def test_imagepalette(): im = hopper("P") im.putpalette(ImagePalette.negative()) assert_image_equal_tofile(im.convert("RGB"), "Tests/images/palette_negative.png") im.putpalette(ImagePalette.random()) im.putpalette(ImagePalette.sepia()) assert_image_equal_tofile(im.convert("RGB"), "Tests/images/palette_sepia.png") im.putpalette(ImagePalette.wedge()) assert_image_equal_tofile(im.convert("RGB"), "Tests/images/palette_wedge.png")
def test_imagepalette(self): im = hopper("P") im.putpalette(ImagePalette.negative()) im.putpalette(ImagePalette.random()) im.putpalette(ImagePalette.sepia()) im.putpalette(ImagePalette.wedge())
def test_imagepalette(): im = lena("P") assert_no_exception(lambda: im.putpalette(ImagePalette.negative())) assert_no_exception(lambda: im.putpalette(ImagePalette.random())) assert_no_exception(lambda: im.putpalette(ImagePalette.sepia())) assert_no_exception(lambda: im.putpalette(ImagePalette.wedge()))
import os #os.environ['CUDA_HOME'] = '/opt/anaconda3/lib/python3.6/site-packages/torch/cuda' from torch.nn import functional as F from torchvision.transforms import ToTensor, Normalize, Compose import torch print(torch.cuda.is_available()) import cv2 import random from pathlib import Path # In[3]: random.seed(42) NUCLEI_PALETTE = ImagePalette.random() random.seed() # In[4]: rcParams['figure.figsize'] = 15, 15 # In[5]: from models.ternausnet2 import TernausNetV2 # print('...') # In[6]: def get_model(model_path):
def test_imagepalette(self): im = lena("P") im.putpalette(ImagePalette.negative()) im.putpalette(ImagePalette.random()) im.putpalette(ImagePalette.sepia()) im.putpalette(ImagePalette.wedge())