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aee66d8
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Parent(s):
2cc8d92
Update utils.py
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utils.py
CHANGED
@@ -8,36 +8,34 @@ from PIL import Image
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def gen_labels():
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train = 'Dataset/Train'
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train_generator = ImageDataGenerator(rescale
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train_generator = train_generator.flow_from_directory(train,
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labels =
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labels = dict((v,k) for k,v in labels.items())
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return labels
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def preprocess(image):
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image = np.array(image.resize((256, 256), Image.LANCZOS))
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image =
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image = np.array(image) / 255.0
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return image
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def model_arc():
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model = Sequential()
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# Convolution blocks
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model.add(Conv2D(32, kernel_size=(3,3), padding='same', input_shape=(256, 256, 3), activation='relu'))
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model.add(MaxPooling2D(pool_size=2))
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model.add(Conv2D(64, kernel_size=(3,3), padding='same', activation='relu'))
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model.add(MaxPooling2D(pool_size=2))
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model.add(Conv2D(32, kernel_size=(3,3), padding='same', activation='relu'))
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model.add(MaxPooling2D(pool_size=2))
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# Classification layers
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model.add(Flatten())
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def gen_labels():
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train = 'Dataset/Train'
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train_generator = ImageDataGenerator(rescale=1/255)
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train_generator = train_generator.flow_from_directory(train,
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target_size=(256, 256),
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batch_size=32,
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class_mode='sparse')
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labels = train_generator.class_indices
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labels = dict((v, k) for k, v in labels.items())
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return labels
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def preprocess(image):
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image = np.array(image.resize((256, 256), Image.LANCZOS))
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image = image.astype('float32') / 255.0
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return image
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def model_arc():
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model = Sequential()
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# Convolution blocks
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model.add(Conv2D(32, kernel_size=(3, 3), padding='same', input_shape=(256, 256, 3), activation='relu'))
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model.add(MaxPooling2D(pool_size=2))
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model.add(Conv2D(64, kernel_size=(3, 3), padding='same', activation='relu'))
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model.add(MaxPooling2D(pool_size=2))
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model.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu'))
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model.add(MaxPooling2D(pool_size=2))
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# Classification layers
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model.add(Flatten())
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