Device Recommendation – Best camera and protocol for embedded real-time CNN project

It has something to do with photography, so I hope this fits into this forum.

I want to develop a standalone real-time CNN application for outdoor imaging and cannot deal with the myriad of camera options and their communication protocols.
The goal is a Linux embedded device (e.g. Beaglebone + Edge TPU). and the top language is python. (if it is inevitable, I can write a c ++ driver)

I have done a number of projects with webcams using OpenCV. However, this project requires a more serious camera with motorized zoom and auto focus.
with at least 3MP and 30fps.

  1. There are numerous Chinese IP camera blocks with zoom functions.
    but they're streaming H.265, and I'm not sure how OpenCV would handle it.
    It is also not clear how the zoom function in one of them should be controlled via Python.
  2. Then there are USB2 camera blocks that seem to be of lower but adequate quality, but I couldn't find one with motorized zoom.
  3. Then there are the cameras & # 39; Gige & # 39; and & # 39; USB3 Vision & # 39; which appear optimal, but are prohibitively expensive (over $ 500).

This is kind of an open question, but I couldn't find a lot of information on the subject online, so I hope to find wisdom here.

So I wondered if anyone has any advice or recommendations.

Unit – access to the intermediate layers of a tensorflow CNN model?

I am new to Unity scripting and am doing a project that captures video from my laptop's camera and then displays a heat map in AR using the information at an intermediate level from a CNN model.

I've checked out tutorials online, but I've never seen anyone speak about accessing intermediate levels from a model.

Basically, I want to show this in Unity.

    intermediate_layer_model = Model(inputs=model.input,
                                     outputs=model.get_layer(layer_name).output)

    layer_output = intermediate_layer_model.predict(Image)

Is that possible and can someone point me in the right direction?

Cheers.

Machine Learning – CNN Model for CIFAR-10?

Does anyone know of a pre-trained model for the CIFAR 10 data set? I've seen how to get the datasets with different flavors from

 ResourceData("CIFAR-10")

But I can't find a CNN model for it. Yes, there is a sample notebook that trains a logistic regression classifier, but I've seen examples that say that a CNN can potentially achieve accuracies in excess of 90%. When training with 10,000 samples, the logistic regression is 84.8% accurate, which is good but below some reported CNN models. I use CIFAR-10_1_examples.nb for the data I have reported.

Python – VGG19 codes from a CNN model

I've just read some tutorials on how to create a CNN and read some things about VGG16 and VGG19. Can someone check my code if it is correct? I have no mistakes, I just want to know if I did it right, because I handled this code like Idk, all I wanted to make it work

model = Sequential()

model.add(Conv2D(64, (3, 3), input_shape=X.shape(1:)))
model.add(Activation('relu'))
padding='same'
model.add(Conv2D(64, (3, 3), input_shape=X.shape(1:)))
model.add(Activation('relu'))
padding='same'
model.add(MaxPooling2D(pool_size=(1, 1)))

model.add(Conv2D(128, (3, 3), input_shape=X.shape(1:)))
model.add(Activation('relu'))
padding='same'
model.add(Conv2D(128, (3, 3), input_shape=X.shape(1:)))
model.add(Activation('relu'))
padding='same'
model.add(MaxPooling2D(pool_size=(1, 1)))

model.add(Conv2D(256, (3, 3), input_shape=X.shape(1:)))
model.add(Activation('relu'))
padding='same'
model.add(Conv2D(256, (3, 3), input_shape=X.shape(1:)))
model.add(Activation('relu'))
padding='same'
model.add(Conv2D(256, (3, 3), input_shape=X.shape(1:)))
model.add(Activation('relu'))
padding='same'
model.add(Conv2D(256, (3, 3), input_shape=X.shape(1:)))
model.add(Activation('relu'))
padding='same'
model.add(MaxPooling2D(pool_size=(1, 1)))

model.add(Conv2D(512, (3, 3), input_shape=X.shape(1:)))
model.add(Activation('relu'))
padding='same'
model.add(Conv2D(512, (3, 3), input_shape=X.shape(1:)))
model.add(Activation('relu'))
padding='same'
model.add(Conv2D(512, (3, 3), input_shape=X.shape(1:)))
model.add(Activation('relu'))
padding='same'
model.add(Conv2D(512, (3, 3), input_shape=X.shape(1:)))
model.add(Activation('relu'))
padding='same'
model.add(MaxPooling2D(pool_size=(1, 1)))

model.add(Conv2D(512, (3, 3), input_shape=X.shape(1:)))
model.add(Activation('relu'))
padding='same'
model.add(Conv2D(512, (3, 3), input_shape=X.shape(1:)))
model.add(Activation('relu'))
padding='same'
model.add(Conv2D(512, (3, 3), input_shape=X.shape(1:)))
model.add(Activation('relu'))
padding='same'
model.add(Conv2D(512, (3, 3), input_shape=X.shape(1:)))
model.add(Activation('relu'))
padding='same'
model.add(MaxPooling2D(pool_size=(1, 1)))

model.add(Flatten())

model.add(Dense(64))
model.add(Activation('relu'))

model.add(Dense(1))
model.add(Activation('sigmoid'))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=('accuracy'))
model.fit(X, y, batch_size=15, epochs=1, validation_split=0.1)

from sklearn.metrics import confusion_matrix
pred = model.predict(X)
pred = np.round(pred)

conf = confusion_matrix(y, pred)

import seaborn as sns
sns.heatmap(conf, annot=True)

plt.show()

model.save('64x2-CNN.model')

Devin Nunes sued CNN for reporting lies about him. Nicholas Sandman will soon be wealthy on CNN lies. In fact, fake news?

He does not sue her and it would be incredibly stupid to do that. The US Defamation Law makes it virtually impossible for a person like Nunes to sue for defamation. The bigger problem for Nunes is that the truth of the allegations is defense against defamation. If he sued CNN, they automatically got the right to inform him about his dealings with Ukraine and to subpoena virtually all of his records that could theoretically relate to it. It would invite CNN to engage with the court in depth to gain access to all relevant documents and to Nunes himself. Do you see how Nunes just refused to answer CNN when they tried to question him about his activities in Ukraine? If he sued her, he could not do that. The lawyers of CNN could sell him extensively. He would have to answer her questions and he would have to do it truthfully. An example of the futility of sueing the media is Nunes God, Trump. A few years ago, a journalist by the name of Timothy O & Brien wrote a book in which he estimated the value of Trump at only about $ 150 million. Trump, who has falsely claimed to be a billionaire for decades, sued. The lawsuit was eventually dismissed, but not before O & Brien saw Trump's tax returns. To date, he and his lawyers are among the few people outside of Trump's orbit who have seen his tax returns.

Would you be happy if CNN, MSNBC, CBS, ABC, NBC, NYT and all other MSMs were banned in the US?

Then we would only have right propaganda. I will get REAL news, thank you.

It's really strange that the only way Trumpsters can cover their heads with fake reality is to assume that ANY OTHER MESSAGE ORGANIZATION LIES ON THE PLANET. This includes international news organizations like BBC News.

Is Anderson Cooper of CNN anti-white?

Disgusting, right?

It is now fashionable to openly celebrate the decline of a particular race in the country.

When the Democrats passed the Hart Cellar Act of 1965, they assured the Senate that the law would not change the country's ethnic composition. But now they say that this is a good thing.

If this is the policy of the Democrats, why should not whites organize and start the policy of white identity and take care of our racial interests?

Anderson Cooper could be white. They are elites who live 97 percent in lily-white quarters. They do not experience the non-white quarters.

They signal virtue for money in MSM.

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