doodle/create_encoder_dataset.py at master · luketzh/doodle · GitHub

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import argparse
import h5py
import imageio
import numpy as np
import glob
import os
import matplotlib.pyplot as plt
import random

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--object_type", required=False, type=str, default="*",
        help="The name of the object to be created (cat, dog, etc).")
    parser.add_argument("--flipping_augment", required=False, type=bool, default=False,
        help="Add flipping across y axis to all images for augmentation")
    parser.add_argument("--num_pairs", required=True, type=int,
        help="How many image/sketch pairs do you want in the dataset?")
    parser.add_argument("--input_location", required=False, type=str,default='./' ,
        help="relative path to the Sketchy Dataset 256x256 folder")
    parser.add_argument("--output_file", required=False, type=str, default="output",
        help="The name of the output folder")
    parser.add_argument("--create_test_set", required=False, type=bool, default=False,
        help="Creates a test set, 1000 triplets not in the train set.")

    args = parser.parse_args()

    if args.output_file[-5:] == '.hdf5':
        args.output_file = args.output_file[:-5]

    if os.path.exists(args.output_file + ".hdf5") and os.path.isfile(args.output_file + ".hdf5"):
        user_input = input("Output file: \'" + args.output_file + ".hdf5\' already exists, would you like to overwrite it? (y/n)")
        if user_input != 'y' and user_input.lower() != 'yes':
            print("Ok, exiting now")
            return
        os.remove(args.output_file + ".hdf5")

    images_data, sketches_data = read_images_and_sketches(args)
    # images_data, sketches_data = perform_augmentation(args, images_data, sketches_data)


def read_images_and_sketches(args):
    count = 0
    output_images = np.zeros((min(1000,args.num_pairs),256,256,3),dtype='i8')
    output_sketches = np.zeros((min(1000,args.num_pairs),256,256,3),dtype='i8')
    bad_output_sketches = np.zeros((min(1000,args.num_pairs),256,256,3),dtype='i8')
    folder_prefix = args.input_location + "/256x256/"
    invalid_ambiguous = set(line.strip() for line in open("./data/sketchy/info/invalid-ambiguous.txt"))
    invalid_context = set(line.strip() for line in open("./data/sketchy/info/invalid-context.txt"))
    invalid_error = set(line.strip() for line in open("./data/sketchy/info/invalid-error.txt"))
    invalid_pose = set(line.strip() for line in open("./data/sketchy/info/invalid-pose.txt"))
    sketch_index_start = 1
    file_list = glob.glob(folder_prefix + "/photo/tx_000100000000/" + args.object_type + "/*.jpg")
    sketch_file_list = glob.glob(folder_prefix + "/sketch/tx_000100000000/" + args.object_type + "/*.png")

    output = h5py.File(args.output_file + ".hdf5", "a")
    image_dataset = output.create_dataset("image_dataset", (1,256,256,3),dtype='i8',compression='gzip', maxshape=(None,None,None,None,))
    sketch_dataset = output.create_dataset("sketch_dataset", (1,256,256,3), dtype='i8',compression='gzip', maxshape=(None,None,None,None,))
    bad_sketch_dataset = output.create_dataset("bad_sketch_dataset", (1,256,256,3), dtype='i8',compression='gzip', maxshape=(None,None,None,None,))

    string_datatype = h5py.string_dtype(encoding='ascii')
    sketch_type_dataset = output.create_dataset("sketch_type_dataset", (1,1),dtype=string_datatype,compression='gzip', maxshape=(None,None,))
    bad_sketch_type_dataset = output.create_dataset("bad_sketch_type_dataset", (1,1),dtype=string_datatype,compression='gzip', maxshape=(None,None,))
    good_sketch_type_list = []
    bad_sketch_type_list = []

    # Get 1000 images at a time
    temp_storage_counter = 0
    index=0
    end=min(1000, args.num_pairs)

    #So we pick from all object types
    random.shuffle(file_list)
    while count < args.num_pairs:
        for file_name_and_loc in file_list:
            output_images[temp_storage_counter] = np.array(imageio.imread(file_name_and_loc),dtype='i8')
            file_name = file_name_and_loc.split('/')[-1][:-4] #This isolates the file name, and drops the file type
            object_type = file_name_and_loc.split('/')[-2]

            sketch_index = sketch_index_start
            sketch_name = file_name + "-" + str(sketch_index)

            #make sure we dont use an invalid sketch
            while sketch_name in invalid_ambiguous or sketch_name in invalid_context or \
                    sketch_name in invalid_error or sketch_name in invalid_pose:
                sketch_index += 1
                sketch_name = file_name + "-" + str(sketch_index)

            output_sketches[temp_storage_counter] = np.array(imageio.imread(folder_prefix + "sketch/tx_000100000000/" +
                    object_type + "/" + sketch_name + ".png"),dtype='i8')

            bad_sketch_file = sketch_file_list[random.randint(0,len(sketch_file_list))]
            bad_output_sketches[temp_storage_counter] = np.array(imageio.imread(bad_sketch_file),dtype='i8')

            good_sketch_type_list.append([object_type])
            bad_object_type = bad_sketch_file.split('/')[-2]
            bad_sketch_type_list.append([bad_object_type])

            count += 1
            temp_storage_counter += 1

            # Saves 1000 images in the file, so we don't have an array that is too long
            if temp_storage_counter == 1000 or count == args.num_pairs:
                # end = min(index + 1000, args.num_pairs)
                image_dataset.resize(end,axis=0)
                image_dataset[index:end] = output_images
                sketch_dataset.resize(end,axis=0)
                sketch_dataset[index:end] = output_sketches
                bad_sketch_dataset.resize(end,axis=0)
                bad_sketch_dataset[index:end] = bad_output_sketches

                sketch_type_dataset.resize(end,axis=0)
                sketch_type_dataset[index:end] = np.array(good_sketch_type_list)
                bad_sketch_type_dataset.resize(end,axis=0)
                bad_sketch_type_dataset[index:end] = np.array(bad_sketch_type_list)


                if args.flipping_augment:
                    diff = end - index
                    index = end
                    end += diff
                    output_images = np.flip(output_images,axis=2)
                    output_sketches = np.flip(output_sketches,axis=2)
                    bad_output_sketches = np.flip(bad_output_sketches,axis=2)
                    image_dataset.resize(end,axis=0)
                    image_dataset[index:end] = output_images
                    sketch_dataset.resize(end,axis=0)
                    sketch_dataset[index:end] = output_sketches
                    bad_sketch_dataset.resize(end,axis=0)
                    bad_sketch_dataset[index:end] = bad_output_sketches

                    sketch_type_dataset.resize(end,axis=0)
                    sketch_type_dataset[index:end] = good_sketch_type_list
                    bad_sketch_type_dataset.resize(end,axis=0)
                    bad_sketch_type_dataset[index:end] = bad_sketch_type_list

                index = end
                end += min(1000, args.num_pairs - count)
                print("image data shape: ", image_dataset.shape)
                print("sketch data shape: ", sketch_dataset.shape)

                temp_storage_counter = 0
                output_images = np.zeros((min(1000,args.num_pairs-count),256,256,3),dtype='i8')
                output_sketches = np.zeros((min(1000,args.num_pairs-count),256, 256, 3),dtype='i8')
                bad_output_sketches = np.zeros((min(1000,args.num_pairs-count),256, 256, 3),dtype='i8')

                good_sketch_type_list = []
                bad_sketch_type_list = []

            if count >= args.num_pairs:
                break
            if count % 100 == 0:
                print('Read ' + str(count) + ' images')
        sketch_index_start += 1

    # Creates 1000 test images
    if args.create_test_set:
        output_images = np.zeros((min(1000,args.num_pairs),256,256,3),dtype='i8')
        output_sketches = np.zeros((min(1000,args.num_pairs),256,256,3),dtype='i8')
        test_images = output.create_dataset("test_images", (1000,256,256,3),dtype='i8',compression='gzip', maxshape=(None,None,None,None,))
        test_sketches = output.create_dataset("test_sketches", (1000,256,256,3), dtype='i8',compression='gzip', maxshape=(None,None,None,None,))

        test_type_dataset = output.create_dataset("test_type_dataset", (1000,1),dtype=string_datatype,compression='gzip', maxshape=(None,None,))
        test_type_list = []

        for i in range(1000):
            image_index = i + count % len(file_list)
            file_name_and_loc = file_list[image_index]

            output_images[i] = np.array(imageio.imread(file_name_and_loc),dtype='i8')
            file_name = file_name_and_loc.split('/')[-1][:-4] #This isolates the file name, and drops the file type
            object_type = file_name_and_loc.split('/')[-2]
            test_type_list.append([object_type])

            sketch_index = sketch_index_start
            sketch_name = file_name + "-" + str(sketch_index)

            #make sure we dont use an invalid sketch
            while sketch_name in invalid_ambiguous or sketch_name in invalid_context or \
                    sketch_name in invalid_error or sketch_name in invalid_pose:
                sketch_index += 1
                sketch_name = file_name + "-" + str(sketch_index)

            output_sketches[i] = np.array(imageio.imread(folder_prefix + "sketch/tx_000100000000/" +
                    object_type + "/" + sketch_name + ".png"),dtype='i8')

        # Write to the test set
        test_images[0:1000] = output_images
        test_sketches[0:1000] = output_sketches
        test_type_dataset[0:1000] = np.array(test_type_list)

    return image_dataset, sketch_dataset

def perform_augmentation(args, images_data, sketches_data):
    if args.flipping_augment:
        images_copy = np.append(images_copy,np.flip(images_copy,axis=2),axis=0)
        sketches_copy = np.append(sketches_copy, np.flip(sketches_copy,axis=2),axis=0)
    return images_copy, sketches_copy

if __name__ == "__main__":
    main()