data/scripts/real_time_inference.py

from pathlib import Path
from surg_seg.Utils.ImageSubscriber import ImageSubscriber
import cv2
import torch
import numpy as np

from monai.visualize.utils import blend_images
from surg_seg.Networks.Models import FlexibleUnet1InferencePipe
from surg_seg.Datasets.ImageDataset import ImageTransforms


def main():

    image_saver = ImageSubscriber()

    device = "cuda"
    path_to_weights = Path("./assets/weights/myweights_3d_med_2_all_ds3/myweights.pt")
    model_pipe = FlexibleUnet1InferencePipe(path_to_weights, device, out_channels=5)

    while True:
        frame = image_saver.get_current_frame("left")

        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        input_tensor, inferred_single_ch = model_pipe.infer(frame)

        inferred_single_ch = inferred_single_ch.detach().cpu()
        input_tensor = input_tensor.detach().cpu()[0]

        blended = blend_images(input_tensor, inferred_single_ch, cmap="viridis", alpha=0.8).numpy()
        blended = (np.transpose(blended, (1, 2, 0)) * 254).astype(np.uint8)
        blended = cv2.cvtColor(blended, cv2.COLOR_RGB2BGR)

        cv2.imshow("image", blended)

        if cv2.waitKey(30) & 0xFF == ord("q"):
            cv2.destroyAllWindows()
            break


if __name__ == "__main__":

    with torch.no_grad():
        main()