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import streamlit as st
import os
from PIL import Image
import numpy as np
import cv2
from Utils import *
from huggingface_hub import hf_hub_download,from_pretrained_keras
model = from_pretrained_keras("SerdarHelli/Knee-View-Merchant-Landmark-Detection")
st.subheader("Upload Merchant Knee View")
image_file = st.file_uploader("Upload Images", type=["dcm"])
examples=["1.3.46.670589.30.1.6.1.149885691756583.1510655758812.1.dcm"
,"1.2.392.200036.9125.9.0.235868094.418384128.208354950.dcm",
"1.2.392.200036.9107.500.304.423.20170526.173028.10423.dcm"]
colx1, colx2, colx3 = st.columns(3)
st.text("Merchant Knee View Dicom Examples ")
with colx1:
st.text("Example -1 ")
if st.button('Example 1'):
image_file=examples[0]
with colx2:
st.text("Example -2 ")
if st.button('Example 2'):
image_file=examples[1]
with colx3:
st.text("Example -3 ")
if st.button('Example 3'):
image_file=examples[2]
if image_file is not None:
st.text("Making A Prediction ....")
try:
data,PatientName,PatientID,SOPInstanceUID,StudyDate,InstitutionAddress,PatientAge,PatientSex=read_dicom(image_file,False,True)
except:
data,PatientName,PatientID,SOPInstanceUID,StudyDate,InstitutionAddress,PatientAge,PatientSex=read_dicom(image_file,True,True)
pass
img = np.copy(data)
#Denoise Image
kernel =( np.ones((5,5), dtype=np.float32))
img2=cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel,iterations=2 )
img2=cv2.erode(img2,kernel,iterations =2)
if len(img2.shape)==3:
img2=img2[:,:,0]
#Threshhold 100- 4096
ret,thresh = cv2.threshold(img2,100, 4096, cv2.THRESH_BINARY)
#To Thresh uint8 becasue "findContours" doesnt accept uint16
thresh =((thresh/np.max(thresh))*255).astype('uint8')
a1,b1=thresh.shape
#Find Countours
contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
#If There is no countour
if len(contours)==0:
roi= thresh
else:
#Get Areas
c_area=np.zeros([len(contours)])
for i in range(len(contours)):
c_area[i]= cv2.contourArea(contours[i])
#Find Max Countour
cnts=contours[np.argmax(c_area)]
x, y, w, h = cv2.boundingRect(cnts)
#Posibble Square
roi = croping(data, x, y, w, h)
# Absolute Square
roi=modification_cropping(roi)
# Resize to 256x256 with Inter_Nearest
roi=cv2.resize(roi,(256,256),interpolation=cv2.INTER_NEAREST)
pre=predict(roi,model)
heatpoint=points_max_value(pre)
output=put_text_point(roi,heatpoint)
output,PatellerCongruenceAngle,ParalelTiltAngle=draw_angle(output,heatpoint)
data_text = {'PatientID': PatientID, 'PatientName': PatientName,
'Pateller_Congruence_Angle': PatellerCongruenceAngle,
'Paralel_Tilt_Angle':ParalelTiltAngle,
'SOP_Instance_UID':SOPInstanceUID,
"StudyDate" :StudyDate,
"InstitutionName" :InstitutionAddress,
"PatientAge" :PatientAge ,
"PatientSex" :PatientSex,
}
st.text("Original Dicom Image")
st.image(np.uint8((data/np.max(data)*255)),width=450)
st.text("Predicted and Cropped-Resized Image ")
st.image(np.uint8(output),width=450)
st.write(data_text)
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