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=.1, scale=.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