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import gradio as gr | |
from openvino.inference_engine import IECore | |
import cv2 | |
import numpy as np | |
from PIL import Image | |
# Load the OpenVINO model | |
model_xml = 'model.xml' | |
model_bin = 'model.bin' | |
ie = IECore() | |
net = ie.read_network(model=model_xml, weights=model_bin) | |
exec_net = ie.load_network(network=net, device_name="CPU") | |
# Define the function for image processing | |
def upscale_image(input_image): | |
# Convert Gradio PIL Image to numpy array | |
input_image = np.array(input_image) | |
# Preprocess the input image | |
image = cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR) | |
image = cv2.resize(image, (224, 224)) | |
image = image / 255.0 | |
image = np.transpose(image, (2, 0, 1)) | |
image = image.reshape(1, 3, 224, 224) | |
# Run inference | |
outputs = exec_net.infer(inputs={'input': image}) | |
# Post-process the output | |
output_image = outputs['output'][0] | |
output_image = np.transpose(output_image, (1, 2, 0)) | |
output_image = np.clip(output_image, 0, 1) * 255 | |
output_image = output_image.astype(np.uint8) | |
output_image = cv2.cvtColor(output_image, cv2.COLOR_RGB2BGR) | |
return output_image | |
# Create the Gradio interface | |
inputs = gr.inputs.Image(type="pil", label="Input Image") # Use 'pil' type for uploaded images | |
outputs = gr.outputs.Image(type="pil", label="Upscaled Image") | |
title = "Image Upscaling App" | |
description = "Upload an image and see the upscaled result." | |
iface = gr.Interface(fn=upscale_image, inputs=inputs, outputs=outputs, title=title, description=description) | |
# Launch the Gradio interface | |
iface.launch() |