Data frame Creation
# Creating DataFrame of image and mask
all_val_img = sorted([os.path.join(VAL_DIR,i) for i in os.listdir(VAL_DIR)])
all_val_mask = sorted([os.path.join(VAL_MASK_DIR,i) for i in os.listdir(VAL_MASK_DIR)])
#DataFrame
val_data_df = pd.DataFrame(zip(all_val_img,all_val_mask), columns = ['photos', 'mask'])
I have Data Frame that looks like this(below). and I want to create a tensor dataset out of it.
photos mask
4691 dataset/val2017/000000546556.jpg dataset/panoptic_val2017/000000546556.png
1191 dataset/val2017/000000140286.jpg dataset/panoptic_val2017/000000140286.png
3041 dataset/val2017/000000351823.jpg dataset/panoptic_val2017/000000351823.png
2552 dataset/val2017/000000294163.jpg dataset/panoptic_val2017/000000294163.png
3070 dataset/val2017/000000356169.jpg dataset/panoptic_val2017/000000356169.png
I Converted the data frame into tensor data. and want to map function to make them image.
val_data = tf.data.Dataset.from_tensor_slices(val_data_df)
so wrote a function to map on the dataset.but it did not work.
def make_it_image(image, label):
image_raw = tf.io.read_file(image)
image = tf.image.decode_image(image_raw)
label_raw = tf.io.read_file(label)
label = tf.image.decode_image(label_raw)
# normalize
image = image /255
label = label /255
return image, label
when I mapped the function. Result was
val_data = val_data.map(make_it_image).cache().batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)
Error :
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-72-c6fd8ebb8233> in <module>
----> 1 val_data = val_data.map(make_it_image).cache().batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)
10 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py in wrapper(*args, **kwargs)
690 except Exception as e: # pylint:disable=broad-except
691 if hasattr(e, 'ag_error_metadata'):
--> 692 raise e.ag_error_metadata.to_exception(e)
693 else:
694 raise
TypeError: in user code:
TypeError: tf__make_it_image() missing 1 required positional argument: 'label'
OR
tell me how to create a dataset from two image directories one as image, one as mask?
Combine the data
val_data = tf.data.Dataset.from_tensor_slices((np.array(all_val_img),
np.array(all_val_mask)))
map the dataset
def make_image(x,y):
image = tf.io.read_file(x)
image = tf.image.decode_png(image, channels=3)
image = image/255
label = tf.io.read_file(y)
label = tf.image.decode_png(label, channels=3)
label = label/255
return image,label
val_data = val_data.map(make_image)
and it works
val_data
# <MapDataset element_spec=(TensorSpec(shape=(None, None, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None, None, 3), dtype=tf.float32, name=None))>