"%s files were found under current folder. return tfrecord_listdef main(): tfrecord_list = tfrecord_auto_traversal()if __name__ == "__main__": main(). such as placeholder or image reverse APIs.At last, do not forget about the all mighty Github, another branch of tensorflow has a few open source network structures. The problem currently is how to handle multiple return values from tf.graph(). ; Create a dataset from Images for Object Classification. ")def _int64_feature(value): """Wrapper for inserting int64 features into Example proto.""" '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.' Following the approach, outlined here, you don’t have to depend on Tensorboard or any third-party software. Interested in high performance computing and machine learning. How to scrape google images and build a deep learning image dataset in 12 lines of code? 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. ", 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), _, serialized_example = reader.read(filename_queue), features = tf.parse_single_example(serialized_example, features = {, image_raw = tf.image.decode_jpeg(image_encoded, channels=, current_image_object.image = tf.image.resize_image_with_crop_or_pad(image_raw, FLAGS.image_height, FLAGS.image_width), # current_image_object.image = tf.cast(image_crop, tf.float32) * (1./255) - 0.5, current_image_object.filename = features[, current_image_object.label = tf.cast(features[, filename_queue = tf.train.string_input_producer(, current_image_object = read_and_decode(filename_queue), threads = tf.train.start_queue_runners(coord=coord), "Write cropped and resized image to the folder './resized_image'", pre_image, pre_label = sess.run([current_image_object.image, current_image_object.label]), "cd to current directory, the folder 'resized_image' should contains %d images with %dx%d size. 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). 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! # Create a generic TensorFlow-based utility for converting all image codings. I highly recommend you read this article Hello, tensorflow, and this tutorial LearningTensorflow.The last two articles are really helpful to me, they tell you how tensorflow actually works and how to correctly use some of the key op. ")flags.DEFINE_integer("image_width", 299, "Width of the output image after crop and resize. ', (len(filenames), len(unique_labels), data_dir)), (name, directory, num_shards, labels_file). neural network. image_buffer: string, JPEG encoding of RGB image. In the official basic tutorials, they provided the way to decode the mnist dataset and cifar10 dataset, both were binary format, but our own image usually is .jpeg or .png format.So, here I decided to summarize my experience on how to feed your own image data to tensorflow and build a simple conv. Default is 299. But it didn’t help much.Then I tried to find some tutorials which are more basic. In othe r words, a data set corresponds to the contents of a single database table, or a single statistical data matrix, where every column of the table represents a particular variable, and each row corresponds to a given member of the data set in question. 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.") Annotate images. 'dog', labels: list of integer; each integer identifies the ground truth. They both are very good machine learning tools for neural network. Keras Computer Vision Datasets 2. 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.""" The drawback, I think, there are at least two, first, the efficiency is low; second, too much APIs to remember. 'dog', example = tf.train.Example(features=tf.train.Features(feature={, """Helper class that provides TensorFlow image coding utilities.""". In order to create a dataset, you must put the raw data in a folder on the shared file system that IBM Spectrum Conductor Deep Learning Impact has access to. 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. labels: list of integer; each integer identifies the ground truth. Return the list of names of the tfrecord files. height: integer, image height in pixels. If TFRecords was selected, select how to generate records, either by shard or class. texts: list of strings; each string is the class, e.g. """, (filename, image_buffer, label, text, height, width). Assumes that the image data set resides in JPEG files located in the following directory structure. This python script let’s you download hundreds of images from Google Images num_threads = len(ranges) assert not num_shards % num_threads num_shards_per_batch = int(num_shards / num_threads) shard_ranges = np.linspace(ranges[thread_index][0], ranges[thread_index][1], num_shards_per_batch + 1).astype(int) num_files_in_thread = ranges[thread_index][1] - ranges[thread_index][0] counter = 0 for s in xrange(num_shards_per_batch): # Generate a sharded version of the file name, e.g. Then, here’s my road to tensorflow:I learn basic python syntax from this well known book: A Byte of Python. label_index = 1 # Construct the list of JPEG files and labels. 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. You signed in with another tab or window. where 'dog' is the label associated with these images. 4.The training accuracy is about 97% after 2000 epochs. If nothing happens, download the GitHub extension for Visual Studio and try again. to build your own image into tfrecord. 5 simple steps for Deep Learning. ", "Height of the output image after crop and resize. PyImageSearch – 9 Apr 18 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." 'dog'. I’m too busy to update the blog. Try to display the label and the image at the same time, generate the preprocessed images according to their labels. data_dir/dog/another-image.JPEG data_dir/dog/my-image.jpg where 'dog' is the label associated with these images. # Assumes that the file contains entries as such: # where each line corresponds to a label. Train neural network. name: string, unique identifier specifying the data set. num_shards: integer number of shards for this data set. We showed how you can create a dashboard of living, breathing visualizations of a deep learning model performance, with simple code snippets. Checkout Part 1 here. 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. # Each thread produces N shards where N = int(num_shards / num_threads). Prepare the training dataset with flower images and its corresponding labels. 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. ", Creative Commons Attribution 4.0 International License. MNIST Dataset 3. You have a stellar concept that can be implemented using a machine learning … This article is a comprehensive review of Data Augmentation techniques for Deep Learning, specific to images. A data set is a collection of data. A simple 6 layers model is applied to train these images. The goal of this article is to hel… You can feed your own image data to the network simply by change the I/O path in python code. Args: filename: string, path of the image file. Work fast with our official CLI. Assumes that the file contains entries as such: dog cat flower where each line corresponds to a label. I followed that document, it’s working.So far, I suppose that is the best document for Tensorflow, because Inception-v3 is one of a few the state-of-art architectures and tensorflow is a very powerful deep learning tool.Google open sourced Inception-resnet-v2 yesterday (02/09/2016), what can I say~ :), There’s a lot of data I/O api in python, so it’s not a difficult task. % (datetime.now(), len(filenames))) sys.stdout.flush()def _find_image_files(data_dir, labels_file): """Build a list of all images files and labels in the data set. Each Category has 36 to 40 images and that's a small dataset to be used in deep learning methods. % (datetime.now(), thread_index, counter, num_files_in_thread)) sys.stdout.flush()def _process_image_files(name, filenames, texts, labels, num_shards): """Process and save list of images as TFRecord of Example protos. 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!" image_data = tf.gfile.FastGFile(filename, 'r').read() # Convert any PNG to JPEG's for consistency. print('Determining list of input files and labels from %s.' Fashion-MNIST Dataset 4. 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. """Processes and saves list of images as TFRecord in 1 thread. (coder, thread_index, ranges, name, filenames. Then I found the following script in tensorflow repo. I used to analyze the C code of the Torch7, I should say Torch7 should be a very fast framework and the drawback is that I think Torch7 is a little bit more resource consuming, it achieves faster training and inference speed at the cost of requiring more memory.Another point is that Torch7’s I/O API (Application Programming Interface) is so user friendly, the only thing that you need to load an image it to call an imread function with the argument of “/path/of/your/image/data.jpg”.But, for tensorflow, the basic tutorial didn’t tell you how to load your own data to form an efficient input data. % 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. filenames, texts, labels = _find_image_files(directory, labels_file) _process_image_files(name, filenames, texts, labels, num_shards)def main(unused_argv): assert not FLAGS.train_shards % FLAGS.num_threads, ( 'Please make the FLAGS.num_threads commensurate with FLAGS.train_shards') assert not FLAGS.validation_shards % FLAGS.num_threads, ( 'Please make the FLAGS.num_threads commensurate with ' 'FLAGS.validation_shards') print('Saving results to %s !' Deep Learning with Your Own Image Dataset. Maybe. 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. Can not find any TFRecord files each string is the class, e.g creating the hdf5 file and the... A Convolutional neural network to do the task, but i am of! For TensorFlow name it ‘ train set ’, http: //machinelearninguru.com/deep_learning/data_preparation/hdf5/hdf5.html TensorFlow repo label 0! In 12 lines of code entries as such: # update the blog,. Located in neural Networks need proper images to create your own datasets very quickly that only end... 'Found % d images in data set contains 12500 dog pictures and 12500 cat.. Associated with these images. ' that 's a small dataset to fit this model cats_dogs_model.py a... Labels in the data set with Python library: h5py '/path/to/example.JPG ' specify image storage format, by. Segmentation, deep learning to solve your own problems dataset from images. ' the necessary code to records... Fixed ) techniques in Convolutional neural network for image classfication 80 % of output... Learning image dataset ; train SSD ; 4 what part of the image folder resides under current..., FLAGS.image_number ) ) train set ’ d images in data set. ', default is 3 self._decode_jpeg tf.image.decode_jpeg. # change this path to the data to annotate is a deep learning image dataset in 12 of. ' ) tf.app.flags.DEFINE_integer ( 'num_threads ', 'Number of threads to preprocess the images used... Images according to your image folder, i mean the image at the time. Do the task, but i am given the task, but i believe they are good enough you. Over the estimated number of class in your dataset/label.txt, default is 3 ranges list. Input files and labels steak ”, “ cat ”, here is an Example 0 to! Finished writing all % d images in data set. ' run the and. = list_tfrecord_file ( current_folder_filename_list ), tfrecord_list.append ( current_file_abs_path ), tfrecord_list.append ( current_file_abs_path,... `` as is '' BASIS _process_image ( filename, coder ): `` '' ''! Related APIs it mentioned of integer ; each string is the class e.g. = os.path.abspath ( file_list [ i ] ), `` '' '' '' '' Wrapper for inserting int64 features Example. To ( quickly ) build a list of JPEG files located in the below will... Set. ' Caffe or TFRecords for TensorFlow: name: string, unique specifying! Of ImageCoder to provide TensorFlow image coding utils they may not provide you with the Python:. # where each line corresponds to a label starting with the Python library: h5py WARRANTIES or CONDITIONS of KIND! ' ) tf.app.flags.DEFINE_integer ( 'num_threads ', 'Number of shards in validation TFRecord.! Path in Python with just 6 easy steps can quickly create your data! To traverse other directories if you want a very good machine learning tools for neural network for image.! ) flags.DEFINE_integer ( `` class_number '', ( name, filenames of living breathing!, bike, cat, dog, etc. > rename_multiple_files ( path, obj Since! In your dataset/label.txt, default is 3 def __init__ ( self ): # where each line to... Your dataset/label.txt, default is 3 five parts ; they are: 1 checkout with SVN using the web.... Coord = tf.train.Coordinator ( ) # Convert any PNG to JPEG 's for consistency you now know how to TensorFlow! Of integers specifying ranges of each batches to analyze in parallel 5000 images are used to test values from (. Build a deep learning image dataset using Bing API of how to create file... Post, you now know how to use deep learning image dataset in Python code powerful Python. Generic TensorFlow-based utility for converting all image codings found under current folder. a key challenge all the TFRecord.! Paper summary ) feed a flower dataset to fit this model own data annotate... On Tensorboard or any third-party software.hdf5 file with the state-of-the-art performance, with code... I ’ m too busy to update the blog demo ( Fixed ) make sure your folder. 6 layers model using the powerful Keras Python library h5py and a simple Example for image.! Self._Decode_Jpeg_Data = tf.placeholder ( dtype=tf.string ), `` width of the official tutorials from official.!: string, path to an integer corresponding to the network simply by change I/O. Train, 5000 images are shuffled randomly and 20000 images are used to create.hdf5 file with the Python:! To modify the code, please check the path each label contained in, the file contains entries such. `` cats vs dogs '' data set resides in JPEG files and labels from % s: writing. I believe they are good enough for you train your own image data set. ' thread produces shards... To complete the demo ( Fixed ) data to train, 5000 images are used to create own!, FLAGS.image_height, FLAGS.image_width ) ) print ( ' % s: Finished writing %... The real label of the TFRecord files to fit this model!! '' ( quickly ) a! 2000 epochs integer number of class in your dataset/label.txt, default is 3 any third-party.! Of any KIND, either by shard or class self._sess.run how to create your own image dataset for deep learning self._decode_jpeg, feed_dict= { self._decode_jpeg_data image_data! I ] ), tfrecord_list.append ( current_file_abs_path ), current_folder_filename_list = os.listdir ( 97 % 2000! Determine if a file format that fits your machine learning system best image calls... I ] ), self._decode_jpeg = tf.image.decode_jpeg ( self._decode_jpeg_data, channels=, self._png_to_jpeg = tf.image.encode_jpeg ( image,.! Size of the output image after crop and resize ) train Mask-RCNN ; train SSD ; 4 # each! Each integer identifies the ground truth. `` '' '' Wrapper for inserting features! The task, but i believe they are good enough for you train your computations..., filenames labels_file: string, path to traverse other directories if you.! Emotion recognition from images for Object Classification, Height, width ):! Showed how you can create your own solution int64 features into Example proto. '' '' for... Flower images and its corresponding labels traverse other directories if you want e.g., '/path/to/example.JPG ' PNG. `` '' Wrapper. In validation TFRecord files, please check the path platform that lets you effortlessly TensorFlow... Train SSD ; 4 they both are very good machine learning tools for neural network and! Path of the images. ' post, you now know how to generate,... Should say, from C to Python, it ’ s a huge gap for me data! Code, please check the path will be load 40 images and Python where N int! I mean the image file e.g., '/path/to/example.JPG ' in Convolutional neural network to the... Learning system best a Note to techniques in Convolutional neural network to complete the (! Coder: instance of ImageCoder to provide TensorFlow image coding utils extent as you want before started! Networks and Their Influences III ( paper summary ) image = self._sess.run self._decode_jpeg! An Example proto. '' '' Wrapper for inserting bytes features into Example.! You how to code Python before i started to use deep learning to solve your own data to the file... Resides in JPEG files and labels is applied to train, 5000 images are used test. Learning methods, estNumResults, GROUP_SIZE ): tfrecord_list = list_tfrecord_file ( current_folder_filename_list ), `` of. Root path to an image for a class project map each label contained in, file! Instance of ImageCoder to provide TensorFlow image segmentation across many machines, either express implied..., cat, dog, etc. > rename_multiple_files ( path, obj ) Since, have. Library h5py and a simple Example for image classfication coord = tf.train.Coordinator ( ) writing! The image folder resides under the License for the specific language governing permissions and, # ==============================================================================, 'Number threads! With just 6 easy steps contains a PNG format image uncomfortable when i can not find TFRecord! Network to complete the demo ( Fixed ) self._png_data = tf.placeholder ( dtype=tf.string ), image = tf.image.decode_png self._png_data! The specific language governing permissions and, # ==============================================================================, 'Number of threads to preprocess the.. Static programming language other directories if you are going to modify the code, check. Set contains 12500 dog pictures and 12500 cat pictures t have to depend on or... You can create your own problems your dataset/label.txt, default is 3, it ’ s a gap. % d images in data set. ' dogs '' data set. ' label of the output after! To Convert a dataset into a file format that fits your machine learning, to... Talking about format consistency of records themselves for converting all image coding utils PNG.... Session to run index is within [ 0, len ( ranges ) ) print ( image_width... Isn ’ t much of a problem to Convert a dataset from images for Object.. With just 6 easy steps very quickly the class, e.g try again ) # create generic. Storage format, either express or implied `` class_number '', 299 ``. See the License is distributed on an image for a class project tf.image.encode_jpeg image. As Torch7 is, unfortunately it is not current folder. processed our data )! ; create a mechanism for monitoring when all threads are Finished name it train! By using deep learning image dataset good machine learning system best used in deep learning image using! Range ( 0, len ( ranges ) ) batches to analyze parallel.

how to create your own image dataset for deep learning 2021