import cv2 import numpy as np import torch import torch.nn.functional as F 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 """ 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): """ 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 """ 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): """ 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 """ 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 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] """ # Rescale coordinates (xyxy) from im1_shape to im0_shape if ratio_pad is None: # calculate from im0_shape gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding else: pad = ratio_pad[1] top, left = int(pad[1]), int(pad[0]) # y, x bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0]) if len(masks.shape) < 2: raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}') masks = masks[top:bottom, left:right] # masks = masks.permute(2, 0, 1).contiguous() # masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0] # masks = masks.permute(1, 2, 0).contiguous() masks = cv2.resize(masks, (im0_shape[1], im0_shape[0])) if len(masks.shape) == 2: masks = masks[:, :, None] return masks def mask_iou(mask1, mask2, eps=1e-7): """ mask1: [N, n] m1 means number of predicted objects mask2: [M, n] m2 means number of gt objects Note: n means image_w x image_h return: masks iou, [N, M] """ intersection = torch.matmul(mask1, mask2.t()).clamp(0) union = (mask1.sum(1)[:, None] + mask2.sum(1)[None]) - intersection # (area1 + area2) - intersection return intersection / (union + eps) def masks_iou(mask1, mask2, eps=1e-7): """ mask1: [N, n] m1 means number of predicted objects mask2: [N, n] m2 means number of gt objects Note: n means image_w x image_h return: masks iou, (N, ) """ intersection = (mask1 * mask2).sum(1).clamp(0) # (N, ) union = (mask1.sum(1) + mask2.sum(1))[None] - intersection # (area1 + area2) - intersection return intersection / (union + eps) def masks2segments(masks, strategy='largest'): # Convert masks(n,160,160) into segments(n,xy) 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