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 | 1 | initial version |
const char* keys =
"{ help h | | Print help message. }"
"{ device | 1 | camera device number. }"
"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera. }"
"{ model m | yolov2-tiny.weights | 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).}"
"{ config c | yolov2-tiny.cfg | Path to a text file of model contains network configuration. "
"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 | coco.names | 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. }"
"{ scale | 0.003921.f | Preprocess input image by multiplying on a scale factor. }"
"{ width | 416 | Preprocess input image by resizing to a specific width. }"
"{ height | 416 | Preprocess input image by resizing to a specific height. }"
"{ rgb | 1 | Indicate that model works with RGB input images instead BGR ones. }"
"{ thr | .5 | Confidence threshold. }"
"{ backend | 0 | Choose one of computation backends: "
"0: automatically (by default), "
"1: Halide language (http://halide-lang.org/), "
"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"3: OpenCV implementation }"
"{ target | 0 | Choose one of target computation devices: "
"0: CPU target (by default), "
"1: OpenCL, "
"2: OpenCL fp16 (half-float precision), "
"3: VPU }";
This is how I configured it. It works well.