Refactored feature detection code into a class

wagtail/wagtailimages/backends/base.py

@@ -60,7 +60,8 @@ class BaseImageBackend(object):
         image_mode, image_data = self.image_data_as_rgb(image)
 
         # Use feature detection to find a focal point
-        focal_point = feature_detection.get_focal_point(image.size, image_mode, image_data)
+        feature_detector = feature_detection.FeatureDetector(image.size, image_mode, image_data)
+        focal_point = feature_detector.get_focal_point()
         if focal_point:
             return self.crop_to_point(image, size, focal_point)
 

wagtail/wagtailimages/utils/feature_detection.py

@@ -13,25 +13,30 @@ except ImportError:
         opencv_available = False
 
 
-from wagtail.wagtailimages.utils.focal_point import FocalPoint, combine_points
+from wagtail.wagtailimages.utils.focal_point import FocalPoint, combine_focal_points
 
 
-def get_cv_gray_image(image_size, image_mode, image_data):
+class FeatureDetector(object):
-    image = cv.CreateImageHeader(image_size, cv.IPL_DEPTH_8U, 3)
+    def __init__(self, image_size, image_mode, image_data):
-    cv.SetData(image, image_data)
+        self.image_size = image_size
+        self.image_mode = image_mode
+        self.image_data = image_data
 
-    gray_image = cv.CreateImage(image_size, 8, 1)
+    def opencv_grey_image(self):
-    convert_mode = getattr(cv, 'CV_%s2GRAY' % image_mode)
+        image = cv.CreateImageHeader(self.image_size, cv.IPL_DEPTH_8U, 3)
+        cv.SetData(image, self.image_data)
+
+        gray_image = cv.CreateImage(self.image_size, 8, 1)
+        convert_mode = getattr(cv, 'CV_%s2GRAY' % self.image_mode)
         cv.CvtColor(image, gray_image, convert_mode)
 
         return gray_image
 
-
+    def detect_features(self):
-def detect_features(image_size, image_mode, image_data):
         if opencv_available:
-        image = get_cv_gray_image(image_size, image_mode, image_data)
+            image = self.opencv_grey_image()
-        rows = image_size[0]
+            rows = self.image_size[0]
-        cols = image_size[1]
+            cols = self.image_size[1]
 
             eig_image = cv.CreateMat(rows, cols, cv.CV_32FC1)
             temp_image = cv.CreateMat(rows, cols, cv.CV_32FC1)
@@ -42,12 +47,11 @@ def detect_features(image_size, image_mode, image_data):
 
         return []
 
-
+    def detect_faces(self):
-def detect_faces(image_size, image_mode, image_data):
         if opencv_available:
             cascade_filename = os.path.join(os.path.dirname(__file__), 'face_detection', 'haarcascade_frontalface_alt2.xml')
             cascade = cv.Load(cascade_filename)
-        image = get_cv_gray_image(image_size, image_mode, image_data)
+            image = self.opencv_grey_image()
 
             cv.EqualizeHist(image, image)
 
@@ -66,16 +70,15 @@ def detect_faces(image_size, image_mode, image_data):
 
         return []
 
-
+    def get_focal_point(self):
-def get_focal_point(image_size, image_mode, image_data):
         # Face detection
-    faces = feature_detection.detect_faces(image_size, image_mode, image_data)
+        faces = self.detect_faces()
 
         if faces:
-        return combine_points(faces)
+            return combine_focal_points(faces)
 
         # Feature detection
-    features = feature_detection.detect_features(image_size, image_mode, image_data)
+        features = self.detect_features()
 
         if features:
-        return focal_point.combine_points(features)
+            return combine_focal_points(features)