Michio JP

Michio JP


Aloception | Collection of Packages for Computer Vision


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


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")


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():


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.


# 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")


# 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")


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


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




Model nameLinkalonet locationLearn more
deformable-detrhttps://arxiv.org/abs/2010.04159alonet.deformable_detr.DeformableDETRDeformable 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")

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")


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


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.


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


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Aloception | Collection of Packages for Computer Vision
Joseph  Murray

Joseph Murray


Collection vs Collections in Java: Difference Between Collection & Collections in Java


This article will be looking into one of the most popular questions in Java Language – What is Collection in Java? Also, what do you mean by Collections in Java? Are Collection and Collections the same or different in Java?

What is Collection?

What is Collections?


#full stack development #collection #collection vs collections in java #collections in java #difference between collection and collections in java

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.

* **Manufacturing — : **In the Field of manufacturing Computer vision can help in **predictive maintenance **of the machines.

*** Healthcare — : In the field of healthcare computer Vision can be used in medical image analysis.** Images from CT scans and X-rays are analyzed to find anomalies such as tumors or search for signs of neurological illnesses.

* **Autonomous vehicles — : **The field of computer vision plays a central role in the domain of autonomous vehicles since it allows them to perceive and understand the environment around them in order to operate correctly. One of the most exciting challenges in computer vision is object detection in images and videos. This involves locating a varying number of objects and the ability to classify them, in order to distinguish if an object is a traffic light, a car, or a person, as in the video below.

#computer-vision #opencv #mediapipe #anaconda-navigator #python #computer vision using mediapipe

Macey  Kling

Macey Kling


How To Deter Adversarial Attacks In Computer Vision Models

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.

As Kar shared, AI has become the new target for attackers, and the instances of manipulation and adversaries have increased dramatically over the last few years. From companies such as Google and Tesla to startups are affected by adversarial attacks.

“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.

#developers corner #adversarial attacks #computer vision #computer vision adversarial attack

Osiki  Douglas

Osiki Douglas


The Best Project to Start in Computer Vision with Python

GrabCut — A Google Colab NoteBook implementation for Image Matting (background removal)

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!

Image matting is an extremely interesting task where the goal is to find any object of interest, or human, in a picture and remove its background. This task is hard to achieve due to its complexity, finding the person, people, or objects with the perfect contour. This post reviews an exciting technique using basic computer vision algorithms to achieve this task. The GrabCut algorithm. It is swift but not very precise for complex objects like humans or animals. Nonetheless, it can be handy in specific contexts and is a perfect applied first project to start in computer vision and python! As mentioned above, the implementation uses Google Colab, thus having no requirements or setup needed, making it an exciting project to duplicate for learning.

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

Dominic  Feeney

Dominic Feeney


Computer Vision Using TensorFlow Keras - Analytics India Magazine

Computer Vision attempts to perform the tasks that a human brain does with the aid of human eyes. Computer Vision is a branch of Deep Learning that deals with images and videos. Computer Vision tasks can be roughly classified into two categories:

  1. Discriminative tasks
  2. Generative tasks

Discriminative tasks, in general, are about predicting the probability of occurrence (e.g. class of an image) given probability distribution (e.g. features of an image). Generative tasks, in general, are about generating the probability distribution (e.g. generating an image) given the probability of occurrence (e.g. class of an image) and/or other conditions.

Discriminative Computer Vision finds applications in image classificationobject detectionobject recognitionshape detectionpose estimationimage segmentation, etc. Generative Computer Vision finds applications in photo enhancementimage synthesisaugmentationdeepfake videos, etc.

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!

Readers are expected to have a basic understanding of deep learning. This article, “Getting Started With Deep Learning Using TensorFlow Keras”, helps one grasp the fundamentals of deep learning.

#developers corner #computer vision #fashion mnist #image #image classification #keras #tensorflow #vision