zhreshold commented on a change in pull request #9784: Fix for the case where 
there are no detections
URL: https://github.com/apache/incubator-mxnet/pull/9784#discussion_r168291198
 
 

 ##########
 File path: example/ssd/detect/detector.py
 ##########
 @@ -136,31 +132,52 @@ class names
         height = img.shape[0]
         width = img.shape[1]
         colors = dict()
-        for i in range(dets.shape[0]):
-            cls_id = int(dets[i, 0])
-            if cls_id >= 0:
-                score = dets[i, 1]
-                if score > thresh:
-                    if cls_id not in colors:
-                        colors[cls_id] = (random.random(), random.random(), 
random.random())
-                    xmin = int(dets[i, 2] * width)
-                    ymin = int(dets[i, 3] * height)
-                    xmax = int(dets[i, 4] * width)
-                    ymax = int(dets[i, 5] * height)
-                    rect = plt.Rectangle((xmin, ymin), xmax - xmin,
-                                         ymax - ymin, fill=False,
-                                         edgecolor=colors[cls_id],
-                                         linewidth=3.5)
-                    plt.gca().add_patch(rect)
-                    class_name = str(cls_id)
-                    if classes and len(classes) > cls_id:
-                        class_name = classes[cls_id]
-                    plt.gca().text(xmin, ymin - 2,
-                                    '{:s} {:.3f}'.format(class_name, score),
-                                    bbox=dict(facecolor=colors[cls_id], 
alpha=0.5),
+        for det in dets:
+            (klass, score, x0, y0, x1, y1) = det
+            if score < thresh:
+                continue
+            cls_id = int(klass)
+            if cls_id not in colors:
+                colors[cls_id] = (random.random(), random.random(), 
random.random())
+            xmin = int(x0 * width)
+            ymin = int(y0 * height)
+            xmax = int(x1 * width)
+            ymax = int(y1 * height)
+            rect = plt.Rectangle((xmin, ymin), xmax - xmin,
+                                 ymax - ymin, fill=False,
+                                 edgecolor=colors[cls_id],
+                                 linewidth=3.5)
+            plt.gca().add_patch(rect)
+            class_name = str(cls_id)
+            if classes and len(classes) > cls_id:
+                class_name = classes[cls_id]
+            plt.gca().text(xmin, ymin - 2,
+                            '{:s} {:.3f}'.format(class_name, score),
+                            bbox=dict(facecolor=colors[cls_id], alpha=0.5),
                                     fontsize=12, color='white')
         plt.show()
 
+    @staticmethod
+    def filter_positive_detections(detections):
+        """
+        First column (class id) is -1 for negative detections
+        :param detections:
+        :return:
+        """
+        class_idx = 0
+        assert(isinstance(detections, mx.nd.NDArray) or isinstance(detections, 
np.ndarray))
+        detections_per_image = []
+        # for each image
+        for i in range(detections.shape[0]):
+            result = []
+            det = detections[i, :, :]
+            for obj in det:
+                if obj[class_idx] >= 0:
 
 Review comment:
   How about self.class_idx with default 0 in `__init__`

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