161 lines
5.7 KiB
Python
161 lines
5.7 KiB
Python
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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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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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protos: [mask_dim, mask_h, mask_w]
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masks_in: [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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def process_mask_native(protos, masks_in, bboxes, shape):
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"""
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Crop after upsample.
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protos: [mask_dim, mask_h, mask_w]
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masks_in: [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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gain = min(mh / shape[0], mw / shape[1]) # gain = old / new
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pad = (mw - shape[1] * gain) / 2, (mh - shape[0] * gain) / 2 # wh padding
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top, left = int(pad[1]), int(pad[0]) # y, x
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bottom, right = int(mh - pad[1]), int(mw - pad[0])
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masks = masks[:, top:bottom, left:right]
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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 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 mask_iou(mask1, mask2, eps=1e-7):
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"""
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mask1: [N, n] m1 means number of predicted objects
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mask2: [M, n] m2 means number of gt objects
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Note: n means image_w x image_h
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return: masks iou, [N, M]
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"""
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intersection = torch.matmul(mask1, mask2.t()).clamp(0)
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union = (mask1.sum(1)[:, None] + mask2.sum(1)[None]) - intersection # (area1 + area2) - intersection
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return intersection / (union + eps)
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def masks_iou(mask1, mask2, eps=1e-7):
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"""
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mask1: [N, n] m1 means number of predicted objects
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mask2: [N, n] m2 means number of gt objects
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Note: n means image_w x image_h
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return: masks iou, (N, )
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"""
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intersection = (mask1 * mask2).sum(1).clamp(0) # (N, )
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union = (mask1.sum(1) + mask2.sum(1))[None] - intersection # (area1 + area2) - intersection
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return intersection / (union + eps)
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def masks2segments(masks, strategy='largest'):
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# Convert masks(n,160,160) into segments(n,xy)
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segments = []
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for x in masks.int().cpu().numpy().astype('uint8'):
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c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]
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if c:
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if strategy == 'concat': # concatenate all segments
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c = np.concatenate([x.reshape(-1, 2) for x in c])
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elif strategy == 'largest': # select largest segment
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c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2)
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else:
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c = np.zeros((0, 2)) # no segments found
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segments.append(c.astype('float32'))
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return segments
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