<filename>DN/ioUtils.py
import numpy as np
import skimage
import skimage.io
import scipy.io as sio
import skimage.transform
import sys

np.random.seed(0)

VGG_MEAN = [103.939, 116.779, 123.68]


def read_mat(path):
    return np.load(path)


def write_mat(path, m):
    np.save(path, m)


def read_ids(path):
    return [line.rstrip('\n') for line in open(path)]


class Batch_Feeder:
    def __init__(self, dataset, indices, train, batchSize, padWidth=None, padHeight=None, flip=False, keepEmpty=True):
        self._epochs_completed = 0
        self._index_in_epoch = 0
        self._dataset = dataset
        self._indices = indices
        self._train = train
        self._batchSize = batchSize
        self._padWidth = padWidth
        self._padHeight = padHeight
        self._flip = flip
        self._keepEmpty = keepEmpty

    def set_paths(self, idList=None, imageDir=None, gtDir=None, ssDir=None):
        self._paths = []

        if self._train:
            for id in idList:
                self._paths.append([id, imageDir + '/' + id + '_leftImg8bit.png',
                                        gtDir + '/' + id + '_unified_GT.mat',
                                        ssDir + '/' + id + '_unified_ss.mat'])
            self.shuffle()
        else:
            for id in idList:
                self._paths.append([id, imageDir + '/' + id + '_leftImg8bit.png',
                                    ssDir + '/' + id + '_unified_ss.mat'])

        self._numData = len(self._paths)

        if self._numData < self._batchSize:
            self._batchSize = self._numData

    def shuffle(self):
        np.random.shuffle(self._paths)

    def next_batch(self):
        idBatch = []
        imageBatch = []
        gtBatch = []
        ssBatch = []
        ssMaskBatch = []
        weightBatch = []

        if self._train:
            while(len(idBatch) < self._batchSize):
                ss = (sio.loadmat(self._paths[self._index_in_epoch][3])['mask']).astype(float)
                ssMask = ss
                ss = np.sum(ss[:,:,self._indices], 2)

                background = np.zeros(ssMask.shape[0:2] + (1,))
                ssMask = np.concatenate((ssMask[:,:,[1,2,3,4]], background, ssMask[:,:,[0,5,6,7]]), axis=-1)
                ssMask = np.argmax(ssMask, axis=-1)
                ssMask = ssMask.astype(float)
                ssMask = (ssMask - 4) * 32 # centered at 0, with 0 being background, spaced 32 apart for classes

                if ss.sum() > 0 or self._keepEmpty:
                    idBatch.append(self._paths[self._index_in_epoch][0])

                    image = (self.image_scaling(skimage.io.imread(self._paths[self._index_in_epoch][1]))).astype(float)
                    gt = (sio.loadmat(self._paths[self._index_in_epoch][2])['dir_map']).astype(float)
                    weight = (sio.loadmat(self._paths[self._index_in_epoch][2])['weight_map']).astype(float)

                    imageBatch.append(self.pad(image))
                    gtBatch.append(self.pad(gt))
                    weightBatch.append(self.pad(weight))
                    ssBatch.append(self.pad(ss))
                    ssMaskBatch.append(self.pad(ssMask))
                else:
                    pass
                    # raw_input("skipping " + self._paths[self._index_in_epoch][0])
                self._index_in_epoch += 1
                if self._index_in_epoch == self._numData:
                    self._index_in_epoch = 0
                    self.shuffle()

            imageBatch = np.array(imageBatch)
            gtBatch = np.array(gtBatch)
            ssBatch = np.array(ssBatch)
            ssMaskBatch = np.array(ssMaskBatch)
            weightBatch = np.array(weightBatch)

            if self._flip and np.random.uniform() > 0.5:
                for i in range(len(imageBatch)):
                    for j in range(3):
                        imageBatch[i,:,:,j] = np.fliplr(imageBatch[i,:,:,j])

                    weightBatch[i] = np.fliplr(weightBatch[i])
                    ssBatch[i] = np.fliplr(ssBatch[i])
                    ssMaskBatch[i] = np.fliplr(ssMaskBatch[i])

                    for j in range(2):
                        gtBatch[i,:,:,j] = np.fliplr(gtBatch[i,:,:,j])

                    gtBatch[i,:,:,0] = -1 * gtBatch[i,:,:,0]
            return imageBatch, gtBatch, weightBatch, ssBatch, ssMaskBatch, idBatch
        else:
            for example in self._paths[self._index_in_epoch:min(self._index_in_epoch+self._batchSize, self._numData)]:
                imageBatch.append(self.pad((self.image_scaling(skimage.io.imread(example[1]))).astype(float)))
                idBatch.append(example[0])
                ss = (sio.loadmat(example[2])['mask']).astype(float)
                ssMask = ss
                ss = np.sum(ss[:, :, self._indices], 2)
                background = np.zeros(ssMask.shape[0:2] + (1,))
                ssMask = np.concatenate((ssMask[:,:,[1,2,3,4]], background, ssMask[:,:,[0,5,6,7]]), axis=-1)
                ssMask = np.argmax(ssMask, axis=-1)
                ssMask = ssMask.astype(float)
                ssMask = (ssMask - 4) * 32 # centered at 0, with 0 being background, spaced 32 apart for classes
                ssBatch.append(self.pad(ss))
                ssMaskBatch.append(self.pad(ssMask))
            imageBatch = np.array(imageBatch)
            ssBatch = np.array(ssBatch)
            ssMaskBatch = np.array(ssMaskBatch)

            self._index_in_epoch += self._batchSize
            return imageBatch, ssBatch, ssMaskBatch, idBatch

    def total_samples(self):
        return self._numData

    def image_scaling(self, rgb_in):
        if rgb_in.dtype == np.float32:
            rgb_in = rgb_in*255
        elif rgb_in.dtype == np.uint8:
            rgb_in = rgb_in.astype(np.float32)

        # VGG16 was trained using opencv which reads images as BGR, but skimage reads images as RGB
        rgb_out = np.zeros(rgb_in.shape).astype(np.float32)
        rgb_out[:,:,0] = rgb_in[:,:,2] - VGG_MEAN[2]
        rgb_out[:,:,1] = rgb_in[:,:,1] - VGG_MEAN[1]
        rgb_out[:,:,2] = rgb_in[:,:,0] - VGG_MEAN[0]

        return rgb_out

    def pad(self, data):
        if self._padHeight and self._padWidth:
            if data.ndim == 3:
                npad = ((0,self._padHeight-data.shape[0]),(0,self._padWidth-data.shape[1]),(0,0))
            elif data.ndim == 2:
                npad = ((0, self._padHeight - data.shape[0]), (0, self._padWidth - data.shape[1]))
            padData = np.pad(data, npad, mode='constant', constant_values=0)

        else:
            padData = data

        return padData


<filename>DN/ioUtils.py
import numpy as np
import skimage
import skimage.io
import scipy.io as sio
import skimage.transform
import sys

np.random.seed(0)

VGG_MEAN = [103.939, 116.779, 123.68]


def read_mat(path):
    return np.load(path)


def write_mat(path, m):
    np.save(path, m)


def read_ids(path):
    return [line.rstrip('\n') for line in open(path)]


class Batch_Feeder:
    def __init__(self, dataset, indices, train, batchSize, padWidth=None, padHeight=None, flip=False, keepEmpty=True):
        self._epochs_completed = 0
        self._index_in_epoch = 0
        self._dataset = dataset
        self._indices = indices
        self._train = train
        self._batchSize = batchSize
        self._padWidth = padWidth
        self._padHeight = padHeight
        self._flip = flip
        self._keepEmpty = keepEmpty

    def set_paths(self, idList=None, imageDir=None, gtDir=None, ssDir=None):
        self._paths = []

        if self._train:
            for id in idList:
                self._paths.append([id, imageDir + '/' + id + '_leftImg8bit.png',
                                        gtDir + '/' + id + '_unified_GT.mat',
                                        ssDir + '/' + id + '_unified_ss.mat'])
            self.shuffle()
        else:
            for id in idList:
                self._paths.append([id, imageDir + '/' + id + '_leftImg8bit.png',
                                    ssDir + '/' + id + '_unified_ss.mat'])

        self._numData = len(self._paths)

        if self._numData < self._batchSize:
            self._batchSize = self._numData

    def shuffle(self):
        np.random.shuffle(self._paths)

    def next_batch(self):
        idBatch = []
        imageBatch = []
        gtBatch = []
        ssBatch = []
        ssMaskBatch = []
        weightBatch = []

        if self._train:
            while(len(idBatch) < self._batchSize):
                ss = (sio.loadmat(self._paths[self._index_in_epoch][3])['mask']).astype(float)
                ssMask = ss
                ss = np.sum(ss[:,:,self._indices], 2)

                background = np.zeros(ssMask.shape[0:2] + (1,))
                ssMask = np.concatenate((ssMask[:,:,[1,2,3,4]], background, ssMask[:,:,[0,5,6,7]]), axis=-1)
                ssMask = np.argmax(ssMask, axis=-1)
                ssMask = ssMask.astype(float)
                ssMask = (ssMask - 4) * 32 # centered at 0, with 0 being background, spaced 32 apart for classes

                if ss.sum() > 0 or self._keepEmpty:
                    idBatch.append(self._paths[self._index_in_epoch][0])

                    image = (self.image_scaling(skimage.io.imread(self._paths[self._index_in_epoch][1]))).astype(float)
                    gt = (sio.loadmat(self._paths[self._index_in_epoch][2])['dir_map']).astype(float)
                    weight = (sio.loadmat(self._paths[self._index_in_epoch][2])['weight_map']).astype(float)

                    imageBatch.append(self.pad(image))
                    gtBatch.append(self.pad(gt))
                    weightBatch.append(self.pad(weight))
                    ssBatch.append(self.pad(ss))
                    ssMaskBatch.append(self.pad(ssMask))
                else:
                    pass
                    # raw_input("skipping " + self._paths[self._index_in_epoch][0])
                self._index_in_epoch += 1
                if self._index_in_epoch == self._numData:
                    self._index_in_epoch = 0
                    self.shuffle()

            imageBatch = np.array(imageBatch)
            gtBatch = np.array(gtBatch)
            ssBatch = np.array(ssBatch)
            ssMaskBatch = np.array(ssMaskBatch)
            weightBatch = np.array(weightBatch)

            if self._flip and np.random.uniform() > 0.5:
                for i in range(len(imageBatch)):
                    for j in range(3):
                        imageBatch[i,:,:,j] = np.fliplr(imageBatch[i,:,:,j])

                    weightBatch[i] = np.fliplr(weightBatch[i])
                    ssBatch[i] = np.fliplr(ssBatch[i])
                    ssMaskBatch[i] = np.fliplr(ssMaskBatch[i])

                    for j in range(2):
                        gtBatch[i,:,:,j] = np.fliplr(gtBatch[i,:,:,j])

                    gtBatch[i,:,:,0] = -1 * gtBatch[i,:,:,0]
            return imageBatch, gtBatch, weightBatch, ssBatch, ssMaskBatch, idBatch
        else:
            for example in self._paths[self._index_in_epoch:min(self._index_in_epoch+self._batchSize, self._numData)]:
                imageBatch.append(self.pad((self.image_scaling(skimage.io.imread(example[1]))).astype(float)))
                idBatch.append(example[0])
                ss = (sio.loadmat(example[2])['mask']).astype(float)
                ssMask = ss
                ss = np.sum(ss[:, :, self._indices], 2)
                background = np.zeros(ssMask.shape[0:2] + (1,))
                ssMask = np.concatenate((ssMask[:,:,[1,2,3,4]], background, ssMask[:,:,[0,5,6,7]]), axis=-1)
                ssMask = np.argmax(ssMask, axis=-1)
                ssMask = ssMask.astype(float)
                ssMask = (ssMask - 4) * 32 # centered at 0, with 0 being background, spaced 32 apart for classes
                ssBatch.append(self.pad(ss))
                ssMaskBatch.append(self.pad(ssMask))
            imageBatch = np.array(imageBatch)
            ssBatch = np.array(ssBatch)
            ssMaskBatch = np.array(ssMaskBatch)

            self._index_in_epoch += self._batchSize
            return imageBatch, ssBatch, ssMaskBatch, idBatch

    def total_samples(self):
        return self._numData

    def image_scaling(self, rgb_in):
        if rgb_in.dtype == np.float32:
            rgb_in = rgb_in*255
        elif rgb_in.dtype == np.uint8:
            rgb_in = rgb_in.astype(np.float32)

        # VGG16 was trained using opencv which reads images as BGR, but skimage reads images as RGB
        rgb_out = np.zeros(rgb_in.shape).astype(np.float32)
        rgb_out[:,:,0] = rgb_in[:,:,2] - VGG_MEAN[2]
        rgb_out[:,:,1] = rgb_in[:,:,1] - VGG_MEAN[1]
        rgb_out[:,:,2] = rgb_in[:,:,0] - VGG_MEAN[0]

        return rgb_out

    def pad(self, data):
        if self._padHeight and self._padWidth:
            if data.ndim == 3:
                npad = ((0,self._padHeight-data.shape[0]),(0,self._padWidth-data.shape[1]),(0,0))
            elif data.ndim == 2:
                npad = ((0, self._padHeight - data.shape[0]), (0, self._padWidth - data.shape[1]))
            padData = np.pad(data, npad, mode='constant', constant_values=0)

        else:
            padData = data

        return padData


# centered at 0, with 0 being background, spaced 32 apart for classes # raw_input("skipping " + self._paths[self._index_in_epoch][0]) # centered at 0, with 0 being background, spaced 32 apart for classes # VGG16 was trained using opencv which reads images as BGR, but skimage reads images as RGB