remi.bussiere
/
CV-Project-1-BUSSIERE-BEAUD
1
1
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Mise à jour de 'README.md'

This commit is contained in:
Rémi BUSSIERE 2023-02-19 19:34:49 +01:00
parent add3aec7d0
commit d1a0963ec3
1 changed files with 6 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
README.md
View File

@ -2,7 +2,7 @@
This project has an objective of determine the heartrate of a person by analysing a 1 minute long video taken by a computer camera. This project has an objective of determine the heartrate of a person by analysing a 1 minute long video taken by a computer camera.
FIRST STEP - Get the video ##FIRST STEP - Get the video
To get the video, we need a normal camera ( like a laptop camera ) and record a ~1min long video of ourselves, in this case : Rémi. To get the video, we need a normal camera ( like a laptop camera ) and record a ~1min long video of ourselves, in this case : Rémi.
Then, we use the ffmpeg package* to convert this video into a 15fps, 640x480 video. Then, we use the ffmpeg package* to convert this video into a 15fps, 640x480 video.
@ -11,7 +11,7 @@ Then, we use the ffmpeg package* to convert this video into a 15fps, 640x480 vid
SECOND STEP - Get the ROI position ##SECOND STEP - Get the ROI position
The ROI ( Region of interest ) is important here because we only need the average color of the person's head, the surrounding is considerate here as noise. The ROI ( Region of interest ) is important here because we only need the average color of the person's head, the surrounding is considerate here as noise.
@ -24,7 +24,7 @@ The haarcascades is a pre-train model of machine learning developped for OpenCV
THIRD STEP - Calculate the average color ##THIRD STEP - Calculate the average color
Then, we have the coordinates, and we compute the average between each red composents of each pixel inside the square, and we repeat it for blue and green. Then, we have as an output one color/frame. Then, we have the coordinates, and we compute the average between each red composents of each pixel inside the square, and we repeat it for blue and green. Then, we have as an output one color/frame.
@ -38,5 +38,7 @@ We then export a .txt file with all the average color to use it in an octave cod
POSSIBLE ALTENATIVES POSSIBLE ALTENATIVES
In order to find the best possible ROI, we need some other algorithm than haarcascades. So that's why we used the mediapipe library, wich is a way better algorithm, it's also a pre-trained model but much more complete. We can see here a picture of the results using the mediapipe library, where the ROI is more precise In order to find the best possible ROI, we need some other algorithm than haarcascades. So that's why we used the mediapipe* library, wich is a way better algorithm, it's also a pre-trained model but much more complete. We can see here a picture of the results using the mediapipe library, where the ROI is more precise.
*pip install mediapipe
more informations here : https://github.com/google/mediapipe