Update build script and worked on texture model creation

README.md

@@ -2,8 +2,12 @@
 Run the following command
 ~~~~
 $ make
+$ source bin/activate
+$ make show_reconstruction
 ~~~~
 
+
+
 Will get the data needed for this tool.
 
 ## References

actions.mk

+TARGETS += data/pca_shape_train_model.npy
+.PHONY := train_model show_pca test_model show_reconstruction
+
+data/imm_face_db: data/imm_face_db.tar.gz
+	(cd data; mkdir -p imm_face_db; \
+		tar -xvzf imm_face_db.tar.gz -C imm_face_db
+	)
+
+train_model: data/pca_shape_model.npy data/pca_texture_model.npy
+
+data/imm_face_db.tar.gz:
+	(cd data; wget http://www.imm.dtu.dk/~aam/datasets/imm_face_db.tar.gz)
+
+data/pca_shape_model.npy:
+	python src/main.py \
+		--save_pca_shape \
+		--asf `./scripts/imm_train_set.sh` \
+		--model_shape_file data/pca_shape_model
+
+data/pca_texture_model.npy:
+	python src/main.py \
+		--save_pca_texture \
+		--asf `./scripts/imm_train_set.sh` \
+		--model_texture_file data/pca_texture_model \
+		--model_shape_file data/pca_shape_model.npy
+
+show_pca:
+	python src/main.py \
+		--show_pca \
+		--asf data/imm_face_db/*.asf \
+		--model_shape_file data/pca_model.npy
+
+test_model:
+	python src/main.py \
+		--reconstruct \
+		--asf `./scripts/imm_test_set.sh` \
+		--model_shape_file data/pca_shape_model.npy \
+		--n_components 6
+
+show_reconstruction:
+	python src/main.py \
+		--reconstruct \
+		--asf data/imm_face_db/*.asf \
+		--model_shape_file data/pca_shape_model.npy \
+		--model_texture_file data/pca_texture_model.npy \
+		--n_components 6
+
+test:
+	python -m py.test -f src/*_test.py

makefile

-all: data
+VIRTUALENV := venv
+PYTHON := python2.7
+PYTHON_BIN_PATH := /usr/local/bin/$(PYTHON)
+SITE_PACKAGES := $(VIRTUALENV)/lib/$(PYTHON)/site-packages
 
-data: data/imm_face_db
-
-data/imm_face_db: data/imm_face_db.tar.gz
-	(cd data; mkdir -p imm_face_db; \
-		tar -xvzf imm_face_db.tar.gz -C imm_face_db
-	)
+OPENCV:= $(SITE_PACKAGES)/cv.py $(SITE_PACKAGES)/cv2.so
 
-data/imm_face_db.tar.gz:
-	(cd data; wget http://www.imm.dtu.dk/~aam/datasets/imm_face_db.tar.gz)
+TARGETS:= $(VIRTUALENV) data build
+all: $(TARGETS)
 
-train_model: data/pca_train_model.npy
-	python src/main.py \
-		--save_pca \
-		--asf data/imm_face_db/*.asf \
-		--model_file data/pca_train_model
+include actions.mk
+include build.mk
 
-show_pca:
-	python src/main.py \
-		--show_pca \
-		--asf data/imm_face_db/*.asf \
-		--model_file data/pca_model.npy
+data: data/imm_face_db
 
-test_model:
-	python src/main.py \
-		--reconstruct \
-		--asf `./scripts/imm_test_set.sh` \
-		--model_file data/pca_train_model.npy \
-		--n_components 6
+build: $(OPENCV)
+	@(source $(VIRTUALENV)/bin/activate; \
+		pip install -r requirements.txt; \
+	);
 
-show_reconstruction:
-	python src/main.py \
-		--reconstruct \
-		--asf data/imm_face_db/*.asf \
-		--model_file data/pca_train_model.npy \
-		--n_components 6
+$(VIRTUALENV):
+	virtualenv -p $(PYTHON_BIN_PATH) venv
 
-test:
-	python -m py.test -f src/*_test.py
+$(SITE_PACKAGES)/cv%:
+	@/bin/ln -s `scripts/get_site_package_location.sh`/$(shell basename $@) $@
+	@ls $@

requirements.txt

+cycler==0.10.0
+Cython==0.24
+Kivy==1.9.1
+Kivy-Garden==0.1.4
+matplotlib==1.5.1
+numpy==1.11.0
+pyparsing==2.1.2
+python-dateutil==2.5.3
+pytz==2016.4
+requests==2.10.0
+six==1.10.0

scripts/get_site_package_location.sh

+#!/bin/bash
+ python -c "from distutils.sysconfig import get_python_lib; print(get_python_lib())"

src/aam.py

+import logging
+
 from matplotlib.tri import Triangulation
 import numpy as np
 import cv2
-
 import pca
 
+from utils.generate_head_texture import fill_triangle, get_colors_triangle
+
+logging.basicConfig(level=logging.INFO,
+                    format='%(asctime)s %(levelname)s %(name)s: %(message)s')
+logger = logging.getLogger(__name__)
+
 
-def get_mean(imm_points):
+def get_mean(vector):
     """ construct a mean from a matrix of x,y values
     Args:
         imm_points(numpy array) that follows the following structure:
@@ -36,7 +43,7 @@ def get_mean(imm_points):
 
             mean. [[x_mean_0, y_mean_0], ... [x_mean_n, y_mean_n]]
     """
-    return np.mean(imm_points, axis=0)
+    return np.mean(vector, axis=0)
 
 
 def get_triangles(x_vector, y_vector):
@@ -63,6 +70,97 @@ def build_feature_vectors(files, get_points, flattened=False):
     return points
 
 
+def cartesian2barycentric(r1, r2, r3, r):
+    x, y = r
+    x1, y1 = r1
+    x2, y2 = r2
+    x3, y3 = r3
+
+    a = np.array([[x1, x2, x3], [y1, y2, y3], [1, 1, 1]])
+    b = np.array([x, y, 1])
+
+    return np.linalg.solve(a, b)
+
+
+def barycentric2cartesian(r1, r2, r3, L):
+    x1, y1 = r1
+    x2, y2 = r2
+    x3, y3 = r3
+
+    a = np.array([[x1, x2, x3], [y1, y2, y3], [1, 1, 1]])
+    b = np.array(L)
+
+    return np.asarray(np.dot(a, b), dtype=np.uint32)
+
+
+def sample_from_triangles(b, points2d_b, triangles, n_samples=20):
+    all_triangles = []
+    h, w, c = b.shape
+
+    for tri in triangles:
+        p1_b = points2d_b[tri[0]]
+        p2_b = points2d_b[tri[1]]
+        p3_b = points2d_b[tri[2]]
+
+        cv2.line(b,
+                 tuple(p1_b),
+                 tuple(p2_b), (0, 255, 0), 1)
+        cv2.line(b,
+                 tuple(p2_b),
+                 tuple(p3_b), (0, 255, 0), 1)
+        cv2.line(b,
+                 tuple(p3_b),
+                 tuple(p1_b), (0, 255, 0), 1)
+
+        bary_centric_range = np.linspace(0, 1, num=n_samples)
+        pixels = np.full((n_samples * n_samples, 3), fill_value=-1, dtype=np.int)
+        L = np.zeros((3, 1))
+
+        for s_i, s in enumerate(bary_centric_range):
+            for t_i, t in enumerate(bary_centric_range):
+                # make sure the coordinates are inside the triangle
+                if s + t <= 1:
+                    # build lambda's
+                    L[0] = s
+                    L[1] = t
+                    L[2] = 1 - s - t
+
+                    # cartesian x, y coordinates inside the triangle
+                    cart_x, cart_y, _ = barycentric2cartesian(p1_b, p2_b, p3_b, L)
+                    pixels[s_i * n_samples + t_i, :] = b[cart_y, cart_x, :]
+
+                    # cv2.circle(b, tuple([cart_x, cart_y]), 1, color=(0, 255, 100))
+
+        all_triangles.append(pixels[np.where(pixels >= 0)])
+
+    return np.asarray(all_triangles, dtype=np.uint8)
+
+
+def build_texture_feature_vector(files, get_image_with_landmarks, triangles):
+    mean_texture = []
+
+    for i, f in enumerate(files[:10]):
+        image, landmarks = get_image_with_landmarks(f)
+        h, w, c = image.shape
+        landmarks[:, 0] = landmarks[:, 0] * w
+        landmarks[:, 1] = landmarks[:, 1] * h
+
+        triangles_colors = sample_from_triangles(
+            image, landmarks, triangles
+        )
+
+        mean_texture.append(triangles_colors)
+        logger.info('processed file: {} {}/{}'.format(f, i, len(files)))
+
+        # cv2.imshow('image', image)
+        # k = cv2.waitKey(0) & 0xFF
+
+        # if k == 27:
+        #     break
+
+    return np.asarray(mean_texture)
+
+
 def get_pixel_values(image, points):
     h, w, c = image.shape
 
@@ -78,16 +176,16 @@ def get_pixel_values(image, points):
     x, y, w, h = rect
 
     # pixels = np.zeros((h, w, c), dtype=np.uint8)
-
-    for i in np.linspace(0, 1, num=100):
-        for j in np.linspace(0, 1, num=100):
+    for i in np.linspace(0, 1, num=150):
+        for j in np.linspace(0, 1, num=150):
             y_loc_g = int(i * h + y)
             x_loc_g = int(j * w + x)
 
-            y_loc = min(int(i * h), h - 1)
-            x_loc = min(int(j * w), w - 1)
-
             if cv2.pointPolygonTest(hull, (x_loc_g, y_loc_g), measureDist=False) >= 0:
+                image[y_loc_g][x_loc_g][0] = 0
+                image[y_loc_g][x_loc_g][1] = 0
+                image[y_loc_g][x_loc_g][2] = 0
                 pixels.extend(image[y_loc_g][x_loc_g])
 
-    return np.asarray(pixels, dtype=np.uint8), hull
+    # return np.asarray(pixels, dtype=np.uint8), hull
+    return image, hull

src/aam_test.py

 import numpy as np
 
-from aam import get_mean, get_pixel_values
 import imm_points
+import cv2
+import aam
 import pca
 
 
@@ -16,7 +17,7 @@ def test_build_mean_aan():
         [2.5, 5.]
     ])
 
-    mean = get_mean(imm_points)
+    mean = aam.get_mean(imm_points)
 
     np.testing.assert_array_equal(mean, expected)
 
@@ -32,7 +33,7 @@ def test_zero_mean_aan():
         [0.5, 0.5, 0.5, 1.0],
     ])
 
-    mean = get_mean(imm_points)
+    mean = aam.get_mean(imm_points)
     zero_mean = imm_points - mean
 
     # test that zero mean has indeed zero mean
@@ -50,6 +51,49 @@ def test_get_pixel_values():
     points = imm.get_points()
     image = imm.get_image()
 
-    pixels, hull = get_pixel_values(image, points)
+    pixels, hull = aam.get_pixel_values(image, points)
+
+
+def test_sample_from_triangles():
+    blue_points = [[20, 20], [50, 160], [160, 20],
+                   [50, 20], [60, 200], [180, 20]]
+
+    red_points = [[40, 80], [130, 150], [40, 150],
+                  [40, 80], [60, 82], [60, 100]]
+
+    # blue_image = cv2.imread('../data/test_data/blue.png')
+    #red_image = cv2.imread('../data/test_data/red.png')
+    blue_image = cv2.imread('../data/imm_face_db/01-1m.jpg')
+    red_image = cv2.imread('../data/imm_face_db/02-1m.jpg')
+
+    triangles = [[0, 1, 2]]
+
+    for tri in triangles:
+        cv2.line(blue_image,
+                 tuple(blue_points[tri[0]]),
+                 tuple(blue_points[tri[1]]), (0, 255, 0), 1)
+        cv2.line(blue_image,
+                 tuple(blue_points[tri[1]]),
+                 tuple(blue_points[tri[2]]), (0, 255, 0), 1)
+        cv2.line(blue_image,
+                 tuple(blue_points[tri[2]]),
+                 tuple(blue_points[tri[0]]), (0, 255, 0), 1)
+
+    for tri in triangles:
+        cv2.line(red_image,
+                 tuple(red_points[tri[0]]),
+                 tuple(red_points[tri[1]]), (0, 255, 0), 1)
+        cv2.line(red_image,
+                 tuple(red_points[tri[1]]),
+                 tuple(red_points[tri[2]]), (0, 255, 0), 1)
+        cv2.line(red_image,
+                 tuple(red_points[tri[2]]),
+                 tuple(red_points[tri[0]]), (0, 255, 0), 1)
+
+    all_triangles = aam.sample_from_triangles(
+        red_image, red_points, triangles
+    )
 
-    assert False
+    cv2.imshow('blue_image', blue_image)
+    cv2.imshow('red_image', red_image)
+    cv2.waitKey(0)

src/imm_points.py

@@ -24,7 +24,6 @@ class IMMPoints():
         return self.points
 
     def get_image(self):
-        cv2.imread(self.image_file)
         return cv2.imread(self.image_file)
 
     def import_file(self, filename):
@@ -39,11 +38,12 @@ class IMMPoints():
 
         self.points = np.asarray(self.points, dtype='f')
 
-    def draw_triangles(self, img, points):
-        h, w, c = img.shape
+    def draw_triangles(self, img, points, multiply=True):
+        if multiply:
+            h, w, c = img.shape
 
-        points[:, 0] = points[:, 0] * w
-        points[:, 1] = points[:, 1] * h
+            points[:, 0] = points[:, 0] * w
+            points[:, 1] = points[:, 1] * h
 
         point_indices = list(range(0, 58))
         triangles = Triangulation(points[:, 0], points[:, 1])
@@ -60,8 +60,8 @@ class IMMPoints():
                         cv2.FONT_HERSHEY_SIMPLEX, .5, (100, 0, 255))
             cv2.circle(img, tuple(p), 3, color=(0, 255, 100))
 
-    def show_on_img(self, img, window_name='image'):
-        self.draw_triangles(img, self.points)
+    def show_on_img(self, img, window_name='image', multiply=True):
+        self.draw_triangles(img, self.points, multiply=multiply)
 
     def show(self, window_name='image'):
         """show the image and datapoints on the image"""
@@ -83,6 +83,11 @@ def get_imm_landmarks(files):
     return np.asarray(points)
 
 
+def get_imm_image_with_landmarks(filename):
+    imm = IMMPoints(filename=filename)
+    return imm.get_image(), imm.get_points()
+
+
 def add_parser_options():
     parser = argparse.ArgumentParser(description='IMMPoints tool')
 

src/main.py

@@ -8,7 +8,7 @@ import aam
 import imm_points as imm
 
 logging.basicConfig(level=logging.INFO,
-        format='%(asctime)s %(levelname)s %(name)s: %(message)s')
+                    format='%(asctime)s %(levelname)s %(name)s: %(message)s')
 logger = logging.getLogger(__name__)
 
 
@@ -22,8 +22,13 @@ def add_parser_options():
     )
 
     pca_group.add_argument(
-        '--save_pca', action='store_true',
-        help='save the pca model'
+        '--save_pca_shape', action='store_true',
+        help='save the pca shape model'
+    )
+
+    pca_group.add_argument(
+        '--save_pca_texture', action='store_true',
+        help='save the pca texture model'
     )
 
     pca_group.add_argument(
@@ -42,14 +47,53 @@ def add_parser_options():
     )
 
     pca_group.add_argument(
-        '--model_file', type=str,
-        help='pca model file that contains or is going to contain the pca model'
+        '--model_shape_file', type=str,
+        help='pca model file that contains or is going to contain the pca shape model'
+    )
+
+    pca_group.add_argument(
+        '--model_texture_file', type=str,
+        help='pca model file that contains or is going to contain the pca texture model'
     )
 
     return parser
 
 
-def save_pca_model(args):
+def save_pca_model_texture(args):
+    """
+    save the U, s, Vt and mean of all the asf datafiles given by the asf
+    files.
+
+    It is saved in the following way:
+
+        np.load(filename, np.assary([Vt, mean_values])
+
+        And accessed by:
+
+        Vtm = np.load(args.model_file_texture)
+
+        Vt = Vtm[0]
+        mean_values = Vtm[1][0]
+
+    """
+    assert args.asf, '--asf files should be given'
+    assert args.model_shape_file, '--model_texture_file needs to be provided to save the pca model'
+    assert args.model_texture_file, '--model_texture_file needs to be provided to save the pca model'
+
+    Vt, mean_values, triangles = pca.load(args.model_shape_file)
+
+    textures = aam.build_texture_feature_vector(
+        args.asf, imm.get_imm_image_with_landmarks, triangles
+    )
+
+    mean_texture = aam.get_mean(textures)
+    _, _, Vt = pca.pca(textures, mean_texture)
+
+    pca.save(Vt, mean_texture, triangles, args.model_texture_file)
+    logger.info('texture pca model saved in %s', args.model_texture_file)
+
+
+def save_pca_model_shape(args):
     """
     save the U, s, Vt and mean of all the asf datafiles given by the asf
     files.
@@ -60,36 +104,32 @@ def save_pca_model(args):
 
         And accessed by:
 
-        Vtm = np.load(args.model_file)
+        Vtm = np.load(args.model_shape_file)
 
         Vt = Vtm[0]
         mean_values = Vtm[1][0]
 
     """
     assert args.asf, '--asf files should be given'
-    assert args.model_file, '--model_file needs to be provided to save the pca model'
+    assert args.model_shape_file, '--model_shape_file needs to be provided to save the pca model'
 
     points = aam.build_feature_vectors(args.asf,
             imm.get_imm_landmarks, flattened=True)
+
     mean_values = aam.get_mean(points)
 
     _, _, Vt = pca.pca(points, mean_values)
 
-    pca.save(Vt, mean_values, args.model_file)
+    mean_xy = mean_values.reshape((-1, 2))
+    triangles = aam.get_triangles(mean_xy[:, 0], mean_xy[:, 1])
 
-    logger.info('saved pca model in %s', args.model_file)
-
-
-def show_pca_model(args):
-    assert args.asf, '--asf files should be given to allow the image to be shown'
-    assert args.model_file, '--model_file needs to be provided to get the pca model'
-
-    Vt, mean_values = pca.load(args.model_file)
+    pca.save(Vt, mean_values, triangles, args.model_shape_file)
+    logger.info('shape pca model saved in %s', args.model_shape_file + '_shape')
 
 
 def reconstruct_with_model(args):
     assert args.asf, '--asf files should be given to allow the image to be shown'
-    assert args.model_file, '--model_file needs to be provided to get the pca model'
+    assert args.model_shape_file, '--model_shape_file needs to be provided to get the pca model'
 
     # clear sys args. arguments are conflicting with parseargs
     # kivy will parse args upon import and will crash if it finds our
@@ -97,24 +137,33 @@ def reconstruct_with_model(args):
     sys.argv[1:] = []
 
     from view.reconstruct import ReconstructApp
+    Vt_shape, mean_values_shape, triangles = pca.load(args.model_shape_file)
+    Vt_texture, mean_values_texture, _ = pca.load(args.model_texture_file)
+
+    app = ReconstructApp()
+
+    app.set_values(
+        args=args,
+        eigenv_shape=Vt_shape,
+        eigenv_texture=Vt_texture,
+        mean_values_shape=mean_values_shape,
+        mean_values_texture=mean_values_texture,
+        triangles=triangles
+    )
 
-    Vt, mean_values = pca.load(args.model_file)
-    ReconstructApp(
-        args=args, eigen_vectors=Vt, mean_values=mean_values,
-    ).run()
+    app.run()
 
 
 def main():
     parser = add_parser_options()
     args = parser.parse_args()
 
-    if args.show_pca:
-        show_pca_model(args)
-    elif args.save_pca:
-        save_pca_model(args)
+    if args.save_pca_shape:
+        save_pca_model_shape(args)
+    elif args.save_pca_texture:
+        save_pca_model_texture(args)
     elif args.reconstruct:
         reconstruct_with_model(args)
 
-
 if __name__ == '__main__':
     main()

src/pca.py

@@ -36,7 +36,7 @@ def reconstruct(feature_vector, Vt, mean_values, n_components=10):
     return np.dot(Vt[:n_components].T, yk) + mean_values
 
 
-def save(Vt, mean_values, filename):
+def save(Vt, mean_values, triangles, filename):
     """
     Store the U, s, Vt and mean of all the asf datafiles given by the asf
     files.
@@ -49,9 +49,10 @@ def save(Vt, mean_values, filename):
 
         Vt = Vtm[0]
         mean_values = Vtm[1][0]
+        triangles = Vtm[2]
 
     """
-    saving = np.asarray([Vt, [mean_values]])
+    saving = np.asarray([Vt, [mean_values], triangles])
     np.save(filename, saving)
 
 
@@ -76,8 +77,9 @@ def load(filename):
 
     Vt = Vtm[0]
     mean_values = Vtm[1][0]
+    triangles = Vtm[2]
 
-    return Vt, mean_values
+    return Vt, mean_values, triangles
 
 
 def flatten_feature_vectors(data):

src/utils/generate_head_texture.pyx

+cimport cython
+import numpy as np
+cimport numpy as np
+from cpython cimport array as c_array
+
+DTYPE_Int = np.int
+ctypedef np.int_t DTYPE_t
+
+
+cdef inline float cross_product(int v1_x, int v1_y, int v2_x, int v2_y):
+    # Cross product of two 2d vectors
+    return (v1_x * v2_y) - (v1_y * v2_x)
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def fill_triangle(np.ndarray[unsigned char, ndim=3] src,
+                  np.ndarray[unsigned char, ndim=3] dst,
+                  int x1, int y1, int x2, int y2, int x3, int y3):
+    """
+    Fill a triangle by applying the Barycentric Algorithm for deciding if a
+    point lies inside or outside a triangle.
+    """
+
+    # Get the bounding box of the triangle
+    cdef int x_min = min(x1, min(x2, x3))
+    cdef int x_max = max(x1, max(x2, x3))
+    cdef int y_min = min(y1, min(y2, y3))
+    cdef int y_max = max(y1, max(y2, y3))
+
+    cdef int vs1_x = x2 - x1
+    cdef int vs1_y = y2 - y1
+    cdef int vs2_x = x3 - x1
+    cdef int vs2_y = y3 - y1
+
+    cdef float s
+    cdef float t
+
+    for y in xrange(y_min, y_max):
+        for x in xrange(x_min, x_max):
+            q_x = x - x1
+            q_y = y - y1
+
+            s = cross_product(q_x, q_y, vs2_x, vs2_y) / \
+                cross_product(vs1_x, vs1_y, vs2_x, vs2_y)
+            t = cross_product(vs1_x, vs1_y, q_x, q_y) / \
+                cross_product(vs1_x, vs1_y, vs2_x, vs2_y)
+
+            if s >= 0 and t >= 0 and s + t <= 1:
+                dst[y, x, :] = src[y, x, :]
+
+
+def get_colors_triangle(np.ndarray[unsigned char, ndim=3] src,
+                        np.ndarray[unsigned char, ndim=3] dst,
+                        int x1, int y1, int x2, int y2, int x3, int y3):
+    """
+    Fill a triangle by applying the Barycentric Algorithm for deciding if a
+    point lies inside or outside a triangle.
+    """
+
+    # Get the bounding box of the triangle
+    cdef int x_min = min(x1, min(x2, x3))
+    cdef int x_max = max(x1, max(x2, x3))
+    cdef int y_min = min(y1, min(y2, y3))
+    cdef int y_max = max(y1, max(y2, y3))
+
+    cdef int vs1_x = x2 - x1
+    cdef int vs1_y = y2 - y1
+    cdef int vs2_x = x3 - x1
+    cdef int vs2_y = y3 - y1
+
+    cdef float s
+    cdef float t
+
+    y_count = 0
+    x_count = 0
+
+    for y in xrange(y_min, y_max):
+        for x in xrange(x_min, x_max):
+            a = np.ndarray([[x1, x2, x3], [y1, y2, y3], [1, 1, 1]], dtype=DTYPE_Int)
+            b = np.ndarray([x, y, 1], dtype=DTYPE_Int)
+            L = np.solve(a, b)
+
+            #L[0]
+
+            #if s >= 0 and t >= 0 and s + t <= 1:
+            #    dst[y_count * x_max + x_count] = src[y, x, :]
+            #    y_count += 1
+            #    x_count += 1

src/utils/setup.py

+import numpy as np
+from distutils.core import setup
+from distutils.extension import Extension
+
+from Cython.Build import cythonize
+extensions = [
+    Extension(
+        "generate_head_texture",
+        ["generate_head_texture.pyx"],
+        include_dirs=[np.get_include()],
+)]
+
+setup(
+    ext_modules=cythonize(extensions),
+)

src/view.py.bak

+import cv2
+
+
+class ViewState():
+    def __init__(self):
+        self.variables = {}
+
+    def update(self, key, value):
+        self.variables[key] = value
+
+
+def init_eigenvalue_trackbars(n_components, s, callback, window='image'):
+    cv2.namedWindow(window)
+
+    for i in range(n_components):
+        cv2.createTrackbar('{}'.format(i), 'eigenvalues', 50, 100, callback)