enhance: non maximum suppression

model/ssd.py

@@ -195,6 +195,63 @@ def jacc_filter(plabel, ploc):
                 det_labels.append(plabel)
 
         return det_locs, det_labels
+
+
+    def non_maximum_suppression(self, candidates, overlap_threshold):
+        """
+        this is nms(non maximum_suppression) which filters predicted objects.
+
+        Args:
+            predicted bounding boxes
+        Returns:
+            detected bounding boxes and its label
+        """
+
+        label = candidates[:,4]
+        boxes = candidates[label<classes]
+
+        if len(boxes) == 0:
+            return []
+
+        picked = []
+
+        x1 = boxes[:,0]
+        y1 = boxes[:,1]
+        x2 = boxes[:,2] + x1
+        y2 = boxes[:,3] + y1
+
+        area = (x2 - x1 + 1) * (y2 - y1 + 1)
+        idxs = np.argsort(y2)
+
+        while len(idxs) > 0:
+            last = len(idxs) - 1
+            i = idxs[last]
+            picked.append(i)
+            suppress = [last]
+
+            for pos in range(0, last):
+                j = idxs[pos]
+
+                # extract smallest and largest bouding boxes
+                xx1 = max(x1[i], x1[j])
+                yy1 = max(y1[i], y1[j])
+                xx2 = min(x2[i], x2[j])
+                yy2 = min(y2[i], y2[j])
+
+                w = max(0, xx2 - xx1 + 1)
+                h = max(0, yy2 - yy1 + 1)
+
+                # overlap of current box and those in area list
+                overlap = float(w * h) / area[j]
+
+                # suppress current box
+                if overlap > overlap_threshold:
+                    suppress.append(pos)
+
+            # delete suppressed indexes
+            idxs = np.delete(idxs, suppress)
+
+        return boxes[picked]
 
 
     def detect_objects(self, pred_confs, pred_locs):
@@ -219,9 +276,16 @@ def detect_objects(self, pred_confs, pred_locs):
         # extract top 200 by confidence
         possibilities = [np.amax(np.exp(conf)) / (np.sum(np.exp(conf)) + 1e-3) for conf in pred_confs[0]]
         indicies = np.argpartition(possibilities, -200)[-200:]
-        top200 = np.asarray(possibilities)[indicies]
-        slicer = indicies[0.7 < top200]
-
-        locations, labels = self._filter(pred_confs[0][slicer], pred_locs[0][slicer])
+        # top200 = np.asarray(possibilities)[indicies]
+        # slicer = indicies[0.7 < top200]
+        # locations, labels = self._filter(pred_confs[0][slicer], pred_locs[0][slicer])
+
+        locations, labels = pred_locs[0][indicies], np.argmax(pred_confs[0][indicies], axis=1)
+        labels = np.asarray(labels).reshape(len(labels), 1)
+        with_labels = np.concatenate((locations, labels), axis=1)
 
-        return locations, labels
+        # labels, locations = image.non_max_suppression(boxes, possibilities, 10)
+        filtered = self.non_maximum_suppression(with_labels, 0.5)
+        # locations, labels = pred_confs[0][indices], pred_locs[0][indices]
+
+        return filtered[:,:4], filtered[:,4]