Importing Annotations
You can import your annotations when you create a Dataset. Visual Layer Profiler supports the following two annotations types:
- Image annotations - Images with class labels
- Object annotations - Objects annotated with bounding boxes and their corresponding annotations
There are two supported annotations formats:
- Parquet / CSV annotations
- Json annotations - in the COCO format
Limitations:
- Please note that as of now, annotations can't be added after a Dataset is created
- Use the following file names:
- Json: annotations.json
- CSV:
- image_annotations.csv
- object_annotations.csv
- Parquet:
- image_annotations.parquet
- object_annotations.parquet
Parquet / CSV annotations
If you like to use a parquet like annotation file, here are a couple of examples for full image class label and bounding box object location and class label. Just put a file with the name image_annotations.parquet
and object_annotations.parquet
or image_annotations.csv
and object_annotations.csv
in the root folder of your bucket or tar/zip file with images. Make sure the image filenames under the filename
column is pointing to the relative location of the file inside the folder.
import pandas as pd
df = pd.read_parquet('image_annotations.parquet')
df
filename label
0 IDX_DF_SIG21341_PlasmasNeg.png IDX_DF
1 IDX_DF_ALM00324_PlasmasPos.png IDX_DF
2 IDX_DF_ALM00331_PlasmasPos.png IDX_DF
3 IDX_DF_ALM00340_PlasmasPos.png IDX_DF
4 IDX_DF_ALM00355_PlasmasPos.png IDX_DF
... ... ...
1375 IDX_RC_ALM04559_PlasmasNeg.png IDX_RC
1376 IDX_RC_ALM04529_PlasmasPos.png IDX_RC
1377 IDX_RC_ALM00521_PlasmasNeg.png IDX_RC
1378 IDX_RC_ALM00534_PlasmasNeg.png IDX_RC
1379 IDX_RC_ALM00544_PlasmasPos.png IDX_RC
[1380 rows x 2 columns]
For Object annotations:
import pandas as pd
df = pd.read_parquet('object_annotations.parquet')
df
filename col_x row_y width height label
0 Kitti/raw/training/image_2/006149.png 0 240 135 133 Car
1 Kitti/raw/training/image_2/006149.png 608 169 59 43 Car
2 Kitti/raw/training/image_2/006149.png 285 205 81 51 Car
3 Kitti/raw/training/image_2/006149.png 187 206 108 61 Car
4 Kitti/raw/training/image_2/006149.png 949 162 226 70 Car
... ... ... ... ... ...
40565 Kitti/raw/training/image_2/001472.png 726 165 22 19 Van
40566 Kitti/raw/training/image_2/001472.png 623 176 50 28 Car
40567 Kitti/raw/training/image_2/001472.png 336 118 185 106 Truck
40568 Kitti/raw/training/image_2/001472.png 543 177 115 63 Car
40569 Kitti/raw/training/image_2/001472.png 577 159 62 29 Tram
[40570 rows x 7 columns]
Json annotations
To have your annotations imported, you need to:
- Have them in a single file, named annotations.json
- Place this file in the root folder of the file you upload or in the S3 bucket you are creating the Dataset from
- Have the file there when you create the Dataset. For now, you can't import any annotations after you have already created a Dataset.
Here is an example of an annotations file:
Note: "bbox": [col_x,row_y,width,height]. Example: "bbox": [100, 100, 200, 200].
Please make sure you remove the comment for the JSON before importing it to Visual Layer.
{
"images": [
{
"id": 1,
"width": 640,
"height": 480,
"file_name": "image1.jpg"
},
{
"id": 2,
"width": 800,
"height": 600,
"file_name": "image2.jpg"
}
],
"categories": [
{
"id": 1,
"name": "cat"
},
{
"id": 2,
"name": "dog"
},
{
"id": 3,
"name": "t-rex"
}
],
"annotations": [
# Two object annotations
{
"id": 1,
"image_id": 1,
"category_id": 1,
"bbox": [100, 100, 200, 200]
},
{
"id": 2,
"image_id": 2,
"category_id": 2,
"bbox": [50, 50, 150, 150]
},
# Two image annotations
{
"id": 3,
"image_id": 1,
"category_id": 3
},
{
"id": 4,
"image_id": 2,
"category_id": 3
}
]
}
Updated 2 months ago