Fix empty detection bug (#135)

Closes: #134

Fixes regression in post-processing from #129.

Signed-off-by: Justin Georgi <justin.georgi@gmail.com>

Reviewed-on: #135

src/pages/detection-mixin.js

@@ -43,48 +43,50 @@ export default {
         rawBoxes.push(boxCalc)
         rawScores.push(getScores)
       }
-      const tBoxes = tf.tensor2d(rawBoxes)
-      let tScores = null
-      let structureScores = null
-      let boxes_data = []
-      let scores_data = []
-      let classes_data = []
-      for (var c = 0; c < outputSize - 4; c++) {
-        structureScores = rawScores.map(x => x[c])
-        tScores = tf.tensor1d(structureScores)
-        var validBoxes = await tf.image.nonMaxSuppressionAsync(tBoxes,tScores,10,0.5,.05)
-        validBoxes = validBoxes.dataSync()
-        if (validBoxes) {
-          boxes_data.push(...rawBoxes.filter( (_, idx) => validBoxes.includes(idx)))
-          var outputScores = structureScores.filter( (_, idx) => validBoxes.includes(idx))
-          scores_data.push(...outputScores)
-          classes_data.push(...outputScores.fill(c))
+
+      if (rawBoxes.length > 0) {
+        const tBoxes = tf.tensor2d(rawBoxes)
+        let tScores = null
+        let structureScores = null
+        let boxes_data = []
+        let scores_data = []
+        let classes_data = []
+        for (var c = 0; c < outputSize - 4; c++) {
+          structureScores = rawScores.map(x => x[c])
+          tScores = tf.tensor1d(structureScores)
+          var validBoxes = await tf.image.nonMaxSuppressionAsync(tBoxes,tScores,10,0.5,.05)
+          validBoxes = validBoxes.dataSync()
+          if (validBoxes) {
+            boxes_data.push(...rawBoxes.filter( (_, idx) => validBoxes.includes(idx)))
+            var outputScores = structureScores.filter( (_, idx) => validBoxes.includes(idx))
+            scores_data.push(...outputScores)
+            classes_data.push(...outputScores.fill(c))
+          }
+        }
+
+        tf.dispose(tBoxes)
+        tf.dispose(tScores)
+        const valid_detections_data = classes_data.length
+        var output = {
+          detections: []
+        }
+        for (var i =0; i < valid_detections_data; i++) {
+          var [dLeft, dTop, dRight, dBottom] = boxes_data[i]
+          output.detections.push({
+            "top": dTop,
+            "left": dLeft,
+            "bottom": dBottom,
+            "right": dRight,
+            "label": this.detectorLabels[classes_data[i]].name,
+            "confidence": scores_data[i] * 100
+          })
         }
       }
-
-      const valid_detections_data = classes_data.length
-      var output = {
-        detections: []
-      }
-      for (var i =0; i < valid_detections_data; i++) {
-        var [dLeft, dTop, dRight, dBottom] = boxes_data[i]
-        output.detections.push({
-          "top": dTop,
-          "left": dLeft,
-          "bottom": dBottom,
-          "right": dRight,
-          "label": this.detectorLabels[classes_data[i]].name,
-          "confidence": scores_data[i] * 100
-        })
-      }
-
       tf.dispose(res)
-      tf.dispose(tBoxes)
-      tf.dispose(tScores)
       tf.dispose(input)
       console.timeEnd('post-process')
 
-      return output
+      return output || { detections: [] }
     },
     getRemoteLabels() {
       var self = this