Created
December 9, 2020 01:42
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# Processing image files into numpy array | |
def process_label(label): | |
label = [i == unique_label for i in label] | |
label = np.array(label).astype(int) | |
return label | |
def processImage(path): | |
image = tf.io.read_file(path) | |
image = tf.image.decode_jpeg(image,channels=3) | |
image = tf.image.convert_image_dtype(image,tf.float32) | |
image = tf.image.resize(image,size=[224,224]) | |
return image | |
# Create Batch data of numpy array | |
def pairData(image,label): | |
return processImage(image),label | |
def batchData(image,label=None,for_valid=False,for_test=False): | |
if for_test: | |
data = tf.data.Dataset.from_tensor_slices((image)) | |
batch = data.map(processImage).batch(32) | |
return batch | |
elif for_valid: | |
data = tf.data.Dataset.from_tensor_slices((tf.constant(image),tf.constant(label))) | |
batch = data.map(pairData).batch(32) | |
return batch | |
else: | |
data = tf.data.Dataset.from_tensor_slices((tf.constant(image),tf.constant(label))) | |
data = data.shuffle(buffer_size=len(image)) | |
batch = data.map(pairData).batch(32) | |
return batch | |
unique_label = np.unique(y_test) | |
y_test = process_label(y_test) | |
y_train = process_label(y_train) | |
train_data = batchData(x_train,y_train) | |
valid_data = batchData(x_test,y_test,for_valid=True) |
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