s444517/AI2020_Project
1
2
Fork 0
Code Issues Pull Requests Releases Wiki Activity

grammatical errors

Browse Source
This commit is contained in:
Kamila Bobkowska 2020-06-07 14:01:25 +00:00
parent 8c0b6aa04b
commit 3da2fff556
1 changed files with 3 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
final-evaluation.md
View File

@ -2,7 +2,7 @@
## General information
This is the final report for our **Intelligent Garbage Truck** AI program. Our program works as following - a **20x20** grid is generated with 20 random dumpsters, the garbage dump and our garbage truck(the agent). All the time we make sure that while they randomly generate they do not overlap each other.
After that our garbage truck chooses the closest dumpsters to him and collects trah whilst making sure the house is our client. After he collected all trask (aka visited all dumpsters) he immediately goes to the garabage dump and sorts the trash.
The dumpsters have a predeterminded colour and type but still can contain incorrectly sorted trash. If a house is not our clinet it is visited but no trash is picked up.
The dumpsters have a predeterminded colour and type but still can contain incorrectly sorted trash. If a house is not our client it is visited but no trash is picked up.
## How to run our program
In order to succesfully open our program one needs to make sure he has the following libraries installed:
@ -51,7 +51,7 @@ Since there are three people in our group we have created and combined three sub
The first subproject was created by Patryk Krawiec (the report can be found [here](https://git.wmi.amu.edu.pl/s444517/AI2020_Project/src/master/Report_Patryk_Krawiec.md)).
### Second subproject
The second subproject was created by Kamila Bobkowska (the report can be found [here](https://git.wmi.amu.edu.pl/s444517/AI2020_Project/src/master/Report_Kamila_Bobkowska.md)). This project used **Convolutional Neural Networks** to detect what kind of trash is in the dumpster and sorts it accordingly. It was mainly implemented to to the class `Garbagetruck.py` and `dumpster.py` . It used photos of garbage to determine whether it was sorted correctly. It did so by using CNNs - extracting the most important features from the photos with convolution layers, then using pooling layers and flattening. Along with implementing CNNs the person created a function that assigns trash to dumspters (between 1 to 3 trash correctly sorted and 0 to 2 incorrectly sorted trash). The data set used was from [kaggle](https://www.kaggle.com/asdasdasasdas/garbage-classification).
The second subproject was created by Kamila Bobkowska (the report can be found [here](https://git.wmi.amu.edu.pl/s444517/AI2020_Project/src/master/Report_Kamila_Bobkowska.md)). This project used **Convolutional Neural Networks** to detect what kind of trash is in the dumpster and sorts it accordingly. It was mainly implemented to to the class `Garbagetruck.py` and `dumpster.py` . It used photos of garbage to determine whether it was sorted correctly. It did so by using CNNs - extracting the most important features from the photos with convolution layers, then using pooling layers and flattening. Along with implementing CNNs the person created a function that assigns trash to dumpsters (between 1 to 3 trash correctly sorted and 0 to 2 incorrectly sorted trash). The data set used was from [kaggle](https://www.kaggle.com/asdasdasasdas/garbage-classification).
### Thrid subproject
The third subproject was created by Klaudia Przybylska (the report can be found [here](https://git.wmi.amu.edu.pl/s444517/AI2020_Project/src/master/Report_Klaudia_Przybylska.md)). This subproject used **Random Forest Classifier** to make sure that when the garbage truck reaches the garbage dump, all the thrash inside is correctly classified before taking it out of the truck and sorting it accordingly. From the pictures of different kinds of waste, it extracts three features: Hu moments (shapes), Color Histogram (colors) and Haralick Texture (textures), that are given to the classifier. It is mainly implemented in `garbageDumpSorting.py` class. The data set is the same as in the second project - [kaggle](https://www.kaggle.com/asdasdasasdas/garbage-classification).
@ -61,7 +61,7 @@ How the UI looks like:
![ ](https://i.imgur.com/Gjdn1ek.png)
As seen on the picture we have our main grid where everything takes place and a panel on the right telling the user how many dumpsters are left and how much trash is in the garbage truck. At the end it shows the information about how much trash is in the garbage dump (the garbage dump can have different number of trash to the garbage truck as it once again checks if it has been correctlty sorted). Additional information (like whether a house is a client, if trash was correctly sorted in the truck and dump) is displayed on the console:
As seen on the picture we have our main grid where everything takes place and a panel on the right telling the user how many dumpsters are left and how much trash is in the garbage truck. At the end it shows the information about how much trash is in the garbage dump (the garbage dump can have different number of trash to the garbage truck as it once again checks if it has been correctly sorted). Additional information (like whether a house is a client, if trash was correctly sorted in the truck and dump) is displayed on the console:
a) info about house numbers and trash from individual dumspters:
![ ](https://i.imgur.com/cWmLka3.png)