Michio JP

Michio JP

1633069926

Aloception | Collection of Packages for Computer Vision

Aloception

Aloception is a set of packages for computer vision built on top of popular deep learning libraries: pytorch and pytorch lightning.

Aloscene

Aloscene extend the use of tensors with Augmented Tensors designed to facilitate the use of computer vision data (such as frames, 2d boxes, 3d boxes, optical flow, disparity, camera parameters...).

frame = aloscene.Frame("/path/to/image.jpg")
frame = frame.to("cpu")
frame.get_view().render()

Alodataset

Alodataset implement ready-to-use datasets for computer vision with the help of aloscene and augmented tensors to make it easier to transform and display your vision data.

coco_dataset = alodataset.CocoDetectionDataset(sample=True)
for frame in coco_dataset.stream_loader():
    frame.get_view().render()

Alonet

Alonet integrates several promising computer vision architectures. You can use it for research purposes or to finetune and deploy your model using TensorRT. Alonet is mainly built on top of lightning with the help of aloscene and alodataset.

Training

# Init the training pipeline
detr = alonet.detr.LitDetr()
# Init the data module
coco_loader = alonet.detr.CocoDetection2Detr()
# Run the training using the two components
detr.run_train(data_loader=coco_loader, project="detr", expe_name="test_experiment")

Inference

# Load model
model = alonet.detr.DetrR50(num_classes=91, weights="detr-r50").eval()

# Open and normalized frame
frame = aloscene.Frame("/path/to/image.jpg").norm_resnet()

# Run inference
pred_boxes = model.inference(model([frame]))

# Add and display the predicted boxes
frame.append_boxes2d(pred_boxes[0], "pred_boxes")
frame.get_view().render()

Note

One can use aloscene independently than the two other packages to handle computer vision data, or to improve its training pipelines with augmented tensors.

Install

Aloception's packages are built on top of multiple libraries. Most of them are listed in the requirements.txt

pip install -r requirements.txt

Once the others packages are installed, you still need to install pytorch based on your hardware and environment configuration. Please, ref to the pytorch website <https://pytorch.org/>_ for this install.

Getting started

Tutorials

Alonet

Models

Model nameLinkalonet locationLearn more
detr-r50https://arxiv.org/abs/2005.12872alonet.detr.DetrR50Detr
deformable-detrhttps://arxiv.org/abs/2010.04159alonet.deformable_detr.DeformableDETRDeformable detr
RAFThttps://arxiv.org/abs/2003.12039alonet.raft.RAFTRAFT

Detr

Here is a simple example to get started with Detr and aloception. To learn more about Detr, you can checkout the Tutorials or the detr README.

# Load model
model = alonet.detr.DetrR50(num_classes=91, weights="detr-r50").eval()

# Open and normalized frame
frame = aloscene.Frame("/path/to/image.jpg").norm_resnet()

# Run inference
pred_boxes = model.inference(model([frame]))

# Add and display the predicted boxes
frame.append_boxes2d(pred_boxes[0], "pred_boxes")
frame.get_view().render()

Deformable Detr

Here is a simple example to get started with Deformable Detr and aloception. To learn more about Deformable, you can checkout the Tutorials or the deformable detr README.

# Loading Deformable model
model = alonet.deformable_detr.DeformableDetrR50(num_classes=91, weights="deformable-detr-r50").eval()

# Open, normalize frame and send frame on the device
frame = aloscene.Frame("/home/thibault/Desktop/yoga.jpg").norm_resnet().to(torch.device("cuda"))

# Run inference
pred_boxes = model.inference(model([frame]))

# Add and display the predicted boxes
frame.append_boxes2d(pred_boxes[0], "pred_boxes")
frame.get_view().render()

RAFT

Here is a simple example to get started with RAFT and aloception. To learn more about RAFT, you can checkout the raft README.

# Use the left frame from the  Sintel Flow dataset and normalize the frame for the RAFT Model
frame = alodataset.SintelFlowDataset(sample=True).getitem(0)["left"].norm_minmax_sym()

# Load the model using the sintel weights
raft = alonet.raft.RAFT(weights="raft-sintel")

# Compute optical flow
padder = alonet.raft.utils.Padder()
flow = raft.inference(raft(padder.pad(frame[0:1]), padder.pad(frame[1:2])))

# Render the flow along with the first frame
flow[0].get_view().render()

Alodataset

Here is a list of all the datasets you can use on Aloception. If you're dataset is not in the list but is important for computer vision. Please let us know using the issues or feel free to contribute.

Datasets

Dataset namealodataset locationTo try
CocoDetectionalodataset.CocoDetectionDatasetpython alodataset/coco_detection_dataset.py
CrowdHumanalodataset.CrowdHumanDatasetpython alodataset/crowd_human_dataset.py
Waymoalodataset.WaymoDatasetpython alodataset/waymo_dataset.py
ChairsSDHomalodataset.ChairsSDHomDatasetpython alodataset/chairssdhom_dataset.py
FlyingThings3DSubsetalodataset.FlyingThings3DSubsetDatasetpython alodataset/flyingthings3D_subset_dataset.py
FlyingChairs2alodataset.FlyingChairs2Datasetpython alodataset/flying_chairs2_dataset.py
SintelDisparityDatasetalodataset.SintelDisparityDatasetpython alodataset/sintel_disparity_dataset.py
SintelFlowDatasetalodataset.SintelFlowDatasetpython alodataset/sintel_flow_dataset.py
MOT17alodataset.Mot17python alodataset/mot17.py

Unit tests

python -m pytest

 

Download Details:
 

Author: Visual-Behavior
Download Link: Download The Source Code
Official Website: https://github.com/Visual-Behavior/aloception 
License: View license

#python 

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Aloception | Collection of Packages for Computer Vision
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Computer Vision using Mediapipe

Computer vision can be defined as a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects and then react to what they “see.”

Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos.

In today’s world computer vision is very useful in many fields such as — :

*_ Inventory management — : _**In the case of inventory management, the applications can be in the field of security camera image analysis where a computer vision algorithm can generate a very accurate estimate of the items available in the store. Another field can be Analyzing the use of shelf space to identify suboptimal configurations.

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While computer vision has become one of the most used technologies across the globe, computer vision models are not immune to threats. One of the reasons for this threat is the underlying lack of robustness of the models. Indrajit Kar, who is the Principal Solution Architect at Accenture, took through a talk at CVDC 2020 on how to make AI more resilient to attack.

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“While we celebrate advancements in AI, deep neural networks (DNNs)—the algorithms intrinsic to much of AI—have recently been proven to be at risk from attack through seemingly benign inputs. It is possible to fool DNNs by making subtle alterations to input data that often either remain undetected or are overlooked if presented to a human,” he said.

Type Of Adversarial Attacks

Alterations to images that are so small as to remain unnoticed by humans can cause DNNs to misinterpret the image content. As many AI systems take their input from external sources—voice recognition devices or social media upload, for example—this ability to be tricked by adversarial input opens a new, often intriguing, security threat. This has called for an increase in cybersecurity which is coming together to address the crevices in computer vision and machine learning.

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Follow the article along with the complete code implementation on GitHub. Open the notebook in Google Colab, import your image(s), and run the cells!Originally published on louisbouchard.ai, read it 2 days before on my blog!

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#computer-vision #python #ai #machine-learning #artificial-intelligence #the best project to start in computer vision with python

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This article aims to give a strong foundation to Computer Vision by exploring image classification tasks using Convolutional Neural Networks built with TensorFlow Keras. More importance has been given to both the coding part and the key concepts of theory and math behind each operation. Let’s start our Computer Vision journey!

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