def test_random_sized_crop_size(): image = np.ones((100, 100, 3)) bboxes = [[0.2, 0.3, 0.6, 0.8], [0.3, 0.4, 0.7, 0.9, 99]] aug = RandomSizedCrop((70, 90), 50, 50, p=1.) transformed = aug(image=image, bboxes=bboxes) assert transformed['image'].shape == (50, 50, 3) assert len(bboxes) == len(transformed['bboxes'])
def test_random_sized_crop_size(): image = np.ones((100, 100, 3)) keypoints = [(0.2, 0.3, 0.6, 0.8), (0.3, 0.4, 0.7, 0.9, 99)] aug = RandomSizedCrop(min_max_height=(70, 90), height=50, width=50, p=1.0) transformed = aug(image=image, keypoints=keypoints) assert transformed["image"].shape == (50, 50, 3) assert len(keypoints) == len(transformed["keypoints"])
def test_random_sized_crop_size(): image = np.ones((100, 100, 3)) bboxes = [[0.2, 0.3, 0.6, 0.8], [0.3, 0.4, 0.7, 0.9, 99]] aug = RandomSizedCrop(min_max_height=(70, 90), height=50, width=50, p=1.0) transformed = aug(image=image, bboxes=bboxes) assert transformed["image"].shape == (50, 50, 3) assert len(bboxes) == len(transformed["bboxes"])
import os from PIL import Image from torch.utils import data import numpy as np from torchvision import transforms as T import cv2 ##导入albumentations来做图像增强 input_size = 224 import albumentations from albumentations.augmentations.transforms import Resize, RandomSizedCrop, ShiftScaleRotate, HorizontalFlip, Normalize, RandomBrightnessContrast, MotionBlur, Blur, GaussNoise, JpegCompression train_transform = albumentations.Compose([ RandomSizedCrop(min_max_height=(input_size//3,input_size//3),height=input_size,width=input_size), ShiftScaleRotate(p=0.3, scale_limit=0.25, border_mode=1, rotate_limit=25), HorizontalFlip(p=0.2), RandomBrightnessContrast(p=0.3, brightness_limit=0.25, contrast_limit=0.5), MotionBlur(p=.2), GaussNoise(p=.2), JpegCompression(p=.2, quality_lower=50), Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) val_transform = albumentations.Compose([ Resize(input_size,input_size), Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) class MyDataset(data.Dataset): def __init__(self,root,transforms = None,is_train=True):
def __init__(self, **kwargs): self.obj = RandomSizedCrop(**kwargs)