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[Docs]: Link buttons, add autobackend, BaseModel and ops (#130)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Ayush Chaurasia
2023-01-02 20:42:30 +05:30
committed by GitHub
parent af6e3c536b
commit 8996c5c6cf
10 changed files with 562 additions and 96 deletions
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ultralytics/yolo/utils/ops.py
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@ -47,6 +47,17 @@ def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper)
def segment2box(segment, width=640, height=640):
"""
> Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to
(xyxy)
Args:
segment: the segment label
width: the width of the image. Defaults to 640
height: The height of the image. Defaults to 640
Returns:
the minimum and maximum x and y values of the segment.
"""
# Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
x, y = segment.T # segment xy
inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
@ -55,7 +66,18 @@ def segment2box(segment, width=640, height=640):
def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):
# Rescale boxes (xyxy) from img1_shape to img0_shape
"""
> Rescale boxes (xyxy) from img1_shape to img0_shape
Args:
img1_shape: The shape of the image that the bounding boxes are for.
boxes: the bounding boxes of the objects in the image
img0_shape: the shape of the original image
ratio_pad: a tuple of (ratio, pad)
Returns:
The boxes are being returned.
"""
#
if ratio_pad is None: # calculate from img0_shape
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
@ -70,18 +92,6 @@ def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):
return boxes
def clip_boxes(boxes, shape):
# Clip boxes (xyxy) to image shape (height, width)
if isinstance(boxes, torch.Tensor): # faster individually
boxes[..., 0].clamp_(0, shape[1]) # x1
boxes[..., 1].clamp_(0, shape[0]) # y1
boxes[..., 2].clamp_(0, shape[1]) # x2
boxes[..., 3].clamp_(0, shape[0]) # y2
else: # np.array (faster grouped)
boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2
boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2
def make_divisible(x, divisor):
# Returns nearest x divisible by divisor
if isinstance(divisor, torch.Tensor):
@ -101,7 +111,7 @@ def non_max_suppression(
nm=0, # number of masks
):
"""
Perform non-maximum suppression (NMS) on a set of boxes, with support for masks and multiple labels per box.
> Perform non-maximum suppression (NMS) on a set of boxes, with support for masks and multiple labels per box.
Arguments:
prediction (torch.Tensor): A tensor of shape (batch_size, num_boxes, num_classes + 4 + num_masks)
@ -217,6 +227,25 @@ def non_max_suppression(
return output
def clip_boxes(boxes, shape):
"""
> It takes a list of bounding boxes and a shape (height, width) and clips the bounding boxes to the
shape
Args:
boxes: the bounding boxes to clip
shape: the shape of the image
"""
if isinstance(boxes, torch.Tensor): # faster individually
boxes[..., 0].clamp_(0, shape[1]) # x1
boxes[..., 1].clamp_(0, shape[0]) # y1
boxes[..., 2].clamp_(0, shape[1]) # x2
boxes[..., 3].clamp_(0, shape[0]) # y2
else: # np.array (faster grouped)
boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2
boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2
def clip_coords(boxes, shape):
# Clip bounding xyxy bounding boxes to image shape (height, width)
if isinstance(boxes, torch.Tensor): # faster individually
@ -231,9 +260,16 @@ def clip_coords(boxes, shape):
def scale_image(im1_shape, masks, im0_shape, ratio_pad=None):
"""
img1_shape: model input shape, [h, w]
img0_shape: origin pic shape, [h, w, 3]
masks: [h, w, num]
> It takes a mask, and resizes it to the original image size
Args:
im1_shape: model input shape, [h, w]
masks: [h, w, num]
im0_shape: the original image shape
ratio_pad: the ratio of the padding to the original image.
Returns:
The masks are being returned.
"""
# Rescale coordinates (xyxy) from im1_shape to im0_shape
if ratio_pad is None: # calculate from im0_shape
@ -258,7 +294,16 @@ def scale_image(im1_shape, masks, im0_shape, ratio_pad=None):
def xyxy2xywh(x):
# Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
"""
> It takes a list of bounding boxes, and converts them from the format [x1, y1, x2, y2] to [x, y, w,
h] where xy1=top-left, xy2=bottom-right
Args:
x: the input tensor
Returns:
the center of the box, the width and the height of the box.
"""
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 0] = (x[..., 0] + x[..., 2]) / 2 # x center
y[..., 1] = (x[..., 1] + x[..., 3]) / 2 # y center
@ -268,7 +313,15 @@ def xyxy2xywh(x):
def xywh2xyxy(x):
# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
"""
> It converts the bounding box from x,y,w,h to x1,y1,x2,y2 where xy1=top-left, xy2=bottom-right
Args:
x: the input tensor
Returns:
the top left and bottom right coordinates of the bounding box.
"""
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x
y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y
@ -278,7 +331,19 @@ def xywh2xyxy(x):
def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
# Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
"""
> It converts the normalized coordinates to the actual coordinates [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
Args:
x: the bounding box coordinates
w: width of the image. Defaults to 640
h: height of the image. Defaults to 640
padw: padding width. Defaults to 0
padh: height of the padding. Defaults to 0
Returns:
the xyxy coordinates of the bounding box.
"""
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw # top left x
y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh # top left y
@ -288,7 +353,20 @@ def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
# Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right
"""
> It takes in a list of bounding boxes, and returns a list of bounding boxes, but with the x and y
coordinates normalized to the width and height of the image
Args:
x: the bounding box coordinates
w: width of the image. Defaults to 640
h: height of the image. Defaults to 640
clip: If True, the boxes will be clipped to the image boundaries. Defaults to False
eps: the minimum value of the box's width and height.
Returns:
the xywhn format of the bounding boxes.
"""
if clip:
clip_boxes(x, (h - eps, w - eps)) # warning: inplace clip
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
@ -300,7 +378,19 @@ def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
def xyn2xy(x, w=640, h=640, padw=0, padh=0):
# Convert normalized segments into pixel segments, shape (n,2)
"""
> It converts normalized segments into pixel segments of shape (n,2)
Args:
x: the normalized coordinates of the bounding box
w: width of the image. Defaults to 640
h: height of the image. Defaults to 640
padw: padding width. Defaults to 0
padh: padding height. Defaults to 0
Returns:
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
y[..., 1] = h * x[..., 1] + padh # top left y
@ -308,7 +398,15 @@ def xyn2xy(x, w=640, h=640, padw=0, padh=0):
def xywh2ltwh(x):
# Convert nx4 boxes from [x, y, w, h] to [x1, y1, w, h] where xy1=top-left
"""
> It converts the bounding box from [x, y, w, h] to [x1, y1, w, h] where xy1=top-left
Args:
x: the x coordinate of the center of the bounding box
Returns:
the top left x and y coordinates of the bounding box.
"""
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
@ -316,7 +414,15 @@ def xywh2ltwh(x):
def xyxy2ltwh(x):
# Convert nx4 boxes from [x1, y1, x2, y2] to [x1, y1, w, h] where xy1=top-left, xy2=bottom-right
"""
> Convert nx4 boxes from [x1, y1, x2, y2] to [x1, y1, w, h] where xy1=top-left, xy2=bottom-right
Args:
x: the input tensor
Returns:
the xyxy2ltwh function.
"""
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[:, 2] = x[:, 2] - x[:, 0] # width
y[:, 3] = x[:, 3] - x[:, 1] # height
@ -324,7 +430,12 @@ def xyxy2ltwh(x):
def ltwh2xywh(x):
# Convert nx4 boxes from [x1, y1, w, h] to [x, y, w, h] where xy1=top-left, xy=center
"""
> Convert nx4 boxes from [x1, y1, w, h] to [x, y, w, h] where xy1=top-left, xy=center
Args:
x: the input tensor
"""
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[:, 0] = x[:, 0] + x[:, 2] / 2 # center x
y[:, 1] = x[:, 1] + x[:, 3] / 2 # center y
@ -332,7 +443,16 @@ def ltwh2xywh(x):
def ltwh2xyxy(x):
# Convert nx4 boxes from [x1, y1, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
"""
> It converts the bounding box from [x1, y1, w, h] to [x1, y1, x2, y2] where xy1=top-left,
xy2=bottom-right
Args:
x: the input image
Returns:
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
y[:, 3] = x[:, 3] + x[:, 1] # height
@ -340,7 +460,16 @@ def ltwh2xyxy(x):
def segments2boxes(segments):
# Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)
"""
> It converts segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)
Args:
segments: list of segments, each segment is a list of points, each point is a list of x, y
coordinates
Returns:
the xywh coordinates of the bounding boxes.
"""
boxes = []
for s in segments:
x, y = s.T # segment xy
@ -349,7 +478,17 @@ def segments2boxes(segments):
def resample_segments(segments, n=1000):
# Up-sample an (n,2) segment
"""
> It takes a list of segments (n,2) and returns a list of segments (n,2) where each segment has been
up-sampled to n points
Args:
segments: a list of (n,2) arrays, where n is the number of points in the segment.
n: number of points to resample the segment to. Defaults to 1000
Returns:
the resampled segments.
"""
for i, s in enumerate(segments):
s = np.concatenate((s, s[0:1, :]), axis=0)
x = np.linspace(0, len(s) - 1, n)
@ -360,13 +499,15 @@ def resample_segments(segments, n=1000):
def crop_mask(masks, boxes):
"""
"Crop" predicted masks by zeroing out everything not in the predicted bbox.
Vectorized by Chong (thanks Chong).
Args:
- masks should be a size [h, w, n] tensor of masks
- boxes should be a size [n, 4] tensor of bbox coords in relative point form
"""
> It takes a mask and a bounding box, and returns a mask that is cropped to the bounding box
Args:
masks: [h, w, n] tensor of masks
boxes: [n, 4] tensor of bbox coords in relative point form
Returns:
The masks are being cropped to the bounding box.
"""
n, h, w = masks.shape
x1, y1, x2, y2 = torch.chunk(boxes[:, :, None], 4, 1) # x1 shape(1,1,n)
r = torch.arange(w, device=masks.device, dtype=x1.dtype)[None, None, :] # rows shape(1,w,1)
@ -377,14 +518,18 @@ def crop_mask(masks, boxes):
def process_mask_upsample(protos, masks_in, bboxes, shape):
"""
Crop after upsample.
proto_out: [mask_dim, mask_h, mask_w]
out_masks: [n, mask_dim], n is number of masks after nms
bboxes: [n, 4], n is number of masks after nms
shape:input_image_size, (h, w)
return: h, w, n
"""
> It takes the output of the mask head, and applies the mask to the bounding boxes. This produces masks of higher
quality but is slower.
Args:
protos: [mask_dim, mask_h, mask_w]
masks_in: [n, mask_dim], n is number of masks after nms
bboxes: [n, 4], n is number of masks after nms
shape: the size of the input image
Returns:
mask
"""
c, mh, mw = protos.shape # CHW
masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)
masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW
@ -394,12 +539,17 @@ def process_mask_upsample(protos, masks_in, bboxes, shape):
def process_mask(protos, masks_in, bboxes, shape, upsample=False):
"""
Crop before upsample.
proto_out: [mask_dim, mask_h, mask_w]
out_masks: [n, mask_dim], n is number of masks after nms
bboxes: [n, 4], n is number of masks after nms
shape:input_image_size, (h, w)
return: h, w, n
> It takes the output of the mask head, and applies the mask to the bounding boxes. This is faster but produces
downsampled quality of mask
Args:
protos: [mask_dim, mask_h, mask_w]
masks_in: [n, mask_dim], n is number of masks after nms
bboxes: [n, 4], n is number of masks after nms
shape: the size of the input image
Returns:
mask
"""
c, mh, mw = protos.shape # CHW
@ -420,12 +570,16 @@ def process_mask(protos, masks_in, bboxes, shape, upsample=False):
def process_mask_native(protos, masks_in, bboxes, shape):
"""
Crop after upsample.
protos: [mask_dim, mask_h, mask_w]
masks_in: [n, mask_dim], n is number of masks after nms
bboxes: [n, 4], n is number of masks after nms
shape: input_image_size, (h, w)
return: h, w, n
> It takes the output of the mask head, and crops it after upsampling to the bounding boxes.
Args:
protos: [mask_dim, mask_h, mask_w]
masks_in: [n, mask_dim], n is number of masks after nms
bboxes: [n, 4], n is number of masks after nms
shape: input_image_size, (h, w)
Returns:
masks: [h, w, n]
"""
c, mh, mw = protos.shape # CHW
masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)
@ -441,7 +595,19 @@ def process_mask_native(protos, masks_in, bboxes, shape):
def scale_segments(img1_shape, segments, img0_shape, ratio_pad=None, normalize=False):
# Rescale coords (xyxy) from img1_shape to img0_shape
"""
> Rescale segment coords (xyxy) from img1_shape to img0_shape
Args:
img1_shape: The shape of the image that the segments are from.
segments: the segments to be scaled
img0_shape: the shape of the image that the segmentation is being applied to
ratio_pad: the ratio of the image size to the padded image size.
normalize: If True, the coordinates will be normalized to the range [0, 1]. Defaults to False
Returns:
the segmented image.
"""
if ratio_pad is None: # calculate from img0_shape
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
@ -460,7 +626,16 @@ def scale_segments(img1_shape, segments, img0_shape, ratio_pad=None, normalize=F
def masks2segments(masks, strategy='largest'):
# Convert masks(n,160,160) into segments(n,xy)
"""
> It takes a list of masks(n,h,w) and returns a list of segments(n,xy)
Args:
masks: the output of the model, which is a tensor of shape (batch_size, 160, 160)
strategy: 'concat' or 'largest'. Defaults to largest
Returns:
segments (List): list of segment masks
"""
segments = []
for x in masks.int().cpu().numpy().astype('uint8'):
c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]
@ -476,7 +651,14 @@ def masks2segments(masks, strategy='largest'):
def clip_segments(segments, shape):
# Clip segments (xy1,xy2,...) to image shape (height, width)
"""
> It takes a list of line segments (x1,y1,x2,y2) and clips them to the image shape (height, width)
Args:
segments: a list of segments, each segment is a list of points, each point is a list of x,y
coordinates
shape: the shape of the image
"""
if isinstance(segments, torch.Tensor): # faster individually
segments[:, 0].clamp_(0, shape[1]) # x
segments[:, 1].clamp_(0, shape[0]) # y