In this code pattern, we will guide you through the process of analyzing an image dataset using a pre-trained convolution network (VGG16) and extracting feature vectors for each image using a Jupyter notebook. This is a computationally expensive process, which takes 300 times longer on a CPU versus a GPU. We'll use the GPU environment on Watson Studio or on your local machine to accelerate feature extraction. Post analysis, we will try to demonstrate 'reverse image search', one of the widely popular applications of image analysis. Reverse image search is a content-based image retrieval (CBIR) query technique that involves providing the CBIR system with a sample image that it will then base its search upon; in terms of information retrieval, the sample image is what formulates a search query. In particular, reverse image search is characterized by a lack of search terms. Learn more about Reverse Image Search on its Wikipedia page.

When you have completed this code pattern, you will understand how to:

• Use GPU acceleration in Watson Studio or locally to greatly improve performance of feature extraction.
• Download VGG16 pre-trained model using keras.
• Perform Feature Extraction. Here we remove the last layer ie.,the softmax classification layer so our output model now has only 12 layers and the last layer would be fc2(Dense), a fully connected layer.
• Get feature vectors for all the images, then scale them down using PCA.
• Use cosine distance between PCA features to compare the query image to 5 number of closest images and return them as thumbnails.

1. User interacts with Jupyter notebook to import and use Python modules.
2. 101_Object_Categories from caltech-101 are imported for image search.
3. Keras VGG16 model is imported, with weights pre-trained on ImageNet.
4. User can perform feature extraction using GPU for increased performance.

Note: Click on the image below to view the video on YouTube. For Google Chrome, press the Ctrl key + the left mouse button and say Open link.

1. Clone the repository
2. Perform either i or ii:
   1. Create a notebook in IBM Watson Studio
   2. Create notebook locally
3. Run the notebook

git clone https://github.com/IBM/reverse-image-search-gpu-studio
cd reverse-image-search-gpu-studio

Either in Watson Studio or locally.

• Sign up for IBM's Watson Studio. By creating a project in Watson Studio a free tier Object Storage service will be created in your IBM Cloud account. Take note of your service names as you will need to select them in the following steps.

Note: When creating your Object Storage service, select the Free storage type in order to avoid having to pay an upgrade fee.

• Create a new Project in Watson Studio (New --> Empty project)

• Create a GPU Environment (Environment --> New Environment --> GPU)

• Create a new Notebook (Add to project --> Notebook --> From URL)
• Provision the notebook on newly created GPU Environment

• Stop the Environment after usage

• Clone the repository

git clone https://github.com/IBM/reverse-image-search-gpu-studio

• Navigate into the directory

cd reverse-image-search-gpu-studio

• Run using Jupyter notebooks, choosing the data/ReverseImageSearch.ipynb notebook

jupyter notebook

When a notebook is executed, each code cell in the notebook is executed, in order, from top to bottom.

Each code cell is selectable and is preceded by a tag in the left margin. The tag format is In [x]:. Depending on the state of the notebook, the x can be:

• A blank, this indicates that the cell has never been executed.
• A number, this number represents the relative order this code step was executed.
• A *, this indicates that the cell is currently executing.

There are several ways to execute the code cells in your notebook:

• One cell at a time.
  • Select the cell, and then press the Play button in the toolbar.
• Batch mode, in sequential order.
  • From the Cell menu bar, there are several options available. For example, you can Run All cells in your notebook, or you can Run All Below, that will start executing from the first cell under the currently selected cell, and then continue executing all cells that follow.
• At a scheduled time.
  • Press the Schedule button located in the top right section of your notebook panel. Here you can schedule your notebook to be executed once at some future time, or repeatedly at your specified interval.

With GPU on Watson Studio:

tic = time.time()
features = []
for i, image_path in enumerate(images):
  if i%500 == 0:
    toc = time.time()
    elap = toc-tic;
    print("analyzing image %d / %d. Time taken : %4.4f seconds"%(i,len(images),elap))
    tic= time.time()
  img,x = load_image(image_path)
  feat = feat_extractor.predict(x)[0]
  features.append(feat)
print('finished extracting features for %d images' % len(images))

analyzing image 0 / 9144. Time taken : 0.0000 seconds
analyzing image 500 / 9144. Time taken : 14.2453 seconds
analyzing image 1000 / 9144. Time taken : 14.6271 seconds
analyzing image 1500 / 9144. Time taken : 14.8962 seconds
analyzing image 2000 / 9144. Time taken : 15.4356 seconds
analyzing image 2500 / 9144. Time taken : 14.4446 seconds
analyzing image 3000 / 9144. Time taken : 15.7983 seconds
analyzing image 3500 / 9144. Time taken : 14.3962 seconds
analyzing image 4000 / 9144. Time taken : 14.6742 seconds
analyzing image 4500 / 9144. Time taken : 14.5494 seconds
analyzing image 5000 / 9144. Time taken : 14.2647 seconds
analyzing image 5500 / 9144. Time taken : 14.5833 seconds
analyzing image 6000 / 9144. Time taken : 14.6735 seconds
analyzing image 6500 / 9144. Time taken : 14.2645 seconds
analyzing image 7000 / 9144. Time taken : 14.6823 seconds
analyzing image 7500 / 9144. Time taken : 14.6732 seconds
analyzing image 8000 / 9144. Time taken : 15.3673 seconds
analyzing image 8500 / 9144. Time taken : 14.6734 seconds
analyzing image 9000 / 9144. Time taken : 13.9992 seconds
finished extracting features for 9144 images

Without GPU (CPU only):

analyzing image 0 / 9144. Time taken : 0.0000 seconds
analyzing image 500 / 9144. Time taken : 1445.3647 seconds
analyzing image 1000 / 9144. Time taken : 1462.7562 seconds
analyzing image 1500 / 9144. Time taken : 1443.5652 seconds
analyzing image 2000 / 9144. Time taken : 1417.2242 seconds
analyzing image 2500 / 9144. Time taken : 1438.3622 seconds
analyzing image 3000 / 9144. Time taken : 1387.2346 seconds
analyzing image 3500 / 9144. Time taken : 1453.6862 seconds
analyzing image 4000 / 9144. Time taken : 1382.9572 seconds
analyzing image 4500 / 9144. Time taken : 1418.3552 seconds
analyzing image 5000 / 9144. Time taken : 1387.3456 seconds
analyzing image 5500 / 9144. Time taken : 1425.2452 seconds
analyzing image 6000 / 9144. Time taken : 1460.4654 seconds
analyzing image 6500 / 9144. Time taken : 1428.4565 seconds
analyzing image 7000 / 9144. Time taken : 1456.3365 seconds
analyzing image 7500 / 9144. Time taken : 1411.2468 seconds
analyzing image 8000 / 9144. Time taken : 1428.5892 seconds
analyzing image 8500 / 9144. Time taken : 1450.2576 seconds
analyzing image 9000 / 9144. Time taken : 1412.5300 seconds
finished extracting features for 9144 images

Example Notebook

This code pattern is licensed under the Apache License, Version 2. Separate third-party code objects invoked within this code pattern are licensed by their respective providers pursuant to their own separate licenses. Contributions are subject to the Developer Certificate of Origin, Version 1.1 and the Apache License, Version 2.

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