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labelme2COCO.py
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import argparse
import json
import matplotlib.pyplot as plt
import skimage.io as io
import cv2
from labelme import utils
import numpy as np
import glob
import PIL.Image
REQUIRE_MASK = False
class labelme2coco(object):
def __init__(self,labelme_json=[],save_json_path='./new.json'):
'''
:param labelme_json: the list of all labelme json file paths
:param save_json_path: the path to save new json
'''
self.labelme_json=labelme_json
self.save_json_path=save_json_path
self.images=[]
self.categories=[]
self.annotations=[]
# self.data_coco = {}
self.label=[]
self.annID=1
self.height=0
self.width=0
self.require_mask = REQUIRE_MASK
self.save_json()
def data_transfer(self):
for num,json_file in enumerate(self.labelme_json):
if not json_file == self.save_json_path:
with open(json_file,'r') as fp:
data = json.load(fp)
self.images.append(self.image(data,num))
for shapes in data['shapes']:
print("label is ")
print(shapes['label'])
label=shapes['label']
# if label[1] not in self.label:
if label not in self.label:
print("find new category: ")
self.categories.append(self.categorie(label))
print(self.categories)
# self.label.append(label[1])
self.label.append(label)
points=shapes['points']
self.annotations.append(self.annotation(points,label,num))
self.annID+=1
def image(self,data,num):
image={}
img = utils.img_b64_to_arr(data['imageData'])
# img=io.imread(data['imagePath'])
# img = cv2.imread(data['imagePath'], 0)
height, width = img.shape[:2]
img = None
image['height']=height
image['width'] = width
image['id']=num+1
image['file_name'] = data['imagePath'].split('/')[-1]
self.height=height
self.width=width
return image
def categorie(self,label):
categorie={}
categorie['supercategory'] = label
# categorie['supercategory'] = label
categorie['id']=len(self.label)+1
categorie['name'] = label
# categorie['name'] = label[1]
return categorie
def annotation(self,points,label,num):
annotation={}
print(points)
x1 = points[0][0]
y1 = points[0][1]
x2 = points[1][0]
y2 = points[1][1]
contour = np.array([[x1, y1], [x2, y1], [x2, y2], [x1, y2]]) #points = [[x1, y1], [x2, y2]] for rectangle
contour = contour.astype(int)
area = cv2.contourArea(contour)
print("contour is ", contour, " area = ", area)
annotation['segmentation']= [list(np.asarray([[x1, y1], [x2, y1], [x2, y2], [x1, y2]]).flatten())]
#[list(np.asarray(contour).flatten())]
annotation['iscrowd'] = 0
annotation['area'] = area
annotation['image_id'] = num+1
if self.require_mask:
annotation['bbox'] = list(map(float,self.getbbox(points)))
else:
x1 = points[0][0]
y1 = points[0][1]
width = points[1][0] - x1
height = points[1][1] - y1
annotation['bbox']= list(np.asarray([x1, y1, width, height]).flatten())
annotation['category_id'] = self.getcatid(label)
annotation['id'] = self.annID
return annotation
def getcatid(self,label):
for categorie in self.categories:
# if label[1]==categorie['name']:
if label == categorie['name']:
return categorie['id']
return -1
def getbbox(self,points):
polygons = points
mask = self.polygons_to_mask([self.height,self.width], polygons)
return self.mask2box(mask)
def mask2box(self, mask):
# np.where(mask==1)
index = np.argwhere(mask == 1)
rows = index[:, 0]
clos = index[:, 1]
left_top_r = np.min(rows) # y
left_top_c = np.min(clos) # x
right_bottom_r = np.max(rows)
right_bottom_c = np.max(clos)
# return [(left_top_r,left_top_c),(right_bottom_r,right_bottom_c)]
# return [(left_top_c, left_top_r), (right_bottom_c, right_bottom_r)]
# return [left_top_c, left_top_r, right_bottom_c, right_bottom_r] # [x1,y1,x2,y2]
return [left_top_c, left_top_r, right_bottom_c-left_top_c, right_bottom_r-left_top_r]
def polygons_to_mask(self,img_shape, polygons):
mask = np.zeros(img_shape, dtype=np.uint8)
mask = PIL.Image.fromarray(mask)
xy = list(map(tuple, polygons))
PIL.ImageDraw.Draw(mask).polygon(xy=xy, outline=1, fill=1)
mask = np.array(mask, dtype=bool)
return mask
def data2coco(self):
data_coco={}
data_coco['images']=self.images
data_coco['categories']=self.categories
data_coco['annotations']=self.annotations
return data_coco
def save_json(self):
print("in save_json")
self.data_transfer()
self.data_coco = self.data2coco()
print(self.save_json_path)
json.dump(self.data_coco, open(self.save_json_path, 'w'), indent=4)
labelme_json=glob.glob('./validate/*.json')
labelme2coco(labelme_json,'./annotations/validate.json')