Removed references to boost.python since it's no longer needed.

docs/docs/faq.xml

@@ -54,33 +54,6 @@ cmake --build . --config Release
 
       <!-- ****************************************** -->
 
-      <question text="Why won't the Python bindings compile/work?">
-         To compile dlib's Python bindings you need a correctly installed copy of Boost.Python.  
-         If you are getting errors either during compile time or when you try to import dlib
-         then it is <b>very likely</b> that you have installed Boost.Python incorrectly.
-         <p>
-            <b>It is critical that the copy of Boost.Python you are using is compiled against the specific
-            version of the Python interpreter you are trying to use.</b>  If this is not the case then it won't work.
-            Here are some reasons why you might not have a correct install of Boost.Python
-            <ul>
-               <li>You downloaded Boost.Python binaries from the internet and
-                  they aren't build for whatever Python you are using.  The fix
-                  for this is to either find the correct Boost.Python binaries
-                  or build Boost.Python yourself.</li>
-               <li>You have more than one copy of Python installed on your computer.  The build scripts will try to use
-               whatever python is in your path when you build.  So you need to be consistent about what python interpreter you are using.
-               Everything, both dlib and boost, need to be compiled against the specific Python interpreter you want to use. The simplest thing to do is to 
-               delete other Python interpreters from your machine so there is no confusion.
-               </li>
-            </ul>
-            Other problems users have reported are compiler errors related to X11.  This is pretty much always
-            caused by Anaconda, which includes broken X11 headers in its distribution.  Delete Anaconda if you have
-            this problem.
-         </p>
-      </question>
-
-      <!-- ****************************************** -->
-
       <question text="Why is dlib slow?">
          Dlib isn't slow.  I get this question many times a week and 95% of the time it's from someone
          using Visual Studio who has compiled their program in Debug mode rather than the optimized

python_examples/cnn_face_detector.py

@@ -29,9 +29,9 @@
 #   and you have CUDA installed since this makes things run *much* faster.
 #
 #   Compiling dlib should work on any operating system so long as you have
-#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
-#   running the command:
-#       sudo apt-get install libboost-python-dev cmake
+#   CMake installed.  On Ubuntu, this can be done easily by running the
+#   command:
+#       sudo apt-get install cmake
 #
 #   Also note that this example requires scikit-image which can be installed
 #   via the command:

python_examples/correlation_tracker.py

@@ -28,9 +28,9 @@
 #   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
-#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
-#   running the command:
-#       sudo apt-get install libboost-python-dev cmake
+#   CMake installed.  On Ubuntu, this can be done easily by running the
+#   command:
+#       sudo apt-get install cmake
 #
 #   Also note that this example requires scikit-image which can be installed
 #   via the command:

python_examples/face_alignment.py

@@ -17,9 +17,9 @@
 #   installed.
 #
 #   Compiling dlib should work on any operating system so long as you have
-#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
-#   running the command:
-#       sudo apt-get install libboost-python-dev cmake
+#   CMake installed.  On Ubuntu, this can be done easily by running the
+#   command:
+#       sudo apt-get install cmake
 #
 #   Also note that this example requires OpenCV and Numpy which can be installed
 #   via the command:

python_examples/face_clustering.py

@@ -24,9 +24,9 @@
 #   installed.
 #
 #   Compiling dlib should work on any operating system so long as you have
-#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
-#   running the command:
-#       sudo apt-get install libboost-python-dev cmake
+#   CMake installed.  On Ubuntu, this can be done easily by running the
+#   command:
+#       sudo apt-get install cmake
 #
 #   Also note that this example requires scikit-image which can be installed
 #   via the command:

python_examples/face_detector.py

@@ -33,9 +33,9 @@
 #   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
-#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
-#   running the command:
-#       sudo apt-get install libboost-python-dev cmake
+#   CMake installed.  On Ubuntu, this can be done easily by running the
+#   command:
+#       sudo apt-get install cmake
 #
 #   Also note that this example requires scikit-image which can be installed
 #   via the command:

python_examples/face_jitter.py

@@ -21,9 +21,9 @@
 #   installed.
 #
 #   Compiling dlib should work on any operating system so long as you have
-#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
-#   running the command:
-#       sudo apt-get install libboost-python-dev cmake
+#   CMake installed.  On Ubuntu, this can be done easily by running the
+#   command:
+#       sudo apt-get install cmake
 #
 #   Also note that this example requires OpenCV and Numpy which can be installed
 #   via the command:

python_examples/face_landmark_detection.py

@@ -41,9 +41,9 @@
 #   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
-#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
-#   running the command:
-#       sudo apt-get install libboost-python-dev cmake
+#   CMake installed.  On Ubuntu, this can be done easily by running the
+#   command:
+#       sudo apt-get install cmake
 #
 #   Also note that this example requires scikit-image which can be installed
 #   via the command:

python_examples/face_recognition.py

@@ -36,9 +36,9 @@
 #   installed.
 #
 #   Compiling dlib should work on any operating system so long as you have
-#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
-#   running the command:
-#       sudo apt-get install libboost-python-dev cmake
+#   CMake installed.  On Ubuntu, this can be done easily by running the
+#   command:
+#       sudo apt-get install cmake
 #
 #   Also note that this example requires scikit-image which can be installed
 #   via the command:

python_examples/find_candidate_object_locations.py

@@ -27,9 +27,9 @@
 #   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
-#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
-#   running the command:
-#       sudo apt-get install libboost-python-dev cmake
+#   CMake installed.  On Ubuntu, this can be done easily by running the
+#   command:
+#       sudo apt-get install cmake
 #
 #   Also note that this example requires scikit-image which can be installed
 #   via the command:

python_examples/global_optimization.py

@@ -21,9 +21,9 @@
 #       python setup.py install
 #
 #   Compiling dlib should work on any operating system so long as you have
-#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
-#   running the command:
-#       sudo apt-get install libboost-python-dev cmake
+#   CMake installed.  On Ubuntu, this can be done easily by running the
+#   command:
+#       sudo apt-get install cmake
 #
 
 import dlib

python_examples/max_cost_assignment.py

@@ -18,9 +18,9 @@
 #   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
-#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
-#   running the command:
-#       sudo apt-get install libboost-python-dev cmake
+#   CMake installed.  On Ubuntu, this can be done easily by running the
+#   command:
+#       sudo apt-get install cmake
 #
 
 import dlib

python_examples/sequence_segmenter.py

@@ -23,9 +23,9 @@
 #   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
-#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
-#   running the command:
-#       sudo apt-get install libboost-python-dev cmake
+#   CMake installed.  On Ubuntu, this can be done easily by running the
+#   command:
+#       sudo apt-get install cmake
 #
 import sys
 import dlib

python_examples/svm_binary_classifier.py

@@ -20,9 +20,9 @@
 #   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
-#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
-#   running the command:
-#       sudo apt-get install libboost-python-dev cmake
+#   CMake installed.  On Ubuntu, this can be done easily by running the
+#   command:
+#       sudo apt-get install cmake
 #
 
 import dlib

python_examples/svm_rank.py

@@ -27,9 +27,9 @@
 #   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
-#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
-#   running the command:
-#       sudo apt-get install libboost-python-dev cmake
+#   CMake installed.  On Ubuntu, this can be done easily by running the
+#   command:
+#       sudo apt-get install cmake
 #
 
 import dlib

python_examples/svm_struct.py

@@ -27,9 +27,9 @@
 #   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
-#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
-#   running the command:
-#       sudo apt-get install libboost-python-dev cmake
+#   CMake installed.  On Ubuntu, this can be done easily by running the
+#   command:
+#       sudo apt-get install cmake
 #
 
 import dlib

python_examples/train_object_detector.py

@@ -21,9 +21,9 @@
 #   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
-#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
-#   running the command:
-#       sudo apt-get install libboost-python-dev cmake
+#   CMake installed.  On Ubuntu, this can be done easily by running the
+#   command:
+#       sudo apt-get install cmake
 #
 #   Also note that this example requires scikit-image which can be installed
 #   via the command:

python_examples/train_shape_predictor.py

@@ -29,9 +29,9 @@
 #   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
-#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
-#   running the command:
-#       sudo apt-get install libboost-python-dev cmake
+#   CMake installed.  On Ubuntu, this can be done easily by running the
+#   command:
+#       sudo apt-get install cmake
 #
 #   Also note that this example requires scikit-image which can be installed
 #   via the command: