| Name | ArtificialVision JSON |
| Version |
0.1.4
JSON |
| download |
| home_page | None |
| Summary | Artificial Vision Library |
| upload_time | 2024-05-04 10:34:11 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | Copyright (c) 2012-2024 Scott Chacon and others Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
hwk060023
vision
ai
ml
dl
cv
artificialvision
artificialintelligence
machinelearning
deeplearning
computervision
python
pypi
package
tutorial
research
development
library
framework
tool
utility
module
class
function
method
variable
constant
parameter
argument
hyperparameter
model
dataset
preprocessing
augmentation
training
validation
testing
evaluation
prediction
inference
analysis
visualization
benchmark
comparison
documentation
reference
paper
|
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# ArtificialVision
<div align="center">
<img src="https://github.com/hwk06023/ArtificialVision/blob/master/artificialvision/img/logo.png?raw=true" width=550> <br/>
[](https://badge.fury.io/py/artificialvision)
[](https://opensource.org/licenses/MIT)
<br/>
**❗️ The package is still under development and has not been released yet ❗️** <br/>
**If the version exceeds 1.0.0, this message will be removed, and the package will become available.** <br/>
</div>
<br/>
## Installation
```bash
pip install artificialvision
```
<br/>
## What is ArtificialVision?
ArtificialVision is the package for makes it easy to get the outcomes of various Machine Learning & Computer Vision technologies.
This package's aims are improving the quality and increasing the productivity by using it for convenience in various research and development experiments. <br/>
In this Version, just inference & getting the various results are supported. Support for training and fine-tuning will be added in the future. <br/>
<br/>
## Contributing to ArtificialVision (Not yet)
All the contributions are welcome ! <br/>
Check the [ContributeGuide.md](ContributeGuide.md) for more information. <br/>
## TODO
### Primary Methods
- [ ] Classification
- [ ] Object Detection
- [ ] Segmentation
- [ ] Pose Estimation
- [ ] feature Extraction
### Secondary Methods
- [ ] Matching
- [ ] Tracking
- [ ] Generation
- [ ] Restoration
- [ ] Super-Resolution
### Contributors
<a href="https://github.com/hwk06023/ArtificialVision/graphs/contributors">
<img src="https://contrib.rocks/image?repo=hwk06023/ArtificialVision" />
</a>
<br/> <br/>
## Methods Tutorial
### Image Classification
**example** <br/>
```python
from artificialvision import ImgClassification
import cv2
# Read the image
img = cv2.imread('PATH of Image file')
# Get the classification result
ImgClassification.get_result(img)
```
<br/>
**Currently, only models pretrained on ImageNet are available.** <br/>
<br/>
### Object Detection
**example** <br/>
```python
from artificialvision import ObjDetection
import cv2
''' Image '''
# Read the image
img = cv2.imread('PATH of Image file')
# Get the detection result with the bounding box
ObjDetection.get_result(img)
# Get the bounding box only
ObjDetection.get_result_with_box(img)
''' Video '''
# Read the video
video = cv2.VideoCapture('PATH of Video file', type=1)
# Get the detection result with the bounding box
ObjDetection.get_result(video)
# Get the bounding box only
ObjDetection.get_result_with_box(video)
```
**hyperparameters** <br/>
- `type` : int, default is 0
- 0 : Image
- 1 : Video
<br/>
**Currently, only image and video matching are supported.** <br/>
<br/>
### Segmentation
**example** <br/>
```python
from artificialvision import Segmentation
import cv2
''' Image '''
# Read the image
img = cv2.imread('PATH of Image file')
# Get the segmentation result
Segmentation.get_result(img)
# Get only the segment map
Segmentation.get_segment_map(img)
''' Video '''
# Read the video
video = cv2.VideoCapture('PATH of Video file', type=1)
# Get the segmentation result
Segmentation.get_result(video)
# Get only the segment map
Segmentation.get_segment_map(video)
''' Webcam (real-time) '''
# start the webcam(recording)
# if finished, press 'q' to stop & get the result
Segmentation.get_result(type=2)
```
**hyperparameters** <br/>
- `type` : int, default is 0
- 0 : Image
- 1 : Video
- 2 : Webcam (real-time)
- `category` : int, default is 0
- segmentation category
- 0 : Semantic Segmentation
- 1 : Instance Segmentation
- 2 : Panoptic Segmentation
- `detail` : int, default is 0
- segmentation detail
- 0 : Segmentation Result (Overlayed Image)
- 1 : Segmentation Map
- `get_poligon` : bool, default is False
- If True, get the poligon points of the segmentation result. (Only for the instance segmentation)
<br/>
**Currently, only image and video matching are supported.** <br/>
<br/>
### Image Matching
**example** <br/>
```python
from artificialvision import ImgMatching
import cv2
''' Image '''
# Read the images
img1 = cv2.imread('PATH of Image1 file')
img2 = cv2.imread('PATH of Image2 file')
# Get the matching score
ImgMatching.get_matching_score(img1, img2)
# Get the matching result
ImgMatching.get_matching_result(img1, img2)
''' Video '''
# Read the videos
video1 = cv2.VideoCapture('PATH of Video1 file')
video2 = cv2.VideoCapture('PATH of Video2 file')
# Get the matching score
ImgMatching.get_matching_score(video1, video2, type=1)
# Get the matching result
ImgMatching.get_matching_result(video1, video2, type=1)
''' Mixed '''
# Read the images for matchin
img_list = [img1, img2, img3, ...]
# Get the matching score
ImgMatching.get_matching_score(img_list, video1, type=2)
# Get the matching result
ImgMatching.get_matching_result(img_list, video1, type=2)
''' Webcam (real-time) '''
# start the webcam(recording)
# if finished, press 'q' to stop & get the result
ImgMatching.get_matching_result(img_list, type=3)
```
**hyperparameters** <br/>
- `type` : int, default is 0
- 0 : Image
- 1 : Video
- 2 : Mixed
- 3 : Webcam (real-time)
- `threshold` : float, default is 0.5
- The threshold for the matching score. If the matching score is less than the threshold, it is considered as a matching result. Range is 0.0 ~ 1.0. Recommended is +-0.1 from the default value.
<br/>
**Currently, only image and video matching are supported.** <br/>
<br/>
## Format
### Inference Data Format
| Inference data format | Type in python | Usage Example |
| ----------------------------------------------------------------------------------------------- | ----------------------------------------------------- | ------------------------------ |
| [Path of the data](#Methods-Tutorial) | ```str``` | '/Path/to/data/file.extension' |
| [List](#Methods-Tutorial) | ```list``` |
| [Numpy Array](#Methods-Tutorial) | ```numpy.ndarray``` |
| [Pytorch Tensor](#Methods-Tutorial) | ```torch.Tensor``` |
| [Tensorflow Tensor](#Methods-Tutorial) | ```tensorflow.python.framework.ops.EagerTensor``` |
### Inference Model Format
| Inference model format | `export.py --include` | Model |
|:---------------------------------------------------------------------------|:----------------------|:--------------------------|
| [PyTorch](https://pytorch.org/) | - | `model.pt` |
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `model.torchscript` |
| [ONNX](https://onnx.ai/) | `onnx` | `model.onnx` |
| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `model_openvino_model/` |
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `model.engine` |
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `model.mlmodel` |
| [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `model_saved_model/` |
| [TensorFlow GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `model.pb` |
| [TensorFlow Lite](https://www.tensorflow.org/lite) | `tflite` | `model.tflite` |
| [TensorFlow Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `model_edgetpu.tflite` |
| [TensorFlow.js](https://www.tensorflow.org/js) | `tfjs` | `model_web_model/` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `model_paddle_model/` |
------
<br/>
**If you want more information, check the [Official Docs(Not yet)]()**
<br/>
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"description": "# ArtificialVision\n\n<div align=\"center\">\n<img src=\"https://github.com/hwk06023/ArtificialVision/blob/master/artificialvision/img/logo.png?raw=true\" width=550> <br/>\n\n[](https://badge.fury.io/py/artificialvision)\n[](https://opensource.org/licenses/MIT)\n\n\n\n\n<br/>\n\n**\u2757\ufe0f The package is still under development and has not been released yet \u2757\ufe0f** <br/>\n\n**If the version exceeds 1.0.0, this message will be removed, and the package will become available.** <br/>\n\n</div>\n\n<br/>\n\n## Installation\n\n\n```bash\npip install artificialvision\n```\n\n<br/>\n\n## What is ArtificialVision?\n\nArtificialVision is the package for makes it easy to get the outcomes of various Machine Learning & Computer Vision technologies.\nThis package's aims are improving the quality and increasing the productivity by using it for convenience in various research and development experiments. <br/>\n\nIn this Version, just inference & getting the various results are supported. Support for training and fine-tuning will be added in the future. <br/>\n\n<br/>\n\n## Contributing to ArtificialVision (Not yet)\n\nAll the contributions are welcome ! <br/>\n\nCheck the [ContributeGuide.md](ContributeGuide.md) for more information. <br/>\n\n## TODO\n\n### Primary Methods\n\n- [ ] Classification\n- [ ] Object Detection\n- [ ] Segmentation\n- [ ] Pose Estimation\n- [ ] feature Extraction\n\n### Secondary Methods\n\n- [ ] Matching\n- [ ] Tracking\n- [ ] Generation\n- [ ] Restoration\n- [ ] Super-Resolution\n\n\n### Contributors\n\n<a href=\"https://github.com/hwk06023/ArtificialVision/graphs/contributors\">\n <img src=\"https://contrib.rocks/image?repo=hwk06023/ArtificialVision\" />\n</a>\n\n<br/> <br/>\n\n\n## Methods Tutorial\n\n### Image Classification\n\n**example** <br/>\n\n```python\nfrom artificialvision import ImgClassification\nimport cv2 \n\n# Read the image\nimg = cv2.imread('PATH of Image file')\n\n# Get the classification result\nImgClassification.get_result(img)\n```\n\n<br/>\n\n**Currently, only models pretrained on ImageNet are available.** <br/>\n\n<br/>\n\n\n### Object Detection\n\n**example** <br/>\n\n```python\nfrom artificialvision import ObjDetection\nimport cv2\n\n''' Image '''\n# Read the image\nimg = cv2.imread('PATH of Image file')\n\n# Get the detection result with the bounding box\nObjDetection.get_result(img)\n\n# Get the bounding box only\nObjDetection.get_result_with_box(img)\n\n''' Video '''\n# Read the video\nvideo = cv2.VideoCapture('PATH of Video file', type=1)\n\n# Get the detection result with the bounding box\nObjDetection.get_result(video)\n\n# Get the bounding box only\nObjDetection.get_result_with_box(video)\n```\n\n**hyperparameters** <br/>\n\n- `type` : int, default is 0\n - 0 : Image\n - 1 : Video \n\n<br/> \n\n**Currently, only image and video matching are supported.** <br/>\n\n<br/>\n\n\n### Segmentation\n\n**example** <br/>\n\n```python\nfrom artificialvision import Segmentation\nimport cv2\n\n''' Image '''\n# Read the image\nimg = cv2.imread('PATH of Image file')\n\n# Get the segmentation result\nSegmentation.get_result(img)\n\n# Get only the segment map\nSegmentation.get_segment_map(img)\n''' Video '''\n# Read the video\nvideo = cv2.VideoCapture('PATH of Video file', type=1)\n\n# Get the segmentation result\nSegmentation.get_result(video)\n\n# Get only the segment map\nSegmentation.get_segment_map(video)\n''' Webcam (real-time) '''\n# start the webcam(recording)\n# if finished, press 'q' to stop & get the result\nSegmentation.get_result(type=2)\n```\n\n**hyperparameters** <br/>\n\n- `type` : int, default is 0\n - 0 : Image\n - 1 : Video \n - 2 : Webcam (real-time)\n\n- `category` : int, default is 0\n - segmentation category\n - 0 : Semantic Segmentation\n - 1 : Instance Segmentation\n - 2 : Panoptic Segmentation\n\n- `detail` : int, default is 0\n - segmentation detail\n - 0 : Segmentation Result (Overlayed Image)\n - 1 : Segmentation Map\n\n- `get_poligon` : bool, default is False\n - If True, get the poligon points of the segmentation result. (Only for the instance segmentation)\n\n<br/> \n\n**Currently, only image and video matching are supported.** <br/>\n\n<br/>\n\n\n### Image Matching\n\n**example** <br/>\n \n```python\nfrom artificialvision import ImgMatching\nimport cv2 \n\n''' Image '''\n# Read the images\nimg1 = cv2.imread('PATH of Image1 file')\nimg2 = cv2.imread('PATH of Image2 file')\n\n# Get the matching score\nImgMatching.get_matching_score(img1, img2)\n\n# Get the matching result\nImgMatching.get_matching_result(img1, img2)\n\n\n''' Video '''\n# Read the videos\nvideo1 = cv2.VideoCapture('PATH of Video1 file')\nvideo2 = cv2.VideoCapture('PATH of Video2 file')\n\n# Get the matching score\nImgMatching.get_matching_score(video1, video2, type=1)\n\n# Get the matching result\nImgMatching.get_matching_result(video1, video2, type=1)\n\n''' Mixed '''\n# Read the images for matchin\nimg_list = [img1, img2, img3, ...]\n\n# Get the matching score\nImgMatching.get_matching_score(img_list, video1, type=2)\n\n# Get the matching result\nImgMatching.get_matching_result(img_list, video1, type=2)\n\n''' Webcam (real-time) '''\n# start the webcam(recording)\n# if finished, press 'q' to stop & get the result\nImgMatching.get_matching_result(img_list, type=3)\n```\n\n**hyperparameters** <br/>\n\n- `type` : int, default is 0\n - 0 : Image\n - 1 : Video \n - 2 : Mixed\n - 3 : Webcam (real-time)\n- `threshold` : float, default is 0.5\n - The threshold for the matching score. If the matching score is less than the threshold, it is considered as a matching result. Range is 0.0 ~ 1.0. Recommended is +-0.1 from the default value.\n\n<br/>\n\n**Currently, only image and video matching are supported.** <br/>\n\n<br/> \n\n## Format\n\n### Inference Data Format\n\n| Inference data format | Type in python | Usage Example |\n| ----------------------------------------------------------------------------------------------- | ----------------------------------------------------- | ------------------------------ |\n| [Path of the data](#Methods-Tutorial) | ```str``` | '/Path/to/data/file.extension' |\n| [List](#Methods-Tutorial) | ```list``` | \n| [Numpy Array](#Methods-Tutorial) | ```numpy.ndarray``` |\n| [Pytorch Tensor](#Methods-Tutorial) | ```torch.Tensor``` |\n| [Tensorflow Tensor](#Methods-Tutorial) | ```tensorflow.python.framework.ops.EagerTensor``` |\n\n### Inference Model Format\n\n| Inference model format | `export.py --include` | Model |\n|:---------------------------------------------------------------------------|:----------------------|:--------------------------|\n| [PyTorch](https://pytorch.org/) | - | `model.pt` |\n| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `model.torchscript` |\n| [ONNX](https://onnx.ai/) | `onnx` | `model.onnx` |\n| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `model_openvino_model/` |\n| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `model.engine` |\n| [CoreML](https://github.com/apple/coremltools) | `coreml` | `model.mlmodel` |\n| [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `model_saved_model/` |\n| [TensorFlow GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `model.pb` |\n| [TensorFlow Lite](https://www.tensorflow.org/lite) | `tflite` | `model.tflite` |\n| [TensorFlow Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `model_edgetpu.tflite` |\n| [TensorFlow.js](https://www.tensorflow.org/js) | `tfjs` | `model_web_model/` |\n| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `model_paddle_model/` |\n\n\n------\n\n<br/>\n\n**If you want more information, check the [Official Docs(Not yet)]()**\n\n<br/>\n\n\n\n",
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