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704 lines
28 KiB
704 lines
28 KiB
import contextlib
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import math
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import re
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import time
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import cv2
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import numpy as np
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import torch
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import torch.nn.functional as F
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import torchvision
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from ultralytics.yolo.utils import LOGGER
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from .metrics import box_iou
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class Profile(contextlib.ContextDecorator):
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"""
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YOLOv8 Profile class.
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Usage: as a decorator with @Profile() or as a context manager with 'with Profile():'
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"""
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def __init__(self, t=0.0):
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"""
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Initialize the Profile class.
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Args:
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t (float): Initial time. Defaults to 0.0.
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"""
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self.t = t
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self.cuda = torch.cuda.is_available()
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def __enter__(self):
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"""
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Start timing.
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"""
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self.start = self.time()
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return self
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def __exit__(self, type, value, traceback):
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"""
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Stop timing.
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"""
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self.dt = self.time() - self.start # delta-time
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self.t += self.dt # accumulate dt
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def time(self):
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"""
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Get current time.
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"""
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if self.cuda:
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torch.cuda.synchronize()
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return time.time()
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def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper)
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# https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
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# a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
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# b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
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# x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco
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# x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet
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return [
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1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
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35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
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64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
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def segment2box(segment, width=640, height=640):
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"""
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Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
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Args:
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segment (torch.Tensor): the segment label
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width (int): the width of the image. Defaults to 640
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height (int): The height of the image. Defaults to 640
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Returns:
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(np.ndarray): the minimum and maximum x and y values of the segment.
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"""
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# Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
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x, y = segment.T # segment xy
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inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
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x, y, = x[inside], y[inside]
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return np.array([x.min(), y.min(), x.max(), y.max()], dtype=segment.dtype) if any(x) else np.zeros(
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4, dtype=segment.dtype) # xyxy
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def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):
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"""
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Rescales bounding boxes (in the format of xyxy) from the shape of the image they were originally specified in
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(img1_shape) to the shape of a different image (img0_shape).
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Args:
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img1_shape (tuple): The shape of the image that the bounding boxes are for, in the format of (height, width).
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boxes (torch.Tensor): the bounding boxes of the objects in the image, in the format of (x1, y1, x2, y2)
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img0_shape (tuple): the shape of the target image, in the format of (height, width).
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ratio_pad (tuple): a tuple of (ratio, pad) for scaling the boxes. If not provided, the ratio and pad will be
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calculated based on the size difference between the two images.
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Returns:
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boxes (torch.Tensor): The scaled bounding boxes, in the format of (x1, y1, x2, y2)
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"""
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if ratio_pad is None: # calculate from img0_shape
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gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
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pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
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else:
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gain = ratio_pad[0][0]
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pad = ratio_pad[1]
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boxes[..., [0, 2]] -= pad[0] # x padding
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boxes[..., [1, 3]] -= pad[1] # y padding
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boxes[..., :4] /= gain
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clip_boxes(boxes, img0_shape)
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return boxes
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def make_divisible(x, divisor):
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"""
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Returns the nearest number that is divisible by the given divisor.
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Args:
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x (int): The number to make divisible.
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divisor (int or torch.Tensor): The divisor.
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Returns:
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int: The nearest number divisible by the divisor.
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"""
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if isinstance(divisor, torch.Tensor):
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divisor = int(divisor.max()) # to int
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return math.ceil(x / divisor) * divisor
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def non_max_suppression(
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prediction,
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conf_thres=0.25,
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iou_thres=0.45,
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classes=None,
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agnostic=False,
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multi_label=False,
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labels=(),
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max_det=300,
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nc=0, # number of classes (optional)
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max_time_img=0.05,
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max_nms=30000,
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max_wh=7680,
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):
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"""
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Perform non-maximum suppression (NMS) on a set of boxes, with support for masks and multiple labels per box.
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Arguments:
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prediction (torch.Tensor): A tensor of shape (batch_size, num_boxes, num_classes + 4 + num_masks)
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containing the predicted boxes, classes, and masks. The tensor should be in the format
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output by a model, such as YOLO.
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conf_thres (float): The confidence threshold below which boxes will be filtered out.
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Valid values are between 0.0 and 1.0.
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iou_thres (float): The IoU threshold below which boxes will be filtered out during NMS.
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Valid values are between 0.0 and 1.0.
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classes (List[int]): A list of class indices to consider. If None, all classes will be considered.
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agnostic (bool): If True, the model is agnostic to the number of classes, and all
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classes will be considered as one.
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multi_label (bool): If True, each box may have multiple labels.
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labels (List[List[Union[int, float, torch.Tensor]]]): A list of lists, where each inner
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list contains the apriori labels for a given image. The list should be in the format
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output by a dataloader, with each label being a tuple of (class_index, x1, y1, x2, y2).
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max_det (int): The maximum number of boxes to keep after NMS.
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nc (int): (optional) The number of classes output by the model. Any indices after this will be considered masks.
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max_time_img (float): The maximum time (seconds) for processing one image.
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max_nms (int): The maximum number of boxes into torchvision.ops.nms().
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max_wh (int): The maximum box width and height in pixels
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Returns:
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(List[torch.Tensor]): A list of length batch_size, where each element is a tensor of
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shape (num_boxes, 6 + num_masks) containing the kept boxes, with columns
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(x1, y1, x2, y2, confidence, class, mask1, mask2, ...).
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"""
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# Checks
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assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
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assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
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if isinstance(prediction, (list, tuple)): # YOLOv8 model in validation model, output = (inference_out, loss_out)
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prediction = prediction[0] # select only inference output
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device = prediction.device
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mps = 'mps' in device.type # Apple MPS
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if mps: # MPS not fully supported yet, convert tensors to CPU before NMS
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prediction = prediction.cpu()
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bs = prediction.shape[0] # batch size
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nc = nc or (prediction.shape[1] - 4) # number of classes
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nm = prediction.shape[1] - nc - 4
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mi = 4 + nc # mask start index
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xc = prediction[:, 4:mi].amax(1) > conf_thres # candidates
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# Settings
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# min_wh = 2 # (pixels) minimum box width and height
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time_limit = 0.5 + max_time_img * bs # seconds to quit after
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redundant = True # require redundant detections
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multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img)
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merge = False # use merge-NMS
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t = time.time()
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output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs
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for xi, x in enumerate(prediction): # image index, image inference
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# Apply constraints
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# x[((x[:, 2:4] < min_wh) | (x[:, 2:4] > max_wh)).any(1), 4] = 0 # width-height
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x = x.transpose(0, -1)[xc[xi]] # confidence
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# Cat apriori labels if autolabelling
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if labels and len(labels[xi]):
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lb = labels[xi]
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v = torch.zeros((len(lb), nc + nm + 5), device=x.device)
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v[:, :4] = lb[:, 1:5] # box
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v[range(len(lb)), lb[:, 0].long() + 4] = 1.0 # cls
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x = torch.cat((x, v), 0)
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# If none remain process next image
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if not x.shape[0]:
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continue
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# Detections matrix nx6 (xyxy, conf, cls)
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box, cls, mask = x.split((4, nc, nm), 1)
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box = xywh2xyxy(box) # center_x, center_y, width, height) to (x1, y1, x2, y2)
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if multi_label:
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i, j = (cls > conf_thres).nonzero(as_tuple=False).T
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x = torch.cat((box[i], x[i, 4 + j, None], j[:, None].float(), mask[i]), 1)
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else: # best class only
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conf, j = cls.max(1, keepdim=True)
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x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres]
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# Filter by class
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if classes is not None:
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x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
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# Apply finite constraint
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# if not torch.isfinite(x).all():
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# x = x[torch.isfinite(x).all(1)]
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# Check shape
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n = x.shape[0] # number of boxes
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if not n: # no boxes
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continue
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x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence and remove excess boxes
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# Batched NMS
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c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
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boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
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i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
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i = i[:max_det] # limit detections
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if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)
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# update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
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iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
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weights = iou * scores[None] # box weights
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x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
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if redundant:
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i = i[iou.sum(1) > 1] # require redundancy
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output[xi] = x[i]
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if mps:
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output[xi] = output[xi].to(device)
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if (time.time() - t) > time_limit:
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LOGGER.warning(f'WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded')
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break # time limit exceeded
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return output
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def clip_boxes(boxes, shape):
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"""
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It takes a list of bounding boxes and a shape (height, width) and clips the bounding boxes to the
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shape
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Args:
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boxes (torch.Tensor): the bounding boxes to clip
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shape (tuple): the shape of the image
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"""
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if isinstance(boxes, torch.Tensor): # faster individually
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boxes[..., 0].clamp_(0, shape[1]) # x1
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boxes[..., 1].clamp_(0, shape[0]) # y1
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boxes[..., 2].clamp_(0, shape[1]) # x2
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boxes[..., 3].clamp_(0, shape[0]) # y2
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else: # np.array (faster grouped)
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boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2
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boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2
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def clip_coords(coords, shape):
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"""
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Clip line coordinates to the image boundaries.
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Args:
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coords (torch.Tensor) or (numpy.ndarray): A list of line coordinates.
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shape (tuple): A tuple of integers representing the size of the image in the format (height, width).
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Returns:
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(None): The function modifies the input `coordinates` in place, by clipping each coordinate to the image boundaries.
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"""
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if isinstance(coords, torch.Tensor): # faster individually
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coords[..., 0].clamp_(0, shape[1]) # x
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coords[..., 1].clamp_(0, shape[0]) # y
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else: # np.array (faster grouped)
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coords[..., 0] = coords[..., 0].clip(0, shape[1]) # x
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coords[..., 1] = coords[..., 1].clip(0, shape[0]) # y
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def scale_image(masks, im0_shape, ratio_pad=None):
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"""
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Takes a mask, and resizes it to the original image size
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Args:
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masks (torch.Tensor): resized and padded masks/images, [h, w, num]/[h, w, 3].
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im0_shape (tuple): the original image shape
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ratio_pad (tuple): the ratio of the padding to the original image.
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Returns:
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masks (torch.Tensor): The masks that are being returned.
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"""
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# Rescale coordinates (xyxy) from im1_shape to im0_shape
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im1_shape = masks.shape
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if im1_shape[:2] == im0_shape[:2]:
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return masks
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if ratio_pad is None: # calculate from im0_shape
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gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new
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pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding
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else:
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gain = ratio_pad[0][0]
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pad = ratio_pad[1]
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top, left = int(pad[1]), int(pad[0]) # y, x
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bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0])
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if len(masks.shape) < 2:
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raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}')
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masks = masks[top:bottom, left:right]
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# masks = masks.permute(2, 0, 1).contiguous()
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# masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0]
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# masks = masks.permute(1, 2, 0).contiguous()
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masks = cv2.resize(masks, (im0_shape[1], im0_shape[0]))
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if len(masks.shape) == 2:
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masks = masks[:, :, None]
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return masks
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def xyxy2xywh(x):
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"""
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Convert bounding box coordinates from (x1, y1, x2, y2) format to (x, y, width, height) format.
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Args:
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x (np.ndarray) or (torch.Tensor): The input bounding box coordinates in (x1, y1, x2, y2) format.
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Returns:
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y (np.ndarray) or (torch.Tensor): The bounding box coordinates in (x, y, width, height) format.
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"""
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y[..., 0] = (x[..., 0] + x[..., 2]) / 2 # x center
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y[..., 1] = (x[..., 1] + x[..., 3]) / 2 # y center
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y[..., 2] = x[..., 2] - x[..., 0] # width
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y[..., 3] = x[..., 3] - x[..., 1] # height
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return y
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def xywh2xyxy(x):
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"""
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Convert bounding box coordinates from (x, y, width, height) format to (x1, y1, x2, y2) format where (x1, y1) is the
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top-left corner and (x2, y2) is the bottom-right corner.
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Args:
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x (np.ndarray) or (torch.Tensor): The input bounding box coordinates in (x, y, width, height) format.
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Returns:
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y (np.ndarray) or (torch.Tensor): The bounding box coordinates in (x1, y1, x2, y2) format.
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"""
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x
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y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y
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y[..., 2] = x[..., 0] + x[..., 2] / 2 # bottom right x
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y[..., 3] = x[..., 1] + x[..., 3] / 2 # bottom right y
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return y
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def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
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"""
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Convert normalized bounding box coordinates to pixel coordinates.
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Args:
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x (np.ndarray) or (torch.Tensor): The bounding box coordinates.
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w (int): Width of the image. Defaults to 640
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h (int): Height of the image. Defaults to 640
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padw (int): Padding width. Defaults to 0
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padh (int): Padding height. Defaults to 0
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Returns:
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y (np.ndarray) or (torch.Tensor): The coordinates of the bounding box in the format [x1, y1, x2, y2] where
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x1,y1 is the top-left corner, x2,y2 is the bottom-right corner of the bounding box.
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"""
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw # top left x
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y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh # top left y
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y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw # bottom right x
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y[..., 3] = h * (x[..., 1] + x[..., 3] / 2) + padh # bottom right y
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return y
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def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
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"""
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Convert bounding box coordinates from (x1, y1, x2, y2) format to (x, y, width, height, normalized) format.
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x, y, width and height are normalized to image dimensions
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Args:
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x (np.ndarray) or (torch.Tensor): The input bounding box coordinates in (x1, y1, x2, y2) format.
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w (int): The width of the image. Defaults to 640
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h (int): The height of the image. Defaults to 640
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clip (bool): If True, the boxes will be clipped to the image boundaries. Defaults to False
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eps (float): The minimum value of the box's width and height. Defaults to 0.0
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Returns:
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y (np.ndarray) or (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
|
|
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
|
|
y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w # x center
|
|
y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h # y center
|
|
y[..., 2] = (x[..., 2] - x[..., 0]) / w # width
|
|
y[..., 3] = (x[..., 3] - x[..., 1]) / h # height
|
|
return y
|
|
|
|
|
|
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
|
|
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 = 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
|
|
return y
|
|
|
|
|
|
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
|
|
Returns:
|
|
y (np.ndarray) or (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
|
|
y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
|
|
return y
|
|
|
|
|
|
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
|
|
Returns:
|
|
y (np.ndarray) or (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
|
|
y[:, 3] = x[:, 3] - x[:, 1] # height
|
|
return y
|
|
|
|
|
|
def ltwh2xywh(x):
|
|
"""
|
|
Convert nx4 boxes from [x1, y1, w, h] to [x, y, w, h] where xy1=top-left, xy=center
|
|
|
|
Args:
|
|
x (torch.Tensor): 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
|
|
return y
|
|
|
|
|
|
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
|
|
|
|
Returns:
|
|
y (np.ndarray) or (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
|
|
y[:, 3] = x[:, 3] + x[:, 1] # height
|
|
return y
|
|
|
|
|
|
def segments2boxes(segments):
|
|
"""
|
|
It converts segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)
|
|
|
|
Args:
|
|
segments (list): list of segments, each segment is a list of points, each point is a list of x, y coordinates
|
|
|
|
Returns:
|
|
(np.ndarray): the xywh coordinates of the bounding boxes.
|
|
"""
|
|
boxes = []
|
|
for s in segments:
|
|
x, y = s.T # segment xy
|
|
boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy
|
|
return xyxy2xywh(np.array(boxes)) # cls, xywh
|
|
|
|
|
|
def resample_segments(segments, n=1000):
|
|
"""
|
|
Inputs a list of segments (n,2) and returns a list of segments (n,2) up-sampled to n points each.
|
|
|
|
Args:
|
|
segments (list): a list of (n,2) arrays, where n is the number of points in the segment.
|
|
n (int): number of points to resample the segment to. Defaults to 1000
|
|
|
|
Returns:
|
|
segments (list): 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)
|
|
xp = np.arange(len(s))
|
|
segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)],
|
|
dtype=np.float32).reshape(2, -1).T # segment xy
|
|
return segments
|
|
|
|
|
|
def crop_mask(masks, boxes):
|
|
"""
|
|
It takes a mask and a bounding box, and returns a mask that is cropped to the bounding box
|
|
|
|
Args:
|
|
masks (torch.Tensor): [h, w, n] tensor of masks
|
|
boxes (torch.Tensor): [n, 4] tensor of bbox coordinates in relative point form
|
|
|
|
Returns:
|
|
(torch.Tensor): 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)
|
|
c = torch.arange(h, device=masks.device, dtype=x1.dtype)[None, :, None] # cols shape(h,1,1)
|
|
|
|
return masks * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2))
|
|
|
|
|
|
def process_mask_upsample(protos, masks_in, bboxes, shape):
|
|
"""
|
|
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 (torch.Tensor): [mask_dim, mask_h, mask_w]
|
|
masks_in (torch.Tensor): [n, mask_dim], n is number of masks after nms
|
|
bboxes (torch.Tensor): [n, 4], n is number of masks after nms
|
|
shape (tuple): the size of the input image (h,w)
|
|
|
|
Returns:
|
|
(torch.Tensor): The upsampled masks.
|
|
"""
|
|
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
|
|
masks = crop_mask(masks, bboxes) # CHW
|
|
return masks.gt_(0.5)
|
|
|
|
|
|
def process_mask(protos, masks_in, bboxes, shape, upsample=False):
|
|
"""
|
|
Apply masks to bounding boxes using the output of the mask head.
|
|
|
|
Args:
|
|
protos (torch.Tensor): A tensor of shape [mask_dim, mask_h, mask_w].
|
|
masks_in (torch.Tensor): A tensor of shape [n, mask_dim], where n is the number of masks after NMS.
|
|
bboxes (torch.Tensor): A tensor of shape [n, 4], where n is the number of masks after NMS.
|
|
shape (tuple): A tuple of integers representing the size of the input image in the format (h, w).
|
|
upsample (bool): A flag to indicate whether to upsample the mask to the original image size. Default is False.
|
|
|
|
Returns:
|
|
(torch.Tensor): A binary mask tensor of shape [n, h, w], where n is the number of masks after NMS, and h and w
|
|
are the height and width of the input image. The mask is applied to the bounding boxes.
|
|
"""
|
|
|
|
c, mh, mw = protos.shape # CHW
|
|
ih, iw = shape
|
|
masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) # CHW
|
|
|
|
downsampled_bboxes = bboxes.clone()
|
|
downsampled_bboxes[:, 0] *= mw / iw
|
|
downsampled_bboxes[:, 2] *= mw / iw
|
|
downsampled_bboxes[:, 3] *= mh / ih
|
|
downsampled_bboxes[:, 1] *= mh / ih
|
|
|
|
masks = crop_mask(masks, downsampled_bboxes) # CHW
|
|
if upsample:
|
|
masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW
|
|
return masks.gt_(0.5)
|
|
|
|
|
|
def process_mask_native(protos, masks_in, bboxes, shape):
|
|
"""
|
|
It takes the output of the mask head, and crops it after upsampling to the bounding boxes.
|
|
|
|
Args:
|
|
protos (torch.Tensor): [mask_dim, mask_h, mask_w]
|
|
masks_in (torch.Tensor): [n, mask_dim], n is number of masks after nms
|
|
bboxes (torch.Tensor): [n, 4], n is number of masks after nms
|
|
shape (tuple): the size of the input image (h,w)
|
|
|
|
Returns:
|
|
masks (torch.Tensor): The returned masks with dimensions [h, w, n]
|
|
"""
|
|
c, mh, mw = protos.shape # CHW
|
|
masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)
|
|
gain = min(mh / shape[0], mw / shape[1]) # gain = old / new
|
|
pad = (mw - shape[1] * gain) / 2, (mh - shape[0] * gain) / 2 # wh padding
|
|
top, left = int(pad[1]), int(pad[0]) # y, x
|
|
bottom, right = int(mh - pad[1]), int(mw - pad[0])
|
|
masks = masks[:, top:bottom, left:right]
|
|
|
|
masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW
|
|
masks = crop_mask(masks, bboxes) # CHW
|
|
return masks.gt_(0.5)
|
|
|
|
|
|
def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None, normalize=False):
|
|
"""
|
|
Rescale segment coordinates (xyxy) from img1_shape to img0_shape
|
|
|
|
Args:
|
|
img1_shape (tuple): The shape of the image that the coords are from.
|
|
coords (torch.Tensor): the coords to be scaled
|
|
img0_shape (tuple): the shape of the image that the segmentation is being applied to
|
|
ratio_pad (tuple): the ratio of the image size to the padded image size.
|
|
normalize (bool): If True, the coordinates will be normalized to the range [0, 1]. Defaults to False
|
|
|
|
Returns:
|
|
coords (torch.Tensor): 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
|
|
else:
|
|
gain = ratio_pad[0][0]
|
|
pad = ratio_pad[1]
|
|
|
|
coords[..., 0] -= pad[0] # x padding
|
|
coords[..., 1] -= pad[1] # y padding
|
|
coords[..., 0] /= gain
|
|
coords[..., 1] /= gain
|
|
clip_coords(coords, img0_shape)
|
|
if normalize:
|
|
coords[..., 0] /= img0_shape[1] # width
|
|
coords[..., 1] /= img0_shape[0] # height
|
|
return coords
|
|
|
|
|
|
def masks2segments(masks, strategy='largest'):
|
|
"""
|
|
It takes a list of masks(n,h,w) and returns a list of segments(n,xy)
|
|
|
|
Args:
|
|
masks (torch.Tensor): the output of the model, which is a tensor of shape (batch_size, 160, 160)
|
|
strategy (str): '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]
|
|
if c:
|
|
if strategy == 'concat': # concatenate all segments
|
|
c = np.concatenate([x.reshape(-1, 2) for x in c])
|
|
elif strategy == 'largest': # select largest segment
|
|
c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2)
|
|
else:
|
|
c = np.zeros((0, 2)) # no segments found
|
|
segments.append(c.astype('float32'))
|
|
return segments
|
|
|
|
|
|
def clean_str(s):
|
|
"""
|
|
Cleans a string by replacing special characters with underscore _
|
|
|
|
Args:
|
|
s (str): a string needing special characters replaced
|
|
|
|
Returns:
|
|
(str): a string with special characters replaced by an underscore _
|
|
"""
|
|
return re.sub(pattern='[|@#!¡·$€%&()=?¿^*;:,¨´><+]', repl='_', string=s)
|