-Modified Dockerfile.base to cudagl

-Changed demo to take in input from demoConfig.yaml file
-Readme changes for demo.md

docs/demo.md

@@ -30,31 +30,24 @@ cmake .. -DCMAKE_BUILD_TYPE=Debug -DDEBUG=True
 make
 ```
 
-Once you have successfully created your rt file, run the demo(yolo) :
+Once you have successfully created your rt file, run the demo:
 ```
-./demo yolo4_fp32.rt ../demo/yolo_test.mp4 y 80 ../tests/darknet/cfg/yolo4.cfg ../tests/darknet/names/coco.names 
+./ demo <path-to-config>
 ```
+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"```.
 
-To run demo for mobilenet and centernet for the created rt file :
-```
-./demo mobilenetv2ssd_fp32.rt m 20
-```
-
-In general the demo program takes 7 parameters:
-```
-./demo <network-rt-file> <path-to-video> <kind-of-network> <number-of-classes> <cfg-path> <name-path>  <n-batches> <show-flag> <conf-thresh>
-```
-where
-
-* ```<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
-* ```<cfg-path> ```is the relative path to the config file (only for darknet based networks) used to train the network
-* ```<name-path>```is the relative path to the names file (only for darknet based networks) used to train the network
-* ```<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)
+* ```cfg_input``` (for linux) \ ```cfg_win_input``` (for windows)  is the location of the cfg path of the network for mobilenet and centernet networks use ```" "```
+* ```name_input``` (for linux) \ ```name_win_input``` (for windows)  is the location of the name path of the network for mobilenet and centernet networks use ```" "```
 
 N.B. By default it is used FP32 inference
 
@@ -69,7 +62,8 @@ To run the demo with FP16 inference follow these steps (example with yolov3):
 export TKDNN_MODE=FP16  # set the half floating point optimization
 rm yolo4_fp16.rt        # be sure to delete(or move) old tensorRT files
 ./test_yolo4            # run the yolo test (is slow)
-./demo yolo4_fp16.rt ../demo/yolo_test.mp4 y 80 ../tests/darknet/cfg/yolo4.cfg ../tests/darknet/names/coco.names 
+#set net: yolo4_fp16.rt in the config file
+./demo
 ```
 N.B. Using FP16 inference will lead to some errors in the results (first or second decimal). 
 
@@ -94,7 +88,8 @@ export TKDNN_CALIB_LABEL_PATH=../demo/COCO_val2017/all_labels.txt
 export TKDNN_CALIB_IMG_PATH=../demo/COCO_val2017/all_images.txt
 rm yolo4_int8.rt        # be sure to delete(or move) old tensorRT files
 ./test_yolo4            # run the yolo test (is slow)
-./demo yolo4_int8.rt ../demo/yolo_test.mp4 y 80 ../tests/darknet/cfg/yolo4.cfg ../tests/darknet/names/coco.names 
+#set net: yolo4_int8.rt in the config file
+./demo
 ```
 N.B.