Add smart library demo

demos/python_demos/smart_library_demo/README.md

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+# Smart Library Demo
+The applicattion is a demo of automated “smart library”. 
+It involves the registration of the reader; authorization of the reader through face recognition; 
+receiving and returning books by recognizing QR codes generated for each book in the library. 
+The following pretrained models can be used:
+
+* `face-detection-retail-0004`, to detect faces and predict their bounding boxes;
+* `landmarks-regression-retail-0009`, to predict face keypoints;
+* `face-reidentification-retail-0095`, to recognize readers.
+
+For more information about the pre-trained models, refer to the [model documentation](../../../models/intel/index.md).
+
+### How it works
+
+The application is started from command line. It accepted several parameters. 
+It reads video stream frame-by-frame from a web-camera device and performs independent analysis
+of each frame. To make predictions the application using 3 models. An input frame is processed by
+the face detection model to predict face bounding boxes. Then, face keypoints
+are predicted by the facial landmarks regression model. Keypoints are using
+to align the face and match it with face in data base. 
+To register in library press `r` on keyboard. Once reader has beeen registered, he will be autothorized 
+through face recognition. Registration allows to receiving and ruturning books by recognizing QR codes for 
+each book. To make book recognition press `b` on keyboard. Also applications provides some 
+extra statictics in console, like list of registered readers, full list of books in the library,
+hystory of borrowing books. To change information in console press `f` on keyboard. To exit press `q`.
+
+### Creating QR-codes for books
+Next two files are using to create QR-codes for books:
+`library.json` file contains information about books in the library. 
+`createQRCodes.py` script generates QR-codes for each book in `library.json` file.
+
+usage: createQRCodes.py [-h] [-i LIB] [-o OUT]
+
+optional arguments:
+  -h, --help  show this help message and exit
+  -i LIB      unput `.json` file with info
+  -o OUT      directory to save generated QR-codes
+
+### Installation and dependencies
+
+The demo depends on:
+- OpenVINO toolkit (2019R3 or newer)
+- Python (any of 2.7+ or 3.4+, which is supported by OpenVINO)
+- OpenCV (>=3.4.0)
+
+To install all the required Python modules you can use:
+
+pip install -r requirements.txt
+
+
+### Running the demo:
+
+Running the application with the `-h` option or without
+any arguments yields the following message:
+
+``` sh
+python ./smart_library_demo.py -h
+
+usage: smart_library_demo.py [-h] -reid RDDET -m_rd RDMODEL -fd FDDET -m_fd
+                             FDMODEL -lm LMDET -m_lm LMMODEL [-w_rd RDWIDTH]
+                             [-h_rd RDHEIGHT] [-t_rd RDTHRESHOLD]
+                             [-w_fd FDWIDTH] [-h_fd FDHEIGHT]
+                             [-t_fd FDTHRESHOLD] [-w_lm LMWIDTH]
+                             [-h_lm LMHEIGHT] [-br BR] [-lib LIB] [-w WEB]
+
+Smart Library Sample
+
+Optional arguments:
+  -h, --help         show this help message and exit
+  -w_rd RDWIDTH      Optional. Image width to resize
+  -h_rd RDHEIGHT     Optional. Image height to resize
+  -t_rd RDTHRESHOLD  Optional. Probability threshold for face detections.
+  -w_fd FDWIDTH      Optional. Image width to resize
+  -h_fd FDHEIGHT     Optional. Image height to resize
+  -t_fd FDTHRESHOLD  Optional. Probability threshold for face detections.
+  -w_lm LMWIDTH      Optional. Image width to resize
+  -h_lm LMHEIGHT     Optional. Image height to resize
+  -br BR             Optional. Type - QR
+  -lib LIB           Optional. Path to library.
+  -w WEB             Optional. Specify index of web-camera to open. Default is
+                     0
+
+Models:
+  -reid RDDET        Required. Type of recognizer. Available DNN face
+                     recognizer - DNNfr
+  -m_rd RDMODEL      Required. Path to .xml file
+  -fd FDDET          Required. Type of detector. Available DNN face detector -
+                     DNNfd
+  -m_fd FDMODEL      Required. Path to .xml file
+  -lm LMDET          Required. Type of detector. Available DNN landmarks
+                     regression - DNNlm
+  -m_lm LMMODEL      Required. Path to .xml file
+
+
+Example of a valid command line to run the application:
+
+Linux (`sh`, `bash`, ...) (assuming OpenVINO installed in `/opt/intel/openvino`):
+
+``` sh
+# Set up the environment
+source /opt/intel/openvino/bin/setupvars.sh
+
+python ./smart_library_demo.py \
+-reid='DNNfr' \
+-m_rd=<path_to_model>/face-reidentification-retail-0095.xml \
+-fd='DNNfd' -m_fd=<path_to_model>/face-detection-retail-0004.xml  \
+-lm='DNNlm' \
+-m_lm=<path_to_model>/landmarks-regression-retail-0009.xml \
+```
+
+Windows (`cmd`, `powershell`) (assuming OpenVINO installed in `C:/Program Files (x86)/IntelSWTools/openvino/`):
+
+``` powershell
+# Set up the environment
+call C:/Program Files (x86)/IntelSWTools/openvino_2019.3.334/bin/setupvars.bat
+
+python smart_library_demo.py  -reid='DNNfr' -m_rd=<path_to_model>/face-reidentification-retail-0095.xml 
+                              -fd='DNNfd' -m_fd=<path_to_model>/face-detection-retail-0004.xml 
+                              -lm='DNNlm' -m_lm=<path_to_model>/landmarks-regression-retail-0009.xml
+
+Notice that the custom networks should be converted to the
+Inference Engine format (*.xml + *bin) first. To do this use the
+[Model Optimizer](https://software.intel.com/en-us/articles/OpenVINO-ModelOptimizer) tool.
+
+### Demo output
+
+The demo uses OpenCV window to display the resulting video frame and detections.
+It outputs logs to the terminal.
+
+## See also
+* [Using Inference Engine Demos](../../README.md)

demos/python_demos/smart_library_demo/createQRCodes.py

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+import sys, os
+import argparse
+import json
+sys.path.append("src/modules")
+import QR_generator as qr
+
+
+def createArgparse():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-i', type=str, dest = 'lib',  default='library.json')
+    parser.add_argument('-o', type=str, dest = 'out',  default='qr-codes')
+    return parser.parse_args()
+def main():
+    args = createArgparse()
+    gen = qr.QRgenerator()
+
+
+    if (args.lib != None and  os.path.isfile(args.lib) and args.out != None ):
+        try:
+            os.mkdir(args.out)
+        except OSError:
+            print ("Creation of the directory %s failed" % args.out)
+        else:
+            print ("Successfully created the directory %s " % args.out)
+            with open(args.lib, 'r', encoding='utf-8') as lib:
+                data = json.load(lib)
+
+            for book in data['books']:
+                strData = (str(book['id'])+ ' ' + book['title'] + ' ' + 
+                        book['author'] + ' ' + book['publisher'] + ' ' +
+                        str(book['year']))
+                qr = gen.makeQR(strData)
+                print(strData)
+                qr.save(args.out + '/' + str(book['id']) + '.png')
+    else:
+        print('File or directory not exists!')
+
+if __name__ == '__main__':
+    sys.exit(main() or 0)

demos/python_demos/smart_library_demo/library.json

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+{
+    "books": 
+    [
+        {
+            "id": 1,
+            "title": "book 1",
+            "author": "Author 1, Author 2",
+            "publisher": "pub",
+            "year": 2018
+        },
+        {
+            "id": 2,
+            "title": "book 2",
+            "author": "Author 1, Author 2",
+            "publisher": "pub",
+            "year": 2017
+        } 
+    ]
+}

demos/python_demos/smart_library_demo/models.lst

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+# This file can be used with the --list option of the model downloader.
+face-detection-retail-????
+face-reidentification-retail-????
+landmarks-regression-retail-????
+

demos/python_demos/smart_library_demo/requirements.txt

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+pyzbar>=0.1.8
+qrcode>=6.1