def build_index_and_search_through_it(images_features, file_index):
# Decide whether to do only image search or hybrid search
if not features_from_new_model_boolean:
# This is pure image search
image_index = vector_search.index_features(images_features, dims=4096)
search_key = get_index(input_image, file_index)
results = vector_search.search_index_by_key(search_key, image_index,
file_index)
print(results)
else:
word_vectors = vector_search.load_glove_vectors(glove_path)
# If we are searching for tags for an image
if not input_word:
# Work on a single image instead of indexing
search_key = get_index(input_image, file_index)
word_index, word_mapping = vector_search.build_word_index(
word_vectors)
results = vector_search.search_index_by_value(
images_features[search_key], word_index, word_mapping)
# If we are using words to search through our images
else:
image_index = vector_search.index_features(images_features,
dims=300)
results = vector_search.search_index_by_value(
word_vectors[input_word], image_index, file_index)
print(results)
def build_index_and_search_through_it(images_features, file_index):
# Decide whether to do only image search or hybrid search
if not features_from_new_model_boolean:
# This is pure image search
image_index = vector_search.index_features(images_features, dims=4096)
search_key = get_index(input_image, file_index)
results = vector_search.search_index_by_key(search_key, image_index, file_index)
print(results)
st.write('\n'.join('- `%s`' % elt for elt in results))
demo.show_top_n(9, results, search_by_img=False)
else:
word_vectors = vector_search.load_glove_vectors(glove_path)
# If we are searching for tags for an image
if not input_word:
# Work on a single image instead of indexing
search_key = get_index(input_image, file_index)
word_index, word_mapping = vector_search.build_word_index(word_vectors)
results = vector_search.search_index_by_value(images_features[search_key], word_index, word_mapping)
for result in results:
st.write(result)
image_index = vector_search.index_features(images_features, dims=300)
results = vector_search.search_index_by_value(word_vectors[result[1]], image_index, file_index)
st.write('\n'.join('- `%s`' % elt for elt in results))
demo.show_top_n(9, results, search_by_img=False)
# If we are using words to search through our images
else:
image_index = vector_search.index_features(images_features, dims=300)
st.write(word_vectors[input_word])
results = vector_search.search_index_by_value(word_vectors[input_word], image_index, file_index)
st.write('\n'.join('- `%s`' % elt for elt in results))
demo.show_top_n(9, results, search_by_img=False)
print(results)
st.image(Image.open('assets/vgg16_chopped.jpg'), width=800, caption="Our model")
st.write("This is how we get such a model in practice")
show_source(vector_search.load_headless_pretrained_model)
st.write("""
What do we mean by generating embeddings? Well we just use our pre-trained model up to the penultimate layer, and
store the value of the activations.""")
show_source(vector_search.generate_features)
st.write('Here are what the embeddings look like for the first 20 images. Each image is now represented by a '
'sparse vector of size 4096:')
st.write(images_features[:20])
st.write("Now that we have the features, we will build a fast index to search through them using Annoy.")
with st.echo():
image_index = vector_search.index_features(images_features)
show_source(vector_search.index_features)
st.header("Using our embeddings to search through images")
st.write("""
We can now simply take in an image, get its **embedding** (saved to disk), and look in our fast index to
find **similar embeddings, and thus similar images**.
This is especially useful, since image labels are often noisy, so there is more to an image than it's label.
In our dataset for example, we have both a class `cat`, and a class `bottle`.
Which class do you think this image is labeled as?
""")
st.image(to_array(image_paths[search_key]), caption="Cat or bottle")
loaded_model = vector_search.load_headless_pretrained_model()
if model_path:
loaded_model = load_model(model_path)
# Decide whether to index or search
if indexing:
features, index = index_images(index_folder, features_path,
file_mapping, loaded_model)
print("Indexed %s images" % len(features))
else:
images_features, file_index = vector_search.load_features(
features_path, file_mapping)
# Decide whether to do only image search or hybrid search
if pure_image_embedding:
image_index = vector_search.index_features(images_features,
dims=4096)
search_key = get_index(input_image, file_index)
results = vector_search.search_index_by_key(
search_key, image_index, file_index)
print(results)
else:
word_vectors = vector_search.load_glove_vectors(glove_path)
# If we are searching for tags for an image
if input_image:
search_key = get_index(input_image, file_index)
word_index, word_mapping = vector_search.build_word_index(
word_vectors)
results = vector_search.search_index_by_value(
images_features[search_key], word_index, word_mapping)
# If we are using words to search through our images
else:
features)
with open(
'./object_detection/feature_extraction/ref_img_filemapping_no_aug.json',
'w') as f:
json.dump(file_mapping, f)
# %% Build database reference features
'''
Build and save an ANNOY search index for fast searching.
'''
# Load the saved features and file mappings and compile Annoy search indices
features, file_index = vector_search.load_features(
'object_detection/feature_extraction/ref_img_features_no_aug',
'./object_detection/feature_extraction/ref_img_filemapping_no_aug')
ref_img_index = vector_search.index_features(features, n_trees=8000)
ref_img_index.save(
'./object_detection/feature_extraction/ref_img_index_no_aug.ann')
# %%
'''
Get and save hash dictionary mapping between the file_mapping index,
and each the item name, category, source website, local source file path,
and the local source file name.
'''
with open(
'./object_detection/feature_extraction/ref_img_filemapping_no_aug.json',
'r') as f:
ref_img_filemapping = json.load(f)