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. # Each thread produces N shards where N = int(num_shards / num_threads). 'train-00002-of-00010', shard = thread_index * num_shards_per_batch + s, output_file = os.path.join(FLAGS.output_directory, output_filename), writer = tf.python_io.TFRecordWriter(output_file), files_in_shard = np.arange(shard_ranges[s], shard_ranges[s +, image_buffer, height, width = _process_image(filename, coder). ")flags.DEFINE_integer("image_width", 299, "Width of the output image after crop and resize. num_shards: integer number of shards for this data set. # Change this PATH to traverse other directories if you want. It also helps manage large data sets, view hyperparameters and metrics across your entire team on a convenient dashboard, and manage thousands of experiments easily. 'dog', labels: list of integer; each integer identifies the ground truth. print('Launching %d threads for spacings: %s' % (FLAGS.num_threads, ranges)) sys.stdout.flush() # Create a mechanism for monitoring when all threads are finished. 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. This blog aims to teach you how to use your own data to train a convolutional neural network for image recognition in tensorflow. The focus will be given to how to feed your own data to the network instead of how to design the network architecture.Before I started to survey tensorflow, me and my colleagues were using Torch7 or caffe. % (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.' thread_index: integer, unique batch to run index is within [0, len(ranges)). Be noted that this script must be used along the above script, otherwise, believe me, it wouldn’t work.This program will call the first script to find all the tfrecord files, then extract the images, label, filenames etc. Keras Computer Vision Datasets 2. """Build a list of all images files and labels in the data set. data_dir/dog/another-image.JPEG data_dir/dog/my-image.jpg where 'dog' is the label associated with these images. ; Create a dataset from Images for Object Classification. 1.The famous data set "cats vs dogs" data set is used to create.hdf5 file with the Python library: h5py. I did go through 80% of the official tutorials from official tutorials. ... you can quickly create your own image and video segmentation data in no time!! download the GitHub extension for Visual Studio, http://machinelearninguru.com/deep_learning/data_preparation/hdf5/hdf5.html. 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. Specify your own configurations in conf.json file. Then I found the following script in tensorflow repo. % (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. Args: name: string, unique identifier specifying the data set. 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. texts: list of strings; each string is the class, e.g. # See the License for the specific language governing permissions and, # ==============================================================================, 'Number of shards in training TFRecord files. Args: coder: instance of ImageCoder to provide TensorFlow image coding utils. Create your own data set with Python library h5py and a simple example for image classfication. 2.The data set contains 12500 dog pictures and 12500 cat pictures. ", Creative Commons Attribution 4.0 International License. % (i, FLAGS.image_number)) print("Complete!!") Maybe. ', 'Number of threads to preprocess the images.'. % (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. # saved TFRecord files. 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). What I’m gonna do here is to write a python script to turn all the images and associated label from a folder (folder name afters the label) into a tfRecord file, then feed the tfRecord into the network.The related skills I think maybe covers: python-numpy, python-os, python-scipy, python-pillow, protocol buffers, tensorflow.Let’s get started on directory traversal script, this scrpit will do the directory traversal to your current directory, list all the file names or folder names, and select all the files end with .tfrecord. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. """, """Wrapper for inserting bytes features into Example proto. Each Category has 36 to 40 images and that's a small dataset to be used in deep learning methods. For ex. Learn more about machine learning, image processing, image segmentation, deep learning Image Acquisition Toolbox, Deep Learning Toolbox. The problem currently is how to handle multiple return values from tf.graph(). A data set is a collection of data. (coder, thread_index, ranges, name, filenames. ● create h5 file.py: use your own images to create a hdf5 data set. 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. 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! Specifically, you learned the six key steps in using Keras to create a neural network or deep learning model, step-by-step including: How to load data. We map each label contained in. """Determine if a file contains a PNG format image. Make the randomization repeatable. % len(current_folder_filename_list)) print("Please be noted that only files end with '*.tfrecord' will be load!") ", "Number of class in your dataset/label.txt, default is 3. We showed how you can create a dashboard of living, breathing visualizations of a deep learning model performance, with simple code snippets. % (FLAGS.image_number,FLAGS.image_height, FLAGS.image_width)). ", "Width of the output image after crop and resize. File1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980818283import tensorflow as tfimport numpy as npimport osfrom PIL import Imagefrom dir_traversal_tfrecord import tfrecord_auto_traversalflags = tf.app.flagsFLAGS = flags.FLAGSflags.DEFINE_integer("image_number", 300, "Number of images in your tfrecord, default is 300. # Create a generic TensorFlow-based utility for converting all image codings. data_dir: string, path to the root directory of images. # Create a single Session to run all image coding calls. % (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. This article is a comprehensive review of Data Augmentation techniques for Deep Learning, specific to images. 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. # Create a mechanism for monitoring when all threads are finished. self._decode_jpeg_data = tf.placeholder(dtype=tf.string), self._decode_jpeg = tf.image.decode_jpeg(self._decode_jpeg_data, channels=. ", "Height of the output image after crop and resize. Try to display the label and the image at the same time, generate the preprocessed images according to their labels. ● cats_dogs_batch.py: read your hdf5 file and prepare the train batch, test batch. 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!" The script named flower_train_cnn.py is a script to feed a flower dataset to a typical CNN from scratch. Or at least Jack or 10. 'dog'. directory: string, root path to the data set. In this post, you discovered how to create your first neural network model using the powerful Keras Python library for deep learning. def __init__(self): # Create a single Session to run all image coding calls. Create a label.txt file under your current directory. ')tf.app.flags.DEFINE_integer('validation_shards', 0, 'Number of shards in validation TFRecord files. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. Return the list of names of the tfrecord files. 1.The famous data set "cats vs dogs" data set is used to create .hdf5 file with the Python library: h5py. image_data = tf.gfile.FastGFile(filename, 'r').read() # Convert any PNG to JPEG's for consistency. Annotate images. Deciding what part of the data to annotate is a key challenge. Assumes that the file contains entries as such: dog cat flower where each line corresponds to a label. 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 … ")def _int64_feature(value): """Wrapper for inserting int64 features into Example proto.""" 1. "%s files were found under current folder. _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. '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 image storage format, either LMDB for Caffe or TFRecords for TensorFlow.. 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. """Processes and saves list of images as TFRecord in 1 thread. # Leave label index 0 empty as a background class. labels_file: string, path to the labels file. The goal of this article is to hel… name: string, unique identifier specifying the data set. such as “sushi”, “steak”, “cat”, “dog”, here is an example. 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." 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. If nothing happens, download the GitHub extension for Visual Studio and try again. label_index = 1 # Construct the list of JPEG files and labels. example = _convert_to_example(filename, image_buffer, label, writer.write(example.SerializeToString()), '%s [thread %d]: Processed %d of %d images in thread batch. 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. Feed the images to a network to complete the demo (Fixed). # Break all images into batches with a [ranges[i][0], ranges[i][1]]. Deep Learning with Your Own Image Dataset. image_data = tf.gfile.FastGFile(filename. Default is 299. Default is 299. 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. Args: 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. 5 simple steps for Deep Learning. PyImageSearch – 9 Apr 18 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. Then, here’s my road to tensorflow:I learn basic python syntax from this well known book: A Byte of Python. This is Part 2 of How to use Deep Learning when you have Limited Data. # Initializes function that decodes RGB JPEG data. They both are very good machine learning tools for neural network. There is a very good blog written by Dr Adrian Rosebrock for building a deep learning image dataset using Bing API. 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. % 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. Args: filename: string, path to an image file e.g., '/path/to/example.JPG'. The original propose for turning to tensorflow is that we believe tensorflow will have a better support on mobile side, as we all know that Android) and tensorflow are both dominated by Google.If you are really hurry with importing data to your program, visit my Github repo. Well, you now know how to create your own Image Dataset in python with just 6 easy steps. How to scrape google images and build a deep learning image dataset in 12 lines of code? if current_folder_filename_list != None: print("%s files were found under current folder. " # define a function to list tfrecord files. Python is much more easier than static programming language. Create your own emoji with deep learning. If nothing happens, download GitHub Desktop and try again. image_data = coder.png_to_jpeg(image_data), # image = tf.Session().run(tf.image.resize_image_with_crop_or_pad(image, 128, 128)), # image_data = tf.image.encode_jpeg(image), # img.save(os.path.join("./re_steak/"+str(i)+".jpeg")). # The labels file contains a list of valid labels are held in this file. such as “sushi”, “steak”, “cat”, “dog”, here is an. 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. ", 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. IBM Spectrum Conductor Deep Learning Impact assumes that you have collected your raw data and labeled the raw data using a label file or organized the data into folders. directory: string, root path to the data set. ) that I called it puzzle dataset from natural images with 7 categories. 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. Download the dataset from the above link. Merge the content of ‘car’ and ‘bikes’ folder and name it ‘train set’. neural network. assert len(filenames) == len(texts) assert len(filenames) == len(labels) # Break all images into batches with a [ranges[i][0], ranges[i][1]]. Train FCN (Fully Convolutional Network) Train Mask-RCNN; Train SSD; 4. 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. Make sure your image folder resides under the current folder. filename: string, path of the image file. Make the randomization repeatable. That’s essentially saying that I’d be an expert programmer for knowing how to type: print(“Hello World”). 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. 'dog', example = tf.train.Example(features=tf.train.Features(feature={, """Helper class that provides TensorFlow image coding utilities.""". I have used Bing API multiple times for building my custom dataset.Yes can you gather a bunch of images from Bing API and build your own dataset . We have all been there. width: integer, image width in pixels. """

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