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face mask detection perameter #3
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U r sending the parameter but which environment u r using
If u r using jupyter notebook u can pass the local machine folders
But if u r using the Google colab it cannot access the local machine
folders u have to upload it to the cloud.
…On Thu, 2 Jul, 2020, 7:32 pm clownpreet22, ***@***.***> wrote:
what would be the perameter of this fun
def split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE):
dataset = []
for unitData in os.listdir(SOURCE):
data = SOURCE + unitData
if(os.path.getsize(data) > 0):
dataset.append(unitData)
else:
print('Skipped ' + unitData)
print('Invalid file i.e zero size')
train_set_length = int(len(dataset) * SPLIT_SIZE)
test_set_length = int(len(dataset) - train_set_length)
shuffled_set = random.sample(dataset, len(dataset))
train_set = dataset[0:train_set_length]
test_set = dataset[-test_set_length:]
for unitData in train_set:
temp_train_set = SOURCE + unitData
final_train_set = TRAINING + unitData
copyfile(temp_train_set, final_train_set)
for unitData in test_set:
temp_test_set = SOURCE + unitData
final_test_set = TESTING + unitData
copyfile(temp_test_set, final_test_set)
YES_SOURCE_DIR = "dest_folder/val/yes/"
TRAINING_YES_DIR = "dest_folder/train/yes/"
TESTING_YES_DIR = "dest_folder/test/yes/"
NO_SOURCE_DIR = "dest_folder/val/no/"
TRAINING_NO_DIR = "dest_folder/val/no/"
TESTING_NO_DIR = "dest_folder/test/no/"
split_size = .8
split_data(YES_SOURCE_DIR, TRAINING_YES_DIR, TESTING_YES_DIR, split_size)
split_data(NO_SOURCE_DIR, TRAINING_NO_DIR, TESTING_NO_DIR, split_size)
[image: location of dataset]
<https://user-images.githubusercontent.com/42198801/86368406-c2f45780-bc9a-11ea-8b0d-1dcef380a2ae.png>
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Im using jupyter notebook but i don't understand what will be the code
On Thu, 2 Jul 2020, 8:13 pm Ravikiran Dhulipala, <notifications@github.com>
wrote:
… U r sending the parameter but which environment u r using
If u r using jupyter notebook u can pass the local machine folders
But if u r using the Google colab it cannot access the local machine
folders u have to upload it to the cloud.
On Thu, 2 Jul, 2020, 7:32 pm clownpreet22, ***@***.***>
wrote:
> what would be the perameter of this fun
> def split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE):
> dataset = []
>
> for unitData in os.listdir(SOURCE):
> data = SOURCE + unitData
> if(os.path.getsize(data) > 0):
> dataset.append(unitData)
> else:
> print('Skipped ' + unitData)
> print('Invalid file i.e zero size')
>
> train_set_length = int(len(dataset) * SPLIT_SIZE)
> test_set_length = int(len(dataset) - train_set_length)
> shuffled_set = random.sample(dataset, len(dataset))
> train_set = dataset[0:train_set_length]
> test_set = dataset[-test_set_length:]
>
> for unitData in train_set:
> temp_train_set = SOURCE + unitData
> final_train_set = TRAINING + unitData
> copyfile(temp_train_set, final_train_set)
>
> for unitData in test_set:
> temp_test_set = SOURCE + unitData
> final_test_set = TESTING + unitData
> copyfile(temp_test_set, final_test_set)
>
> YES_SOURCE_DIR = "dest_folder/val/yes/"
> TRAINING_YES_DIR = "dest_folder/train/yes/"
> TESTING_YES_DIR = "dest_folder/test/yes/"
> NO_SOURCE_DIR = "dest_folder/val/no/"
> TRAINING_NO_DIR = "dest_folder/val/no/"
> TESTING_NO_DIR = "dest_folder/test/no/"
> split_size = .8
> split_data(YES_SOURCE_DIR, TRAINING_YES_DIR, TESTING_YES_DIR, split_size)
> split_data(NO_SOURCE_DIR, TRAINING_NO_DIR, TESTING_NO_DIR, split_size)
> [image: location of dataset]
> <
https://user-images.githubusercontent.com/42198801/86368406-c2f45780-bc9a-11ea-8b0d-1dcef380a2ae.png
>
>
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The parameters passed to function are the source file ie., yes or no files
Training dataset folder ,testing dataset folder ,and the size in which the
dataset has to be splitted
On Thu, 2 Jul 2020 at 9:18 PM, clownpreet22 <notifications@github.com>
wrote:
… Im using jupyter notebook but i don't understand what will be the code
On Thu, 2 Jul 2020, 8:13 pm Ravikiran Dhulipala, ***@***.***
>
wrote:
> U r sending the parameter but which environment u r using
> If u r using jupyter notebook u can pass the local machine folders
>
> But if u r using the Google colab it cannot access the local machine
> folders u have to upload it to the cloud.
>
> On Thu, 2 Jul, 2020, 7:32 pm clownpreet22, ***@***.***>
> wrote:
>
> > what would be the perameter of this fun
> > def split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE):
> > dataset = []
> >
> > for unitData in os.listdir(SOURCE):
> > data = SOURCE + unitData
> > if(os.path.getsize(data) > 0):
> > dataset.append(unitData)
> > else:
> > print('Skipped ' + unitData)
> > print('Invalid file i.e zero size')
> >
> > train_set_length = int(len(dataset) * SPLIT_SIZE)
> > test_set_length = int(len(dataset) - train_set_length)
> > shuffled_set = random.sample(dataset, len(dataset))
> > train_set = dataset[0:train_set_length]
> > test_set = dataset[-test_set_length:]
> >
> > for unitData in train_set:
> > temp_train_set = SOURCE + unitData
> > final_train_set = TRAINING + unitData
> > copyfile(temp_train_set, final_train_set)
> >
> > for unitData in test_set:
> > temp_test_set = SOURCE + unitData
> > final_test_set = TESTING + unitData
> > copyfile(temp_test_set, final_test_set)
> >
> > YES_SOURCE_DIR = "dest_folder/val/yes/"
> > TRAINING_YES_DIR = "dest_folder/train/yes/"
> > TESTING_YES_DIR = "dest_folder/test/yes/"
> > NO_SOURCE_DIR = "dest_folder/val/no/"
> > TRAINING_NO_DIR = "dest_folder/val/no/"
> > TESTING_NO_DIR = "dest_folder/test/no/"
> > split_size = .8
> > split_data(YES_SOURCE_DIR, TRAINING_YES_DIR, TESTING_YES_DIR,
split_size)
> > split_data(NO_SOURCE_DIR, TRAINING_NO_DIR, TESTING_NO_DIR, split_size)
> > [image: location of dataset]
> > <
>
https://user-images.githubusercontent.com/42198801/86368406-c2f45780-bc9a-11ea-8b0d-1dcef380a2ae.png
> >
> >
> > —
> > You are receiving this because you are subscribed to this thread.
> > Reply to this email directly, view it on GitHub
> > <#3>, or
> > unsubscribe
> > <
>
https://github.com/notifications/unsubscribe-auth/AIAR4NWSLZY6CAPBAEIC2Y3RZSHQ3ANCNFSM4OO5NI2Q
> >
> > .
> >
>
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> <
#3 (comment)
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Thank you so much
On Thu, 2 Jul 2020, 9:21 pm Ravikiran Dhulipala, <notifications@github.com>
wrote:
… The parameters passed to function are the source file ie., yes or no files
Training dataset folder ,testing dataset folder ,and the size in which the
dataset has to be splitted
On Thu, 2 Jul 2020 at 9:18 PM, clownpreet22 ***@***.***>
wrote:
> Im using jupyter notebook but i don't understand what will be the code
>
> On Thu, 2 Jul 2020, 8:13 pm Ravikiran Dhulipala, <
***@***.***
> >
> wrote:
>
> > U r sending the parameter but which environment u r using
> > If u r using jupyter notebook u can pass the local machine folders
> >
> > But if u r using the Google colab it cannot access the local machine
> > folders u have to upload it to the cloud.
> >
> > On Thu, 2 Jul, 2020, 7:32 pm clownpreet22, ***@***.***>
> > wrote:
> >
> > > what would be the perameter of this fun
> > > def split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE):
> > > dataset = []
> > >
> > > for unitData in os.listdir(SOURCE):
> > > data = SOURCE + unitData
> > > if(os.path.getsize(data) > 0):
> > > dataset.append(unitData)
> > > else:
> > > print('Skipped ' + unitData)
> > > print('Invalid file i.e zero size')
> > >
> > > train_set_length = int(len(dataset) * SPLIT_SIZE)
> > > test_set_length = int(len(dataset) - train_set_length)
> > > shuffled_set = random.sample(dataset, len(dataset))
> > > train_set = dataset[0:train_set_length]
> > > test_set = dataset[-test_set_length:]
> > >
> > > for unitData in train_set:
> > > temp_train_set = SOURCE + unitData
> > > final_train_set = TRAINING + unitData
> > > copyfile(temp_train_set, final_train_set)
> > >
> > > for unitData in test_set:
> > > temp_test_set = SOURCE + unitData
> > > final_test_set = TESTING + unitData
> > > copyfile(temp_test_set, final_test_set)
> > >
> > > YES_SOURCE_DIR = "dest_folder/val/yes/"
> > > TRAINING_YES_DIR = "dest_folder/train/yes/"
> > > TESTING_YES_DIR = "dest_folder/test/yes/"
> > > NO_SOURCE_DIR = "dest_folder/val/no/"
> > > TRAINING_NO_DIR = "dest_folder/val/no/"
> > > TESTING_NO_DIR = "dest_folder/test/no/"
> > > split_size = .8
> > > split_data(YES_SOURCE_DIR, TRAINING_YES_DIR, TESTING_YES_DIR,
> split_size)
> > > split_data(NO_SOURCE_DIR, TRAINING_NO_DIR, TESTING_NO_DIR,
split_size)
> > > [image: location of dataset]
> > > <
> >
>
https://user-images.githubusercontent.com/42198801/86368406-c2f45780-bc9a-11ea-8b0d-1dcef380a2ae.png
> > >
> > >
> > > —
> > > You are receiving this because you are subscribed to this thread.
> > > Reply to this email directly, view it on GitHub
> > > <#3>, or
> > > unsubscribe
> > > <
> >
>
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> > >
> > > .
> > >
> >
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> > <
>
#3 (comment)
> >,
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Sir while i run history it runs till some epochs and then stops why this
happened
…On Thu 2 Jul, 2020, 9:49 PM Patel Preet, ***@***.***> wrote:
Thank you so much
On Thu, 2 Jul 2020, 9:21 pm Ravikiran Dhulipala, ***@***.***>
wrote:
> The parameters passed to function are the source file ie., yes or no files
> Training dataset folder ,testing dataset folder ,and the size in which the
> dataset has to be splitted
>
> On Thu, 2 Jul 2020 at 9:18 PM, clownpreet22 ***@***.***>
> wrote:
>
> > Im using jupyter notebook but i don't understand what will be the code
> >
> > On Thu, 2 Jul 2020, 8:13 pm Ravikiran Dhulipala, <
> ***@***.***
> > >
> > wrote:
> >
> > > U r sending the parameter but which environment u r using
> > > If u r using jupyter notebook u can pass the local machine folders
> > >
> > > But if u r using the Google colab it cannot access the local machine
> > > folders u have to upload it to the cloud.
> > >
> > > On Thu, 2 Jul, 2020, 7:32 pm clownpreet22, ***@***.***>
> > > wrote:
> > >
> > > > what would be the perameter of this fun
> > > > def split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE):
> > > > dataset = []
> > > >
> > > > for unitData in os.listdir(SOURCE):
> > > > data = SOURCE + unitData
> > > > if(os.path.getsize(data) > 0):
> > > > dataset.append(unitData)
> > > > else:
> > > > print('Skipped ' + unitData)
> > > > print('Invalid file i.e zero size')
> > > >
> > > > train_set_length = int(len(dataset) * SPLIT_SIZE)
> > > > test_set_length = int(len(dataset) - train_set_length)
> > > > shuffled_set = random.sample(dataset, len(dataset))
> > > > train_set = dataset[0:train_set_length]
> > > > test_set = dataset[-test_set_length:]
> > > >
> > > > for unitData in train_set:
> > > > temp_train_set = SOURCE + unitData
> > > > final_train_set = TRAINING + unitData
> > > > copyfile(temp_train_set, final_train_set)
> > > >
> > > > for unitData in test_set:
> > > > temp_test_set = SOURCE + unitData
> > > > final_test_set = TESTING + unitData
> > > > copyfile(temp_test_set, final_test_set)
> > > >
> > > > YES_SOURCE_DIR = "dest_folder/val/yes/"
> > > > TRAINING_YES_DIR = "dest_folder/train/yes/"
> > > > TESTING_YES_DIR = "dest_folder/test/yes/"
> > > > NO_SOURCE_DIR = "dest_folder/val/no/"
> > > > TRAINING_NO_DIR = "dest_folder/val/no/"
> > > > TESTING_NO_DIR = "dest_folder/test/no/"
> > > > split_size = .8
> > > > split_data(YES_SOURCE_DIR, TRAINING_YES_DIR, TESTING_YES_DIR,
> > split_size)
> > > > split_data(NO_SOURCE_DIR, TRAINING_NO_DIR, TESTING_NO_DIR,
> split_size)
> > > > [image: location of dataset]
> > > > <
> > >
> >
> https://user-images.githubusercontent.com/42198801/86368406-c2f45780-bc9a-11ea-8b0d-1dcef380a2ae.png
> > > >
> > > >
> > > > —
> > > > You are receiving this because you are subscribed to this thread.
> > > > Reply to this email directly, view it on GitHub
> > > > <#3>, or
> > > > unsubscribe
> > > > <
> > >
> >
> https://github.com/notifications/unsubscribe-auth/AIAR4NWSLZY6CAPBAEIC2Y3RZSHQ3ANCNFSM4OO5NI2Q
> > > >
> > > > .
> > > >
> > >
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> > > <
> >
> #3 (comment)
> > >,
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> > > <
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If u have high end local machine u can run all the epochs. I want to
mention one thing that u cannot train Machine learning models accurately on
local machine Unless u have high end configurations
try it in cloud env
On Thu, 2 Jul 2020 at 9:54 PM, clownpreet22 <notifications@github.com>
wrote:
… Sir while i run history it runs till some epochs and then stops why this
happened
On Thu 2 Jul, 2020, 9:49 PM Patel Preet, ***@***.***> wrote:
> Thank you so much
>
> On Thu, 2 Jul 2020, 9:21 pm Ravikiran Dhulipala, <
***@***.***>
> wrote:
>
>> The parameters passed to function are the source file ie., yes or no
files
>> Training dataset folder ,testing dataset folder ,and the size in which
the
>> dataset has to be splitted
>>
>> On Thu, 2 Jul 2020 at 9:18 PM, clownpreet22 ***@***.***>
>> wrote:
>>
>> > Im using jupyter notebook but i don't understand what will be the code
>> >
>> > On Thu, 2 Jul 2020, 8:13 pm Ravikiran Dhulipala, <
>> ***@***.***
>> > >
>> > wrote:
>> >
>> > > U r sending the parameter but which environment u r using
>> > > If u r using jupyter notebook u can pass the local machine folders
>> > >
>> > > But if u r using the Google colab it cannot access the local machine
>> > > folders u have to upload it to the cloud.
>> > >
>> > > On Thu, 2 Jul, 2020, 7:32 pm clownpreet22, <
***@***.***>
>> > > wrote:
>> > >
>> > > > what would be the perameter of this fun
>> > > > def split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE):
>> > > > dataset = []
>> > > >
>> > > > for unitData in os.listdir(SOURCE):
>> > > > data = SOURCE + unitData
>> > > > if(os.path.getsize(data) > 0):
>> > > > dataset.append(unitData)
>> > > > else:
>> > > > print('Skipped ' + unitData)
>> > > > print('Invalid file i.e zero size')
>> > > >
>> > > > train_set_length = int(len(dataset) * SPLIT_SIZE)
>> > > > test_set_length = int(len(dataset) - train_set_length)
>> > > > shuffled_set = random.sample(dataset, len(dataset))
>> > > > train_set = dataset[0:train_set_length]
>> > > > test_set = dataset[-test_set_length:]
>> > > >
>> > > > for unitData in train_set:
>> > > > temp_train_set = SOURCE + unitData
>> > > > final_train_set = TRAINING + unitData
>> > > > copyfile(temp_train_set, final_train_set)
>> > > >
>> > > > for unitData in test_set:
>> > > > temp_test_set = SOURCE + unitData
>> > > > final_test_set = TESTING + unitData
>> > > > copyfile(temp_test_set, final_test_set)
>> > > >
>> > > > YES_SOURCE_DIR = "dest_folder/val/yes/"
>> > > > TRAINING_YES_DIR = "dest_folder/train/yes/"
>> > > > TESTING_YES_DIR = "dest_folder/test/yes/"
>> > > > NO_SOURCE_DIR = "dest_folder/val/no/"
>> > > > TRAINING_NO_DIR = "dest_folder/val/no/"
>> > > > TESTING_NO_DIR = "dest_folder/test/no/"
>> > > > split_size = .8
>> > > > split_data(YES_SOURCE_DIR, TRAINING_YES_DIR, TESTING_YES_DIR,
>> > split_size)
>> > > > split_data(NO_SOURCE_DIR, TRAINING_NO_DIR, TESTING_NO_DIR,
>> split_size)
>> > > > [image: location of dataset]
>> > > > <
>> > >
>> >
>>
https://user-images.githubusercontent.com/42198801/86368406-c2f45780-bc9a-11ea-8b0d-1dcef380a2ae.png
>> > > >
>> > > >
>> > > > —
>> > > > You are receiving this because you are subscribed to this thread.
>> > > > Reply to this email directly, view it on GitHub
>> > > > <#3>,
or
>> > > > unsubscribe
>> > > > <
>> > >
>> >
>>
https://github.com/notifications/unsubscribe-auth/AIAR4NWSLZY6CAPBAEIC2Y3RZSHQ3ANCNFSM4OO5NI2Q
>> > > >
>> > > > .
>> > > >
>> > >
>> > > —
>> > > You are receiving this because you authored the thread.
>> > > Reply to this email directly, view it on GitHub
>> > > <
>> >
>>
#3 (comment)
>> > >,
>> > > or unsubscribe
>> > > <
>> >
>>
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>> > >
>> > > .
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>> >
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Thank u so much sir
Your idea helps me a lot
Code completely run on colab platform
Just it shows small *error* pls help me out from this
On Thu 2 Jul, 2020, 9:57 PM Ravikiran Dhulipala, <notifications@github.com>
wrote:
… If u have high end local machine u can run all the epochs. I want to
mention one thing that u cannot train Machine learning models accurately on
local machine Unless u have high end configurations
try it in cloud env
On Thu, 2 Jul 2020 at 9:54 PM, clownpreet22 ***@***.***>
wrote:
> Sir while i run history it runs till some epochs and then stops why this
> happened
>
> On Thu 2 Jul, 2020, 9:49 PM Patel Preet, ***@***.***>
wrote:
>
> > Thank you so much
> >
> > On Thu, 2 Jul 2020, 9:21 pm Ravikiran Dhulipala, <
> ***@***.***>
> > wrote:
> >
> >> The parameters passed to function are the source file ie., yes or no
> files
> >> Training dataset folder ,testing dataset folder ,and the size in which
> the
> >> dataset has to be splitted
> >>
> >> On Thu, 2 Jul 2020 at 9:18 PM, clownpreet22 ***@***.***
>
> >> wrote:
> >>
> >> > Im using jupyter notebook but i don't understand what will be the
code
> >> >
> >> > On Thu, 2 Jul 2020, 8:13 pm Ravikiran Dhulipala, <
> >> ***@***.***
> >> > >
> >> > wrote:
> >> >
> >> > > U r sending the parameter but which environment u r using
> >> > > If u r using jupyter notebook u can pass the local machine folders
> >> > >
> >> > > But if u r using the Google colab it cannot access the local
machine
> >> > > folders u have to upload it to the cloud.
> >> > >
> >> > > On Thu, 2 Jul, 2020, 7:32 pm clownpreet22, <
> ***@***.***>
> >> > > wrote:
> >> > >
> >> > > > what would be the perameter of this fun
> >> > > > def split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE):
> >> > > > dataset = []
> >> > > >
> >> > > > for unitData in os.listdir(SOURCE):
> >> > > > data = SOURCE + unitData
> >> > > > if(os.path.getsize(data) > 0):
> >> > > > dataset.append(unitData)
> >> > > > else:
> >> > > > print('Skipped ' + unitData)
> >> > > > print('Invalid file i.e zero size')
> >> > > >
> >> > > > train_set_length = int(len(dataset) * SPLIT_SIZE)
> >> > > > test_set_length = int(len(dataset) - train_set_length)
> >> > > > shuffled_set = random.sample(dataset, len(dataset))
> >> > > > train_set = dataset[0:train_set_length]
> >> > > > test_set = dataset[-test_set_length:]
> >> > > >
> >> > > > for unitData in train_set:
> >> > > > temp_train_set = SOURCE + unitData
> >> > > > final_train_set = TRAINING + unitData
> >> > > > copyfile(temp_train_set, final_train_set)
> >> > > >
> >> > > > for unitData in test_set:
> >> > > > temp_test_set = SOURCE + unitData
> >> > > > final_test_set = TESTING + unitData
> >> > > > copyfile(temp_test_set, final_test_set)
> >> > > >
> >> > > > YES_SOURCE_DIR = "dest_folder/val/yes/"
> >> > > > TRAINING_YES_DIR = "dest_folder/train/yes/"
> >> > > > TESTING_YES_DIR = "dest_folder/test/yes/"
> >> > > > NO_SOURCE_DIR = "dest_folder/val/no/"
> >> > > > TRAINING_NO_DIR = "dest_folder/val/no/"
> >> > > > TESTING_NO_DIR = "dest_folder/test/no/"
> >> > > > split_size = .8
> >> > > > split_data(YES_SOURCE_DIR, TRAINING_YES_DIR, TESTING_YES_DIR,
> >> > split_size)
> >> > > > split_data(NO_SOURCE_DIR, TRAINING_NO_DIR, TESTING_NO_DIR,
> >> split_size)
> >> > > > [image: location of dataset]
> >> > > > <
> >> > >
> >> >
> >>
>
https://user-images.githubusercontent.com/42198801/86368406-c2f45780-bc9a-11ea-8b0d-1dcef380a2ae.png
> >> > > >
> >> > > >
> >> > > > —
> >> > > > You are receiving this because you are subscribed to this
thread.
> >> > > > Reply to this email directly, view it on GitHub
> >> > > > <#3
>,
> or
> >> > > > unsubscribe
> >> > > > <
> >> > >
> >> >
> >>
>
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> >> > > >
> >> > > > .
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> >> >
> >>
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> >> > >,
> >> > > or unsubscribe
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> >> >
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Can u show me that error so that I can help u
On Fri, 3 Jul, 2020, 12:38 am clownpreet22, <notifications@github.com>
wrote:
… Thank u so much sir
Your idea helps me a lot
Code completely run on colab platform
Just it shows small *error* pls help me out from this
On Thu 2 Jul, 2020, 9:57 PM Ravikiran Dhulipala, ***@***.***
>
wrote:
> If u have high end local machine u can run all the epochs. I want to
> mention one thing that u cannot train Machine learning models accurately
on
> local machine Unless u have high end configurations
> try it in cloud env
>
> On Thu, 2 Jul 2020 at 9:54 PM, clownpreet22 ***@***.***>
> wrote:
>
> > Sir while i run history it runs till some epochs and then stops why
this
> > happened
> >
> > On Thu 2 Jul, 2020, 9:49 PM Patel Preet, ***@***.***>
> wrote:
> >
> > > Thank you so much
> > >
> > > On Thu, 2 Jul 2020, 9:21 pm Ravikiran Dhulipala, <
> > ***@***.***>
> > > wrote:
> > >
> > >> The parameters passed to function are the source file ie., yes or no
> > files
> > >> Training dataset folder ,testing dataset folder ,and the size in
which
> > the
> > >> dataset has to be splitted
> > >>
> > >> On Thu, 2 Jul 2020 at 9:18 PM, clownpreet22 <
***@***.***
> >
> > >> wrote:
> > >>
> > >> > Im using jupyter notebook but i don't understand what will be the
> code
> > >> >
> > >> > On Thu, 2 Jul 2020, 8:13 pm Ravikiran Dhulipala, <
> > >> ***@***.***
> > >> > >
> > >> > wrote:
> > >> >
> > >> > > U r sending the parameter but which environment u r using
> > >> > > If u r using jupyter notebook u can pass the local machine
folders
> > >> > >
> > >> > > But if u r using the Google colab it cannot access the local
> machine
> > >> > > folders u have to upload it to the cloud.
> > >> > >
> > >> > > On Thu, 2 Jul, 2020, 7:32 pm clownpreet22, <
> > ***@***.***>
> > >> > > wrote:
> > >> > >
> > >> > > > what would be the perameter of this fun
> > >> > > > def split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE):
> > >> > > > dataset = []
> > >> > > >
> > >> > > > for unitData in os.listdir(SOURCE):
> > >> > > > data = SOURCE + unitData
> > >> > > > if(os.path.getsize(data) > 0):
> > >> > > > dataset.append(unitData)
> > >> > > > else:
> > >> > > > print('Skipped ' + unitData)
> > >> > > > print('Invalid file i.e zero size')
> > >> > > >
> > >> > > > train_set_length = int(len(dataset) * SPLIT_SIZE)
> > >> > > > test_set_length = int(len(dataset) - train_set_length)
> > >> > > > shuffled_set = random.sample(dataset, len(dataset))
> > >> > > > train_set = dataset[0:train_set_length]
> > >> > > > test_set = dataset[-test_set_length:]
> > >> > > >
> > >> > > > for unitData in train_set:
> > >> > > > temp_train_set = SOURCE + unitData
> > >> > > > final_train_set = TRAINING + unitData
> > >> > > > copyfile(temp_train_set, final_train_set)
> > >> > > >
> > >> > > > for unitData in test_set:
> > >> > > > temp_test_set = SOURCE + unitData
> > >> > > > final_test_set = TESTING + unitData
> > >> > > > copyfile(temp_test_set, final_test_set)
> > >> > > >
> > >> > > > YES_SOURCE_DIR = "dest_folder/val/yes/"
> > >> > > > TRAINING_YES_DIR = "dest_folder/train/yes/"
> > >> > > > TESTING_YES_DIR = "dest_folder/test/yes/"
> > >> > > > NO_SOURCE_DIR = "dest_folder/val/no/"
> > >> > > > TRAINING_NO_DIR = "dest_folder/val/no/"
> > >> > > > TESTING_NO_DIR = "dest_folder/test/no/"
> > >> > > > split_size = .8
> > >> > > > split_data(YES_SOURCE_DIR, TRAINING_YES_DIR, TESTING_YES_DIR,
> > >> > split_size)
> > >> > > > split_data(NO_SOURCE_DIR, TRAINING_NO_DIR, TESTING_NO_DIR,
> > >> split_size)
> > >> > > > [image: location of dataset]
> > >> > > > <
> > >> > >
> > >> >
> > >>
> >
>
https://user-images.githubusercontent.com/42198801/86368406-c2f45780-bc9a-11ea-8b0d-1dcef380a2ae.png
> > >> > > >
> > >> > > >
> > >> > > > —
> > >> > > > You are receiving this because you are subscribed to this
> thread.
> > >> > > > Reply to this email directly, view it on GitHub
> > >> > > > <
#3
> >,
> > or
> > >> > > > unsubscribe
> > >> > > > <
> > >> > >
> > >> >
> > >>
> >
>
https://github.com/notifications/unsubscribe-auth/AIAR4NWSLZY6CAPBAEIC2Y3RZSHQ3ANCNFSM4OO5NI2Q
> > >> > > >
> > >> > > > .
> > >> > > >
> > >> > >
> > >> > > —
> > >> > > You are receiving this because you authored the thread.
> > >> > > Reply to this email directly, view it on GitHub
> > >> > > <
> > >> >
> > >>
> >
>
#3 (comment)
> > >> > >,
> > >> > > or unsubscribe
> > >> > > <
> > >> >
> > >>
> >
>
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> > >> > >
> > >> > > .
> > >> > >
> > >> >
> > >> > —
> > >> > You are receiving this because you commented.
> > >> >
> > >> >
> > >> > Reply to this email directly, view it on GitHub
> > >> > <
> > >>
> >
>
#3 (comment)
> > >> >,
> > >> > or unsubscribe
> > >> > <
> > >>
> >
>
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> > >> >
> > >> > .
> > >> >
> > >>
> > >> —
> > >> You are receiving this because you authored the thread.
> > >> Reply to this email directly, view it on GitHub
> > >> <
> >
>
#3 (comment)
> > >,
> > >> or unsubscribe
> > >> <
> >
>
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> > >
> > >> .
> > >>
> > >
> >
> > —
> > You are receiving this because you commented.
> >
> >
> > Reply to this email directly, view it on GitHub
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>
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|
Sure Sir,
here is the code img and error img attached
On Fri, Jul 3, 2020 at 8:39 AM Ravikiran Dhulipala <notifications@github.com>
wrote:
… Can u show me that error so that I can help u
On Fri, 3 Jul, 2020, 12:38 am clownpreet22, ***@***.***>
wrote:
> Thank u so much sir
> Your idea helps me a lot
> Code completely run on colab platform
>
>
> Just it shows small *error* pls help me out from this
>
> On Thu 2 Jul, 2020, 9:57 PM Ravikiran Dhulipala, <
***@***.***
> >
> wrote:
>
> > If u have high end local machine u can run all the epochs. I want to
> > mention one thing that u cannot train Machine learning models
accurately
> on
> > local machine Unless u have high end configurations
> > try it in cloud env
> >
> > On Thu, 2 Jul 2020 at 9:54 PM, clownpreet22 ***@***.***>
> > wrote:
> >
> > > Sir while i run history it runs till some epochs and then stops why
> this
> > > happened
> > >
> > > On Thu 2 Jul, 2020, 9:49 PM Patel Preet, ***@***.***>
> > wrote:
> > >
> > > > Thank you so much
> > > >
> > > > On Thu, 2 Jul 2020, 9:21 pm Ravikiran Dhulipala, <
> > > ***@***.***>
> > > > wrote:
> > > >
> > > >> The parameters passed to function are the source file ie., yes or
no
> > > files
> > > >> Training dataset folder ,testing dataset folder ,and the size in
> which
> > > the
> > > >> dataset has to be splitted
> > > >>
> > > >> On Thu, 2 Jul 2020 at 9:18 PM, clownpreet22 <
> ***@***.***
> > >
> > > >> wrote:
> > > >>
> > > >> > Im using jupyter notebook but i don't understand what will be
the
> > code
> > > >> >
> > > >> > On Thu, 2 Jul 2020, 8:13 pm Ravikiran Dhulipala, <
> > > >> ***@***.***
> > > >> > >
> > > >> > wrote:
> > > >> >
> > > >> > > U r sending the parameter but which environment u r using
> > > >> > > If u r using jupyter notebook u can pass the local machine
> folders
> > > >> > >
> > > >> > > But if u r using the Google colab it cannot access the local
> > machine
> > > >> > > folders u have to upload it to the cloud.
> > > >> > >
> > > >> > > On Thu, 2 Jul, 2020, 7:32 pm clownpreet22, <
> > > ***@***.***>
> > > >> > > wrote:
> > > >> > >
> > > >> > > > what would be the perameter of this fun
> > > >> > > > def split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE):
> > > >> > > > dataset = []
> > > >> > > >
> > > >> > > > for unitData in os.listdir(SOURCE):
> > > >> > > > data = SOURCE + unitData
> > > >> > > > if(os.path.getsize(data) > 0):
> > > >> > > > dataset.append(unitData)
> > > >> > > > else:
> > > >> > > > print('Skipped ' + unitData)
> > > >> > > > print('Invalid file i.e zero size')
> > > >> > > >
> > > >> > > > train_set_length = int(len(dataset) * SPLIT_SIZE)
> > > >> > > > test_set_length = int(len(dataset) - train_set_length)
> > > >> > > > shuffled_set = random.sample(dataset, len(dataset))
> > > >> > > > train_set = dataset[0:train_set_length]
> > > >> > > > test_set = dataset[-test_set_length:]
> > > >> > > >
> > > >> > > > for unitData in train_set:
> > > >> > > > temp_train_set = SOURCE + unitData
> > > >> > > > final_train_set = TRAINING + unitData
> > > >> > > > copyfile(temp_train_set, final_train_set)
> > > >> > > >
> > > >> > > > for unitData in test_set:
> > > >> > > > temp_test_set = SOURCE + unitData
> > > >> > > > final_test_set = TESTING + unitData
> > > >> > > > copyfile(temp_test_set, final_test_set)
> > > >> > > >
> > > >> > > > YES_SOURCE_DIR = "dest_folder/val/yes/"
> > > >> > > > TRAINING_YES_DIR = "dest_folder/train/yes/"
> > > >> > > > TESTING_YES_DIR = "dest_folder/test/yes/"
> > > >> > > > NO_SOURCE_DIR = "dest_folder/val/no/"
> > > >> > > > TRAINING_NO_DIR = "dest_folder/val/no/"
> > > >> > > > TESTING_NO_DIR = "dest_folder/test/no/"
> > > >> > > > split_size = .8
> > > >> > > > split_data(YES_SOURCE_DIR, TRAINING_YES_DIR,
TESTING_YES_DIR,
> > > >> > split_size)
> > > >> > > > split_data(NO_SOURCE_DIR, TRAINING_NO_DIR, TESTING_NO_DIR,
> > > >> split_size)
> > > >> > > > [image: location of dataset]
> > > >> > > > <
> > > >> > >
> > > >> >
> > > >>
> > >
> >
>
https://user-images.githubusercontent.com/42198801/86368406-c2f45780-bc9a-11ea-8b0d-1dcef380a2ae.png
> > > >> > > >
> > > >> > > >
> > > >> > > > —
> > > >> > > > You are receiving this because you are subscribed to this
> > thread.
> > > >> > > > Reply to this email directly, view it on GitHub
> > > >> > > > <
> #3
> > >,
> > > or
> > > >> > > > unsubscribe
> > > >> > > > <
> > > >> > >
> > > >> >
> > > >>
> > >
> >
>
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> > > >> > > >
> > > >> > > > .
> > > >> > > >
> > > >> > >
> > > >> > > —
> > > >> > > You are receiving this because you authored the thread.
> > > >> > > Reply to this email directly, view it on GitHub
> > > >> > > <
> > > >> >
> > > >>
> > >
> >
>
#3 (comment)
> > > >> > >,
> > > >> > > or unsubscribe
> > > >> > > <
> > > >> >
> > > >>
> > >
> >
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> > > >>
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> > > >> >
> > > >> > .
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I'm sorry I couldn't find any images |
let me share a code and error as a text
*CODE*
labels_dict={0:'without_mask',1:'with_mask'}
color_dict={0:(0,0,255),1:(0,255,0)}
size = 4
webcam = cv2.VideoCapture(0) #Use camera 0
# We load the xml file
classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
while True:
(rval, im) = webcam.read()
im=cv2.flip(im,1,1) #Flip to act as a mirror
# Resize the image to speed up detection
mini = cv2.resize(im, ( im.shape[1] // size, im.shape[0] // size))
# detect MultiScale / faces
faces = classifier.detectMultiScale(mini)
# Draw rectangles around each face
for f in faces:
(x, y, w, h) = [v * size for v in f] #Scale the shapesize backup
#Save just the rectangle faces in SubRecFaces
face_img = im[y:y+h, x:x+w]
resized=cv2.resize(face_img,(150,150))
normalized=resized/255.0
reshaped=np.reshape(normalized,(1,150,150,3))
reshaped = np.vstack([reshaped])
result=model.predict(reshaped)
#print(result)
label=np.argmax(result,axis=1)[0]
cv2.rectangle(im,(x,y),(x+w,y+h),color_dict[label],2)
cv2.rectangle(im,(x,y-40),(x+w,y),color_dict[label],-1)
cv2.putText(im, labels_dict[label], (x, y-10
),cv2.FONT_HERSHEY_SIMPLEX,0.8,(255,255,255),2)
# Show the image
cv2.imshow('LIVE', im)
key = cv2.waitKey(10)
# if Esc key is press then break out of the loop
if key == 27: #The Esc key
break
# Stop video
webcam.release()
# Close all started windows
cv2.destroyAllWindows()
*ERROR*
…---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-39-ec73b1f468a0> <https://localhost:8080/#> in
<module>() 13 14 # Resize the image to speed up
detection---> 15 mini = cv2.resize(im, ( im.shape[1] // size,
im.shape[0] // size)) 16 17 # detect MultiScale / faces
AttributeError: 'NoneType' object has no attribute 'shape'
On Fri, Jul 3, 2020 at 9:36 AM Ravikiran Dhulipala ***@***.***> wrote:
I'm sorry I couldn't find any images
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Just make sure that #flip to act as mirror variable is actually getting the
values and it is a numpy variable
Actually only the numpy variables will only have the shape function to it
Just make sure that
…On Fri, 3 Jul, 2020, 9:40 am clownpreet22, ***@***.***> wrote:
let me share a code and error as a text
*CODE*
labels_dict={0:'without_mask',1:'with_mask'}
color_dict={0:(0,0,255),1:(0,255,0)}
size = 4
webcam = cv2.VideoCapture(0) #Use camera 0
# We load the xml file
classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
while True:
(rval, im) = webcam.read()
im=cv2.flip(im,1,1) #Flip to act as a mirror
# Resize the image to speed up detection
mini = cv2.resize(im, ( im.shape[1] // size, im.shape[0] // size))
# detect MultiScale / faces
faces = classifier.detectMultiScale(mini)
# Draw rectangles around each face
for f in faces:
(x, y, w, h) = [v * size for v in f] #Scale the shapesize backup
#Save just the rectangle faces in SubRecFaces
face_img = im[y:y+h, x:x+w]
resized=cv2.resize(face_img,(150,150))
normalized=resized/255.0
reshaped=np.reshape(normalized,(1,150,150,3))
reshaped = np.vstack([reshaped])
result=model.predict(reshaped)
#print(result)
label=np.argmax(result,axis=1)[0]
cv2.rectangle(im,(x,y),(x+w,y+h),color_dict[label],2)
cv2.rectangle(im,(x,y-40),(x+w,y),color_dict[label],-1)
cv2.putText(im, labels_dict[label], (x, y-10
),cv2.FONT_HERSHEY_SIMPLEX,0.8,(255,255,255),2)
# Show the image
cv2.imshow('LIVE', im)
key = cv2.waitKey(10)
# if Esc key is press then break out of the loop
if key == 27: #The Esc key
break
# Stop video
webcam.release()
# Close all started windows
cv2.destroyAllWindows()
*ERROR*
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-39-ec73b1f468a0> <https://localhost:8080/#> in
<module>() 13 14 # Resize the image to speed up
detection---> 15 mini = cv2.resize(im, ( im.shape[1] // size,
im.shape[0] // size)) 16 17 # detect MultiScale / faces
AttributeError: 'NoneType' object has no attribute 'shape'
On Fri, Jul 3, 2020 at 9:36 AM Ravikiran Dhulipala <
***@***.***>
wrote:
> I'm sorry I couldn't find any images
>
> —
> You are receiving this because you authored the thread.
> Reply to this email directly, view it on GitHub
> <
#3 (comment)
>,
> or unsubscribe
> <
https://github.com/notifications/unsubscribe-auth/AKB6OENLPDB63ZKKRCADEZTRZVKLZANCNFSM4OO5NI2Q
>
> .
>
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where i have to change pls tell what would i put to run code successfully
actually i am not aware about this line of code too mch
pls tell me the like where i need to chnage and what change that is?
On Fri, Jul 3, 2020 at 9:46 AM Ravikiran Dhulipala <notifications@github.com>
wrote:
… Just make sure that #flip to act as mirror variable is actually getting the
values and it is a numpy variable
Actually only the numpy variables will only have the shape function to it
Just make sure that
On Fri, 3 Jul, 2020, 9:40 am clownpreet22, ***@***.***>
wrote:
> let me share a code and error as a text
>
> *CODE*
>
> labels_dict={0:'without_mask',1:'with_mask'}
> color_dict={0:(0,0,255),1:(0,255,0)}
>
> size = 4
> webcam = cv2.VideoCapture(0) #Use camera 0
>
> # We load the xml file
> classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
>
> while True:
> (rval, im) = webcam.read()
> im=cv2.flip(im,1,1) #Flip to act as a mirror
>
> # Resize the image to speed up detection
> mini = cv2.resize(im, ( im.shape[1] // size, im.shape[0] // size))
>
> # detect MultiScale / faces
> faces = classifier.detectMultiScale(mini)
>
> # Draw rectangles around each face
> for f in faces:
> (x, y, w, h) = [v * size for v in f] #Scale the shapesize backup
> #Save just the rectangle faces in SubRecFaces
> face_img = im[y:y+h, x:x+w]
> resized=cv2.resize(face_img,(150,150))
> normalized=resized/255.0
> reshaped=np.reshape(normalized,(1,150,150,3))
> reshaped = np.vstack([reshaped])
> result=model.predict(reshaped)
> #print(result)
>
> label=np.argmax(result,axis=1)[0]
>
> cv2.rectangle(im,(x,y),(x+w,y+h),color_dict[label],2)
> cv2.rectangle(im,(x,y-40),(x+w,y),color_dict[label],-1)
> cv2.putText(im, labels_dict[label], (x, y-10
> ),cv2.FONT_HERSHEY_SIMPLEX,0.8,(255,255,255),2)
>
> # Show the image
> cv2.imshow('LIVE', im)
> key = cv2.waitKey(10)
> # if Esc key is press then break out of the loop
> if key == 27: #The Esc key
> break
> # Stop video
> webcam.release()
>
> # Close all started windows
> cv2.destroyAllWindows()
>
> *ERROR*
>
>
---------------------------------------------------------------------------
>
> AttributeError Traceback (most recent call last)
>
> <ipython-input-39-ec73b1f468a0> <https://localhost:8080/#> in
> <module>() 13 14 # Resize the image to speed up
> detection---> 15 mini = cv2.resize(im, ( im.shape[1] // size,
> im.shape[0] // size)) 16 17 # detect MultiScale / faces
>
> AttributeError: 'NoneType' object has no attribute 'shape'
>
>
> On Fri, Jul 3, 2020 at 9:36 AM Ravikiran Dhulipala <
> ***@***.***>
> wrote:
>
> > I'm sorry I couldn't find any images
> >
> > —
> > You are receiving this because you authored the thread.
> > Reply to this email directly, view it on GitHub
> > <
>
#3 (comment)
> >,
> > or unsubscribe
> > <
>
https://github.com/notifications/unsubscribe-auth/AKB6OENLPDB63ZKKRCADEZTRZVKLZANCNFSM4OO5NI2Q
> >
> > .
> >
>
> —
> You are receiving this because you commented.
> Reply to this email directly, view it on GitHub
> <
#3 (comment)
>,
> or unsubscribe
> <
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@clownpreet22 - Were you able to resolve the shape issue...if so could you please guide me? |
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what would be the perameter of this fun
def split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE):
dataset = []
YES_SOURCE_DIR = "dest_folder/val/yes/"
![location of dataset](https://user-images.githubusercontent.com/42198801/86368406-c2f45780-bc9a-11ea-8b0d-1dcef380a2ae.png)
TRAINING_YES_DIR = "dest_folder/train/yes/"
TESTING_YES_DIR = "dest_folder/test/yes/"
NO_SOURCE_DIR = "dest_folder/val/no/"
TRAINING_NO_DIR = "dest_folder/val/no/"
TESTING_NO_DIR = "dest_folder/test/no/"
split_size = .8
split_data(YES_SOURCE_DIR, TRAINING_YES_DIR, TESTING_YES_DIR, split_size)
split_data(NO_SOURCE_DIR, TRAINING_NO_DIR, TESTING_NO_DIR, split_size)
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