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Learn OpenCV in 4 Hours - Code used in my Python and OpenCV course on freeCodeCamp.

Home Page: https://youtu.be/oXlwWbU8l2o

License: MIT License

Python 62.91% Jupyter Notebook 37.09%

opencv opencv-course opencv-python python faces face-recognition face-detection concepts caer recognition

opencv-course's Introduction

OpenCV with Python in 4 Hours

Notes and code used in my Python and OpenCV course on freeCodeCamp.org. You can find me on Twitter for more info on courses I'm working on currently.

Important Updates:

caer.train_val_split() is a deprecated feature in caer. Use sklearn.model_selection.train_test_split() instead. See #9 for more details.

Course Outline (with timestamps)

1. Installation

Besides installing OpenCV, we cover the installation of the following package:

Caer is a lightweight, high-performance Vision library for high-performance AI research. It simplifies your approach towards Computer Vision by abstracting away unnecessary boilerplate code giving you the flexibility to quickly prototype deep learning models and research ideas.

$ pip install caer

2. Basic Concepts:

Reading Images and Video (0:04:12)
Resizing and Rescaling Images and Video Frames (0:12:57)
Drawing Shapes and Placing text on images (0:20:21)
5 Essential Methods in OpenCV (0:31:55)
Image Transformations (0:44:13)
Contour Detection (0:57:06)

3. Advanced Concepts:

Switching between Colour Spaces (RGB, BGR, Grayscale, HSV and Lab) (1:12:53)
Splitting and Merging Colour Channels (1:23:10)
Blurring (1:31:03)
BITWISE operations (1:44:27)
Masking (1:53:06)
Histogram Computation (2:01:43)
Thresholding/Binarizing Images (2:15:22)
Advanced Edge Detection (2:26:27)

4. Face Detection and Recognition

Face Detection using Haar Cascades (2:35:25)
Face Recognition using OpenCV's LBPHFaceRecognizer algorithm (2:49:05)

5. Capstone: Deep Computer Vision

Building a Deep Computer Vision model to classify between the characters in the popular TV series The Simpsons (3:11:57)

Credits

The images in the Photos and Videos folders were downloaded from Unsplash and Pixabay, unless otherwise mentioned.

The images in the Faces folder were procurred from a repo on Kaggle.

opencv-course's People

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opencv-course's Issues

module 'sklearn' has no attribute 'model_selection'

In the simpsons project when we are splitting the test and train data you have used "sklearn.model_selection" I was getting the error "module 'sklearn' has no attribute 'model_selection'", the fix is very simple just add "import sklearn.model_selection".

Underestimate the number of faces

In the image in the face_detect.py file, the number of faces is 26. But the program counts 19.

(-215:Assertion failed) s >= 0 in function 'cv::setSize'

I was following your Youtube tutorial but got stuck in this particular problem. I searched a lot but couldn't find any solution.

Video with Timestamp - https://youtu.be/oXlwWbU8l2o?t=10728

Traceback (most recent call last): File "face_recognize.py", line 43, in <module> face_recognizer.train(features, labels) cv2.error: OpenCV(4.4.0) C:\Users\appveyor\AppData\Local\Temp\1\pip-req-build-8ely825f\opencv\modules\core\src\matrix.cpp:235: error: (-215:Assertion failed) s >= 0 in function 'cv::setSize'

Program error

I downloaded the Simpsons dataset, and then implemented the Deep Computer Vision: The Simpsons codes in Jupyter. But when training the model, I'm getting an error.

Error description:

Epoch 1/10
345/345 [==============================] - ETA: 0s - loss: 0.3237 - accuracy: 0.1610

ValueError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_2992/1828796816.py in
3 '''
4
----> 5 training=model.fit(train_gen,
6 steps_per_epoch=len(x_train)//BATCH_SIZE,
7 epochs=EPOCHS,

c:\users\niladri\appdata\local\programs\python\python39\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1199 # Create data_handler for evaluation and cache it.
1200 if getattr(self, '_eval_data_handler', None) is None:
-> 1201 self._eval_data_handler = data_adapter.get_data_handler(
1202 x=val_x,
1203 y=val_y,

c:\users\niladri\appdata\local\programs\python\python39\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py in get_data_handler(*args, **kwargs)
1362 if getattr(kwargs["model"], "_cluster_coordinator", None):
1363 return _ClusterCoordinatorDataHandler(*args, **kwargs)
-> 1364 return DataHandler(*args, **kwargs)
1365
1366

c:\users\niladri\appdata\local\programs\python\python39\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py in init(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution, distribute)
1152 adapter_cls = select_data_adapter(x, y)
1153 self._verify_data_adapter_compatibility(adapter_cls)
-> 1154 self._adapter = adapter_cls(
1155 x,
1156 y,

c:\users\niladri\appdata\local\programs\python\python39\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py in init(self, x, y, sample_weights, sample_weight_modes, batch_size, epochs, steps, shuffle, **kwargs)
256
257 num_samples = set(int(i.shape[0]) for i in nest.flatten(inputs)).pop()
--> 258 _check_data_cardinality(inputs)
259
260 # If batch_size is not passed but steps is, calculate from the input data.

c:\users\niladri\appdata\local\programs\python\python39\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py in _check_data_cardinality(data)
1628 label, ", ".join(str(i.shape[0]) for i in nest.flatten(single_data)))
1629 msg += "Make sure all arrays contain the same number of samples."
-> 1630 raise ValueError(msg)
1631
1632

ValueError: Data cardinality is ambiguous:
x sizes: 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 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10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10
Make sure all arrays contain the same number of samples.

I have copied and pasted the entire errors screen from Jupyter. I would be glad if someone explained me where the error occurs.

Thank you

ValueError: Traceback (most recent call last)

# Create our model (returns a compiled model)
model = canaro.models.createSimpsonsModel(IMG_SIZE=IMG_SIZE, channels=channels, output_dim=len(characters), 
                                         loss='binary_crossentropy', decay=1e-7, learning_rate=0.001, momentum=0.9,
                                         nesterov=True)

ValueError: decay is deprecated in the new Keras optimizer, pleasecheck the docstring for valid arguments, or use the legacy optimizer, e.g., tf.keras.optimizers.legacy.SGD.

how to fix this?

Execution of the simpsons script breaks

Execution breaks at

opencv-course/Section #4 - Capstone/simpsons.py

Line 75 in 76e3dc5

train_gen = datagen.flow(x_train, y_train, batch_size=BATCH_SIZE)

with
ValueError: x(images tensor) andy (labels) should have the same length. Found: x.shape = (80, 80, 1), y.shape = (11047, 10)

At this point, I've not investigated and have no idea why. I encountered the problem following the 4 hour video course and reproducing it on Kaggle.

should we use caer.train_val_split or sklearn.model_selection when splitting data on line 62?

@jasmcaus pls help!

import matplotlib.pyplot as plt
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.callbacks import LearningRateScheduler
from sklearn.model_selection import train_test_split

IMG_SIZE = (80,80)
@@ -58,9 +59,10 @@
labels = to_categorical(labels, len(characters))

Creating train and validation data

split_data = caer.train_val_split(featureSet, labels, val_ratio=.2)

this produces error:

InvalidArgumentError Traceback (most recent call last)
in
47 validation_data=(x_val,y_val),
48 validation_steps=len(y_val)//BATCH_SIZE,
---> 49 callbacks = callbacks_list)
50
51 characters

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1098 _r=1):
1099 callbacks.on_train_batch_begin(step)
-> 1100 tmp_logs = self.train_function(iterator)
1101 if data_handler.should_sync:
1102 context.async_wait()

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in call(self, *args, **kwds)
826 tracing_count = self.experimental_get_tracing_count()
827 with trace.Trace(self._name) as tm:
--> 828 result = self._call(*args, **kwds)
829 compiler = "xla" if self._experimental_compile else "nonXla"
830 new_tracing_count = self.experimental_get_tracing_count()

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
886 # Lifting succeeded, so variables are initialized and we can run the
887 # stateless function.
--> 888 return self._stateless_fn(*args, **kwds)
889 else:
890 _, _, _, filtered_flat_args = \

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in call(self, *args, **kwargs)
2941 filtered_flat_args) = self._maybe_define_function(args, kwargs)
2942 return graph_function._call_flat(
-> 2943 filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access
2944
2945 @Property

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1917 # No tape is watching; skip to running the function.
1918 return self._build_call_outputs(self._inference_function.call(
-> 1919 ctx, args, cancellation_manager=cancellation_manager))
1920 forward_backward = self._select_forward_and_backward_functions(
1921 args,

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager)
558 inputs=args,
559 attrs=attrs,
--> 560 ctx=ctx)
561 else:
562 outputs = execute.execute_with_cancellation(

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
58 ctx.ensure_initialized()
59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
62 if name is not None:

InvalidArgumentError: Can not squeeze dim[2], expected a dimension of 1, got 10
[[node binary_crossentropy/remove_squeezable_dimensions/Squeeze (defined at :49) ]] [Op:__inference_train_function_35716]

Function call stack:
train_function

OR
split_data = train_test_split(featureSet, labels, val_ratio=.2)

produces sequential model and error using model.fit method

The following code from section 4 of the openCV tutorial produces a sequential model instead of functional model as mentioned in the video, why is this?

Also the following code produces the error as follows:
#train the model
from tensorflow.keras.callbacks import LearningRateScheduler
callbacks_list = [LearningRateScheduler(canaro.lr_schedule)]
training = model.fit(train_gen,
steps_per_epoch=len(x_train)//BATCH_SIZE,
epochs=EPOCHS,
validation_data=(x_val,y_val),
validation_steps=len(y_val)//BATCH_SIZE,
callbacks = callbacks_list)

produces the following error, I can only see that more parameters are needed in the call to model.fit() or that the error says data cardinality is ambiguous and the tutorial needs to be updated to make sure all arrays contain the same number of samples. How do we do this?

ValueError Traceback (most recent call last)
in
7 validation_data=(x_val,y_val),
8 validation_steps=len(y_val)//BATCH_SIZE,
----> 9 callbacks = callbacks_list)

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1128 use_multiprocessing=use_multiprocessing,
1129 model=self,
-> 1130 steps_per_execution=self._steps_per_execution)
1131 val_logs = self.evaluate(
1132 x=val_x,

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py in init(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution)
1110 use_multiprocessing=use_multiprocessing,
1111 distribution_strategy=ds_context.get_strategy(),
-> 1112 model=model)
1113
1114 strategy = ds_context.get_strategy()

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py in init(self, x, y, sample_weights, sample_weight_modes, batch_size, epochs, steps, shuffle, **kwargs)
272
273 num_samples = set(int(i.shape[0]) for i in nest.flatten(inputs)).pop()
--> 274 _check_data_cardinality(inputs)
275
276 # If batch_size is not passed but steps is, calculate from the input data.

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py in _check_data_cardinality(data)
1527 label, ", ".join(str(i.shape[0]) for i in nest.flatten(single_data)))
1528 msg += "Make sure all arrays contain the same number of samples."
-> 1529 raise ValueError(msg)
1530
1531

ValueError: Data cardinality is ambiguous:
x sizes: 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 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y sizes: 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 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10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10
Make sure all arrays contain the same number of samples.

execution of line 63 produces error

TypeError Traceback (most recent call last)
in
59
60 # Creating train and validation data
---> 61 split_data = train_test_split(featureSet, labels, val_ratio=.2)
62 x_train, x_val, y_train, y_val = (np.array(item) for item in split_data)
63

/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_split.py in train_test_split(*arrays, **options)
2123
2124 if options:
-> 2125 raise TypeError("Invalid parameters passed: %s" % str(options))
2126
2127 arrays = indexable(*arrays)

TypeError: Invalid parameters passed: {'val_ratio': 0.2}

Has something to do with line 63, and change to train_test_split function:

Creating train and validation data

split_data = train_test_split(featureSet, labels, val_ratio=.2)
x_train, x_val, y_train, y_val = (np.array(item) for item in split_data)

Contribute

I want to contribute with you on this project. But I can't create pull request.
I forked it to.

Can't find file Boston.jpg

In Lesson 4, he used boston.jpg for some effects which I was unable to find, which was changed at 33:36 in the course

Face Recognition, AttributeError: module 'cv2.cv2' has no attribute 'face_LBPHFaceRecognizer'

I have this error, and I can't resolve this issue

This is the program

import os
import cv2 as cv
import numpy as np

#people = ['...','....']

people = []
for i in os.listdir(r'C:\Users\owner\_Learning_\_Computer Vision - OpenCV_\OpenCV - Python\OpenCV Course - Full Tutorial with Python - YouTube\Resources\Faces\train'):
    people.append(i)
print(people)

DIR = r'C:\Users\owner\_Learning_\_Computer Vision - OpenCV_\OpenCV - Python\OpenCV Course - Full Tutorial with Python - YouTube\Resources\Faces\train'

features = []
labels = []
haar_cascade = cv.CascadeClassifier('haarcascade_frontalface_default.xml')

def create_train():
    for person in people:
        print("Person:", person)
        path = os.path.join(DIR, person)
        label = people.index(person)
        #print("Label:", label)
        
        for img in os.listdir(path):
            img_path = os.path.join(path,img)
            #print("img_path:", img_path)
            
            img_array = cv.imread(img_path)
            gray = cv.cvtColor(img_array, cv.COLOR_BGR2GRAY)
            
            faces_rect = haar_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=4)
            
            for (x,y,w,h) in faces_rect:
                faces_roi = gray[y:y+h, x:x+w]
                
                features.append(faces_roi)
                labels.append(label)
                
                #cv.imshow(img, faces_roi)
        
create_train()
print('Training Done-------------')
#print(f'length of features = {len(features)}')
#print(f'length of labels = {len(labels)}')

features = np.array(features, dtype='object')
labels = np.array(labels)

face_recognizer = cv.face.LBPHFaceRecognizer_create()

#train
face_recognizer.train(features, labels)

face_recognizer.save('3.2_face_trained.yml')
np.save('3.2_features.py', features)
np.save('3.2_labels.py', labels)



cv.waitKey(0)
cv.destroyAllWindows()

Can't obtain more than 30% accuracy in the training model

I have followed all the steps and have also replaced the deprecated methods with the new method as it has been instructed.
But still, I am not getting more than 30% accuracy while training model.

Please help @jasmcaus

Error in Capstone Project Code

There is an error in the Capstone Project Code ('Section #4 - Capstone/simpsons.ipynb').

x_train, x_val, y_train, y_val = caer.train_test_split(featureSet, labels, val_ratio=.2)

This should be
x_train, x_val, y_train, y_val = caer.train_val_split(featureSet, labels, val_ratio=.2)

your scipt is not working sinse the standards all changed and im stuck!

I want to detect my own image against image of me and script not working can you make a script that uses a live ip cam (mp4) and just looks for 1 authorized image a pic (jpg) and echo matched when matched and not matched if not matched please? i cant do it and i dont know how to do it!

im on linux and use python 3

using your script i get error

python3 ./faces_train.py authorized/
Training done ---------------
Traceback (most recent call last):
File "./faces_train.py", line 49, in
recognizer.train(features,labels)
TypeError: src data type = 17 is not supported

as iv tried making it work with the new way that it works


#!/usr/bin/env python3
#pylint:disable=no-member
#  python3 ./faces_train.py authorized/
import os
import cv2 as cv
import numpy as np

people = ['Jaymee']
DIR = 'authorized'

haar_cascade = cv.CascadeClassifier('haar_face.xml')

features = []
labels = []

def rescaleFrame(frame, scale=0.75):
    # Images, Videos and Live Video
    width = int(frame.shape[1] * scale)
    height = int(frame.shape[0] * scale)

    dimensions = (width,height)

    return cv.resize(frame, dimensions, interpolation=cv.INTER_AREA)

def changeRes(width,height):
    # Live video
    capture.set(3,width)
    capture.set(4,height)
    
def create_train():
    for person in people:
        path = os.path.join(DIR, person)
        label = people.index(person)

        for img in os.listdir(path):
            img_path = os.path.join(path,img)

            img_array = cv.imread(img_path)
            if img_array is None:
                continue 
                
            gray = cv.cvtColor(img_array, cv.COLOR_BGR2GRAY)

            faces_rect = haar_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=4)

            for (x,y,w,h) in faces_rect:
                faces_roi = gray[y:y+h, x:x+w]
                features.append(faces_roi)
                labels.append(label)

create_train()
print('Training done ---------------')

features = np.array(features, dtype='object')
labels = np.array(labels)

#face_recognizer = cv.face.LBPHFaceRecognizer_create()
recognizer = cv.face.LBPHFaceRecognizer_create()
# if you get arror on above line, do following setup!
# sudo python3 -m pip install opencv-contrib-python==3.3.0.9

# Train the Recognizer on the features list and the labels list
recognizer.train(features,labels)
# https://ninghang.blogspot.com/2012/11/list-of-mat-type-in-opencv.html
#
# (supported > single-channel, 8-bit or 32-bit floating point)
# which means that the type must be either CV_8UC1 or CV_32FC1

recognizer.save('face_trained.yml')
np.save('features.npy', features)
np.save('labels.npy', labels)

VSCode suggestions from capture variable

When creating capture instance I wanted to see suggested methods and properties on VSCode.
Anyway to achieve that?

capture = cv.VideoCapture('Resources/Videos/dog.mp4')

Unable to create model

I got to the point in the course where I was instructed to create a model using this code:
model = canaro.models.createSimpsonsModel(IMG_SIZE=IMG_SIZE, channels=channels, output_dim=len(characters), loss='binary_crossentropy', decay=1e-7, learning_rate=0.001, momentum=0.9, nesterov=True)

But I received this error:
`---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
in
2 model = canaro.models.createSimpsonsModel(IMG_SIZE=IMG_SIZE, channels=channels, output_dim=len(characters),
3 loss='binary_crossentropy', decay=1e-7, learning_rate=0.001, momentum=0.9,
----> 4 nesterov=True)

TypeError: createSimpsonsModel() got an unexpected keyword argument 'loss'`

module 'caer' has no attribute 'reshape'

I am getting this error

~/PythonProjects/ImageAndVideo/simpsons.py in prepare(img)
      94     img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
      95     img = cv.resize(img, IMG_SIZE)
----> 96     img = caer.reshape(img, IMG_SIZE, 1)
      97     return img

jasmcaus / opencv-course Goto Github PK

opencv-course's Introduction

OpenCV with Python in 4 Hours

Important Updates:

Course Outline (with timestamps)

1. Installation

2. Basic Concepts:

3. Advanced Concepts:

4. Face Detection and Recognition

5. Capstone: Deep Computer Vision

Credits

opencv-course's People

Contributors

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opencv-course's Issues

Error description:

Epoch 1/10 345/345 [==============================] - ETA: 0s - loss: 0.3237 - accuracy: 0.1610

Creating train and validation data

this produces error:

produces the following error, I can only see that more parameters are needed in the call to model.fit() or that the error says data cardinality is ambiguous and the tutorial needs to be updated to make sure all arrays contain the same number of samples. How do we do this?

Creating train and validation data

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Epoch 1/10
345/345 [==============================] - ETA: 0s - loss: 0.3237 - accuracy: 0.1610