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| from openvino.runtime import Core | |
| import gradio as gr | |
| import numpy as np | |
| from PIL import Image | |
| import cv2 | |
| from torchvision import models,transforms | |
| core = Core() | |
| # Read model to OpenVINO Runtime | |
| model_onnx = core.read_model(model="Davinci_eye.onnx") | |
| compiled_model_onnx = core.compile_model(model=model_onnx, device_name='CPU') | |
| tfms = transforms.Compose([ | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # imagenet | |
| ]) | |
| def normalize(img): | |
| img=img.astype(np.float32) | |
| mean=(0.485, 0.456, 0.406) | |
| std=(0.229, 0.224, 0.225) | |
| img/=255.0 | |
| img-=mean | |
| img/std | |
| return img | |
| def segment_image(filepath): | |
| image=cv2.imread(filepath) | |
| image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB) | |
| image = cv2.resize(image, (512,512)) | |
| x=tfms(image.copy()/255.) | |
| #ort_input={ort_session.get_inputs()[0].name:x.cpu().unsqueeze(0).float().numpy()} | |
| #out=ort_session.run(None,ort_input) | |
| out = compiled_model_onnx([x.unsqueeze(0).float().cpu().numpy()]) | |
| pred_mask=np.squeeze(np.argmax(out[0],1)).astype(np.uint8) | |
| color_mask=cv2.applyColorMap(pred_mask,cv2.COLORMAP_MAGMA)*10 | |
| masked_image=cv2.addWeighted(image,0.6,color_mask,0.4,0.1) | |
| return Image.fromarray(masked_image),Image.fromarray((color_mask)) | |
| demo=gr.Interface(fn=segment_image,inputs=gr.Image(type='filepath'), | |
| outputs=[gr.Image(type="pil"),gr.Image(type="pil")], | |
| examples=["R001_ch1_video_03_00-29-13-03.jpg", | |
| "R002_ch1_video_01_01-07-25-19.jpg", | |
| "R003_ch1_video_05_00-22-42-23.jpg", | |
| "R004_ch1_video_01_01-12-22-00.jpg", | |
| "R005_ch1_video_03_00-19-10-11.jpg", | |
| "R006_ch1_video_01_00-45-02-10.jpg", | |
| "R013_ch1_video_03_00-40-17-11.jpg"], | |
| themes=gr.themes.Glass(primary_hue=gr.themes.colors.blue,secondary_hue=gr.themes.colors.blue), | |
| title="Davinci Eye(CPU version)") | |
| demo.launch() |