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import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from torchvision import models, transforms
from PIL import Image
from matplotlib import pyplot as plt
def get_index_to_class_mapping():
indices = np.arange(1, 16)
class_names = ['Atelectasis', 'Cardiomegaly', 'Consolidation', 'Edema', 'Effusion', 'Emphysema', 'Fibrosis',
'Hernia', 'Infiltration', 'Mass', 'No Finding', 'Nodule', 'Pleural Thickening', 'Pneumonia',
'Pneumothorax']
mapping = dict(zip(indices, class_names))
return mapping
def load_classifier_from_file(ckpt_file, location='cpu'):
checkpoint = torch.load(ckpt_file, map_location=location)
model = checkpoint['model']
return model
def transform_pil_to_tensor(pil_image, device='cpu'):
# ImageNet mean and std
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
# transformation to be applied
transform = transforms.Compose([
transforms.Resize(224),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
tensor = transform(pil_image).to(device)
return tensor
def noise_process(numpy_image, steps=10):
noisy_image_list = []
noisy_image = numpy_image
noisy_image_list.append(noisy_image)
for step in range(steps):
noise = 255 * np.random.normal(0, (step+1) * 0.1, numpy_image.size).reshape(numpy_image.shape)
noisy_image = noisy_image + noise
noisy_image_list.append(noisy_image)
plt.imshow(noisy_image, cmap='gray')
plt.show()
return noisy_image_list
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