Merge branch 'python38' into 'master'

Support for python-3.8

See merge request bob/bob.conda!443

conda/aniso8601/meta.yaml

-{% set name = "aniso8601" %}
-{% set version = "4.0.1" %}
-{% set sha256 = "e7560de91bf00baa712b2550a2fdebf0188c5fce2fcd1162fbac75c19bb29c95" %}
-
-package:
-  name: {{ name }}
-  version: {{ version }}
-
-source:
-  fn: {{ name }}-{{ version }}.tar.gz
-  url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/{{ name }}-{{ version }}.tar.gz
-  sha256: {{ sha256 }}
-
-build:
-  number: 0
-  script: python -m pip install --no-deps --ignore-installed .
-
-requirements:
-  build:
-    - pip
-    - python
-    - setuptools
-
-  run:
-    - python
-    - python-dateutil
-
-test:
-  imports:
-    - aniso8601
-
-about:
-  home: https://bitbucket.org/nielsenb/aniso8601
-  license: BSD 3-Clause
-  summary: A library for parsing ISO 8601 strings.
-
-extra:
-  recipe-maintainers:
-    - anjos

conda/aspy.yaml/meta.yaml

-{% set name = "aspy.yaml" %}
-{% set version = "1.1.1" %}
-{% set file_ext = "tar.gz" %}
-{% set hash_type = "sha256" %}
-{% set hash_value = "0a77e23fafe7b242068ffc0252cee130d3e509040908fc678d9d1060e7494baa" %}
-
-package:
-  name: '{{ name|lower }}'
-  version: '{{ version }}'
-
-source:
-  fn: '{{ name }}-{{ version }}.{{ file_ext }}'
-  url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/{{ name }}-{{ version }}.{{ file_ext }}
-  '{{ hash_type }}': '{{ hash_value }}'
-
-build:
-  number: 0
-  entry_points:
-    - identify-cli=identify.cli:main
-  script: "{{ PYTHON }} -m pip install . --no-deps --ignore-installed --no-cache-dir -vvv"
-
-requirements:
-  host:
-    - pip
-    - python
-    - setuptools
-  run:
-    - python
-    - pyyaml
-
-test:
-  imports:
-    - aspy
-    - aspy.yaml
-
-about:
-  home: https://github.com/asottile/aspy.yaml
-  license: MIT
-  license_family: MIT
-  summary: A few extensions to pyyaml.
-
-extra:
-  recipe-maintainers: sgaist

conda/beat-devel/meta.yaml

@@ -18,11 +18,11 @@ requirements:
     - python {{ python }}
     - docker-py 4.2.0
     - jsonschema 3.2.0
+    - ncurses 6.2
     - python-graphviz 0.13.2
     - pyzmq 18.1.1
     - simplejson 3.17.0
     - sphinxcontrib-httpdomain 1.7.0
-    - ncurses 6.2
   run:
     - python
   run_constrained:

conda/bob-devel/meta.yaml

@@ -23,72 +23,59 @@ requirements:
 
     # Dependency list of bob packages. Everything is pinned to allow for better
     # reproducibility.
-    - boost 1.67.0
-    - caffe 1.0  # [linux]
-    - click 7.1.1
-    - click-plugins 1.1.1
-    - cmake 3.14.0
-    - coverage 5.0
-    - cyvlfeat 0.5.1
-    - docopt 0.6.2
-    # to make sure we get ffmpeg from our own channel which has more codecs
-    - ffmpeg 4.0 hadceb68_1  # [linux]
-    - ffmpeg 4.0 h2047f9e_1  # [osx]
-    - freetype 2.9.1
-    - giflib 5.1.4
-    - h5py 2.8.0
-    - hdf5 1.10.2
-    - jinja2 2.11.1
-    - jpeg 9b
-    - kaldi 5.5.164  # [linux]
-    - libblitz 1.0.1
-    - libmatio 1.5.17
-    - libogg 1.3.2
-    - libpng 1.6.37
-    - libsvm 3.24
-    - libtiff 4.1.0
-    - matplotlib 3.1.3
-    - menpo 0.9.2
-    - menpofit 0.6.1
-    - mkl 2019.4
-    - mne 0.18.1
-    - mr.developer 2.0.0
-    - nose 1.3.7
-    - numpy 1.18.1
-    - opencv 3.4.2
-    - pillow 6.2.1
-    - pkg-config 0.29.2
-    - pyedflib 0.1.14
-    - cudatoolkit 10.0  # [linux]
-    - pandas 1.0.3
-    - pytorch 1.3.1
-    - pyyaml 5.3.1
-    - requests 2.23.0
-    - schema 0.7.1
-    - scikit-image 0.16.2
-    - scikit-learn 0.22.1
-    - scipy 1.4.1
-    - setuptools 46.1.1
-    - six 1.14.0
-    - sox 14.4.2
-    - sphinx 2.4.4
-    - sphinx_rtd_theme 0.4.3
-    - sphinxcontrib-programoutput 0.16
-    - sqlalchemy 1.3.15
-    - tabulate 0.8.3
-    - tensorflow 1.15.0
-    - torchvision 0.4.2  # [linux]
-    - tqdm 4.43.0
-    - vlfeat 0.9.21
-    - zc.buildout 2.13.3
-    - zc.recipe.egg 2.0.7
+    - boost
+    - click
+    - click-plugins
+    - cmake
+    - coverage
+    - docopt
+    - ffmpeg
+    - freetype
+    - giflib
+    - h5py
+    - hdf5
+    - jinja2
+    - jpeg
+    - libblitz
+    - libmatio
+    - libpng
+    - libsvm
+    - libtiff
+    - matplotlib
+    - mkl
+    - nose
+    - numpy
+    - opencv
+    - pandas
+    - pillow
+    - pkg-config
+    - pybind11
+    - pytorch
+    - pyyaml
+    - requests
+    - schema
+    - scikit-image
+    - scikit-learn
+    - scipy
+    - setuptools
+    - six
+    - sox
+    - sphinx
+    - sphinx_rtd_theme
+    - sphinxcontrib-programoutput
+    - sqlalchemy
+    - tabulate
+    - tensorflow
+    - torchvision
+    - tqdm
+    - vlfeat
+    - zc.buildout
+    - zc.recipe.egg
     # beat-devel direct dependencies that leak to bob-devel as indirect
     # dependencies:
-    - termcolor 1.1.0
-    - psycopg2 2.8.4
+    - termcolor
+    - psycopg2
     # semi-supported dependencies (drop them if they conflict):
-    - dlib 19.7
-    - pybind11 2.4.3
   run:
     - python
   run_constrained:
@@ -102,8 +89,8 @@ test:
   requires:
     - numpy
     - ffmpeg
-    - pytorch  # [linux]
-    - torchvision  # [linux]
+    - pytorch
+    - torchvision
   commands:
     # we expect these features from ffmpeg:
     - ffmpeg -codecs | grep "DEVI.S zlib"  # [unix]

conda/cfgv/meta.yaml

-{% set name = "cfgv" %}
-{% set version = "1.4.0" %}
-{% set file_ext = "tar.gz" %}
-{% set hash_type = "sha256" %}
-{% set hash_value = "39d9055c47e3932908fe25abd5807e21dc002630db01c7a5f05738d027e2b706" %}
-
-package:
-  name: '{{ name|lower }}'
-  version: '{{ version }}'
-
-source:
-  fn: '{{ name }}-{{ version }}.{{ file_ext }}'
-  url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/{{ name }}-{{ version }}.{{ file_ext }}
-  '{{ hash_type }}': '{{ hash_value }}'
-
-build:
-  number: 0
-  entry_points:
-    - identify-cli=identify.cli:main
-  script: "{{ PYTHON }} -m pip install . --no-deps --ignore-installed --no-cache-dir -vvv"
-
-requirements:
-  host:
-    - pip
-    - python
-  build:
-    - six
-  run:
-    - python
-    - six
-
-test:
-  imports:
-    - cfgv
-
-about:
-  home: https://github.com/asottile/cfgv
-  license: MIT
-  license_family: MIT
-  summary: Validate configuration and produce human readable error messages.
-
-extra:
-  recipe-maintainers: sgaist

conda/cyvlfeat/bld.bat

-"%PYTHON%" setup.py install --single-version-externally-managed --record=%TEMP%record.txt
-
-if errorlevel 1 exit 1

conda/cyvlfeat/build.sh

-#!/bin/sh
-
-if [ "$(uname)" == "Linux" ]; then
-  CFLAGS="${CFLAGS} -I${CONDA_PREFIX}/include" LDFLAGS="-L${CONDA_PREFIX}/lib -lvl -Wl,-rpath=${CONDA_PREFIX}/lib "
-else
-  CFLAGS="${CFLAGS} -I${CONDA_PREFIX}/include" LDFLAGS="-L${CONDA_PREFIX}/lib -lvl "
-fi
-
-"$PYTHON" setup.py install --single-version-externally-managed --record=record.txt
-
-if [ "$(uname -s)" == "Darwin" ]; then
-  # For some reason Cython refuses to generate @rpath/vlfeat.dylib - so
-  # we manually do it here
-  find $SP_DIR/cyvlfeat -name "*.so" -print0 | while read -d $'\0' file
-  do
-    install_name_tool -change @loader_path/libvl.dylib @rpath/libvl.dylib $file
-  done
-fi

conda/cyvlfeat/meta.yaml

-{% set name = "cyvlfeat" %}
-{% set version = "0.5.1" %}
-{% set sha256 = "816d0609c5f5a1b4e4a620a9cb204abe26c8cd6a166d077923242f6cf15c2c09" %}
-
-package:
-  name: {{ name }}
-  version: {{ version }}
-
-source:
-  fn: {{ name }}-v{{ version }}.tar.gz
-  url: https://github.com/menpo/cyvlfeat/archive/v{{ version }}.tar.gz
-  sha256: {{ sha256 }}
-
-build:
-  number: 1
-
-requirements:
-  build:
-    - {{ compiler('c') }}
-    - {{ compiler('cxx') }}
-
-  host:
-    - python
-    - setuptools {{ setuptools }}
-    - cython {{ cython }}
-    - numpy {{ numpy }}
-    - vlfeat {{ vlfeat }}
-
-  run:
-    - python
-    - {{ pin_compatible('numpy') }}
-
-test:
-  requires:
-    - nose
-    - pytest
-    - coverage
-
-  imports:
-    - cyvlfeat.sift.sift
-    - cyvlfeat.sift.dsift
-    - cyvlfeat.fisher.fisher
-    - cyvlfeat.hog.hog
-    - cyvlfeat.kmeans.kmeans
-    - cyvlfeat.generic.generic
-
-  commands:
-    - nosetests {{ name }} -v --cover-package={{ name }}
-    - conda inspect linkages -p $PREFIX {{ name }}  # [not win]
-    - conda inspect objects -p $PREFIX {{ name }}  # [osx]
-
-about:
-  home: https://github.com/menpo/cyvlfeat/
-  summary: A thin Cython wrapper around select areas of vlfeat
-  license: BSD
-
-extra:
-  recipe-maintainers:
-    - anjos

conda/ddt/meta.yaml

@@ -13,7 +13,7 @@ source:
   sha256: {{ sha256 }}
 
 build:
-  number: 1
+  number: 2
   script: python -m pip install --no-deps --ignore-installed .
 
 requirements:

conda/dlib/FACE_LICENSE

-The images face.jpg/face.png are used under the Creative Commons 2.0 License, 
-which can be found here:
-
-https://creativecommons.org/licenses/by/2.0/
-
-The image was found on Flickr under the following URL:
-
-https://flic.kr/p/x1TgvF
-
-and was provided by user Jane Kostenko (layma1999): 
-
-https://www.flickr.com/photos/133533365@N06/
-
-The "medium" version of the image was downloaded on 21/07/2016 and was modified
-only via reencoding to png.
-
-The intended use of this image is for testing purposes only.

conda/dlib/bld.bat

-@echo ON
-
-mkdir build
-cd build
-
-rem The Python lib has no period in the version string, so we remove it here.
-set PY_VER_NO_DOT=%PY_VER:.=%
-rem Required as the png locator does some processing that chokes on \
-set "PNG_LIBRARY=%LIBRARY_LIB%\libpng16.lib"
-set "PNG_LIBRARY=%PNG_LIBRARY:\=/%"
-
-rem There is a bug whereby linking to jpeg causes a link crash, so we allow
-rem dlib to build it
-cmake ..\tools\python -LAH -G"NMake Makefiles"              ^
--DCMAKE_BUILD_TYPE="Release"                                ^
--DCMAKE_PREFIX_PATH="%LIBRARY_PREFIX%"                      ^
--DBoost_USE_STATIC_LIBS=0                                   ^
--DBoost_USE_STATIC_RUNTIME=0                                ^
--DBOOST_ROOT="%LIBRARY_PREFIX%"                             ^
--DBOOST_INCLUDEDIR="%LIBRARY_INC%"                          ^
--DBOOST_LIBRARYDIR="%LIBRARY_LIB%"                          ^
--DPYTHON3=%PY3K%                                            ^
--DPYTHON_LIBRARY="%PREFIX%\libs\python%PY_VER_NO_DOT%.lib"  ^
--DPYTHON_INCLUDE_DIR="%PREFIX%\include"                     ^
--DDLIB_LINK_WITH_SQLITE3=0                                  ^
--DDLIB_PNG_SUPPORT=1                                        ^
--DPNG_INCLUDE_DIR="%LIBRARY_INC%"                           ^
--DPNG_PNG_INCLUDE_DIR="%LIBRARY_INC%"                       ^
--DPNG_LIBRARY="%PNG_LIBRARY%"                               ^
--DZLIB_INCLUDE_DIRS="%LIBRARY_INC%"                         ^
--DZLIB_LIBRARIES="%LIBRARY_BIN%\zlib.dll"                   ^
--DDLIB_JPEG_SUPPORT=1                                       ^
--DDLIB_USE_BLAS=0                                           ^
--DDLIB_USE_LAPACK=0                                         ^
--DUSE_SSE2_INSTRUCTIONS=1                                   ^
--DUSE_SSE4_INSTRUCTIONS=0                                   ^
--DUSE_AVX_INSTRUCTIONS=0                                    ^
--DDLIB_USE_CUDA=0                                           ^
--DDLIB_GIF_SUPPORT=0
-if errorlevel 1 exit 1
-
-cmake --build . --target INSTALL --config Release
-if errorlevel 1 exit 1
-
-rem Copy the dlib library to site packages
-move "..\python_examples\dlib.pyd" "%SP_DIR%\dlib.pyd"
-if errorlevel 1 exit 1

conda/dlib/build.sh

-#!/bin/bash
-
-set +x
-
-rm -fr build
-mkdir -p build
-cd build
-
-if [ $PY3K -eq 1 ]; then
-  export PY_STR="${PY_VER}m"
-else
-  export PY_STR="${PY_VER}"
-fi
-
-# Make the probably sensible assumption that a 64-bit
-# machine supports SSE4 instructions - if this becomes
-# a problem we should turn this off.
-if [ $ARCH -eq 64 ]; then
-  USE_SSE4=1
-else
-  USE_SSE4=0
-fi
-
-PYTHON_LIBRARY_PATH="$PREFIX/lib/libpython$PY_STR$SHLIB_EXT"
-
-cmake -LAH ../tools/python                              \
-  -DCMAKE_PREFIX_PATH="$PREFIX"                         \
-  -DCMAKE_BUILD_TYPE="Release"                          \
-  -DBoost_USE_STATIC_LIBS=0                             \
-  -DBoost_USE_STATIC_RUNTIME=0                          \
-  -DBOOST_ROOT="$PREFIX"                                \
-  -DBOOST_INCLUDEDIR="$PREFIX/include"                  \
-  -DBOOST_LIBRARYDIR="$PREFIX/lib"                      \
-  -DPYTHON_LIBRARY="$PYTHON_LIBRARY_PATH"               \
-  -DPYTHON_INCLUDE_DIR="$PREFIX/include/python$PY_STR"  \
-  -DPYTHON3=$PY3K                                       \
-  -DDLIB_PNG_SUPPORT=1                                  \
-  -DPNG_INCLUDE_DIR="$PREFIX/include"                   \
-  -DPNG_PNG_INCLUDE_DIR="$PREFIX/include"               \
-  -DPNG_LIBRARY="$PREFIX/lib/libpng$SHLIB_EXT"          \
-  -DZLIB_INCLUDE_DIRS="$PREFIX/include"                 \
-  -DZLIB_LIBRARIES="$PREFIX/lib/libz$SHLIB_EXT"         \
-  -DDLIB_JPEG_SUPPORT=1                                 \
-  -DJPEG_INCLUDE_DIR="$PREFIX/include"                  \
-  -DJPEG_LIBRARY="$PREFIX/lib/libjpeg$SHLIB_EXT"        \
-  -DDLIB_LINK_WITH_SQLITE3=1                            \
-  -DDLIB_NO_GUI_SUPPORT=1                               \
-  -DUSE_SSE2_INSTRUCTIONS=1                             \
-  -DUSE_SSE4_INSTRUCTIONS=$USE_SSE4                     \
-  -DUSE_AVX_INSTRUCTIONS=0                              \
-  -DDLIB_USE_BLAS=1                                     \
-  -DDLIB_USE_LAPACK=1                                   \
-  -DDLIB_USE_CUDA=0                                     \
-  -DDLIB_GIF_SUPPORT=0
-
-make -j$CPU_COUNT
-# Non-standard installation - copy manually
-cp dlib.so $SP_DIR

conda/dlib/images.xml

-<?xml version='1.0' encoding='ISO-8859-1'?>
-<?xml-stylesheet type='text/xsl' href='image_metadata_stylesheet.xsl'?>
-<dataset>
-<name>Training faces</name>
-<comment>See the FACE_LICENSE file for more information.</comment>
-<images>
-  <image file='face.jpg'>
-    <box top='125' left='156' width='278' height='240'/>
-  </image>
-  <image file='face.png'>
-    <box top='125' left='156' width='278' height='240'/>
-  </image>
-</images>
-</dataset>

conda/dlib/lapack_sgetrf.patch

---- dlib/cmake_utils/cmake_find_blas.txt
-+++ dlib/cmake_utils/cmake_find_blas.txt
-@@ -156,15 +156,8 @@
-          set(blas_found 1)
-          message(STATUS "Found OpenBLAS library")
-          set(CMAKE_REQUIRED_LIBRARIES ${blas_libraries})
--         # If you compiled OpenBLAS with LAPACK in it then it should have the
--         # sgetrf_single function in it.  So if we find that function in
--         # OpenBLAS then just use OpenBLAS's LAPACK. 
--         CHECK_FUNCTION_EXISTS(sgetrf_single OPENBLAS_HAS_LAPACK)
--         if (OPENBLAS_HAS_LAPACK)
--            message(STATUS "Using OpenBLAS's built in LAPACK")
--            # set(lapack_libraries gfortran) 
--            set(lapack_found 1)
--         endif()
-+         message(STATUS "Using OpenBLAS's built in LAPACK")
-+         set(lapack_found 1)
-       endif()
-       mark_as_advanced( cblas_lib)
-    endif()
-

conda/dlib/meta.yaml

-{% set name = "dlib" %}
-{% set version = "19.7" %}
-
-package:
-  name: {{ name }}
-  version: {{ version }}
-
-source:
-  url: https://github.com/davisking/{{ name }}/archive/v{{ version }}.tar.gz
-  sha1: 6dfc0dfa1cd45baa39a9b03238b222cd59041194
-
-  patches:
-    - osx_jpeg.patch           # [osx]
-    - lapack_sgetrf.patch      # [unix]
-    - win_boost.patch          # [win]
-    - win_jpeg_png.patch       # [win]
-    - win_release_flags.patch  # [win]
-    - win_thread_fix.patch     # [win]
-
-build:
-  number: 3
-  skip: True  # [blas_impl == 'mkl' and win]
-
-requirements:
-  build:
-    - cmake
-    - {{ compiler('c') }}
-    - {{ compiler('cxx') }}
-    - pkg-config
-
-  host:
-    - python
-    - numpy {{ numpy }}
-    - py-boost {{ boost }}
-    - libpng {{ libpng }}
-    - mkl-devel {{ mkl }}  # [blas_impl == 'mkl']
-    - jpeg {{ jpeg }}  # [not win]
-    - sqlite {{ sqlite }}  # [not win]
-
-  run:
-    - python
-    - {{ pin_compatible('numpy') }}
-    - py-boost {{ boost }}
-
-test:
-  files:
-    - face.jpg
-    - face.png
-    - images.xml
-
-  requires:
-    - tqdm
-    - pillow
-
-  imports:
-    - dlib
-
-  commands:
-    - conda inspect linkages -p $PREFIX {{ name }}  # [not win]
-    - conda inspect objects -p $PREFIX {{ name }}  # [osx]
-
-about:
-  home: http://dlib.net/
-  license: Boost 1.0
-  license_file: dlib/LICENSE.txt
-  summary: Modern C++ toolkit containing machine learning algorithms with Python bindings
-
-extra:
-  recipe-maintainers:
-    - tiago.pereira
-    - anjos
-    - amohammadi

conda/dlib/osx_jpeg.patch

---- dlib/CMakeLists.txt
-+++ dlib/CMakeLists.txt
-@@ -403,7 +403,7 @@
-             set(CMAKE_REQUIRED_LIBRARIES ${JPEG_LIBRARY})
-             CHECK_FUNCTION_EXISTS(jpeg_read_header LIBJPEG_IS_GOOD)
-          endif()
--         if (JPEG_FOUND AND LIBJPEG_IS_GOOD AND NOT APPLE)
-+         if (JPEG_FOUND AND LIBJPEG_IS_GOOD)
-             include_directories(${JPEG_INCLUDE_DIR})
-             set (dlib_needed_libraries ${dlib_needed_libraries} ${JPEG_LIBRARY})
-          else()
-

conda/dlib/run_test.py

-import os
-import sys
-import bz2
-import unittest
-from functools import partial
-
-import dlib
-import numpy as np
-from PIL import Image
-from tqdm import tqdm
-
-
-SHAPE_PREDICTOR_FNAME = 'shape_predictor_68_face_landmarks.dat'
-SHAPE_PREDICTOR_BZ2_FNAME = SHAPE_PREDICTOR_FNAME + '.bz2'
-SHAPE_PREDICTOR_URL = 'http://dlib.net/files/{}'.format(SHAPE_PREDICTOR_BZ2_FNAME)
-
-
-def _download_file(url, out_path):
-    try:
-        from urllib import urlretrieve          # Python 2
-    except ImportError:
-        from urllib.request import urlretrieve  # Python 3
-
-    # Wrap tqdm instance with urlretrieve compatible function
-    # Abuse mutable [] argument to give function 'memory'
-    # First argument will be supplied using partial (an instance of tqdm)
-    def reporthook(t, b=1, bsize=1, tsize=None, last_b=[0]):
-        if tsize is not None:
-            t.total = tsize
-        t.update((b - last_b[0]) * bsize)
-        last_b[0] = b
-
-    with tqdm(unit='B', unit_scale=True, miniters=1, desc=out_path) as t:
-        urlretrieve(url, filename=out_path, reporthook=partial(reporthook, t))
-
-
-def _bz2_decompress_inplace(path, out_path):
-    with open(path, 'rb') as source, open(out_path, 'wb') as dest:
-        dest.write(bz2.decompress(source.read()))
-
-
-def _load_image_using_pillow(path):
-    return np.array(Image.open(path))
-
-
-class TestDlib(unittest.TestCase):
-
-    @classmethod
-    def setUpClass(cls):
-        # Get paths to test data
-        test_dir_path = os.path.dirname(os.path.abspath(__file__))
-        cls.face_jpg_path = os.path.join(test_dir_path, 'face.jpg')
-        cls.face_png_path = os.path.join(test_dir_path, 'face.png')
-
-        # Download shape_predictor model
-        print('Downloading {} to ./{}'.format(SHAPE_PREDICTOR_URL, 
-                                              SHAPE_PREDICTOR_BZ2_FNAME))
-        _download_file(SHAPE_PREDICTOR_URL, SHAPE_PREDICTOR_BZ2_FNAME)
-        _bz2_decompress_inplace(SHAPE_PREDICTOR_BZ2_FNAME,
-                                SHAPE_PREDICTOR_FNAME)
-
-    def test_builtin_frontal_face_detection(self):
-        detector = dlib.get_frontal_face_detector()
-        image = _load_image_using_pillow(self.face_jpg_path)
-        results = detector(image)
-        self.assertEqual(len(results), 1)
-
-    def test_shape_predictor(self):
-        predictor = dlib.shape_predictor(SHAPE_PREDICTOR_FNAME)
-        image = _load_image_using_pillow(self.face_jpg_path)
-
-        # This is the output of the detector, hardcoded
-        detection = dlib.rectangle(left=125, top=56, right=434, bottom=365)
-        shape = predictor(image, detection)
-        self.assertEqual(len(shape.parts()), 68)
-        for p in shape.parts():
-            self.assertGreater(p.x, 0)
-            self.assertGreater(p.y, 0)
-
-    def test_train_xml_detector(self):
-        # This effectively tests that we can successfully load images
-        options = dlib.simple_object_detector_training_options()
-        options.add_left_right_image_flips = True
-        options.C = 1
-        options.num_threads = 1
-
-        dlib.train_simple_object_detector('images.xml', "test.svm", options)