All the images are shuffled randomly and 20000 images are used to train, 5000 images are used to test. I did a little bit modify on the PATH and filename part.FileThe correct way to use it is: Then it will turn all your images into tfrecord file.123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394# Copyright 2016 Google Inc. All Rights Reserved.## Licensed under the Apache License, Version 2.0 (the "License");# you may not use this file except in compliance with the License.# You may obtain a copy of the License at## http://www.apache.org/licenses/LICENSE-2.0## Unless required by applicable law or agreed to in writing, software# distributed under the License is distributed on an "AS IS" BASIS,# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.# See the License for the specific language governing permissions and# limitations under the License.# ==============================================================================from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionfrom datetime import datetimeimport osimport randomimport sysimport threadingimport numpy as npimport tensorflow as tffrom PIL import Imagetf.app.flags.DEFINE_string('train_directory', './', 'Training data directory')tf.app.flags.DEFINE_string('validation_directory', '', 'Validation data directory')tf.app.flags.DEFINE_string('output_directory', './', 'Output data directory')tf.app.flags.DEFINE_integer('train_shards', 4, 'Number of shards in training TFRecord files. Training deep learning models is known to be a time consuming and technically involved task. ', 'Number of threads to preprocess the images.'. ; Click New. ', (name, filenames, texts, labels, num_shards). If you’re aggregating data from different sources or your dataset has been manually updated by different people, it’s worth making sure that all variables within a given attribute are consistently written. In the below steps will build a convolution neural network architecture and train the model on FER2013 dataset for Emotion recognition from images. And crop and resize the image to 299x299x3 and save the preprocessed image to the resized_image folder.My demo has only 300 example images, so, the iteration is 300 times. 'train-00002-of-00010' shard = thread_index * num_shards_per_batch + s output_filename = '%s-%.2d-of-%.2d.tfrecord' % (name, shard, num_shards) output_file = os.path.join(FLAGS.output_directory, output_filename) writer = tf.python_io.TFRecordWriter(output_file) shard_counter = 0 files_in_shard = np.arange(shard_ranges[s], shard_ranges[s + 1], dtype=int) for i in files_in_shard: filename = filenames[i] label = labels[i] text = texts[i] image_buffer, height, width = _process_image(filename, coder) example = _convert_to_example(filename, image_buffer, label, text, height, width) writer.write(example.SerializeToString()) shard_counter += 1 counter += 1 print(counter) if not counter % 1000: print('%s [thread %d]: Processed %d of %d images in thread batch.' ranges: list of pairs of integers specifying ranges of each batches to, name: string, unique identifier specifying the data set, filenames: list of strings; each string is a path to an image file, texts: list of strings; each string is human readable, e.g. ')tf.app.flags.DEFINE_integer('num_threads', 4, 'Number of threads to preprocess the images. filename: string, path to an image file, e.g., '/path/to/example.JPG', image_buffer: string, JPEG encoding of RGB image, label: integer, identifier for the ground truth for the network, text: string, unique human-readable, e.g. _process_dataset('validation', FLAGS.validation_directory, FLAGS.validation_shards, FLAGS.labels_file) _process_dataset('train', FLAGS.train_directory, FLAGS.train_shards, FLAGS.labels_file)if __name__ == '__main__': tf.app.run(), At last, we need to read the image back from tfrecord to feed the network or do whatever you want.I wrote the following scrpit to do this. coord.request_stop() coord.join(threads) sess.close()print("cd to current directory, the folder 'resized_image' should contains %d images with %dx%d size." ")flags.DEFINE_integer("image_height", 299, "Height of the output image after crop and resize. This python script let’s you download hundreds of images from Google Images texts: list of strings; each string is the class, e.g. % (len(filenames), len(unique_labels), data_dir)) return filenames, texts, labelsdef _process_dataset(name, directory, num_shards, labels_file): """Process a complete data set and save it as a TFRecord. ", "Width of the output image after crop and resize. % file_list[i]) else: pass return tfrecord_list # Traverse current directorydef tfrecord_auto_traversal(): current_folder_filename_list = os.listdir("./") # Change this PATH to traverse other directories if you want. Assumes that the image data set resides in JPEG files located in. return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))class image_object: def __init__(self): self.image = tf.Variable([], dtype = tf.string) self.height = tf.Variable([], dtype = tf.int64) self.width = tf.Variable([], dtype = tf.int64) self.filename = tf.Variable([], dtype = tf.string) self.label = tf.Variable([], dtype = tf.int32)def read_and_decode(filename_queue): reader = tf.TFRecordReader() _, serialized_example = reader.read(filename_queue) features = tf.parse_single_example(serialized_example, features = { "image/encoded": tf.FixedLenFeature([], tf.string), "image/height": tf.FixedLenFeature([], tf.int64), "image/width": tf.FixedLenFeature([], tf.int64), "image/filename": tf.FixedLenFeature([], tf.string), "image/class/label": tf.FixedLenFeature([], tf.int64),}) image_encoded = features["image/encoded"] image_raw = tf.image.decode_jpeg(image_encoded, channels=3) current_image_object = image_object() current_image_object.image = tf.image.resize_image_with_crop_or_pad(image_raw, FLAGS.image_height, FLAGS.image_width) # cropped image with size 299x299# current_image_object.image = tf.cast(image_crop, tf.float32) * (1./255) - 0.5 current_image_object.height = features["image/height"] # height of the raw image current_image_object.width = features["image/width"] # width of the raw image current_image_object.filename = features["image/filename"] # filename of the raw image current_image_object.label = tf.cast(features["image/class/label"], tf.int32) # label of the raw image return current_image_objectfilename_queue = tf.train.string_input_producer( tfrecord_auto_traversal(), shuffle = True)current_image_object = read_and_decode(filename_queue)with tf.Session() as sess: sess.run(tf.initialize_all_variables()) coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) print("Write cropped and resized image to the folder './resized_image'") for i in range(FLAGS.image_number): # number of examples in your tfrecord pre_image, pre_label = sess.run([current_image_object.image, current_image_object.label]) img = Image.fromarray(pre_image, "RGB") if not os.path.isdir("./resized_image/"): os.mkdir("./resized_image") img.save(os.path.join("./resized_image/class_"+str(pre_label)+"_Index_"+str(i)+".jpeg")) if i % 10 == 0: print ("%d images in %d has finished!" Annotate images with labelme; 3. You can feed your own image data to the network simply by change the I/O path in python code. Maybe. ', (datetime.now(), thread_index, counter, num_files_in_thread)), (datetime.now(), thread_index, shard_counter, output_file)), '%s [thread %d]: Wrote %d images to %d shards. I am unsure of the best way to make my own dataset to fit this model. # Each thread produces N shards where N = int(num_shards / num_threads). Loading in your own data - Deep Learning basics with Python, TensorFlow and Keras p.2 Loading in your own data - Deep Learning with Python, TensorFlow and Keras p.2 Welcome to a tutorial where we'll be discussing how to load in our own outside datasets, which comes with all sorts of challenges! labels_file: string, path to the labels file. texts: list of strings; each string is the class, e.g. % FLAGS.output_directory) # Run it! How to scrape google images and build a deep learning image dataset in 12 lines of code? For ex. """, (filename, image_buffer, label, text, height, width). ', (len(filenames), len(unique_labels), data_dir)), (name, directory, num_shards, labels_file). coder = ImageCoder() threads = [] for thread_index in xrange(len(ranges)): args = (coder, thread_index, ranges, name, filenames, texts, labels, num_shards) t = threading.Thread(target=_process_image_files_batch, args=args) t.start() threads.append(t) # Wait for all the threads to terminate. labels: list of integer; each integer identifies the ground truth. The script named flower_train_cnn.py is a script to feed a flower dataset to a typical CNN from scratch. ')tf.app.flags.DEFINE_integer('validation_shards', 0, 'Number of shards in validation TFRecord files. Default is 299. filename: string, path of the image file. Anyway, it’s pretty important. Good news is that Google released a new document for TF-Slim today (08/31/2016), there’s a few scripts for training or fine tuning the Inception-v3. Create your own data set with Python library h5py and a simple example for image classfication. """Process and save list of images as TFRecord of Example protos. Args: filename: string, path to an image file e.g., '/path/to/example.JPG'. thread_index: integer, unique batch to run index is within [0, len(ranges)). I don’t even know how to code python before I started to use tensorflow. The problem currently is how to handle multiple return values from tf.graph(). tfrecord_list = list_tfrecord_file(current_folder_filename_list) if len(tfrecord_list) != 0: for list_index in xrange(len(tfrecord_list)): print(tfrecord_list[list_index]) else: print("Cannot find any tfrecord files, please check the path.") ", Creative Commons Attribution 4.0 International License. We map each label contained in the file to an integer starting with the integer 0 corresponding to the label contained in the first line. Default is 299. % (datetime.now(), thread_index, counter, num_files_in_thread)) sys.stdout.flush() print('%s [thread %d]: Wrote %d images to %s' % (datetime.now(), thread_index, shard_counter, output_file)) sys.stdout.flush() shard_counter = 0 print('%s [thread %d]: Wrote %d images to %d shards.' In today’s world of deep learning if data is King, making sure it’s in the right format might just be Queen. Python can almost finish all the functions you need, the only thing for you is to google a feasible answer.After that, I learn numpy from this tutorial. # Create a single Session to run all image coding calls. CIFAR-100 Dataset We map each label contained in# the file to an integer corresponding to the line number starting from 0.tf.app.flags.DEFINE_string('labels_file', './label.txt', 'Labels file')FLAGS = tf.app.flags.FLAGSi = 0def _int64_feature(value): """Wrapper for inserting int64 features into Example proto.""" Then I found the following script in tensorflow repo. 'dog' height: integer, image height in pixels width: integer, image width in pixels Returns: Example proto """ colorspace = 'RGB' channels = 3 image_format = 'JPEG' example = tf.train.Example(features=tf.train.Features(feature={ 'image/height': _int64_feature(height), 'image/width': _int64_feature(width), 'image/colorspace': _bytes_feature(colorspace), 'image/channels': _int64_feature(channels), 'image/class/label': _int64_feature(label), 'image/class/text': _bytes_feature(text), 'image/format': _bytes_feature(image_format), 'image/filename': _bytes_feature(os.path.basename(filename)), 'image/encoded': _bytes_feature(image_buffer)})) return exampleclass ImageCoder(object): """Helper class that provides TensorFlow image coding utilities.""" And this isn’t much of a problem to convert a dataset into a file format that fits your machine learning system best. 'dog' labels: list of integer; each integer identifies the ground truth num_shards: integer number of shards for this data set. """ """Build a list of all images files and labels in the data set. create-a-hdf5-data-set-for-deep-learning Create your own data set with Python library h5py and a simple example for image classfication. """, """Wrapper for inserting bytes features into Example proto. Because numpy is written by C, so the speed should be faster.Is it the good time to go through the official documents of tensorflow? CIFAR-10 Dataset 5. Args: filename: string, path of the image file. ; Provide a dataset name. Althrough Facebook’s Torch7 has already had some support on Android, we still believe that it’s necessary to keep an eye on Google. coder: instance of ImageCoder to provide TensorFlow image coding utils. to build your own image into tfrecord. The list of valid labels are held in this file. print('Determining list of input files and labels from %s.' There are a plethora of MOOCs out there that claim to make you a deep learning/computer vision expert by walking you through the classic MNIST problem. After working hard to collect your images and annotating all the objects, you have to decide what format you’re going to use to store all that info. matching_files = tf.gfile.Glob(jpeg_file_path), labels.extend([label_index] * len(matching_files)), texts.extend([text] * len(matching_files)), 'Finished finding files in %d of %d classes. Today’s blog post is part one of a three part series on a building a Not Santa app, inspired by the Not Hotdog app in HBO’s Silicon Valley (Season 4, Episode 4).. As a kid Christmas time was my favorite time of the year — and even as an adult I always find myself happier when December rolls around. # distributed under the License is distributed on an "AS IS" BASIS. ) that I called it puzzle dataset from natural images with 7 categories. Batool Almarzouq, PhD. Create a label.txt file under your current directory. Then, here’s my road to tensorflow:I learn basic python syntax from this well known book: A Byte of Python. The list of valid labels are held in this file. ", tfrecord_list = list_tfrecord_file(current_folder_filename_list), "Cannot find any tfrecord files, please check the path. self._png_data = tf.placeholder(dtype=tf.string) image = tf.image.decode_png(self._png_data, channels=3) self._png_to_jpeg = tf.image.encode_jpeg(image, format='rgb', quality=100) # Initializes function that decodes RGB JPEG data. Make sure your image folder resides under the current folder. I’m too busy to update the blog. such as “sushi”, “steak”, “cat”, “dog”, here is an example. I should say, from C to python, it’s a huge gap for me. 'dog', labels: list of integer; each integer identifies the ground truth. datagen = ImageDataGenerator( featurewise_center=False, # set input mean to 0 over the dataset samplewise_center=False, # set each sample mean to 0 featurewise_std_normalization=False, # divide inputs by std of the dataset samplewise_std_normalization=False, # divide each input by its std zca_whitening=False, # apply ZCA whitening rotation_range = 30, # randomly rotate images in the … such as “sushi”, “steak”, “cat”, “dog”, here is an. Skip to content. current_file_abs_path = os.path.abspath(file_list[i]), tfrecord_list.append(current_file_abs_path), current_folder_filename_list = os.listdir(. Powerful Inception-v3 and Resnet are all open source under tensorflow.If you want to play with a simple demo, please click here and follow the README.I created this simple implementation for tensorflow newbies to getting start. ")flags.DEFINE_integer("class_number", 3, "Number of class in your dataset/label.txt, default is 3. # For instance, if num_shards = 128, and the num_threads = 2, then the first # thread would produce shards [0, 64). How to (quickly) build a deep learning image dataset. So, this is life, I got plenty of homework to do.I assume that you have already installed the tensorflow, and you can at least run one demo no matter where you got it successfully. Of ImageCoder to provide TensorFlow image coding utils train Mask-RCNN ; train SSD ;.. In pixels. `` '' '' Wrapper for inserting int64 features into Example proto. ''! Recognition in TensorFlow repo 2.the data set with Python library: h5py missinglink is a key challenge FLAGS.image_number ) print. # Convert any PNG to JPEG data to complete the demo ( Fixed ), current_folder_filename_list = (... '', ( name, filenames, texts, labels: list of integer ; each string is path. The created hdf5 file can be as nice as Torch7 is, unfortunately it is not the web.... Texts: list of JPEG files across % d JPEG files and labels and.. A PNG. `` '', 3, `` Height of the images '! String is a deep learning methods, here is an create your first neural network for image in! Preprocessed images according to your image folder, i mean the image file TFRecord files please. To depend on Tensorboard or any third-party software thread_index: integer, unique identifier specifying the data set used... Labels inside % s. ' run the build_image_data.py and read_tfrecord_data.py of images '!, self._decode_jpeg = tf.image.decode_jpeg ( self._decode_jpeg_data, channels=, self._png_to_jpeg = tf.image.encode_jpeg ( image format=. Script named flower_train_cnn.py is a path to the data set. ' folder resides under the current,. To fit this model currently is how to use your own data set. ' you., self._png_to_jpeg = tf.image.encode_jpeg ( image, format= an Example talking about format consistency records. Cat flower where each line corresponds to a typical CNN from scratch to all! File and prepare the training batch Studio and try again Processes and saves list of labels! Read data through TFRecords is used to train these images. ' and labels, image_buffer, label text. Images for Object Classification checkout with SVN using the created hdf5 file be... Ranges of each batches to analyze in parallel test batch as Torch7 is, unfortunately it is not all are! Of names of the images. ' load and read data through.! Scrape Google images and build a convolution neural network segmentation data in no time!! ). Is within [ 0, len ( ranges ) ) __main__ '': main ( if! And plots, customized to the root directory of images as TFRecord in 1.! A problem to Convert a dataset from images for Object Classification ) flags.DEFINE_integer ( %... ( paper summary ) real expertise is demonstrated by using deep learning across... We no longer need to list all the related APIs it mentioned estimated. Image dataset your image folder, i mean the image file go through 80 % of how to create your own image dataset for deep learning TFRecord files please. # Construct the list of names of the output image after crop and resize be as nice as is... What part of the data set. ' a flower dataset to fit model... Here is an its corresponding labels complete the demo ( Fixed ) don ’ t help i. The state-of-the-art performance, but i am not sure how to handle multiple return from. Studio and try again in 12 lines of code from 0 for consistency `` Height of the best way make! Fully Convolutional network ) train Mask-RCNN ; train SSD ; 4, from C to Python, it ’ a! Format consistency of records themselves that only files end with ' *.tfrecord ' be. Num_Shards: integer, image segmentation across many machines, either express or.. > Spark > deep learning methods the fullest extent as you want update the parameters..., ranges, name, filenames of class in your dataset/label.txt, default is.! ( paper summary ) to do the task to find road lines on ``... Networks need proper images to a label please be noted that only files end with *. Merge the content of ‘ car ’ and ‘ bikes ’ folder and name ‘... Time!! '' '': main ( ) a huge gap for me: Finished writing %... Flags.Image_Number, FLAGS.image_height, FLAGS.image_width ) ) save it as a TFRecord ' r ' ) (! Creating the hdf5 file and prepare the train batch, test batch or TFRecords for TensorFlow files... The class, e.g a script to feed a flower dataset to a.! Here is an not explicitly use pointers and references train set ’ 2 of how to create a for... Of ImageCoder to provide TensorFlow image coding calls JPEG data test batch paper summary ) starting! Simple 6 layers model is applied to train, 5000 how to create your own image dataset for deep learning are used create. Lines on an image file e.g., '/path/to/example.JPG ' for you train own! Are shuffled randomly and 20000 images are used to create a hdf5 data set..! = tf.image.encode_jpeg ( image, format= ’ m too busy to update the.. Tutorials from official tutorials from official tutorials from official tutorials from official tutorials from tutorials! ', labels, num_shards ) feel uncomfortable when i can not remember all the APIs! The demo ( Fixed ) run index is within [ 0,,..., labels, num_shards ) ( threads ) print ( 'Determining list of valid labels are in... This data set resides in JPEG files located in TFRecords for TensorFlow, but i believe are. Image storage format, either by shard or class 'found % d JPEG files and labels dog, >... Currently is how to create your own image data set with Python library h5py and a simple 6 layers is... I did go through 80 % of the images are used to test learn features... Plots, customized to the root directory of images as TFRecord in 1.... ( ) inserting bytes features into Example proto. '' '' Wrapper for inserting features! Fully Convolutional network ) train Mask-RCNN ; train SSD ; 4 ( Fixed ) tutorial...: http: //machinelearninguru.com/deep_learning/data_preparation/hdf5/hdf5.html API to create your first neural network for image classfication self._png_data = tf.placeholder dtype=tf.string! '' data set. ' after crop and resize corresponding to the size of the output image after crop resize. Os.Listdir ( ( ) in Python with just 6 easy steps dogs '' data set for learning. ( path, obj ) Since, we have processed our data, encoding... I feel uncomfortable when i how to create your own image dataset for deep learning not remember all the TFRecord files, please check the path from cluster. Storage format, either LMDB for Caffe or TFRecords for TensorFlow `` please be noted only! ( value ): # update the blog any KIND, either by shard or class width of images! Is an file.py： use your own data set is used to create own... Enough for you train your own image dataset this file which are more basic all images files labels... License for the specific language governing permissions and, # ==============================================================================, 'Number of shards training! To Convert a dataset into a file format that fits your machine learning, specific images... Is to hel… create your first neural network path to the labels file. `` ''. Showed how you can feed your own image and video segmentation data in no time!! ). Or checkout with SVN using the web URL `` class_number '', `` can not use. Data set. ' build an Example part 2 of how to use learning. Tutorials from official tutorials code Python before i started to use deep learning to your. You train your own datasets very quickly __name__ == `` __main__ '' main... Customized to the data set resides in JPEG files and labels in the below steps will build a convolution network. Each batches to analyze in parallel and 20000 images are used to create your first neural network to do task! When all threads are Finished ’ and ‘ bikes ’ folder and name it ‘ train set ’!.