chore: 🧹 clean up documentation and improve formatting
Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>
This commit is contained in:
Onuralp SEZER
parent
58a9588a53
commit
78f17cba2e
|
|
@ -38,3 +38,18 @@ repos:
|
|||
args: [--fix, --exit-non-zero-on-fix]
|
||||
- id: ruff-format
|
||||
types_or: [ python, pyi, jupyter ]
|
||||
|
||||
# - repo: https://github.com/executablebooks/mdformat
|
||||
# rev: 0.7.17
|
||||
# hooks:
|
||||
# - id: mdformat
|
||||
# additional_dependencies:
|
||||
# - "mdformat-mkdocs[recommended]>=2.1.0"
|
||||
# args: ["--number"]
|
||||
|
||||
- repo: https://github.com/codespell-project/codespell
|
||||
rev: v2.2.6
|
||||
hooks:
|
||||
- id: codespell
|
||||
additional_dependencies:
|
||||
- tomli
|
||||
|
|
|
|||
|
|
@ -1,4 +1,3 @@
|
|||
|
||||
# Contributor Covenant Code of Conduct
|
||||
|
||||
## Our Pledge
|
||||
|
|
@ -18,24 +17,24 @@ diverse, inclusive, and healthy community.
|
|||
Examples of behavior that contributes to a positive environment for our
|
||||
community include:
|
||||
|
||||
* Demonstrating empathy and kindness toward other people
|
||||
* Being respectful of differing opinions, viewpoints, and experiences
|
||||
* Giving and gracefully accepting constructive feedback
|
||||
* Accepting responsibility and apologizing to those affected by our mistakes,
|
||||
and learning from the experience
|
||||
* Focusing on what is best not just for us as individuals, but for the overall
|
||||
community
|
||||
- Demonstrating empathy and kindness toward other people
|
||||
- Being respectful of differing opinions, viewpoints, and experiences
|
||||
- Giving and gracefully accepting constructive feedback
|
||||
- Accepting responsibility and apologizing to those affected by our mistakes,
|
||||
and learning from the experience
|
||||
- Focusing on what is best not just for us as individuals, but for the overall
|
||||
community
|
||||
|
||||
Examples of unacceptable behavior include:
|
||||
|
||||
* The use of sexualized language or imagery, and sexual attention or advances of
|
||||
any kind
|
||||
* Trolling, insulting or derogatory comments, and personal or political attacks
|
||||
* Public or private harassment
|
||||
* Publishing others' private information, such as a physical or email address,
|
||||
without their explicit permission
|
||||
* Other conduct which could reasonably be considered inappropriate in a
|
||||
professional setting
|
||||
- The use of sexualized language or imagery, and sexual attention or advances of
|
||||
any kind
|
||||
- Trolling, insulting or derogatory comments, and personal or political attacks
|
||||
- Public or private harassment
|
||||
- Publishing others' private information, such as a physical or email address,
|
||||
without their explicit permission
|
||||
- Other conduct which could reasonably be considered inappropriate in a
|
||||
professional setting
|
||||
|
||||
## Enforcement Responsibilities
|
||||
|
||||
|
|
@ -121,14 +120,14 @@ version 2.1, available at
|
|||
[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1].
|
||||
|
||||
Community Impact Guidelines were inspired by
|
||||
[Mozilla's code of conduct enforcement ladder][Mozilla CoC].
|
||||
[Mozilla's code of conduct enforcement ladder][mozilla coc].
|
||||
|
||||
For answers to common questions about this code of conduct, see the FAQ at
|
||||
[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at
|
||||
[https://www.contributor-covenant.org/faq][faq]. Translations are available at
|
||||
[https://www.contributor-covenant.org/translations][translations].
|
||||
|
||||
[faq]: https://www.contributor-covenant.org/faq
|
||||
[homepage]: https://www.contributor-covenant.org
|
||||
[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html
|
||||
[Mozilla CoC]: https://github.com/mozilla/diversity
|
||||
[FAQ]: https://www.contributor-covenant.org/faq
|
||||
[mozilla coc]: https://github.com/mozilla/diversity
|
||||
[translations]: https://www.contributor-covenant.org/translations
|
||||
[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html
|
||||
|
|
|
|||
|
|
@ -11,13 +11,13 @@ Please read and adhere to our [Code of Conduct](https://supervision.roboflow.com
|
|||
## Table of Contents
|
||||
|
||||
- [Contribution Guidelines](#contribution-guidelines)
|
||||
- [Contributing Features](#contributing-features)
|
||||
- [Contributing Features](#contributing-features)
|
||||
- [How to Contribute Changes](#how-to-contribute-changes)
|
||||
- [Installation for Contributors](#installation-for-contributors)
|
||||
- [Code Style and Quality](#code-style-and-quality)
|
||||
- [Pre-commit tool](#pre-commit-tool)
|
||||
- [Docstrings](#docstrings)
|
||||
- [Type checking](#type-checking)
|
||||
- [Pre-commit tool](#pre-commit-tool)
|
||||
- [Docstrings](#docstrings)
|
||||
- [Type checking](#type-checking)
|
||||
- [Documentation](#documentation)
|
||||
- [Cookbooks](#cookbooks)
|
||||
- [Tests](#tests)
|
||||
|
|
@ -83,7 +83,7 @@ git push -u origin <your_branch_name>
|
|||
|
||||
Use conventional commit messages to clearly describe your changes. The format is:
|
||||
|
||||
<type>[optional scope]: <description>
|
||||
<type>\[optional scope\]: <description>
|
||||
|
||||
Common types include:
|
||||
|
||||
|
|
@ -130,45 +130,46 @@ Before starting your work on the project, set up your development environment:
|
|||
|
||||
1. Clone your fork of the project:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/YOUR_USERNAME/supervision.git
|
||||
cd supervision
|
||||
```
|
||||
```bash
|
||||
git clone https://github.com/YOUR_USERNAME/supervision.git
|
||||
cd supervision
|
||||
```
|
||||
|
||||
Replace `YOUR_USERNAME` with your GitHub username.
|
||||
Replace `YOUR_USERNAME` with your GitHub username.
|
||||
|
||||
2. Create and activate a virtual environment:
|
||||
|
||||
```bash
|
||||
python3 -m venv .venv
|
||||
source .venv/bin/activate
|
||||
```
|
||||
```bash
|
||||
python3 -m venv .venv
|
||||
source .venv/bin/activate
|
||||
```
|
||||
|
||||
3. Install Poetry:
|
||||
|
||||
Using pip:
|
||||
Using pip:
|
||||
|
||||
```bash
|
||||
pip install -U pip setuptools
|
||||
pip install poetry
|
||||
```
|
||||
```bash
|
||||
pip install -U pip setuptools
|
||||
pip install poetry
|
||||
```
|
||||
|
||||
Or using pipx (recommended for global installation):
|
||||
Or using pipx (recommended for global installation):
|
||||
|
||||
```bash
|
||||
pipx install poetry
|
||||
```
|
||||
```bash
|
||||
pipx install poetry
|
||||
```
|
||||
|
||||
4. Install project dependencies:
|
||||
|
||||
```bash
|
||||
poetry install
|
||||
```
|
||||
```bash
|
||||
poetry install
|
||||
```
|
||||
|
||||
5. Run pytest to verify the setup:
|
||||
```bash
|
||||
poetry run pytest
|
||||
```
|
||||
|
||||
```bash
|
||||
poetry run pytest
|
||||
```
|
||||
|
||||
## 🎨 Code Style and Quality
|
||||
|
||||
|
|
|
|||
121
README.md
121
README.md
|
|
@ -8,11 +8,11 @@
|
|||
</a>
|
||||
</p>
|
||||
|
||||
<br>
|
||||
<br>
|
||||
|
||||
[notebooks](https://github.com/roboflow/notebooks) | [inference](https://github.com/roboflow/inference) | [autodistill](https://github.com/autodistill/autodistill) | [maestro](https://github.com/roboflow/multimodal-maestro)
|
||||
|
||||
<br>
|
||||
<br>
|
||||
|
||||
[](https://badge.fury.io/py/supervision)
|
||||
[](https://pypistats.org/packages/supervision)
|
||||
|
|
@ -23,6 +23,7 @@
|
|||
[](https://huggingface.co/spaces/Roboflow/Annotators)
|
||||
[](https://discord.gg/GbfgXGJ8Bk)
|
||||
[](https://squidfunk.github.io/mkdocs-material/)
|
||||
|
||||
</div>
|
||||
|
||||
## 👋 hello
|
||||
|
|
@ -54,7 +55,7 @@ import supervision as sv
|
|||
from ultralytics import YOLO
|
||||
|
||||
image = cv2.imread(...)
|
||||
model = YOLO('yolov8s.pt')
|
||||
model = YOLO("yolov8s.pt")
|
||||
result = model(image)[0]
|
||||
detections = sv.Detections.from_ultralytics(result)
|
||||
|
||||
|
|
@ -97,10 +98,7 @@ image = cv2.imread(...)
|
|||
detections = sv.Detections(...)
|
||||
|
||||
box_annotator = sv.BoxAnnotator()
|
||||
annotated_frame = box_annotator.annotate(
|
||||
scene=image.copy(),
|
||||
detections=detections
|
||||
)
|
||||
annotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections)
|
||||
```
|
||||
|
||||
https://github.com/roboflow/supervision/assets/26109316/691e219c-0565-4403-9218-ab5644f39bce
|
||||
|
|
@ -133,88 +131,69 @@ for path, image, annotation in ds:
|
|||
|
||||
- load
|
||||
|
||||
```python
|
||||
dataset = sv.DetectionDataset.from_yolo(
|
||||
images_directory_path=...,
|
||||
annotations_directory_path=...,
|
||||
data_yaml_path=...
|
||||
)
|
||||
```python
|
||||
dataset = sv.DetectionDataset.from_yolo(
|
||||
images_directory_path=..., annotations_directory_path=..., data_yaml_path=...
|
||||
)
|
||||
|
||||
dataset = sv.DetectionDataset.from_pascal_voc(
|
||||
images_directory_path=...,
|
||||
annotations_directory_path=...
|
||||
)
|
||||
dataset = sv.DetectionDataset.from_pascal_voc(
|
||||
images_directory_path=..., annotations_directory_path=...
|
||||
)
|
||||
|
||||
dataset = sv.DetectionDataset.from_coco(
|
||||
images_directory_path=...,
|
||||
annotations_path=...
|
||||
)
|
||||
```
|
||||
dataset = sv.DetectionDataset.from_coco(images_directory_path=..., annotations_path=...)
|
||||
```
|
||||
|
||||
- split
|
||||
|
||||
```python
|
||||
train_dataset, test_dataset = dataset.split(split_ratio=0.7)
|
||||
test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5)
|
||||
```python
|
||||
train_dataset, test_dataset = dataset.split(split_ratio=0.7)
|
||||
test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5)
|
||||
|
||||
len(train_dataset), len(test_dataset), len(valid_dataset)
|
||||
# (700, 150, 150)
|
||||
```
|
||||
len(train_dataset), len(test_dataset), len(valid_dataset)
|
||||
# (700, 150, 150)
|
||||
```
|
||||
|
||||
- merge
|
||||
|
||||
```python
|
||||
ds_1 = sv.DetectionDataset(...)
|
||||
len(ds_1)
|
||||
# 100
|
||||
ds_1.classes
|
||||
# ['dog', 'person']
|
||||
```python
|
||||
ds_1 = sv.DetectionDataset(...)
|
||||
len(ds_1)
|
||||
# 100
|
||||
ds_1.classes
|
||||
# ['dog', 'person']
|
||||
|
||||
ds_2 = sv.DetectionDataset(...)
|
||||
len(ds_2)
|
||||
# 200
|
||||
ds_2.classes
|
||||
# ['cat']
|
||||
ds_2 = sv.DetectionDataset(...)
|
||||
len(ds_2)
|
||||
# 200
|
||||
ds_2.classes
|
||||
# ['cat']
|
||||
|
||||
ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
|
||||
len(ds_merged)
|
||||
# 300
|
||||
ds_merged.classes
|
||||
# ['cat', 'dog', 'person']
|
||||
```
|
||||
ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
|
||||
len(ds_merged)
|
||||
# 300
|
||||
ds_merged.classes
|
||||
# ['cat', 'dog', 'person']
|
||||
```
|
||||
|
||||
- save
|
||||
|
||||
```python
|
||||
dataset.as_yolo(
|
||||
images_directory_path=...,
|
||||
annotations_directory_path=...,
|
||||
data_yaml_path=...
|
||||
)
|
||||
```python
|
||||
dataset.as_yolo(
|
||||
images_directory_path=..., annotations_directory_path=..., data_yaml_path=...
|
||||
)
|
||||
|
||||
dataset.as_pascal_voc(
|
||||
images_directory_path=...,
|
||||
annotations_directory_path=...
|
||||
)
|
||||
dataset.as_pascal_voc(images_directory_path=..., annotations_directory_path=...)
|
||||
|
||||
dataset.as_coco(
|
||||
images_directory_path=...,
|
||||
annotations_path=...
|
||||
)
|
||||
```
|
||||
dataset.as_coco(images_directory_path=..., annotations_path=...)
|
||||
```
|
||||
|
||||
- convert
|
||||
|
||||
```python
|
||||
sv.DetectionDataset.from_yolo(
|
||||
images_directory_path=...,
|
||||
annotations_directory_path=...,
|
||||
data_yaml_path=...
|
||||
).as_pascal_voc(
|
||||
images_directory_path=...,
|
||||
annotations_directory_path=...
|
||||
)
|
||||
```
|
||||
```python
|
||||
sv.DetectionDataset.from_yolo(
|
||||
images_directory_path=..., annotations_directory_path=..., data_yaml_path=...
|
||||
).as_pascal_voc(images_directory_path=..., annotations_directory_path=...)
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
|
@ -266,7 +245,7 @@ We love your input! Please see our [contributing guide](https://github.com/robof
|
|||
|
||||
<div align="center">
|
||||
|
||||
<div align="center">
|
||||
<div align="center">
|
||||
<a href="https://youtube.com/roboflow">
|
||||
<img
|
||||
src="https://media.roboflow.com/notebooks/template/icons/purple/youtube.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949634652"
|
||||
|
|
|
|||
|
|
@ -13,7 +13,6 @@ To install the Supervision assets utility, you can use `pip`. This utility is av
|
|||
as an extra within the Supervision package.
|
||||
|
||||
!!! example "pip install"
|
||||
|
||||
```bash
|
||||
pip install "supervision[assets]"
|
||||
```
|
||||
|
|
|
|||
|
|
@ -1,3 +1,5 @@
|
|||
# CHANGELOG
|
||||
|
||||
### 0.23.0 <small>Aug 28, 2024</small>
|
||||
|
||||
- Added [#930](https://github.com/roboflow/supervision/pull/930): `IconAnnotator`, a [new annotator](https://supervision.roboflow.com/0.23.0/detection/annotators/#supervision.annotators.core.IconAnnotator) that allows drawing icons on each detection. Useful if you want to draw a specific icon for each class.
|
||||
|
|
@ -80,10 +82,7 @@ detections = sv.Detections.from_transformers(
|
|||
|
||||
```python
|
||||
import supervision as sv
|
||||
from segment_anything import (
|
||||
sam_model_registry,
|
||||
SamAutomaticMaskGenerator
|
||||
)
|
||||
from segment_anything import sam_model_registry, SamAutomaticMaskGenerator
|
||||
|
||||
sam_model_reg = sam_model_registry[MODEL_TYPE]
|
||||
sam = sam_model_reg(checkpoint=CHECKPOINT_PATH).to(device=DEVICE)
|
||||
|
|
@ -116,19 +115,15 @@ for frame in sv.get_video_frames_generator(
|
|||
- Fix [#1424](https://github.com/roboflow/supervision/pull/1424): `plot_image` function now clearly indicates that the size is in inches.
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
The `track_buffer`, `track_thresh`, and `match_thresh` parameters in [`ByteTrack`](trackers.md/#supervision.tracker.byte_tracker.core.ByteTrack) are deprecated and were removed as of `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
The `triggering_position ` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) was removed as of `supervision-0.23.0`. Use `triggering_anchors ` instead.
|
||||
The `triggering_position` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) was removed as of `supervision-0.23.0`. Use `triggering_anchors` instead.
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
`overlap_filter_strategy` in `InferenceSlicer.__init__` is deprecated and will be removed in `supervision-0.27.0`. Use `overlap_strategy` instead.
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
`overlap_ratio_wh` in `InferenceSlicer.__init__` is deprecated and will be removed in `supervision-0.27.0`. Use `overlap_wh` instead.
|
||||
|
||||
### 0.22.0 <small>Jul 12, 2024</small>
|
||||
|
|
@ -136,11 +131,9 @@ for frame in sv.get_video_frames_generator(
|
|||
- Added [#1326](https://github.com/roboflow/supervision/pull/1326): [`sv.DetectionsDataset`](https://supervision.roboflow.com/0.22.0/datasets/core/#supervision.dataset.core.DetectionDataset) and [`sv.ClassificationDataset`](https://supervision.roboflow.com/0.22.0/datasets/core/#supervision.dataset.core.ClassificationDataset) allowing to load the images into memory only when necessary (lazy loading).
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
Constructing `DetectionDataset` with parameter `images` as `Dict[str, np.ndarray]` is deprecated and will be removed in `supervision-0.26.0`. Please pass a list of paths `List[str]` instead.
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
The `DetectionDataset.images` property is deprecated and will be removed in `supervision-0.26.0`. Please loop over images with `for path, image, annotation in dataset:`, as that does not require loading all images into memory.
|
||||
|
||||
```python
|
||||
|
|
@ -197,7 +190,7 @@ annotated_frame = mask_annotator.annotate(scene=image.copy(), detections=detecti
|
|||
```
|
||||
|
||||
- Added [#1277](https://github.com/roboflow/supervision/pull/1277): if you provide a font that supports symbols of a language, [`sv.RichLabelAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.LabelAnnotator.annotate) will draw them on your images.
|
||||
- Various other annotators have been revised to ensure proper in-place functionality when used with `numpy` arrays. Additionally, we fixed a bug where `sv.ColorAnnotator` was filling boxes with solid color when used in-place.
|
||||
- Various other annotators have been revised to ensure proper in-place functionality when used with `numpy` arrays. Additionally, we fixed a bug where `sv.ColorAnnotator` was filling boxes with solid color when used in-place.
|
||||
|
||||
```python
|
||||
import cv2
|
||||
|
|
@ -223,7 +216,7 @@ train_ds = sv.DetectionDataset.from_yolo(
|
|||
images_directory_path="/content/dataset/train/images",
|
||||
annotations_directory_path="/content/dataset/train/labels",
|
||||
data_yaml_path="/content/dataset/data.yaml",
|
||||
is_obb=True
|
||||
is_obb=True,
|
||||
)
|
||||
|
||||
_, image, detections in train_ds[0]
|
||||
|
|
@ -235,11 +228,9 @@ annotated_image = obb_annotator.annotate(scene=image.copy(), detections=detectio
|
|||
- Fixed [#1312](https://github.com/roboflow/supervision/pull/1312): Fixed [`CropAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.TraceAnnotator.annotate).
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
`BoxAnnotator` was removed, however `BoundingBoxAnnotator` has been renamed to `BoxAnnotator`. Use a combination of [`BoxAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.BoxAnnotator) and [`LabelAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.LabelAnnotator) to simulate old `BoundingBox` behavior.
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
The name `BoundingBoxAnnotator` has been deprecated and will be removed in `supervision-0.26.0`. It has been renamed to [`BoxAnnotator`](https://supervision.roboflow.com/0.22.0/detection/annotators/#supervision.annotators.core.BoxAnnotator).
|
||||
|
||||
- Added [#975](https://github.com/roboflow/supervision/pull/975) 📝 New Cookbooks: serialize detections into [json](https://github.com/roboflow/supervision/blob/de896189b83a1f9434c0a37dd9192ee00d2a1283/docs/notebooks/serialise-detections-to-json.ipynb) and [csv](https://github.com/roboflow/supervision/blob/de896189b83a1f9434c0a37dd9192ee00d2a1283/docs/notebooks/serialise-detections-to-csv.ipynb).
|
||||
|
|
@ -249,35 +240,27 @@ annotated_image = obb_annotator.annotate(scene=image.copy(), detections=detectio
|
|||
- Added [#1340](https://github.com/roboflow/supervision/pull/1340): Two new methods for converting between bounding box formats - [`xywh_to_xyxy`](https://supervision.roboflow.com/0.22.0/detection/utils/#supervision.detection.utils.xywh_to_xyxy) and [`xcycwh_to_xyxy`](https://supervision.roboflow.com/0.22.0/detection/utils/#supervision.detection.utils.xcycwh_to_xyxy)
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
`from_roboflow` method has been removed due to deprecation. Use [from_inference](https://supervision.roboflow.com/0.22.0/detection/core/#supervision.detection.core.Detections.from_inference) instead.
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
`Color.white()` has been removed due to deprecation. Use `color.WHITE` instead.
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
`Color.black()` has been removed due to deprecation. Use `color.BLACK` instead.
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
`Color.red()` has been removed due to deprecation. Use `color.RED` instead.
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
`Color.green()` has been removed due to deprecation. Use `color.GREEN` instead.
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
`Color.blue()` has been removed due to deprecation. Use `color.BLUE` instead.
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
`ColorPalette.default()` has been removed due to deprecation. Use [ColorPalette.DEFAULT](https://supervision.roboflow.com/0.22.0/utils/draw/#supervision.draw.color.ColorPalette.DEFAULT) instead.
|
||||
|
||||
!!! failure "Removed"
|
||||
|
||||
`FPSMonitor.__call__` has been removed due to deprecation. Use the attribute [FPSMonitor.fps](https://supervision.roboflow.com/0.22.0/utils/video/#supervision.utils.video.FPSMonitor.fps) instead.
|
||||
|
||||
### 0.21.0 <small>Jun 5, 2024</small>
|
||||
|
|
@ -294,7 +277,7 @@ detections = sv.Detections.from_lmm(
|
|||
sv.LMM.PALIGEMMA,
|
||||
paligemma_result,
|
||||
resolution_wh=(1000, 1000),
|
||||
classes=['cat', 'dog']
|
||||
classes=["cat", "dog"],
|
||||
)
|
||||
detections.xyxy
|
||||
# array([[250., 250., 750., 750.]])
|
||||
|
|
@ -311,14 +294,8 @@ import supervision as sv
|
|||
image = ...
|
||||
key_points = sv.KeyPoints(...)
|
||||
|
||||
edge_annotator = sv.EdgeAnnotator(
|
||||
color=sv.Color.GREEN,
|
||||
thickness=5
|
||||
)
|
||||
annotated_frame = edge_annotator.annotate(
|
||||
scene=image.copy(),
|
||||
key_points=key_points
|
||||
)
|
||||
edge_annotator = sv.EdgeAnnotator(color=sv.Color.GREEN, thickness=5)
|
||||
annotated_frame = edge_annotator.annotate(scene=image.copy(), key_points=key_points)
|
||||
```
|
||||
|
||||
- Added [#1147](https://github.com/roboflow/supervision/pull/1147): [`sv.KeyPoints.from_inference`](https://supervision.roboflow.com/0.21.0/keypoint/core/#supervision.keypoint.core.KeyPoints.from_inference) allowing to create [`sv.KeyPoints`](https://supervision.roboflow.com/0.21.0/keypoint/core/#supervision.keypoint.core.KeyPoints) from [Inference](https://github.com/roboflow/inference) result.
|
||||
|
|
@ -386,7 +363,6 @@ annotated_image = edge_annotators.annotate(image.copy(), keypoints)
|
|||
- Changed [#1109](https://github.com/roboflow/supervision/pull/1109): [`sv.PolygonZone`](/0.20.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) such that the `frame_resolution_wh` argument is no longer required to initialize `sv.PolygonZone`.
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
The `frame_resolution_wh` parameter in `sv.PolygonZone` is deprecated and will be removed in `supervision-0.24.0`.
|
||||
|
||||
- Changed [#1084](https://github.com/roboflow/supervision/pull/1084): [`sv.get_polygon_center`](/0.20.0/utils/geometry/#supervision.geometry.core.utils.get_polygon_center) to calculate a more accurate polygon centroid.
|
||||
|
|
@ -492,13 +468,11 @@ annotated_frame = crop_annotator.annotate(
|
|||
- Changed [#787](https://github.com/roboflow/supervision/pull/787): [`sv.ByteTrack`](/0.19.0/trackers/#supervision.tracker.ByteTrack) input arguments and docstrings updated to improve readability and ease of use.
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
The `track_buffer`, `track_thresh`, and `match_thresh` parameters in `sv.ByteTrack` are deprecated and will be removed in `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
|
||||
|
||||
- Changed [#910](https://github.com/roboflow/supervision/pull/910): [`sv.PolygonZone`](/0.19.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) to now accept a list of specific box anchors that must be in zone for a detection to be counted.
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
The `triggering_position ` parameter in `sv.PolygonZone` is deprecated and will be removed in `supervision-0.23.0`. Use `triggering_anchors` instead.
|
||||
|
||||
- Changed [#875](https://github.com/roboflow/supervision/pull/875): annotators adding support for Pillow images. All supervision Annotators can now accept an image as either a numpy array or a Pillow Image. They automatically detect its type, draw annotations, and return the output in the same format as the input.
|
||||
|
|
@ -562,7 +536,6 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
|
|||
- Changed [#756](https://github.com/roboflow/supervision/pull/756): [`sv.Color`](/0.18.0/draw/color/#color)'s and [`sv.ColorPalette`](/0.18.0/draw/color/#colorpalette)'s method of accessing predefined colors, transitioning from a function-based approach (`sv.Color.red()`) to a more intuitive and conventional property-based method (`sv.Color.RED`).
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
`sv.ColorPalette.default()` is deprecated and will be removed in `supervision-0.22.0`. Use `sv.ColorPalette.DEFAULT` instead.
|
||||
|
||||
- Changed [#769](https://github.com/roboflow/supervision/pull/769): [`sv.ColorPalette.DEFAULT`](/0.18.0/draw/color/#colorpalette) value, giving users a more extensive set of annotation colors.
|
||||
|
|
@ -570,7 +543,6 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
|
|||
- Changed [#677](https://github.com/roboflow/supervision/pull/677): `sv.Detections.from_roboflow` to [`sv.Detections.from_inference`](/0.18.0/detection/core/#supervision.detection.core.Detections.from_inference) streamlining its functionality to be compatible with both the both [inference](https://github.com/roboflow/inference) pip package and the Robloflow [hosted API](https://docs.roboflow.com/deploy/hosted-api).
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
`Detections.from_roboflow()` is deprecated and will be removed in `supervision-0.22.0`. Use `Detections.from_inference` instead.
|
||||
|
||||
- Fixed [#735](https://github.com/roboflow/supervision/pull/735): [`sv.LineZone`](/0.18.0/detection/tools/line_zone/#linezone) functionality to accurately update the counter when an object crosses a line from any direction, including from the side. This enhancement enables more precise tracking and analytics, such as calculating individual in/out counts for each lane on the road.
|
||||
|
|
@ -668,7 +640,6 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
|
|||
- Fixed [#430](https://github.com/roboflow/supervision/pull/430): [`sv.ByteTrack`](/0.16.0/trackers/#supervision.tracker.byte_tracker.core.ByteTrack) to return `np.array([], dtype=int)` when `svDetections` is empty.
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
`sv.Detections.from_yolov8` and `sv.Classifications.from_yolov8` as those are now replaced by [`sv.Detections.from_ultralytics`](/0.16.0/detection/core/#supervision.detection.core.Detections.from_ultralytics) and [`sv.Classifications.from_ultralytics`](/0.16.0/classification/core/#supervision.classification.core.Classifications.from_ultralytics).
|
||||
|
||||
### 0.15.0 <small>October 5, 2023</small>
|
||||
|
|
@ -736,7 +707,6 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
|
|||
- Added [#281](https://github.com/roboflow/supervision/pull/281): [`sv.Classifications.from_ultralytics`](/0.14.0/classification/core/#supervision.classification.core.Classifications.from_ultralytics) to enable seamless integration with [Ultralytics](https://github.com/ultralytics/ultralytics) framework. This will enable you to use supervision with all [models](https://docs.ultralytics.com/models/) that Ultralytics supports.
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
[sv.Detections.from_yolov8](/0.14.0/detection/core/#supervision.detection.core.Detections.from_yolov8) and [sv.Classifications.from_yolov8](/0.14.0/classification/core/#supervision.classification.core.Classifications.from_yolov8) are now deprecated and will be removed with `supervision-0.16.0` release.
|
||||
|
||||
- Added [#341](https://github.com/roboflow/supervision/pull/341): First supervision usage example script showing how to detect and track objects on video using YOLOv8 + Supervision.
|
||||
|
|
@ -774,7 +744,6 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
|
|||
- Added [#222](https://github.com/roboflow/supervision/pull/222): [`sv.Detections.from_ultralytics`](/0.13.0/detection/core/#supervision.detection.core.Detections.from_ultralytics) to enable seamless integration with [Ultralytics](https://github.com/ultralytics/ultralytics) framework. This will enable you to use `supervision` with all [models](https://docs.ultralytics.com/models/) that Ultralytics supports.
|
||||
|
||||
!!! failure "Deprecated"
|
||||
|
||||
[`sv.Detections.from_yolov8`](/0.13.0/detection/core/#supervision.detection.core.Detections.from_yolov8) is now deprecated and will be removed with `supervision-0.15.0` release.
|
||||
|
||||
- Added [#191](https://github.com/roboflow/supervision/pull/191): [`sv.Detections.from_paddledet`](/0.13.0/detection/core/#supervision.detection.core.Detections.from_paddledet) to enable seamless integration with [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) framework.
|
||||
|
|
@ -784,7 +753,6 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
|
|||
### 0.12.0 <small>July 24, 2023</small>
|
||||
|
||||
!!! failure "Python 3.7. Support Terminated"
|
||||
|
||||
With the `supervision-0.12.0` release, we are terminating official support for Python 3.7.
|
||||
|
||||
- Added [#177](https://github.com/roboflow/supervision/pull/177): initial support for object detection model benchmarking with [`sv.ConfusionMatrix`](/0.12.0/metrics/detection/#confusionmatrix).
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
--8<-- "CONTRIBUTING.md"
|
||||
--8\<-- "CONTRIBUTING.md"
|
||||
|
|
|
|||
|
|
@ -5,7 +5,6 @@ comments: true
|
|||
# Datasets
|
||||
|
||||
!!! warning
|
||||
|
||||
Dataset API is still fluid and may change. If you use Dataset API in your project until further notice, freeze the
|
||||
`supervision` version in your `requirements.txt` or `setup.py`.
|
||||
|
||||
|
|
|
|||
|
|
@ -10,10 +10,13 @@ These features are phased out due to better alternatives or potential issues in
|
|||
- The `frame_resolution_wh ` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) will be removed in `supervision-0.24.0`.
|
||||
|
||||
- Constructing [`DetectionDataset`](https://supervision.roboflow.com/latest/datasets/core/#supervision.dataset.core.DetectionDataset) and [`ClassificationDataset`](https://supervision.roboflow.com/latest/datasets/core/#supervision.dataset.core.ClassificationDataset) with parameter `images` as `Dict[str, np.ndarray]` will be removed in `supervision-0.26.0`. Please pass a list of paths `List[str]` instead.
|
||||
|
||||
- The `DetectionDataset.images` property will be removed in `supervision-0.26.0`. Please loop over images with `for path, image, annotation in dataset:`, as that does not require loading all images into memory.
|
||||
|
||||
- `BoundingBoxAnnotator` has been renamed to `BoxAnnotator` after the old implementation of [`BoxAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.BoxAnnotator) has been removed. `BoundingBoxAnnotator` will be removed in `supervision-0.26.0`.
|
||||
|
||||
- `overlap_filter_strategy` in [`InferenceSlicer.__init__`](https://supervision.roboflow.com/latest/detection/tools/inference_slicer/) is deprecated and will be removed in `supervision-0.27.0`. Use `overlap_strategy` instead.
|
||||
|
||||
- `overlap_ratio_wh` in [`InferenceSlicer.__init__`](https://supervision.roboflow.com/latest/detection/tools/inference_slicer/) is deprecated and will be removed in `supervision-0.27.0`. Use `overlap_wh` instead.
|
||||
|
||||
# Removed
|
||||
|
|
|
|||
|
|
@ -20,7 +20,6 @@ First, you'll need to obtain predictions from your object detection or segmentat
|
|||
model.
|
||||
|
||||
=== "Inference"
|
||||
|
||||
```python
|
||||
import cv2
|
||||
from inference import get_model
|
||||
|
|
@ -31,7 +30,6 @@ model.
|
|||
```
|
||||
|
||||
=== "Ultralytics"
|
||||
|
||||
```python
|
||||
import cv2
|
||||
from ultralytics import YOLO
|
||||
|
|
@ -42,7 +40,6 @@ model.
|
|||
```
|
||||
|
||||
=== "Transformers"
|
||||
|
||||
```python
|
||||
import torch
|
||||
from PIL import Image
|
||||
|
|
@ -68,7 +65,6 @@ model.
|
|||
Now that we have predictions from a model, we can load them into Supervision.
|
||||
|
||||
=== "Inference"
|
||||
|
||||
We can do so using the [`sv.Detections.from_inference`](/latest/detection/core/#supervision.detection.core.Detections.from_inference) method, which accepts model results from both detection and segmentation models.
|
||||
|
||||
```{ .py hl_lines="2 8" }
|
||||
|
|
@ -83,7 +79,6 @@ Now that we have predictions from a model, we can load them into Supervision.
|
|||
```
|
||||
|
||||
=== "Ultralytics"
|
||||
|
||||
We can do so using the [`sv.Detections.from_ultralytics`](/latest/detection/core/#supervision.detection.core.Detections.from_ultralytics) method, which accepts model results from both detection and segmentation models.
|
||||
|
||||
```{ .py hl_lines="2 8" }
|
||||
|
|
@ -98,7 +93,6 @@ Now that we have predictions from a model, we can load them into Supervision.
|
|||
```
|
||||
|
||||
=== "Transformers"
|
||||
|
||||
We can do so using the [`sv.Detections.from_transformers`](/latest/detection/core/#supervision.detection.core.Detections.from_transformers) method, which accepts model results from both detection and segmentation models.
|
||||
|
||||
```{ .py hl_lines="2 19-21" }
|
||||
|
|
@ -138,7 +132,6 @@ You can load predictions from other computer vision frameworks and libraries usi
|
|||
Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoxAnnotator`](/latest/detection/annotators/#supervision.annotators.core.BoxAnnotator) and [`sv.LabelAnnotator`](/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator) classes.
|
||||
|
||||
=== "Inference"
|
||||
|
||||
```{ .py hl_lines="10-16" }
|
||||
import cv2
|
||||
import supervision as sv
|
||||
|
|
@ -159,7 +152,6 @@ Finally, we can annotate the image with the predictions. Since we are working wi
|
|||
```
|
||||
|
||||
=== "Ultralytics"
|
||||
|
||||
```{ .py hl_lines="10-16" }
|
||||
import cv2
|
||||
import supervision as sv
|
||||
|
|
@ -180,7 +172,6 @@ Finally, we can annotate the image with the predictions. Since we are working wi
|
|||
```
|
||||
|
||||
=== "Transformers"
|
||||
|
||||
```{ .py hl_lines="23-30" }
|
||||
import torch
|
||||
import supervision as sv
|
||||
|
|
@ -222,7 +213,6 @@ will label each detection with its `class_name` (if possible) or `class_id`. You
|
|||
override this behavior by passing a list of custom `labels` to the `annotate` method.
|
||||
|
||||
=== "Inference"
|
||||
|
||||
```{ .py hl_lines="13-17 22" }
|
||||
import cv2
|
||||
import supervision as sv
|
||||
|
|
@ -249,7 +239,6 @@ override this behavior by passing a list of custom `labels` to the `annotate` me
|
|||
```
|
||||
|
||||
=== "Ultralytics"
|
||||
|
||||
```{ .py hl_lines="13-17 22" }
|
||||
import cv2
|
||||
import supervision as sv
|
||||
|
|
@ -276,7 +265,6 @@ override this behavior by passing a list of custom `labels` to the `annotate` me
|
|||
```
|
||||
|
||||
=== "Transformers"
|
||||
|
||||
```{ .py hl_lines="26-30 35" }
|
||||
import torch
|
||||
import supervision as sv
|
||||
|
|
@ -326,7 +314,6 @@ is a drop-in replacement for
|
|||
that will allow you to draw masks instead of boxes.
|
||||
|
||||
=== "Inference"
|
||||
|
||||
```python
|
||||
import cv2
|
||||
import supervision as sv
|
||||
|
|
@ -347,7 +334,6 @@ that will allow you to draw masks instead of boxes.
|
|||
```
|
||||
|
||||
=== "Ultralytics"
|
||||
|
||||
```python
|
||||
import cv2
|
||||
import supervision as sv
|
||||
|
|
@ -368,7 +354,6 @@ that will allow you to draw masks instead of boxes.
|
|||
```
|
||||
|
||||
=== "Transformers"
|
||||
|
||||
```python
|
||||
import torch
|
||||
import supervision as sv
|
||||
|
|
|
|||
|
|
@ -20,7 +20,6 @@ Small object detection in high-resolution images presents challenges due to the
|
|||
size relative to the image resolution.
|
||||
|
||||
=== "Inference"
|
||||
|
||||
```python
|
||||
import cv2
|
||||
import supervision as sv
|
||||
|
|
@ -41,7 +40,6 @@ size relative to the image resolution.
|
|||
```
|
||||
|
||||
=== "Ultralytics"
|
||||
|
||||
```python
|
||||
import cv2
|
||||
import supervision as sv
|
||||
|
|
@ -62,7 +60,6 @@ size relative to the image resolution.
|
|||
```
|
||||
|
||||
=== "Transformers"
|
||||
|
||||
```python
|
||||
import torch
|
||||
import supervision as sv
|
||||
|
|
@ -108,7 +105,6 @@ identification at the cost of processing speed and increased memory usage. This
|
|||
is less effective for ultra-high-resolution images (4K and above).
|
||||
|
||||
=== "Inference"
|
||||
|
||||
```{ .py hl_lines="5" }
|
||||
import cv2
|
||||
import supervision as sv
|
||||
|
|
@ -129,7 +125,6 @@ is less effective for ultra-high-resolution images (4K and above).
|
|||
```
|
||||
|
||||
=== "Ultralytics"
|
||||
|
||||
```{ .py hl_lines="7" }
|
||||
import cv2
|
||||
import supervision as sv
|
||||
|
|
@ -162,7 +157,6 @@ objects within each, and aggregating the results.
|
|||
</video>
|
||||
|
||||
=== "Inference"
|
||||
|
||||
```{ .py hl_lines="9-14" }
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
|
@ -189,7 +183,6 @@ objects within each, and aggregating the results.
|
|||
```
|
||||
|
||||
=== "Ultralytics"
|
||||
|
||||
```{ .py hl_lines="9-14" }
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
|
@ -216,7 +209,6 @@ objects within each, and aggregating the results.
|
|||
```
|
||||
|
||||
=== "Transformers"
|
||||
|
||||
```{ .py hl_lines="13-28" }
|
||||
import cv2
|
||||
import torch
|
||||
|
|
@ -269,7 +261,6 @@ objects within each, and aggregating the results.
|
|||
[`InferenceSlicer`](/latest/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer) can perform segmentation tasks too.
|
||||
|
||||
=== "Inference"
|
||||
|
||||
```{ .py hl_lines="6 16 19-20" }
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
|
@ -296,7 +287,6 @@ objects within each, and aggregating the results.
|
|||
```
|
||||
|
||||
=== "Ultralytics"
|
||||
|
||||
```{ .py hl_lines="6 16 19-20" }
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
|
|
|||
|
|
@ -15,7 +15,6 @@ the filters in their applications.
|
|||
Allows you to select detections that belong only to one selected class.
|
||||
|
||||
=== "After"
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
|
||||
|
|
@ -30,7 +29,6 @@ Allows you to select detections that belong only to one selected class.
|
|||
</div>
|
||||
|
||||
=== "Before"
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
|
||||
|
|
@ -49,7 +47,6 @@ Allows you to select detections that belong only to one selected class.
|
|||
Allows you to select detections that belong only to selected set of classes.
|
||||
|
||||
=== "After"
|
||||
|
||||
```python
|
||||
import numpy as np
|
||||
import supervision as sv
|
||||
|
|
@ -66,7 +63,6 @@ Allows you to select detections that belong only to selected set of classes.
|
|||
</div>
|
||||
|
||||
=== "Before"
|
||||
|
||||
```python
|
||||
import numpy as np
|
||||
import supervision as sv
|
||||
|
|
@ -87,7 +83,6 @@ Allows you to select detections that belong only to selected set of classes.
|
|||
Allows you to select detections with specific confidence value, for example higher than selected threshold.
|
||||
|
||||
=== "After"
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
|
||||
|
|
@ -102,7 +97,6 @@ Allows you to select detections with specific confidence value, for example high
|
|||
</div>
|
||||
|
||||
=== "Before"
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
|
||||
|
|
@ -122,7 +116,6 @@ Allows you to select detections based on their size. We define the area as the n
|
|||
detection in the image. In the example below, we have sifted out the detections that are too small.
|
||||
|
||||
=== "After"
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
|
||||
|
|
@ -137,7 +130,6 @@ detection in the image. In the example below, we have sifted out the detections
|
|||
</div>
|
||||
|
||||
=== "Before"
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
|
||||
|
|
@ -159,7 +151,6 @@ but small on a 3840x2160 image. In such cases, we can filter out detections base
|
|||
occupied by them. In the example below, we remove too large detections.
|
||||
|
||||
=== "After"
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
|
||||
|
|
@ -178,7 +169,6 @@ occupied by them. In the example below, we remove too large detections.
|
|||
</div>
|
||||
|
||||
=== "Before"
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
|
||||
|
|
@ -203,7 +193,6 @@ can be criteria for rejecting detection. Implementing such filtering requires a
|
|||
simple and fast.
|
||||
|
||||
=== "After"
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
|
||||
|
|
@ -220,7 +209,6 @@ simple and fast.
|
|||
</div>
|
||||
|
||||
=== "Before"
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
|
||||
|
|
@ -242,7 +230,6 @@ Allows you to use `Detections` in combination with `PolygonZone` to weed out bou
|
|||
zone. In the example below you can see how to filter out all detections located in the lower part of the image.
|
||||
|
||||
=== "After"
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
|
||||
|
|
@ -259,7 +246,6 @@ zone. In the example below you can see how to filter out all detections located
|
|||
</div>
|
||||
|
||||
=== "Before"
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
|
||||
|
|
@ -280,7 +266,6 @@ zone. In the example below you can see how to filter out all detections located
|
|||
`Detections`' greatest strength, however, is that you can build arbitrarily complex logical conditions by simply combining separate conditions using `&` or `|`.
|
||||
|
||||
=== "After"
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
|
||||
|
|
@ -297,7 +282,6 @@ zone. In the example below you can see how to filter out all detections located
|
|||
</div>
|
||||
|
||||
=== "Before"
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
|
||||
|
|
|
|||
|
|
@ -19,7 +19,6 @@ model. You can learn more on this topic in our
|
|||
[How to Detect and Annotate](/latest/how_to/detect_and_annotate.md) guide.
|
||||
|
||||
=== "Inference"
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
from inference import get_model
|
||||
|
|
@ -34,7 +33,6 @@ model. You can learn more on this topic in our
|
|||
```
|
||||
|
||||
=== "Ultralytics"
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
from ultralytics import YOLO
|
||||
|
|
@ -49,7 +47,6 @@ model. You can learn more on this topic in our
|
|||
```
|
||||
|
||||
=== "Transformers"
|
||||
|
||||
```python
|
||||
import torch
|
||||
import supervision as sv
|
||||
|
|
@ -83,7 +80,6 @@ and then pass the
|
|||
object resulting from the inference to it. Its fields are parsed and saved on disk.
|
||||
|
||||
=== "Inference"
|
||||
|
||||
```{ .py hl_lines="7 12" }
|
||||
import supervision as sv
|
||||
from inference import get_model
|
||||
|
|
@ -100,7 +96,6 @@ object resulting from the inference to it. Its fields are parsed and saved on di
|
|||
```
|
||||
|
||||
=== "Ultralytics"
|
||||
|
||||
```{ .py hl_lines="7 12" }
|
||||
import supervision as sv
|
||||
from ultralytics import YOLO
|
||||
|
|
@ -117,7 +112,6 @@ object resulting from the inference to it. Its fields are parsed and saved on di
|
|||
```
|
||||
|
||||
=== "Transformers"
|
||||
|
||||
```{ .py hl_lines="9 23" }
|
||||
import torch
|
||||
import supervision as sv
|
||||
|
|
@ -144,11 +138,11 @@ object resulting from the inference to it. Its fields are parsed and saved on di
|
|||
sink.append(detections, {})
|
||||
```
|
||||
|
||||
| x_min | y_min | x_max | y_max | class_id | confidence | tracker_id | class_name |
|
||||
|---------|----------|---------|----------|----------|------------|------------|------------|
|
||||
| 2941.14 | 1269.31 | 3220.77 | 1500.67 | 2 | 0.8517 | | car |
|
||||
| 944.889 | 899.641 | 1235.42 | 1308.80 | 7 | 0.6752 | | truck |
|
||||
| 1439.78 | 1077.79 | 1621.27 | 1231.40 | 2 | 0.6450 | | car |
|
||||
| x_min | y_min | x_max | y_max | class_id | confidence | tracker_id | class_name |
|
||||
| ------- | ------- | ------- | ------- | -------- | ---------- | ---------- | ---------- |
|
||||
| 2941.14 | 1269.31 | 3220.77 | 1500.67 | 2 | 0.8517 | | car |
|
||||
| 944.889 | 899.641 | 1235.42 | 1308.80 | 7 | 0.6752 | | truck |
|
||||
| 1439.78 | 1077.79 | 1621.27 | 1231.40 | 2 | 0.6450 | | car |
|
||||
|
||||
## Custom Fields
|
||||
|
||||
|
|
@ -160,7 +154,6 @@ also allows you to add custom information to each row, which can be passed via t
|
|||
frame index from which the detections originate.
|
||||
|
||||
=== "Inference"
|
||||
|
||||
```{ .py hl_lines="8 12" }
|
||||
import supervision as sv
|
||||
from inference import get_model
|
||||
|
|
@ -177,7 +170,6 @@ frame index from which the detections originate.
|
|||
```
|
||||
|
||||
=== "Ultralytics"
|
||||
|
||||
```{ .py hl_lines="8 12" }
|
||||
import supervision as sv
|
||||
from ultralytics import YOLO
|
||||
|
|
@ -194,7 +186,6 @@ frame index from which the detections originate.
|
|||
```
|
||||
|
||||
=== "Transformers"
|
||||
|
||||
```{ .py hl_lines="10 23" }
|
||||
import torch
|
||||
import supervision as sv
|
||||
|
|
@ -221,11 +212,11 @@ frame index from which the detections originate.
|
|||
sink.append(detections, {"frame_index": frame_index})
|
||||
```
|
||||
|
||||
| x_min | y_min | x_max | y_max | class_id | confidence | tracker_id | class_name | frame_index |
|
||||
|---------|----------|---------|----------|----------|------------|------------|------------|-------------|
|
||||
| 2941.14 | 1269.31 | 3220.77 | 1500.67 | 2 | 0.8517 | | car | 0 |
|
||||
| 944.889 | 899.641 | 1235.42 | 1308.80 | 7 | 0.6752 | | truck | 0 |
|
||||
| 1439.78 | 1077.79 | 1621.27 | 1231.40 | 2 | 0.6450 | | car | 0 |
|
||||
| x_min | y_min | x_max | y_max | class_id | confidence | tracker_id | class_name | frame_index |
|
||||
| ------- | ------- | ------- | ------- | -------- | ---------- | ---------- | ---------- | ----------- |
|
||||
| 2941.14 | 1269.31 | 3220.77 | 1500.67 | 2 | 0.8517 | | car | 0 |
|
||||
| 944.889 | 899.641 | 1235.42 | 1308.80 | 7 | 0.6752 | | truck | 0 |
|
||||
| 1439.78 | 1077.79 | 1621.27 | 1231.40 | 2 | 0.6450 | | car | 0 |
|
||||
|
||||
## Save Detections as JSON
|
||||
|
||||
|
|
@ -236,7 +227,6 @@ with
|
|||
[`sv.JSONSink`](/latest/detection/tools/save_detections/#supervision.detection.tools.csv_sink.JSONSink).
|
||||
|
||||
=== "Inference"
|
||||
|
||||
```{ .py hl_lines="7" }
|
||||
import supervision as sv
|
||||
from inference import get_model
|
||||
|
|
@ -253,7 +243,6 @@ with
|
|||
```
|
||||
|
||||
=== "Ultralytics"
|
||||
|
||||
```{ .py hl_lines="7" }
|
||||
import supervision as sv
|
||||
from ultralytics import YOLO
|
||||
|
|
@ -270,7 +259,6 @@ with
|
|||
```
|
||||
|
||||
=== "Transformers"
|
||||
|
||||
```{ .py hl_lines="9" }
|
||||
import torch
|
||||
import supervision as sv
|
||||
|
|
|
|||
|
|
@ -41,7 +41,6 @@ This `callback` function will be essential in the subsequent steps of the tutori
|
|||
it will be modified to include tracking, labeling, and trace annotations.
|
||||
|
||||
=== "Ultralytics"
|
||||
|
||||
```{ .py }
|
||||
import numpy as np
|
||||
import supervision as sv
|
||||
|
|
@ -63,7 +62,6 @@ it will be modified to include tracking, labeling, and trace annotations.
|
|||
```
|
||||
|
||||
=== "Inference"
|
||||
|
||||
```{ .py }
|
||||
import numpy as np
|
||||
import supervision as sv
|
||||
|
|
@ -97,7 +95,6 @@ functionality, each detected object is assigned a unique tracker ID,
|
|||
enabling the continuous following of the object's motion path across different frames.
|
||||
|
||||
=== "Ultralytics"
|
||||
|
||||
```{ .py hl_lines="6 12" }
|
||||
import numpy as np
|
||||
import supervision as sv
|
||||
|
|
@ -121,7 +118,6 @@ enabling the continuous following of the object's motion path across different f
|
|||
```
|
||||
|
||||
=== "Inference"
|
||||
|
||||
```{ .py hl_lines="6 12" }
|
||||
import numpy as np
|
||||
import supervision as sv
|
||||
|
|
@ -153,7 +149,6 @@ in Supervision, we can overlay the tracker IDs and class labels on the detected
|
|||
offering a clear visual representation of each object's class and unique identifier.
|
||||
|
||||
=== "Ultralytics"
|
||||
|
||||
```{ .py hl_lines="8 15-19 23-24" }
|
||||
import numpy as np
|
||||
import supervision as sv
|
||||
|
|
@ -188,7 +183,6 @@ offering a clear visual representation of each object's class and unique identif
|
|||
```
|
||||
|
||||
=== "Inference"
|
||||
|
||||
```{ .py hl_lines="8 15-19 23-24" }
|
||||
import numpy as np
|
||||
import supervision as sv
|
||||
|
|
@ -235,7 +229,6 @@ allows for visualizing the trajectories of objects, helping in understanding the
|
|||
movement patterns and interactions between objects in the video.
|
||||
|
||||
=== "Ultralytics"
|
||||
|
||||
```{ .py hl_lines="9 26-27" }
|
||||
import numpy as np
|
||||
import supervision as sv
|
||||
|
|
@ -273,7 +266,6 @@ movement patterns and interactions between objects in the video.
|
|||
```
|
||||
|
||||
=== "Inference"
|
||||
|
||||
```{ .py hl_lines="9 26-27" }
|
||||
import numpy as np
|
||||
import supervision as sv
|
||||
|
|
|
|||
|
|
@ -34,9 +34,7 @@ You can install `supervision` in a
|
|||
[**Python>=3.8**](https://www.python.org/) environment.
|
||||
|
||||
!!! example "pip install (recommended)"
|
||||
|
||||
=== "pip"
|
||||
|
||||
[](https://badge.fury.io/py/supervision)
|
||||
[](https://pypistats.org/packages/supervision)
|
||||
[](https://github.com/roboflow/supervision/blob/main/LICENSE.md)
|
||||
|
|
@ -47,9 +45,7 @@ You can install `supervision` in a
|
|||
```
|
||||
|
||||
!!! example "conda/mamba install"
|
||||
|
||||
=== "conda"
|
||||
|
||||
[](https://anaconda.org/conda-forge/supervision) [](https://anaconda.org/conda-forge/supervision) [](https://anaconda.org/conda-forge/supervision) [](https://anaconda.org/conda-forge/supervision)
|
||||
|
||||
```bash
|
||||
|
|
@ -57,7 +53,6 @@ You can install `supervision` in a
|
|||
```
|
||||
|
||||
=== "mamba"
|
||||
|
||||
[](https://anaconda.org/conda-forge/supervision) [](https://anaconda.org/conda-forge/supervision) [](https://anaconda.org/conda-forge/supervision) [](https://anaconda.org/conda-forge/supervision)
|
||||
|
||||
```bash
|
||||
|
|
@ -65,9 +60,7 @@ You can install `supervision` in a
|
|||
```
|
||||
|
||||
!!! example "git clone (for development)"
|
||||
|
||||
=== "virtualenv"
|
||||
|
||||
```bash
|
||||
# clone repository and navigate to root directory
|
||||
git clone https://github.com/roboflow/supervision.git
|
||||
|
|
@ -83,7 +76,6 @@ You can install `supervision` in a
|
|||
```
|
||||
|
||||
=== "poetry"
|
||||
|
||||
```bash
|
||||
# clone repository and navigate to root directory
|
||||
git clone https://github.com/roboflow/supervision.git
|
||||
|
|
@ -103,48 +95,48 @@ You can install `supervision` in a
|
|||
|
||||
- **Detect and Annotate**
|
||||
|
||||
***
|
||||
---
|
||||
|
||||
Annotate predictions from a range of object detection and segmentation models
|
||||
Annotate predictions from a range of object detection and segmentation models
|
||||
|
||||
[:octicons-arrow-right-24: Tutorial](how_to/detect_and_annotate.md)
|
||||
[:octicons-arrow-right-24: Tutorial](how_to/detect_and_annotate.md)
|
||||
|
||||
- **Track Objects**
|
||||
|
||||
***
|
||||
---
|
||||
|
||||
Discover how to enhance video analysis by implementing seamless object tracking
|
||||
Discover how to enhance video analysis by implementing seamless object tracking
|
||||
|
||||
[:octicons-arrow-right-24: Tutorial](how_to/track_objects.md)
|
||||
[:octicons-arrow-right-24: Tutorial](how_to/track_objects.md)
|
||||
|
||||
- **Detect Small Objects**
|
||||
|
||||
***
|
||||
---
|
||||
|
||||
Learn how to detect small objects in images
|
||||
Learn how to detect small objects in images
|
||||
|
||||
[:octicons-arrow-right-24: Tutorial](how_to/detect_small_objects.md)
|
||||
[:octicons-arrow-right-24: Tutorial](how_to/detect_small_objects.md)
|
||||
|
||||
- **Count Objects Crossing Line**
|
||||
|
||||
***
|
||||
---
|
||||
|
||||
Explore methods to accurately count and analyze objects crossing a predefined line
|
||||
Explore methods to accurately count and analyze objects crossing a predefined line
|
||||
|
||||
[:octicons-arrow-right-24: Notebook](https://supervision.roboflow.com/latest/notebooks/count-objects-crossing-the-line/)
|
||||
[:octicons-arrow-right-24: Notebook](https://supervision.roboflow.com/latest/notebooks/count-objects-crossing-the-line/)
|
||||
|
||||
- > **Filter Objects in Zone**
|
||||
|
||||
***
|
||||
---
|
||||
|
||||
Master the techniques to selectively filter and focus on objects within a specific zone
|
||||
Master the techniques to selectively filter and focus on objects within a specific zone
|
||||
|
||||
- **Cheatsheet**
|
||||
|
||||
***
|
||||
---
|
||||
|
||||
Access a quick reference guide to the most common `supervision` functions
|
||||
Access a quick reference guide to the most common `supervision` functions
|
||||
|
||||
[:octicons-arrow-right-24: Cheatsheet](https://roboflow.github.io/cheatsheet-supervision/)
|
||||
[:octicons-arrow-right-24: Cheatsheet](https://roboflow.github.io/cheatsheet-supervision/)
|
||||
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -16,67 +16,76 @@ https://github.com/roboflow/supervision/assets/26109316/f84db7b5-79e2-4142-a1da-
|
|||
|
||||
- clone repository and navigate to example directory
|
||||
|
||||
```bash
|
||||
git clone https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/count_people_in_zone
|
||||
```
|
||||
```bash
|
||||
git clone https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/count_people_in_zone
|
||||
```
|
||||
|
||||
- setup python environment and activate it [optional]
|
||||
- setup python environment and activate it \[optional\]
|
||||
|
||||
```bash
|
||||
python3 -m venv venv
|
||||
source venv/bin/activate
|
||||
```
|
||||
```bash
|
||||
python3 -m venv venv
|
||||
source venv/bin/activate
|
||||
```
|
||||
|
||||
- install required dependencies
|
||||
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
- download `traffic_analysis.pt` and `traffic_analysis.mov` files
|
||||
|
||||
```bash
|
||||
./setup.sh
|
||||
```
|
||||
```bash
|
||||
./setup.sh
|
||||
```
|
||||
|
||||
## 🛠️ script arguments
|
||||
|
||||
- ultralytics
|
||||
|
||||
- `--source_weights_path` (optional): The path to the YOLO model's weights file.
|
||||
Defaults to `"yolov8x.pt"` if not specified.
|
||||
- `--source_weights_path` (optional): The path to the YOLO model's weights file.
|
||||
Defaults to `"yolov8x.pt"` if not specified.
|
||||
|
||||
- `--zone_configuration_path`: Specifies the path to the JSON file containing zone
|
||||
configurations. This file defines the polygonal areas in the video where objects will
|
||||
be counted.
|
||||
- `--source_video_path`: The path to the source video file that will be analyzed.
|
||||
- `--target_video_path` (optional): The path to save the output video with annotations.
|
||||
If not provided, the processed video will be displayed in real-time.
|
||||
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model
|
||||
to filter detections. Default is `0.3`.
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
|
||||
for the model. Default is `0.7`.
|
||||
- `--zone_configuration_path`: Specifies the path to the JSON file containing zone
|
||||
configurations. This file defines the polygonal areas in the video where objects will
|
||||
be counted.
|
||||
|
||||
- `--source_video_path`: The path to the source video file that will be analyzed.
|
||||
|
||||
- `--target_video_path` (optional): The path to save the output video with annotations.
|
||||
If not provided, the processed video will be displayed in real-time.
|
||||
|
||||
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model
|
||||
to filter detections. Default is `0.3`.
|
||||
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
|
||||
for the model. Default is `0.7`.
|
||||
|
||||
- inference
|
||||
|
||||
- `--roboflow_api_key` (optional): The API key for Roboflow services. If not provided
|
||||
directly, the script tries to fetch it from the `ROBOFLOW_API_KEY` environment
|
||||
variable. Follow [this guide](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)
|
||||
to acquire your `API KEY`.
|
||||
- `--model_id` (optional): Designates the Roboflow model ID to be used. The default
|
||||
value is `"yolov8x-1280"`.
|
||||
- `--roboflow_api_key` (optional): The API key for Roboflow services. If not provided
|
||||
directly, the script tries to fetch it from the `ROBOFLOW_API_KEY` environment
|
||||
variable. Follow [this guide](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)
|
||||
to acquire your `API KEY`.
|
||||
|
||||
- `--zone_configuration_path`: Specifies the path to the JSON file containing zone
|
||||
configurations. This file defines the polygonal areas in the video where objects will
|
||||
be counted.
|
||||
- `--source_video_path`: The path to the source video file that will be analyzed.
|
||||
- `--target_video_path` (optional): The path to save the output video with annotations.
|
||||
If not provided, the processed video will be displayed in real-time.
|
||||
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model
|
||||
to filter detections. Default is `0.3`.
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
|
||||
for the model. Default is `0.7`.
|
||||
- `--model_id` (optional): Designates the Roboflow model ID to be used. The default
|
||||
value is `"yolov8x-1280"`.
|
||||
|
||||
- `--zone_configuration_path`: Specifies the path to the JSON file containing zone
|
||||
configurations. This file defines the polygonal areas in the video where objects will
|
||||
be counted.
|
||||
|
||||
- `--source_video_path`: The path to the source video file that will be analyzed.
|
||||
|
||||
- `--target_video_path` (optional): The path to save the output video with annotations.
|
||||
If not provided, the processed video will be displayed in real-time.
|
||||
|
||||
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model
|
||||
to filter detections. Default is `0.3`.
|
||||
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
|
||||
for the model. Default is `0.7`.
|
||||
|
||||
## 📌 zone configuration
|
||||
|
||||
|
|
@ -89,35 +98,35 @@ https://github.com/roboflow/supervision/assets/26109316/f84db7b5-79e2-4142-a1da-
|
|||
|
||||
- ultralytics
|
||||
|
||||
```bash
|
||||
python ultralytics_example.py \
|
||||
--zone_configuration_path data/multi-zone-config.json \
|
||||
--source_video_path data/market-square.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5
|
||||
```
|
||||
```bash
|
||||
python ultralytics_example.py \
|
||||
--zone_configuration_path data/multi-zone-config.json \
|
||||
--source_video_path data/market-square.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5
|
||||
```
|
||||
|
||||
- inference
|
||||
|
||||
```bash
|
||||
python inference_example.py \
|
||||
--roboflow_api_key <ROBOFLOW API KEY> \
|
||||
--zone_configuration_path data/multi-zone-config.json \
|
||||
--source_video_path data/market-square.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5
|
||||
```
|
||||
```bash
|
||||
python inference_example.py \
|
||||
--roboflow_api_key <ROBOFLOW API KEY> \
|
||||
--zone_configuration_path data/multi-zone-config.json \
|
||||
--source_video_path data/market-square.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5
|
||||
```
|
||||
|
||||
## © license
|
||||
|
||||
This demo integrates two main components, each with its own licensing:
|
||||
|
||||
- ultralytics: The object detection model used in this demo, YOLOv8, is distributed
|
||||
under the [AGPL-3.0 license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
|
||||
You can find more details about this license here.
|
||||
under the [AGPL-3.0 license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
|
||||
You can find more details about this license here.
|
||||
|
||||
- supervision: The analytics code that powers the zone-based analysis in this demo is
|
||||
based on the Supervision library, which is licensed under the
|
||||
[MIT license](https://github.com/roboflow/supervision/blob/develop/LICENSE.md). This
|
||||
makes the Supervision part of the code fully open source and freely usable in your
|
||||
projects.
|
||||
based on the Supervision library, which is licensed under the
|
||||
[MIT license](https://github.com/roboflow/supervision/blob/develop/LICENSE.md). This
|
||||
makes the Supervision part of the code fully open source and freely usable in your
|
||||
projects.
|
||||
|
|
|
|||
|
|
@ -6,7 +6,6 @@ This script performs heatmap and tracking analysis using YOLOv8, an object-detec
|
|||
ByteTrack, a simple yet effective online multi-object tracking method. It uses the
|
||||
supervision package for multiple tasks such as drawing heatmap annotations, tracking objects, etc.
|
||||
|
||||
|
||||
## 💻 install
|
||||
|
||||
- clone repository and navigate to example directory
|
||||
|
|
@ -16,7 +15,7 @@ supervision package for multiple tasks such as drawing heatmap annotations, trac
|
|||
cd supervision/examples/heatmap_and_track
|
||||
```
|
||||
|
||||
- setup python environment and activate it [optional]
|
||||
- setup python environment and activate it \[optional\]
|
||||
|
||||
```bash
|
||||
python3 -m venv venv
|
||||
|
|
@ -32,17 +31,17 @@ supervision package for multiple tasks such as drawing heatmap annotations, trac
|
|||
## 🛠️ script arguments
|
||||
|
||||
- `--source_weights_path`: Required. Specifies the path to the weights file for the
|
||||
YOLO model. This file contains the trained model data necessary for object detection.
|
||||
YOLO model. This file contains the trained model data necessary for object detection.
|
||||
- `--source_video_path` (optional): The path to the source video file that will be
|
||||
analyzed. This is the input video on which crowd analysis will be performed.
|
||||
If not specified default is `people-walking.mp4` from supervision assets
|
||||
analyzed. This is the input video on which crowd analysis will be performed.
|
||||
If not specified default is `people-walking.mp4` from supervision assets
|
||||
- `--target_video_path` (optional): The path to save the output.mp4 video with annotations.
|
||||
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model
|
||||
to filter detections. Default is `0.3`. This determines how confident the model should
|
||||
be to recognize an object in the video.
|
||||
to filter detections. Default is `0.3`. This determines how confident the model should
|
||||
be to recognize an object in the video.
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
|
||||
for the model. Default is 0.7. This value is used to manage object detection accuracy,
|
||||
particularly in distinguishing between different objects.
|
||||
for the model. Default is 0.7. This value is used to manage object detection accuracy,
|
||||
particularly in distinguishing between different objects.
|
||||
- `--heatmap_alpha` (optional): Opacity of the overlay mask, between 0 and 1.
|
||||
- `--radius` (optional): Radius of the heat circle.
|
||||
- `--track_threshold` (optional): Detection confidence threshold for track activation.
|
||||
|
|
@ -53,11 +52,11 @@ particularly in distinguishing between different objects.
|
|||
|
||||
```bash
|
||||
python script.py \
|
||||
--source_weights_path weight.pt \
|
||||
--source_video_path input_video.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5 \
|
||||
--target_video_path output_video.mp4
|
||||
--source_weights_path weight.pt \
|
||||
--source_video_path input_video.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5 \
|
||||
--target_video_path output_video.mp4
|
||||
```
|
||||
|
||||
## © license
|
||||
|
|
@ -65,11 +64,11 @@ python script.py \
|
|||
This demo integrates two main components, each with its own licensing:
|
||||
|
||||
- ultralytics: The object detection model used in this demo, YOLOv8, is distributed
|
||||
under the [AGPL-3.0 license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
|
||||
You can find more details about this license here.
|
||||
under the [AGPL-3.0 license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
|
||||
You can find more details about this license here.
|
||||
|
||||
- supervision: The analytics code that powers the zone-based analysis in this demo is
|
||||
based on the Supervision library, which is licensed under the
|
||||
[MIT license](https://github.com/roboflow/supervision/blob/develop/LICENSE.md). This
|
||||
makes the Supervision part of the code fully open source and freely usable in your
|
||||
projects.
|
||||
based on the Supervision library, which is licensed under the
|
||||
[MIT license](https://github.com/roboflow/supervision/blob/develop/LICENSE.md). This
|
||||
makes the Supervision part of the code fully open source and freely usable in your
|
||||
projects.
|
||||
|
|
|
|||
|
|
@ -11,7 +11,7 @@ supervision package for multiple tasks such as tracking, annotations, etc.
|
|||
|
||||
https://github.com/roboflow/supervision/assets/26109316/d50118c1-2ae4-458d-915a-5d860fd36f71
|
||||
|
||||
> [!IMPORTANT]
|
||||
> \[!IMPORTANT\]
|
||||
> Adjust the [`SOURCE`](https://github.com/roboflow/supervision/blob/e32b05a636dab2ea1f39299e529c4b22b8baa8da/examples/speed_estimation/ultralytics_example.py#L10)
|
||||
> and [`TARGET`](https://github.com/roboflow/supervision/blob/e32b05a636dab2ea1f39299e529c4b22b8baa8da/examples/speed_estimation/ultralytics_example.py#L15)
|
||||
> configuration if you plan to run a speed estimation script on your video file. Those must be adjusted separately for each camera view. You can learn more
|
||||
|
|
@ -21,97 +21,102 @@ https://github.com/roboflow/supervision/assets/26109316/d50118c1-2ae4-458d-915a-
|
|||
|
||||
- clone repository and navigate to example directory
|
||||
|
||||
```bash
|
||||
git clone https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/speed_estimation
|
||||
```
|
||||
```bash
|
||||
git clone https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/speed_estimation
|
||||
```
|
||||
|
||||
- setup python environment and activate it [optional]
|
||||
- setup python environment and activate it \[optional\]
|
||||
|
||||
```bash
|
||||
python3.10 -m venv venv
|
||||
source venv/bin/activate
|
||||
```
|
||||
```bash
|
||||
python3.10 -m venv venv
|
||||
source venv/bin/activate
|
||||
```
|
||||
|
||||
- install required dependencies
|
||||
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
- download `vehicles.mp4` file
|
||||
|
||||
```bash
|
||||
python3.10 video_downloader.py
|
||||
```
|
||||
```bash
|
||||
python3.10 video_downloader.py
|
||||
```
|
||||
|
||||
## 🛠️ script arguments
|
||||
|
||||
- `--roboflow_api_key` (optional): The API key for Roboflow services. If not provided
|
||||
directly, the script tries to fetch it from the `ROBOFLOW_API_KEY` environment
|
||||
variable. Follow [this guide](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)
|
||||
to acquire your `API KEY`.
|
||||
directly, the script tries to fetch it from the `ROBOFLOW_API_KEY` environment
|
||||
variable. Follow [this guide](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)
|
||||
to acquire your `API KEY`.
|
||||
|
||||
- `--model_id` (optional): Designates the Roboflow model ID to be used. The default
|
||||
value is `"yolov8x-1280"`.
|
||||
value is `"yolov8x-1280"`.
|
||||
|
||||
- `--source_weights_path`: Required. Specifies the path to the YOLO model's weights
|
||||
file, which is essential for the object detection process. This file contains the
|
||||
data that the model uses to identify objects in the video.
|
||||
file, which is essential for the object detection process. This file contains the
|
||||
data that the model uses to identify objects in the video.
|
||||
|
||||
- `--source_video_path`: Required. The path to the source video file that will be
|
||||
analyzed. This is the input video on which traffic flow analysis will be performed.
|
||||
analyzed. This is the input video on which traffic flow analysis will be performed.
|
||||
|
||||
- `--target_video_path`: The path to save the output video with
|
||||
annotations. If not specified, the processed video will be displayed in real-time
|
||||
without being saved.
|
||||
annotations. If not specified, the processed video will be displayed in real-time
|
||||
without being saved.
|
||||
|
||||
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO
|
||||
model to filter detections. Default is `0.3`. This determines how confident the
|
||||
model should be to recognize an object in the video.
|
||||
model to filter detections. Default is `0.3`. This determines how confident the
|
||||
model should be to recognize an object in the video.
|
||||
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
|
||||
for the model. Default is 0.7. This value is used to manage object detection
|
||||
accuracy, particularly in distinguishing between different objects.
|
||||
for the model. Default is 0.7. This value is used to manage object detection
|
||||
accuracy, particularly in distinguishing between different objects.
|
||||
|
||||
## ⚙️ run
|
||||
|
||||
- yolo-nas
|
||||
|
||||
```bash
|
||||
```bash
|
||||
python yolo_nas_example.py \
|
||||
--source_video_path data/vehicles.mp4 \
|
||||
--target_video_path data/vehicles-result.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5
|
||||
```
|
||||
--source_video_path data/vehicles.mp4 \
|
||||
--target_video_path data/vehicles-result.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5
|
||||
```
|
||||
|
||||
- inference
|
||||
|
||||
```bash
|
||||
```bash
|
||||
python inference_example.py \
|
||||
--roboflow_api_key <ROBOFLOW API KEY> \
|
||||
--source_video_path data/vehicles.mp4 \
|
||||
--target_video_path data/vehicles-result.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5
|
||||
```
|
||||
--roboflow_api_key <ROBOFLOW API KEY> \
|
||||
--source_video_path data/vehicles.mp4 \
|
||||
--target_video_path data/vehicles-result.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5
|
||||
```
|
||||
|
||||
- ultralytics
|
||||
|
||||
```bash
|
||||
```bash
|
||||
python ultralytics_example.py \
|
||||
--source_video_path data/vehicles.mp4 \
|
||||
--target_video_path data/vehicles-result.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5
|
||||
```
|
||||
--source_video_path data/vehicles.mp4 \
|
||||
--target_video_path data/vehicles-result.mp4 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5
|
||||
```
|
||||
|
||||
## © license
|
||||
|
||||
This demo integrates two main components, each with its own licensing:
|
||||
|
||||
- ultralytics: The object detection model used in this demo, YOLOv8, is distributed
|
||||
under the [AGPL-3.0 license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
|
||||
You can find more details about this license here.
|
||||
under the [AGPL-3.0 license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
|
||||
You can find more details about this license here.
|
||||
|
||||
- supervision: The analytics code that powers the zone-based analysis in this demo is
|
||||
based on the Supervision library, which is licensed under the
|
||||
[MIT license](https://github.com/roboflow/supervision/blob/develop/LICENSE.md). This
|
||||
makes the Supervision part of the code fully open source and freely usable in your
|
||||
projects.
|
||||
based on the Supervision library, which is licensed under the
|
||||
[MIT license](https://github.com/roboflow/supervision/blob/develop/LICENSE.md). This
|
||||
makes the Supervision part of the code fully open source and freely usable in your
|
||||
projects.
|
||||
|
|
|
|||
|
|
@ -15,23 +15,23 @@ https://github.com/roboflow/supervision/assets/26109316/d051cc8a-dd15-41d4-aa36-
|
|||
|
||||
- clone repository and navigate to example directory
|
||||
|
||||
```bash
|
||||
git clone https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/time_in_zone
|
||||
```
|
||||
```bash
|
||||
git clone https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/time_in_zone
|
||||
```
|
||||
|
||||
- setup python environment and activate it [optional]
|
||||
- setup python environment and activate it \[optional\]
|
||||
|
||||
```bash
|
||||
python3 -m venv venv
|
||||
source venv/bin/activate
|
||||
```
|
||||
```bash
|
||||
python3 -m venv venv
|
||||
source venv/bin/activate
|
||||
```
|
||||
|
||||
- install required dependencies
|
||||
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
## 🛠 scripts
|
||||
|
||||
|
|
@ -45,16 +45,16 @@ This script allows you to download a video from YouTube.
|
|||
|
||||
```bash
|
||||
python scripts/download_from_youtube.py \
|
||||
--url "https://www.youtube.com/watch?v=-8zyEwAa50Q" \
|
||||
--output_path "data/checkout" \
|
||||
--file_name "video.mp4"
|
||||
--url "https://www.youtube.com/watch?v=-8zyEwAa50Q" \
|
||||
--output_path "data/checkout" \
|
||||
--file_name "video.mp4"
|
||||
```
|
||||
|
||||
```bash
|
||||
python scripts/download_from_youtube.py \
|
||||
--url "https://www.youtube.com/watch?v=MNn9qKG2UFI" \
|
||||
--output_path "data/traffic" \
|
||||
--file_name "video.mp4"
|
||||
--url "https://www.youtube.com/watch?v=MNn9qKG2UFI" \
|
||||
--output_path "data/traffic" \
|
||||
--file_name "video.mp4"
|
||||
```
|
||||
|
||||
### `stream_from_file`
|
||||
|
|
@ -68,14 +68,14 @@ mock a live video stream for local testing. Video will be streamed in a loop und
|
|||
|
||||
```bash
|
||||
python scripts/stream_from_file.py \
|
||||
--video_directory "data/checkout" \
|
||||
--number_of_streams 1
|
||||
--video_directory "data/checkout" \
|
||||
--number_of_streams 1
|
||||
```
|
||||
|
||||
```bash
|
||||
python scripts/stream_from_file.py \
|
||||
--video_directory "data/traffic" \
|
||||
--number_of_streams 1
|
||||
--video_directory "data/traffic" \
|
||||
--number_of_streams 1
|
||||
```
|
||||
|
||||
### `draw_zones`
|
||||
|
|
@ -86,24 +86,27 @@ window where you can draw polygons on the source image or video file. The polygo
|
|||
be saved as a JSON file.
|
||||
|
||||
- `--source_path`: Path to the source image or video file for drawing polygons.
|
||||
|
||||
- `--zone_configuration_path`: Path where the polygon annotations will be saved as a JSON file.
|
||||
|
||||
|
||||
- `enter` - finish drawing the current polygon.
|
||||
|
||||
- `escape` - cancel drawing the current polygon.
|
||||
|
||||
- `q` - quit the drawing window.
|
||||
|
||||
- `s` - save zone configuration to a JSON file.
|
||||
|
||||
```bash
|
||||
python scripts/draw_zones.py \
|
||||
--source_path "data/checkout/video.mp4" \
|
||||
--zone_configuration_path "data/checkout/config.json"
|
||||
--source_path "data/checkout/video.mp4" \
|
||||
--zone_configuration_path "data/checkout/config.json"
|
||||
```
|
||||
|
||||
```bash
|
||||
python scripts/draw_zones.py \
|
||||
--source_path "data/traffic/video.mp4" \
|
||||
--zone_configuration_path "data/traffic/config.json"
|
||||
--source_path "data/traffic/video.mp4" \
|
||||
--zone_configuration_path "data/traffic/config.json"
|
||||
```
|
||||
|
||||
https://github.com/roboflow/supervision/assets/26109316/9d514c9e-2a61-418b-ae49-6ac1ad6ae5ac
|
||||
|
|
@ -114,33 +117,33 @@ https://github.com/roboflow/supervision/assets/26109316/9d514c9e-2a61-418b-ae49-
|
|||
|
||||
Script to run object detection on a video file using the Roboflow Inference model.
|
||||
|
||||
- `--zone_configuration_path`: Path to the zone configuration JSON file.
|
||||
- `--source_video_path`: Path to the source video file.
|
||||
- `--model_id`: Roboflow model ID.
|
||||
- `--classes`: List of class IDs to track. If empty, all classes are tracked.
|
||||
- `--confidence_threshold`: Confidence level for detections (`0` to `1`). Default is `0.3`.
|
||||
- `--iou_threshold`: IOU threshold for non-max suppression. Default is `0.7`.
|
||||
- `--zone_configuration_path`: Path to the zone configuration JSON file.
|
||||
- `--source_video_path`: Path to the source video file.
|
||||
- `--model_id`: Roboflow model ID.
|
||||
- `--classes`: List of class IDs to track. If empty, all classes are tracked.
|
||||
- `--confidence_threshold`: Confidence level for detections (`0` to `1`). Default is `0.3`.
|
||||
- `--iou_threshold`: IOU threshold for non-max suppression. Default is `0.7`.
|
||||
|
||||
```bash
|
||||
python inference_file_example.py \
|
||||
--zone_configuration_path "data/checkout/config.json" \
|
||||
--source_video_path "data/checkout/video.mp4" \
|
||||
--model_id "yolov8x-640" \
|
||||
--classes 0 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.7
|
||||
--zone_configuration_path "data/checkout/config.json" \
|
||||
--source_video_path "data/checkout/video.mp4" \
|
||||
--model_id "yolov8x-640" \
|
||||
--classes 0 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.7
|
||||
```
|
||||
|
||||
https://github.com/roboflow/supervision/assets/26109316/d051cc8a-dd15-41d4-aa36-d38b86334c39
|
||||
|
||||
```bash
|
||||
python inference_file_example.py \
|
||||
--zone_configuration_path "data/traffic/config.json" \
|
||||
--source_video_path "data/traffic/video.mp4" \
|
||||
--model_id "yolov8x-640" \
|
||||
--classes 2 5 6 7 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.7
|
||||
--zone_configuration_path "data/traffic/config.json" \
|
||||
--source_video_path "data/traffic/video.mp4" \
|
||||
--model_id "yolov8x-640" \
|
||||
--classes 2 5 6 7 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.7
|
||||
```
|
||||
|
||||
https://github.com/roboflow/supervision/assets/26109316/5ec896d7-4b39-4426-8979-11e71666878b
|
||||
|
|
@ -149,31 +152,31 @@ https://github.com/roboflow/supervision/assets/26109316/5ec896d7-4b39-4426-8979-
|
|||
|
||||
Script to run object detection on a video stream using the Roboflow Inference model.
|
||||
|
||||
- `--zone_configuration_path`: Path to the zone configuration JSON file.
|
||||
- `--rtsp_url`: Complete RTSP URL for the video stream.
|
||||
- `--model_id`: Roboflow model ID.
|
||||
- `--classes`: List of class IDs to track. If empty, all classes are tracked.
|
||||
- `--confidence_threshold`: Confidence level for detections (`0` to `1`). Default is `0.3`.
|
||||
- `--iou_threshold`: IOU threshold for non-max suppression. Default is `0.7`.
|
||||
- `--zone_configuration_path`: Path to the zone configuration JSON file.
|
||||
- `--rtsp_url`: Complete RTSP URL for the video stream.
|
||||
- `--model_id`: Roboflow model ID.
|
||||
- `--classes`: List of class IDs to track. If empty, all classes are tracked.
|
||||
- `--confidence_threshold`: Confidence level for detections (`0` to `1`). Default is `0.3`.
|
||||
- `--iou_threshold`: IOU threshold for non-max suppression. Default is `0.7`.
|
||||
|
||||
```bash
|
||||
python inference_stream_example.py \
|
||||
--zone_configuration_path "data/checkout/config.json" \
|
||||
--rtsp_url "rtsp://localhost:8554/live0.stream" \
|
||||
--model_id "yolov8x-640" \
|
||||
--classes 0 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.7
|
||||
--zone_configuration_path "data/checkout/config.json" \
|
||||
--rtsp_url "rtsp://localhost:8554/live0.stream" \
|
||||
--model_id "yolov8x-640" \
|
||||
--classes 0 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.7
|
||||
```
|
||||
|
||||
```bash
|
||||
python inference_stream_example.py \
|
||||
--zone_configuration_path "data/traffic/config.json" \
|
||||
--rtsp_url "rtsp://localhost:8554/live0.stream" \
|
||||
--model_id "yolov8x-640" \
|
||||
--classes 2 5 6 7 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.7
|
||||
--zone_configuration_path "data/traffic/config.json" \
|
||||
--rtsp_url "rtsp://localhost:8554/live0.stream" \
|
||||
--model_id "yolov8x-640" \
|
||||
--classes 2 5 6 7 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.7
|
||||
```
|
||||
|
||||
<details>
|
||||
|
|
@ -183,68 +186,68 @@ python inference_stream_example.py \
|
|||
|
||||
Script to run object detection on a video file using the Ultralytics YOLOv8 model.
|
||||
|
||||
- `--zone_configuration_path`: Path to the zone configuration JSON file.
|
||||
- `--source_video_path`: Path to the source video file.
|
||||
- `--weights`: Path to the model weights file. Default is `'yolov8s.pt'`.
|
||||
- `--device`: Computation device (`'cpu'`, `'mps'` or `'cuda'`). Default is `'cpu'`.
|
||||
- `--classes`: List of class IDs to track. If empty, all classes are tracked.
|
||||
- `--confidence_threshold`: Confidence level for detections (`0` to `1`). Default is `0.3`.
|
||||
- `--iou_threshold`: IOU threshold for non-max suppression. Default is `0.7`.
|
||||
- `--zone_configuration_path`: Path to the zone configuration JSON file.
|
||||
- `--source_video_path`: Path to the source video file.
|
||||
- `--weights`: Path to the model weights file. Default is `'yolov8s.pt'`.
|
||||
- `--device`: Computation device (`'cpu'`, `'mps'` or `'cuda'`). Default is `'cpu'`.
|
||||
- `--classes`: List of class IDs to track. If empty, all classes are tracked.
|
||||
- `--confidence_threshold`: Confidence level for detections (`0` to `1`). Default is `0.3`.
|
||||
- `--iou_threshold`: IOU threshold for non-max suppression. Default is `0.7`.
|
||||
|
||||
```bash
|
||||
python ultralytics_file_example.py \
|
||||
--zone_configuration_path "data/checkout/config.json" \
|
||||
--source_video_path "data/checkout/video.mp4" \
|
||||
--weights "yolov8x.pt" \
|
||||
--device "cpu" \
|
||||
--classes 0 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.7
|
||||
--zone_configuration_path "data/checkout/config.json" \
|
||||
--source_video_path "data/checkout/video.mp4" \
|
||||
--weights "yolov8x.pt" \
|
||||
--device "cpu" \
|
||||
--classes 0 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.7
|
||||
```
|
||||
|
||||
```bash
|
||||
python ultralytics_file_example.py \
|
||||
--zone_configuration_path "data/traffic/config.json" \
|
||||
--source_video_path "data/traffic/video.mp4" \
|
||||
--weights "yolov8x.pt" \
|
||||
--device "cpu" \
|
||||
--classes 2 5 6 7 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.7
|
||||
--zone_configuration_path "data/traffic/config.json" \
|
||||
--source_video_path "data/traffic/video.mp4" \
|
||||
--weights "yolov8x.pt" \
|
||||
--device "cpu" \
|
||||
--classes 2 5 6 7 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.7
|
||||
```
|
||||
|
||||
### `ultralytics_stream_example`
|
||||
|
||||
Script to run object detection on a video stream using the Ultralytics YOLOv8 model.
|
||||
|
||||
- `--zone_configuration_path`: Path to the zone configuration JSON file.
|
||||
- `--rtsp_url`: Complete RTSP URL for the video stream.
|
||||
- `--weights`: Path to the model weights file. Default is `'yolov8s.pt'`.
|
||||
- `--device`: Computation device (`'cpu'`, `'mps'` or `'cuda'`). Default is `'cpu'`.
|
||||
- `--classes`: List of class IDs to track. If empty, all classes are tracked.
|
||||
- `--confidence_threshold`: Confidence level for detections (`0` to `1`). Default is `0.3`.
|
||||
- `--iou_threshold`: IOU threshold for non-max suppression. Default is `0.7`.
|
||||
- `--zone_configuration_path`: Path to the zone configuration JSON file.
|
||||
- `--rtsp_url`: Complete RTSP URL for the video stream.
|
||||
- `--weights`: Path to the model weights file. Default is `'yolov8s.pt'`.
|
||||
- `--device`: Computation device (`'cpu'`, `'mps'` or `'cuda'`). Default is `'cpu'`.
|
||||
- `--classes`: List of class IDs to track. If empty, all classes are tracked.
|
||||
- `--confidence_threshold`: Confidence level for detections (`0` to `1`). Default is `0.3`.
|
||||
- `--iou_threshold`: IOU threshold for non-max suppression. Default is `0.7`.
|
||||
|
||||
```bash
|
||||
python ultralytics_stream_example.py \
|
||||
--zone_configuration_path "data/checkout/config.json" \
|
||||
--rtsp_url "rtsp://localhost:8554/live0.stream" \
|
||||
--weights "yolov8x.pt" \
|
||||
--device "cpu" \
|
||||
--classes 0 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.7
|
||||
--zone_configuration_path "data/checkout/config.json" \
|
||||
--rtsp_url "rtsp://localhost:8554/live0.stream" \
|
||||
--weights "yolov8x.pt" \
|
||||
--device "cpu" \
|
||||
--classes 0 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.7
|
||||
```
|
||||
|
||||
```bash
|
||||
python ultralytics_stream_example.py \
|
||||
--zone_configuration_path "data/traffic/config.json" \
|
||||
--rtsp_url "rtsp://localhost:8554/live0.stream" \
|
||||
--weights "yolov8x.pt" \
|
||||
--device "cpu" \
|
||||
--classes 2 5 6 7 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.7
|
||||
--zone_configuration_path "data/traffic/config.json" \
|
||||
--rtsp_url "rtsp://localhost:8554/live0.stream" \
|
||||
--weights "yolov8x.pt" \
|
||||
--device "cpu" \
|
||||
--classes 2 5 6 7 \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.7
|
||||
```
|
||||
|
||||
</details>
|
||||
|
|
@ -254,11 +257,11 @@ python ultralytics_stream_example.py \
|
|||
This demo integrates two main components, each with its own licensing:
|
||||
|
||||
- ultralytics: The object detection model used in this demo, YOLOv8, is distributed
|
||||
under the [AGPL-3.0 license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
|
||||
You can find more details about this license here.
|
||||
under the [AGPL-3.0 license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
|
||||
You can find more details about this license here.
|
||||
|
||||
- supervision: The analytics code that powers the zone-based analysis in this demo is
|
||||
based on the Supervision library, which is licensed under the
|
||||
[MIT license](https://github.com/roboflow/supervision/blob/develop/LICENSE.md). This
|
||||
makes the Supervision part of the code fully open source and freely usable in your
|
||||
projects.
|
||||
based on the Supervision library, which is licensed under the
|
||||
[MIT license](https://github.com/roboflow/supervision/blob/develop/LICENSE.md). This
|
||||
makes the Supervision part of the code fully open source and freely usable in your
|
||||
projects.
|
||||
|
|
|
|||
|
|
@ -9,93 +9,100 @@ detection and Supervision for tracking and annotation.
|
|||
|
||||
- clone repository and navigate to example directory
|
||||
|
||||
```bash
|
||||
git clone https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/tracking
|
||||
```
|
||||
```bash
|
||||
git clone https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/tracking
|
||||
```
|
||||
|
||||
- setup python environment and activate it [optional]
|
||||
- setup python environment and activate it \[optional\]
|
||||
|
||||
```bash
|
||||
python3 -m venv venv
|
||||
source venv/bin/activate
|
||||
```
|
||||
```bash
|
||||
python3 -m venv venv
|
||||
source venv/bin/activate
|
||||
```
|
||||
|
||||
- install required dependencies
|
||||
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
## 🛠️ script arguments
|
||||
|
||||
- ultralytics
|
||||
|
||||
- `--source_weights_path`: Required. Specifies the path to the YOLO model's weights
|
||||
file, which is essential for the object detection process. This file contains the data
|
||||
that the model uses to identify objects in the video.
|
||||
- `--source_weights_path`: Required. Specifies the path to the YOLO model's weights
|
||||
file, which is essential for the object detection process. This file contains the data
|
||||
that the model uses to identify objects in the video.
|
||||
|
||||
- `--source_video_path`: Required. The path to the source video file to be processed.
|
||||
This is the video on which object detection and annotation will be performed.
|
||||
- `--target_video_path`: Required. The path where the processed video, with annotations
|
||||
added, will be saved. This is your output video file.
|
||||
- `--confidence_threshold` (optional): Sets the confidence level at which the model
|
||||
identifies objects in the video. Default is `0.3`. A higher threshold makes the model
|
||||
more selective, while a lower threshold makes it more inclusive in identifying objects.
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
|
||||
for the model, defaulting to `0.7`. This parameter helps in differentiating between
|
||||
distinct objects, especially in crowded scenes.
|
||||
- `--source_video_path`: Required. The path to the source video file to be processed.
|
||||
This is the video on which object detection and annotation will be performed.
|
||||
|
||||
- `--target_video_path`: Required. The path where the processed video, with annotations
|
||||
added, will be saved. This is your output video file.
|
||||
|
||||
- `--confidence_threshold` (optional): Sets the confidence level at which the model
|
||||
identifies objects in the video. Default is `0.3`. A higher threshold makes the model
|
||||
more selective, while a lower threshold makes it more inclusive in identifying objects.
|
||||
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
|
||||
for the model, defaulting to `0.7`. This parameter helps in differentiating between
|
||||
distinct objects, especially in crowded scenes.
|
||||
|
||||
- inference
|
||||
|
||||
- `--roboflow_api_key` (optional): The API key for Roboflow services. If not provided
|
||||
directly, the script tries to fetch it from the `ROBOFLOW_API_KEY` environment
|
||||
variable. Follow [this guide](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)
|
||||
to acquire your `API KEY`.
|
||||
- `--model_id` (optional): Designates the Roboflow model ID to be used. The default
|
||||
value is `"yolov8x-1280"`.
|
||||
- `--roboflow_api_key` (optional): The API key for Roboflow services. If not provided
|
||||
directly, the script tries to fetch it from the `ROBOFLOW_API_KEY` environment
|
||||
variable. Follow [this guide](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)
|
||||
to acquire your `API KEY`.
|
||||
|
||||
- `--source_video_path`: Required. The path to the source video file to be processed.
|
||||
This is the video on which object detection and annotation will be performed.
|
||||
- `--target_video_path`: Required. The path where the processed video, with annotations
|
||||
added, will be saved. This is your output video file.
|
||||
- `--confidence_threshold` (optional): Sets the confidence level at which the model
|
||||
identifies objects in the video. Default is `0.3`. A higher threshold makes the model
|
||||
more selective, while a lower threshold makes it more inclusive in identifying objects.
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
|
||||
for the model, defaulting to `0.7`. This parameter helps in differentiating between
|
||||
distinct objects, especially in crowded scenes.
|
||||
- `--model_id` (optional): Designates the Roboflow model ID to be used. The default
|
||||
value is `"yolov8x-1280"`.
|
||||
|
||||
- `--source_video_path`: Required. The path to the source video file to be processed.
|
||||
This is the video on which object detection and annotation will be performed.
|
||||
|
||||
- `--target_video_path`: Required. The path where the processed video, with annotations
|
||||
added, will be saved. This is your output video file.
|
||||
|
||||
- `--confidence_threshold` (optional): Sets the confidence level at which the model
|
||||
identifies objects in the video. Default is `0.3`. A higher threshold makes the model
|
||||
more selective, while a lower threshold makes it more inclusive in identifying objects.
|
||||
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
|
||||
for the model, defaulting to `0.7`. This parameter helps in differentiating between
|
||||
distinct objects, especially in crowded scenes.
|
||||
|
||||
## ⚙️ run
|
||||
|
||||
- inference
|
||||
|
||||
```bash
|
||||
python inference_example.py \
|
||||
--roboflow_api_key <ROBOFLOW API KEY> \
|
||||
--source_video_path input.mp4 \
|
||||
--target_video_path tracking_result.mp4
|
||||
```
|
||||
```bash
|
||||
python inference_example.py \
|
||||
--roboflow_api_key <ROBOFLOW API KEY> \
|
||||
--source_video_path input.mp4 \
|
||||
--target_video_path tracking_result.mp4
|
||||
```
|
||||
|
||||
- ultralytics
|
||||
|
||||
```bash
|
||||
python ultralytics_example.py \
|
||||
--source_weights_path yolov8s.pt \
|
||||
--source_video_path input.mp4 \
|
||||
--target_video_path tracking_result.mp4
|
||||
```
|
||||
```bash
|
||||
python ultralytics_example.py \
|
||||
--source_weights_path yolov8s.pt \
|
||||
--source_video_path input.mp4 \
|
||||
--target_video_path tracking_result.mp4
|
||||
```
|
||||
|
||||
## © license
|
||||
|
||||
This demo integrates two main components, each with its own licensing:
|
||||
|
||||
- ultralytics: The object detection model used in this demo, YOLOv8, is distributed
|
||||
under the [AGPL-3.0 license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
|
||||
You can find more details about this license here.
|
||||
under the [AGPL-3.0 license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
|
||||
You can find more details about this license here.
|
||||
|
||||
- supervision: The analytics code that powers the zone-based analysis in this demo is
|
||||
based on the Supervision library, which is licensed under the
|
||||
[MIT license](https://github.com/roboflow/supervision/blob/develop/LICENSE.md). This
|
||||
makes the Supervision part of the code fully open source and freely usable in your
|
||||
projects.
|
||||
based on the Supervision library, which is licensed under the
|
||||
[MIT license](https://github.com/roboflow/supervision/blob/develop/LICENSE.md). This
|
||||
makes the Supervision part of the code fully open source and freely usable in your
|
||||
projects.
|
||||
|
|
|
|||
|
|
@ -12,105 +12,112 @@ https://github.com/roboflow/supervision/assets/26109316/c9436828-9fbf-4c25-ae8c-
|
|||
|
||||
- clone repository and navigate to example directory
|
||||
|
||||
```bash
|
||||
git clone https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/traffic_analysis
|
||||
```
|
||||
```bash
|
||||
git clone https://github.com/roboflow/supervision.git
|
||||
cd supervision/examples/traffic_analysis
|
||||
```
|
||||
|
||||
- setup python environment and activate it [optional]
|
||||
- setup python environment and activate it \[optional\]
|
||||
|
||||
```bash
|
||||
python3 -m venv venv
|
||||
source venv/bin/activate
|
||||
```
|
||||
```bash
|
||||
python3 -m venv venv
|
||||
source venv/bin/activate
|
||||
```
|
||||
|
||||
- install required dependencies
|
||||
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
- download `traffic_analysis.pt` and `traffic_analysis.mov` files
|
||||
|
||||
```bash
|
||||
./setup.sh
|
||||
```
|
||||
```bash
|
||||
./setup.sh
|
||||
```
|
||||
|
||||
## 🛠️ script arguments
|
||||
|
||||
- ultralytics
|
||||
|
||||
- `--source_weights_path`: Required. Specifies the path to the YOLO model's weights
|
||||
file, which is essential for the object detection process. This file contains the
|
||||
data that the model uses to identify objects in the video.
|
||||
- `--source_weights_path`: Required. Specifies the path to the YOLO model's weights
|
||||
file, which is essential for the object detection process. This file contains the
|
||||
data that the model uses to identify objects in the video.
|
||||
|
||||
- `--source_video_path`: Required. The path to the source video file that will be
|
||||
analyzed. This is the input video on which traffic flow analysis will be performed.
|
||||
- `--target_video_path` (optional): The path to save the output video with
|
||||
annotations. If not specified, the processed video will be displayed in real-time
|
||||
without being saved.
|
||||
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO
|
||||
model to filter detections. Default is `0.3`. This determines how confident the
|
||||
model should be to recognize an object in the video.
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
|
||||
for the model. Default is 0.7. This value is used to manage object detection
|
||||
accuracy, particularly in distinguishing between different objects.
|
||||
- `--source_video_path`: Required. The path to the source video file that will be
|
||||
analyzed. This is the input video on which traffic flow analysis will be performed.
|
||||
|
||||
- `--target_video_path` (optional): The path to save the output video with
|
||||
annotations. If not specified, the processed video will be displayed in real-time
|
||||
without being saved.
|
||||
|
||||
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO
|
||||
model to filter detections. Default is `0.3`. This determines how confident the
|
||||
model should be to recognize an object in the video.
|
||||
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
|
||||
for the model. Default is 0.7. This value is used to manage object detection
|
||||
accuracy, particularly in distinguishing between different objects.
|
||||
|
||||
- inference
|
||||
|
||||
- `--roboflow_api_key` (optional): The API key for Roboflow services. If not provided
|
||||
directly, the script tries to fetch it from the `ROBOFLOW_API_KEY` environment
|
||||
variable. Follow [this guide](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)
|
||||
to acquire your `API KEY`.
|
||||
- `--model_id` (optional): Designates the Roboflow model ID to be used. The default
|
||||
value is `"vehicle-count-in-drone-video/6"`.
|
||||
- `--roboflow_api_key` (optional): The API key for Roboflow services. If not provided
|
||||
directly, the script tries to fetch it from the `ROBOFLOW_API_KEY` environment
|
||||
variable. Follow [this guide](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)
|
||||
to acquire your `API KEY`.
|
||||
|
||||
- `--source_video_path`: Required. The path to the source video file that will be
|
||||
analyzed. This is the input video on which traffic flow analysis will be performed.
|
||||
- `--target_video_path` (optional): The path to save the output video with
|
||||
annotations. If not specified, the processed video will be displayed in real-time
|
||||
without being saved.
|
||||
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO
|
||||
model to filter detections. Default is `0.3`. This determines how confident the
|
||||
model should be to recognize an object in the video.
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
|
||||
for the model. Default is 0.7. This value is used to manage object detection
|
||||
accuracy, particularly in distinguishing between different objects.
|
||||
- `--model_id` (optional): Designates the Roboflow model ID to be used. The default
|
||||
value is `"vehicle-count-in-drone-video/6"`.
|
||||
|
||||
- `--source_video_path`: Required. The path to the source video file that will be
|
||||
analyzed. This is the input video on which traffic flow analysis will be performed.
|
||||
|
||||
- `--target_video_path` (optional): The path to save the output video with
|
||||
annotations. If not specified, the processed video will be displayed in real-time
|
||||
without being saved.
|
||||
|
||||
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO
|
||||
model to filter detections. Default is `0.3`. This determines how confident the
|
||||
model should be to recognize an object in the video.
|
||||
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
|
||||
for the model. Default is 0.7. This value is used to manage object detection
|
||||
accuracy, particularly in distinguishing between different objects.
|
||||
|
||||
## ⚙️ run
|
||||
|
||||
- ultralytics
|
||||
|
||||
```bash
|
||||
python ultralytics_example.py \
|
||||
--source_weights_path data/traffic_analysis.pt \
|
||||
--source_video_path data/traffic_analysis.mov \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5 \
|
||||
--target_video_path data/traffic_analysis_result.mov
|
||||
```
|
||||
```bash
|
||||
python ultralytics_example.py \
|
||||
--source_weights_path data/traffic_analysis.pt \
|
||||
--source_video_path data/traffic_analysis.mov \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5 \
|
||||
--target_video_path data/traffic_analysis_result.mov
|
||||
```
|
||||
|
||||
- inference
|
||||
|
||||
```bash
|
||||
python inference_example.py \
|
||||
--roboflow_api_key <ROBOFLOW API KEY> \
|
||||
--source_video_path data/traffic_analysis.mov \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5 \
|
||||
--target_video_path data/traffic_analysis_result.mov
|
||||
```
|
||||
```bash
|
||||
python inference_example.py \
|
||||
--roboflow_api_key <ROBOFLOW API KEY> \
|
||||
--source_video_path data/traffic_analysis.mov \
|
||||
--confidence_threshold 0.3 \
|
||||
--iou_threshold 0.5 \
|
||||
--target_video_path data/traffic_analysis_result.mov
|
||||
```
|
||||
|
||||
## © license
|
||||
|
||||
This demo integrates two main components, each with its own licensing:
|
||||
|
||||
- ultralytics: The object detection model used in this demo, YOLOv8, is distributed
|
||||
under the [AGPL-3.0 license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
|
||||
You can find more details about this license here.
|
||||
under the [AGPL-3.0 license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
|
||||
You can find more details about this license here.
|
||||
|
||||
- supervision: The analytics code that powers the zone-based analysis in this demo is
|
||||
based on the Supervision library, which is licensed under the
|
||||
[MIT license](https://github.com/roboflow/supervision/blob/develop/LICENSE.md). This
|
||||
makes the Supervision part of the code fully open source and freely usable in your
|
||||
projects.
|
||||
based on the Supervision library, which is licensed under the
|
||||
[MIT license](https://github.com/roboflow/supervision/blob/develop/LICENSE.md). This
|
||||
makes the Supervision part of the code fully open source and freely usable in your
|
||||
projects.
|
||||
|
|
|
|||
|
|
@ -236,6 +236,12 @@ skip-magic-trailing-comma = false
|
|||
# Like Black, automatically detect the appropriate line ending.
|
||||
line-ending = "auto"
|
||||
|
||||
[tool.codespell]
|
||||
skip = "*.ipynb,poetry.lock"
|
||||
count = true
|
||||
quiet-level = 3
|
||||
ignore-words-list = "STrack,sTrack,strack"
|
||||
|
||||
[tool.setuptools]
|
||||
include-package-data = false
|
||||
|
||||
|
|
|
|||
|
|
@ -6,19 +6,19 @@ It assumes you already have the code changes, as well as a draft of the release
|
|||
|
||||
1. Make sure you have all required changes were merged into `develop`.
|
||||
2. Create and merge a PR, merging `develop` into `main`, containing:
|
||||
- A commit that updates the project version in `pyproject.toml`.
|
||||
- All changes made during the release.
|
||||
- A commit that updates the project version in `pyproject.toml`.
|
||||
- All changes made during the release.
|
||||
3. Tag the commit with the new supervision version.
|
||||
- make sure to pull from `main` !
|
||||
- Verify that the latest merge commits exists. `git log`.
|
||||
- Run `git tag x.y.z`, with your version
|
||||
- Check with `git log`.
|
||||
- Run `git push origin --tags`
|
||||
- Upon pushing the tag, the [PyPi](https://pypi.org/project/supervision/) should update to the new version. Check this!
|
||||
- make sure to pull from `main` !
|
||||
- Verify that the latest merge commits exists. `git log`.
|
||||
- Run `git tag x.y.z`, with your version
|
||||
- Check with `git log`.
|
||||
- Run `git push origin --tags`
|
||||
- Upon pushing the tag, the [PyPi](https://pypi.org/project/supervision/) should update to the new version. Check this!
|
||||
4. Open and merge a PR, merging `main` into `develop`.
|
||||
5. Update the docs by running the [Supervision Release Documentation Workflow 📚](https://github.com/roboflow/supervision/actions/workflows/publish-release-docs.yml) workflow from GitHub.
|
||||
- Select the `main` branch from the dropdown.
|
||||
- Select the `main` branch from the dropdown.
|
||||
6. Create a release on GitHub.
|
||||
- Go to releases
|
||||
- Assign the release notes to the tag created in step 3.
|
||||
- Publish the release.
|
||||
- Go to releases
|
||||
- Assign the release notes to the tag created in step 3.
|
||||
- Publish the release.
|
||||
|
|
|
|||
|
|
@ -369,7 +369,7 @@ class LineZoneAnnotator:
|
|||
label is rectangular.
|
||||
|
||||
Returns:
|
||||
Tuple[int, int]: xy, pont in an image where the label will be placed.
|
||||
Tuple[int, int]: xy, point in an image where the label will be placed.
|
||||
"""
|
||||
line_angle = self._get_line_angle(line_zone)
|
||||
|
||||
|
|
|
|||
|
|
@ -308,7 +308,7 @@ def test_group_coco_annotations_by_image_id(
|
|||
),
|
||||
),
|
||||
DoesNotRaise(),
|
||||
), # two image annotations with mask, one mask as polygon ans second as RLE
|
||||
), # two image annotations with mask, one mask as polygon and second as RLE
|
||||
(
|
||||
[
|
||||
mock_coco_annotation(
|
||||
|
|
|
|||
Loading…
Reference in New Issue