jasmcaus / opencv-course Goto Github PK
View Code? Open in Web Editor NEWLearn OpenCV in 4 Hours - Code used in my Python and OpenCV course on freeCodeCamp.
Home Page: https://youtu.be/oXlwWbU8l2o
License: MIT License
Learn OpenCV in 4 Hours - Code used in my Python and OpenCV course on freeCodeCamp.
Home Page: https://youtu.be/oXlwWbU8l2o
License: MIT License
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
I want to contribute with you on this project. But I can't create pull request.
I forked it to.
# 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?
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'`
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".
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'
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
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:
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)
@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))
split_data = caer.train_val_split(featureSet, labels, val_ratio=.2)
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)
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)
The model creation part of the course at 3:31:00 raises some errors due to the new versions of TensorFlow changing some things around with how optimizers work. I recommend modifying the simpsons.py file as specified by this answer here.
The issue is specifically with this code in the tutorial:
model = canaro.models.createSimpsonsModel(IMG_SIZE=imageSize, channels=channels, output_dim=len(characters),
loss='binary_crossentropy', decay=1e-7, learning_rate=0.001, momentum=0.9,
nesterov=True)
Which can be fixed by adding this line in the simpsons.py file right before the return line:
import tensorflow as tf
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate=0.01,
decay_steps=10000,
decay_rate=0.9)
optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)
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.
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
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
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
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)
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, 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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
Make sure all arrays contain the same number of samples.
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)
Execution breaks at
ValueError:
x(images tensor) and
y (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.
In the image in the face_detect.py file, the number of faces is 26. But the program counts 19.
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()
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