YOLOv3 detectiongit add experimental/dog.jpg!

experiencor · Apr 3, 2018 · b751077 · b751077
1 parent e82ecd8
commit b751077
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Showing 5 changed files with 18 additions and 5 deletions.
diff --git a/Yolo Step-by-Step.ipynb b/Yolo Step-by-Step.ipynb
@@ -867,6 +867,18 @@
     "}"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def normalize(image):\n",
+    "    return image / 255."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 16,
@@ -998,7 +1010,8 @@
      "end_time": "2018-04-04T00:19:07.263359",
      "start_time": "2018-04-04T00:19:05.658285"
     },
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": true
    },
    "outputs": [
     {

diff --git a/experimental/README.md b/experimental/README.md
@@ -2,4 +2,4 @@
 
 This is a work in progress. Only detection with YOLOv3 is supported at the moment.
 
-python yolo3_detect.py -w yolov3.weights -i dog.jpg
+python yolo3_detect.py -w yolov3.weights -i dog.jpg 
diff --git a/experimental/yolo3_detect.py b/experimental/yolo3_detect.py
@@ -421,7 +421,7 @@ def _main_(args):
     do_nms(boxes, nms_thresh)     
 
     # draw bounding boxes on the image using labels
-    draw_boxes(image, boxes, labels, obj_thresh)
+    draw_boxes(image, boxes, labels, obj_thresh) 
 
     # write the image with bounding boxes to file
     cv2.imwrite(image_path[:-4] + '_detected' + image_path[-4:], (image).astype('uint8'))

diff --git a/frontend.py b/frontend.py
@@ -451,7 +451,7 @@ def evaluate(self,
             precision = true_positives / np.maximum(true_positives + false_positives, np.finfo(np.float64).eps)
 
             # compute average precision
-            average_precision  = compute_ap(recall, precision)
+            average_precision  = compute_ap(recall, precision) 
             average_precisions[label] = average_precision
 
         return average_precisions    

diff --git a/utils.py b/utils.py
@@ -44,7 +44,7 @@ def reset(self):
 
 def bbox_iou(box1, box2):
     intersect_w = _interval_overlap([box1.xmin, box1.xmax], [box2.xmin, box2.xmax])
-    intersect_h = _interval_overlap([box1.ymin, box1.ymax], [box2.ymin, box2.ymax])
+    intersect_h = _interval_overlap([box1.ymin, box1.ymax], [box2.ymin, box2.ymax]) 
 
     intersect = intersect_w * intersect_h