Whats the right way of using openCV with openVINO? - opencv

Question

Dislcaimer: I have never used openCV or openVINO or for the fact anything even close to ML before. However I've been slamming my head studying neural-networks(reading material online) because I've to work with intel's openVINO on an edge device.
Here's what the official documentation says about using openCV with openVINO(using openVINO's inference engine with openCV).
->Optimize the pretrained model with openVINO's model optimizer(creating the IR file pair)
use these IR files with
openCV's dnn.readnet() //this is where the inference engine gets set?
https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_raspbian.html
Tried digging more and found a third party reference. Here a difference approach is taken.
->Intermediatte files (bin/xml are not created. Instead caffe model file is used)
->the inference engine is defined explicitly with the following line
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_INFERENCE_ENGINE)
https://www.learnopencv.com/using-openvino-with-opencv/
Now I know to utilize openCV we have to use it's inference engine with pretrained models. I want to know which of the two approach is the correct(or preferred) one, and if rather I'm missing out no something.

Answer 1

You can get started using OpenVino from: https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_windows.html
You would require a set of pre-requsites to run your sample. OpenCV is your Computer Vision package which can used for Image processing.
Openvino inference requires you to convert any of your trained models(.caffemodel,.pb,etc.) to Intermediate representations(.xml,.bin) files.
For a better understanding and sample demos on OpenVino, watch the videos/subscribe to the OpenVino Youtube channel: https://www.youtube.com/channel/UCkN8KINLvP1rMkL4trkNgTg

Answer 2

If the topology that you are using is supported by OpenVino,the best way to use is the opencv that comes with openvino. For that you need to
1.Initialize the openvino environment by running the setupvars.bat in your openvino path(C:\Program Files (x86)\IntelSWTools\openvino\bin)
2.Generate the IR file (xml&bin)for your model using model optimizer.
3.Run using inference engine samples in the path /inference_engine_samples_build/
If the topology is not supported, then you can go for the other procedure that you mentioned.

Answer 3

The most common issues I ran into:
setupvars.bat must be run within the same terminal, or use os.environ["varname"] = varvalue
OpenCV needs to be built with support for the inference engines (ie DLDT). There are pre-built binaries here: https://github.com/opencv/opencv/wiki/Intel%27s-Deep-Learning-Inference-Engine-backend
Target inference engine: net.setPreferableBackend(cv2.dnn.DNN_BACKEND_INFERENCE_ENGINE)
Target NCS2: net.setPreferableTarget(cv2.dnn.DNN_TARGET_MYRIAD)
The OpenCV pre-built binary located in the OpenVino directory already has IE support and is also an option.
Note that the Neural Compute Stick 2 AKA NCS2 (OpenVino IE/VPU/MYRIAD) requires FP16 model formats (float16). Also try to keep you image in this format to avoid conversion penalties. You can input images as any of these formats though: FP32, FP16, U8
I found this guide helpful: https://learnopencv.com/using-openvino-with-opencv/
Here's an example targetting the NCS2 from https://medium.com/sclable/intel-openvino-with-opencv-f5ad03363a38:
# Load the model.
net = cv2.dnn.readNet(ARCH_FPATH, MODEL_FPATH)
# Specify target device.
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_INFERENCE_ENGINE)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_MYRIAD) # NCS 2
# Read an image.
print("Processing input image...")
img = cv2.imread(IMG_FPATH)
if img is None:
raise Exception(f'Image not found here: {IMG_FPATH}')
# Prepare input blob and perform inference
blob = cv2.dnn.blobFromImage(img, size=(672, 384), ddepth=cv2.CV_8U)
net.setInput(blob)
out = net.forward()
# Draw detected faces
for detect in out.reshape(-1, 7):
conf = float(detect[2])
xmin = int(detect[3] * frame.shape[1])
ymin = int(detect[4] * frame.shape[0])
xmax = int(detect[5] * frame.shape[1])
ymax = int(detect[6] * frame.shape[0])
if conf > CONF_THRESH:
cv2.rectangle(img, (xmin, ymin), (xmax, ymax), color=(0, 255, 0))
There are more samples here (jupyter notebook/python): https://github.com/sclable/openvino_opencv