Moved imm_points to imm, in preperation of including a dataset implementation capability

src/aam.py

@@ -51,7 +51,7 @@ class AAMPoints():
 
         return self.bounding_box
 
-    def get_scaled_points(self, shape, rescale=False):
+    def get_scaled_points(self, width_height_dimensions, rescale=False):
         """
         Scale the normalized flattened points list to a scale given by 'shape'.
         The x and y values should be scaled to the width and height of the image.
@@ -66,11 +66,14 @@ class AAMPoints():
         if self.points_list is None or rescale:
             self.points_list = self.normalized_flattened_points_list
 
+            # shape into [[x, y(, z)], [x, y, (,z)]]
+            # we use the 'actual_shape' which is known from the creation of
+            # this object.
             if len(self.actual_shape):
                 self.points_list = self.points_list.reshape(self.actual_shape)
 
-            h = shape[0]
-            w = shape[1]
+            h = width_height_dimensions[0]
+            w = width_height_dimensions[1]
 
             self.points_list[:, 0] = self.points_list[:, 0] * w
             self.points_list[:, 1] = self.points_list[:, 1] * h
@@ -88,11 +91,13 @@ class AAMPoints():
         width)
         """
         assert self.points_list is not None, \
-            'the list points already need to be scaled order to correctly work'
+            'the list points already need to be scaled order to correctly work,\
+            this requires that get_scaled_points is executed first.'
 
         hull = cv2.convexHull(self.points_list, returnPoints=True)
         return cv2.boundingRect(hull)
 
+    # TODO: impove by not using opencv but just min-max of the lists
     def get_bounding_box_2(self):
         pass
         #hull = cv2.convexHull(self.points_list, returnPoints=True)
@@ -103,7 +108,7 @@ class AAMPoints():
 def get_mean(vector):
     """ construct a mean from a matrix of x,y values
     Args:
-        imm_points(numpy array) that follows the following structure:
+        points(numpy array) that follows the following structure:
 
     Returns:
         mean_values (numpy array)
@@ -187,8 +192,8 @@ def build_texture_feature_vectors(files, get_image_with_points, mean_points, tri
     """
     Args:
         files (list): list files
-        flattened (bool): Flatten the inner feature vectors, see
-            flatten_feature_vectors.
+        get_image_with_points (function): That can return the image together
+            with the location.
         mean_points(AAMPoints): AAMPoints object
 
     Returns:
@@ -197,8 +202,7 @@ def build_texture_feature_vectors(files, get_image_with_points, mean_points, tri
     mean_texture = []
 
     image, points = get_image_with_points(files[0])
-    mean_points.get_scaled_points(image.shape)
-
+    mean_points.get_scaled_points(image.shape) # improve this, see issue #1
     x, y, w_slice, h_slice = mean_points.get_bounding_box()
 
     for i, f in enumerate(files):

src/aam_test.py

@@ -4,7 +4,7 @@ import pytest
 
 import aam
 import pca
-import imm_points as imm
+import imm
 
 from reconstruction import triangles as tri
 

src/main.py

@@ -10,7 +10,7 @@ import cv2
 # local imports
 import pca
 import aam
-import imm_points as imm
+import imm
 
 from reconstruction import reconstruction
 
@@ -67,6 +67,11 @@ def add_parser_options():
         help='pca model file that contains or is going to contain the pca shape model'
     )
 
+    pca_group.add_argument(
+        '--shape_type', type=str, choices=['imm'],
+        help='type of shape, annotated dataset'
+    )
+
     pca_group.add_argument(
         '--model_texture_file', type=str,
         help='pca model file that contains or is going to contain the pca texture model'
@@ -135,7 +140,8 @@ def save_pca_model_shape(args):
     assert args.model_shape_file, '--model_shape_file needs to be provided to save the pca model'
 
     points = aam.build_shape_feature_vectors(
-        args.files, imm.get_imm_points, flattened=True)
+        args.files, imm.get_imm_points, flattened=True
+    )
 
     mean_values = aam.get_mean(points)
 
@@ -213,6 +219,7 @@ def show_pca_model(args):
 
 
 def generate_call_graph(args):
+    """Performance debug function, will be (re)moved later. """
     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'
 

src/view/reconstruct.py

@@ -19,7 +19,7 @@ from kivy.graphics.context_instructions import Color
 from functools import partial
 from math import cos, sin, pi
 
-import imm_points as imm
+import imm
 from utils import utils
 from utils.texture import fill_triangle
 #import IMMPoints, build_feature_vectors, \