app.py

from openvino.runtime import Core
import gradio as gr 
import numpy as np 
from PIL import Image
import cv2
from torchvision import models,transforms
from typing import Iterable
import gradio as gr
from gradio.themes.base import Base
from gradio.themes.utils import colors, fonts, sizes
import time
core = Core()

# Read model to OpenVINO Runtime
model_ir = core.read_model(model="Davinci_eye.onnx")
compiled_model_ir = core.compile_model(model=model_ir, device_name='CPU')

tfms = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # imagenet
])
color_map={
    (251,244,5): 1,
    (37,250,5):2, 
    (0,21,209):3, 
    (172,21,2): 4,
    (172,21,229): 5,
    (6,254,249): 6,
    (141,216,23):7,
    (96,13,13):8,
    (65,214,24):9,
    (124,3,252):10,
    (214,55,153):11,
    (48,61,173):12,
    (110,31,254):13,
    (249,37,14):14,
    (249,137,254):15,
    (34,255,113):16,
    (169,52,14):17,
    (124,49,176):18,
    (4,88,238):19,
    (115,214,178):20,
    (115,63,178):21,
    (115,214,235):22,
    (63,63,178): 23,
    (130,34,26):24,
    (220,158,161):25,
    (201,117,56):26,
    (121,16,40):27,
    (15,126,0):28,
    (0,50,70):29,
    (20,20,0):30,
    (20,20,0):31,
     }

items = {
    1: "HarmonicAce_Head",
    2: "HarmonicAce_Body",
    3: "MarylandBipolarForceps_Head",
    4: "MarylandBipolarForceps_Wrist",
    5: "MarylandBipolarForceps_Body",
    6: "CadiereForceps_Head",
    7: "CadiereForceps_Wrist",
    8: "CadiereForceps_Body",
    9: "CurvedAtraumaticGrasper_Head",
    10: "CurvedAtraumaticGrasper_Body",
    11: "Stapler_Head",
    12: "Stapler_Body",
    13: "MediumLargeClipApplier_Head",
    14: "MediumLargeClipApplier_Wrist",
    15: "MediumLargeClipApplier_Body",
    16: "SmallClipApplier_Head",
    17: "SmallClipApplier_Wrist",
    18: "SmallClipApplier_Body",
    19: "SuctionIrrigation",
    20: "Needle",
    21: "Endotip",
    22: "Specimenbag",
    23: "DrainTube",
    24: "Liver",
    25: "Stomach",
    26: "Pancreas",
    27: "Spleen",
    28: "Gallbladder",
    29:"Gauze",
    30:"TheOther_Instruments",
    31:"TheOther_Tissues",


}
class Davinci_Eye(Base):
    def __init__(
        self,
        *,
        primary_hue: colors.Color | str = colors.stone,
        secondary_hue: colors.Color | str = colors.blue,
        neutral_hue: colors.Color | str = colors.gray,
        spacing_size: sizes.Size | str = sizes.spacing_md,
        radius_size: sizes.Size | str = sizes.radius_md,
        text_size: sizes.Size | str = sizes.text_lg,
        font: fonts.Font
        | str
        | Iterable[fonts.Font | str] = (
            fonts.GoogleFont("IBM Plex Mono"),
            "ui-sans-serif",
            "sans-serif",
        ),
        font_mono: fonts.Font
        | str
        | Iterable[fonts.Font | str] = (
            fonts.GoogleFont("IBM Plex Mono"),
            "ui-monospace",
            "monospace",
        ),
    ):
        super().__init__(
            primary_hue=primary_hue,
            secondary_hue=secondary_hue,
            neutral_hue=neutral_hue,
            spacing_size=spacing_size,
            radius_size=radius_size,
            text_size=text_size,
            font=font,
            font_mono=font_mono,
        )


davincieye = Davinci_Eye()

colormap={v:[i for i in k] for k,v in color_map.items()}

def convert_mask_to_rgb(pred_mask):
    rgb_mask=np.zeros((pred_mask.shape[0],pred_mask.shape[1],3),dtype=np.uint8)
    for k,v in colormap.items():
        rgb_mask[pred_mask==k]=v
    return rgb_mask



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())
#ort_input={ort_session.get_inputs()[0].name:x.cpu().unsqueeze(0).float().numpy()}
#out=ort_session.run(None,ort_input)
  out = compiled_model_ir(x.unsqueeze(0).float().cpu().numpy())
  pred_mask=np.squeeze(np.argmax(out[0],1)).astype(np.uint8)
  color_mask=convert_mask_to_rgb(pred_mask)
  masked_image=cv2.addWeighted(image,0.6,color_mask,0.4,0.1)
  pred_keys=pred_mask[np.nonzero(pred_mask)]
  objects=[items[k] for k in pred_keys]
  surgery_items=np.unique(np.array(objects),axis=0)
  surg=""
  for item in surgery_items:
        surg+=item+","+" "
  return Image.fromarray(masked_image),surg

demo=gr.Interface(fn=segment_image,inputs=gr.Image(type='filepath'),
                  outputs=[gr.Image(type="pil"),gr.Text()],
                  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"],
                  theme=davincieye.set(loader_color='#65aab1'),
                  title="Davinci Eye(Quantized for CPU)")
demo.launch()