added spatial colour and texture

added imload normalisation
added fixes for 0 edge histogram

vision/descriptor/spatial.py

@@ -43,7 +43,7 @@ def extract_spatial_texture(height: int,
         for seg in segments:
             img_edge = edge.get_edge_info(pixels=seg)
             hist = edge.get_edge_angle_hist(img_edge, bins=bins, threshold=threshold)
-            descriptor = np.append(descriptor, hist[0])
+            descriptor = np.append(descriptor, hist)
         return descriptor
 
     if images is not None:
@@ -68,7 +68,7 @@ def extract_spatial_average_rgb(height: int,
         raise KeyError('no image provided')
 
     def extract(i):
-        segments = grid_image(height, width, pixels)
+        segments = grid_image(height, width, i)
         descriptor = np.array([])
         for seg in segments:
             descriptor = np.append(descriptor, rgb.extract_average_rgb(pixels=seg))
@@ -84,3 +84,37 @@ def extract_spatial_average_rgb(height: int,
         image.descriptor = extract(image.pixels)
     else:
         return extract(pixels)
+
+
+def extract_spatial_avg_rgb_texture(height: int,
+                                    width: int,
+                                    bins: int,
+                                    threshold: float,
+                                    pixels: np.array = None,
+                                    image: Image = None,
+                                    images: List[Image] = None):
+
+    if pixels is None and image is None and images is None:
+        raise KeyError('no image provided')
+
+    def extract(i):
+        segments = grid_image(height, width, i)
+        descriptor = np.array([])
+        for seg in segments:
+            img_edge = edge.get_edge_info(pixels=seg)
+            hist = edge.get_edge_angle_hist(img_edge, bins=bins, threshold=threshold)
+            descriptor = np.append(descriptor, hist)
+
+            descriptor = np.append(descriptor, rgb.extract_average_rgb(pixels=seg))
+        return descriptor
+
+    if images is not None:
+        length = len(images)
+        for index, image in enumerate(images):
+            logger.debug(f'generating {index} of {length}')
+            image.descriptor = extract(image.pixels)
+        return
+    elif image is not None:
+        image.descriptor = extract(image.pixels)
+    else:
+        return extract(pixels)

vision/io/__init__.py

@@ -25,7 +25,15 @@ def load_set(path: str) -> List[Image]:
     logger.info(f'loading set from {path}')
 
     files = load_path(path)
-    images = [Image(cv2.imread(i))[:, :, ::-1] for i in files]
+    images = [Image(cv2.normalize(
+                        cv2.imread(i)[:, :, ::-1].  # slice to go from BGR to RGB
+                        astype('float'),
+                        None,  # output obj none
+                        alpha=0,
+                        beta=1,
+                        norm_type=cv2.NORM_MINMAX,
+                        dtype=cv2.CV_32F)
+                ) for i in files]
     return images
 
 
@@ -40,7 +48,14 @@ def load_msrc(path: str, descriptor_path=None) -> List[Image]:
         file_name_split = file_name.split('_')
         category = int(file_name_split[0])
         name = int(file_name_split[1])
-        images.append(Image(cv2.imread(image)[:, :, ::-1],
+        images.append(Image(cv2.normalize(
+                                cv2.imread(image)[:, :, ::-1].  # slice to go from BGR to RGB
+                                astype('float'),
+                                None,  # output obj none
+                                alpha=0,
+                                beta=1,
+                                norm_type=cv2.NORM_MINMAX,
+                                dtype=cv2.CV_32F),
                             category=category,
                             name=name))
 

vision/util/edge.py

@@ -21,7 +21,11 @@ def get_edge_angle_hist(edge: Edge, bins: int, threshold: float):
 
                 bin_val = m.floor((edge.angle[i, j] / (2*np.pi)) * bins)
                 angle_vals.append(bin_val)
-    return np.histogram(angle_vals, bins=bins, density=True)
+
+    if len(angle_vals) > 0:
+        return np.histogram(angle_vals, bins=bins, density=True)[0]
+    else:
+        return np.zeros(bins)
 
 
 def get_edge_info(pixels: np.array = None,
@@ -39,7 +43,7 @@ def get_edge_info(pixels: np.array = None,
         grey = cv2.cvtColor(i, cv2.COLOR_BGR2GRAY)
 
         if blur is True:
-            i = convolve2d(grey, np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]]) / 9)
+            grey = convolve2d(grey, np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]]) / 9)
 
         dx = convolve2d(grey, kx)
         dy = convolve2d(grey, ky)