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# -*- coding: utf-8 -*-
"""
Created on Wed Nov 13 18:37:31 2024
@author: sabar
"""
import gradio as gr
import cv2
import numpy as np
import os
import json
from openvino.runtime import Core # Assuming you're using OpenVINO
from tqdm import tqdm
from tf_post_processing import non_max_suppression #,optimized_object_detection
# Load the OpenVINO model
classification_model_xml = "./model/best_openvino_model\best.xml"
core = Core()
config = {
"INFERENCE_NUM_THREADS": 2,
"ENABLE_CPU_PINNING": True
}
model = core.read_model(model=classification_model_xml)
compiled_model = core.compile_model(model=model, device_name="CPU", config=config)
# Function to perform inference
def predict_image(image):
# Resize, preprocess, and reshape the input image
img_size = 960
resized_image = cv2.resize(image, (img_size, img_size)) / 255.0
resized_image = resized_image.transpose(2, 0, 1)
reshaped_image = np.expand_dims(resized_image, axis=0).astype(np.float32)
im_height, im_width, _ = image.shape
output_numpy = compiled_model(reshaped_image)[0]
results = non_max_suppression(output_numpy, conf_thres=0.2, iou_thres=0.6, max_wh=15000)[0]
# Prepare output paths
output_path = "D:/Research/Auto_CAD_detection/output_file_train/"
output_image_folder = os.path.join(output_path, "images_alienware_openvino/")
os.makedirs(output_image_folder, exist_ok=True)
output_json_folder = os.path.join(output_path, "json_output/")
os.makedirs(output_json_folder, exist_ok=True)
predictions = []
# Draw boxes and collect prediction data
for result in results:
boxes = result[:4]
prob = result[4]
classes = int(result[5])
x1, y1, x2, y2 = np.uint16([
boxes[0] * im_width,
boxes[1] * im_height,
boxes[2] * im_width,
boxes[3] * im_height
])
if prob > 0.2:
cv2.rectangle(image, (x1, y1), (x2, y2), (255, 255, 0), 2)
label_text = f"{classes} {round(prob, 2)}"
cv2.putText(image, label_text, (x1, y1), 0, 0.5, (0, 255, 0), 2)
# Store prediction info in a JSON-compatible format
predictions.append({
"class": classes,
"probability": round(float(prob), 2),
"coordinates": {
"xmin": int(x1),
"ymin": int(y1),
"xmax": int(x2),
"ymax": int(y2)
}
})
# Save the processed image and JSON file
output_image_path = os.path.join(output_image_folder, "result_image.jpg")
cv2.imwrite(output_image_path, image)
output_json_path = os.path.join(output_json_folder, "predictions.json")
with open(output_json_path, 'w') as f:
json.dump(predictions, f, indent=4)
return output_image_path, predictions
# Set up Gradio interface
def gradio_interface(image):
output_image_path, predictions = predict_image(image)
return output_image_path, json.dumps(predictions, indent=4)
# Launch the Gradio app
gr.Interface(
fn=gradio_interface,
inputs="image",
outputs=["image", "json"],
title="OpenVINO Model Inference with Gradio",
description="Upload an image to get model predictions with bounding boxes and probabilities."
).launch()
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