Docs updates: Add Explorer to tab, YOLOv5 in Guides and Usage in Quickstart (#7438)
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>
This commit is contained in:
Ayush Chaurasia
GitHub
parent
53150a925b
commit
a92adf8231
30 changed files with 227 additions and 105 deletions
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@ -1,3 +1,5 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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from .utils import plot_query_result
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__all__ = ['plot_query_result']
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@ -1,3 +1,5 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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from io import BytesIO
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from pathlib import Path
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from typing import Any, List, Tuple, Union
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@ -24,9 +26,8 @@ class ExplorerDataset(YOLODataset):
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def __init__(self, *args, data: dict = None, **kwargs) -> None:
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super().__init__(*args, data=data, **kwargs)
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# NOTE: Load the image directly without any resize operations.
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def load_image(self, i: int) -> Union[Tuple[np.ndarray, Tuple[int, int], Tuple[int, int]], Tuple[None, None, None]]:
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"""Loads 1 image from dataset index 'i', returns (im, resized hw)."""
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"""Loads 1 image from dataset index 'i' without any resize ops."""
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im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i]
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if im is None: # not cached in RAM
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if fn.exists(): # load npy
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@ -41,6 +42,7 @@ class ExplorerDataset(YOLODataset):
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return self.ims[i], self.im_hw0[i], self.im_hw[i]
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def build_transforms(self, hyp: IterableSimpleNamespace = None):
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"""Creates transforms for dataset images without resizing."""
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return Format(
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bbox_format='xyxy',
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normalize=False,
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@ -122,7 +124,7 @@ class Explorer:
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self.table = table
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def _yield_batches(self, dataset: ExplorerDataset, data_info: dict, model: YOLO, exclude_keys: List[str]):
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# Implement Batching
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"""Generates batches of data for embedding, excluding specified keys."""
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for i in tqdm(range(len(dataset))):
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self.progress = float(i + 1) / len(dataset)
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batch = dataset[i]
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@ -143,7 +145,7 @@ class Explorer:
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limit (int): Number of results to return.
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Returns:
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An arrow table containing the results. Supports converting to:
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(pyarrow.Table): An arrow table containing the results. Supports converting to:
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- pandas dataframe: `result.to_pandas()`
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- dict of lists: `result.to_pydict()`
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@ -175,7 +177,7 @@ class Explorer:
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return_type (str): Type of the result to return. Can be either 'pandas' or 'arrow'. Defaults to 'pandas'.
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Returns:
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An arrow table containing the results.
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(pyarrow.Table): An arrow table containing the results.
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Example:
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```python
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@ -216,7 +218,7 @@ class Explorer:
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labels (bool): Whether to plot the labels or not.
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Returns:
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PIL Image containing the plot.
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(PIL.Image): Image containing the plot.
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Example:
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```python
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@ -248,7 +250,7 @@ class Explorer:
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return_type (str): Type of the result to return. Can be either 'pandas' or 'arrow'. Defaults to 'pandas'.
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Returns:
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A table or pandas dataframe containing the results.
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(pandas.DataFrame): A dataframe containing the results.
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Example:
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```python
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@ -282,7 +284,7 @@ class Explorer:
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limit (int): Number of results to return. Defaults to 25.
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Returns:
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PIL Image containing the plot.
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(PIL.Image): Image containing the plot.
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Example:
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```python
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@ -306,11 +308,12 @@ class Explorer:
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Args:
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max_dist (float): maximum L2 distance between the embeddings to consider. Defaults to 0.2.
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top_k (float): Percentage of the closest data points to consider when counting. Used to apply limit when running
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vector search. Defaults: None.
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vector search. Defaults: None.
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force (bool): Whether to overwrite the existing similarity index or not. Defaults to True.
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Returns:
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A pandas dataframe containing the similarity index.
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(pandas.DataFrame): A dataframe containing the similarity index. Each row corresponds to an image, and columns
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include indices of similar images and their respective distances.
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Example:
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```python
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@ -340,6 +343,7 @@ class Explorer:
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sim_table = self.connection.create_table(sim_idx_table_name, schema=get_sim_index_schema(), mode='overwrite')
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def _yield_sim_idx():
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"""Generates a dataframe with similarity indices and distances for images."""
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for i in tqdm(range(len(embeddings))):
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sim_idx = self.table.search(embeddings[i]).limit(top_k).to_pandas().query(f'_distance <= {max_dist}')
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yield [{
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@ -364,7 +368,7 @@ class Explorer:
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force (bool): Whether to overwrite the existing similarity index or not. Defaults to True.
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Returns:
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PIL.PngImagePlugin.PngImageFile containing the plot.
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(PIL.Image): Image containing the plot.
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Example:
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```python
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@ -416,7 +420,7 @@ class Explorer:
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query (str): Question to ask.
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Returns:
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Answer from AI.
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(pandas.DataFrame): A dataframe containing filtered results to the SQL query.
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Example:
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```python
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@ -436,14 +440,17 @@ class Explorer:
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def visualize(self, result):
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"""
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Visualize the results of a query.
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Visualize the results of a query. TODO.
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Args:
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result (arrow table): Arrow table containing the results of a query.
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result (pyarrow.Table): Table containing the results of a query.
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"""
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# TODO:
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pass
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def generate_report(self, result):
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"""Generate a report of the dataset."""
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"""
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Generate a report of the dataset.
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TODO
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"""
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pass
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@ -0,0 +1 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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@ -1,3 +1,5 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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import time
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from threading import Thread
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@ -7,13 +9,13 @@ from ultralytics import Explorer
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from ultralytics.utils import ROOT, SETTINGS
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from ultralytics.utils.checks import check_requirements
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check_requirements('streamlit>=1.29.0')
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check_requirements('streamlit-select>=0.2')
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check_requirements(('streamlit>=1.29.0', 'streamlit-select>=0.2'))
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import streamlit as st
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from streamlit_select import image_select
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def _get_explorer():
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"""Initializes and returns an instance of the Explorer class."""
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exp = Explorer(data=st.session_state.get('dataset'), model=st.session_state.get('model'))
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thread = Thread(target=exp.create_embeddings_table,
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kwargs={'force': st.session_state.get('force_recreate_embeddings')})
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@ -28,6 +30,7 @@ def _get_explorer():
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def init_explorer_form():
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"""Initializes an Explorer instance and creates embeddings table with progress tracking."""
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datasets = ROOT / 'cfg' / 'datasets'
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ds = [d.name for d in datasets.glob('*.yaml')]
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models = [
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@ -46,6 +49,7 @@ def init_explorer_form():
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def query_form():
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"""Sets up a form in Streamlit to initialize Explorer with dataset and model selection."""
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with st.form('query_form'):
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col1, col2 = st.columns([0.8, 0.2])
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with col1:
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@ -58,6 +62,7 @@ def query_form():
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def ai_query_form():
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"""Sets up a Streamlit form for user input to initialize Explorer with dataset and model selection."""
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with st.form('ai_query_form'):
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col1, col2 = st.columns([0.8, 0.2])
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with col1:
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@ -67,6 +72,7 @@ def ai_query_form():
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def find_similar_imgs(imgs):
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"""Initializes a Streamlit form for AI-based image querying with custom input."""
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exp = st.session_state['explorer']
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similar = exp.get_similar(img=imgs, limit=st.session_state.get('limit'), return_type='arrow')
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paths = similar.to_pydict()['im_file']
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@ -74,6 +80,7 @@ def find_similar_imgs(imgs):
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def similarity_form(selected_imgs):
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"""Initializes a form for AI-based image querying with custom input in Streamlit."""
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st.write('Similarity Search')
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with st.form('similarity_form'):
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subcol1, subcol2 = st.columns([1, 1])
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@ -109,6 +116,7 @@ def similarity_form(selected_imgs):
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def run_sql_query():
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"""Executes an SQL query and returns the results."""
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st.session_state['error'] = None
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query = st.session_state.get('query')
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if query.rstrip().lstrip():
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@ -118,6 +126,7 @@ def run_sql_query():
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def run_ai_query():
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"""Execute SQL query and update session state with query results."""
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if not SETTINGS['openai_api_key']:
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st.session_state[
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'error'] = 'OpenAI API key not found in settings. Please run yolo settings openai_api_key="..."'
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@ -134,12 +143,14 @@ def run_ai_query():
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def reset_explorer():
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"""Resets the explorer to its initial state by clearing session variables."""
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st.session_state['explorer'] = None
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st.session_state['imgs'] = None
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st.session_state['error'] = None
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def utralytics_explorer_docs_callback():
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"""Resets the explorer to its initial state by clearing session variables."""
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with st.container(border=True):
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st.image('https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Original.svg',
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width=100)
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@ -151,6 +162,7 @@ def utralytics_explorer_docs_callback():
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def layout():
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"""Resets explorer session variables and provides documentation with a link to API docs."""
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st.set_page_config(layout='wide', initial_sidebar_state='collapsed')
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st.markdown("<h1 style='text-align: center;'>Ultralytics Explorer Demo</h1>", unsafe_allow_html=True)
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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import getpass
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from typing import List
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@ -14,6 +16,7 @@ from ultralytics.utils.plotting import plot_images
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def get_table_schema(vector_size):
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"""Extracts and returns the schema of a database table."""
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from lancedb.pydantic import LanceModel, Vector
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class Schema(LanceModel):
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@ -29,6 +32,7 @@ def get_table_schema(vector_size):
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def get_sim_index_schema():
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"""Returns a LanceModel schema for a database table with specified vector size."""
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from lancedb.pydantic import LanceModel
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class Schema(LanceModel):
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def sanitize_batch(batch, dataset_info):
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"""Sanitizes input batch for inference, ensuring correct format and dimensions."""
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batch['cls'] = batch['cls'].flatten().int().tolist()
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box_cls_pair = sorted(zip(batch['bboxes'].tolist(), batch['cls']), key=lambda x: x[1])
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batch['bboxes'] = [box for box, _ in box_cls_pair]
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def prompt_sql_query(query):
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"""Plots images with optional labels from a similar data set."""
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check_requirements('openai>=1.6.1')
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from openai import OpenAI
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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import itertools
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import os
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from glob import glob
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def load_yolo_dota(data_root, split='train'):
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"""Load DOTA dataset.
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"""
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Load DOTA dataset.
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Args:
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data_root (str): Data root.
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split (str): The split data set, could be train or val.
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Notes:
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The directory structure assumed for the DOTA dataset:
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- data_root
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@ -133,7 +138,7 @@ def get_window_obj(anno, windows, iof_thr=0.7):
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label[:, 1::2] *= w
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label[:, 2::2] *= h
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iofs = bbox_iof(label[:, 1:], windows)
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# unnormalized and misaligned coordinates
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# Unnormalized and misaligned coordinates
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window_anns = [(label[iofs[:, i] >= iof_thr]) for i in range(len(windows))]
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else:
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window_anns = [np.zeros((0, 9), dtype=np.float32) for _ in range(len(windows))]
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def crop_and_save(anno, windows, window_objs, im_dir, lb_dir):
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"""Crop images and save new labels.
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"""
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Crop images and save new labels.
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Args:
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anno (dict): Annotation dict, including `filepath`, `label`, `ori_size` as its keys.
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windows (list): A list of windows coordinates.
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window_objs (list): A list of labels inside each window.
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im_dir (str): The output directory path of images.
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lb_dir (str): The output directory path of labels.
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Notes:
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The directory structure assumed for the DOTA dataset:
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- data_root
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@ -185,7 +193,7 @@ def split_images_and_labels(data_root, save_dir, split='train', crop_sizes=[1024
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"""
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Split both images and labels.
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NOTES:
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Notes:
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The directory structure assumed for the DOTA dataset:
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- data_root
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- images
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@ -215,7 +223,7 @@ def split_trainval(data_root, save_dir, crop_size=1024, gap=200, rates=[1.0]):
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"""
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Split train and val set of DOTA.
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NOTES:
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Notes:
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The directory structure assumed for the DOTA dataset:
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- data_root
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- images
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@ -245,7 +253,7 @@ def split_test(data_root, save_dir, crop_size=1024, gap=200, rates=[1.0]):
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"""
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Split test set of DOTA, labels are not included within this set.
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NOTES:
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Notes:
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The directory structure assumed for the DOTA dataset:
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- data_root
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- images
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@ -107,6 +107,7 @@ class RTDETRValidator(DetectionValidator):
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return outputs
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def _prepare_batch(self, si, batch):
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"""Prepares a batch for training or inference by applying transformations."""
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idx = batch['batch_idx'] == si
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cls = batch['cls'][idx].squeeze(-1)
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bbox = batch['bboxes'][idx]
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@ -121,6 +122,7 @@ class RTDETRValidator(DetectionValidator):
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return prepared_batch
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def _prepare_pred(self, pred, pbatch):
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"""Prepares and returns a batch with transformed bounding boxes and class labels."""
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predn = pred.clone()
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predn[..., [0, 2]] *= pbatch['ori_shape'][1] / self.args.imgsz # native-space pred
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predn[..., [1, 3]] *= pbatch['ori_shape'][0] / self.args.imgsz # native-space pred
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@ -87,6 +87,7 @@ class DetectionValidator(BaseValidator):
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max_det=self.args.max_det)
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def _prepare_batch(self, si, batch):
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"""Prepares a batch of images and annotations for validation."""
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idx = batch['batch_idx'] == si
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cls = batch['cls'][idx].squeeze(-1)
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bbox = batch['bboxes'][idx]
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@ -100,6 +101,7 @@ class DetectionValidator(BaseValidator):
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return prepared_batch
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def _prepare_pred(self, pred, pbatch):
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"""Prepares a batch of images and annotations for validation."""
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predn = pred.clone()
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ops.scale_boxes(pbatch['imgsz'], predn[:, :4], pbatch['ori_shape'],
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ratio_pad=pbatch['ratio_pad']) # native-space pred
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@ -23,6 +23,7 @@ class OBBPredictor(DetectionPredictor):
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"""
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
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"""Initializes OBBPredictor with optional model and data configuration overrides."""
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super().__init__(cfg, overrides, _callbacks)
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self.args.task = 'obb'
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@ -65,6 +65,7 @@ class OBBValidator(DetectionValidator):
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return self.match_predictions(detections[:, 5], gt_cls, iou)
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def _prepare_batch(self, si, batch):
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"""Prepares and returns a batch for OBB validation."""
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idx = batch['batch_idx'] == si
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cls = batch['cls'][idx].squeeze(-1)
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bbox = batch['bboxes'][idx]
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@ -78,6 +79,7 @@ class OBBValidator(DetectionValidator):
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return prepared_batch
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def _prepare_pred(self, pred, pbatch):
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"""Prepares and returns a batch for OBB validation with scaled and padded bounding boxes."""
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predn = pred.clone()
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ops.scale_boxes(pbatch['imgsz'], predn[:, :4], pbatch['ori_shape'], ratio_pad=pbatch['ratio_pad'],
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xywh=True) # native-space pred
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|
|
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@ -69,6 +69,7 @@ class PoseValidator(DetectionValidator):
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self.stats = dict(tp_p=[], tp=[], conf=[], pred_cls=[], target_cls=[])
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||||
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||||
def _prepare_batch(self, si, batch):
|
||||
"""Prepares a batch for processing by converting keypoints to float and moving to device."""
|
||||
pbatch = super()._prepare_batch(si, batch)
|
||||
kpts = batch['keypoints'][batch['batch_idx'] == si]
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h, w = pbatch['imgsz']
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@ -80,6 +81,7 @@ class PoseValidator(DetectionValidator):
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|||
return pbatch
|
||||
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||||
def _prepare_pred(self, pred, pbatch):
|
||||
"""Prepares and scales keypoints in a batch for pose processing."""
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||||
predn = super()._prepare_pred(pred, pbatch)
|
||||
nk = pbatch['kpts'].shape[1]
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||||
pred_kpts = predn[:, 6:].view(len(predn), nk, -1)
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||||
|
|
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|||
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|
@ -72,12 +72,14 @@ class SegmentationValidator(DetectionValidator):
|
|||
return p, proto
|
||||
|
||||
def _prepare_batch(self, si, batch):
|
||||
"""Prepares a batch for training or inference by processing images and targets."""
|
||||
prepared_batch = super()._prepare_batch(si, batch)
|
||||
midx = [si] if self.args.overlap_mask else batch['batch_idx'] == si
|
||||
prepared_batch['masks'] = batch['masks'][midx]
|
||||
return prepared_batch
|
||||
|
||||
def _prepare_pred(self, pred, pbatch, proto):
|
||||
"""Prepares a batch for training or inference by processing images and targets."""
|
||||
predn = super()._prepare_pred(pred, pbatch)
|
||||
pred_masks = self.process(proto, pred[:, 6:], pred[:, :4], shape=pbatch['imgsz'])
|
||||
return predn, pred_masks
|
||||
|
|
|
|||
|
|
@ -116,6 +116,7 @@ class OBB(Detect):
|
|||
"""YOLOv8 OBB detection head for detection with rotation models."""
|
||||
|
||||
def __init__(self, nc=80, ne=1, ch=()):
|
||||
"""Initialize OBB with number of classes `nc` and layer channels `ch`."""
|
||||
super().__init__(nc, ch)
|
||||
self.ne = ne # number of extra parameters
|
||||
self.detect = Detect.forward
|
||||
|
|
@ -124,6 +125,7 @@ class OBB(Detect):
|
|||
self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.ne, 1)) for x in ch)
|
||||
|
||||
def forward(self, x):
|
||||
"""Concatenates and returns predicted bounding boxes and class probabilities."""
|
||||
bs = x[0].shape[0] # batch size
|
||||
angle = torch.cat([self.cv4[i](x[i]).view(bs, self.ne, -1) for i in range(self.nl)], 2) # OBB theta logits
|
||||
# NOTE: set `angle` as an attribute so that `decode_bboxes` could use it.
|
||||
|
|
|
|||
|
|
@ -306,6 +306,7 @@ class OBBModel(DetectionModel):
|
|||
super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)
|
||||
|
||||
def init_criterion(self):
|
||||
"""Initialize the loss criterion for the model."""
|
||||
return v8OBBLoss(self)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -153,6 +153,7 @@ class ObjectCounter:
|
|||
self.selected_point = None
|
||||
|
||||
def extract_and_process_tracks(self, tracks):
|
||||
"""Extracts and processes tracks for object counting in a video stream."""
|
||||
boxes = tracks[0].boxes.xyxy.cpu()
|
||||
clss = tracks[0].boxes.cls.cpu().tolist()
|
||||
track_ids = tracks[0].boxes.id.int().cpu().tolist()
|
||||
|
|
|
|||
|
|
@ -55,6 +55,7 @@ class BaseTrack:
|
|||
_count = 0
|
||||
|
||||
def __init__(self):
|
||||
"""Initializes a new track with unique ID and foundational tracking attributes."""
|
||||
self.track_id = 0
|
||||
self.is_activated = False
|
||||
self.state = TrackState.New
|
||||
|
|
|
|||
|
|
@ -245,6 +245,7 @@ def set_logging(name=LOGGING_NAME, verbose=True):
|
|||
class CustomFormatter(logging.Formatter):
|
||||
|
||||
def format(self, record):
|
||||
"""Sets up logging with UTF-8 encoding and configurable verbosity."""
|
||||
return emojis(super().format(record))
|
||||
|
||||
formatter = CustomFormatter('%(message)s') # Use CustomFormatter to eliminate UTF-8 output as last recourse
|
||||
|
|
|
|||
|
|
@ -206,7 +206,7 @@ def check_disk_space(url='https://ultralytics.com/assets/coco128.zip', sf=1.5, h
|
|||
# Check file size
|
||||
gib = 1 << 30 # bytes per GiB
|
||||
data = int(r.headers.get('Content-Length', 0)) / gib # file size (GB)
|
||||
total, used, free = (x / gib for x in shutil.disk_usage('/')) # bytes
|
||||
total, used, free = (x / gib for x in shutil.disk_usage(Path.cwd())) # bytes
|
||||
if data * sf < free:
|
||||
return True # sufficient space
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue