from iaugmentor import IAugmentor
from sklearn.preprocessing import LabelEncoder
from imutils import paths
import os
import cv2
from utils.aspectawarepreprocessor import AspectAwarePreprocessor
from utils.hdf5datasetwriter import HDF5DatasetWriterClassification
import xml.etree.ElementTree as ET
import progressbar
# This class serves to generate images for a classification
# problem where all the images are organized by folders
# distributed by labels. Example:
# - Folder
# |- cats
#    |- image1.jpg
#    |- image2.jpg
#    |- ...
# |- dogs
#    |- image1.jpg
#    |- image2.jpg
#    |- ...
class HDF5LinearLocalizationAugmentor:

    # All images must have same width and height
    def __init__(self,inputPath,parameters):
        IAugmentor.__init__(self)
        self.inputPath = inputPath
        # output path represents the h5py file where dataset will be stored
        # output path represents the h5py file where dataset will be stored
        if parameters["outputPath"]:
            self.outputPath = parameters["outputPath"]
        else:
            raise ValueError("You should provide an output path in the parameters")

        self.generators = []
        if parameters["width"]:
            self.width = parameters["width"]
        else:
            raise ValueError("You should provide a width in the parameters")
        if parameters["height"]:
            self.width = parameters["height"]
        else:
            raise ValueError("You should provide a height in the parameters")
        self.aw = AspectAwarePreprocessor(self.width,self.height)

    def addGenerator(self, generator):
        self.generators.append(generator)

    def readImagesAndAnnotations(self):
        self.imagePaths = list(paths.list_images(self.inputPath))


    def applyAugmentation(self):
        self.readImagesAndAnnotations()
        le = LabelEncoder()
        writer = HDF5DatasetWriterClassification((len(self.imagePaths)*len(self.generators),self.width,self.height,3),
                                   self.outputPath)
        # We need to define this function outside to work in parallel.
        writer.storeClassLabels(le.classes_)
        widgets = ["Processing images: ", progressbar.Percentage(), " ",
                   progressbar.Bar(), " ", progressbar.ETA()]

        pbar = progressbar.ProgressBar(maxval=len(self.imagePaths),
                                   widgets=widgets).start()
        for i_and_imagePath in enumerate(self.imagePaths):
            (i, imagePath) = i_and_imagePath
            image = cv2.imread(imagePath)
            name = imagePath.split(os.path.sep)[-1]
            labelPath = '/'.join(imagePath.split(os.path.sep)[:-1]) + "/" + name[0:name.rfind(".")] + ".xml"
            tree = ET.parse(labelPath)
            root = tree.getroot()
            objects = root.findall('object')
            if (len(objects) != 1):
                raise Exception("Since this is a localization problem, the xml should only contain one object")
            object = objects[0]
            category = object.find('name').text
            bndbox = object.find('bndbox')
            x = int(bndbox.find('xmin').text)
            y = int(bndbox.find('ymin').text)
            w = int(bndbox.find('ymax').text) - y
            h = int(bndbox.find('xmax').text) - x

            for (j, generator) in enumerate(self.generators):
                (newimage, box) = generator.applyForLocalization(image, (category, (x, y, w, h)))
                writer.add([newimage],[0,box[1][0],box[1][1],box[1][2],box[1][3]])
            pbar.update(i)
        pbar.finish()
        writer.close()

# # Example
# augmentor = HDF5LinearClassificationAugmentor(
#     "/home/joheras/datasets/cats_and_dogs_small/train/",
#     "/home/joheras/datasets/cats_and_dogs_small/database.hdf5"
# )
#
# from techniques.averageBlurringAugmentationTechnique import averageBlurringAugmentationTechnique
# from techniques.bilateralBlurringAugmentationTechnique import bilateralBlurringAugmentationTechnique
# from techniques.gaussianNoiseAugmentationTechnique import gaussianNoiseAugmentationTechnique
# from techniques.rotateAugmentationTechnique import rotateAugmentationTechnique
# from techniques.flipAugmentationTechnique import flipAugmentationTechnique
# from techniques.noneAugmentationTechnique import noneAugmentationTechnique
# from generator import Generator
# import time
# augmentor.addGenerator(Generator(noneAugmentationTechnique()))
# augmentor.addGenerator(Generator(averageBlurringAugmentationTechnique()))
# augmentor.addGenerator(Generator(bilateralBlurringAugmentationTechnique()))
# augmentor.addGenerator(Generator(gaussianNoiseAugmentationTechnique()))
# augmentor.addGenerator(Generator(rotateAugmentationTechnique()))
# augmentor.addGenerator(Generator(flipAugmentationTechnique()))
# start = time.time()
# augmentor.applyAugmentation()
# end = time.time()
# print(end - start)
