Our own export format that uses a similar structure to COCO, but builds on top of it with added complexity
Hello, thank you for using the code provided by Hasty. Please note that some code blocks might not be 100% complete and ready to be run as is. This is done intentionally as we focus on implementing only the most challenging parts that might be tough to pick up from scratch. View our code block as a LEGO block - you can’t use it as a standalone solution, but you can take it and add to your system to complement it. If you have questions about using the tool, please get in touch with us to get direct help from the Hasty team.
{
"project_name": "Project Name",
"create_date": "2019-10-22 19:04:16Z",
"export_format_version": "1.1",
"export_date": "2019-11-19 16:21:18Z",
"attributes": [
{
"name": "ewerwer",
"type": "TEXT",
"values": ["black", "white"]
},
{...},
],
"label_classes": [
{
"class_name": "cat",
"color": "#1f78b44d",
"class_type": "object",
"attributes": ["black", "white"]
},
{
"class_name": "dog",
"color": "#e31a1c4d",
"class_type": "object"
}
],
"images": [
{
"image_name": "IMG_000002.jpg",
"dataset_name": "train dataset",
"width": 500,
"height": 430,
"image_status": "TO REVIEW",
"labels": [
{
"class_name": "cat",
"bbox": [102, 45, 420, 404],
"polygon": null,
"mask": null,
"z_index": 1,
"attributes":{"attribute_name":"xyz"}
},
{...},
{...}
],
"tags":["xyz","pqr"]
},
{...},
{...}
]
}
project_name string\
The name of the project
create_date string\
The project creation date in format YYYY-MM-DD HH:MI:SSZ
export_format_version string\
Internal file format version
export_date string\
The project export date in format YYYY-MM-DD HH:MI:SSZ
label_classes list of Label Class objects\
Projects label classes
attributes list of attribute objects
images list of Image objects\
The list of images and associated labels
class_name string\
The name of the class
color string\
Associated with the label class color, in format #RRGGBBAA
class_type string Class type, "object" or "background"
attributes list of strings\
attributes of the label class
image_name string\
The image filename
dataset_name string\
Dataset name
width integer\
Image width in pixels
height integer\
Image height in pixels
image_status string\
Image status. Possible values:
labels _list o_f label object\
The list of labels associated with the image
tags list\
list of strings
class_name string\
The name of the class
bbox list of integers or null\
Bounding box label. 4 numbers. [X_top_left, Y_top_left, X_bottom_right, Y_bottom_right].
polygon list of the list of integers or null\
Polygon coordinates, list of polygon vertices (x0, y0), (x1, y1), ...
mask list of integers or null\
RLE Encoded mask
z_index integer\
The z-index property specifies the stack order of an element. An element with a greater stack order is always in front of an element with a lower stack order.
attributes dictionary\
Attribute object
Attributes
name string\
The name of the attribute
type string\
Possible values:
value list\
The list of the values of attributes
Hello, thank you for using the code provided by Hasty. Please note that some code blocks might not be 100% complete and ready to be run as is. This is done intentionally as we focus on implementing only the most challenging parts that might be tough to pick up from scratch. View our code block as a LEGO block - you can’t use it as a standalone solution, but you can take it and add to your system to complement it. If you have questions about using the tool, please get in touch with us to get direct help from the Hasty team.
python
import numpy as np
def rle_decode(mask_rle, shape):
"""
mask_rle: run-length as string formatted (start length)
shape: (width, height) of array to return
Returns numpy array, 1 - mask, 0 - background
"""
s = mask_rle
starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]
starts -= 1
ends = starts + lengths
shape = shape[1], shape[0]
img = np.zeros(shape[0] * shape[1], dtype=np.uint8)
for lo, hi in zip(starts, ends):
img[lo:hi] = 1
return img.reshape(shape)
bbox = [24, 307, 43, 320]
mask_rle = [11, 2, 26, 2, 30, 2, 45, 3, 49, 3, 60, 2, 65, 7, 79, 4, 84, 7, 99, 12, 115, 1, 119, 8, 128, 3, 134, 10, 149, 2, 154, 9, 164, 3, 169, 2, 173, 9, 189, 2, 193, 8, 206, 3, 214, 13, 234, 10]
mask = rle_decode(mask_rle, (bbox[2]-bbox[0], bbox[3]-bbox[1]))
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