Text Spotting Python* Demo¶
This demo shows how to run Text Spotting models. Text Spotting models allow us to simultaneously detect and recognize text.
Only batch size of 1 is supported.
How It Works¶
The demo application expects a text spotting model that is split into three parts. Every model part must be in the Intermediate Representation (IR) format.
First model is Mask-RCNN like text detector with the following constraints:
im_datafor input image and
im_infofor meta-information about the image (actual height, width and scale).
At least five outputs including:
boxeswith absolute bounding box coordinates of the input image
scoreswith confidence scores for all bounding boxes
classeswith object class IDs for all bounding boxes
raw_maskswith fixed-size segmentation heat maps for all classes of all bounding boxes
text_featureswith text features which are fed to Text Recognition Head further
Second model is Text Recognition Encoder that takes
text_features as input and produces
Third model is Text Recognition Decoder that takes
encoded text from Text Recognition Encoder ,
previous symbol and
hidden state. On the first step special
Start Of Sequence (SOS) symbol and zero
hidden state are fed to Text Recognition Decoder. The decoder produces
current hidden state each step until
End Of Sequence (EOS) symbol is generated.
Examples of valid inputs to specify with a command-line argument
-i are a path to a video file or a numeric ID of a web camera.
The demo workflow is the following:
The demo application reads frames from the provided input, resizes them to fit into the input image blob of the network (
im_infoinput blob passes resulting resolution and scale of a pre-processed image to the network to perform inference of Mask-RCNN-like text detector.
The Text Recognition Encoder takes input from the text detector and produces output.
The Text Recognition Decoder takes output from the Text Recognition Encoder output as input and produces output.
The demo visualizes the resulting text spotting results. Certain command-line options affect the visualization:
If you specify
--show_scoresarguments, bounding boxes and confidence scores are also shown.
By default, tracking is used to show text instance with the same color throughout the whole video. It assumes more or less static scene with instances in two frames being a part of the same track if intersection over union of the masks is greater than the 0.5 threshold. To disable tracking, specify the
By default, Open Model Zoo demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the demo application or reconvert your model using the Model Optimizer tool with the
--reverse_input_channels argument specified. For more information about the argument, refer to When to Reverse Input Channels section of Embedding Preprocessing Computation.
Preparing to Run¶
For demo input image or video files, refer to the section Media Files Available for Demos in the Open Model Zoo Demos Overview. The list of models supported by the demo is in
<omz_dir>/demos/text_spotting_demo/python/models.lst file. This file can be used as a parameter for Model Downloader and Converter to download and, if necessary, convert models to OpenVINO IR format (*.xml + *.bin).
An example of using the Model Downloader:
omz_downloader --list models.lst
An example of using the Model Converter:
omz_converter --list models.lst
Run the application with the
-h option to see the following usage message:
usage: text_spotting_demo.py [-h] -m_m "<path>" -m_te "<path>" -m_td "<path>" -i INPUT [--loop] [-o OUTPUT] [-limit OUTPUT_LIMIT] [-d "<device>"] [--delay "<num>"] [-pt "<num>"] [-a ALPHABET] [--trd_input_prev_symbol TRD_INPUT_PREV_SYMBOL] [--trd_input_prev_hidden TRD_INPUT_PREV_HIDDEN] [--trd_input_encoder_outputs TRD_INPUT_ENCODER_OUTPUTS] [--trd_output_symbols_distr TRD_OUTPUT_SYMBOLS_DISTR] [--trd_output_cur_hidden TRD_OUTPUT_CUR_HIDDEN] [-trt "<num>"] [--keep_aspect_ratio] [--no_track] [--show_scores] [--show_boxes] [-r] [--no_show] [-u UTILIZATION_MONITORS] Options: -h, --help Show this help message and exit. -m_m "<path>", --mask_rcnn_model "<path>" Required. Path to an .xml file with a trained Mask- RCNN model with additional text features output. -m_te "<path>", --text_enc_model "<path>" Required. Path to an .xml file with a trained text recognition model (encoder part). -m_td "<path>", --text_dec_model "<path>" Required. Path to an .xml file with a trained text recognition model (decoder part). -i INPUT, --input INPUT Required. An input to process. The input must be a single image, a folder of images, video file or camera id. --loop Optional. Enable reading the input in a loop. -o OUTPUT, --output OUTPUT Optional. Name of the output file(s) to save. -limit OUTPUT_LIMIT, --output_limit OUTPUT_LIMIT Optional. Number of frames to store in output. If 0 is set, all frames are stored. -d "<device>", --device "<device>" Optional. Specify the target device to infer on, i.e. CPU, GPU. The demo will look for a suitable plugin for device specified (by default, it is CPU). Please refer to OpenVINO documentation for the list of devices supported by the model. --delay "<num>" Optional. Interval in milliseconds of waiting for a key to be pressed. -pt "<num>", --prob_threshold "<num>" Optional. Probability threshold for detections filtering. -a ALPHABET, --alphabet ALPHABET Optional. Alphabet that is used for decoding. --trd_input_prev_symbol TRD_INPUT_PREV_SYMBOL Optional. Name of previous symbol input node to text recognition head decoder part. --trd_input_prev_hidden TRD_INPUT_PREV_HIDDEN Optional. Name of previous hidden input node to text recognition head decoder part. --trd_input_encoder_outputs TRD_INPUT_ENCODER_OUTPUTS Optional. Name of encoder outputs input node to text recognition head decoder part. --trd_output_symbols_distr TRD_OUTPUT_SYMBOLS_DISTR Optional. Name of symbols distribution output node from text recognition head decoder part. --trd_output_cur_hidden TRD_OUTPUT_CUR_HIDDEN Optional. Name of current hidden output node from text recognition head decoder part. -trt "<num>", --tr_threshold "<num>" Optional. Text recognition confidence threshold. --keep_aspect_ratio Optional. Force image resize to keep aspect ratio. --no_track Optional. Disable tracking. --show_scores Optional. Show detection scores. --show_boxes Optional. Show bounding boxes. -r, --raw_output_message Optional. Output inference results raw values. --no_show Optional. Don't show output -u UTILIZATION_MONITORS, --utilization_monitors UTILIZATION_MONITORS Optional. List of monitors to show initially.
Running the application with an empty list of options yields the short version of the usage message and an error message.
To run the demo, please provide paths to the model in the IR format and to an input with images:
python3 text_spotting_demo.py \ -m_m <path_to_model>/text-spotting-0005-detector.xml \ -m_te <path_to_model>/text-spotting-0005-recognizer-encoder.xml \ -m_td <path_to_model>/text-spotting-0005-recognizer-decoder.xml \ -i 0
> NOTE : If you provide a single image as an input, the demo processes and renders it quickly, then exits. To continuously visualize inference results on the screen, apply the
loop option, which enforces processing a single image in a loop.
You can save processed results to a Motion JPEG AVI file or separate JPEG or PNG files using the
To save processed results in an AVI file, specify the name of the output file with
aviextension, for example:
To save processed results as images, specify the template name of the output image file with
pngextension, for example:
-o output_%03d.jpg. The actual file names are constructed from the template at runtime by replacing regular expression
%03dwith the frame number, resulting in the following:
output_001.jpg, and so on. To avoid disk space overrun in case of continuous input stream, like camera, you can limit the amount of data stored in the output file(s) with the
limitoption. The default value is 1000. To change it, you can apply the
-limit Noption, where
Nis the number of frames to store.
> NOTE : Windows* systems may not have the Motion JPEG codec installed by default. If this is the case, you can download OpenCV FFMPEG back end using the PowerShell script provided with the OpenVINO install package and located at
<INSTALL_DIR>/opencv/ffmpeg-download.ps1. The script should be run with administrative privileges if OpenVINO is installed in a system protected folder (this is a typical case). Alternatively, you can save results as images.
The application uses OpenCV to display resulting text instances. The demo reports
FPS : average rate of video frame processing (frames per second).
Latency : average time required to process one frame (from reading the frame to displaying the results). You can use both of these metrics to measure application-level performance.