Deep Learning for Computer Vision, by Packt

License: MIT License

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deep-learning-for-computer-vision's Introduction

Deep-Learning-for-Computer-Vision

Code repository for Deep Learning for Computer Vision, by Packt

This is the code repository for Deep Learning for Computer Vision, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

About the Book

Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning.

Instructions and Navigation

All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.

The code will look like the following:

merged_summary_operation = tf.summary.merge_all()
train_summary_writer = tf.summary.FileWriter('/tmp/train' , session.graph)
test_summary_writer = tf.summary.FileWriter('/tmp/test' )

The examples covered in this book can be run with Windows, Ubuntu, or Mac. All the installation instructions are covered. Basic knowledge of Python and machine learning is required. It's preferable that the reader has GPU hardware but it's not necessary

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deep-learning-for-computer-vision's Issues

3_satellite from Chapter 5

Dear author,

I have encountered an error when running your code for modelling FCN for segmentation.

Traceback (most recent call last): File "d:\git\Deep-Learning-for-Computer-Vision\Chapter05\3_satellite.py", line 14, in <module> resnet50_model = ResNet50(include_top=False, weights='imagenet', input_tensor=input_tensor) File "C:\Users\gesu270495\AppData\Local\Programs\Python\Python36\lib\site-packages\keras_applications\resnet50.py", line 212, in ResNet50 x = layers.ZeroPadding2D(padding=(3, 3), name='conv1_pad')(img_input) File "C:\Users\gesu270495\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\base_layer.py", line 446, in __call__ previous_mask = _collect_previous_mask(inputs) File "C:\Users\gesu270495\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\base_layer.py", line 1328, in _collect_previous_mask mask = node.output_masks[tensor_index] AttributeError: 'Node' object has no attribute 'output_masks'

So far, I haven't been able to fix it. Any suggestions about what might cause this error would help.

Best Regards,

Shirll

ValueError in Chapter08/1_style_transfer.py

In Chapter 8, I modified vgg16_avg.py to match keras 2.0.

But I get an error on line 100 in 1_style_transfer.py, but I do not know how to fix it.

line 100 is as follows,

style_loss = sum(style_mse_loss(l1[0], l2[0]) for l1, l2 in zip(style_features, style_targets))

And the error is as follows,

ValueError: Dimensions must be equal, but are 64 and 128 for 'add_1' (op: 'Add') with input shapes: [64], [128].

I would appreciate it if you could tell me how to fix the error.

8_cat_vs_dog_fine_tune.py

I cannot find out 'fc_model.h5'.
Where can i find it out?

Nerve Segmentation from Chapter 5

Dear author;
I got this error when running the code:
ValueError: Negative dimension size caused by subtracting 3 from 2 for 'conv2d_93/Conv2D' (op: 'Conv2D') with input shapes: [?,2,2,256], [3,3,256,512].

and also :
TypeError: init() missing 1 required positional argument: 'kernel_size'

which tensorflow version used in this book?

about the categorical_crossentropy in line 51

Dear author,
the code 7_cat_vs_dog_bottleneck.py
I encountered an error when running your code. This issue is related to the line 51. However, after changing categorical_crossentropy to binary_crossentropy the problem was solved.

Could you check this problem?
Thanks very much!

Best,
Helen

Chapter3 Serving Client Solution

1> Conda Environment Configuration
#tensorflow 2.0 Environment Configuration (Client)
#conda create -n keras keras tensorflow
#source activate keras
#pip install numpy scipy scikit-learn pillow h5py
#pip install grpcio
#pip install tensorflow-serving-api
#pip install tensorflow_datasets
#conda deactivate

2> Docker Server Environment Configuration
------------------------- DockerFile --------------------------
FROM ubuntu:16.04

RUN apt-get update && apt-get install -y software-properties-common && add-apt-repository ppa:deadsnakes/ppa &&
apt-get update && apt-get install -y python3.6 python3.6-dev python3-pip git

RUN ln -sfn /usr/bin/python3.6 /usr/bin/python3 && ln -sfn /usr/bin/python3 /usr/bin/python && ln -sfn /usr/bin/pip3 /usr/bin/pip

docker build -t docker-ubuntu16-python3.6 .
docker run -it docker-ubuntu16-python3.6 bash
apt-get install pkg-config zip g++ zlib1g-dev unzip

apt-get install wget
wget https://github.com/bazelbuild/bazel/releases/download/0.15.0/bazel-0.15.0-installer-linux-x86_64.sh
chmod +x bazel-0.15.0-installer-linux-x86_64.sh
./bazel-0.15.0-installer-linux-x86_64.sh
export PATH="$PATH:$HOME/bin"
apt-get install sudo
sudo apt-get update && sudo apt-get install -y
automake
build-essential
curl
libcurl3-dev
git
libtool
libfreetype6-dev
libpng12-dev
libzmq3-dev
pkg-config
python-dev
python-numpy
python-pip
software-properties-common
swig
zip
zlib1g-dev
pip install --upgrade pip
pip install grpcio
git clone --recursive https://github.com/tensorflow/serving
echo "deb [arch=amd64] http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal" | sudo tee /etc/apt/sources.list.d/tensorflow-serving.list
curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | sudo apt-key add -
sudo apt-get update && sudo apt-get install tensorflow-model-server
pip install tensorflow-serving-api
cd serving/
python tensorflow_serving/example/mnist_saved_model.py /tmp/mnist_model

tensorflow_model_server --port=9000 --model_name=mnist --model_base_path=/tmp/mnist_model

3> Source Customization (TensorFlow 2.0)
from grpc.beta import implementations
import numpy
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2
import matplotlib.pyplot as plt
import os

mnist = tfds.load("mnist", shuffle_files=False, as_supervised=False)
mnist.keys()

print(mnist.keys())

concurrency = 1
num_tests = 100
host = ''
port = 9000
work_dir = './tmp/4'

try:
if not(os.path.isdir(work_dir)):
os.makedirs(os.path.join(work_dir))
except OSError:
print('Failed to create directory!' + work_dir)

def _create_rpc_callback():
def _callback(result):
response = numpy.array(
result.result().outputs['y'].float_val)
prediction = numpy.argmax(response)
print(prediction)
return _callback

train_data, test_data = mnist["train"], mnist["test"]
#test_data_set = mnist["test"]
#test_image = mnist.test.images[0]

train_data, test_data = tfds.load(
"mnist",
split=(tfds.Split.TRAIN, tfds.Split.TEST),
shuffle_files=False, as_supervised=False
)
print(train_data.output_types)
print(train_data.output_shapes)

data = []
iterator = iter(train_data.batch(2))
data.append(next(iterator))
data.append(next(iterator))
data.append(next(iterator))

print(data[0].keys())
print(data[0]["image"].shape)
print(data[0]["label"].shape)

plt.figure(figsize=(10,2))
for i in range(3):
for j in range(2):
plt.subplot(1,6,2*i+j+1)
plt.imshow(data[i]["image"][j,:,:,0])
plt.axis("off")
#plt.tight_layout()
#plt.show()

print(data[0]["label"].numpy(), data[1]["label"].numpy(), data[2]["label"].numpy())

test_image = data[0]["image"].numpy()

#print('test_image:'+ test_image)

predict_request = predict_pb2.PredictRequest()
predict_request.model_spec.name = 'mnist'
predict_request.model_spec.signature_name = 'prediction'

predict_channel = implementations.insecure_channel(host, int(port))
predict_stub = prediction_service_pb2.beta_create_PredictionService_stub(predict_channel)

#print(test_image)

predict_request.inputs['x'].CopyFrom(
tf.make_tensor_proto(test_image, shape=[1, test_image.size]))
result = predict_stub.Predict.future(predict_request, 3.0)
result.add_done_callback(
_create_rpc_callback())

InvalidArgumentError in Chapter02/2_mnist_cnn.py

Hi!
In Chapter 2, 2_mnist_cnn.py, I am getting an InvalidArgumentError.
Can someone please have a look at it and find out why it's happening?
Thanks

ImportError: No module named fiducial_data

In chapter 6, when running 5_fiducial_points.py, error happens.
How to import it?

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