105 lines
3.7 KiB
Python
105 lines
3.7 KiB
Python
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Image augmentation functions
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"""
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import math
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import random
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import cv2
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import numpy as np
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from ..augmentations import box_candidates
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from ..general import resample_segments, segment2box
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def mixup(im, labels, segments, im2, labels2, segments2):
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# Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
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r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
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im = (im * r + im2 * (1 - r)).astype(np.uint8)
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labels = np.concatenate((labels, labels2), 0)
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segments = np.concatenate((segments, segments2), 0)
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return im, labels, segments
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def random_perspective(im,
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targets=(),
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segments=(),
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degrees=10,
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translate=.1,
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scale=.1,
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shear=10,
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perspective=0.0,
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border=(0, 0)):
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# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
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# targets = [cls, xyxy]
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height = im.shape[0] + border[0] * 2 # shape(h,w,c)
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width = im.shape[1] + border[1] * 2
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# Center
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C = np.eye(3)
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C[0, 2] = -im.shape[1] / 2 # x translation (pixels)
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C[1, 2] = -im.shape[0] / 2 # y translation (pixels)
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# Perspective
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P = np.eye(3)
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P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)
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P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)
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# Rotation and Scale
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R = np.eye(3)
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a = random.uniform(-degrees, degrees)
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# a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
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s = random.uniform(1 - scale, 1 + scale)
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# s = 2 ** random.uniform(-scale, scale)
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R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
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# Shear
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S = np.eye(3)
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S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
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S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
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# Translation
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T = np.eye(3)
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T[0, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * width) # x translation (pixels)
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T[1, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * height) # y translation (pixels)
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# Combined rotation matrix
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M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
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if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
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if perspective:
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im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))
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else: # affine
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im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
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# Visualize
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# import matplotlib.pyplot as plt
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# ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
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# ax[0].imshow(im[:, :, ::-1]) # base
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# ax[1].imshow(im2[:, :, ::-1]) # warped
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# Transform label coordinates
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n = len(targets)
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new_segments = []
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if n:
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new = np.zeros((n, 4))
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segments = resample_segments(segments) # upsample
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for i, segment in enumerate(segments):
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xy = np.ones((len(segment), 3))
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xy[:, :2] = segment
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xy = xy @ M.T # transform
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xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]) # perspective rescale or affine
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# clip
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new[i] = segment2box(xy, width, height)
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new_segments.append(xy)
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# filter candidates
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i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01)
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targets = targets[i]
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targets[:, 1:5] = new[i]
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new_segments = np.array(new_segments)[i]
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return im, targets, new_segments
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