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| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | |
| """ | |
| Utilities for bounding box manipulation and GIoU. | |
| """ | |
| import torch | |
| from torchvision.ops.boxes import box_area | |
| import pdb | |
| def box_cxcywh_to_xyxy(x): | |
| x_c, y_c, w, h = x.unbind(-1) | |
| b = [(x_c - 0.5 * w), (y_c - 0.5 * h), | |
| (x_c + 0.5 * w), (y_c + 0.5 * h)] | |
| return torch.stack(b, dim=-1) | |
| def box_xyxy_to_cxcywh(x): | |
| x0, y0, x1, y1 = x.unbind(-1) | |
| b = [(x0 + x1) / 2, (y0 + y1) / 2, | |
| (x1 - x0), (y1 - y0)] | |
| return torch.stack(b, dim=-1) | |
| def rescale_bboxes(out_bbox, size): | |
| img_h, img_w = size | |
| b = out_bbox | |
| #b = box_cxcywh_to_xyxy(out_bbox) | |
| b = b * torch.tensor([img_w, img_h, img_w, img_h], dtype=torch.float32, device=b.device) | |
| return b | |
| # modified from torchvision to also return the union | |
| def box_iou(boxes1, boxes2): | |
| area1 = box_area(boxes1) | |
| area2 = box_area(boxes2) | |
| lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] | |
| rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] | |
| wh = (rb - lt).clamp(min=0) # [N,M,2] | |
| inter = wh[:, :, 0] * wh[:, :, 1] # [N,M] | |
| union = area1[:, None] + area2 - inter | |
| iou = inter / union | |
| return iou, union | |
| def generalized_box_iou(boxes1, boxes2): | |
| """ | |
| Generalized IoU from https://giou.stanford.edu/ | |
| The boxes should be in [x0, y0, x1, y1] format | |
| Returns a [N, M] pairwise matrix, where N = len(boxes1) | |
| and M = len(boxes2) | |
| """ | |
| # degenerate boxes gives inf / nan results | |
| # so do an early check | |
| assert (boxes1[:, 2:] >= boxes1[:, :2]).all() | |
| assert (boxes2[:, 2:] >= boxes2[:, :2]).all() | |
| iou, union = box_iou(boxes1, boxes2) | |
| lt = torch.min(boxes1[:, None, :2], boxes2[:, :2]) | |
| rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) | |
| wh = (rb - lt).clamp(min=0) # [N,M,2] | |
| area = wh[:, :, 0] * wh[:, :, 1] | |
| return iou - (area - union) / area | |
| def masks_to_boxes(masks): | |
| """Compute the bounding boxes around the provided masks | |
| The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions. | |
| Returns a [N, 4] tensors, with the boxes in xyxy format | |
| """ | |
| if masks.numel() == 0: | |
| return torch.zeros((0, 4), device=masks.device) | |
| h, w = masks.shape[-2:] | |
| y = torch.arange(0, h, dtype=torch.float) | |
| x = torch.arange(0, w, dtype=torch.float) | |
| y, x = torch.meshgrid(y, x) | |
| x_mask = (masks * x.unsqueeze(0)) | |
| x_max = x_mask.flatten(1).max(-1)[0] | |
| x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] | |
| y_mask = (masks * y.unsqueeze(0)) | |
| y_max = y_mask.flatten(1).max(-1)[0] | |
| y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] | |
| return torch.stack([x_min, y_min, x_max, y_max], 1) |