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import numpy as np
import pandas as pd
from PIL import Image
from tqdm import tqdm
from scipy.sparse import csr_matrix
from typing import Mapping, List, Tuple, Union
from transformers.pipelines import Pipeline, pipeline
from bertopic.representation._mmr import mmr
from bertopic.representation._base import BaseRepresentation
class VisualRepresentation(BaseRepresentation):
""" From a collection of representative documents, extract
images to represent topics. These topics are represented by a
collage of images.
Arguments:
nr_repr_images: Number of representative images to extract
nr_samples: The number of candidate documents to extract per cluster.
image_height: The height of the resulting collage
image_square: Whether to resize each image in the collage
to a square. This can be visually more appealing
if all input images are all almost squares.
image_to_text_model: The model to caption images.
batch_size: The number of images to pass to the
`image_to_text_model`.
Usage:
```python
from bertopic.representation import VisualRepresentation
from bertopic import BERTopic
# The visual representation is typically not a core representation
# and is advised to pass to BERTopic as an additional aspect.
# Aspects can be labeled with dictionaries as shown below:
representation_model = {
"Visual_Aspect": VisualRepresentation()
}
# Use the representation model in BERTopic as a separate aspect
topic_model = BERTopic(representation_model=representation_model)
```
"""
def __init__(self,
nr_repr_images: int = 9,
nr_samples: int = 500,
image_height: Tuple[int, int] = 600,
image_squares: bool = False,
image_to_text_model: Union[str, Pipeline] = None,
batch_size: int = 32):
self.nr_repr_images = nr_repr_images
self.nr_samples = nr_samples
self.image_height = image_height
self.image_squares = image_squares
# Text-to-image model
if isinstance(image_to_text_model, Pipeline):
self.image_to_text_model = image_to_text_model
elif isinstance(image_to_text_model, str):
self.image_to_text_model = pipeline("image-to-text", model=image_to_text_model)
elif image_to_text_model is None:
self.image_to_text_model = None
else:
raise ValueError("Please select a correct transformers pipeline. For example:"
"pipeline('image-to-text', model='nlpconnect/vit-gpt2-image-captioning')")
self.batch_size = batch_size
def extract_topics(self,
topic_model,
documents: pd.DataFrame,
c_tf_idf: csr_matrix,
topics: Mapping[str, List[Tuple[str, float]]]
) -> Mapping[str, List[Tuple[str, float]]]:
""" Extract topics
Arguments:
topic_model: A BERTopic model
documents: All input documents
c_tf_idf: The topic c-TF-IDF representation
topics: The candidate topics as calculated with c-TF-IDF
Returns:
representative_images: Representative images per topic
"""
# Extract image ids of most representative documents
images = documents["Image"].values.tolist()
(_, _, _,
repr_docs_ids) = topic_model._extract_representative_docs(c_tf_idf,
documents,
topics,
nr_samples=self.nr_samples,
nr_repr_docs=self.nr_repr_images)
unique_topics = sorted(list(topics.keys()))
# Combine representative images into a single representation
representative_images = {}
for topic in tqdm(unique_topics):
# Get and order represetnative images
sliced_examplars = repr_docs_ids[topic+topic_model._outliers]
sliced_examplars = [sliced_examplars[i:i + 3] for i in
range(0, len(sliced_examplars), 3)]
images_to_combine = [
[Image.open(images[index]) if isinstance(images[index], str)
else images[index] for index in sub_indices]
for sub_indices in sliced_examplars
]
# Concatenate representative images
representative_image = get_concat_tile_resize(images_to_combine,
self.image_height,
self.image_squares)
representative_images[topic] = representative_image
# Make sure to properly close images
if isinstance(images[0], str):
for image_list in images_to_combine:
for image in image_list:
image.close()
return representative_images
def _convert_image_to_text(self,
images: List[str],
verbose: bool = False) -> List[str]:
""" Convert a list of images to captions.
Arguments:
images: A list of images or words to be converted to text.
verbose: Controls the verbosity of the process
Returns:
List of captions
"""
# Batch-wise image conversion
if self.batch_size is not None:
documents = []
for batch in tqdm(self._chunks(images), disable=not verbose):
outputs = self.image_to_text_model(batch)
captions = [output[0]["generated_text"] for output in outputs]
documents.extend(captions)
# Convert images to text
else:
outputs = self.image_to_text_model(images)
documents = [output[0]["generated_text"] for output in outputs]
return documents
def image_to_text(self, documents: pd.DataFrame, embeddings: np.ndarray) -> pd.DataFrame:
""" Convert images to text """
# Create image topic embeddings
topics = documents.Topic.values.tolist()
images = documents.Image.values.tolist()
df = pd.DataFrame(np.hstack([np.array(topics).reshape(-1, 1), embeddings]))
image_topic_embeddings = df.groupby(0).mean().values
# Extract image centroids
image_centroids = {}
unique_topics = sorted(list(set(topics)))
for topic, topic_embedding in zip(unique_topics, image_topic_embeddings):
indices = np.array([index for index, t in enumerate(topics) if t == topic])
top_n = min([self.nr_repr_images, len(indices)])
indices = mmr(topic_embedding.reshape(1, -1), embeddings[indices], indices, top_n=top_n, diversity=0.1)
image_centroids[topic] = indices
# Extract documents
documents = pd.DataFrame(columns=["Document", "ID", "Topic", "Image"])
current_id = 0
for topic, image_ids in tqdm(image_centroids.items()):
selected_images = [Image.open(images[index]) if isinstance(images[index], str) else images[index] for index in image_ids]
text = self._convert_image_to_text(selected_images)
for doc, image_id in zip(text, image_ids):
documents.loc[len(documents), :] = [doc, current_id, topic, images[image_id]]
current_id += 1
# Properly close images
if isinstance(images[image_ids[0]], str):
for image in selected_images:
image.close()
return documents
def _chunks(self, images):
for i in range(0, len(images), self.batch_size):
yield images[i:i + self.batch_size]
def get_concat_h_multi_resize(im_list):
"""
Code adapted from: https://note.nkmk.me/en/python-pillow-concat-images/
"""
min_height = min(im.height for im in im_list)
min_height = max(im.height for im in im_list)
im_list_resize = []
for im in im_list:
im.resize((int(im.width * min_height / im.height), min_height), resample=0)
im_list_resize.append(im)
total_width = sum(im.width for im in im_list_resize)
dst = Image.new('RGB', (total_width, min_height), (255, 255, 255))
pos_x = 0
for im in im_list_resize:
dst.paste(im, (pos_x, 0))
pos_x += im.width
return dst
def get_concat_v_multi_resize(im_list):
"""
Code adapted from: https://note.nkmk.me/en/python-pillow-concat-images/
"""
min_width = min(im.width for im in im_list)
min_width = max(im.width for im in im_list)
im_list_resize = [im.resize((min_width, int(im.height * min_width / im.width)), resample=0)
for im in im_list]
total_height = sum(im.height for im in im_list_resize)
dst = Image.new('RGB', (min_width, total_height), (255, 255, 255))
pos_y = 0
for im in im_list_resize:
dst.paste(im, (0, pos_y))
pos_y += im.height
return dst
def get_concat_tile_resize(im_list_2d, image_height=600, image_squares=False):
"""
Code adapted from: https://note.nkmk.me/en/python-pillow-concat-images/
"""
images = [[image.copy() for image in images] for images in im_list_2d]
# Create
if image_squares:
width = int(image_height / 3)
height = int(image_height / 3)
images = [[image.resize((width, height)) for image in images] for images in im_list_2d]
# Resize images based on minimum size
else:
min_width = min([min([img.width for img in imgs]) for imgs in im_list_2d])
min_height = min([min([img.height for img in imgs]) for imgs in im_list_2d])
for i, imgs in enumerate(images):
for j, img in enumerate(imgs):
if img.height > img.width:
images[i][j] = img.resize((int(img.width * min_height / img.height), min_height), resample=0)
elif img.width > img.height:
images[i][j] = img.resize((min_width, int(img.height * min_width / img.width)), resample=0)
else:
images[i][j] = img.resize((min_width, min_width))
# Resize grid image
images = [get_concat_h_multi_resize(im_list_h) for im_list_h in images]
img = get_concat_v_multi_resize(images)
height_percentage = (image_height/float(img.size[1]))
adjusted_width = int((float(img.size[0])*float(height_percentage)))
img = img.resize((adjusted_width, image_height), Image.Resampling.LANCZOS)
return img
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