json/info.yaml')
shapes = []
obj = dict()
obj['version'] = labelme_version
obj['flags'] = {}
for la in labels:
if la[1] > 0:
# img = label_img[label_img == i[1]]
img = copy.deepcopy(label_img)
img[img == la[1]] = 255
img[img != 255] = 0
region = process(img.astype(np.uint8))
"""
this if...else... is unnecessary if using getShape.getMultiRegion
"""
if isinstance(region, np.ndarray):
points = []
for i in range(0, region.shape[0]):
# print(region[i][0])
points.append(region[i][0].tolist())
shape = dict()
shape['label'] = la[0]
shape['points'] = points
shape['group_id'] = 'null'
shape['shape_type'] = 'polygon'
shape['flags'] = {}
shapes.append(shape)
elif isinstance(region, list):
for subregion in region:
points = []
for i in range(0, subregion.shape[0]):
# print(region[i][0])
points.append(subregion[i][0].tolist())
shape = dict()
shape['label'] = la[0]
shape['points'] = points
shape['group_id'] = 'null'
shape['shape_type'] = 'polygon'
shape['flags'] = {}
shapes.append(shape)
obj['shapes'] = shapes
obj['imagePath'] = oriImgPath.split(os.sep)[-1]
obj['imageData'] = str(imgEncode(oriImgPath))
obj['imageHeight'] = labelShape[0]
obj['imageWidth'] = labelShape[1]
j = json.dumps(obj, sort_keys=True, indent=4)
with open(BASE_DIR + '/static/multi_objs.json', 'w') as f:
f.write(j)
rmQ.rm(BASE_DIR + '/static/multi_objs.json')
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
import os
from convertmask.utils.methods.img2base64 import imgEncode
from skimage import io
try:
from labelme import __version__
except:
__version__ = '4.2.9'
BASE_DIR = os.path.abspath(os.path.dirname(os.getcwd())) + os.sep + 'static'
imgPath = BASE_DIR + os.sep + 'multi_objs_test.jpg'
if __name__ == "__main__":
image = io.imread(imgPath)
base64Code = imgEncode(image).decode()
# print(base64Code)
(fatherPath, filename_ext) = os.path.split(imgPath)
(filename, _) = os.path.splitext(filename_ext)
ob = dict()
ob['imageData'] = base64Code
ob['flags'] = {}
ob['version'] = __version__
ob['imagePath'] = filename_ext
ob['shapes'] = []
ob['imageHeight'] = image.shape[0]
ob['imageWidth'] = image.shape[1]
# ob['shapes'] = shapes