@@ -6,8 +6,6 @@ datasetstools. Tools for working with different datasets.
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@@ -6,8 +6,6 @@ datasetstools. Tools for working with different datasets.
The datasetstools module includes classes for working with different datasets.
The datasetstools module includes classes for working with different datasets.
First version of this module was implemented for **Fall2014 OpenCV Challenge**.
Action Recognition
Action Recognition
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@@ -50,13 +48,13 @@ FR_lfw
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@@ -50,13 +48,13 @@ FR_lfw
Implements loading dataset:
Implements loading dataset:
_`"Labeled Faces in the Wild-a"`: http://www.openu.ac.il/home/hassner/data/lfwa/
_`"Labeled Faces in the Wild"`: http://vis-www.cs.umass.edu/lfw/
.. note:: Usage
.. note:: Usage
1. From link above download dataset file: lfwa.tar.gz.
1. From link above download any dataset file: lfw.tgz\lfwa.tar.gz\lfw-deepfunneled.tgz\lfw-funneled.tgz and file with 10 test splits: pairs.txt.
2. Unpack it.
2. Unpack dataset file and place pairs.txt in created folder.
3. To load data run: ./opencv/build/bin/example_datasetstools_fr_lfw -p=/home/user/path_to_unpacked_folder/lfw2/
3. To load data run: ./opencv/build/bin/example_datasetstools_fr_lfw -p=/home/user/path_to_unpacked_folder/lfw2/
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@@ -75,9 +73,11 @@ _`"ChaLearn Looking at People"`: http://gesture.chalearn.org/
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@@ -75,9 +73,11 @@ _`"ChaLearn Looking at People"`: http://gesture.chalearn.org/
1. Follow instruction from site above, download files for dataset "Track 3: Gesture Recognition": Train1.zip-Train5.zip, Validation1.zip-Validation3.zip (Register on site: www.codalab.org and accept the terms and conditions of competition: https://www.codalab.org/competitions/991#learn_the_details There are three mirrors for downloading dataset files. When I downloaded data only mirror: "Universitat Oberta de Catalunya" works).
1. Follow instruction from site above, download files for dataset "Track 3: Gesture Recognition": Train1.zip-Train5.zip, Validation1.zip-Validation3.zip (Register on site: www.codalab.org and accept the terms and conditions of competition: https://www.codalab.org/competitions/991#learn_the_details There are three mirrors for downloading dataset files. When I downloaded data only mirror: "Universitat Oberta de Catalunya" works).
2. Unpack train archives Train1.zip-Train5.zip to one folder (currently loading validation files wasn't implemented)
2. Unpack train archives Train1.zip-Train5.zip to folder Train/, validation archives Validation1.zip-Validation3.zip to folder Validation/
3. To load data run: ./opencv/build/bin/example_datasetstools_gr_chalearn -p=/home/user/path_to_unpacked_folder/
3. Unpack all archives in Train/ & Validation/ in the folders with the same names, for example: Sample0001.zip to Sample0001/
4. To load data run: ./opencv/build/bin/example_datasetstools_gr_chalearn -p=/home/user/path_to_unpacked_folders/
GR_skig
GR_skig
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@@ -239,13 +239,29 @@ Currently implemented loading full list with urls. Planned to implement dataset
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@@ -239,13 +239,29 @@ Currently implemented loading full list with urls. Planned to implement dataset
3. To load data run: ./opencv/build/bin/example_datasetstools_or_imagenet -p=/home/user/path_to_unpacked_file/
3. To load data run: ./opencv/build/bin/example_datasetstools_or_imagenet -p=/home/user/path_to_unpacked_file/
OR_mnist
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.. ocv:class:: OR_mnist
Implements loading dataset:
_`"MNIST"`: http://yann.lecun.com/exdb/mnist/
.. note:: Usage
1. From link above download dataset files: t10k-images-idx3-ubyte.gz, t10k-labels-idx1-ubyte.gz, train-images-idx3-ubyte.gz, train-labels-idx1-ubyte.gz.
2. Unpack them.
3. To load data run: ./opencv/build/bin/example_datasetstools_or_mnist -p=/home/user/path_to_unpacked_files/
OR_sun
OR_sun
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.. ocv:class:: OR_sun
.. ocv:class:: OR_sun
Implements loading dataset:
Implements loading dataset:
_`"SUN Database"`: http://sun.cs.princeton.edu/
_`"SUN Database"`: http://sundatabase.mit.edu/
Currently implemented loading "Scene Recognition Benchmark. SUN397". Planned to implement also "Object Detection Benchmark. SUN2012".
Currently implemented loading "Scene Recognition Benchmark. SUN397". Planned to implement also "Object Detection Benchmark. SUN2012".