Update app.py

app.py

@@ -33,117 +33,61 @@ import h5py
 model_file_path = "mobile_net_occ.h5"
 
 
-page_names = ["Blurred or Not Blurred Prediction","Occluded or Not Occluded Prediction"]
+#page_names = ["Blurred or Not Blurred Prediction","Occluded or Not Occluded Prediction"]
 page = st.sidebar.radio('Navigation',page_names)
 #st.write("Welcome to the Project")
 
-if page == "Blurred or Not Blurred Prediction":
-    st.title("""
+
+st.title("""
          Image Blurriness Occluded
          """)
-    st.subheader("Prediction of Blur or NotBlur Image")
-    #images = ["blur1.png","blurimg2.png","blurimg3.png","images_11.jpeg"]
-    #with st.sidebar:
-     #  st.write("choose an image")
-      # st.image(images)
-    #model_file_path = "mobile_net_occ.h5"
+st.subheader("Prediction of Blur or NotBlur Image")
+images = ["blur1.png","blurimg2.png","blurimg3.png","images_11.jpeg"]
+with st.sidebar:
+    st.write("choose an image")
+    st.image(images)
+#model_file_path = "mobile_net_occ.h5"
 
     ##Blurriness Features
 
-    plt. figure(figsize=(10,9))
-    def variance_of_laplacian(image):
-        return cv2.Laplacian(image, cv2.CV_64F).var()
+plt. figure(figsize=(10,9))
+def variance_of_laplacian(image):
+    return cv2.Laplacian(image, cv2.CV_64F).var()
 
-    def threshold(value, thresh):
-        if value > thresh:
-            return "Not Blur"
-        else:
-            return "Blur"  
-    def blurr_predict(img_iter):
-  
-      def make_prediction(img_content):
-        pil_image = Image.open(img_content)
-        imgplot = plt.imshow(pil_image)
-        #st.image(pil_image)
-        plt.show()
-        gray_cvimage = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2GRAY)
+def threshold(value, thresh):
+    if value > thresh:
+        return "Not Blur"
+    else:
+        return "Blur"  
+def blurr_predict(img_iter):
+  def make_prediction(img_content):
+      pil_image = Image.open(img_content)
+      imgplot = plt.imshow(pil_image)
+      #st.image(pil_image)
+      plt.show()
+      gray_cvimage = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2GRAY)
         #print(gray_cvimage)
-        variance_laplacian = variance_of_laplacian(gray_cvimage)
+      variance_laplacian = variance_of_laplacian(gray_cvimage)
         #print(variance_laplacian)
-        return variance_laplacian
+      return variance_laplacian
 
-      variance_score = make_prediction(img_iter)
-      thresh = 2000
-      variance_score = variance_score/thresh
-      predicted_label = threshold(variance_score, 1)
-      return predicted_label,variance_score
+    variance_score = make_prediction(img_iter)
+    thresh = 2000
+    variance_score = variance_score/thresh
+    predicted_label = threshold(variance_score, 1)
+    return predicted_label,variance_score
 
     #image_path = "images_11.jpeg"
-    file = st.file_uploader('Upload an Image',type=(["jpeg","jpg","png"]))
+file = st.file_uploader('Upload an Image',type=(["jpeg","jpg","png"]))
 
-    if file is None:
-        st.write("Please upload an image file")
-    else:
-        image= Image.open(file)
-        st.image(image,use_column_width = True)
-        predicted_label,variance_score = blurr_predict(file)
-        #st.header(predicted_label)
-        #st.header(str(round(variance_score,2)))
-        string = "The image is," + str(predicted_label) + " with the score value of  " + str(round(variance_score,2))
-        st.subheader(string)
+if file is None:
+    st.write("Please upload an image file")
 else:
-    st.title("Prediction of Occluded or not Occluded ")
-    #plt. figure(figsize=(10,9))
-    def occ_predict(img_content):
-        im = []
-        image = cv2.imread(img_content)
-        imgplot = plt.show(image)
-
-        #st.write(img_content)
-        #st.write(type(img_content))
-
-        img = Image.fromarray(image, 'RGB') 
-        #st.write(type(image1))
-        resize_image = img.resize((50, 50))
-        im.append(np.array(resize_image))
-        fv = np.array(im)
-        np_array_img = fv.astype('float32') / 255
-        model_gcs = h5py.File(model_file_path, 'r')
-        myModel = load_model(model_gcs)
-        prediction = myModel.predict(np_array_img)
-        score = prediction[0][0].item()
-        
-        thresh = 0.5
-        if score > thresh:
-            return "Not Occluded",score
-        else:
-            return "Occluded",score
-
-    f = st.file_uploader('Upload an Image',type=(["jpeg","jpg","png"]))
-    st.write(f)
-    #st.subheader("Prediction of Occluded or Not Occluded")
-    #images1 = ["img1.png","img2.png","img3.png","img4.png"]
-   # with st.sidebar:
-        #st.write("choose an image")
-        #st.image(images1)
-
-    if f is None:
-        st.write("Please upload an image file")
-    else:        
-        #stringio = StringIO(f.getvalue())
-        #f = stringio.read()
-        image1= Image.open(f)
-        #st.write(type(f.name))
-        st.image(image1,use_column_width = True)
-        #image_path = Path(f.name)
-        #st.write(image_path)
-        
-        
-        predicted_label,variance_score = occ_predict(f)
+    image= Image.open(file)
+    st.image(image,use_column_width = True)
+    predicted_label,variance_score = blurr_predict(file)
         #st.header(predicted_label)
         #st.header(str(round(variance_score,2)))
-        string1 = "The image is," + predicted_label + " with the score value of  " + str(round(variance_score,2))
-        st.subheader(string1)
+    string = "The image is," + str(predicted_label) + " with the score value of  " + str(round(variance_score,2))
+    st.subheader(string)
 
-#predicted_label, score = occ_predict("/content/drive/MyDrive/Occulded.jpg")
-#print("The image is", '\033[1m' + predicted_label1 + '\033[0m', "with the score value of" ,round(score,2))