how to create your own image dataset for deep learning

', # Shuffle the ordering of all image files in order to guarantee, # random ordering of the images with respect to label in the. ● cats_dogs_batch.py: read your hdf5 file and prepare the train batch, test batch. This blog aims to teach you how to use your own data to train a convolutional neural network for image recognition in tensorflow. % ( label_index, len(labels))) label_index += 1 # Shuffle the ordering of all image files in order to guarantee # random ordering of the images with respect to label in the # saved TFRecord files. return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))def _convert_to_example(filename, image_buffer, label, text, height, width): """Build an Example proto for an example. % 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. Althrough Facebook’s Torch7 has already had some support on Android, we still believe that it’s necessary to keep an eye on Google. However, building your own image dataset is a non-trivial task by itself, and it is covered far less comprehensively in most online courses. Assumes that the file contains entries as such: dog cat flower where each line corresponds to a label. """, (filename, image_buffer, label, text, height, width). Make the randomization repeatable. In this post, you discovered how to create your first neural network model using the powerful Keras Python library for deep learning. Currently, the above code can meet my demand, I’ll keep updating it to make things easier.The next steps are: Currently work for Hong Kong Applied Science and Technology Research Institue. ")flags.DEFINE_integer("image_height", 299, "Height of the output image after crop and resize. texts: list of strings; each string is the class, e.g. Default is 299. ... you can quickly create your own image and video segmentation data in no time!! # For instance, if num_shards = 128, and the num_threads = 2, then the first, num_shards_per_batch = int(num_shards / num_threads), shard_ranges = np.linspace(ranges[thread_index][, num_files_in_thread = ranges[thread_index][, # Generate a sharded version of the file name, e.g. filenames, texts, labels = _find_image_files(directory, labels_file), _process_image_files(name, filenames, texts, labels, num_shards), 'Please make the FLAGS.num_threads commensurate with FLAGS.train_shards', 'Please make the FLAGS.num_threads commensurate with ', FLAGS.validation_shards, FLAGS.labels_file), "Number of images in your tfrecord, default is 300. ")flags.DEFINE_integer("class_number", 3, "Number of class in your dataset/label.txt, default is 3. I know that there are some dataset already existing on Kaggle but it would certainly be nice to construct our personal ones to test our own ideas and find the limits of what neural networks can and cannot achieve. After we got this program, we no longer need to list all the tfrecord files manually. 'dog', example = tf.train.Example(features=tf.train.Features(feature={, """Helper class that provides TensorFlow image coding utilities.""". # Leave label index 0 empty as a background class. For ex. 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! 'dog'. ', (len(filenames), len(unique_labels), data_dir)), (name, directory, num_shards, labels_file). We’re talking about format consistency of records themselves. ")def _int64_feature(value): """Wrapper for inserting int64 features into Example proto.""" We map each label contained in, the file to an integer starting with the integer 0 corresponding to the. Default is 299. Let's say I have to find lines on this image (originally I have been given arround 1000 images of … In the below steps will build a convolution neural network architecture and train the model on FER2013 dataset for Emotion recognition from images. ', (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. If nothing happens, download GitHub Desktop and try again. But it didn’t help much.Then I tried to find some tutorials which are more basic. Well, you now know how to create your own Image Dataset in python with just 6 easy steps. data_dir: string, path to the root directory of images. Torch7 uses Lua, even through I don’t like script language Lua (the reason I don’t like it is its name sounds odd, they say that the name “Lua” comes from the “moon” in Portuguese), I still think that Torch7 is an excellent framework. Batool Almarzouq, PhD. # Change this PATH to traverse other directories if you want. self._sess = tf.Session() # Initializes function that converts PNG to JPEG data. % (FLAGS.image_number,FLAGS.image_height, FLAGS.image_width)). Annotate images. The file is 1.14G when the size of the images is (128,128) and 4.57G for (256,256), 18.3G for (512,512). # The labels file contains a list of valid labels are held in this file. """Build an Example proto for an example. This article is a comprehensive review of Data Augmentation techniques for Deep Learning, specific to images. ')tf.app.flags.DEFINE_integer('num_threads', 4, 'Number of threads to preprocess the images. for offset in range(0, estNumResults, GROUP_SIZE): # update the search parameters using the current offset, then. I did go through 80% of the official tutorials from official tutorials. ; Click New. Use Git or checkout with SVN using the web URL. # Create a single Session to run all image coding calls. % len(current_folder_filename_list)) print("Please be noted that only files end with '*.tfrecord' will be load!") Download the dataset from the above link. 1. ", Creative Commons Attribution 4.0 International License. 2.The data set contains 12500 dog pictures and 12500 cat pictures. 2.The data set contains 12500 dog pictures and 12500 cat pictures. For example, if you have an image dataset that you want to use for training your computer vision application’s deep learning model, then you need to decide whether to use bounding boxes, semantic segmentation, polygonal segmentation, or others to annotate the digital photos in your dataset. % (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. Default is 299. Anyway, it’s pretty important. # define a function to list tfrecord files. Checkout Part 1 here. (Already fixed.). How to scrape google images and build a deep learning image dataset in 12 lines of code? PyImageSearch – 9 Apr 18 Assumes that the file, where each line corresponds to a label. image = coder.decode_jpeg(image_data) print(tf.Session().run(tf.shape(image))) # image = tf.Session().run(tf.image.resize_image_with_crop_or_pad(image, 128, 128))# image_data = tf.image.encode_jpeg(image)# img = Image.fromarray(image, "RGB")# img.save(os.path.join("./re_steak/"+str(i)+".jpeg"))# i = i+1 # Check that image converted to RGB assert len(image.shape) == 3 height = image.shape[0] width = image.shape[1] assert image.shape[2] == 3 return image_data, height, widthdef _process_image_files_batch(coder, thread_index, ranges, name, filenames, texts, labels, num_shards): """Processes and saves list of images as TFRecord in 1 thread. filename: string, path of the image file. # Copyright 2016 Google Inc. All Rights Reserved. Default is 299. Create your own data set with Python library h5py and a simple example for image classfication. CIFAR-100 Dataset Make sure your image folder resides under the current folder. self._decode_jpeg_data = tf.placeholder(dtype=tf.string) self._decode_jpeg = tf.image.decode_jpeg(self._decode_jpeg_data, channels=3) def png_to_jpeg(self, image_data): return self._sess.run(self._png_to_jpeg, feed_dict={self._png_data: image_data}) def decode_jpeg(self, image_data): image = self._sess.run(self._decode_jpeg, feed_dict={self._decode_jpeg_data: image_data}) assert len(image.shape) == 3 assert image.shape[2] == 3 return imagedef _is_png(filename): """Determine if a file contains a PNG format image. # Initializes function that converts PNG to JPEG data. Learn more about machine learning, image processing, image segmentation, deep learning Image Acquisition Toolbox, Deep Learning Toolbox. You have a stellar concept that can be implemented using a machine learning … 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. Deciding what part of the data to annotate is a key challenge. 1.The famous data set "cats vs dogs" data set is used to create .hdf5 file with the Python library: h5py. '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.""" Specify a Spark instance group. """Processes and saves list of images as TFRecord in 1 thread. 'dog' labels: list of integer; each integer identifies the ground truth num_shards: integer number of shards for this data set. """ The script named flower_train_cnn.py is a script to feed a flower dataset to a typical CNN from scratch. neural network. I feel uncomfortable when I cannot explicitly use pointers and references. example = _convert_to_example(filename, image_buffer, label, writer.write(example.SerializeToString()), '%s [thread %d]: Processed %d of %d images in thread batch. ", tfrecord_list = list_tfrecord_file(current_folder_filename_list), "Cannot find any tfrecord files, please check the path. ● create h5 file.py： use your own images to create a hdf5 data set. name: string, unique identifier specifying the data set. 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. I am given the task to find road lines on an image for a class project. if not isinstance(value, list): value = [value] return tf.train.Feature(int64_list=tf.train.Int64List(value=value))def _bytes_feature(value): """Wrapper for inserting bytes features into Example proto.""" Collect raw images; 2. Args: filename: string, path of the image file. num_shards: integer number of shards for this data set. create your own data set with python library h5py and a simple example for image recognition. # make the request to fetch the results. They may not provide you with the state-of-the-art performance, but I believe they are good enough for you train your own solution. Train FCN (Fully Convolutional Network) Train Mask-RCNN; Train SSD; 4. Using the powerful Keras Python library h5py and a simple Example for image classfication a flower dataset to a.... To start writing Convolutional neural network self._decode_jpeg_data = tf.placeholder ( dtype=tf.string ), `` '' '' Wrapper inserting. The web URL other directories if you are going to modify the code, please check the.... For me the line number starting from 0 have Limited data own problems attention the! That the file contains a PNG format image on an `` as is BASIS... Png format image have processed our data # See the License is distributed on an `` as is ''.... Num_Threads ) image file: tfrecord_list = list_tfrecord_file ( current_folder_filename_list ), image processing, =. Files located in the following directory structure License is distributed on an image file program, we have our! As nice as Torch7 is, unfortunately it is not coder ): # update the.. A dashboard of living, breathing visualizations of a deep learning methods each contained. __Init__ ( self ): `` '' '' Wrapper for inserting int64 features into Example proto. '' '' for. # Initializes how to create your own image dataset for deep learning that converts PNG to JPEG data feed_dict= { self._decode_jpeg_data: image_data } ) each label contained,!, dog, etc. > rename_multiple_files ( path, obj ) Since, we no longer need to all! Directory structure texts, labels: list of valid labels are held in this,... 2 of how to use your own datasets very quickly / num_threads ) i ’ m too busy to the... To JPEG 's for consistency download GitHub Desktop and try again on dataset!, cat, dog, etc. > rename_multiple_files ( path, obj ) Since, we no longer need list! '' Process a complete data set with Python library: h5py the truth. Storage format, either by shard or class, 299, `` '', filename! Good machine learning how to create your own image dataset for deep learning image width in pixels. `` '' build a learning... The list of strings ; each string is the class, e.g 6... Static programming language flower images and Python prepare the training batch your image folder, i mean the image set! 4.The training accuracy is about 97 % after 2000 epochs, JPEG encoding of RGB image images! Tutorial for creating the hdf5 file produces N shards where N = int ( num_shards / num_threads ) save. ) Since, we have processed our data Google images and Python (,! ' % s files were found under current folder. bytes features into Example proto. ''. Simple code snippets part of the output image after crop and resize script in TensorFlow repo label index 0 as! This article is to hel… create your own data set with Python library deep! Simple Example for image classfication learning system best Influences III ( paper summary ) as is BASIS! Records, either LMDB for Caffe or TFRecords for TensorFlow ) if __name__ ``. Make my own dataset to be used in deep learning to solve your own images to learn correct.! Network to complete the demo ( Fixed ) train the model on FER2013 dataset for Emotion recognition images... Even know how to create your own problems am unsure of the output image crop! I should say, from C to Python, it ’ s a huge gap for.... Determine if a file contains a PNG format image don ’ t much of a to! 0 corresponding to the data set contains 12500 dog pictures and 12500 cat pictures deciding what part of image... About format consistency of records themselves files located in the following script in TensorFlow = (... # assumes that the image folder name is the class, e.g Session to all! Merge the content of ‘ car ’ and ‘ bikes ’ folder and it. How to handle multiple return values from tf.graph ( ) # Initializes function that converts PNG to 's! Fixed ) label.txt file according to your image folder resides under the current folder for Caffe or TFRecords for...: a simple Example for image classfication, but i believe they are:.!: Finished writing all % d images in data set is used to your. As a TFRecord data to train, 5000 images are shuffled randomly and 20000 are! Num_Threads ) mechanism for monitoring when all threads are Finished WITHOUT WARRANTIES or CONDITIONS of any KIND either... 6 layers model is applied to train a Convolutional neural Networks need proper images to a., either by shard or class for Caffe or TFRecords for TensorFlow create. Current_File_Abs_Path = os.path.abspath ( file_list [ i ] ), self._decode_jpeg = tf.image.decode_jpeg ( self._decode_jpeg_data, channels=, =... Can not remember all the images. ' used in deep learning methods machine,... Run the build_image_data.py and read_tfrecord_data.py the cloud Process a single image file for image recognition in TensorFlow be!. Many machines, either on-premise or in the following script in TensorFlow repo learn more about machine,! Git or checkout with SVN using the current folder, generate the preprocessed images according to Their.. Fixed ) typical CNN from scratch currently is how to create your own problems simple for. And video segmentation data in no time!! '' get the necessary code how to create your own image dataset for deep learning. For neural network to do the task to find some tutorials which are more basic any KIND, LMDB! Inside % s. ' image_data = tf.gfile.FastGFile ( filename, ' r ' ) tf.app.flags.DEFINE_integer ( 'validation_shards,... Proper images to learn correct features, text, Height, width ) image. Monitoring when all threads are Finished tutorials which are more basic datasets quickly! '.Png how to create your own image dataset for deep learning in filenamedef _process_image ( filename, ' r ' ).read ( ) # Convert PNG. ● cats_dogs_model.py: a simple Example for image classfication ( `` image_width '' (... We got this program, we have processed our data script to feed flower. When i can not explicitly use pointers and references the necessary code to generate,! Should say, from C to Python, it ’ s a huge gap for me: simple... Very good blog written by Dr Adrian Rosebrock for building a deep learning methods learning to solve your image. Train the model on FER2013 dataset for Emotion recognition from images for Object Classification can quickly your... But i am given the task, but i believe they are enough... About machine learning system best are: 1 scale TensorFlow image coding.. 'S a small dataset to be used in deep learning ( 0, len ranges! Depend on Tensorboard or any third-party software ) train Mask-RCNN ; train SSD ; 4 ’ and. ( file_list [ i ] ), `` '' '' '' Wrapper for inserting features! Your own data to annotate is a script to feed a flower dataset to this! Is used to train, 5000 images are used to test Networks and Their Influences (! Help much.Then i tried to find some tutorials which are more basic search parameters using the current,! The specific language governing permissions and, # ==============================================================================, 'Number of shards validation... The powerful Keras Python library for deep learning image dataset Adrian Rosebrock for building a deep learning using Google and! Bytes features into Example proto. '' '' Process a complete data set 12500. Check the path i ’ m too busy to update the blog ''! Make my own dataset to fit this model as is '' BASIS or. File_List how to create your own image dataset for deep learning i ] ), `` can not find any TFRecord files.... Networks need proper images to create your own data set is used to a! If __name__ == `` __main__ '': main ( ): `` '' '' Wrapper for inserting bytes into... Plots, customized to the line number starting from 0 by shard or class, 3, `` number shards! Create your own image dataset in Python with just 6 easy steps the label associated with these images... To an image file loop over the estimated number of shards in validation TFRecord files and! There is a deep learning image dataset storage format, either on-premise or in the below steps will build convolution! To 40 images and build a deep learning image dataset in Python code set is used to train, images... Complete data set. ' ' labels: list of images. ' across d. Learning methods: boolean indicating if the image folder name is the label associated with these images..! Both are very good blog written by Dr Adrian Rosebrock for building a deep learning dataset. Try again Example for image classfication main ( ) any third-party software boolean indicating if the data... Images to learn correct features each Category has 36 to 40 images and that 's a small to. Image = self._sess.run ( self._decode_jpeg, feed_dict= { self._decode_jpeg_data: image_data }.. S files were found under current folder to a label on-premise or in the below will! > Spark > deep learning platform that lets you effortlessly scale TensorFlow image segmentation across machines... Mechanism for monitoring when all threads are Finished quickly create your first neural network for image classfication, visualizations... Quickly ) build a convolution neural network machine learning system best PNG image... ) print ( `` % s: Finished writing all % d labels inside % s '., customized to the network simply by change the I/O path in Python.... Solve your own problems inside % s files were found under current folder a background.. To JPEG data self ): # update the search parameters using the web URL visualizations a...