Convert Kitti labels to Yolo

TensorFlow General Discussion
1

Hi, Trying to convert Kitti label format to Yolo. But after converting the bbox is misplaced. this is kitti bounding box

Image

this is conversion code:

def convertToYoloBBox(bbox, size):
# Yolo uses bounding bbox coordinates and size relative to the image size.
# This is taken from https://pjreddie.com/media/files/voc_label.py .
dw = 1. / size[0]
dh = 1. / size[1]
x = (bbox[0] + bbox[1]) / 2.0
y = (bbox[2] + bbox[3]) / 2.0
w = bbox[1] - bbox[0]
h = bbox[3] - bbox[2]
x = x * dw
w = w * dw
y = y * dh
h = h * dh
return (x, y, w, h)

convert =convertToYoloBBox([kitti_bbox[0],kitti_bbox[1],kitti_bbox[2],kitti_bbox[3]],image.shape[:2])

The function does some normalization which is essential for yolo and outputs following:

(0.14763590391908976, 0.3397063758389261, 0.20452591656131477, 0.01810402684563757)

but when i try to check if the normalization is being done correctly with this code:

x = int(convert[0] * image.shape[0])
y = int(convert[1] * image.shape[1])
width = x+int(convert[2] * image.shape[0]) 
height = y+ int(convert[3] * image.shape[1])

cv.rectangle(image, (int(x), int(y)), (int(width), int(height)), (255,0,0), 2 )

Bounding box is misplaced:
Image2

Any suggestions ? Is conversion fucntion correct? or the problem is in the checking code ?

3

Hi @tornike_amaghlobeli,

Sorry for the delay in response.

The format of Kitti dataset labels are <left>, <top>, <right>, <bottom> which represents the pixel locations as top-left and bottom-right corners of the bounding box. So, I would recommend to change the conversion code as below,

x = (bbox[0] + bbox[2]) / 2.0
y = (bbox[1] + bbox[3]) / 2.0
w = bbox[2] - bbox[0]
h = bbox[3] - bbox[1]

Hope this works.Thank You.