Generate image analogies using neural matching and blending

Last update: Jan 08, 2023

Related tags

Overview

neural image analogies

This is basically an implementation of this "Image Analogies" paper, In our case, we use feature maps from VGG16. The patch matching and blending is inspired by the method described in "Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis". Effects similar to that paper can be achieved by turning off the analogy loss (or leave it on!) --analogy-w=0 and turning on the B/B' content weighting via the --b-content-w parameter. Also, instead of using brute-force patch matching we use the PatchMatch algorithm to approximate the best patch matches. Brute-force matching can be re-enabled by setting --model=brute

The initial code was adapted from the Keras "neural style transfer" example.

The example arch images are from the "Image Analogies" website. They have some other good examples from their own implementation which are worth a look. Their paper discusses the various applications of image analogies so you might want to take a look for inspiration.

Installation

This requires either TensorFlow or Theano. If you don't have a GPU you'll want to use TensorFlow. GPU users may find to Theano to be faster at the expense of longer startup times. Here's the Theano GPU guide.

Here's how to configure the backend with Keras and set your default device (e.g. cpu, gpu0).

To install via virtualenv run the following commands.

virtualenv venv
source venv/bin/activate
pip install neural-image-analogies

If you have trouble with the above method, follow these directions to Install latest keras and theano or TensorFlow

The script make_image_analogy.py should now be on your path.

Before running this script, download the weights for the VGG16 model. This file contains only the convolutional layers of VGG16 which is 10% of the full size. Original source of full weights. The script assumes the weights are in the current working directory. If you place them somewhere else make sure to pass the --vgg-weights= parameter or set the VGG_WEIGHT_PATH environment variable.

Example script usage: make_image_analogy.py image-A image-A-prime image-B prefix_for_output

e.g.:

make_image_analogy.py images/arch-mask.jpg images/arch.jpg images/arch-newmask.jpg out/arch

The examples directory has a script, render_example.sh which accepts an example name prefix and, optionally the location of your vgg weights.

./render_example.sh arch /path/to/your/weights.h5

Currently, A and A' must be the same size, the same holds for B and B'. Output size is the same as Image B, unless specified otherwise.

It's too slow

If you're not using a GPU, use TensorFlow. My Macbook Pro with with can render a 512x512 image in approximately 12 minutes using TensorFlow and --mrf-w=0. Here are some other options which mostly trade quality for speed.

If you're using Theano enable openmp threading by using env variables THEANO_FLAGS='openmp=1' OMP_NUM_THREADS=. You can read more about multi-core support here.
set --mrf-w=0 to skip optimization of local coherence
use fewer feature layers by setting --mrf-layers=conv4_1 and/or --analogy-layers=conv4_1 (or other layers) which will consider half as many feature layers.
generate a smaller image by either using a smaller source Image B, or setting the --width or --height parameters.
ensure you're not using --model=brute which needs a powerful GPU

I want it to look better

The default settings are somewhat lowered to give the average user a better chance at generating something on whatever computer they may have. If you have a powerful GPU then here are some options for nicer output:

--model=brute will turn on brute-force patch-matching and will be done on GPU. This is Theano-only (default=patchmatch)
--patch-size=3 this will allow for much nicer-looking details (default=1)
--mrf-layers=conv1_1,conv2_1,... add more layers to the mix (also analogy-layers and content-layers)

Parameters

--width Sets image output max width
--height Sets image output max height
--scales Run at N different scales
--iters Number of iterations per scale
--min-scale Smallest scale to iterate
--mrf-w Weight for MRF loss between A' and B'
--analogy-w Weight for analogy loss
--b-content-w Weight for content loss between B and B'
--tv-w Weight for total variation loss
--vgg-weights Path to VGG16 weights
--a-scale-mode Method of scaling A and A' relative to B
- 'match': force A to be the same size as B regardless of aspect ratio (former default)
- 'ratio': apply scale imposed by width/height params on B to A (current default)
- 'none': leave A/A' alone
--a-scale Additional scale factor for A and A'
--pool-mode Pooling style used by VGG
- 'avg': average pooling - generally smoother results
- 'max': max pooling - more noisy but maybe that's what you want (original default)
--contrast adjust the contrast of the output by removing the bottom x percentile and scaling by the (100 - x)th percentile. Defaults to 0.02
--output-full Output all intermediate images at full size regardless of actual scale
--analogy-layers Comma-separated list of layer names to be used for the analogy loss (default: "conv3_1,conv_4_1")
--mrf-layers Comma-separated list of layer names to be used for the MRF loss (default: "conv3_1,conv_4_1")
--content-layers Comma-separated list of layer names to be used for the content loss (default: "conv3_1,conv_4_1")
--patch-size Patch size used for matching (default: 1)
--use-full-analogy match on all of the analogy patches, instead of combining them into one image (slower/more memory but maybe more accurate)
--model Select the patch matching model ('patchmatch' or 'brute') patchmatch is the default and requires less GPU memory but is less accurate then brute.
--nstyle-w Weight for neural style loss between A' and B'
--nstyle-layers Comma-separated list of layer names to be used for the neural style The analogy loss is the amount of influence of B -> A -> A' -> B'. It's a structure-preserving mapping of Image B into A' via A.

The MRF loss (or "local coherence") is the influence of B' -> A' -> B'. In the parlance of style transfer, this is the style loss which gives texture to the image.

The B/B' content loss is set to 0.0 by default. You can get effects similar to CNNMRF by turning this up and setting analogy weight to zero. Or leave the analogy loss on for some extra style guidance.

If you'd like to only visualize the analogy target to see what's happening, set the MRF and content loss to zero: --mrf-w=0 --content-w=0 This is also much faster as MRF loss is the slowest part of the algorithm.

License

The code for this implementation is provided under the MIT license.

The suggested VGG16 weights are originally from here and are licensed http://creativecommons.org/licenses/by-nc/4.0/ Open a ticket if you have a suggestion for a more free-as-in-free-speech license.

The attributions for the example art can be found in examples/images/ATTRIBUTIONS.md

Comments

Specify theano flags in readme and create an out directory
it took me a while to figure out

mkdir out THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python image_analogy.py images/arch-mask.jpg images/arch.jpg images/arch-newmask.jpg out/arch

I also creating an AMI in northern virginia region. will share once its ready.
opened by ghost 12

ValueError: Layer weight shape not compatible with provided weight shape

after installing Tensorflow Python 3 / CPU only on anaconda, I tried to run the script without success:

$ make_image_analogy.py images/a.jpg images/a.jpg images/b.jpg out/b                                       
Using TensorFlow backend.
Tensorflow detected. Forcing --a-scale-mode=match (A images are scaled to same size as B images)
Using PatchMatch model
Scale factor 0.25 "A" shape (1, 3, 48, 64) "B" shape (1, 3, 48, 64)
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these areavailable on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these areavailable on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
Traceback (most recent call last):
  File "/home/dori/.conda/envs/py36/bin/make_image_analogy.py", line 27, in <module>
    image_analogy.main.main(args, model_class)
  File "/home/dori/.conda/envs/py36/lib/python3.6/site-packages/image_analogy/main.py", line 69, in main
    net = vgg16.get_model(img_width, img_height, weights_path=args.vgg_weights, pool_mode=args.pool_mode)
  File "/home/dori/.conda/envs/py36/lib/python3.6/site-packages/image_analogy/vgg16.py", line 89, in get_model
    layer.set_weights(weights)
  File "/home/dori/.conda/envs/py36/lib/python3.6/site-packages/keras/engine/topology.py", line 1154, in set_weights
    'provided weight shape ' + str(w.shape))
ValueError: Layer weight shape (3, 3, 3, 64) not compatible with provided weight shape (64, 3, 3, 3)

Any idea how to solve this issue ?

PS: Note that I renamed all calls Convolution2D(XXX, 3, 3, activation=... into Conv2D(XXX, (3, 3), activation=... to fix the many UserWarnings

/home/dori/.conda/envs/py36/lib/python3.6/site-packages/image_analogy/vgg16.py:71: 
UserWarning: Update your `Conv2D` call to theKeras 2 API: `Conv2D(512, (3, 3), activation="relu", name="conv5_3")`
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_3'))

opened by AdrienLemaire 11

not working as well as it did a few days ago?

Hey, great wrk on this. looks great. However the changes you've made in the past few days have changed the output quite a lot. A few days ago (commit: https://github.com/awentzonline/image-analogies/commit/c6f35e73bfb35035e195dd5a3bb5a018588bec3e ) I would get this (which looks perfect):

but now I get this (with the same settings):

my mask is this:

(also new version is exactly 2x faster, which is great. but I'm not anywhere near the same results)

opened by memo 9
Cuda Dimension Mismatch

when running this command

python2.7 make_image_analogy.py ~/Documents/imganal/examples/images/arch-A.jpg ~/Documents/imganal/examples/images/arch-Ap.jpg ~/Documents/imganal/examples/images/arch-B.jpg ~/Documents/imganal/out/img

on the arch example it runs for one pass (0x0 through 0x4) and then I get the following trace:

Traceback (most recent call last): File "make_image_analogy.py", line 25, in image_analogy.main.main(args, model_class) File "build/bdist.linux-x86_64/egg/image_analogy/main.py", line 69, in main model.build(a_image, ap_image, b_image, (1, img_num_channels, img_height, img_width)) File "build/bdist.linux-x86_64/egg/image_analogy/models/base.py", line 23, in build File "build/bdist.linux-x86_64/egg/image_analogy/models/analogy.py", line 22, in build_loss File "build/bdist.linux-x86_64/egg/image_analogy/models/base.py", line 51, in precompute_static_features File "build/bdist.linux-x86_64/egg/image_analogy/models/base.py", line 60, in get_features File "/usr/local/lib/python2.7/dist-packages/keras/backend/theano_backend.py", line 384, in call return self.function(*inputs) File "/usr/local/lib/python2.7/dist-packages/theano/compile/function_module.py", line 871, in call storage_map=getattr(self.fn, 'storage_map', None)) File "/usr/local/lib/python2.7/dist-packages/theano/gof/link.py", line 314, in raise_with_op reraise(exc_type, exc_value, exc_trace) File "/usr/local/lib/python2.7/dist-packages/theano/compile/function_module.py", line 859, in call outputs = self.fn() ValueError: CudaNdarray_CopyFromCudaNdarray: need same dimensions for dim 2, destination=290, source=289 Apply node that caused the error: GpuIncSubtensor{InplaceSet;::, ::, int64:int64:, int64:int64:}(GpuAlloc{memset_0=True}.0, GpuElemwise{Composite{(i0 * ((i1 + i2) + Abs((i1 + i2))))}}[(0, 1)].0, Constant{1}, Constant{291}, Constant{1}, Constant{201}) Toposort index: 46 Inputs types: [CudaNdarrayType(float32, 4D), CudaNdarrayType(float32, 4D), Scalar(int64), Scalar(int64), Scalar(int64), Scalar(int64)] Inputs shapes: [(1, 64, 292, 202), (1, 64, 289, 200), (), (), (), ()] Inputs strides: [(0, 58984, 202, 1), (0, 57800, 200, 1), (), (), (), ()] Inputs values: ['not shown', 'not shown', 1, 291, 1, 201] Outputs clients: [[GpuContiguous(GpuIncSubtensor{InplaceSet;::, ::, int64:int64:, int64:int64:}.0)]]

all my requirements are up to date/correct (or at least pip install -r requirements.txt says so). I've had this project installed for awhile so it might be that the cruft from older versions is conflicting with this one (it's not in a venv or anything). I think it has something to do with the img heights and widths. If I run with an example where A is not the same dimensions as B, it crashes before the first pass... I vaguely remember this problem happening on an older version of this project but I forget how I fixed it. I'm not sure if it's me or the new version, any ideas?

opened by DrChromaticNo 7
Error while running example ("Cannot convert %s to TensorType" % str_x, type(x))

Hello guys, I am try to run the examples and I keep getting this error.

File "/home/cyb/image-analogies/venv/local/lib/python2.7/site-packages/theano/tensor/basic.py", line 208, in as_tensor_variable raise AsTensorError("Cannot convert %s to TensorType" % str_x, type(x)) theano.tensor.var.AsTensorError: ('Cannot convert Tensor("ExpandDims:0", shape=(1, 256, 32, 22), dtype=float32) to TensorType', <class 'tensorflow.python.framework.ops.Tensor'>)

Would you have any clues how to fix that ? Thanks a lot!

opened by Cybrak 6
Changed make_patches to patches.make_patches

Minor fix to have make_patches method reference the patches module. This bug wouldn't be noticed unless you are trying to use full analogy patch matching.

opened by vonclites 3
Support for Leaf (Tensorflow alternative)

Leaf is an alternative to Tensorflow that is allegedly faster and easier to run in the GPU, thanks to the Rust language capabilities. Torch, on the other hand, is powered by LuaJIT, and seems great, too.

Torch seems very fast, too - http://autumnai.com/deep-learning-benchmarks

Do you think it would be interesting to support any of them? https://github.com/autumnai/leaf https://github.com/torch/torch7

opened by giovannibonetti 3

getting errors installing

I should have all dependencies, theano enabled for opencl.

running

pip install -r requirements.txt I get

pip install -r requirements.txt
Requirement already satisfied (use --upgrade to upgrade): Cython==0.23.4 in ./venv/lib/python2.7/site-packages (from -r requirements.txt (line 1))
Collecting h5py==2.5.0 (from -r requirements.txt (line 2))
  Using cached h5py-2.5.0.tar.gz
Collecting Keras==0.3.2 (from -r requirements.txt (line 3))
Requirement already satisfied (use --upgrade to upgrade): numpy==1.10.4 in ./venv/lib/python2.7/site-packages (from -r requirements.txt (line 4))
Collecting Pillow==3.1.1 (from -r requirements.txt (line 5))
Collecting PyYAML==3.11 (from -r requirements.txt (line 6))
Collecting scipy==0.17.0 (from -r requirements.txt (line 7))
  Using cached scipy-0.17.0.tar.gz
Requirement already satisfied (use --upgrade to upgrade): six==1.10.0 in ./venv/lib/python2.7/site-packages (from -r requirements.txt (line 8))
Obtaining Theano from git+git://github.com/Theano/[email protected]#egg=Theano (from -r requirements.txt (line 9))
  Skipping because already up-to-date.
Building wheels for collected packages: h5py, scipy
  Running setup.py bdist_wheel for h5py ... error
  Complete output from command /home/alex/image-analogies/venv/bin/python -u -c "import setuptools, tokenize;__file__='/tmp/pip-build-g7OTWL/h5py/setup.py';exec(compile(getattr(tokenize, 'open', open)(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))" bdist_wheel -d /tmp/tmpImCcmFpip-wheel- --python-tag cp27:
  running bdist_wheel
  running build
  running build_py
  creating build
  creating build/lib.linux-x86_64-2.7
  creating build/lib.linux-x86_64-2.7/h5py
  copying h5py/highlevel.py -> build/lib.linux-x86_64-2.7/h5py
  copying h5py/__init__.py -> build/lib.linux-x86_64-2.7/h5py
  copying h5py/ipy_completer.py -> build/lib.linux-x86_64-2.7/h5py
  copying h5py/version.py -> build/lib.linux-x86_64-2.7/h5py
  creating build/lib.linux-x86_64-2.7/h5py/_hl
  copying h5py/_hl/base.py -> build/lib.linux-x86_64-2.7/h5py/_hl
  copying h5py/_hl/selections.py -> build/lib.linux-x86_64-2.7/h5py/_hl
  copying h5py/_hl/__init__.py -> build/lib.linux-x86_64-2.7/h5py/_hl
  copying h5py/_hl/selections2.py -> build/lib.linux-x86_64-2.7/h5py/_hl
  copying h5py/_hl/group.py -> build/lib.linux-x86_64-2.7/h5py/_hl
  copying h5py/_hl/datatype.py -> build/lib.linux-x86_64-2.7/h5py/_hl
  copying h5py/_hl/attrs.py -> build/lib.linux-x86_64-2.7/h5py/_hl
  copying h5py/_hl/dims.py -> build/lib.linux-x86_64-2.7/h5py/_hl
  copying h5py/_hl/dataset.py -> build/lib.linux-x86_64-2.7/h5py/_hl
  copying h5py/_hl/files.py -> build/lib.linux-x86_64-2.7/h5py/_hl
  copying h5py/_hl/filters.py -> build/lib.linux-x86_64-2.7/h5py/_hl
  creating build/lib.linux-x86_64-2.7/h5py/tests
  copying h5py/tests/__init__.py -> build/lib.linux-x86_64-2.7/h5py/tests
  copying h5py/tests/common.py -> build/lib.linux-x86_64-2.7/h5py/tests
  creating build/lib.linux-x86_64-2.7/h5py/tests/old
  copying h5py/tests/old/test_dataset.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
  copying h5py/tests/old/test_h5.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
  copying h5py/tests/old/test_h5p.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
  copying h5py/tests/old/test_file.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
  copying h5py/tests/old/__init__.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
  copying h5py/tests/old/test_h5f.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
  copying h5py/tests/old/test_selections.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
  copying h5py/tests/old/test_objects.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
  copying h5py/tests/old/test_dimension_scales.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
  copying h5py/tests/old/test_slicing.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
  copying h5py/tests/old/test_attrs_data.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
  copying h5py/tests/old/test_base.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
  copying h5py/tests/old/test_h5t.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
  copying h5py/tests/old/test_datatype.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
  copying h5py/tests/old/common.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
  copying h5py/tests/old/test_attrs.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
  copying h5py/tests/old/test_group.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
  creating build/lib.linux-x86_64-2.7/h5py/tests/hl
  copying h5py/tests/hl/test_dataset_swmr.py -> build/lib.linux-x86_64-2.7/h5py/tests/hl
  copying h5py/tests/hl/test_file.py -> build/lib.linux-x86_64-2.7/h5py/tests/hl
  copying h5py/tests/hl/__init__.py -> build/lib.linux-x86_64-2.7/h5py/tests/hl
  copying h5py/tests/hl/test_dims_dimensionproxy.py -> build/lib.linux-x86_64-2.7/h5py/tests/hl
  copying h5py/tests/hl/test_dataset_getitem.py -> build/lib.linux-x86_64-2.7/h5py/tests/hl
  copying h5py/tests/hl/test_attribute_create.py -> build/lib.linux-x86_64-2.7/h5py/tests/hl
  running build_ext
  Autodetection skipped [libhdf5.so: cannot open shared object file: No such file or directory]
  ********************************************************************************
                         Summary of the h5py configuration

      Path to HDF5: None
      HDF5 Version: '1.8.4'
       MPI Enabled: False
  Rebuild Required: False

  ********************************************************************************
  Executing api_gen rebuild of defs
  Executing cythonize()
  Compiling /tmp/pip-build-g7OTWL/h5py/h5py/defs.pyx because it changed.
  Compiling /tmp/pip-build-g7OTWL/h5py/h5py/_errors.pyx because it changed.
  Compiling /tmp/pip-build-g7OTWL/h5py/h5py/_objects.pyx because it changed.
  Compiling /tmp/pip-build-g7OTWL/h5py/h5py/_proxy.pyx because it changed.
  Compiling /tmp/pip-build-g7OTWL/h5py/h5py/h5fd.pyx because it changed.
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  Compiling /tmp/pip-build-g7OTWL/h5py/h5py/h5a.pyx because it changed.
  Compiling /tmp/pip-build-g7OTWL/h5py/h5py/h5f.pyx because it changed.
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  [16/22] Cythonizing /tmp/pip-build-g7OTWL/h5py/h5py/h5o.pyx
  [17/22] Cythonizing /tmp/pip-build-g7OTWL/h5py/h5py/h5p.pyx
  [18/22] Cythonizing /tmp/pip-build-g7OTWL/h5py/h5py/h5r.pyx
  [19/22] Cythonizing /tmp/pip-build-g7OTWL/h5py/h5py/h5s.pyx
  [20/22] Cythonizing /tmp/pip-build-g7OTWL/h5py/h5py/h5t.pyx
  [21/22] Cythonizing /tmp/pip-build-g7OTWL/h5py/h5py/h5z.pyx
  [22/22] Cythonizing /tmp/pip-build-g7OTWL/h5py/h5py/utils.pyx
  building 'h5py.defs' extension
  creating build/temp.linux-x86_64-2.7
  creating build/temp.linux-x86_64-2.7/tmp
  creating build/temp.linux-x86_64-2.7/tmp/pip-build-g7OTWL
  creating build/temp.linux-x86_64-2.7/tmp/pip-build-g7OTWL/h5py
  creating build/temp.linux-x86_64-2.7/tmp/pip-build-g7OTWL/h5py/h5py
  x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -fno-strict-aliasing -D_FORTIFY_SOURCE=2 -g -fstack-protector-strong -Wformat -Werror=format-security -fPIC -DH5_USE_16_API -I/tmp/pip-build-g7OTWL/h5py/lzf -I/opt/local/include -I/usr/local/include -I/home/alex/image-analogies/venv/local/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c /tmp/pip-build-g7OTWL/h5py/h5py/defs.c -o build/temp.linux-x86_64-2.7/tmp/pip-build-g7OTWL/h5py/h5py/defs.o
  In file included from /home/alex/image-analogies/venv/local/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1781:0,
                   from /home/alex/image-analogies/venv/local/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                   from /home/alex/image-analogies/venv/local/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                   from /tmp/pip-build-g7OTWL/h5py/h5py/api_compat.h:26,
                   from /tmp/pip-build-g7OTWL/h5py/h5py/defs.c:279:
  /home/alex/image-analogies/venv/local/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
   #warning "Using deprecated NumPy API, disable it by " \
    ^
  In file included from /tmp/pip-build-g7OTWL/h5py/h5py/defs.c:279:0:
  /tmp/pip-build-g7OTWL/h5py/h5py/api_compat.h:27:18: fatal error: hdf5.h: File o directory non esistente
  compilation terminated.
  error: command 'x86_64-linux-gnu-gcc' failed with exit status 1

  ----------------------------------------
  Failed building wheel for h5py
  Running setup.py clean for h5py
  Running setup.py bdist_wheel for scipy ... done
  Stored in directory: /home/alex/.cache/pip/wheels/76/aa/e2/031ee833b4abfd33d8620e4bc36f8178b95cfcf36ec550a6b9
Successfully built scipy
Failed to build h5py
Installing collected packages: h5py, scipy, Theano, PyYAML, Keras, Pillow
  Running setup.py install for h5py ... error
    Complete output from command /home/alex/image-analogies/venv/bin/python -u -c "import setuptools, tokenize;__file__='/tmp/pip-build-g7OTWL/h5py/setup.py';exec(compile(getattr(tokenize, 'open', open)(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))" install --record /tmp/pip-x5IHF0-record/install-record.txt --single-version-externally-managed --compile --install-headers /home/alex/image-analogies/venv/include/site/python2.7/h5py:
    running install
    running build
    running build_py
    creating build
    creating build/lib.linux-x86_64-2.7
    creating build/lib.linux-x86_64-2.7/h5py
    copying h5py/highlevel.py -> build/lib.linux-x86_64-2.7/h5py
    copying h5py/__init__.py -> build/lib.linux-x86_64-2.7/h5py
    copying h5py/ipy_completer.py -> build/lib.linux-x86_64-2.7/h5py
    copying h5py/version.py -> build/lib.linux-x86_64-2.7/h5py
    creating build/lib.linux-x86_64-2.7/h5py/_hl
    copying h5py/_hl/base.py -> build/lib.linux-x86_64-2.7/h5py/_hl
    copying h5py/_hl/selections.py -> build/lib.linux-x86_64-2.7/h5py/_hl
    copying h5py/_hl/__init__.py -> build/lib.linux-x86_64-2.7/h5py/_hl
    copying h5py/_hl/selections2.py -> build/lib.linux-x86_64-2.7/h5py/_hl
    copying h5py/_hl/group.py -> build/lib.linux-x86_64-2.7/h5py/_hl
    copying h5py/_hl/datatype.py -> build/lib.linux-x86_64-2.7/h5py/_hl
    copying h5py/_hl/attrs.py -> build/lib.linux-x86_64-2.7/h5py/_hl
    copying h5py/_hl/dims.py -> build/lib.linux-x86_64-2.7/h5py/_hl
    copying h5py/_hl/dataset.py -> build/lib.linux-x86_64-2.7/h5py/_hl
    copying h5py/_hl/files.py -> build/lib.linux-x86_64-2.7/h5py/_hl
    copying h5py/_hl/filters.py -> build/lib.linux-x86_64-2.7/h5py/_hl
    creating build/lib.linux-x86_64-2.7/h5py/tests
    copying h5py/tests/__init__.py -> build/lib.linux-x86_64-2.7/h5py/tests
    copying h5py/tests/common.py -> build/lib.linux-x86_64-2.7/h5py/tests
    creating build/lib.linux-x86_64-2.7/h5py/tests/old
    copying h5py/tests/old/test_dataset.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
    copying h5py/tests/old/test_h5.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
    copying h5py/tests/old/test_h5p.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
    copying h5py/tests/old/test_file.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
    copying h5py/tests/old/__init__.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
    copying h5py/tests/old/test_h5f.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
    copying h5py/tests/old/test_selections.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
    copying h5py/tests/old/test_objects.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
    copying h5py/tests/old/test_dimension_scales.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
    copying h5py/tests/old/test_slicing.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
    copying h5py/tests/old/test_attrs_data.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
    copying h5py/tests/old/test_base.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
    copying h5py/tests/old/test_h5t.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
    copying h5py/tests/old/test_datatype.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
    copying h5py/tests/old/common.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
    copying h5py/tests/old/test_attrs.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
    copying h5py/tests/old/test_group.py -> build/lib.linux-x86_64-2.7/h5py/tests/old
    creating build/lib.linux-x86_64-2.7/h5py/tests/hl
    copying h5py/tests/hl/test_dataset_swmr.py -> build/lib.linux-x86_64-2.7/h5py/tests/hl
    copying h5py/tests/hl/test_file.py -> build/lib.linux-x86_64-2.7/h5py/tests/hl
    copying h5py/tests/hl/__init__.py -> build/lib.linux-x86_64-2.7/h5py/tests/hl
    copying h5py/tests/hl/test_dims_dimensionproxy.py -> build/lib.linux-x86_64-2.7/h5py/tests/hl
    copying h5py/tests/hl/test_dataset_getitem.py -> build/lib.linux-x86_64-2.7/h5py/tests/hl
    copying h5py/tests/hl/test_attribute_create.py -> build/lib.linux-x86_64-2.7/h5py/tests/hl
    running build_ext
    Autodetection skipped [libhdf5.so: cannot open shared object file: No such file or directory]
    ********************************************************************************
                           Summary of the h5py configuration

        Path to HDF5: None
        HDF5 Version: '1.8.4'
         MPI Enabled: False
    Rebuild Required: False

    ********************************************************************************
    Executing cythonize()
    building 'h5py.defs' extension
    creating build/temp.linux-x86_64-2.7
    creating build/temp.linux-x86_64-2.7/tmp
    creating build/temp.linux-x86_64-2.7/tmp/pip-build-g7OTWL
    creating build/temp.linux-x86_64-2.7/tmp/pip-build-g7OTWL/h5py
    creating build/temp.linux-x86_64-2.7/tmp/pip-build-g7OTWL/h5py/h5py
    x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -fno-strict-aliasing -D_FORTIFY_SOURCE=2 -g -fstack-protector-strong -Wformat -Werror=format-security -fPIC -DH5_USE_16_API -I/tmp/pip-build-g7OTWL/h5py/lzf -I/opt/local/include -I/usr/local/include -I/home/alex/image-analogies/venv/local/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c /tmp/pip-build-g7OTWL/h5py/h5py/defs.c -o build/temp.linux-x86_64-2.7/tmp/pip-build-g7OTWL/h5py/h5py/defs.o
    In file included from /home/alex/image-analogies/venv/local/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1781:0,
                     from /home/alex/image-analogies/venv/local/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                     from /home/alex/image-analogies/venv/local/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                     from /tmp/pip-build-g7OTWL/h5py/h5py/api_compat.h:26,
                     from /tmp/pip-build-g7OTWL/h5py/h5py/defs.c:279:
    /home/alex/image-analogies/venv/local/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
     #warning "Using deprecated NumPy API, disable it by " \
      ^
    In file included from /tmp/pip-build-g7OTWL/h5py/h5py/defs.c:279:0:
    /tmp/pip-build-g7OTWL/h5py/h5py/api_compat.h:27:18: fatal error: hdf5.h: File o directory non esistente
    compilation terminated.
    error: command 'x86_64-linux-gnu-gcc' failed with exit status 1

    ----------------------------------------
Command "/home/alex/image-analogies/venv/bin/python -u -c "import setuptools, tokenize;__file__='/tmp/pip-build-g7OTWL/h5py/setup.py';exec(compile(getattr(tokenize, 'open', open)(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))" install --record /tmp/pip-x5IHF0-record/install-record.txt --single-version-externally-managed --compile --install-headers /home/alex/image-analogies/venv/include/site/python2.7/h5py" failed with error code 1 in /tmp/pip-build-g7OTWL/h5py/

I guess it has something to do with some depricated apis or some missing file, but I really don't get what I should do.

Any help?

opened by rayset 3

add requirements.txt and explicitly state install directions

@awentzonline this is great. I just made my first image analogy, thanks.

I'm pretty new to python so I struggled a bit with the setup. Here is an update to the readme and a requirements.txt file to help others in the future.

opened by mcwhittemore 3
Bump numpy from 1.10.4 to 1.21.0
Bumps numpy from 1.10.4 to 1.21.0.

Release notes

Sourced from numpy's releases.

v1.21.0

NumPy 1.21.0 Release Notes

The NumPy 1.21.0 release highlights are

continued SIMD work covering more functions and platforms,

initial work on the new dtype infrastructure and casting,

universal2 wheels for Python 3.8 and Python 3.9 on Mac,

improved documentation,

improved annotations,

new PCG64DXSM bitgenerator for random numbers.

In addition there are the usual large number of bug fixes and other improvements.

The Python versions supported for this release are 3.7-3.9. Official support for Python 3.10 will be added when it is released.

:warning: Warning: there are unresolved problems compiling NumPy 1.21.0 with gcc-11.1 .

Optimization level -O3 results in many wrong warnings when running the tests.

On some hardware NumPy will hang in an infinite loop.

New functions

Add PCG64DXSM BitGenerator

Uses of the PCG64 BitGenerator in a massively-parallel context have been shown to have statistical weaknesses that were not apparent at the first release in numpy 1.17. Most users will never observe this weakness and are safe to continue to use PCG64. We have introduced a new PCG64DXSM BitGenerator that will eventually become the new default BitGenerator implementation used by default_rng in future releases. PCG64DXSM solves the statistical weakness while preserving the performance and the features of PCG64.

See upgrading-pcg64 for more details.

(gh-18906)

Expired deprecations

The shape argument numpy.unravel_index cannot be passed as dims keyword argument anymore. (Was deprecated in NumPy 1.16.)

... (truncated)

Commits

b235f9e Merge pull request #19283 from charris/prepare-1.21.0-release

34aebc2 MAINT: Update 1.21.0-notes.rst

493b64b MAINT: Update 1.21.0-changelog.rst

07d7e72 MAINT: Remove accidentally created directory.

032fca5 Merge pull request #19280 from charris/backport-19277

7d25b81 BUG: Fix refcount leak in ResultType

fa5754e BUG: Add missing DECREF in new path

61127bb Merge pull request #19268 from charris/backport-19264

143d45f Merge pull request #19269 from charris/backport-19228

d80e473 BUG: Removed typing for == and != in dtypes

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dependencies
opened by dependabot[bot] 2
SyntaxError: Missing parentheses in call to 'print'

try to run example, make_image_analogy.py images/arch-mask.jpg images/arch.jpg images/arch-newmask.jpg out/arch the output is SyntaxError: Missing parentheses in call to 'print' (venv) liudeMacBook-Pro:scripts liu$ make_image_analogy.py images/arch-mask.jpg images/arch.jpg images/arch-newmask.jpg out/arch Using Theano backend. /Users/liu/Code/venv/lib/python3.4/site-packages/theano/tensor/signal/downsample.py:6: UserWarning: downsample module has been moved to the theano.tensor.signal.pool module. "downsample module has been moved to the theano.tensor.signal.pool module.") Theano CPU mode detected. Forcing a-scale-mode to "match" Using PatchMatch model Traceback (most recent call last): File "/Users/liu/Code/venv/bin/make_image_analogy.py", line 21, in from image_analogy.models.nnf import NNFModel as model_class File "/Users/liu/Code/venv/lib/python3.4/site-packages/image_analogy/models/nnf.py", line 7, in from image_analogy.losses.nnf import nnf_analogy_loss, NNFState, PatchMatcher File "/Users/liu/Code/venv/lib/python3.4/site-packages/image_analogy/losses/nnf.py", line 5, in from .patch_matcher import PatchMatcher File "/Users/liu/Code/venv/lib/python3.4/site-packages/image_analogy/losses/patch_matcher.py", line 187 print "[congrid] dimensions error. "
^ SyntaxError: Missing parentheses in call to 'print'

opened by Heipiao 2
Bump pillow from 3.1.1 to 9.3.0
Bumps pillow from 3.1.1 to 9.3.0.

Release notes

Sourced from pillow's releases.

9.3.0

https://pillow.readthedocs.io/en/stable/releasenotes/9.3.0.html

Changes

Initialize libtiff buffer when saving #6699 [@radarhere]

Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [@wiredfool]

Inline fname2char to fix memory leak #6329 [@nulano]

Fix memory leaks related to text features #6330 [@nulano]

Use double quotes for version check on old CPython on Windows #6695 [@hugovk]

GHA: replace deprecated set-output command with GITHUB_OUTPUT file #6697 [@nulano]

Remove backup implementation of Round for Windows platforms #6693 [@cgohlke]

Upload fribidi.dll to GitHub Actions #6532 [@nulano]

Fixed set_variation_by_name offset #6445 [@radarhere]

Windows build improvements #6562 [@nulano]

Fix malloc in _imagingft.c:font_setvaraxes #6690 [@cgohlke]

Only use ASCII characters in C source file #6691 [@cgohlke]

Release Python GIL when converting images using matrix operations #6418 [@hmaarrfk]

Added ExifTags enums #6630 [@radarhere]

Do not modify previous frame when calculating delta in PNG #6683 [@radarhere]

Added support for reading BMP images with RLE4 compression #6674 [@npjg]

Decode JPEG compressed BLP1 data in original mode #6678 [@radarhere]

pylint warnings #6659 [@marksmayo]

Added GPS TIFF tag info #6661 [@radarhere]

Added conversion between RGB/RGBA/RGBX and LAB #6647 [@radarhere]

Do not attempt normalization if mode is already normal #6644 [@radarhere]

Fixed seeking to an L frame in a GIF #6576 [@radarhere]

Consider all frames when selecting mode for PNG save_all #6610 [@radarhere]

Don't reassign crc on ChunkStream close #6627 [@radarhere]

Raise a warning if NumPy failed to raise an error during conversion #6594 [@radarhere]

Only read a maximum of 100 bytes at a time in IMT header #6623 [@radarhere]

Show all frames in ImageShow #6611 [@radarhere]

Allow FLI palette chunk to not be first #6626 [@radarhere]

If first GIF frame has transparency for RGB_ALWAYS loading strategy, use RGBA mode #6592 [@radarhere]

Round box position to integer when pasting embedded color #6517 [@radarhere]

Removed EXIF prefix when saving WebP #6582 [@radarhere]

Pad IM palette to 768 bytes when saving #6579 [@radarhere]

Added DDS BC6H reading #6449 [@ShadelessFox]

Added support for opening WhiteIsZero 16-bit integer TIFF images #6642 [@JayWiz]

Raise an error when allocating translucent color to RGB palette #6654 [@jsbueno]

Moved mode check outside of loops #6650 [@radarhere]

Added reading of TIFF child images #6569 [@radarhere]

Improved ImageOps palette handling #6596 [@PososikTeam]

Defer parsing of palette into colors #6567 [@radarhere]

Apply transparency to P images in ImageTk.PhotoImage #6559 [@radarhere]

Use rounding in ImageOps contain() and pad() #6522 [@bibinhashley]

Fixed GIF remapping to palette with duplicate entries #6548 [@radarhere]

Allow remap_palette() to return an image with less than 256 palette entries #6543 [@radarhere]

Corrected BMP and TGA palette size when saving #6500 [@radarhere]

... (truncated)

Changelog

Sourced from pillow's changelog.

9.3.0 (2022-10-29)

Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [wiredfool]

Initialize libtiff buffer when saving #6699 [radarhere]

Inline fname2char to fix memory leak #6329 [nulano]

Fix memory leaks related to text features #6330 [nulano]

Use double quotes for version check on old CPython on Windows #6695 [hugovk]

Remove backup implementation of Round for Windows platforms #6693 [cgohlke]

Fixed set_variation_by_name offset #6445 [radarhere]

Fix malloc in _imagingft.c:font_setvaraxes #6690 [cgohlke]

Release Python GIL when converting images using matrix operations #6418 [hmaarrfk]

Added ExifTags enums #6630 [radarhere]

Do not modify previous frame when calculating delta in PNG #6683 [radarhere]

Added support for reading BMP images with RLE4 compression #6674 [npjg, radarhere]

Decode JPEG compressed BLP1 data in original mode #6678 [radarhere]

Added GPS TIFF tag info #6661 [radarhere]

Added conversion between RGB/RGBA/RGBX and LAB #6647 [radarhere]

Do not attempt normalization if mode is already normal #6644 [radarhere]

... (truncated)

Commits

d594f4c Update CHANGES.rst [ci skip]

909dc64 9.3.0 version bump

1a51ce7 Merge pull request #6699 from hugovk/security-libtiff_buffer

2444cdd Merge pull request #6700 from hugovk/security-samples_per_pixel-sec

744f455 Added release notes

0846bfa Add to release notes

799a6a0 Fix linting

00b25fd Hide UserWarning in logs

05b175e Tighter test case

13f2c5a Prevent DOS with large SAMPLESPERPIXEL in Tiff IFD

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dependencies
opened by dependabot[bot] 0
Bump numpy from 1.10.4 to 1.22.0
Bumps numpy from 1.10.4 to 1.22.0.

Release notes

Sourced from numpy's releases.

v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.

A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.

NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.

New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.

A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

... (truncated)

Commits

4adc87d Merge pull request #20685 from charris/prepare-for-1.22.0-release

fd66547 REL: Prepare for the NumPy 1.22.0 release.

125304b wip

c283859 Merge pull request #20682 from charris/backport-20416

5399c03 Merge pull request #20681 from charris/backport-20954

f9c45f8 Merge pull request #20680 from charris/backport-20663

794b36f Update armccompiler.py

d93b14e Update test_public_api.py

7662c07 Update init.py

311ab52 Update armccompiler.py

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dependencies
opened by dependabot[bot] 0
Bump pyyaml from 3.11 to 5.4
Bumps pyyaml from 3.11 to 5.4.

Changelog

Sourced from pyyaml's changelog.

5.4 (2021-01-19)

yaml/pyyaml#407 -- Build modernization, remove distutils, fix metadata, build wheels, CI to GHA

yaml/pyyaml#472 -- Fix for CVE-2020-14343, moves arbitrary python tags to UnsafeLoader

yaml/pyyaml#441 -- Fix memory leak in implicit resolver setup

yaml/pyyaml#392 -- Fix py2 copy support for timezone objects

yaml/pyyaml#378 -- Fix compatibility with Jython

5.3.1 (2020-03-18)

yaml/pyyaml#386 -- Prevents arbitrary code execution during python/object/new constructor

5.3 (2020-01-06)

yaml/pyyaml#290 -- Use is instead of equality for comparing with None

yaml/pyyaml#270 -- Fix typos and stylistic nit

yaml/pyyaml#309 -- Fix up small typo

yaml/pyyaml#161 -- Fix handling of slots

yaml/pyyaml#358 -- Allow calling add_multi_constructor with None

yaml/pyyaml#285 -- Add use of safe_load() function in README

yaml/pyyaml#351 -- Fix reader for Unicode code points over 0xFFFF

yaml/pyyaml#360 -- Enable certain unicode tests when maxunicode not > 0xffff

yaml/pyyaml#359 -- Use full_load in yaml-highlight example

yaml/pyyaml#244 -- Document that PyYAML is implemented with Cython

yaml/pyyaml#329 -- Fix for Python 3.10

yaml/pyyaml#310 -- Increase size of index, line, and column fields

yaml/pyyaml#260 -- Remove some unused imports

yaml/pyyaml#163 -- Create timezone-aware datetimes when parsed as such

yaml/pyyaml#363 -- Add tests for timezone

5.2 (2019-12-02)

Repair incompatibilities introduced with 5.1. The default Loader was changed, but several methods like add_constructor still used the old default yaml/pyyaml#279 -- A more flexible fix for custom tag constructors yaml/pyyaml#287 -- Change default loader for yaml.add_constructor yaml/pyyaml#305 -- Change default loader for add_implicit_resolver, add_path_resolver

Make FullLoader safer by removing python/object/apply from the default FullLoader yaml/pyyaml#347 -- Move constructor for object/apply to UnsafeConstructor

Fix bug introduced in 5.1 where quoting went wrong on systems with sys.maxunicode <= 0xffff yaml/pyyaml#276 -- Fix logic for quoting special characters

Other PRs: yaml/pyyaml#280 -- Update CHANGES for 5.1

5.1.2 (2019-07-30)

Re-release of 5.1 with regenerated Cython sources to build properly for Python 3.8b2+

... (truncated)

Commits

58d0cb7 5.4 release

a60f7a1 Fix compatibility with Jython

ee98abd Run CI on PR base branch changes

ddf2033 constructor.timezone: _copy & deepcopy

fc914d5 Avoid repeatedly appending to yaml_implicit_resolvers

a001f27 Fix for CVE-2020-14343

fe15062 Add 3.9 to appveyor file for completeness sake

1e1c7fb Add a newline character to end of pyproject.toml

0b6b7d6 Start sentences and phrases for capital letters

c976915 Shell code improvements

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dependencies
opened by dependabot[bot] 0
FileNotFoundError: [Errno 2] No such file or directory: ''

I followed the installation upto installing venv. tried running the program with my images and the vgg16 file in cwd.

I installed tensorflow on my machine using pip3 install tensorflow --user

venv did not detect tensorflow, so I installed tensorflow with pip install tensorflow while in venv.

then I ran my command again.

(venv) [email protected]:~/Documents/Programming/image$ make_image_analogy.py a.jpg a.jpg b.jpg output Using TensorFlow backend. Tensorflow detected. Forcing --a-scale-mode=match (A images are scaled to same size as B images) Using PatchMatch model Traceback (most recent call last): File "/home/user/.keras/venv/bin/make_image_analogy.py", line 27, in image_analogy.main.main(args, model_class) File "/home/user/.keras/venv/lib/python3.6/site-packages/image_analogy/main.py", line 34, in main os.makedirs(output_dir) File "/home/user/.keras/venv/lib/python3.6/os.py", line 220, in makedirs mkdir(name, mode) FileNotFoundError: [Errno 2] No such file or directory: ''

I am getting this error involving patchmatch model, and I have no clue where to begin troubleshooting it.

opened by Logner 0

Converting images causes unknown bus error.

I got all dependencies installed and I finally got some things to run, however I am getting an error when trying to run 2 really small images against each other with a raspberry pi 3 B+. I'm guessing it doesn't have enough memory to do this or I'm missing something.

Building loss...
WARNING:tensorflow:From /home/pi/.local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:460: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
WARNING:tensorflow:Variable += will be deprecated. Use variable.assign_add if you want assignment to the variable value or 'x = x + y' if you want a new python Tensor object.
Precomputing static features...
Building and combining losses...
/home/pi/.local/lib/python2.7/site-packages/sklearn/feature_extraction/image.py:287: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  indexing_strides = arr[slices].strides
Start of iteration 0 x 0
Bus error

opened by RattleyCooper 0

Add support for video

Hi, do you think it would be hard to implement deepflow/deepmatching to process multiple frames ? Let me know if it seems too difficult or not and I can have a look. I'm interested in making videos with it.

opened by martync 0

Releases(v0.0.5)

v0.0.5(Mar 14, 2016)

Source code(tar.gz)
Source code(zip)
vgg16_weights.h5(56.19 MB)

Generate image analogies using neural matching and blending

Related tags

Overview

neural image analogies

Installation

It's too slow

I want it to look better

Parameters

License

Comments

v1.21.0

NumPy 1.21.0 Release Notes

New functions

Add PCG64DXSM BitGenerator

Expired deprecations

9.3.0

Changes

9.3.0 (2022-10-29)

v1.22.0

NumPy 1.22.0 Release Notes

Expired deprecations

Deprecated numeric style dtype strings have been removed

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

5.2 (2019-12-02)

5.1.2 (2019-07-30)

Releases(v0.0.5)

v0.0.5(Mar 14, 2016)

Owner

Adam Wentz

QAT(quantize aware training) for classification with MQBench

PyTorch implemention of ICCV'21 paper SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation

An open framework for Federated Learning.

Neural Logic Inductive Learning

This repo includes our code for evaluating and improving transferability in domain generalization (NeurIPS 2021)

Pretraining on Dynamic Graph Neural Networks

TransReID: Transformer-based Object Re-Identification

CPU inference engine that delivers unprecedented performance for sparse models

Twins: Revisiting the Design of Spatial Attention in Vision Transformers

Source code for paper: Knowledge Inheritance for Pre-trained Language Models

duralava is a neural network which can simulate a lava lamp in an infinite loop.

Human segmentation models, training/inference code, and trained weights, implemented in PyTorch

It is a system used to detect bone fractures. using techniques deep learning and image processing

A library for preparing, training, and evaluating scalable deep learning hybrid recommender systems using PyTorch.

Development of IP code based on VIPs and AADM

A PyTorch implementation of "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning", IJCAI-21

Pytorch implementation for M^3L

Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes

Improving 3D Object Detection with Channel-wise Transformer

Ready-to-use code and tutorial notebooks to boost your way into few-shot image classification.

Expired deprecations for `loads`, `ndfromtxt`, and `mafromtxt` in npyio