s474137/automatyczny_kelner
3
3
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Neural Network - #4

Browse Source
This commit is contained in:
Mirrowel 2023-06-01 23:23:54 +02:00
parent f4ffb21a27
commit 7cd48f5cba
2 changed files with 48 additions and 19 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

65
TesterRandom.py
View File

@ -11,23 +11,52 @@ model = keras.models.load_model('trained_model.h5')
class_names = ['Empty', 'Food','People'] class_names = ['Empty', 'Food','People']
# Path to the folder containing test images # Path to the folder containing test images
test_images_folder = 'Testing/ test_images_folder = 'Testing/'
# Iterate over the test images # Iterate over the test images
for filename in os.listdir(test_images_folder): i = 0
if filename.endswith('.jpg') or filename.endswith('.jpeg') or filename.endswith('.png'): errorcount = 0
# Load and preprocess the test image for folder_name in os.listdir(test_images_folder):
image_path = os.path.join(test_images_folder, filename) folder_path = os.path.join(test_images_folder, folder_name)
test_image = image.load_img(image_path, target_size=(224, 224)) if os.path.isdir(folder_path):
test_image = image.img_to_array(test_image) print('Testing images in folder:', folder_name)
test_image = np.expand_dims(test_image, axis=0)
test_image = test_image / 255.0 # Normalize the image # True class based on folder name
if folder_name == 'Empty':
# Make predictions true_class = 'Empty'
predictions = model.predict(test_image) elif folder_name == 'Food':
predicted_class_index = np.argmax(predictions[0]) true_class = 'Food'
predicted_class = class_names[predicted_class_index] elif folder_name == 'People':
true_class = 'People'
print('Image:', filename) true_class = folder_name
print('Predicted class:', predicted_class)
print() # Iterate over the files in the subfolder
for filename in os.listdir(folder_path):
if filename.endswith('.jpg') or filename.endswith('.jpeg'):
i+=1
# Load and preprocess the test image
image_path = os.path.join(folder_path, filename)
test_image = image.load_img(image_path, target_size=(100, 100))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis=0)
test_image = test_image / 255.0 # Normalize the image
# Reshape the image array to (1, height, width, channels)
test_image = np.reshape(test_image, (1,100, 100, 3))
# Make predictions
predictions = model.predict(test_image)
predicted_class_index = np.argmax(predictions[0])
predicted_class = class_names[predicted_class_index]
direct = 'Results/'
filename = str(i) + predicted_class + '.jpeg'
test_image = np.reshape(test_image, (100, 100, 3))
tf.keras.preprocessing.image.save_img(direct+filename, test_image)
if predicted_class != true_class:
errorcount += 1
print('Image:', filename)
print('True class:', true_class)
print('Predicted class:', predicted_class)
print()
print('Error count: ', errorcount)

2
testerZBS.py
View File

@ -47,7 +47,7 @@ for i in range(60):
direct = 'Results/' direct = 'Results/'
filename = predicted_class + str(i) + '.jpeg' filename = predicted_class + str(i) + '.jpeg'
tf.keras.preprocessing.image.save_img(direct+filename, val_images[i]) tf.keras.preprocessing.image.save_img(direct+filename, test_image)
if predicted_class != true_class: if predicted_class != true_class:
errorcount += 1 errorcount += 1
print('Image', i+1) print('Image', i+1)