Uniform Plots

.gitignore

@@ -1 +1,3 @@
 *.jpg
+LoackerVenv/
+__pycache__/

app.py

@@ -37,6 +37,24 @@ Please note that this application is intended to provide an estimate only and ma
 image = st.file_uploader(label="Upload your image here",
                          type=['png', 'jpg', 'jpeg'])
 
+def write_area_prediction(fat_ratio):
+    fat_percentage = round(fat_ratio * 100, ndigits=2)
+    choco_percentage = 100 - fat_percentage
+    st.write("Area Covered by Fat (%):", fat_percentage)
+    st.write("Area Covered by Chocolate (%):", choco_percentage)
+    
+    #Define class appartenence
+    if( fat_ratio == 0):
+        st.write("Predicted Area Class:", 0)
+    if( fat_ratio >  0 and fat_ratio < 0.25):
+        st.write("Predicted Area Class:", 1)
+    if( fat_ratio >=  0.25 and fat_ratio < 0.50):
+        st.write("Predicted Area Class:", 2)
+    if( fat_ratio >=  0.5 and fat_ratio < 0.75):
+        st.write("Predicted Area Class:", 3)
+    if( fat_ratio >=  0.75 ):
+        st.write("Predicted Area Class:", 4)
+
 
 def fat_choco_rateo(image, input_image, threshold):
 
@@ -81,10 +99,16 @@ def fat_choco_rateo(image, input_image, threshold):
         st.image(input_image, caption="Original", use_column_width=True)
 
     with col2:
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
         st.image(img, caption="Detected Tortina", use_column_width=True)
 
     with col3:
-        st.image(predicted, caption="Predicted bloom zone",
+        predicted = predicted.min(-1) #take minimum of thresholded channels
+        overlay = img
+        #overlay = cv2.cvtColor(img, cv2.COLOR_RGB2RGBA)
+        #overlay[:,:,-1] = 200 #make half transparent
+        overlay[predicted == 255] = np.array([0, 0, 255])
+        st.image(overlay, caption="Prediction",
                  use_column_width=True)
 
     with col4:
@@ -101,25 +125,28 @@ def fat_choco_rateo(image, input_image, threshold):
         hist_image = cv2.resize(hist_image, (600, 600))
         st.image(hist_image, caption="Histogram", use_column_width=True)
 
-    st.write("Percentuale fat")
-    st.write(int(len(fat) / len(hist_analisy) * 100))
-    st.write("Percentuale choco")
-    st.write(int(len(choco) / len(hist_analisy) * 100))
+    # st.write("Percentuale fat")
+    # st.write(int(len(fat) / len(hist_analisy) * 100))
+    # st.write("Percentuale choco")
+    # st.write(int(len(choco) / len(hist_analisy) * 100))
     
     
-    fat = (len(fat) / len(hist_analisy))
+    # fat = (len(fat) / len(hist_analisy))
     
-    #Define class appartenence
-    if( fat == 0):
-        st.write("Class 0 ")
-    if( fat >  0 and fat < 0.25):
-        st.write("Class 1 ")
-    if( fat >=  0.25 and fat < 0.50):
-        st.write("Class 2 ")
-    if( fat >=  50 and fat < 0.75):
-        st.write("Class 3 ")
-    if( fat >=  75 ):
-        st.write("Class 4 ")
+    # #Define class appartenence
+    # if( fat == 0):
+    #     st.write("Class 0 ")
+    # if( fat >  0 and fat < 0.25):
+    #     st.write("Class 1 ")
+    # if( fat >=  0.25 and fat < 0.50):
+    #     st.write("Class 2 ")
+    # if( fat >=  50 and fat < 0.75):
+    #     st.write("Class 3 ")
+    # if( fat >=  75 ):
+    #     st.write("Class 4 ")
+
+    fat_ratio = (len(fat) / len(hist_analisy))
+    write_area_prediction(fat_ratio)
     
         
 
@@ -146,7 +173,7 @@ def auto_encoder_prediction(image, thresh):
 
     thresholded_anomaly_maps = (
         anomaly_maps_np > thresh).astype(np.uint8).max(-1)
-    postprocessed_anomaly_maps = np.zeros_like(thresholded_anomaly_maps)
+    #postprocessed_anomaly_maps = np.zeros_like(thresholded_anomaly_maps)
 
     postprocessed_anomaly_maps = postprocessing(
         gray, thresholded_anomaly_maps[0])
@@ -160,7 +187,7 @@ def auto_encoder_prediction(image, thresh):
 
 
     # Display results in Streamlit
-    col1, col2, col3, col4 = st.columns([1, 1, 1, 1])
+    col1, col2, col3, col4, col5 = st.columns([1, 1, 1, 1, 1])
 
     with col1:
         st.image(img, caption="Original", use_column_width=True)
@@ -169,15 +196,24 @@ def auto_encoder_prediction(image, thresh):
         detected[postprocessed_anomaly_maps == 0] = 0  
         st.image(detected, caption="Detected Tortina", use_column_width=True)
     with col3:
-        predicted = np.zeros(postprocessed_anomaly_maps.shape, dtype=np.uint8)
-        predicted[postprocessed_anomaly_maps == 2] = 255    
-        st.image(predicted, caption="Predicted bloom zone",use_column_width=True)
+        predicted = detected#np.zeros(img[0].shape, dtype=np.uint8)
+        predicted[(thresholded_anomaly_maps[0] == 1) & (detected.sum(-1) != 0)] = np.array([0, 0, 255])    
+        st.image(predicted, caption="Prediction",use_column_width=True)
+
     with col4:
-        threshold = 0.03    
+        # predicted = np.zeros(postprocessed_anomaly_maps.shape, dtype=np.uint8)
+        # predicted[postprocessed_anomaly_maps == 2] = 255    
+        # st.image(predicted, caption="Predicted bloom zone",use_column_width=True)
+        predicted = np.zeros(img[0].shape, dtype=np.uint8)
+        predicted[postprocessed_anomaly_maps == 1] = img[0][postprocessed_anomaly_maps == 1] 
+        predicted[postprocessed_anomaly_maps == 2] = np.array([0, 0, 255])    
+        st.image(predicted, caption="Post-processed Prediction",use_column_width=True)
+    with col5:
+        #threshold = 0.03    
         hist_data = np.ravel(anomaly_maps_np[0][postprocessed_anomaly_maps != 0])
         fig, ax = plt.subplots(figsize=(6, 4))
         ax.hist(hist_data, 256//10, (0, hist_data.max()))
-        ax.axvline(x=threshold, color='red')
+        ax.axvline(x=thresh, color='red')
         ax.set_title("Histogram")
         # Render plot as image    
         canvas = FigureCanvasAgg(fig)
@@ -189,8 +225,13 @@ def auto_encoder_prediction(image, thresh):
     
     
     #st.image(rgb_output, caption="rgb out", use_column_width=True)
-    st.write("Final class prediction: ", final_predictions)
+    #st.write("Final class prediction: ", final_predictions)
+    fat_pixels = (postprocessed_anomaly_maps == 2).sum()
+    choco_pixels = (postprocessed_anomaly_maps == 1).sum()
     
+    fat_ratio = (fat_pixels) / (fat_pixels + choco_pixels)
+
+    write_area_prediction(fat_ratio)
 
 if image is not None:
 
@@ -206,7 +247,7 @@ if image is not None:
 
     st.title('Deep Learning Approach')
     threshold2 = st.slider(
-        'Threshold (0.33 is the optimal value evalueted by the model)', 0.001, 1.000, 0.033)
+        'Threshold (0.033 is the optimal value evalueted by the model)', 0.001, 1.000, 0.033)
     auto_encoder_prediction("img.jpg", threshold2)
 
 

requirements.txt

@@ -1,6 +1,6 @@
 altair==4.2.2
 attrs==22.2.0
-backports.zoneinfo==0.2.1
+backports.zoneinfo==0.2.1;python_version<"3.9"
 blinker==1.5
 brotlipy==0.7.0
 cachetools==5.3.0