select the device (video capture)

doc/tutorials/dnn/dnn_yolo/dnn_yolo.markdown

@@ -29,7 +29,7 @@ Execute in webcam:
 
 @code{.bash}
 
-$ example_dnn_object_detection --config=[PATH-TO-DARKNET]/cfg/yolo.cfg --model=[PATH-TO-DARKNET]/yolo.weights --classes=object_detection_classes_pascal_voc.txt --width=416 --height=416 --scale=0.00392
+$ example_dnn_object_detection --config=[PATH-TO-DARKNET]/cfg/yolo.cfg --model=[PATH-TO-DARKNET]/yolo.weights --classes=object_detection_classes_pascal_voc.txt --width=416 --height=416 --scale=0.00392 --rgb
 
 @endcode
 
@@ -37,7 +37,7 @@ Execute with image or video file:
 
 @code{.bash}
 
-$ example_dnn_object_detection --config=[PATH-TO-DARKNET]/cfg/yolo.cfg --model=[PATH-TO-DARKNET]/yolo.weights --classes=object_detection_classes_pascal_voc.txt --width=416 --height=416 --scale=0.00392 --input=[PATH-TO-IMAGE-OR-VIDEO-FILE]
+$ example_dnn_object_detection --config=[PATH-TO-DARKNET]/cfg/yolo.cfg --model=[PATH-TO-DARKNET]/yolo.weights --classes=object_detection_classes_pascal_voc.txt --width=416 --height=416 --scale=0.00392 --input=[PATH-TO-IMAGE-OR-VIDEO-FILE] --rgb
 
 @endcode
 

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;

samples/dnn/openpose.cpp

@@ -61,12 +61,16 @@ int main(int argc, char **argv)
         "{ p proto          |       | (required) model configuration, e.g. hand/pose.prototxt }"
         "{ m model          |       | (required) model weights, e.g. hand/pose_iter_102000.caffemodel }"
         "{ i image          |       | (required) path to image file (containing a single person, or hand) }"
+        "{ width            |  368  | Preprocess input image by resizing to a specific width. }"
+        "{ height           |  368  | Preprocess input image by resizing to a specific height. }"
         "{ t threshold      |  0.1  | threshold or confidence value for the heatmap }"
     );
 
     String modelTxt = parser.get<string>("proto");
     String modelBin = parser.get<string>("model");
     String imageFile = parser.get<String>("image");
+    int W_in = parser.get<int>("width");
+    int H_in = parser.get<int>("height");
     float thresh = parser.get<float>("threshold");
     if (parser.get<bool>("help") || modelTxt.empty() || modelBin.empty() || imageFile.empty())
     {
@@ -75,10 +79,6 @@ int main(int argc, char **argv)
         return 0;
     }
 
-    // fixed input size for the pretrained network
-    int W_in = 368;
-    int H_in = 368;
-
     // read the network model
     Net net = readNetFromCaffe(modelTxt, modelBin);
 

samples/dnn/segmentation.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. }"
     "{ colors      | | Optional path to a text file with colors for an every class. "
@@ -111,7 +112,7 @@ 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"));
     //! [Open a video file or an image file or a camera stream]
 
     // Process frames.