# ArtificialVision
<img src="https://lh3.googleusercontent.com/u/0/drive-viewer/AEYmBYSOMvdeaLCLq2djzo1mgZIEd6-Qyll8boR6V7Z1VHYkH2IFJzg8geBFdcxis-KIyVdoawhJTa-mWCLmfImUXQIoCJVv8w=w1762-h1610" 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/>
<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/>
### Contributors
<img src = "https://contrib.rocks/image?repo=hwk06023/ArtificialVision"/>
<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 to more information, check the [Official Docs(Not yet)]()**
<br/>
Raw data
{
"_id": null,
"home_page": "",
"name": "ArtificialVision",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": "",
"keywords": "hwk060023,Vision,AI,ML,DL,CV,ArtificialVision,ArtificialIntelligence,MachineLearning,DeepLearning,ComputerVision,Python,PyPI,Package,Tutorial",
"author": "",
"author_email": "hwk06023 <hwk06023@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/ec/4a/9ed0415bb711ad903bd60f6b8b456968f9ba34c5c7f56f6293f36d31983b/ArtificialVision-0.1.2.tar.gz",
"platform": null,
"description": "# ArtificialVision\n\n<img src=\"https://lh3.googleusercontent.com/u/0/drive-viewer/AEYmBYSOMvdeaLCLq2djzo1mgZIEd6-Qyll8boR6V7Z1VHYkH2IFJzg8geBFdcxis-KIyVdoawhJTa-mWCLmfImUXQIoCJVv8w=w1762-h1610\" 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<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\n\n\n### Contributors\n\n<img src = \"https://contrib.rocks/image?repo=hwk06023/ArtificialVision\"/>\n\n<br/> <br/>\n\n\n\n\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 to more information, check the [Official Docs(Not yet)]()**\n\n<br/>\n\n\n\n",
"bugtrack_url": null,
"license": "",
"summary": "Artificial Vision Library",
"version": "0.1.2",
"project_urls": {
"Source": "https://github.com/hwk06023/ArtificialVision"
},
"split_keywords": [
"hwk060023",
"vision",
"ai",
"ml",
"dl",
"cv",
"artificialvision",
"artificialintelligence",
"machinelearning",
"deeplearning",
"computervision",
"python",
"pypi",
"package",
"tutorial"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "c71d53db85fd9d7321565309f7a8e746471b57f189ceb47d36d306a2ec77ff90",
"md5": "3f06f5c4cbd1a770420f03f724afea2f",
"sha256": "9a1d48ba83e641833985a8e7655e5622f76f78fb5e1174ec9bebb403567b023c"
},
"downloads": -1,
"filename": "ArtificialVision-0.1.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "3f06f5c4cbd1a770420f03f724afea2f",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 7163,
"upload_time": "2024-02-25T08:28:06",
"upload_time_iso_8601": "2024-02-25T08:28:06.628086Z",
"url": "https://files.pythonhosted.org/packages/c7/1d/53db85fd9d7321565309f7a8e746471b57f189ceb47d36d306a2ec77ff90/ArtificialVision-0.1.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "ec4a9ed0415bb711ad903bd60f6b8b456968f9ba34c5c7f56f6293f36d31983b",
"md5": "2debee1c6bc40682495e4207e81ea5c6",
"sha256": "77ddcae5cefb936725a181e9838e64618d03a7343725163653005b1b2058b232"
},
"downloads": -1,
"filename": "ArtificialVision-0.1.2.tar.gz",
"has_sig": false,
"md5_digest": "2debee1c6bc40682495e4207e81ea5c6",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 6566,
"upload_time": "2024-02-25T08:28:08",
"upload_time_iso_8601": "2024-02-25T08:28:08.045580Z",
"url": "https://files.pythonhosted.org/packages/ec/4a/9ed0415bb711ad903bd60f6b8b456968f9ba34c5c7f56f6293f36d31983b/ArtificialVision-0.1.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-02-25 08:28:08",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "hwk06023",
"github_project": "ArtificialVision",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "artificialvision"
}
JSON
