select the device (video capture)

samples/dnn/object_detection.cpp

@@ -7,12 +7,13 @@
 
 const char* keys =
     "{ help  h     | | Print help message. }"
-    "{ input i     | | Path to input image or video file. Skip this argument to capture frames from a camera.}"
+    "{ device      |  0 | camera device number. }"
+    "{ input i     | | Path to input image or video file. Skip this argument to capture frames from a camera. }"
     "{ model m     | | Path to a binary file of model contains trained weights. "
                       "It could be a file with extensions .caffemodel (Caffe), "
-                      ".pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet) }"
+                      ".pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet).}"
     "{ config c    | | Path to a text file of model contains network configuration. "
-                      "It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet) }"
+                      "It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet).}"
     "{ framework f | | Optional name of an origin framework of the model. Detect it automatically if it does not set. }"
     "{ classes     | | Optional path to a text file with names of classes to label detected objects. }"
     "{ mean        | | Preprocess input image by subtracting mean values. Mean values should be in BGR order and delimited by spaces. }"
@@ -91,7 +92,7 @@ int main(int argc, char** argv)
     if (parser.has("input"))
         cap.open(parser.get<String>("input"));
     else
-        cap.open(0);
+        cap.open(parser.get<int>("device"));
 
     // Process frames.
     Mat frame, blob;