210-ct-scan-live-inference: Live Inference and Benchmark CT-scan Data with OpenVINO

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Thu Jun 09 2022 18:51:21 GMT+0000 (UTC)

Saved by @OpenVINOtoolkit #python #openvino #openvino-notebook #ct-scan #healthcareai #unet

# Imports
import os
import sys
import zipfile
from pathlib import Path

import numpy as np
from monai.transforms import LoadImage
from openvino.inference_engine import IECore

sys.path.append("../utils")
from models.custom_segmentation import SegmentationModel
from notebook_utils import benchmark_model, download_file, show_live_inference

# Settings
# The directory that contains the IR model (xml and bin) files
MODEL_PATH = "pretrained_model/quantized_unet_kits19.xml"
# Uncomment the next line to use the FP16 model instead of the quantized model
# MODEL_PATH = "pretrained_model/unet_kits19.xml"

# Benchmark Model Performance
ie = IECore()
# By default, benchmark on MULTI:CPU,GPU if a GPU is available, otherwise on CPU.
device = "MULTI:CPU,GPU" if "GPU" in ie.available_devices else "CPU"
# Uncomment one of the options below to benchmark on other devices
# device = "GPU"
# device = "CPU"
# device = "AUTO"

# Benchmark model
benchmark_model(model_path=MODEL_PATH, device=device, seconds=15)

# Download and Prepare Data
# Directory that contains the CT scan data. This directory should contain subdirectories
# case_00XXX where XXX is between 000 and 299
BASEDIR = Path("kits19_frames_1")
# The CT scan case number. For example: 16 for data from the case_00016 directory
# Currently only 117 is supported
CASE = 117

case_path = BASEDIR / f"case_{CASE:05d}"

if not case_path.exists():
    filename = download_file(
        f"https://storage.openvinotoolkit.org/data/test_data/openvino_notebooks/kits19/case_{CASE:05d}.zip"
    )
    with zipfile.ZipFile(filename, "r") as zip_ref:
        zip_ref.extractall(path=BASEDIR)
    os.remove(filename)  # remove zipfile
    print(f"Downloaded and extracted data for case_{CASE:05d}")
else:
    print(f"Data for case_{CASE:05d} exists")

# Load Model and List of Image Files
ie = IECore()
segmentation_model = SegmentationModel(
    ie=ie, model_path=Path(MODEL_PATH), sigmoid=True, rotate_and_flip=True
)
image_paths = sorted(case_path.glob("imaging_frames/*jpg"))

print(f"{case_path.name}, {len(image_paths)} images")

# Show Inference
# Possible options for device include "CPU", "GPU", "AUTO", "MULTI"
device = "MULTI:CPU,GPU" if "GPU" in ie.available_devices else "CPU"
reader = LoadImage(image_only=True, dtype=np.uint8)

show_live_inference(
    ie=ie, image_paths=image_paths, model=segmentation_model, device=device, reader=reader
)
content_copyCOPY

This tutorial is part of a series on how to train, optimize, quantize and show live inference on a medical segmentation model. The goal is to accelerate inference on a kidney segmentation model. The UNet model is trained from scratch; the data is from Kits19. This tutorial shows how to - Benchmark performance of the model - Show live inference with OpenVINO's async API and MULTI plugin To learn how this model was quantized, please see the Convert and Quantize a UNet Model and Show Live Inference tutorial. If you have not yet installed OpenVINO™, please follow the Installation Guide to install all required dependencies. https://github.com/openvinotoolkit/openvino_notebooks/blob/main/README.md#-installation-guide Link to .bin and .xml files: https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/210-ct-scan-live-inference/pretrained_model

https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/210-ct-scan-live-inference/210-ct-scan-live-inference.ipynb