import torch import torchvision from torchvision import transforms from torch import Tensor import pandas as pd from skimage import io import matplotlib.pyplot as plt from pathlib import Path # Ignore warnings import warnings warnings.filterwarnings("ignore") plt.ion() # interactive mode class MORRIS(torch.utils.data.Dataset): _storage_csv='morris.csv' _storage_jpg='jpgs' def __init__(self,root=Path(__file__).parent,transform=False): self.storage = Path(root) self.transform = transform self.index = pd.read_csv(self.storage / self._storage_csv) def __len__(self): return len(self.index) def __getitem__(self,idx): item = self.index.iloc[idx].to_dict() image = io.imread(self.storage / self._storage_jpg / self.index.iloc[idx].filename) if self.transform: image = self.transform(image) item['image'] = image return item def showitem(self,idx): plt.imshow(transforms.ToPILImage()(self.__getitem__(idx)['image'])) def _self_validate(self): """try loading each image in the dataset""" allgood=True for idx in range(len(self)): try: self[idx] except: allgood=False print(f"couldn't load {self.index.iloc[idx].filename}") if allgood: print(f"All Good! Loaded {len(self)} images.") class Deframe(object): """check for uniform color boundaries on edges of input and crop them away""" def __init__(self,aggressive=False,maxPixelFrame=20): self.alpha = 0.1 if aggressive else 0.01 self.maxPixelFrame = maxPixelFrame def _map2idx(self,frameMap): try: return frameMap.tolist().index(False) except ValueError: return self.maxPixelFrame def _Border(self,img: Tensor): """ take greyscale Tensor return left,right,top,bottom border size identified """ top = left = right = bottom = 0 # expected image variance hvar,wvar = torch.mean(torch.var(img,dim=0)), torch.mean(torch.var(img,dim=1)) # use image variance and alpha to identify too-uniform frame borders top = torch.var(img[:self.maxPixelFrame,:],dim=1) < wvar*(1+self.alpha) top = self._map2idx(top) bottom = torch.var(img[-self.maxPixelFrame:,:],dim=1) < wvar*(1+self.alpha) bottom = self._map2idx(bottom) left = torch.var(img[:,:self.maxPixelFrame],dim=0) < hvar*(1+self.alpha) left = self._map2idx(left) right = torch.var(img[:,-self.maxPixelFrame:],dim=0) < hvar*(1+self.alpha) right = self._map2idx(right) return (top,bottom,right,left) def __call__(self,img: Tensor): top,bottom,right,left = self._Border(torchvision.transforms.Grayscale()(img)[0]) height = img.shape[1]-(top+bottom) width = img.shape[2]-(left+right) print(f"t{top} b{bottom} l{left} r{right}") return torchvision.transforms.functional.crop(img,top,left,height,width)