From bouding boxes to masks - python-3.x

Question

I have a csv dataframe as follow:
filename width height class xmin ymin xmax ymax
0 1.jpg 2048 1251 1 706 513 743 562
1 10.jpg 1600 980 1 715 157 733 181
2 11.jpg 2828 1828 1 460 1530 482 1557
3 12.jpg 1276 1754 1 846 517 878 563
....
19 10.jpg 1600 980 1 428 83 483 145
I would like to get the masks for every image. I've succeded to get them if there is only one box for each image, however some images have multiple bouding boxes (example 10.jpg). How can I be able to add that bounding box to the mask ?
So far my code is as follow ( works good if image has 1 row ):
for idimage in annotations['filename']:
img = cv2.imread('images/'+idimage)
x1 = annotations[annotations['filename'] == idimage]['xmin'][0]
y1 = annotations[annotations['filename'] == idimage]['ymin'][0]
x2 = annotations[annotations['filename'] == idimage]['xmax'][0]
y2 = annotations[annotations['filename'] == idimage]['ymax'][0]
mask = np.zeros((img.shape[0],img.shape[1])).astype('uint8')
mask[y1:y2, x1:x2] = 1
mask = cv2.imwrite('mask/'+idimage,mask)
Thank you !

Answer 1

Actually, this is wrong:
I've succeded to get them if there is only one box for each image
Your code works only for the first row, because you request index 0. All the other rows fails because the dataframes remember their original index.
In this case, groupby does the trick.
for fn, subdf in annotations.groupby('filename'):
img = cv2.imread('images/'+fn)
mask = np.zeros((img.shape[0],img.shape[1])).astype('uint8')
for _, row in subdf.iterrows():
mask[row['ymin']:row['ymax'], row['xmin']:row['xmax']] = 1
cv2.imwrite('mask/'+fn, mask)
Here groupby allows to iterate over a series of subdataframes with the same 'filename'.
Then in a nested loop iterrows is used to iterate over each row of each subdataframe in order to extract the value and build the mask.
As you see, the mask is buildt each iteration of the outer loop, leaving the inner loop to "paint" different rectangles of the mask, one rectangle for each row of the subdataframe.
EDIT
A similar but slightly faster solution for the inner loop, instead of iterrows is:
for x1, y1, x2, y2 in zip(subdf['xmin'], subdf['ymin'], subdf['xmax'], subdf['ymax']):
mask[y1:y2, x1:x2] = 1
If you have a large amount of rows may be useful.