mirror of https://github.com/alibaba/EasyCV.git
125 lines
4.7 KiB
Python
125 lines
4.7 KiB
Python
#borrow some code from https://github.com/xuebinqin/U-2-Net
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import torch
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from torchvision import transforms
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from skimage import transform, color
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import numpy as np
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class RescaleT(object):
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def __init__(self,output_size):
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assert isinstance(output_size,(int,tuple))
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self.output_size = output_size
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def __call__(self,sample):
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imidx, image, label = sample['imidx'], sample['image'],sample['label']
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h, w = image.shape[:2]
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if isinstance(self.output_size,int):
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if h > w:
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new_h, new_w = self.output_size*h/w,self.output_size
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else:
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new_h, new_w = self.output_size,self.output_size*w/h
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else:
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new_h, new_w = self.output_size
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new_h, new_w = int(new_h), int(new_w)
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# #resize the image to new_h x new_w and convert image from range [0,255] to [0,1]
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# img = transform.resize(image,(new_h,new_w),mode='constant')
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# lbl = transform.resize(label,(new_h,new_w),mode='constant', order=0, preserve_range=True)
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img = transform.resize(image,(self.output_size,self.output_size),mode='constant')
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lbl = transform.resize(label,(self.output_size,self.output_size),mode='constant', order=0, preserve_range=True)
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return {'imidx':imidx, 'image':img,'label':lbl}
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class ToTensorLab(object):
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"""Convert ndarrays in sample to Tensors."""
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def __init__(self,flag=0):
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self.flag = flag
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def __call__(self, sample):
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imidx, image, label =sample['imidx'], sample['image'], sample['label']
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tmpLbl = np.zeros(label.shape)
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if(np.max(label)<1e-6):
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label = label
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else:
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label = label/np.max(label)
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# change the color space
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if self.flag == 2: # with rgb and Lab colors
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tmpImg = np.zeros((image.shape[0],image.shape[1],6))
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tmpImgt = np.zeros((image.shape[0],image.shape[1],3))
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if image.shape[2]==1:
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tmpImgt[:,:,0] = image[:,:,0]
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tmpImgt[:,:,1] = image[:,:,0]
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tmpImgt[:,:,2] = image[:,:,0]
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else:
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tmpImgt = image
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tmpImgtl = color.rgb2lab(tmpImgt)
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# nomalize image to range [0,1]
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tmpImg[:,:,0] = (tmpImgt[:,:,0]-np.min(tmpImgt[:,:,0]))/(np.max(tmpImgt[:,:,0])-np.min(tmpImgt[:,:,0]))
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tmpImg[:,:,1] = (tmpImgt[:,:,1]-np.min(tmpImgt[:,:,1]))/(np.max(tmpImgt[:,:,1])-np.min(tmpImgt[:,:,1]))
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tmpImg[:,:,2] = (tmpImgt[:,:,2]-np.min(tmpImgt[:,:,2]))/(np.max(tmpImgt[:,:,2])-np.min(tmpImgt[:,:,2]))
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tmpImg[:,:,3] = (tmpImgtl[:,:,0]-np.min(tmpImgtl[:,:,0]))/(np.max(tmpImgtl[:,:,0])-np.min(tmpImgtl[:,:,0]))
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tmpImg[:,:,4] = (tmpImgtl[:,:,1]-np.min(tmpImgtl[:,:,1]))/(np.max(tmpImgtl[:,:,1])-np.min(tmpImgtl[:,:,1]))
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tmpImg[:,:,5] = (tmpImgtl[:,:,2]-np.min(tmpImgtl[:,:,2]))/(np.max(tmpImgtl[:,:,2])-np.min(tmpImgtl[:,:,2]))
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# tmpImg = tmpImg/(np.max(tmpImg)-np.min(tmpImg))
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tmpImg[:,:,0] = (tmpImg[:,:,0]-np.mean(tmpImg[:,:,0]))/np.std(tmpImg[:,:,0])
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tmpImg[:,:,1] = (tmpImg[:,:,1]-np.mean(tmpImg[:,:,1]))/np.std(tmpImg[:,:,1])
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tmpImg[:,:,2] = (tmpImg[:,:,2]-np.mean(tmpImg[:,:,2]))/np.std(tmpImg[:,:,2])
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tmpImg[:,:,3] = (tmpImg[:,:,3]-np.mean(tmpImg[:,:,3]))/np.std(tmpImg[:,:,3])
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tmpImg[:,:,4] = (tmpImg[:,:,4]-np.mean(tmpImg[:,:,4]))/np.std(tmpImg[:,:,4])
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tmpImg[:,:,5] = (tmpImg[:,:,5]-np.mean(tmpImg[:,:,5]))/np.std(tmpImg[:,:,5])
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elif self.flag == 1: #with Lab color
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tmpImg = np.zeros((image.shape[0],image.shape[1],3))
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if image.shape[2]==1:
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tmpImg[:,:,0] = image[:,:,0]
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tmpImg[:,:,1] = image[:,:,0]
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tmpImg[:,:,2] = image[:,:,0]
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else:
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tmpImg = image
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tmpImg = color.rgb2lab(tmpImg)
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# tmpImg = tmpImg/(np.max(tmpImg)-np.min(tmpImg))
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tmpImg[:,:,0] = (tmpImg[:,:,0]-np.min(tmpImg[:,:,0]))/(np.max(tmpImg[:,:,0])-np.min(tmpImg[:,:,0]))
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tmpImg[:,:,1] = (tmpImg[:,:,1]-np.min(tmpImg[:,:,1]))/(np.max(tmpImg[:,:,1])-np.min(tmpImg[:,:,1]))
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tmpImg[:,:,2] = (tmpImg[:,:,2]-np.min(tmpImg[:,:,2]))/(np.max(tmpImg[:,:,2])-np.min(tmpImg[:,:,2]))
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tmpImg[:,:,0] = (tmpImg[:,:,0]-np.mean(tmpImg[:,:,0]))/np.std(tmpImg[:,:,0])
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tmpImg[:,:,1] = (tmpImg[:,:,1]-np.mean(tmpImg[:,:,1]))/np.std(tmpImg[:,:,1])
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tmpImg[:,:,2] = (tmpImg[:,:,2]-np.mean(tmpImg[:,:,2]))/np.std(tmpImg[:,:,2])
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else: # with rgb color
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tmpImg = np.zeros((image.shape[0],image.shape[1],3))
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image = image/np.max(image)
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if image.shape[2]==1:
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tmpImg[:,:,0] = (image[:,:,0]-0.485)/0.229
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tmpImg[:,:,1] = (image[:,:,0]-0.485)/0.229
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tmpImg[:,:,2] = (image[:,:,0]-0.485)/0.229
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else:
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tmpImg[:,:,0] = (image[:,:,0]-0.485)/0.229
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tmpImg[:,:,1] = (image[:,:,1]-0.456)/0.224
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tmpImg[:,:,2] = (image[:,:,2]-0.406)/0.225
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tmpLbl[:,:,0] = label[:,:,0]
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# change the r,g,b to b,r,g from [0,255] to [0,1]
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#transforms.Normalize(mean = (0.485, 0.456, 0.406), std = (0.229, 0.224, 0.225))
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tmpImg = tmpImg.transpose((2, 0, 1))
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tmpLbl = label.transpose((2, 0, 1))
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return {'imidx':torch.from_numpy(imidx), 'image': torch.from_numpy(tmpImg), 'label': torch.from_numpy(tmpLbl)}
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