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Docs updates: Add Explorer to tab, YOLOv5 in Guides and Usage in Quickstart (#7438)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Haixuan Xavier Tao <tao.xavier@outlook.com>
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Ayush Chaurasia 2024-01-10 04:20:26 +05:30 • committed by GitHub
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commit a92adf8231
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30 changed files with 227 additions and 105 deletions
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2
ultralytics/data/explorer/__init__.py
View file

@ -1,3 +1,5 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
from .utils import plot_query_result
__all__ = ['plot_query_result']

39
ultralytics/data/explorer/explorer.py
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@ -1,3 +1,5 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
from io import BytesIO
from pathlib import Path
from typing import Any, List, Tuple, Union
@ -24,9 +26,8 @@ class ExplorerDataset(YOLODataset):
def __init__(self, *args, data: dict = None, **kwargs) -> None:
super().__init__(*args, data=data, **kwargs)
# NOTE: Load the image directly without any resize operations.
def load_image(self, i: int) -> Union[Tuple[np.ndarray, Tuple[int, int], Tuple[int, int]], Tuple[None, None, None]]:
"""Loads 1 image from dataset index 'i', returns (im, resized hw)."""
"""Loads 1 image from dataset index 'i' without any resize ops."""
im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i]
if im is None: # not cached in RAM
if fn.exists(): # load npy
@ -41,6 +42,7 @@ class ExplorerDataset(YOLODataset):
return self.ims[i], self.im_hw0[i], self.im_hw[i]
def build_transforms(self, hyp: IterableSimpleNamespace = None):
"""Creates transforms for dataset images without resizing."""
return Format(
bbox_format='xyxy',
normalize=False,
@ -122,7 +124,7 @@ class Explorer:
self.table = table
def _yield_batches(self, dataset: ExplorerDataset, data_info: dict, model: YOLO, exclude_keys: List[str]):
# Implement Batching
"""Generates batches of data for embedding, excluding specified keys."""
for i in tqdm(range(len(dataset))):
self.progress = float(i + 1) / len(dataset)
batch = dataset[i]
@ -143,7 +145,7 @@ class Explorer:
limit (int): Number of results to return.
Returns:
An arrow table containing the results. Supports converting to:
(pyarrow.Table): An arrow table containing the results. Supports converting to:
- pandas dataframe: `result.to_pandas()`
- dict of lists: `result.to_pydict()`
@ -175,7 +177,7 @@ class Explorer:
return_type (str): Type of the result to return. Can be either 'pandas' or 'arrow'. Defaults to 'pandas'.
Returns:
An arrow table containing the results.
(pyarrow.Table): An arrow table containing the results.
Example:
```python
@ -216,7 +218,7 @@ class Explorer:
labels (bool): Whether to plot the labels or not.
Returns:
PIL Image containing the plot.
(PIL.Image): Image containing the plot.
Example:
```python
@ -248,7 +250,7 @@ class Explorer:
return_type (str): Type of the result to return. Can be either 'pandas' or 'arrow'. Defaults to 'pandas'.
Returns:
A table or pandas dataframe containing the results.
(pandas.DataFrame): A dataframe containing the results.
Example:
```python
@ -282,7 +284,7 @@ class Explorer:
limit (int): Number of results to return. Defaults to 25.
Returns:
PIL Image containing the plot.
(PIL.Image): Image containing the plot.
Example:
```python
@ -306,11 +308,12 @@ class Explorer:
Args:
max_dist (float): maximum L2 distance between the embeddings to consider. Defaults to 0.2.
top_k (float): Percentage of the closest data points to consider when counting. Used to apply limit when running
vector search. Defaults: None.
vector search. Defaults: None.
force (bool): Whether to overwrite the existing similarity index or not. Defaults to True.
Returns:
A pandas dataframe containing the similarity index.
(pandas.DataFrame): A dataframe containing the similarity index. Each row corresponds to an image, and columns
include indices of similar images and their respective distances.
Example:
```python
@ -340,6 +343,7 @@ class Explorer:
sim_table = self.connection.create_table(sim_idx_table_name, schema=get_sim_index_schema(), mode='overwrite')
def _yield_sim_idx():
"""Generates a dataframe with similarity indices and distances for images."""
for i in tqdm(range(len(embeddings))):
sim_idx = self.table.search(embeddings[i]).limit(top_k).to_pandas().query(f'_distance <= {max_dist}')
yield [{
@ -364,7 +368,7 @@ class Explorer:
force (bool): Whether to overwrite the existing similarity index or not. Defaults to True.
Returns:
PIL.PngImagePlugin.PngImageFile containing the plot.
(PIL.Image): Image containing the plot.
Example:
```python
@ -416,7 +420,7 @@ class Explorer:
query (str): Question to ask.
Returns:
Answer from AI.
(pandas.DataFrame): A dataframe containing filtered results to the SQL query.
Example:
```python
@ -436,14 +440,17 @@ class Explorer:
def visualize(self, result):
"""
Visualize the results of a query.
Visualize the results of a query. TODO.
Args:
result (arrow table): Arrow table containing the results of a query.
result (pyarrow.Table): Table containing the results of a query.
"""
# TODO:
pass
def generate_report(self, result):
"""Generate a report of the dataset."""
"""
Generate a report of the dataset.
TODO
"""
pass

1
ultralytics/data/explorer/gui/__init__.py
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@ -0,0 +1 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license

16
ultralytics/data/explorer/gui/dash.py
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@ -1,3 +1,5 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
import time
from threading import Thread
@ -7,13 +9,13 @@ from ultralytics import Explorer
from ultralytics.utils import ROOT, SETTINGS
from ultralytics.utils.checks import check_requirements
check_requirements('streamlit>=1.29.0')
check_requirements('streamlit-select>=0.2')
check_requirements(('streamlit>=1.29.0', 'streamlit-select>=0.2'))
import streamlit as st
from streamlit_select import image_select
def _get_explorer():
"""Initializes and returns an instance of the Explorer class."""
exp = Explorer(data=st.session_state.get('dataset'), model=st.session_state.get('model'))
thread = Thread(target=exp.create_embeddings_table,
kwargs={'force': st.session_state.get('force_recreate_embeddings')})
@ -28,6 +30,7 @@ def _get_explorer():
def init_explorer_form():
"""Initializes an Explorer instance and creates embeddings table with progress tracking."""
datasets = ROOT / 'cfg' / 'datasets'
ds = [d.name for d in datasets.glob('*.yaml')]
models = [
@ -46,6 +49,7 @@ def init_explorer_form():
def query_form():
"""Sets up a form in Streamlit to initialize Explorer with dataset and model selection."""
with st.form('query_form'):
col1, col2 = st.columns([0.8, 0.2])
with col1:
@ -58,6 +62,7 @@ def query_form():
def ai_query_form():
"""Sets up a Streamlit form for user input to initialize Explorer with dataset and model selection."""
with st.form('ai_query_form'):
col1, col2 = st.columns([0.8, 0.2])
with col1:
@ -67,6 +72,7 @@ def ai_query_form():
def find_similar_imgs(imgs):
"""Initializes a Streamlit form for AI-based image querying with custom input."""
exp = st.session_state['explorer']
similar = exp.get_similar(img=imgs, limit=st.session_state.get('limit'), return_type='arrow')
paths = similar.to_pydict()['im_file']
@ -74,6 +80,7 @@ def find_similar_imgs(imgs):
def similarity_form(selected_imgs):
"""Initializes a form for AI-based image querying with custom input in Streamlit."""
st.write('Similarity Search')
with st.form('similarity_form'):
subcol1, subcol2 = st.columns([1, 1])
@ -109,6 +116,7 @@ def similarity_form(selected_imgs):
def run_sql_query():
"""Executes an SQL query and returns the results."""
st.session_state['error'] = None
query = st.session_state.get('query')
if query.rstrip().lstrip():
@ -118,6 +126,7 @@ def run_sql_query():
def run_ai_query():
"""Execute SQL query and update session state with query results."""
if not SETTINGS['openai_api_key']:
st.session_state[
'error'] = 'OpenAI API key not found in settings. Please run yolo settings openai_api_key="..."'
@ -134,12 +143,14 @@ def run_ai_query():
def reset_explorer():
"""Resets the explorer to its initial state by clearing session variables."""
st.session_state['explorer'] = None
st.session_state['imgs'] = None
st.session_state['error'] = None
def utralytics_explorer_docs_callback():
"""Resets the explorer to its initial state by clearing session variables."""
with st.container(border=True):
st.image('https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Original.svg',
width=100)
@ -151,6 +162,7 @@ def utralytics_explorer_docs_callback():
def layout():
"""Resets explorer session variables and provides documentation with a link to API docs."""
st.set_page_config(layout='wide', initial_sidebar_state='collapsed')
st.markdown("<h1 style='text-align: center;'>Ultralytics Explorer Demo</h1>", unsafe_allow_html=True)

6
ultralytics/data/explorer/utils.py
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@ -1,3 +1,5 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
import getpass
from typing import List
@ -14,6 +16,7 @@ from ultralytics.utils.plotting import plot_images
def get_table_schema(vector_size):
"""Extracts and returns the schema of a database table."""
from lancedb.pydantic import LanceModel, Vector
class Schema(LanceModel):
@ -29,6 +32,7 @@ def get_table_schema(vector_size):
def get_sim_index_schema():
"""Returns a LanceModel schema for a database table with specified vector size."""
from lancedb.pydantic import LanceModel
class Schema(LanceModel):
@ -41,6 +45,7 @@ def get_sim_index_schema():
def sanitize_batch(batch, dataset_info):
"""Sanitizes input batch for inference, ensuring correct format and dimensions."""
batch['cls'] = batch['cls'].flatten().int().tolist()
box_cls_pair = sorted(zip(batch['bboxes'].tolist(), batch['cls']), key=lambda x: x[1])
batch['bboxes'] = [box for box, _ in box_cls_pair]
@ -111,6 +116,7 @@ def plot_query_result(similar_set, plot_labels=True):
def prompt_sql_query(query):
"""Plots images with optional labels from a similar data set."""
check_requirements('openai>=1.6.1')
from openai import OpenAI

20
ultralytics/data/split_dota.py
View file

@ -1,3 +1,5 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
import itertools
import os
from glob import glob
@ -53,10 +55,13 @@ def bbox_iof(polygon1, bbox2, eps=1e-6):
def load_yolo_dota(data_root, split='train'):
"""Load DOTA dataset.
"""
Load DOTA dataset.
Args:
data_root (str): Data root.
split (str): The split data set, could be train or val.
Notes:
The directory structure assumed for the DOTA dataset:
- data_root
@ -133,7 +138,7 @@ def get_window_obj(anno, windows, iof_thr=0.7):
label[:, 1::2] *= w
label[:, 2::2] *= h
iofs = bbox_iof(label[:, 1:], windows)
# unnormalized and misaligned coordinates
# Unnormalized and misaligned coordinates
window_anns = [(label[iofs[:, i] >= iof_thr]) for i in range(len(windows))]
else:
window_anns = [np.zeros((0, 9), dtype=np.float32) for _ in range(len(windows))]
@ -141,13 +146,16 @@ def get_window_obj(anno, windows, iof_thr=0.7):
def crop_and_save(anno, windows, window_objs, im_dir, lb_dir):
"""Crop images and save new labels.
"""
Crop images and save new labels.
Args:
anno (dict): Annotation dict, including `filepath`, `label`, `ori_size` as its keys.
windows (list): A list of windows coordinates.
window_objs (list): A list of labels inside each window.
im_dir (str): The output directory path of images.
lb_dir (str): The output directory path of labels.
Notes:
The directory structure assumed for the DOTA dataset:
- data_root
@ -185,7 +193,7 @@ def split_images_and_labels(data_root, save_dir, split='train', crop_sizes=[1024
"""
Split both images and labels.
NOTES:
Notes:
The directory structure assumed for the DOTA dataset:
- data_root
- images
@ -215,7 +223,7 @@ def split_trainval(data_root, save_dir, crop_size=1024, gap=200, rates=[1.0]):
"""
Split train and val set of DOTA.
NOTES:
Notes:
The directory structure assumed for the DOTA dataset:
- data_root
- images
@ -245,7 +253,7 @@ def split_test(data_root, save_dir, crop_size=1024, gap=200, rates=[1.0]):
"""
Split test set of DOTA, labels are not included within this set.
NOTES:
Notes:
The directory structure assumed for the DOTA dataset:
- data_root
- images