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377 lines
14 KiB
377 lines
14 KiB
import contextlib
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import math
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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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# YOLOv5 Profile class. Usage: @Profile() decorator or 'with Profile():' context manager
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def __init__(self, t=0.0):
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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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self.start = self.time()
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return self
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def __exit__(self, type, value, traceback):
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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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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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# 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()]) if any(x) else np.zeros(4) # xyxy
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def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):
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# Rescale boxes (xyxy) from img1_shape to img0_shape
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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 clip_boxes(boxes, shape):
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# Clip boxes (xyxy) to image shape (height, width)
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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 make_divisible(x, divisor):
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# Returns nearest x divisible by divisor
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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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nm=0, # number of masks
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):
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"""Non-Maximum Suppression (NMS) on inference results to reject overlapping detections
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Returns:
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list of detections, on (n,6) tensor per image [xyxy, conf, cls]
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"""
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if isinstance(prediction, (list, tuple)): # YOLOv5 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 = prediction.shape[2] - nm - 5 # number of classes
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xc = prediction[..., 4] > conf_thres # candidates
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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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# Settings
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# min_wh = 2 # (pixels) minimum box width and height
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max_wh = 7680 # (pixels) maximum box width and height
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max_nms = 30000 # maximum number of boxes into torchvision.ops.nms()
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time_limit = 0.5 + 0.05 * 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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mi = 5 + nc # mask start index
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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[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[:, 4] = 1.0 # conf
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v[range(len(lb)), lb[:, 0].long() + 5] = 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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# Compute conf
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x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf
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# Box/Mask
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box = xywh2xyxy(x[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2)
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mask = x[:, mi:] # zero columns if no masks
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# Detections matrix nx6 (xyxy, conf, cls)
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if multi_label:
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i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T
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x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1)
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else: # best class only
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conf, j = x[:, 5:mi].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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elif n > max_nms: # excess boxes
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x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence
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else:
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x = x[x[:, 4].argsort(descending=True)] # sort by confidence
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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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if i.shape[0] > max_det: # limit detections
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i = i[:max_det]
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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_coords(boxes, shape):
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# Clip bounding xyxy bounding boxes to image shape (height, width)
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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 scale_image(im1_shape, masks, im0_shape, ratio_pad=None):
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"""
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img1_shape: model input shape, [h, w]
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img0_shape: origin pic shape, [h, w, 3]
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masks: [h, w, num]
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"""
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# Rescale coordinates (xyxy) from im1_shape to im0_shape
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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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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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# Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
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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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# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
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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 xywh2ltwh(x):
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# Convert nx4 boxes from [x, y, w, h] to [x1, y1, w, h] where xy1=top-left
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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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return y
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def xyxy2ltwh(x):
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# Convert nx4 boxes from [x1, y1, x2, y2] to [x1, y1, w, h] where xy1=top-left, xy2=bottom-right
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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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 ltwh2xywh(x):
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# Convert nx4 boxes from [x1, y1, w, h] to [x, y, w, h] where xy1=top-left, xy=center
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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 # center x
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y[:, 1] = x[:, 1] + x[:, 3] / 2 # center y
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return y
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def ltwh2xyxy(x):
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# Convert nx4 boxes from [x1, y1, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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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 segments2boxes(segments):
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# Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)
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boxes = []
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for s in segments:
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x, y = s.T # segment xy
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boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy
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return xyxy2xywh(np.array(boxes)) # cls, xywh
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def resample_segments(segments, n=1000):
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# Up-sample an (n,2) segment
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for i, s in enumerate(segments):
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s = np.concatenate((s, s[0:1, :]), axis=0)
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x = np.linspace(0, len(s) - 1, n)
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xp = np.arange(len(s))
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segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy
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return segments
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def crop_mask(masks, boxes):
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"""
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"Crop" predicted masks by zeroing out everything not in the predicted bbox.
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Vectorized by Chong (thanks Chong).
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Args:
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- masks should be a size [h, w, n] tensor of masks
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- boxes should be a size [n, 4] tensor of bbox coords in relative point form
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"""
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n, h, w = masks.shape
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x1, y1, x2, y2 = torch.chunk(boxes[:, :, None], 4, 1) # x1 shape(1,1,n)
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r = torch.arange(w, device=masks.device, dtype=x1.dtype)[None, None, :] # rows shape(1,w,1)
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c = torch.arange(h, device=masks.device, dtype=x1.dtype)[None, :, None] # cols shape(h,1,1)
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return masks * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2))
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def process_mask_upsample(protos, masks_in, bboxes, shape):
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"""
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Crop after upsample.
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proto_out: [mask_dim, mask_h, mask_w]
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out_masks: [n, mask_dim], n is number of masks after nms
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bboxes: [n, 4], n is number of masks after nms
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shape:input_image_size, (h, w)
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return: h, w, n
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"""
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c, mh, mw = protos.shape # CHW
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masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)
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masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW
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masks = crop_mask(masks, bboxes) # CHW
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return masks.gt_(0.5)
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def process_mask(protos, masks_in, bboxes, shape, upsample=False):
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"""
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Crop before upsample.
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proto_out: [mask_dim, mask_h, mask_w]
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out_masks: [n, mask_dim], n is number of masks after nms
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bboxes: [n, 4], n is number of masks after nms
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shape:input_image_size, (h, w)
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return: h, w, n
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"""
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c, mh, mw = protos.shape # CHW
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ih, iw = shape
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masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) # CHW
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downsampled_bboxes = bboxes.clone()
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downsampled_bboxes[:, 0] *= mw / iw
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downsampled_bboxes[:, 2] *= mw / iw
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downsampled_bboxes[:, 3] *= mh / ih
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downsampled_bboxes[:, 1] *= mh / ih
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masks = crop_mask(masks, downsampled_bboxes) # CHW
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if upsample:
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masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW
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return masks.gt_(0.5)
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