convert a .csv file to yolo darknet format - object-detection

Question

I have a few annotations that is originally in .csv format. I would need to convert it to yolo darknet format inorder to train my model with yolov4.
my .csv file :
YOLO format is : object-class x y width height
where, object_class, widht, height is know from my .csv format. But finding x,y is confusing .Note that x and y are center of rectangle (are not top-left corner).
Any help would be appreciated :)

Answer 1

You can use this function to convert bounding boxes to the yolo format. Of course you will need to write some code to read the csv. Just use this function as a template for your needs.
This function was extracted from the labelimg app:
https://github.com/tzutalin/labelImg/blob/master/libs/yolo_io.py
def BndBox2YoloLine(self, box, classList=[]):
xmin = box['xmin']
xmax = box['xmax']
ymin = box['ymin']
ymax = box['ymax']
xcen = float((xmin + xmax)) / 2 / self.imgSize[1]
ycen = float((ymin + ymax)) / 2 / self.imgSize[0]
w = float((xmax - xmin)) / self.imgSize[1]
h = float((ymax - ymin)) / self.imgSize[0]
# PR387
boxName = box['name']
if boxName not in classList:
classList.append(boxName)
classIndex = classList.index(boxName)
return classIndex, xcen, ycen, w, h