@ -123,7 +123,7 @@ def make_divisible(x, divisor):
Args :
x ( int ) : The number to make divisible .
divisor ( int ) or ( torch . Tensor ) : The divisor .
divisor ( int | torch . Tensor ) : The divisor .
Returns :
( int ) : The nearest number divisible by the divisor .
@ -166,7 +166,7 @@ def non_max_suppression(
list contains the apriori labels for a given image . The list should be in the format
output by a dataloader , with each label being a tuple of ( class_index , x1 , y1 , x2 , y2 ) .
max_det ( int ) : The maximum number of boxes to keep after NMS .
nc ( int ): ( optional ) The number of classes output by the model . Any indices after this will be considered masks .
nc ( int , optional ) : The number of classes output by the model . Any indices after this will be considered masks .
max_time_img ( float ) : The maximum time ( seconds ) for processing one image .
max_nms ( int ) : The maximum number of boxes into torchvision . ops . nms ( ) .
max_wh ( int ) : The maximum box width and height in pixels
@ -290,7 +290,7 @@ def clip_coords(coords, shape):
Clip line coordinates to the image boundaries .
Args :
coords ( torch . Tensor ) or ( numpy . ndarray ) : A list of line coordinates .
coords ( torch . Tensor | numpy . ndarray ) : A list of line coordinates .
shape ( tuple ) : A tuple of integers representing the size of the image in the format ( height , width ) .
Returns :
@ -347,9 +347,9 @@ def xyxy2xywh(x):
Convert bounding box coordinates from ( x1 , y1 , x2 , y2 ) format to ( x , y , width , height ) format .
Args :
x ( np . ndarray ) or ( torch . Tensor ) : The input bounding box coordinates in ( x1 , y1 , x2 , y2 ) format .
x ( np . ndarray | torch . Tensor ) : The input bounding box coordinates in ( x1 , y1 , x2 , y2 ) format .
Returns :
y ( np . ndarray ) or ( torch . Tensor ) : The bounding box coordinates in ( x , y , width , height ) format .
y ( np . ndarray | torch . Tensor ) : The bounding box coordinates in ( x , y , width , height ) format .
"""
y = x . clone ( ) if isinstance ( x , torch . Tensor ) else np . copy ( x )
y [ . . . , 0 ] = ( x [ . . . , 0 ] + x [ . . . , 2 ] ) / 2 # x center
@ -365,9 +365,9 @@ def xywh2xyxy(x):
top - left corner and ( x2 , y2 ) is the bottom - right corner .
Args :
x ( np . ndarray ) or ( torch . Tensor ) : The input bounding box coordinates in ( x , y , width , height ) format .
x ( np . ndarray | torch . Tensor ) : The input bounding box coordinates in ( x , y , width , height ) format .
Returns :
y ( np . ndarray ) or ( torch . Tensor ) : The bounding box coordinates in ( x1 , y1 , x2 , y2 ) format .
y ( np . ndarray | torch . Tensor ) : The bounding box coordinates in ( x1 , y1 , x2 , y2 ) format .
"""
y = x . clone ( ) if isinstance ( x , torch . Tensor ) else np . copy ( x )
y [ . . . , 0 ] = x [ . . . , 0 ] - x [ . . . , 2 ] / 2 # top left x
@ -382,13 +382,13 @@ def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
Convert normalized bounding box coordinates to pixel coordinates .
Args :
x ( np . ndarray ) or ( torch . Tensor ) : The bounding box coordinates .
x ( np . ndarray | torch . Tensor ) : The bounding box coordinates .
w ( int ) : Width of the image . Defaults to 640
h ( int ) : Height of the image . Defaults to 640
padw ( int ) : Padding width . Defaults to 0
padh ( int ) : Padding height . Defaults to 0
Returns :
y ( np . ndarray ) or ( torch . Tensor ) : The coordinates of the bounding box in the format [ x1 , y1 , x2 , y2 ] where
y ( np . ndarray | torch . Tensor ) : The coordinates of the bounding box in the format [ x1 , y1 , x2 , y2 ] where
x1 , y1 is the top - left corner , x2 , y2 is the bottom - right corner of the bounding box .
"""
y = x . clone ( ) if isinstance ( x , torch . Tensor ) else np . copy ( x )
@ -405,13 +405,13 @@ def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
x , y , width and height are normalized to image dimensions
Args :
x ( np . ndarray ) or ( torch . Tensor ) : The input bounding box coordinates in ( x1 , y1 , x2 , y2 ) format .
x ( np . ndarray | torch . Tensor ) : The input bounding box coordinates in ( x1 , y1 , x2 , y2 ) format .
w ( int ) : The width of the image . Defaults to 640
h ( int ) : The height of the image . Defaults to 640
clip ( bool ) : If True , the boxes will be clipped to the image boundaries . Defaults to False
eps ( float ) : The minimum value of the box ' s width and height. Defaults to 0.0
Returns :
y ( np . ndarray ) or ( torch . Tensor ) : The bounding box coordinates in ( x , y , width , height , normalized ) format
y ( np . ndarray | torch . Tensor ) : The bounding box coordinates in ( x , y , width , height , normalized ) format
"""
if clip :
clip_boxes ( x , ( h - eps , w - eps ) ) # warning: inplace clip
@ -428,13 +428,13 @@ def xyn2xy(x, w=640, h=640, padw=0, padh=0):
Convert normalized coordinates to pixel coordinates of shape ( n , 2 )
Args :
x ( np . ndarray ) or ( torch . Tensor ) : The input tensor of normalized bounding box coordinates
x ( np . ndarray | torch . Tensor ) : The input tensor of normalized bounding box coordinates
w ( int ) : The width of the image . Defaults to 640
h ( int ) : The height of the image . Defaults to 640
padw ( int ) : The width of the padding . Defaults to 0
padh ( int ) : The height of the padding . Defaults to 0
Returns :
y ( np . ndarray ) or ( torch . Tensor ) : The x and y coordinates of the top left corner of the bounding box
y ( np . ndarray | torch . Tensor ) : The x and y coordinates of the top left corner of the bounding box
"""
y = x . clone ( ) if isinstance ( x , torch . Tensor ) else np . copy ( x )
y [ . . . , 0 ] = w * x [ . . . , 0 ] + padw # top left x
@ -447,9 +447,9 @@ def xywh2ltwh(x):
Convert the bounding box format from [ x , y , w , h ] to [ x1 , y1 , w , h ] , where x1 , y1 are the top - left coordinates .
Args :
x ( np . ndarray ) or ( torch . Tensor ) : The input tensor with the bounding box coordinates in the xywh format
x ( np . ndarray | torch . Tensor ) : The input tensor with the bounding box coordinates in the xywh format
Returns :
y ( np . ndarray ) or ( torch . Tensor ) : The bounding box coordinates in the xyltwh format
y ( np . ndarray | torch . Tensor ) : The bounding box coordinates in the xyltwh format
"""
y = x . clone ( ) if isinstance ( x , torch . Tensor ) else np . copy ( x )
y [ : , 0 ] = x [ : , 0 ] - x [ : , 2 ] / 2 # top left x
@ -462,9 +462,9 @@ def xyxy2ltwh(x):
Convert nx4 bounding boxes from [ x1 , y1 , x2 , y2 ] to [ x1 , y1 , w , h ] , where xy1 = top - left , xy2 = bottom - right
Args :
x ( np . ndarray ) or ( torch . Tensor ) : The input tensor with the bounding boxes coordinates in the xyxy format
x ( np . ndarray | torch . Tensor ) : The input tensor with the bounding boxes coordinates in the xyxy format
Returns :
y ( np . ndarray ) or ( torch . Tensor ) : The bounding box coordinates in the xyltwh format .
y ( np . ndarray | torch . Tensor ) : The bounding box coordinates in the xyltwh format .
"""
y = x . clone ( ) if isinstance ( x , torch . Tensor ) else np . copy ( x )
y [ : , 2 ] = x [ : , 2 ] - x [ : , 0 ] # width
@ -490,10 +490,10 @@ def ltwh2xyxy(x):
It converts the bounding box from [ x1 , y1 , w , h ] to [ x1 , y1 , x2 , y2 ] where xy1 = top - left , xy2 = bottom - right
Args :
x ( np . ndarray ) or ( torch . Tensor ) : the input image
x ( np . ndarray | torch . Tensor ) : the input image
Returns :
y ( np . ndarray ) or ( torch . Tensor ) : the xyxy coordinates of the bounding boxes .
y ( np . ndarray | torch . Tensor ) : the xyxy coordinates of the bounding boxes .
"""
y = x . clone ( ) if isinstance ( x , torch . Tensor ) else np . copy ( x )
y [ : , 2 ] = x [ : , 2 ] + x [ : , 0 ] # width
Glenn Jocher
2 years ago
committed by
GitHub