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| # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license | |
| """Image augmentation functions.""" | |
| import math | |
| import random | |
| import cv2 | |
| import numpy as np | |
| from ..augmentations import box_candidates | |
| from ..general import resample_segments, segment2box | |
| def mixup(im, labels, segments, im2, labels2, segments2): | |
| # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf | |
| r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0 | |
| im = (im * r + im2 * (1 - r)).astype(np.uint8) | |
| labels = np.concatenate((labels, labels2), 0) | |
| segments = np.concatenate((segments, segments2), 0) | |
| return im, labels, segments | |
| def random_perspective( | |
| im, targets=(), segments=(), degrees=10, translate=0.1, scale=0.1, shear=10, perspective=0.0, border=(0, 0) | |
| ): | |
| # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) | |
| # targets = [cls, xyxy] | |
| height = im.shape[0] + border[0] * 2 # shape(h,w,c) | |
| width = im.shape[1] + border[1] * 2 | |
| # Center | |
| C = np.eye(3) | |
| C[0, 2] = -im.shape[1] / 2 # x translation (pixels) | |
| C[1, 2] = -im.shape[0] / 2 # y translation (pixels) | |
| # Perspective | |
| P = np.eye(3) | |
| P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) | |
| P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) | |
| # Rotation and Scale | |
| R = np.eye(3) | |
| a = random.uniform(-degrees, degrees) | |
| # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations | |
| s = random.uniform(1 - scale, 1 + scale) | |
| # s = 2 ** random.uniform(-scale, scale) | |
| R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) | |
| # Shear | |
| S = np.eye(3) | |
| S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) | |
| S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) | |
| # Translation | |
| T = np.eye(3) | |
| T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) | |
| T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) | |
| # Combined rotation matrix | |
| M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT | |
| if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed | |
| if perspective: | |
| im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114)) | |
| else: # affine | |
| im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) | |
| # Visualize | |
| # import matplotlib.pyplot as plt | |
| # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() | |
| # ax[0].imshow(im[:, :, ::-1]) # base | |
| # ax[1].imshow(im2[:, :, ::-1]) # warped | |
| # Transform label coordinates | |
| n = len(targets) | |
| new_segments = [] | |
| if n: | |
| new = np.zeros((n, 4)) | |
| segments = resample_segments(segments) # upsample | |
| for i, segment in enumerate(segments): | |
| xy = np.ones((len(segment), 3)) | |
| xy[:, :2] = segment | |
| xy = xy @ M.T # transform | |
| xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine | |
| # clip | |
| new[i] = segment2box(xy, width, height) | |
| new_segments.append(xy) | |
| # filter candidates | |
| i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01) | |
| targets = targets[i] | |
| targets[:, 1:5] = new[i] | |
| new_segments = np.array(new_segments)[i] | |
| return im, targets, new_segments | |