face mask detection perameter #3

clownpreet22 · 2020-07-02T14:02:37Z

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)

The text was updated successfully, but these errors were encountered:

RAVIKIRAN-DHULIPALA · 2020-07-02T14:43:12Z

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> .

clownpreet22 · 2020-07-02T15:48:04Z

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 > > > — > 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 <https://github.com/notifications/unsubscribe-auth/AKB6OEN7FBIVSNOYNROFHMDRZSMI7ANCNFSM4OO5NI2Q> .

RAVIKIRAN-DHULIPALA · 2020-07-02T15:51:20Z

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 > > > > . > > > > — > 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/AKB6OEN7FBIVSNOYNROFHMDRZSMI7ANCNFSM4OO5NI2Q > > . > — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#3 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AIAR4NVZLY6LVCGB3B3Q5L3RZST4HANCNFSM4OO5NI2Q> .

clownpreet22 · 2020-07-02T16:19:41Z

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 > > > < > > > 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 > > < > https://github.com/notifications/unsubscribe-auth/AKB6OEN7FBIVSNOYNROFHMDRZSMI7ANCNFSM4OO5NI2Q > > > > . > > > > — > You are receiving this because you commented. > > > Reply to this email directly, view it on GitHub > < #3 (comment) >, > or unsubscribe > < https://github.com/notifications/unsubscribe-auth/AIAR4NVZLY6LVCGB3B3Q5L3RZST4HANCNFSM4OO5NI2Q > > . > — 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/AKB6OEJD5PVX6HMCI474RW3RZSUIRANCNFSM4OO5NI2Q> .

clownpreet22 · 2020-07-02T16:23:57Z

Sir while i run history it runs till some epochs and then stops why this happened

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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 > > > < > > > https://github.com/notifications/unsubscribe-auth/AKB6OEN7FBIVSNOYNROFHMDRZSMI7ANCNFSM4OO5NI2Q > > > > > > . > > > > > > > — > > You are receiving this because you commented. > > > > > > Reply to this email directly, view it on GitHub > > < > #3 (comment) > >, > > or unsubscribe > > < > https://github.com/notifications/unsubscribe-auth/AIAR4NVZLY6LVCGB3B3Q5L3RZST4HANCNFSM4OO5NI2Q > > > > . > > > > — > 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/AKB6OEJD5PVX6HMCI474RW3RZSUIRANCNFSM4OO5NI2Q> > . >

RAVIKIRAN-DHULIPALA · 2020-07-02T16:27:37Z

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 >> > > < >> > >> https://github.com/notifications/unsubscribe-auth/AKB6OEN7FBIVSNOYNROFHMDRZSMI7ANCNFSM4OO5NI2Q >> > > >> > > . >> > > >> > >> > — >> > You are receiving this because you commented. >> > >> > >> > Reply to this email directly, view it on GitHub >> > < >> #3 (comment) >> >, >> > or unsubscribe >> > < >> https://github.com/notifications/unsubscribe-auth/AIAR4NVZLY6LVCGB3B3Q5L3RZST4HANCNFSM4OO5NI2Q >> > >> > . >> > >> >> — >> 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/AKB6OEJD5PVX6HMCI474RW3RZSUIRANCNFSM4OO5NI2Q > >> . >> > — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#3 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AIAR4NWGQKAEAPNUNH24MKDRZSYCXANCNFSM4OO5NI2Q> .

clownpreet22 · 2020-07-02T19:08:00Z

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 > >> > > > < > >> > > > >> > > >> > 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 > >> > > < > >> > > >> > https://github.com/notifications/unsubscribe-auth/AKB6OEN7FBIVSNOYNROFHMDRZSMI7ANCNFSM4OO5NI2Q > >> > > > >> > > . > >> > > > >> > > >> > — > >> > You are receiving this because you commented. > >> > > >> > > >> > Reply to this email directly, view it on GitHub > >> > < > >> > #3 (comment) > >> >, > >> > or unsubscribe > >> > < > >> > https://github.com/notifications/unsubscribe-auth/AIAR4NVZLY6LVCGB3B3Q5L3RZST4HANCNFSM4OO5NI2Q > >> > > >> > . > >> > > >> > >> — > >> 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/AKB6OEJD5PVX6HMCI474RW3RZSUIRANCNFSM4OO5NI2Q > > > >> . > >> > > > > — > You are receiving this because you commented. > > > Reply to this email directly, view it on GitHub > < #3 (comment) >, > or unsubscribe > < https://github.com/notifications/unsubscribe-auth/AIAR4NWGQKAEAPNUNH24MKDRZSYCXANCNFSM4OO5NI2Q > > . > — 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/AKB6OEJQIA2VCWYPMK7BNHLRZSYQRANCNFSM4OO5NI2Q> .

RAVIKIRAN-DHULIPALA · 2020-07-03T03:08:51Z

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 > > >> > > < > > >> > > > >> > > > https://github.com/notifications/unsubscribe-auth/AKB6OEN7FBIVSNOYNROFHMDRZSMI7ANCNFSM4OO5NI2Q > > >> > > > > >> > > . > > >> > > > > >> > > > >> > — > > >> > You are receiving this because you commented. > > >> > > > >> > > > >> > Reply to this email directly, view it on GitHub > > >> > < > > >> > > > #3 (comment) > > >> >, > > >> > or unsubscribe > > >> > < > > >> > > > https://github.com/notifications/unsubscribe-auth/AIAR4NVZLY6LVCGB3B3Q5L3RZST4HANCNFSM4OO5NI2Q > > >> > > > >> > . > > >> > > > >> > > >> — > > >> 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/AKB6OEJD5PVX6HMCI474RW3RZSUIRANCNFSM4OO5NI2Q > > > > > >> . > > >> > > > > > > > — > > You are receiving this because you commented. > > > > > > Reply to this email directly, view it on GitHub > > < > #3 (comment) > >, > > or unsubscribe > > < > https://github.com/notifications/unsubscribe-auth/AIAR4NWGQKAEAPNUNH24MKDRZSYCXANCNFSM4OO5NI2Q > > > > . > > > > — > 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/AKB6OEJQIA2VCWYPMK7BNHLRZSYQRANCNFSM4OO5NI2Q > > . > — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#3 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AIAR4NWZKTBASPFCSRCKDZDRZTLJ5ANCNFSM4OO5NI2Q> .

clownpreet22 · 2020-07-03T03:56:08Z

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 > > > >> > > > < > > > >> > > > > > >> > > > > >> > > > > > > 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 > > > >> > > < > > > >> > > > > >> > > > > > > https://github.com/notifications/unsubscribe-auth/AKB6OEN7FBIVSNOYNROFHMDRZSMI7ANCNFSM4OO5NI2Q > > > >> > > > > > >> > > . > > > >> > > > > > >> > > > > >> > — > > > >> > You are receiving this because you commented. > > > >> > > > > >> > > > > >> > Reply to this email directly, view it on GitHub > > > >> > < > > > >> > > > > > > #3 (comment) > > > >> >, > > > >> > or unsubscribe > > > >> > < > > > >> > > > > > > https://github.com/notifications/unsubscribe-auth/AIAR4NVZLY6LVCGB3B3Q5L3RZST4HANCNFSM4OO5NI2Q > > > >> > > > > >> > . > > > >> > > > > >> > > > >> — > > > >> 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/AKB6OEJD5PVX6HMCI474RW3RZSUIRANCNFSM4OO5NI2Q > > > > > > > >> . > > > >> > > > > > > > > > > — > > > You are receiving this because you commented. > > > > > > > > > Reply to this email directly, view it on GitHub > > > < > > > #3 (comment) > > >, > > > or unsubscribe > > > < > > > https://github.com/notifications/unsubscribe-auth/AIAR4NWGQKAEAPNUNH24MKDRZSYCXANCNFSM4OO5NI2Q > > > > > > . > > > > > > > — > > 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/AKB6OEJQIA2VCWYPMK7BNHLRZSYQRANCNFSM4OO5NI2Q > > > > . > > > > — > You are receiving this because you commented. > Reply to this email directly, view it on GitHub > < #3 (comment) >, > or unsubscribe > < https://github.com/notifications/unsubscribe-auth/AIAR4NWZKTBASPFCSRCKDZDRZTLJ5ANCNFSM4OO5NI2Q > > . > — 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/AKB6OEPNBQJ6EFMSF7VA4BDRZVDVBANCNFSM4OO5NI2Q> .

RAVIKIRAN-DHULIPALA · 2020-07-03T04:06:09Z

I'm sorry I couldn't find any images

clownpreet22 · 2020-07-03T04:10:05Z

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> .

RAVIKIRAN-DHULIPALA · 2020-07-03T04:16:05Z

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 <https://github.com/notifications/unsubscribe-auth/AIAR4NX4OSH374CVDJHEZADRZVK2TANCNFSM4OO5NI2Q> .

clownpreet22 · 2020-07-03T04:22:17Z

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 > < https://github.com/notifications/unsubscribe-auth/AIAR4NX4OSH374CVDJHEZADRZVK2TANCNFSM4OO5NI2Q > > . > — 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/AKB6OELWFA47ZEAURM7C6KTRZVLRDANCNFSM4OO5NI2Q> .

vinayakpandi · 2020-11-16T18:05:51Z

@clownpreet22 - Were you able to resolve the shape issue...if so could you please guide me?

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face mask detection perameter #3

face mask detection perameter #3

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