When radiation of high enough energy strikes another atom, it strips away an electron. The resulting positively charged atom is called an ion, which explains why high energy radiation is called ionizing radiation. The release of the electron produces 33 electron volts (eV) of energy, which heats the surrounding tissues and disrupts certain chemical bonds. Extremely high-energy radiation can even destroy the nuclei of atoms, releasing even more energy and causing more damage. Radiation sickness is the cumulative effect of all this damage on a human body that's been bombarded with radiation.
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Ionizing radiation comes in three flavors: alpha particles, beta particles and gamma rays. Alpha particles are the least dangerous in terms of external exposure. Each particle contains a pair of neutrons and a pair of protons. They don't penetrate very deeply into the skin, if at all -- in fact, clothing can stop alpha particles. Unfortunately, alpha particles can be inhaled or ingested, usually in the form of radon gas. Once ingested, alpha particles can be very dangerous. However, even then they don't typically cause radiation sickness -- instead, they lead to lung cancer [source: EPA].
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Beta particles are electrons that move very quickly -- that is, with a lot of energy. Beta particles travel several feet when emitted from a radioactive source, but they're blocked by most solid objects. A beta particle is about 8,000 times smaller than an alpha particle -- and that's what makes them more dangerous. Their small size allows them to penetrate clothing and skin. External exposure can cause burns and tissue damage, along with other symptoms of radiation sickness. If radioactive material enters food or water supplies or is dispersed into the air, people can inhale or ingest beta particle emitters unknowingly. Internal exposure to beta particles causes much more severe symptoms than external exposure.
Gamma rays are the most dangerous form of ionizing radiation. These extremely high energy photons can travel through most forms of matter because they have no mass. It takes several inches of lead -- or several feet of concrete -- to effectively block gamma rays. If you're exposed to gamma rays, they pass through your entire body, affecting all of your tissues from your skin to the marrow of your bones. This causes widespread, systemic damage.
How much radiation does it take to cause radiation sickness, and what effect does this damage have on a human body? That's next. For more detailed information on different types of radiation and where they come from, take a look at How Radiation Works.
Human pose estimation is a fundamental challenge for computer vision. In practice, recognizing the pose of multiple persons in the wild is a lot more challenging than recognizing the pose of a single person in an image.
How Pose Estimation models works ?
Basic working principle of Pose Estimation models are ,
a) Model will try to estimate 18 points of a Person from image.
b) After that it will try to join this points to make a skeleton.
c) There are mainly two approaches which are used to achieve above 2 task :
A) Top Down :-
1) First it will detect the Person from available frame.
2) Then it will get that detected object.
3) And then it will try to estimate 18 key points for each detected Person from that frame and finally try to create the skeleton.
B) Bottom Up :-
1) First it will try to estimate the 18 key points from image for each Person.
2) After that it will try to estimate Person from that key points. i.e joining the key points and making the skeleton.
This is just overview of how Pose Estimation models works.There are many frameworks are available for Pose Estimation but some of the famous one are :-
1) Open Pose
2) Alpha Pose
In this article we will try to explore how we can install and run 'Alpha Pose' on Google Colab.
AlphaPose is an accurate multi-person pose estimator, which is the first open-source system that achieves 70+ mAP (75 mAP) on COCO dataset and 80+ mAP (82.1 mAP) on MPII dataset.
So let's start the Implementation on Google Colab
First we will select GPU on Colab i.e.Select Runtime > Change Runtime type > Hardware accelerator = GPU
Now first uninstall the pre-installed 'Pytorch' version from Colab.
Then we will installed the required 'Pytorch' version.
Now we will mount the drive and set the path to save entire data in our drive.
This will set your drive directory as current working directory.Now we will clone the official github repository of 'Alpha Pose' implementation.
Now again we will set the cloned directory path for further processing.
Now we will install the remaining required packages.
Now we have to download some pretrained models.
1)Download the object detection model manually: yolov3-spp.weights(Google Drive | Baidu pan). Place it into detector/yolo/data. (create 'data' folder manually inside AlphaPose/detector/yolo)
Final directory will be look like :- AlphaPose/detector/yolo/yolov3-spp.weights
2) For pose tracking, download the object tracking model manually: JDE-1088x608-uncertainty(Google Drive | Baidu pan). Place it into detector/tracker/data. (create 'data' folder manually inside AlphaPose/detector/tracker).
Final directory will be look like this :- AlphaPose/detector/tracker/data/jde.1088x608.uncertainty.pt
3) Download pose models. Place them into pretrained_models. All models and details are available in Model Zoo. (from model zoo download 'Fast Pose' model and put into AlphaPose/pretrained_models/ )
Final directory will be look like :- AlphaPose/pretrained_models/fast_res50_256x192.pth
Now all the setup is ready & now we can test it with Images and Videos.
1) Testing with Images
a) Create ' demo_images ' folder inside ' AlphaPose/examples/ ' folder and put your test images there. i.e. AlphaPose/examples/test1.jpg . The output ' Image ' and corresponding ' json ' file which contain all detected key points will be saved by default into 'AlphaPose/examples/res/vis/ ' folder. Now run the below command
2) Testing with Video
a) Create ' demo_video ' folder inside 'AlphaPose/examples/ ' folder and put your test video there. i.e. AlphaPose/examples/demo.mp4 . The output ' Video ' and corresponding ' json ' file which contain all detected key points will be saved into by into ' AlphaPose/examples/res1/ ' folder. Now run the below command
Note :- --gpus: Choose which cuda device to use by index and input comma to use multi gpus, e.g. 0,1,2,3. (input -1 for cpu only)
Complete Code : - https://github.com/akshaykadam771/Human-Pose-Estimation-Using-AlphaPose
So in this article we have learn how we can install and use 'AlphaPose' on 'Google Colab'.In my next article i will try to cover ' How we can use AlphaPose for human Activity Recognition '.
Real-Time Pose Estimation Using AlphaPose, PyTorch, And Deep ...
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