Use yaml config file for the demo instead of param list

Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>

docs/demo.md

@@ -32,21 +32,20 @@ make
 
 Once you have successfully created your rt file, run the demo: 
 ```
-./demo yolo4_fp32.rt ../demo/yolo_test.mp4 y
+./demo <path-to-config>
 ```
-In general the demo program takes 7 parameters:
-```
-./demo <network-rt-file> <path-to-video> <kind-of-network> <number-of-classes> <n-batches> <show-flag> <conf-thresh>
-```
-where
+In general the demo program takes 1 parameter, the ```<path-to-config>``` that is the path to che configuration file. The parameter is optional and its default value is ```"../demo/demoConfig.yaml"```. 
 
-*  ```<network-rt-file>``` is the rt file generated by a test
-*  ```<<path-to-video>``` is the path to a video file or a camera input  
-*  ```<kind-of-network>``` is the type of network. Thee types are currently supported: ```y``` (YOLO family), ```c``` (CenterNet family) and ```m``` (MobileNet-SSD family)
-*  ```<number-of-classes>```is the number of classes the network is trained on
-*  ```<n-batches>``` number of batches to use in inference (N.B. you should first export TKDNN_BATCHSIZE to the required n_batches and create again the rt file for the network).
-*  ```<show-flag>``` if set to 0 the demo will not show the visualization but save the video into result.mp4 (if n-batches ==1)
-*  ```<conf-thresh>``` confidence threshold for the detector. Only bounding boxes with threshold greater than conf-thresh will be displayed.
+The config file is a yaml file with the following attributes:
+*  ```net``` is the rt file generated by a test
+*  ```input``` is the path to a video file or a camera input  (on Linux)
+*  ```win_input``` is the path to a video file or a camera input  (on Windows)
+*  ```ntype``` is the type of network. Thee types are currently supported: ```y``` (YOLO family), ```c``` (CenterNet family) and ```m``` (MobileNet-SSD family)
+*  ```n_classes``` is the number of classes the network is trained on
+*  ```n_batch``` number of batches to use in inference (N.B. you should first export TKDNN_BATCHSIZE to the required n_batches and create again the rt file for the network).
+*  ```conf_thresh``` confidence threshold for the detector. Only bounding boxes with threshold greater than conf-thresh will be displayed.
+*  ```show``` if set to 0 the demo will not show the visualization (if n-batches ==1)
+*  ```save``` if set to 1 the demo will save the video of the demo into result.mp4 (if n-batches ==1)
 
 N.B. By default it is used FP32 inference
 
@@ -61,7 +60,8 @@ To run the demo with FP16 inference follow these steps (example with yolov3):
 export TKDNN_MODE=FP16  # set the half floating point optimization
 rm yolo3_fp16.rt        # be sure to delete(or move) old tensorRT files
 ./test_yolo3            # run the yolo test (is slow)
-./demo yolo3_fp16.rt ../demo/yolo_test.mp4 y
+# set net: yolo3_fp16.rt in the config-file
+./demo 
 ```
 N.B. Using FP16 inference will lead to some errors in the results (first or second decimal). 
 
@@ -86,7 +86,8 @@ export TKDNN_CALIB_LABEL_PATH=../demo/COCO_val2017/all_labels.txt
 export TKDNN_CALIB_IMG_PATH=../demo/COCO_val2017/all_images.txt
 rm yolo3_int8.rt        # be sure to delete(or move) old tensorRT files
 ./test_yolo3            # run the yolo test (is slow)
-./demo yolo3_int8.rt ../demo/yolo_test.mp4 y
+# set net: yolo3_int8.rt in the config-file
+./demo 
 ```
 N.B.