app.py

import time

import gradio as gr
import numpy as np
from pathlib import Path
import time
from anomalib.deploy import OpenVINOInferencer
from openvino.runtime import Core
# Initialize the Core
core = Core() 

# Get the available devices
devices = core.available_devices
inferencer = None
prev_category_selection = None
prev_device_selection = None

example_list = [["bottle/examples/000.png", "anomaly_map", "bottle", "CPU"],
               ["pill/examples/014.png", "heat_map", "pill", "CPU"],
               ["zipper/examples/001.png", "pred_mask", "zipper", "CPU"],
               ["grid/examples/005.png", "segmentations", "grid", "CPU"],
               ["cubes/examples/005.jpg", "heat_map", "cubes", "CPU"]]

def OV_compilemodel(category_choice, device):
    global inferencer
    #Get the available models
    openvino_model_path = Path.cwd() / category_choice / "run" / "weights" / "openvino" / "model.bin"
    metadata_path = Path.cwd() / category_choice / "run" / "weights" / "openvino" / "metadata.json"
    
    inferencer = OpenVINOInferencer(
        path=openvino_model_path,  # Path to the OpenVINO IR model.
        metadata=metadata_path,  # Path to the metadata file.
        device=device,  # We would like to run it on an Intel CPU.
        config= {"INFERENCE_PRECISION_HINT": "f16" } if device != "CPU" else {}
    )
    
    return inferencer

def OV_inference(input_img, operation, category_choice, device):
         
    start_time = time.time()
    predictions = inferencer.predict(image=input_img)
    stop_time = time.time()
    inference_time = stop_time - start_time
    confidence = predictions.pred_score
    
    if operation == "original":
        output_img1 = predictions.image
    elif operation == "anomaly_map":
        output_img1 = predictions.anomaly_map
    elif operation == "heat_map":
        output_img1 = predictions.heat_map
    elif operation == "pred_mask":
        output_img1 = predictions.pred_mask
    elif operation == "segmentations":
        output_img1 = predictions.segmentations
    else: 
        output_img1 = predictions.image
    return output_img1, round(inference_time*1000), round(confidence*100,2)

#Run + Compile the model
def OV_compile_run_model(category_choice, device_choice, image, output_choice):
    #If a different category or device are selected, compile/re-compile the model
    global prev_category_selection
    global prev_device_selection
    if device_choice != prev_device_selection or category_choice != prev_category_selection:
        OV_compilemodel(category_choice, device_choice)
        prev_category_selection = category_choice
        prev_device_selection = device_choice
    #Run model
    print("Running model")
    output_img, output_time, output_confidence = OV_inference(image, output_choice, category_choice, device_choice)
    return output_img, output_time, output_confidence

with gr.Blocks() as demo:

    gr.Markdown(
    """
    <img align="left" width="150" src= "https://github.com/openvinotoolkit/anomalib/assets/10940214/7e61a627-d1b0-4ad4-b602-da9b348c0cbe">   
    <img align="right" width="150" src= "https://github.com/openvinotoolkit/anomalib/assets/10940214/5d6dd038-b40c-441f-ad38-1cf526137de2">   
    
    <h1 align="center"> 🚀 Anomaly detection 🚀 </h1>  

    """
    )
    
    with gr.Row():
        with gr.Column():
            gr.Markdown(
                """
                
                
                Experience the power of the state-of-the-art anomaly detection with Anomalib-OpenVINO Anomaly detection toolbox. This interactive APP leverages the robust capabilities of Anomalib and OpenVINO.
                
                All model are FP32 precision, if you select GPU it will automatically change precision to FP16. Using Anomalib you can also quantize your model in INT8 using NNCF.
                """
            ) 
            
        with gr.Column():
            gr.Markdown(
                """
                <img src="https://github.com/openvinotoolkit/openvino_notebooks/assets/10940214/45dfb61f-c6d1-4098-88d1-8498f0a42e11" alt="drawing" width="500"/>
                """
            ) 
    
    gr.Markdown("## 1. Select the category over you want to detect anormalities.")
    category_choice = gr.Radio(["bottle", "grid", "pill", "zipper", "cubes"], label="Choose the category")
    
    gr.Markdown(
        """
        ## 2. Select the Intel device
        Device Name   | CPU  | GPU.0  | GPU.1 
        ------------- | ------------  |------------- | -------------
        Intel Device  | CPU  |  Integrated GPU  | Discrete GPU  
        
        
        """
    )
    device_choice = gr.Dropdown(devices, label="Choose the device")

    gr.Markdown("## 3. Choose the output you want to visualize.")
    output_choice = gr.Radio(["original", "anomaly_map", "heat_map", "pred_mask", "segmentations"], label="Choose the output")
    
    gr.Markdown("## 4. Drop the image in the input image box and run the inference")
    with gr.Row():
        with gr.Column():
            image = gr.Image(type="numpy", label= "Input image")  
            
        with gr.Column():
            output_img = gr.outputs.Image(type="numpy", label="Anomalib Output")

    compile_inference_btn = gr.Button("Run Inference")
    
    with gr.Row():
        # Create your output components
        #output_prediction = gr.Textbox(label="Prediction")
        output_confidence = gr.Textbox(label="Confidence [%]")
        output_time = gr.Textbox(label="Inference Time [ms]")
    
    gr.Markdown("Changing your choice of category or device will recompile the model.")    

    gr.Markdown("## OR use our image examples for a quick start")
    gr.Examples(
        examples=example_list,
        inputs=[image, output_choice, category_choice, device_choice],
        outputs=[output_img, output_time, output_confidence],
        fn=OV_compile_run_model)

    compile_inference_btn.click(OV_compile_run_model, inputs=[category_choice, device_choice, image, output_choice], 
        outputs=[output_img, output_time, output_confidence])

demo.launch(enable_queue=True)