from PIL import Image
import pytest
import numpy as np
import torch
import torchvision
from torchvision import transforms as trans
from test_cv2_utils import image_similarity_vectors_via_cos
import torchvision_npu
@pytest.mark.parametrize(
["img_path", "transforms"],
[
("./test/Data/fish/fish_11.jpg",
[trans.ToTensor(), trans.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),
("./test/Data/fish/fish_22.jpg",
[trans.RandomResizedCrop(224), trans.RandomHorizontalFlip()]),
("./test/Data/fish/fish_33.jpg",
[trans.Resize(224), trans.CenterCrop(224)]),
("./test/Data/fish/fish_44.jpg",
[trans.RandomRotation(60), trans.GaussianBlur(1, 10)]),
("./test/Data/fish/fish_55.jpg",
[trans.RandomAdjustSharpness(1, 0.7), trans.RandomPerspective(0.5)]),
],
)
def test_compose(img_path, transforms):
pil_img = Image.open(img_path)
torchvision.set_image_backend("PIL")
torch.manual_seed(10)
pil_compose = trans.Compose(transforms=transforms)(
pil_img)
torchvision.set_image_backend("cv2")
torch.manual_seed(10)
cv2_img = np.asarray(pil_img)
cv2_compose = trans.Compose(transforms=transforms)(cv2_img)
if isinstance(pil_compose, torch.Tensor):
pil_compose = trans.ToPILImage()(pil_compose)
cv2_compose = trans.ToPILImage()(cv2_compose)
assert pil_compose.size == cv2_compose.size
assert (np.array(pil_compose) == np.array(cv2_compose)).all()
else:
assert isinstance(pil_compose, Image.Image) and isinstance(cv2_compose, np.ndarray)
assert pil_compose.size == cv2_compose.shape[:2][::-1]
assert image_similarity_vectors_via_cos(pil_compose, Image.fromarray(cv2_compose))
pil_img.close()