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| import os | |
| os.system("pip install openvino-dev==2023.0.1") | |
| 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() |