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

touch ups

Browse Source
This commit is contained in:
Kamila Bobkowska 2020-05-10 16:27:46 +00:00
parent 1aeb9bf1fa
commit e8bc0b6722
1 changed files with 117 additions and 114 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

21
checkingTrash.py
View File

@ -6,6 +6,9 @@ import shutil
from keras.models import Sequential
from keras.layers import Conv2D, Flatten, MaxPooling2D, Dense
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import random
#dataset from https://www.kaggle.com/asdasdasasdas/garbage-classification
@ -42,7 +45,7 @@ classifier.add(Dense(activation = "softmax", units = 5))
classifier.compile(optimizer = "adam", loss = "binary_crossentropy", metrics = ["accuracy"])
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(
rescale=1./255,
@ -76,16 +79,16 @@ test_generator = test_datagen.flow_from_directory(
#Teaching the classifier
'''classifier.fit_generator( train_generator, steps_per_epoch = 150, epochs = 25, validation_data = test_generator )
classifier.save_weights('model_ver_4.h5')'''
'''classifier.fit_generator( train_generator, steps_per_epoch = 165, epochs = 32, validation_data = test_generator )
classifier.save_weights('model_ver_5.h5')'''
import matplotlib.pyplot as plt
labels = (train_generator.class_indices)
labels = dict((value,key) for key,value in labels.items())
classifier.load_weights("model_ver_4.h5")
import random
classifier.load_weights("model_ver_5.h5")
def getTrashPhoto(x, type):
@ -94,13 +97,13 @@ def getTrashPhoto(x, type):
path = "Garbage classification\\testset\\" + kind
file = random.choice(os.listdir(path))
path = "Garbage classification\\testset\\" + kind + "\\" + file
gz = image.load_img(path, target_size = (110,110))
ti = image.img_to_array(gz)
var = image.load_img(path, target_size = (110,110))
ti = image.img_to_array(var)
ti=np.array(ti)/255.0
ti = np.expand_dims(ti, axis = 0)
prediction = classifier.predict(ti)
plt.subplot(1, 3, i+1)
plt.imshow(gz)
plt.imshow(var)
plt.title("AI thinks:%s \nReality:\n %s" % (labels[np.argmax(prediction)], file))
plt.show()