149 lines
4.0 KiB
Python
149 lines
4.0 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import cv2
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import numpy as np
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import torchvision.transforms.functional as TF
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def get_bbox_from_mask(mask):
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h, w = mask.shape[0], mask.shape[1]
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if mask.sum() < 10:
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return 0, h, 0, w
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rows = np.any(mask, axis=1)
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cols = np.any(mask, axis=0)
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y1, y2 = np.where(rows)[0][[0, -1]]
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x1, x2 = np.where(cols)[0][[0, -1]]
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return (y1, y2, x1, x2)
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def pad_to_square(image, pad_value=255, random=False):
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H, W = image.shape[0], image.shape[1]
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if H == W:
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return image, 0, 0
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padd = abs(H - W)
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if random:
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padd_1 = int(np.random.randint(0, padd))
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else:
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padd_1 = int(padd / 2)
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padd_2 = padd - padd_1
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if H > W:
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pad_param = ((0, 0), (padd_1, padd_2), (0, 0))
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else:
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pad_param = ((padd_1, padd_2), (0, 0), (0, 0))
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# print(pad_param, pad_value)
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image = np.pad(image, pad_param, 'constant', constant_values=pad_value)
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return image, padd_1, padd_2
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def box_in_box(small_box, big_box):
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y1, y2, x1, x2 = small_box
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y1_b, _, x1_b, _ = big_box
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y1, y2, x1, x2 = y1 - y1_b, y2 - y1_b, x1 - x1_b, x2 - x1_b
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return (y1, y2, x1, x2)
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def box2squre(image, box):
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H, W = image.shape[0], image.shape[1]
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y1, y2, x1, x2 = box
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cx = (x1 + x2) // 2
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cy = (y1 + y2) // 2
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h, w = y2 - y1, x2 - x1
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if h >= w:
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x1 = cx - h // 2
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x2 = x1 + h
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else:
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y1 = cy - w // 2
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y2 = y1 + w
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x1 = max(0, x1)
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x2 = min(W, x2)
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y1 = max(0, y1)
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y2 = min(H, y2)
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return (y1, y2, x1, x2)
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def expand_bbox(mask,
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yyxx,
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ratio=1.0,
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min_crop=0,
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expand_type='center',
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to_square=False):
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y1, y2, x1, x2 = yyxx
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h = y2 - y1 + 1
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w = x2 - x1 + 1
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H, W = mask.shape[0], mask.shape[1]
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xc, yc = 0.5 * (x1 + x2), 0.5 * (y1 + y2)
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def expand(k):
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if isinstance(ratio, tuple) or isinstance(ratio, list):
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r = np.random.uniform(*ratio)
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k = k * r
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else:
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k = ratio * k
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return k
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new_h = expand(h)
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new_w = expand(w)
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new_h = max(new_h, min_crop)
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new_w = max(new_w, min_crop)
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if to_square:
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if new_w / new_h < 0.334:
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new_w = new_w + 1.0 / 3.0 * new_h
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elif new_h / new_w < 0.334:
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new_h = new_h + 1.0 / 3.0 * new_w
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if expand_type == 'center':
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x1 = max(0, int(xc - new_w * 0.5))
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x2 = min(W, int(xc + new_w * 0.5))
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y1 = max(0, int(yc - new_h * 0.5))
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y2 = min(H, int(yc + new_h * 0.5))
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else:
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x1 = max(0, min(x1,
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int(x2 - new_w * np.random.uniform(w / new_w, 1.0))))
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x2 = min(W, max(x2, x1 + new_w))
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y1 = max(0, min(y1,
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int(y2 - new_h * np.random.uniform(h / new_h, 1.0))))
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y2 = min(H, max(y2, y1 + new_h))
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return (int(y1), int(y2), int(x1), int(x2))
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def crop_back(pred, tar_image, extra_sizes, tar_box_yyxx_crop):
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H1, W1, H2, W2, pad1, pad2 = extra_sizes
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y1, y2, x1, x2 = tar_box_yyxx_crop
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pred = TF.resize(pred, (H2, W2), antialias=True)
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# if W1 == H1:
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# tar_image[:, y1:y2, x1:x2] = pred
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# return tar_image
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# if W1 < W2:
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# pad1 = int((W2 - W1) / 2)
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# pad2 = W2 - W1 - pad1
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# pred = pred[:, :,pad1:-pad2]
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# else:
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# pad1 = int((H2 - H1) / 2)
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# pad2 = H2 - H1 - pad1
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# pred = pred[:, pad1:-pad2, :]
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if W1 < W2:
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# pad width
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assert H1 == H2 and (pad1 + W1) == (W2 - pad2)
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pred = pred[:, :, pad1 + 2:(W2 - pad2 - 2)]
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tar_image[:, y1:y2, x1 + 2:x2 - 2] = pred
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elif H1 < H2:
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# pad height
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assert W1 == W2 and (pad1 + H1) == (H2 - pad2)
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pred = pred[:, pad1 + 2:(H2 - pad2 - 2), :]
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tar_image[:, y1 + 2:y2 - 2, x1:x2] = pred
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else:
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tar_image[:, y1:y2, x1:x2] = pred
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return tar_image
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def save_image(image, save_path):
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image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
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cv2.imwrite(save_path, image)
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