recognize_garbage_photos #36

Merged
s473616 merged 4 commits from recognize_garbage_photos into master 2023-06-19 11:52:44 +02:00
45 changed files with 60 additions and 9 deletions

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@ -1,8 +1,11 @@
import os
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
train_data_dir = "garbage_photos"
location = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
train_data_dir = os.path.join(location, train_data_dir)
input_shape = (150, 150, 3)
num_classes = 5

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@ -2,7 +2,7 @@ import joblib
from sklearn.calibration import LabelEncoder
from agentActionType import AgentActionType
import time
from garbage import GarbageType, RecognizedGarbage
from garbage import Garbage, GarbageType, RecognizedGarbage
from garbageCan import GarbageCan
from turnCar import turn_left_orientation, turn_right_orientation
from garbageTruck import GarbageTruck
@ -14,6 +14,12 @@ import pygame
from bfs import find_path_to_nearest_can
from agentState import AgentState
import tensorflow as tf
from keras.models import load_model
import keras.utils as image
from keras.optimizers import Adam
import numpy as np
def collect_garbage(game_context: GameContext) -> None:
while True:
@ -30,11 +36,12 @@ def collect_garbage(game_context: GameContext) -> None:
pass
def _recognize_garbage(dust_car: GarbageTruck, can: GarbageCan) -> None:
loaded_model = joblib.load('machine_learning/model.pkl')
tree_model = joblib.load('machine_learning/model.pkl')
optimizer = Adam(learning_rate=0.001)
neural_model = load_model('machine_learning/neuralModel.h5', compile=False)
neural_model.compile(optimizer=optimizer)
for garbage in can.garbage:
attributes = [garbage.shape, garbage.flexibility, garbage.does_smell, garbage.weight, garbage.size, garbage.color, garbage.softness, garbage.does_din]
encoded = attributes_to_floats(attributes)
predicted_class = loaded_model.predict([encoded])[0]
predicted_class = predict_class(garbage, tree_model, neural_model)
garbage_type: GarbageType = None
if predicted_class == 'PAPER':
garbage_type = GarbageType.PAPER
@ -50,6 +57,35 @@ def _recognize_garbage(dust_car: GarbageTruck, can: GarbageCan) -> None:
recognized_garbage = RecognizedGarbage(garbage, garbage_type)
dust_car.sort_garbage(recognized_garbage)
def predict_class(garbage: Garbage, tree_model, neural_model) -> str:
if garbage.img is None:
return predict_class_from_tree(garbage, tree_model)
return predict_class_from_neural_model(garbage, neural_model)
def predict_class_from_tree(garbage: Garbage, tree_model) -> str:
attributes = [garbage.shape, garbage.flexibility, garbage.does_smell, garbage.weight, garbage.size, garbage.color, garbage.softness, garbage.does_din]
encoded = attributes_to_floats(attributes)
return tree_model.predict([encoded])[0]
def predict_class_from_neural_model(garbage: Garbage, neural_model) -> str:
img = image.load_img(garbage.img, target_size=(150, 150))
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array /= 255.
predictions = neural_model.predict(img_array)
prediction = np.argmax(predictions[0])
if prediction == 0:
return "BIO"
if prediction == 1:
return "GLASS"
if prediction == 2:
return "MIXED"
if prediction == 3:
return "PAPER"
if prediction == 4:
return "PLASTIC_AND_METAL"
def attributes_to_floats(attributes: list[str]) -> list[float]:
output: list[float] = []
if attributes[0] == 'Longitiudonal':

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@ -36,12 +36,19 @@ def create_city() -> City:
streets = create_streets()
trashcans = create_trashcans()
bumps = create_speed_bumps()
garbage_pieces = create_garbage_pieces()
garbage_pieces = create_garbage_pieces_witout_imgs()
garbage_pieces_counter = 0
for s in streets:
city.add_street(s)
for t in trashcans:
for i in range(4):
for _ in range(4):
t.add_garbage(garbage_pieces[garbage_pieces_counter])
garbage_pieces_counter = garbage_pieces_counter + 1
city.add_can(t)
garbage_pieces = create_garbage_pieces_with_images()
garbage_pieces_counter = 0
for t in trashcans:
for _ in range(3):
t.add_garbage(garbage_pieces[garbage_pieces_counter])
garbage_pieces_counter = garbage_pieces_counter + 1
city.add_can(t)
@ -50,16 +57,21 @@ def create_city() -> City:
return city
def create_garbage_pieces() -> List[Garbage]:
def create_garbage_pieces_witout_imgs() -> List[Garbage]:
garbage_pieces = []
with open('machine_learning/garbage_infill.csv', 'r') as file:
lines = file.readlines()
for line in lines[1:]:
param = line.strip().split(',')
garbage_pieces.append(
Garbage('img', param[0], param[1], param[2], param[3], param[4], param[5], param[6], param[7].strip()))
Garbage(None, param[0], param[1], param[2], param[3], param[4], param[5], param[6], param[7].strip()))
return garbage_pieces
def create_garbage_pieces_with_images() -> list[Garbage]:
garbage_pieces = []
for i in range(1, 22):
garbage_pieces.append(Garbage('machine_learning/photos_not_from_train_set/' + str(i) + '.jpg', None, None, None, None, None, None, None, None))
return garbage_pieces
def create_streets() -> List[Street]:
streets = []