Transforms.py

import numpy as np
import torch
import random
import cv2


#============================================
__author__ = "Sachin Mehta"
__license__ = "MIT"
__maintainer__ = "Sachin Mehta"
#============================================


class Scale(object):
    """
    Randomly crop and resize the given PIL image with a probability of 0.5
    """
    def __init__(self, wi, he):
        '''

        :param wi: width after resizing
        :param he: height after reszing
        '''
        self.w = wi
        self.h = he

    def __call__(self, img, label):
        '''
        :param img: RGB image
        :param label: semantic label image
        :return: resized images
        '''
        #bilinear interpolation for RGB image
        img = cv2.resize(img, (self.w, self.h))
        # nearest neighbour interpolation for label image
        label = cv2.resize(label, (self.w, self.h), interpolation=cv2.INTER_NEAREST)

        return [img, label]



class RandomCropResize(object):
    """
    Randomly crop and resize the given PIL image with a probability of 0.5
    """
    def __init__(self, size):
        '''
        :param crop_area: area to be cropped (this is the max value and we select between o and crop area
        '''
        self.size = size

    def __call__(self, img, label):
        h, w = img.shape[:2]
        x1 = random.randint(0, int(w*0.1)) # 25% to 10%
        y1 = random.randint(0, int(h*0.1))

        img_crop = img[y1:h-y1, x1:w-x1]
        label_crop = label[y1:h-y1, x1:w-x1]

        img_crop = cv2.resize(img_crop, self.size)
        label_crop = cv2.resize(label_crop, self.size, interpolation=cv2.INTER_NEAREST)
        return img_crop, label_crop

class RandomCrop(object):
    '''
    This class if for random cropping
    '''
    def __init__(self, cropArea):
        '''
        :param cropArea: amount of cropping (in pixels)
        '''
        self.crop = cropArea

    def __call__(self, img, label):

        if random.random() < 0.5:
            h, w = img.shape[:2]
            img_crop = img[self.crop:h-self.crop, self.crop:w-self.crop]
            label_crop = label[self.crop:h-self.crop, self.crop:w-self.crop]
            return img_crop, label_crop
        else:
            return [img, label]



class RandomFlip(object):
    """Randomly horizontally flips the given PIL.Image with a probability of 0.5
    """

    def __call__(self, image, label):
        if random.random() < 0.5:
            x1 = 0#random.randint(0, 1) #if you want to do vertical flip, uncomment this line
            if x1 == 0:
                image = cv2.flip(image, 0) # horizontal flip
                label = cv2.flip(label, 0) # horizontal flip
            else:
                image = cv2.flip(image, 1) # veritcal flip
                label = cv2.flip(label, 1)  # veritcal flip
        return [image, label]


class Normalize(object):
    """Given mean: (R, G, B) and std: (R, G, B),
    will normalize each channel of the torch.*Tensor, i.e.
    channel = (channel - mean) / std
    """

    def __init__(self, mean, std):
        '''
        :param mean: global mean computed from dataset
        :param std: global std computed from dataset
        '''
        self.mean = mean
        self.std = std

    def __call__(self, image, label):
        image = image.astype(np.float32)
        for i in range(3):
            image[:,:,i] -= self.mean[i]
        for i in range(3):
            image[:,:, i] /= self.std[i]

        return [image, label]

class ToTensor(object):
    '''
    This class converts the data to tensor so that it can be processed by PyTorch
    '''
    def __init__(self, scale=1):
        '''
        :param scale: ESPNet-C's output is 1/8th of original image size, so set this parameter accordingly
        '''
        self.scale = scale # original images are 2048 x 1024

    def __call__(self, image, label):

        if self.scale != 1:
            h, w = label.shape[:2]
            image = cv2.resize(image, (int(w), int(h)))
            label = cv2.resize(label, (int(w/self.scale), int(h/self.scale)), interpolation=cv2.INTER_NEAREST)

        image = image.transpose((2,0,1))

        image_tensor = torch.from_numpy(image).div(255)
        label_tensor =  torch.LongTensor(np.array(label, dtype=np.int)) #torch.from_numpy(label)

        return [image_tensor, label_tensor]

class Compose(object):
    """Composes several transforms together.
    """

    def __init__(self, transforms):
        self.transforms = transforms

    def __call__(self, *args):
        for t in self.transforms:
            args = t(*args)
        return args