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import gradio as gr |
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import torch |
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import numpy as np |
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import modeling |
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import cv2 |
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def predict(image1, image2, image3,age, BCVA, CDR, IOP): |
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if None in [age, BCVA, CDR, IOP]: |
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empty_fields = [label for label, value in [("Age", age), ("BCVA", BCVA), ("CDR", CDR), ("IOP", IOP)] if value is None] |
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error_message = ", ".join([f"{field} value is empty" for field in empty_fields]) |
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return error_message |
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model = torch.load('./model_save.pth', map_location=torch.device('cpu')) |
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model.eval() |
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x = [] |
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for img in [image1, image2, image3]: |
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pic = cv2.resize(img, (512, 512)) |
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pic = pic[np.newaxis, :] |
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print("image shape(1, 512, 512, 3):", pic.shape) |
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x.append(pic) |
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_img = np.concatenate(x, axis=0) |
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print("image shape(3, 512, 512, 3):", _img.shape) |
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def preprocess_image(img): |
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img = np.array(img).astype(np.float32) |
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img /= 255.0 |
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img -= (0.485, 0.456, 0.406) |
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img /= (0.229, 0.224, 0.225) |
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img = np.transpose(img, (0, 3, 1, 2)) |
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img = np.expand_dims(img, axis=0) |
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img = torch.from_numpy(img).float() |
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return img |
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if age is not None: |
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age /= 100 |
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if IOP is not None: |
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IOP /= 100 |
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tabular = np.array([age, BCVA, CDR, IOP]).astype(np.float32) |
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tabular = np.expand_dims(tabular, axis=0) |
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tabular = torch.from_numpy(tabular).float() |
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img = preprocess_image(_img) |
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with torch.no_grad(): |
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output = model(img, tabular) |
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index_to_label = {0: "Early",1: "Serious"} |
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output_probs = torch.softmax(output, dim=1).numpy()[0] |
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class_proba = {index_to_label[i]: float(prob) for i, prob in enumerate(output_probs)} |
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return class_proba |
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examples = [["1-3.jpg","1-6.jpg","1-9.jpg", 50, 1.5, 0.6, 23], ["2-3.jpg","2-6.jpg","2-9.jpg", 36, 1.2, 0.4, 12],["3-3.jpg","3-6.jpg","3-9.jpg", 36, 1.2, 0.4, 12] ] |
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with gr.Blocks() as demo: |
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gr.Markdown("# Multi-Glau") |
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with gr.Row(): |
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image1 = gr.Image(label="image1") |
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image2 = gr.Image(label="image2") |
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image3 = gr.Image(label="image3") |
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with gr.Row(): |
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Age = gr.Number(label="Age",value=np.nan) |
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BCVA = gr.Number(label="BCVA",value=np.nan) |
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CDR = gr.Number(label="CDR",value=np.nan) |
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IOP = gr.Number(label="IOP",value=np.nan) |
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btn = gr.Button("Submit") |
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output = gr.Label(label="Output") |
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btn.click(fn=predict, inputs=[image1, image2, image3, Age, BCVA, CDR, IOP], outputs=output) |
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gr.Examples(examples, inputs=[image1, image2, image3, Age, BCVA, CDR, IOP]) |
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demo.launch() |
