This repo contains couple python sample applications to teach about Intel(R) Distribution of OpenVINO(TM).
openvino_basic_object_detection.py
This applications intends to showcase how a model is being used with OpenVINO(TM) Toolkit.
Aim is to show initial use case of Inference Engine API and Async Mode.
usage: openvino_basic_object_detection.py [-h] [--model-xml MODEL_XML]
[--model-bin MODEL_BIN]
[--target-device TARGET_DEVICE]
[--input-type INPUT_TYPE]
[--input INPUT]
[--detection-threshold DETECTION_THRESHOLD]
[--async]
[--request-number REQUEST_NUMBER]
Basic OpenVINO Example with MobileNet-SSD
optional arguments:
-h, --help show this help message and exit
--model-xml MODEL_XML
XML File
--model-bin MODEL_BIN
BIN File
--target-device TARGET_DEVICE
Target Plugin: CPU, GPU, FPGA, MYRIAD, MULTI:CPU,GPU,
HETERO:FPGA,CPU
--input-type INPUT_TYPE
Type of Input: image, video, cam
--input INPUT Path to Input: WebCam: 0, Video File or Image file
--detection-threshold DETECTION_THRESHOLD
Object Detection Accuracy Threshold
--async Run Async Mode
--request-number REQUEST_NUMBER
Number of RequestsDownloaded model with OpenVINO(TM) Toolkit Model Downloader
python3 /opt/intel/openvino/deployment_tools/tools/model_downloader/downloader.py \
--name mobilenet-ssd \
--output_dir ~/openvino_models/Converted Model using OpenVINO(TM) Toolkit Model Optimizer
- FP32 Conversion
python3 /opt/intel/openvino/deployment_tools/model_optimizer/mo.py \
--input_model ~/openvino_models/object_detection/common/mobilenet-ssd/caffe/mobilenet-ssd.caffemodel \
--output_dir ~/openvino_models/object_detection/common/mobilenet-ssd/FP32 \
--data_type FP32 \
--scale 127.50223128904757 \
--input_shape [1,3,300,300] \
--mean_values [127.5,127.5,127.5] \
--framework caffe- FP16 Conversion
python3 /opt/intel/openvino/deployment_tools/model_optimizer/mo.py \
--input_model ~/openvino_models/object_detection/common/mobilenet-ssd/caffe/mobilenet-ssd.caffemodel \
--output_dir ~/openvino_models/object_detection/common/mobilenet-ssd/FP16 \
--data_type FP16 \
--scale 127.50223128904757 \
--input_shape [1,3,300,300] \
--mean_values [127.5,127.5,127.5] \
--framework caffeopenvino_basic_object_detection.py
This application intends to showcase how two model from Open Model Zoo (https://github.com/opencv/open_model_zoo) is being used.
Aim is to show initial use case of Inference Engine API Dynamic Batching with using Face Detection and Gender-Age Detection.
usage: openvino_face_detection.py [-h] [--face-model-xml FACE_MODEL_XML]
[--face-model-bin FACE_MODEL_BIN]
[--face-detection-threshold FACE_DETECTION_THRESHOLD]
[--face-target-device FACE_TARGET_DEVICE]
[--ag-model-xml AG_MODEL_XML]
[--ag-model-bin AG_MODEL_BIN]
[--ag-max-batch-size AG_MAX_BATCH_SIZE]
[--ag-dynamic-batch]
[--ag-target-device AG_TARGET_DEVICE]
[--input-type INPUT_TYPE] [--input INPUT]
Basic OpenVINO Example to Face, Age and Gender Detection
optional arguments:
-h, --help show this help message and exit
--face-model-xml FACE_MODEL_XML
Face Detection Model XML File
--face-model-bin FACE_MODEL_BIN
Face Detection Model BIN File
--face-detection-threshold FACE_DETECTION_THRESHOLD
Face Detection Accuracy Threshold
--face-target-device FACE_TARGET_DEVICE
Target Plugin: CPU, GPU, FPGA, MYRIAD, MULTI:CPU,GPU,
HETERO:FPGA,CPU
--ag-model-xml AG_MODEL_XML
Age-Gender Detection XML File
--ag-model-bin AG_MODEL_BIN
Age-Gender Detection BIN File
--ag-max-batch-size AG_MAX_BATCH_SIZE
Age Gender Detection Max Batch Size
--ag-dynamic-batch Age Gender Detection Enable Dynamic Batching
--ag-target-device AG_TARGET_DEVICE
Target Plugin: CPU, GPU, FPGA, MYRIAD, MULTI:CPU,GPU,
HETERO:FPGA,CPU
--input-type INPUT_TYPE
Type of Input: image, video, cam
--input INPUT Path to Input: WebCam: 0, Video File or Image fileDownloaded Face Detection model as below:
python3 /opt/intel/openvino/deployment_tools/tools/model_downloader/downloader.py \
--name face-detection-retail* \
--output_dir ~/openvino_models/Detailed information about the model:
Downloaded Age Gender Detection Model as below:
python3 /opt/intel/openvino/deployment_tools/tools/model_downloader/downloader.py \
--name age-gender-recognition-retail* \
--output_dir ~/openvino_models/openvino_object_detection_configs.py
This application is similar to Object Detection Application above, however some additional configurations options added to see the performance and understand their effect to overall performance.
usage: openvino_object_detection_configs.py [-h] [--model-xml MODEL_XML]
[--model-bin MODEL_BIN]
[--target-device TARGET_DEVICE]
[--input-type INPUT_TYPE]
[--input INPUT]
[--detection-threshold DETECTION_THRESHOLD]
[--async]
[--request-number REQUEST_NUMBER]
[--pc]
[--cpu-num-threads CPU_NUM_THREADS]
[--cpu-bind-thread]
[--cpu-throughput-streams CPU_THROUGHPUT_STREAMS]
[--gpu-throughput-streams GPU_THROUGHPUT_STREAMS]
[--gpu-throttle]
Basic OpenVINO Example to Test Object Detection Model
optional arguments:
-h, --help show this help message and exit
--model-xml MODEL_XML
XML File
--model-bin MODEL_BIN
BIN File
--target-device TARGET_DEVICE
Target Plugin: CPU, GPU, FPGA, MYRIAD, MULTI:CPU,GPU,
HETERO:FPGA,CPU
--input-type INPUT_TYPE
Type of Input: image, video, cam
--input INPUT Path to Input: WebCam: 0, Video File or Image file
--detection-threshold DETECTION_THRESHOLD
Object Detection Accuracy Threshold
--async Run Async Mode
--request-number REQUEST_NUMBER
Number of Requests
--pc Enable Performance Counters
--cpu-num-threads CPU_NUM_THREADS
Limit CPU Threads
--cpu-bind-thread Bind Threads to CPU
--cpu-throughput-streams CPU_THROUGHPUT_STREAMS
Int Values or CPU_THROUGHPUT_NUMA if not set
CPU_THROUGHPUT_AUTO
--gpu-throughput-streams GPU_THROUGHPUT_STREAMS
Int Values if not set GPU_THROUGHPUT_AUTO
--gpu-throttle multi-device execution with the CPU+GPU performs best
with GPU trottling hintThis sample application will be used to compare inference between multiple frameworks; opencv, openvino