How to create mask images from COCO dataset?
Question:
So I have been using this code,. I am trying to generate the raw mask of the images from COCO dataset.
dataDir='G:'
dataType='train2014'
annFile='{}/annotations/instances_{}.json'.format(dataDir,dataType)
coco=COCO(annFile)
annFile = '{}/annotations/person_keypoints_{}.json'.format(dataDir,dataType)
coco_kps=COCO(annFile)
catIds = coco.getCatIds(catNms=['person'])
imgIds = coco.getImgIds(catIds=catIds );
imgIds = coco.getImgIds(imgIds = imgIds[0])
img = coco.loadImgs(imgIds[np.random.randint(0,len(imgIds))])[0]
I = io.imread('G:/train2014/'+img['file_name'])
plt.imshow(I); plt.axis('off')
annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
anns = coco.loadAnns(annIds)
coco.showAnns(anns)
But what i get is some thing like this
But what I want is something like this
How can I get the raw mask against each image ?
Answers:
Not familiar with COCO but I see there’s a annToMask function that should generate a binary mask for each annotation.
So in untested pseudoish code, assuming non overlapping masks, you should have something like:
annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
mask = np.zeros_like(img)
for i, ann in enumerate(annIds):
mask += coco.annToMask(ann) * i
Following Mr Filippo intuition I was able to make the correct code, which looks something like this.
mask = coco.annToMask(anns[0])
for i in range(len(anns)):
mask += coco.annToMask(anns[i])
plt.imshow(mask)
I’m late to the party, but if this can help someone.
I don’t know if your code worked for your application, however, if you want each pixel of the mask to have the value of the annotation category id, then you can’t just add the masks, as some will overlapp. I used a numpy maximum for that :
cat_ids = coco.getCatIds()
anns_ids = coco.getAnnIds(imgIds=img['id'], catIds=cat_ids, iscrowd=None)
anns = coco.loadAnns(anns_ids)
anns_img = np.zeros((img['height'],img['width']))
for ann in anns:
anns_img = np.maximum(anns_img,coco.annToMask(ann)*ann['category_id'])
EDIT :
Here is an example of my code on image 47112 of the 2017 dataset :
The value of the shade of grey is the id of the category as described in the dataset description.
Note that here the pizza overlaps with the table at the edges of its polygon. If we add the masks, the overlap would be given an id corresponding to the sum of the classes of pizza and table. However, using max, only one of the class is kept. In this case, as the class table has a id greater than the id of class pizza, the overlap is affected the class table even if the pizza is visualy above. I am not sure this could be fixed easily though.
The complete code wasn’t in the answer so I post it below.
Please install pycocotools first.
pip install pycocotools
Import the required modules. I’m assuming you’re using a jupyter notebook.
from pycocotools.coco import COCO
import os
from PIL import Image
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
Load the annotations for the coco dataset. Here, specify the 74 image.
coco = COCO('../datasets/coco/annotations/instances_train2017.json')
img_dir = '../datasets/coco/train2017'
image_id = 74
img = coco.imgs[image_id]
# loading annotations into memory...
# Done (t=12.70s)
# creating index...
# index created!
The information of the loaded img is as follows.
img
# {'license': 2,
# 'file_name': '000000000074.jpg',
# 'coco_url': # 'http://images.cocodataset.org/train2017/000000000074.jpg',
# 'height': 426,
# 'width': 640,
# 'date_captured': '2013-11-15 03:08:44',
# 'flickr_url': # 'http://farm5.staticflickr.com/4087/5078192399_aaefdb5074_z.jpg# ',
# 'id': 74}
Display the image as follows.
image = np.array(Image.open(os.path.join(img_dir, img['file_name'])))
plt.imshow(image, interpolation='nearest')
plt.show()
If you want to see the overlay result:
plt.imshow(image)
cat_ids = coco.getCatIds()
anns_ids = coco.getAnnIds(imgIds=img['id'], catIds=cat_ids, iscrowd=None)
anns = coco.loadAnns(anns_ids)
coco.showAnns(anns)
If you just want to see the mask, as Farshid Rayhan replied, do the following:
mask = coco.annToMask(anns[0])
for i in range(len(anns)):
mask += coco.annToMask(anns[i])
plt.imshow(mask)
Just adding variation of the answer, in case if you want to get the binary mask of all the annotations, it can be created as:
#Construct the binary mask
mask = coco.annToMask(anns[0])>0
for i in range(len(anns)):
mask += coco.annToMask(anns[i])>0
plt.imshow(mask,cmap='gray')
So I have been using this code,. I am trying to generate the raw mask of the images from COCO dataset.
dataDir='G:'
dataType='train2014'
annFile='{}/annotations/instances_{}.json'.format(dataDir,dataType)
coco=COCO(annFile)
annFile = '{}/annotations/person_keypoints_{}.json'.format(dataDir,dataType)
coco_kps=COCO(annFile)
catIds = coco.getCatIds(catNms=['person'])
imgIds = coco.getImgIds(catIds=catIds );
imgIds = coco.getImgIds(imgIds = imgIds[0])
img = coco.loadImgs(imgIds[np.random.randint(0,len(imgIds))])[0]
I = io.imread('G:/train2014/'+img['file_name'])
plt.imshow(I); plt.axis('off')
annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
anns = coco.loadAnns(annIds)
coco.showAnns(anns)
But what i get is some thing like this
But what I want is something like this
How can I get the raw mask against each image ?
Not familiar with COCO but I see there’s a annToMask function that should generate a binary mask for each annotation.
So in untested pseudoish code, assuming non overlapping masks, you should have something like:
annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
mask = np.zeros_like(img)
for i, ann in enumerate(annIds):
mask += coco.annToMask(ann) * i
Following Mr Filippo intuition I was able to make the correct code, which looks something like this.
mask = coco.annToMask(anns[0])
for i in range(len(anns)):
mask += coco.annToMask(anns[i])
plt.imshow(mask)
I’m late to the party, but if this can help someone.
I don’t know if your code worked for your application, however, if you want each pixel of the mask to have the value of the annotation category id, then you can’t just add the masks, as some will overlapp. I used a numpy maximum for that :
cat_ids = coco.getCatIds()
anns_ids = coco.getAnnIds(imgIds=img['id'], catIds=cat_ids, iscrowd=None)
anns = coco.loadAnns(anns_ids)
anns_img = np.zeros((img['height'],img['width']))
for ann in anns:
anns_img = np.maximum(anns_img,coco.annToMask(ann)*ann['category_id'])
EDIT :
Here is an example of my code on image 47112 of the 2017 dataset :
The value of the shade of grey is the id of the category as described in the dataset description.
Note that here the pizza overlaps with the table at the edges of its polygon. If we add the masks, the overlap would be given an id corresponding to the sum of the classes of pizza and table. However, using max, only one of the class is kept. In this case, as the class table has a id greater than the id of class pizza, the overlap is affected the class table even if the pizza is visualy above. I am not sure this could be fixed easily though.
The complete code wasn’t in the answer so I post it below.
Please install pycocotools first.
pip install pycocotools
Import the required modules. I’m assuming you’re using a jupyter notebook.
from pycocotools.coco import COCO
import os
from PIL import Image
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
Load the annotations for the coco dataset. Here, specify the 74 image.
coco = COCO('../datasets/coco/annotations/instances_train2017.json')
img_dir = '../datasets/coco/train2017'
image_id = 74
img = coco.imgs[image_id]
# loading annotations into memory...
# Done (t=12.70s)
# creating index...
# index created!
The information of the loaded img is as follows.
img
# {'license': 2,
# 'file_name': '000000000074.jpg',
# 'coco_url': # 'http://images.cocodataset.org/train2017/000000000074.jpg',
# 'height': 426,
# 'width': 640,
# 'date_captured': '2013-11-15 03:08:44',
# 'flickr_url': # 'http://farm5.staticflickr.com/4087/5078192399_aaefdb5074_z.jpg# ',
# 'id': 74}
Display the image as follows.
image = np.array(Image.open(os.path.join(img_dir, img['file_name'])))
plt.imshow(image, interpolation='nearest')
plt.show()
If you want to see the overlay result:
plt.imshow(image)
cat_ids = coco.getCatIds()
anns_ids = coco.getAnnIds(imgIds=img['id'], catIds=cat_ids, iscrowd=None)
anns = coco.loadAnns(anns_ids)
coco.showAnns(anns)
If you just want to see the mask, as Farshid Rayhan replied, do the following:
mask = coco.annToMask(anns[0])
for i in range(len(anns)):
mask += coco.annToMask(anns[i])
plt.imshow(mask)
Just adding variation of the answer, in case if you want to get the binary mask of all the annotations, it can be created as:
#Construct the binary mask
mask = coco.annToMask(anns[0])>0
for i in range(len(anns)):
mask += coco.annToMask(anns[i])>0
plt.imshow(mask,cmap='gray')