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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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import torch |
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import torchvision.transforms as T |
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import torchvision.transforms.functional as TF |
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from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box, xywhn2xyxy |
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from utils.metrics import bbox_ioa |
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IMAGENET_MEAN = 0.485, 0.456, 0.406 |
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IMAGENET_STD = 0.229, 0.224, 0.225 |
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class Albumentations: |
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def __init__(self, size=640): |
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self.transform = None |
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prefix = colorstr('albumentations: ') |
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try: |
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import albumentations as A |
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check_version(A.__version__, '1.0.3', hard=True) |
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T = [ |
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A.RandomResizedCrop(height=size, width=size, scale=(0.8, 1.0), ratio=(0.9, 1.11), p=0.0), |
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A.Blur(p=0.01), |
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A.MedianBlur(p=0.01), |
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A.ToGray(p=0.01), |
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A.CLAHE(p=0.01), |
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A.RandomBrightnessContrast(p=0.0), |
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A.RandomGamma(p=0.0), |
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A.ImageCompression(quality_lower=75, p=0.0)] |
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self.transform = A.Compose(T, bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels'])) |
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LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p)) |
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except ImportError: |
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pass |
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except Exception as e: |
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LOGGER.info(f'{prefix}{e}') |
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def __call__(self, im, labels, p=1.0): |
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if self.transform and random.random() < p: |
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new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) |
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im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])]) |
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return im, labels |
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def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False): |
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return TF.normalize(x, mean, std, inplace=inplace) |
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def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD): |
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for i in range(3): |
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x[:, i] = x[:, i] * std[i] + mean[i] |
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return x |
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def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5): |
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if hgain or sgain or vgain: |
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r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 |
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hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) |
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dtype = im.dtype |
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x = np.arange(0, 256, dtype=r.dtype) |
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lut_hue = ((x * r[0]) % 180).astype(dtype) |
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lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) |
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lut_val = np.clip(x * r[2], 0, 255).astype(dtype) |
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im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) |
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cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) |
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def hist_equalize(im, clahe=True, bgr=False): |
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yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) |
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if clahe: |
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c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) |
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yuv[:, :, 0] = c.apply(yuv[:, :, 0]) |
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else: |
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yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) |
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return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) |
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def replicate(im, labels): |
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h, w = im.shape[:2] |
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boxes = labels[:, 1:].astype(int) |
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x1, y1, x2, y2 = boxes.T |
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s = ((x2 - x1) + (y2 - y1)) / 2 |
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for i in s.argsort()[:round(s.size * 0.5)]: |
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x1b, y1b, x2b, y2b = boxes[i] |
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bh, bw = y2b - y1b, x2b - x1b |
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yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) |
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x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh] |
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im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b] |
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labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0) |
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return im, labels |
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def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32): |
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shape = im.shape[:2] |
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if isinstance(new_shape, int): |
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new_shape = (new_shape, new_shape) |
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r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) |
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if not scaleup: |
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r = min(r, 1.0) |
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ratio = r, r |
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new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) |
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dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] |
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if auto: |
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dw, dh = np.mod(dw, stride), np.mod(dh, stride) |
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elif scaleFill: |
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dw, dh = 0.0, 0.0 |
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new_unpad = (new_shape[1], new_shape[0]) |
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ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] |
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dw /= 2 |
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dh /= 2 |
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if shape[::-1] != new_unpad: |
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im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR) |
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top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) |
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left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) |
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im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) |
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return im, ratio, (dw, dh) |
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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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height = im.shape[0] + border[0] * 2 |
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width = im.shape[1] + border[1] * 2 |
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C = np.eye(3) |
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C[0, 2] = -im.shape[1] / 2 |
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C[1, 2] = -im.shape[0] / 2 |
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P = np.eye(3) |
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P[2, 0] = random.uniform(-perspective, perspective) |
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P[2, 1] = random.uniform(-perspective, perspective) |
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R = np.eye(3) |
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a = random.uniform(-degrees, degrees) |
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s = random.uniform(1 - scale, 1 + scale) |
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R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) |
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S = np.eye(3) |
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S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) |
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S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) |
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T = np.eye(3) |
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T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width |
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T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height |
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M = T @ S @ R @ P @ C |
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if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): |
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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: |
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im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) |
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n = len(targets) |
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if n: |
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use_segments = any(x.any() for x in segments) and len(segments) == n |
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new = np.zeros((n, 4)) |
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if use_segments: |
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segments = resample_segments(segments) |
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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 |
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xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] |
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new[i] = segment2box(xy, width, height) |
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else: |
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xy = np.ones((n * 4, 3)) |
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xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) |
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xy = xy @ M.T |
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xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) |
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x = xy[:, [0, 2, 4, 6]] |
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y = xy[:, [1, 3, 5, 7]] |
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new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T |
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new[:, [0, 2]] = new[:, [0, 2]].clip(0, width) |
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new[:, [1, 3]] = new[:, [1, 3]].clip(0, height) |
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i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10) |
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targets = targets[i] |
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targets[:, 1:5] = new[i] |
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return im, targets |
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def copy_paste(im, labels, segments, p=0.5): |
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n = len(segments) |
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if p and n: |
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h, w, c = im.shape |
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im_new = np.zeros(im.shape, np.uint8) |
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for j in random.sample(range(n), k=round(p * n)): |
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l, s = labels[j], segments[j] |
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box = w - l[3], l[2], w - l[1], l[4] |
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ioa = bbox_ioa(box, labels[:, 1:5]) |
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if (ioa < 0.30).all(): |
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labels = np.concatenate((labels, [[l[0], *box]]), 0) |
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segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1)) |
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cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (1, 1, 1), cv2.FILLED) |
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result = cv2.flip(im, 1) |
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i = cv2.flip(im_new, 1).astype(bool) |
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im[i] = result[i] |
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return im, labels, segments |
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def cutout(im, labels, p=0.5): |
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if random.random() < p: |
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h, w = im.shape[:2] |
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scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 |
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for s in scales: |
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mask_h = random.randint(1, int(h * s)) |
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mask_w = random.randint(1, int(w * s)) |
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xmin = max(0, random.randint(0, w) - mask_w // 2) |
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ymin = max(0, random.randint(0, h) - mask_h // 2) |
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xmax = min(w, xmin + mask_w) |
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ymax = min(h, ymin + mask_h) |
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im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] |
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if len(labels) and s > 0.03: |
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box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) |
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ioa = bbox_ioa(box, xywhn2xyxy(labels[:, 1:5], w, h)) |
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labels = labels[ioa < 0.60] |
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return labels |
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def mixup(im, labels, im2, labels2): |
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r = np.random.beta(32.0, 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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return im, labels |
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def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16): |
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w1, h1 = box1[2] - box1[0], box1[3] - box1[1] |
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w2, h2 = box2[2] - box2[0], box2[3] - box2[1] |
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ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) |
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return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) |
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def classify_albumentations( |
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augment=True, |
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size=224, |
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scale=(0.08, 1.0), |
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ratio=(0.75, 1.0 / 0.75), |
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hflip=0.5, |
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vflip=0.0, |
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jitter=0.4, |
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mean=IMAGENET_MEAN, |
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std=IMAGENET_STD, |
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auto_aug=False): |
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prefix = colorstr('albumentations: ') |
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try: |
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import albumentations as A |
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from albumentations.pytorch import ToTensorV2 |
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check_version(A.__version__, '1.0.3', hard=True) |
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if augment: |
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T = [A.RandomResizedCrop(height=size, width=size, scale=scale, ratio=ratio)] |
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if auto_aug: |
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LOGGER.info(f'{prefix}auto augmentations are currently not supported') |
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else: |
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if hflip > 0: |
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T += [A.HorizontalFlip(p=hflip)] |
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if vflip > 0: |
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T += [A.VerticalFlip(p=vflip)] |
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if jitter > 0: |
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color_jitter = (float(jitter),) * 3 |
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T += [A.ColorJitter(*color_jitter, 0)] |
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else: |
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T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)] |
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T += [A.Normalize(mean=mean, std=std), ToTensorV2()] |
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LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p)) |
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return A.Compose(T) |
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except ImportError: |
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LOGGER.warning(f'{prefix}โ ๏ธ not found, install with `pip install albumentations` (recommended)') |
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except Exception as e: |
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LOGGER.info(f'{prefix}{e}') |
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def classify_transforms(size=224): |
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assert isinstance(size, int), f'ERROR: classify_transforms size {size} must be integer, not (list, tuple)' |
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return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)]) |
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class LetterBox: |
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def __init__(self, size=(640, 640), auto=False, stride=32): |
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super().__init__() |
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self.h, self.w = (size, size) if isinstance(size, int) else size |
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self.auto = auto |
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self.stride = stride |
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def __call__(self, im): |
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imh, imw = im.shape[:2] |
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r = min(self.h / imh, self.w / imw) |
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h, w = round(imh * r), round(imw * r) |
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hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else self.h, self.w |
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top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1) |
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im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype) |
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im_out[top:top + h, left:left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR) |
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return im_out |
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class CenterCrop: |
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def __init__(self, size=640): |
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super().__init__() |
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self.h, self.w = (size, size) if isinstance(size, int) else size |
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def __call__(self, im): |
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imh, imw = im.shape[:2] |
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m = min(imh, imw) |
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top, left = (imh - m) // 2, (imw - m) // 2 |
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return cv2.resize(im[top:top + m, left:left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR) |
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class ToTensor: |
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def __init__(self, half=False): |
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super().__init__() |
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self.half = half |
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def __call__(self, im): |
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im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1]) |
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im = torch.from_numpy(im) |
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im = im.half() if self.half else im.float() |
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im /= 255.0 |
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return im |
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