Showing the indexes of landmark points

.appveyor.yml

@@ -18,6 +18,7 @@ install:
   - pip install nose-exclude
   - pip install dlib
   - pip install imageio
+  - pip install scikit-image
 
 test_script:
   - nosetests -s -v --exclude-dir=mlxtend/plotting

.travis.yml

@@ -1,23 +1,52 @@
-sudo: required
-language: python
+language: generic
+
+cache:
+  apt: true
+  directories:
+  - $HOME/.cache/pip
+  - $HOME/.ccache
+
+dist: trusty
+
+env:
+  global:
+    # Directory where tests are run from
+    - TEST_DIR=/tmp/sklearn
+    - OMP_NUM_THREADS=4
+    - OPENBLAS_NUM_THREADS=4
+
 matrix:
     include:
-        - python: 3.6
-          env: LATEST="false" IMAGE="true" COVERAGE="false" NUMPY_VERSION="1.14.6" SCIPY_VERSION="1.1.0" SKLEARN_VERSION="0.20.1" PANDAS_VERSION="0.23.4"  MINICONDA_PYTHON_VERSION=3.7
-        - python: 3.6 # python 3.7
+        - os: linux
+          sudo: required
+          env: LATEST="false" IMAGE="true" COVERAGE="false" NUMPY_VERSION="1.15.1" SCIPY_VERSION="1.1.0" SKLEARN_VERSION="0.20" PANDAS_VERSION="0.23.4"  MINICONDA_PYTHON_VERSION=3.6
+        - os: linux
+          sudo: required
           env: LATEST="true" IMAGE="true" COVERAGE="true" NOTEBOOKS="true" MINICONDA_PYTHON_VERSION=3.7
-        - python: 3.6 # python 3.7
+        - os: osx
+          sudo: required
+          env: LATEST="true" IMAGE="true" COVERAGE="false" NOTEBOOKS="false" MINICONDA_PYTHON_VERSION=3.7
+        - os: linux
+          sudo: required
           env: LATEST="true" IMAGE="false" COVERAGE="false" NOTEBOOKS="false" MINICONDA_PYTHON_VERSION=3.7
+
+
+before_install:
+    - if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then brew update; fi
+
 install:
-    - sudo apt-get update
+    - if [[ "$TRAVIS_OS_NAME" != "osx" ]]; then sudo apt-get update; fi
     - source ci/.travis_install.sh
+
 script:
     - bash ci/.travis_test.sh
+
 after_success:
     - if [[ "$COVERAGE" == "true" ]]; then coveralls || echo "failed"; fi
+
 notifications:
     email:
       recipients:
           - mail@sebastianraschka.com
     on_success: always
-    on_failure: always
+    on_failure: always

ci/.travis_install.sh

@@ -2,11 +2,22 @@
 
 set -e
 
-if [ "${TRAVIS_PYTHON_VERSION}" == "2.7" ]; then
-    wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
+if [[ "$TRAVIS_OS_NAME" != "osx" ]]; then 
+    if [ "${TRAVIS_PYTHON_VERSION}" == "2.7" ]; then
+
+        wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
+    else
+        wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
+    fi
 else
-    wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
+    if [ "${TRAVIS_PYTHON_VERSION}" == "2.7" ]; then
+
+        wget https://repo.continuum.io/miniconda/Miniconda2-latest-MacOSX-x86_64.sh -O miniconda.sh
+    else
+        wget https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -O miniconda.sh
+    fi
 fi
+
 
 bash miniconda.sh -b -p $HOME/miniconda
 export PATH="$HOME/miniconda/bin:$PATH"
@@ -32,6 +43,7 @@ conda install jupyter
 if [ "${IMAGE}" = "true" ]; then
     pip install dlib
     pip install imageio
+    pip install scikit-image
 fi
 
 if [ "${COVERAGE}" = "true" ]; then

ci/.travis_test.sh

@@ -3,22 +3,29 @@
 set -e
 
 
-if [[ "$COVERAGE" == "true" ]]; then
+if [[ "$TRAVIS_OS_NAME" != "osx" ]]; then 
 
-    if [[ "$IMAGE" == "true" ]]; then
-        nosetests -s -v --with-coverage
-    else
-        nosetests -s -v --with-coverage --exclude-dir=mlxtend/image
-    fi
+
+        if [[ "$COVERAGE" == "true" ]]; then
+
+            if [[ "$IMAGE" == "true" ]]; then
+                nosetests -s -v --with-coverage
+            else
+                nosetests -s -v --with-coverage --exclude-dir=mlxtend/image
+            fi
+
+        else
+            if [[ "$IMAGE" == "true" ]]; then
+                nosetests -s -v
+            else
+                nosetests -s -v --exclude-dir=mlxtend/image
+            fi
+        fi
 
 else
-    if [[ "$IMAGE" == "true" ]]; then
-        nosetests -s -v
-    else
-        nosetests -s -v --exclude-dir=mlxtend/image
-    fi
+    nosetests -s -v --exclude-dir=mlxtend/plotting
 fi
-
+  
 
 if [[ "$NOTEBOOKS" == "true" ]]; then
     cd docs

docs/mkdocs.yml

@@ -98,6 +98,7 @@ nav:
     - user_guide/general_concepts/regularization-linear.md
   - image:
     - user_guide/image/extract_face_landmarks.md
+    - user_guide/image/eyepad_align.md
   - math:
     - user_guide/math/num_combinations.md
     - user_guide/math/num_permutations.md

docs/sources/CHANGELOG.md

@@ -16,6 +16,7 @@ The CHANGELOG for the current development version is available at
 
 ##### New Features
 
+- Added a new transformer class to `mlxtend.image`, `EyepadAlign`, that aligns face images based on the location of the eyes. ([#466](https://github.com/rasbt/mlxtend/pull/466) by [Vahid Mirjalili](https://github.com/vmirly))
 - Adds a new function, `mlxtend.evaluate.bias_variance_decomp` that decomposes the loss of a regressor or classifier into bias and variance terms. ([#470](https://github.com/rasbt/mlxtend/pull/470))
 - Adds a `whitening` parameter to `PrincipalComponentAnalysis`, to optionally whiten the transformed data such that the features have unit variance. ([#475](https://github.com/rasbt/mlxtend/pull/475))
 

mlxtend/image/__init__.py

@@ -5,5 +5,6 @@
 # License: BSD 3 clause
 
 from .extract_face_landmarks import extract_face_landmarks
+from .eyepad_align import EyepadAlign
 
-__all__ = ["extract_face_landmarks"]
+__all__ = ["extract_face_landmarks", "EyepadAlign"]

mlxtend/image/eyepad_align.py

@@ -0,0 +1,120 @@
+# Sebastian Raschka 2014-2018
+# contributor: Vahid Mirjalili
+# mlxtend Machine Learning Library Extensions
+#
+# A class for transforming face images.
+# Author: Sebastian Raschka <sebastianraschka.com>
+#
+# License: BSD 3 clause
+
+from . import extract_face_landmarks
+from .utils import listdir, read_image
+from skimage.transform import warp, AffineTransform
+import numpy as np
+
+left_indx = np.array([36, 37, 38, 39, 40, 41])
+right_indx = np.array([42, 43, 44, 45, 46, 47])
+
+
+class EyepadAlign():
+    """Class to align/transform face images to target landmarks,
+       based on the location of the eyes.
+
+       1. Scaling factor is computed based on distance between the
+        left and right eyes, so that the transformed image will
+        have the same eye distance as target.
+
+       2. Transformation is performed based on the eyes' middle point.
+
+       3. Finally, the transformed image is padded with zeros to match
+        the desired final image size.
+
+    Parameters
+    ----------
+
+    eye_distance:
+
+    verbose :
+
+    Attributes
+    ----------
+
+    target_landmarks_ :
+
+    target_width_ :
+
+    target_height_ :
+
+
+    Examples
+    --------
+
+
+    For more usage examples, please see
+    http://rasbt.github.io/mlxtend/user_guide/image/EyepadAlign/
+
+    """
+    def __init__(self, eye_distance=None, verbose=0):
+        self.eye_distance = eye_distance
+        self.verbose = verbose
+
+    def fit(self, target_image=None,
+            target_img_dir=None, file_extensions='.jpg'):
+        """Fits the target landmarks points:
+             a. if target_image is given, sets the target landmarks
+                to the landmarks of target image.
+             b. otherwise, if a target directory is given,
+                calculates the average landmarks for all face images
+                in the directory which will be set as the target landmark.
+
+        """
+        if target_image is not None:
+            landmarks = extract_face_landmarks(target_image)
+            self.target_landmarks_ = landmarks
+            self.target_width_ = target_image.shape[1]
+            self.target_height_ = target_image.shape[0]
+
+        elif target_img_dir is not None:
+            file_list = listdir(target_img_dir, file_extensions)
+            if self.verbose >= 1:
+                print("Fitting the average facial landmarks "
+                      "for {} face images ".format(len(file_list)))
+            landmarks_list = []
+            for f in file_list:
+                img = read_image(filename=f, path=target_img_dir)
+                landmarks = extract_face_landmarks(img)
+                if landmarks is not None:
+                    landmarks_list.append(landmarks)
+            self.target_landmarks_ = np.mean(landmarks_list, axis=0)
+            self.target_width_ = img.shape[1]
+            self.target_height_ = img.shape[0]
+
+    def _cal_eye_properties(self, landmarks):
+        """ DOCUMENTATION """
+        left_eye = np.mean(landmarks[left_indx], axis=0)
+        right_eye = np.mean(landmarks[right_indx], axis=0)
+        eyes_mid_point = (left_eye + right_eye)/2.0
+        eye_distance = np.sqrt(np.sum(np.square(left_eye - right_eye)))
+
+        return eyes_mid_point, eye_distance
+
+    def transform(self, img):
+        """ DOCUMENTATION """
+        if self.eye_distance is None:
+            props = self._cal_eye_properties(self.target_landmarks_)
+            self.eyes_mid_point = props[0]
+            self.eye_distance = props[1]
+        landmarks = extract_face_landmarks(img)
+        if landmarks is None:
+            return
+        eyes_mid_point, eye_distance = self._cal_eye_properties(landmarks)
+
+        scale = self.eye_distance / eye_distance
+        tr = (self.eyes_mid_point/scale - eyes_mid_point)
+        tr = (int(tr[0]*scale), int(tr[1]*scale))
+
+        tform = AffineTransform(scale=(scale, scale), rotation=0, shear=0,
+                                translation=tr)
+        h, w = self.target_height_, self.target_width_
+        img_tr = warp(img, tform.inverse, output_shape=(h, w))
+        return np.array(img_tr*255, dtype='uint8')

mlxtend/image/tests/test_extract_face_landmarks.py

@@ -43,31 +43,24 @@ def test_jpg():
 
     assert landmarks2.shape == (68, 2)
 
-    if os.name == 'nt':
-        true_vals = np.array([[85, 111],
-                              [84, 120],
-                              [85, 130],
-                              [87, 140],
-                              [91, 148],
-                              [98, 155],
-                              [105, 160],
-                              [113, 164],
-                              [120, 165],
-                              [126, 162]], dtype=np.int32)
+    true_vals = np.array([[85, 110],
+                          [85, 120],
+                          [85, 130],
+                          [87, 140],
+                          [91, 148],
+                          [97, 155],
+                          [104, 160],
+                          [112, 164],
+                          [120, 165],
+                          [125, 162]], dtype=np.int32)
 
+    if os.name == 'nt':
+        # on windows, imageio parses jpgs sometimes differently so pixel values
+        # maybe slightly different
+        assert np.sum(np.abs(landmarks2[:10] - true_vals) > 2) == 0
     else:
-        true_vals = np.array([[85, 110],
-                              [85, 120],
-                              [85, 130],
-                              [87, 140],
-                              [91, 148],
-                              [97, 155],
-                              [104, 160],
-                              [112, 164],
-                              [120, 165],
-                              [125, 162]], dtype=np.int32)
-
-    np.testing.assert_array_equal(landmarks2[:10], true_vals)
+        assert np.sum(np.abs(landmarks2[:10] - true_vals) > 0) == 0
+        np.testing.assert_array_equal(landmarks2[:10], true_vals)
 
 
 def test_grayscale():