SIFT, orthogonal ship image detection (lives), colored debug output

Set up a SIFT method and massively upgraded the debug view to look at the image processing outputs in color.
TODO: the template matching used for unrotated ship icons and asteroids does not work on missiles.

pointcluster.py

@@ -0,0 +1,58 @@
+from utility import *
+
+class PointCluster:
+    def __init__(self):
+        self.points = []
+        self.center = (0, 0)
+        self.max_distance = None
+
+    def update(self):
+        if len(self.points) == 0: return
+        self.center = (sum([p[0] for p in self.points]) / len(self.points),
+                       sum([p[1] for p in self.points]) / len(self.points))
+        self.max_distance = sqrt(max(
+            [squared_distance(self.center, p) for p in self.points]))
+
+    def add(self, pt):
+        self.points.append(pt)
+        self.update()
+
+    def pop(self):
+        p = self.points.pop(-1)
+        self.update()
+        return p
+
+    def __repr__(self):
+        c = f"({self.center[0]:.1f},{self.center[1]:.1f})"
+        return f"<PointCluster center={c}, {len(self.points)} points>"
+
+def cluster_set(points, maxradius):
+    """returns a list of PointCluster objects. Points are fit within circles of maxradius"""
+    clusters = []
+    for pt in points:
+        if len(clusters) == 0:
+            #print("first cluster")
+            clusters.append(PointCluster())
+            clusters[-1].add(pt)
+            continue
+        # add point to its nearest cluster
+        scored_clusters = [(c, squared_distance(pt, c.center)) for c in clusters]
+        scored_clusters.sort(key=lambda i: i[1])
+        winner = scored_clusters[0][0]
+        winner.add(pt)
+        
+        # if maxradius constraint was violated, pop the newest point & add new cluster
+        if winner.max_distance > maxradius:
+            #print(f"{winner.max_distance} > {maxradius}; new cluster")
+            winner.pop()
+            clusters.append(PointCluster())
+            clusters[-1].add(pt)
+            
+    # refine step - accept centers as fixed, put points in closest center
+    new_clusters = {c.center: PointCluster() for c in clusters}
+    closest = lambda pt: sorted(new_clusters.keys(), key= lambda i: squared_distance(pt, i))[0]
+    for point in points:
+        new_clusters[closest(point)].add(point)
+    #print(clusters)
+    #print(new_clusters.values())
+    return new_clusters.values()