Update app.py

app.py

@@ -1,134 +1,134 @@
-
-
-
-
-import streamlit as st 
-
-from PIL import Image
-import numpy as np
-import cv2
-from Utils import *
-from huggingface_hub import 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)
-    
+
+
+
+
+import streamlit as st 
+
+from PIL import Image
+import numpy as np
+import cv2
+from Utils import *
+from huggingface_hub import from_pretrained_keras
+
+
+
+model=from_pretrained_keras("SerdarHelli/Knee-View-Merchant-Landmark-Detection",use_auth_token=True)
+
+
+
+
+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)
+