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- #!/usr/bin/env python
- # -*- coding: utf-8 -*-
- #######################################################################################
- # The MIT License
- # Copyright (c) 2014 Hannes Schulz, University of Bonn <schulz@ais.uni-bonn.de>
- # Copyright (c) 2013 Benedikt Waldvogel, University of Bonn <mail@bwaldvogel.de>
- # Copyright (c) 2008-2009 Sebastian Nowozin <nowozin@gmail.com>
- # Permission is hereby granted, free of charge, to any person obtaining a copy
- # of this software and associated documentation files (the "Software"), to deal
- # in the Software without restriction, including without limitation the rights
- # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- # copies of the Software, and to permit persons to whom the Software is
- # furnished to do so, subject to the following conditions:
- #
- # The above copyright notice and this permission notice shall be included in all
- # copies or substantial portions of the Software.
- #
- # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- # SOFTWARE.
- #######################################################################################
- #
- # See https://github.com/deeplearningais/curfil/wiki/Training-and-Prediction-with-the-NYU-Depth-v2-Dataset
- """Helper script to convert the NYU Depth v2 dataset Matlab file into a set of PNG and JPEG images.
- Receives 3 Files from argparse:
- <h5_file> - Contains the original images, depths maps, and scene types
- <train_test_split> - contains two numpy arrays with the index of the
- images based on the split to train and test sets.
- <out_folder> - Name of the folder to save the original and depth images.
- Every image in the DB will have it's twine B&W image that indicates the depth
- in the image. the images will be read, converted by the convert_image function
- and finally saved to path based on train test split and Scene types.
- """
- from __future__ import print_function
- import h5py
- import numpy as np
- import os
- import scipy.io
- import sys
- import cv2
- from tqdm import tqdm
- def convert_image(index, depth_map, img, output_folder):
- """Processes data images and depth maps
- :param index: int, image index
- :param depth_map: numpy array, image depth - 2D array.
- :param img: numpy array, the original RGB image - 3D array.
- :param output_folder: path to save the image in.
- Receives an image with it's relevant depth map.
- Normalizes the depth map, and adds a 7 px boundary to the original image.
- Saves both image and depth map to the appropriate processed data folder.
- """
- # Normalize the depth image
- # normalized_depth = cv2.normalize(depth_map, None, 0, 255, cv2.NORM_MINMAX)
- img_depth = depth_map * 25.0
- cv2.imwrite("%s/%05d_depth.png" % (output_folder, index), img_depth)
- # Adding black frame to original image
- img = img[:, :, ::-1] # Flipping the image from RGB to BGR for opencv
- image_black_boundary = np.zeros(img.shape, dtype=np.uint8)
- image_black_boundary[7:image_black_boundary.shape[0] - 6, 7:image_black_boundary.shape[1] - 6, :] = \
- img[7:img.shape[0] - 6, 7:img.shape[1] - 6, :]
- cv2.imwrite("%s/%05d.jpg" % (output_folder, index), image_black_boundary)
- if __name__ == "__main__":
- # Check if got all needed input for argparse
- if len(sys.argv) != 4:
- print("usage: %s <h5_file> <train_test_split> <out_folder>" % sys.argv[0], file=sys.stderr)
- sys.exit(0)
- # load arguments to variables
- h5_file = h5py.File(sys.argv[1], "r")
- train_test = scipy.io.loadmat(sys.argv[2]) # h5py is not able to open that file. but scipy is
- out_folder = sys.argv[3]
- # Extract images *indexes* for train and test data sets
- test_images = set([int(x) for x in train_test["testNdxs"]])
- train_images = set([int(x) for x in train_test["trainNdxs"]])
- print("%d training images" % len(train_images))
- print("%d test images" % len(test_images))
- # Grayscale
- depth = h5_file['depths']
- print("Reading", sys.argv[1])
- images = h5_file['images'] # (num_channels, height, width)
- # Extract all sceneTypes per image - "office", "classroom", etc.
- scenes = [u''.join(chr(c[0]) for c in h5_file[obj_ref]) for obj_ref in h5_file['sceneTypes'][0]]
- for i, image in tqdm(enumerate(images), desc="Processing images", total=len(images)):
- idx = int(i) + 1
- if idx in train_images:
- train_test = "train"
- else:
- assert idx in test_images, "index %d neither found in training set nor in test set" % idx
- train_test = "test"
- # Create path to save image in
- folder = "%s/%s/%s" % (out_folder, train_test, scenes[i])
- if not os.path.exists(folder):
- os.makedirs(folder)
- convert_image(i, depth[i, :, :].T, image.T, folder)
- print("Finished")
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