可视化coco格式标注的mask（.json 分割）

目标检测、分割任务中，常用的标注格式是PASCAL VOC或coco数据集格式，其中coco的标签是以,json文件定义的。

以下的代码段可以帮您可视化数据的mask掩码（文末附完整代码）。

key words：语义分割  coco  .json 掩码mask 可视化

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先上结果：（支持单个或多个目标可视化）

 

 

#  首先导入必要的库

import json
import numpy as np
import skimage.io
import cv2
import matplotlib.pyplot as plt
import PIL.Image
import PIL.ImageDraw
import numpy

#  加载图片数据，可使用相对路径，直接使用图片名；也可以使用绝对路径加载图片

data=json.load(open('1.json'))

# img_path=data['imagePath'].split('/')[-1]
img_path = '1.png'
img=skimage.io.imread(img_path)

#  提供了两种polygon2mask的函数

def polygons_to_mask(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 polygons_to_mask2(img_shape, polygons):

    mask = np.zeros(img_shape, dtype=np.uint8)
    polygons = np.asarray([polygons], np.int32) # 这里必须是int32，其他类型使用fillPoly会报错
    # cv2.fillPoly(mask, polygons, 1) # 非int32 会报错
    cv2.fillConvexPoly(mask, polygons, 1)  # 非int32 会报错
    return mask

# 批量读取标签，转化代码

points=[]
labels=[]

annotations = data['annotations']
len_ann = len(annotations)

half_mask=[]
final_mask=[]

for ann in annotations:
    seg1 = ann['segmentation']
    seg = seg1[0]
    for j in range(0,len(seg),2):
        x = seg[j]
        y = seg[j+1]
        mas = [x,y]
        half_mask.append(mas)
    final_mask.append(half_mask)
    half_mask=[]

mask0=polygons_to_mask(img.shape[:2],final_mask[0])

if len_ann>2:
    for i in range(1,len_ann):
        print(i)
        maskany=polygons_to_mask(img.shape[:2],final_mask[i])
        print(mask0)
        print(maskany)
        mask0 += maskany
else:
    maskany=polygons_to_mask(img.shape[:2],final_mask[len_ann-1])
    print(mask0)
    print(maskany)
    mask0 += maskany

# 可视化结果

plt.subplot(121)
plt.imshow(img)
plt.axis('off')
plt.title('original')

plt.subplot(122)
plt.imshow(mask0.astype(np.uint8),'gray')
plt.axis('off')
plt.show()

完整代码如下：

import json
import numpy as np
import skimage.io
import cv2
import matplotlib.pyplot as plt
import PIL.Image
import PIL.ImageDraw
import numpy

def polygons_to_mask(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 polygons_to_mask2(img_shape, polygons):

    mask = np.zeros(img_shape, dtype=np.uint8)
    polygons = np.asarray([polygons], np.int32) # 这里必须是int32，其他类型使用fillPoly会报错
    # cv2.fillPoly(mask, polygons, 1) # 非int32 会报错
    cv2.fillConvexPoly(mask, polygons, 1)  # 非int32 会报错
    return mask

points=[]
labels=[]

annotations = data['annotations']
len_ann = len(annotations)

half_mask=[]
final_mask=[]

for ann in annotations:
    seg1 = ann['segmentation']
    seg = seg1[0]
    for j in range(0,len(seg),2):
        x = seg[j]
        y = seg[j+1]
        mas = [x,y]
        half_mask.append(mas)
    final_mask.append(half_mask)
    half_mask=[]

mask0=polygons_to_mask(img.shape[:2],final_mask[0])

if len_ann>2:
    for i in range(1,len_ann):
        print(i)
        maskany=polygons_to_mask(img.shape[:2],final_mask[i])
        print(mask0)
        print(maskany)
        mask0 += maskany
else:
    maskany=polygons_to_mask(img.shape[:2],final_mask[len_ann-1])
    print(mask0)
    print(maskany)
    mask0 += maskany

plt.subplot(121)
plt.imshow(img)
plt.axis('off')
plt.title('original')

plt.subplot(122)
plt.imshow(mask0.astype(np.uint8),'gray')
plt.axis('off')
plt.show()