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)