我想將存盤為張量流資料集的影像剪切為平方影像。但似乎 tensorflow 不允許使用 ifelse 陳述句的 map 函式。我希望知道我是否可以解決這個問題。提前謝謝了。
def tf_resize(img, new_size=256):
h, w,__ = img.shape
start = math.ceil(abs(w-h))
img_corp = tf.cond(tf.constant(w>h, dtype=tf.bool),
lambda: img[:, start:(start h), :],
lambda: img[start:(start w), :, :])
new_img = tf.image.resize(img_corp, [new_size, new_size])/255.0
return new_img
def load_and_preprocess_image(path, new_size=256):
image = tf.io.read_file(path)
image = tf.image.decode_jpeg(image, channels=3)
return image
data_dir = pathlib.Path(path)
all_image_paths = [str(path) for path in list(data_dir.glob('*/*.jpg'))]
path_ds = tf.data.Dataset.from_tensor_slices(all_image_paths)
image_ds = path_ds.map(load_and_preprocess_image)
image_ds = image_ds.map(tf_resize) # this cause error!!!
錯誤資訊如下所示：
File "/tmp/ipykernel_14519/4089718206.py", line 26, in tf_resize *
start = math.ceil(abs(w-h))
TypeError: unsupported operand type(s) for -: 'NoneType' and 'NoneType'
uj5u.com熱心網友回復：
您的代碼中有一些錯誤。嘗試使用tf.shape獲得動態形狀img：
import tensorflow as tf
def tf_resize(img, new_size=256):
img_shape = tf.cast(tf.shape(img), tf.float32)
w = img_shape[1]
h = img_shape[0]
start = tf.cast(tf.math.ceil(tf.abs(w-h)), dtype=tf.int32)
w = tf.cast(w, dtype=tf.int32)
h = tf.cast(h, dtype=tf.int32)
img_corp = tf.cond(tf.greater(w, h),
lambda: img[:, start:(start h), :],
lambda: img[start:(start w), :, :])
new_img = tf.image.resize(img_corp, [new_size, new_size])/255.0
return new_img
def load_and_preprocess_image(path, new_size=256):
image = tf.io.read_file(path)
image = tf.image.decode_jpeg(image, channels=3)
return image
all_image_paths = ['image.jpg']
path_ds = tf.data.Dataset.from_tensor_slices(all_image_paths)
image_ds = path_ds.map(load_and_preprocess_image)
image_ds = image_ds.map(tf_resize) # this cause error!!!
for d in image_ds.take(1):
print(d.shape)
(256, 256, 3)
uj5u.com熱心網友回復：
您需要指定輸入映射，我已經糾正了您對 Fn 的定義，然后就有了答案。
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
Functions
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
def f1(path, new_size=256) :
New_path = 'C:\\Users\\Jirayu Kaewprateep\\Pictures\\Cats\\samples\\03 28x28.jpg'
New_label = 'Cute'
return New_path, New_label
def f2(img, new_size=256) :
New_label = 'Cute'
return New_label
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: DataSets
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
dataset_cat = tf.data.Dataset.list_files("F:\\datasets\\downloads\\PetImages\\train\\Cat\\*.png")
list_image = [ 'C:\\Users\\Jirayu Kaewprateep\\Pictures\\Cats\\samples\\01 28x28.jpg',
'C:\\Users\\Jirayu Kaewprateep\\Pictures\\Cats\\samples\\02 28x28.jpg',
'C:\\Users\\Jirayu Kaewprateep\\Pictures\\Cats\\samples\\03 28x28.jpg',
'C:\\Users\\Jirayu Kaewprateep\\Pictures\\Cats\\samples\\04 28x28.jpg',
'C:\\Users\\Jirayu Kaewprateep\\Pictures\\Cats\\samples\\05 28x28.jpg',
'C:\\Users\\Jirayu Kaewprateep\\Pictures\\Cats\\samples\\06 28x28.jpg']
list_label = [ 'Shopping', 'Bath', 'Shower', 'Sickness', 'Recover', 'Handsome' ]
dataset = tf.data.Dataset.from_tensor_slices((list_image, list_label))
dataset.take(6)
New_dataset = dataset.map(f1)
for elem in New_dataset.take(6):
print(elem)
### (<tf.Tensor: shape=(), dtype=string, numpy=b'C:\\Users\\Jirayu Kaewprateep\\Pictures\\Cats\\samples\\01 28x28.jpg'>, <tf.Tensor: shape=(), dtype=string, numpy=b'Shopping'>)
print(elem[0].numpy)
### <bound method _EagerTensorBase.numpy of <tf.Tensor: shape=(), dtype=string, numpy=b'C:\\Users\\Jirayu Kaewprateep\\Pictures\\Cats\\samples\\01 28x28.jpg'>>
element_as_string = str(elem[0].numpy()).split('\'')
image = plt.imread(os.fspath(element_as_string[1]))
plt.imshow(image)
plt.show()
plt.close()
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