How to get chosen class images from Imagenet?

Question

Background

I have been playing around with Deep Dream and Inceptionism, using the Caffe framework to visualize layers of GoogLeNet, an architecture built for the Imagenet project, a large visual database designed for use in visual object recognition.

You can find Imagenet here: Imagenet 1000 Classes.

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To probe into the architecture and generate 'dreams', I am using three notebooks:

1. https://github.com/google/deepdream/blob/master/dream.ipynb

2. https://github.com/kylemcdonald/deepdream/blob/master/dream.ipynb

3. https://github.com/auduno/deepdraw/blob/master/deepdraw.ipynb

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The basic idea here is to extract some features from each channel in a specified layer from the model or a 'guide' image.

Then we input an image we wish to modify into the model and extract the features in the same layer specified (for each octave), enhancing the best matching features, i.e., the largest dot product of the two feature vectors.

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So far I've managed to modify input images and control dreams using the following approaches:

• (a) applying layers as 'end' objectives for the input image optimization. (see Feature Visualization)
• (b) using a second image to guide de optimization objective on the input image.
• (c) visualize Googlenet model classes generated from noise.

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However, the effect I want to achieve sits in-between these techniques, of which I haven't found any documentation, paper, or code.

Desired result (not part of the question to be answered)

To have one single class or unit belonging to a given 'end' layer (a) guide the optimization objective (b) and have this class visualized (c) on the input image:

An example where class = 'face' and input_image = 'clouds.jpg':

please note: the image above was generated using a model for face recognition, which was not trained on the Imagenet dataset. For demonstration purposes only.

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Working code

Approach (a)

from cStringIO import StringIO import numpy as np import scipy.ndimage as nd import PIL.Image from IPython.display import clear_output, Image, display from google.protobuf import text_format import matplotlib as plt     import caffe           model_name = 'GoogLeNet'  model_path = 'models/dream/bvlc_googlenet/' # substitute your path here net_fn   = model_path + 'deploy.prototxt' param_fn = model_path + 'bvlc_googlenet.caffemodel'     model = caffe.io.caffe_pb2.NetParameter() text_format.Merge(open(net_fn).read(), model) model.force_backward = True open('models/dream/bvlc_googlenet/tmp.prototxt', 'w').write(str(model))      net = caffe.Classifier('models/dream/bvlc_googlenet/tmp.prototxt', param_fn,                        mean = np.float32([104.0, 116.0, 122.0]), # ImageNet mean, training set dependent                        channel_swap = (2,1,0)) # the reference model has channels in BGR order instead of RGB  def showarray(a, fmt='jpeg'):     a = np.uint8(np.clip(a, 0, 255))     f = StringIO()     PIL.Image.fromarray(a).save(f, fmt)     display(Image(data=f.getvalue()))    # a couple of utility functions for converting to and from Caffe's input image layout def preprocess(net, img):     return np.float32(np.rollaxis(img, 2)[::-1]) - net.transformer.mean['data'] def deprocess(net, img):     return np.dstack((img + net.transformer.mean['data'])[::-1])        def objective_L2(dst):     dst.diff[:] = dst.data   def make_step(net, step_size=1.5, end='inception_4c/output',                jitter=32, clip=True, objective=objective_L2):     '''Basic gradient ascent step.'''      src = net.blobs['data'] # input image is stored in Net's 'data' blob     dst = net.blobs[end]      ox, oy = np.random.randint(-jitter, jitter+1, 2)     src.data[0] = np.roll(np.roll(src.data[0], ox, -1), oy, -2) # apply jitter shift                  net.forward(end=end)     objective(dst)  # specify the optimization objective     net.backward(start=end)     g = src.diff[0]     # apply normalized ascent step to the input image     src.data[:] += step_size/np.abs(g).mean() * g      src.data[0] = np.roll(np.roll(src.data[0], -ox, -1), -oy, -2) # unshift image                  if clip:         bias = net.transformer.mean['data']         src.data[:] = np.clip(src.data, -bias, 255-bias)    def deepdream(net, base_img, iter_n=20, octave_n=4, octave_scale=1.4,                end='inception_4c/output', clip=True, **step_params):     # prepare base images for all octaves     octaves = [preprocess(net, base_img)]          for i in xrange(octave_n-1):         octaves.append(nd.zoom(octaves[-1], (1, 1.0/octave_scale,1.0/octave_scale), order=1))          src = net.blobs['data']          detail = np.zeros_like(octaves[-1]) # allocate image for network-produced details          for octave, octave_base in enumerate(octaves[::-1]):         h, w = octave_base.shape[-2:]                  if octave > 0:             # upscale details from the previous octave             h1, w1 = detail.shape[-2:]             detail = nd.zoom(detail, (1, 1.0*h/h1,1.0*w/w1), order=1)          src.reshape(1,3,h,w) # resize the network's input image size         src.data[0] = octave_base+detail                  for i in xrange(iter_n):             make_step(net, end=end, clip=clip, **step_params)                          # visualization             vis = deprocess(net, src.data[0])                          if not clip: # adjust image contrast if clipping is disabled                 vis = vis*(255.0/np.percentile(vis, 99.98))             showarray(vis)              print octave, i, end, vis.shape             clear_output(wait=True)                      # extract details produced on the current octave         detail = src.data[0]-octave_base     # returning the resulting image     return deprocess(net, src.data[0]) 

I run the code above with:

end = 'inception_4c/output' img = np.float32(PIL.Image.open('clouds.jpg')) _=deepdream(net, img) 

Approach (b)

""" Use one single image to guide  the optimization process.  This affects the style of generated images  without using a different training set. """  def dream_control_by_image(optimization_objective, end):     # this image will shape input img     guide = np.float32(PIL.Image.open(optimization_objective))       showarray(guide)        h, w = guide.shape[:2]     src, dst = net.blobs['data'], net.blobs[end]     src.reshape(1,3,h,w)     src.data[0] = preprocess(net, guide)     net.forward(end=end)      guide_features = dst.data[0].copy()          def objective_guide(dst):         x = dst.data[0].copy()         y = guide_features         ch = x.shape[0]         x = x.reshape(ch,-1)         y = y.reshape(ch,-1)         A = x.T.dot(y) # compute the matrix of dot-products with guide features         dst.diff[0].reshape(ch,-1)[:] = y[:,A.argmax(1)] # select ones that match best      _=deepdream(net, img, end=end, objective=objective_guide) 

and I run the code above with:

end = 'inception_4c/output' # image to be modified img = np.float32(PIL.Image.open('img/clouds.jpg')) guide_image = 'img/guide.jpg' dream_control_by_image(guide_image, end) 

Question

Now the failed approach how I tried to access individual classes, hot encoding the matrix of classes and focusing on one (so far to no avail):

def objective_class(dst, class=50):    # according to imagenet classes     #50: 'American alligator, Alligator mississipiensis',    one_hot = np.zeros_like(dst.data)    one_hot.flat[class] = 1.    dst.diff[:] = one_hot.flat[class] 

To make this clear: the question is not about the dream code, which is the interesting background and which is already working code, but it is about this last paragraph's question only: Could someone please guide me on how to get images of a chosen class (take class #50: 'American alligator, Alligator mississipiensis') from ImageNet (so that I can use them as input - together with the cloud image - to create a dream image)?

Answer 1

The question is how to get images of the chosen class #50: 'American alligator, Alligator mississipiensis' from ImageNet.

1. Go to image-net.org.

2. Go to "Download".

3. Follow the instructions for "Download Image URLs":

How to download the URLs of a synset from your Brower?

1. Type a query in the Search box and click "Search" button 

The alligator is not shown. ImageNet is under maintenance. Only ILSVRC synsets are included in the search results. No problem, we are fine with the similar animal "alligator lizard", since this search is about getting to the right branch of the WordNet treemap. I do not know whether you will get the direct ImageNet images here even if there were no maintenance.

2. Open a synset papge 

Scrolling down:

Scrolling down:

Searching for the American alligator, which happens to be a saurian diapsid reptile as well, as a near neighbour:

3. You will find the "Download URLs" button under the left-bottom corner of the image browsing window. 

You will get all of the URLs with the chosen class. A text file pops up in the browser:

http://image-net.org/api/text/imagenet.synset.geturls?wnid=n01698640

We see here that it is just about knowing the right WordNet id that needs to be put at the end of the URL.

Manual image download

The text file looks as follows:

• http://farm1.static.flickr.com/136/326907154_d975d0c944.jpg
• http://weeksbay.org/photo_gallery/reptiles/American20Alligator.jpg
• ...
• till image number 1261.

As an example, the first URL links to:

And the second is a dead link:

The third link is dead, but the fourth is working.

The images of these URLs are publicly available, but many links are dead, and the pictures are of lower resolution.

Automated image download

From the ImageNet guide again:

How to download by HTTP protocol? To download a synset by HTTP request, you need to obtain the "WordNet ID" (wnid) of a synset first. When you use the explorer to browse a synset, you can find the WordNet ID below the image window.(Click Here and search "Synset WordNet ID" to find out the wnid of "Dog, domestic dog, Canis familiaris" synset). To learn more about the "WordNet ID", please refer to

Mapping between ImageNet and WordNet 

Given the wnid of a synset, the URLs of its images can be obtained at

http://www.image-net.org/api/text/imagenet.synset.geturls?wnid=[wnid] 

You can also get the hyponym synsets given wnid, please refer to API documentation to learn more.

So what is in that API documentation?

There is everything needed to get all of the WordNet IDs (so called "synset IDs") and their words for all synsets, that is, it has any class name and its WordNet ID at hand, for free.

Obtain the words of a synset

Given the wnid of a synset, the words of the synset can be obtained at

http://www.image-net.org/api/text/wordnet.synset.getwords?wnid=[wnid] 

You can also Click Here to download the mapping between WordNet ID and words for all synsets, Click Here to download the mapping between WordNet ID and glosses for all synsets.

If you know the WordNet ids of choice and their class names, you can use the nltk.corpus.wordnet of "nltk" (natural language toolkit), see the WordNet interface.

In our case, we just need the images of class #50: 'American alligator, Alligator mississipiensis', we already know what we need, thus we can leave the nltk.corpus.wordnet aside (see tutorials or Stack Exchange questions for more). We can automate the download of all alligator images by looping through the URLs that are still alive. We could also widen this to the full WordNet with a loop over all WordNet IDs, of course, though this would take far too much time for the whole treemap - and is also not recommended since the images will stop being there if 1000s of people download them daily.

I am afraid I will not take the time to write this Python code that accepts the ImageNet class number "#50" as the argument, though that should be possible as well, using mapping tables from WordNet to ImageNet. Class name and WordNet ID should be enough.

For a single WordNet ID, the code could be as follows:

import urllib.request  import csv  wnid = "n01698640" url = "http://image-net.org/api/text/imagenet.synset.geturls?wnid=" + str(wnid)  # From https://stackoverflow.com/a/45358832/6064933 req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'}) with open(wnid + ".csv", "wb") as f:     with urllib.request.urlopen(req) as r:         f.write(r.read())  with open(wnid + ".csv", "r") as f:     counter = 1     for line in f.readlines():               print(line.strip("\n"))         failed = []         try:             with urllib.request.urlopen(line) as r2:                 with open(f'''{wnid}_{counter:05}.jpg''', "wb") as f2:                     f2.write(r2.read())         except:             failed.append(f'''{counter:05}, {line}'''.strip("\n"))         counter += 1         if counter == 10:             break  with open(wnid + "_failed.csv", "w", newline="") as f3:     writer = csv.writer(f3)     writer.writerow(failed) 

Result:

4. If you need the images even behind the dead links and in original quality, and if your project is non-commercial, you can sign in, see "How do I get a copy of the images?" at the Download FAQ.

• In the URL above, you see the wnid=n01698640 at the end of the URL which is the WordNet id that is mapped to ImageNet.
• Or in the "Images of the Synset" tab, just click on "Wordnet IDs".

To get to:

or right-click -- save as:

You can use the WordNet id to get the original images.

If you are commercial, I would say contact the ImageNet team.

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Add-on

Taking up the idea of a comment: If you do not want many images, but just the "one single class image" that represents the class as much as possible, have a look at Visualizing GoogLeNet Classes and try to use this method with the images of ImageNet instead. Which is using the deepdream code as well.

Visualizing GoogLeNet Classes

29. July 2015

Ever wondered what a deep neural network thinks a Dalmatian should look like? Well, wonder no more.

Recently Google published a post describing how they managed to use deep neural networks to generate class visualizations and modify images through the so called “inceptionism” method. They later published the code to modify images via the inceptionism method yourself, however, they didn’t publish code to generate the class visualizations they show in the same post.

While I never figured out exactly how Google generated their class visualizations, after butchering the deepdream code and this ipython notebook from Kyle McDonald, I managed to coach GoogLeNet into drawing these:

... [with many other example images to follow]