Custom preprocessing_function with tf.image.rgb_to_grayscale - ValueError: setting an array element with a...












0















I am trying to use a custom preprocessing function to convert RGB images to grayscale during training. As such, I try to use tf.image.rbg_to_grayscale for this. My function looks as following:



def prep_data(x):
x = tf.image.rgb_to_grayscale(x)
return x

datagen = ImageDataGenerator(preprocessing_function=prep_data,validation_split=0.15)


The train_generator is defined using datagen.flow_from_dataframe(...). Training without this custom function works just fine, however once I use it I get the following error:




ValueError: setting an array element with a sequence.




Judging from this answer here, I assume I need to change my input to rgb_to_grayscale, but I don't know what's the correct way of passing x to the function.



Any idea on how to solve this?










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  • If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?

    – today
    Nov 26 '18 at 15:56
















0















I am trying to use a custom preprocessing function to convert RGB images to grayscale during training. As such, I try to use tf.image.rbg_to_grayscale for this. My function looks as following:



def prep_data(x):
x = tf.image.rgb_to_grayscale(x)
return x

datagen = ImageDataGenerator(preprocessing_function=prep_data,validation_split=0.15)


The train_generator is defined using datagen.flow_from_dataframe(...). Training without this custom function works just fine, however once I use it I get the following error:




ValueError: setting an array element with a sequence.




Judging from this answer here, I assume I need to change my input to rgb_to_grayscale, but I don't know what's the correct way of passing x to the function.



Any idea on how to solve this?










share|improve this question

























  • If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?

    – today
    Nov 26 '18 at 15:56














0












0








0


1






I am trying to use a custom preprocessing function to convert RGB images to grayscale during training. As such, I try to use tf.image.rbg_to_grayscale for this. My function looks as following:



def prep_data(x):
x = tf.image.rgb_to_grayscale(x)
return x

datagen = ImageDataGenerator(preprocessing_function=prep_data,validation_split=0.15)


The train_generator is defined using datagen.flow_from_dataframe(...). Training without this custom function works just fine, however once I use it I get the following error:




ValueError: setting an array element with a sequence.




Judging from this answer here, I assume I need to change my input to rgb_to_grayscale, but I don't know what's the correct way of passing x to the function.



Any idea on how to solve this?










share|improve this question
















I am trying to use a custom preprocessing function to convert RGB images to grayscale during training. As such, I try to use tf.image.rbg_to_grayscale for this. My function looks as following:



def prep_data(x):
x = tf.image.rgb_to_grayscale(x)
return x

datagen = ImageDataGenerator(preprocessing_function=prep_data,validation_split=0.15)


The train_generator is defined using datagen.flow_from_dataframe(...). Training without this custom function works just fine, however once I use it I get the following error:




ValueError: setting an array element with a sequence.




Judging from this answer here, I assume I need to change my input to rgb_to_grayscale, but I don't know what's the correct way of passing x to the function.



Any idea on how to solve this?







python tensorflow keras image-preprocessing






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edited Nov 22 '18 at 10:15









today

11.2k22139




11.2k22139










asked Nov 21 '18 at 22:26









AaronDTAaronDT

9422527




9422527













  • If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?

    – today
    Nov 26 '18 at 15:56



















  • If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?

    – today
    Nov 26 '18 at 15:56

















If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?

– today
Nov 26 '18 at 15:56





If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?

– today
Nov 26 '18 at 15:56












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Instead, you can use the color_mode argument of flow_from_directory and set it to 'grayscale' to convert the images to grayscale. From Keras docs:




color_mode: One of "grayscale", "rbg", "rgba". Default: "rgb". Whether the images will be converted to have 1, 3, or 4 channels.







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    Instead, you can use the color_mode argument of flow_from_directory and set it to 'grayscale' to convert the images to grayscale. From Keras docs:




    color_mode: One of "grayscale", "rbg", "rgba". Default: "rgb". Whether the images will be converted to have 1, 3, or 4 channels.







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      Instead, you can use the color_mode argument of flow_from_directory and set it to 'grayscale' to convert the images to grayscale. From Keras docs:




      color_mode: One of "grayscale", "rbg", "rgba". Default: "rgb". Whether the images will be converted to have 1, 3, or 4 channels.







      share|improve this answer


























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        Instead, you can use the color_mode argument of flow_from_directory and set it to 'grayscale' to convert the images to grayscale. From Keras docs:




        color_mode: One of "grayscale", "rbg", "rgba". Default: "rgb". Whether the images will be converted to have 1, 3, or 4 channels.







        share|improve this answer













        Instead, you can use the color_mode argument of flow_from_directory and set it to 'grayscale' to convert the images to grayscale. From Keras docs:




        color_mode: One of "grayscale", "rbg", "rgba". Default: "rgb". Whether the images will be converted to have 1, 3, or 4 channels.








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        answered Nov 22 '18 at 10:10









        todaytoday

        11.2k22139




        11.2k22139
































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