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app.py

@@ -123,8 +123,8 @@ with col3:
         label = 'Confidence Threshold',
         min_value = 0.0,
         max_value = 1.0,
-        value = 0.8,
-        step = 0.1
+        value = 0.85,
+        step = 0.05
     )
 
 st.write('##')
@@ -142,7 +142,7 @@ if submit:
     image_size = 4960
 
     with st.spinner(text="Performing prediction ... "):
-        output = sahi_yolov8m_inference(
+        output_visual,coco_df,output_df = sahi_yolov8m_inference(
             image,
             detection_model,
             image_size=image_size,
@@ -153,7 +153,7 @@ if submit:
         )
 
     st.session_state["output_1"] = image
-    st.session_state["output_2"] = output 
+    st.session_state["output_2"] = output_visual
 
 st.write('##')
 
@@ -161,19 +161,12 @@ col1, col2, col3 = st.columns([1, 5, 1], gap='small')
 with col2:  
     st.markdown(f"#### Object Detection Result")
     with st.container(border = True):
-        tab1, tab2 = st.tabs(['Original Image','Inference Prediction'])
+        tab1, tab2, tab3 = st.tabs(['Original Image','Inference Prediction','Data 📊'])
         with tab1:
             st.image(st.session_state["output_1"])
         with tab2:
             st.image(st.session_state["output_2"])
-    #    static_component = image_comparison(
-    #    img1=st.session_state["output_1"],
-    #    img2=st.session_state["output_2"],
-    #    label1='Raw Diagram',
-    #    label2='Inference Prediction',
-    #    width=col2.width,
-    #    starting_position=50,
-    #    show_labels=True,
-    #    make_responsive=True,
-    #    in_memory=True,
-    #    )   
+        with tab3:
+            st.dataframe(coco_df)
+            
+  

utils.py

@@ -1,4 +1,5 @@
 import numpy
+import pandas as pd
 import sahi.predict
 import sahi.utils
 from PIL import Image
@@ -31,7 +32,27 @@ def sahi_yolov8m_inference(
         rect_th=3,
         text_size=2
     )
+    output_visual = Image.fromarray(visual_result["image"])
+    
+    # object prediction annotation
+    coco_annotations = prediction_result.to_coco_annotations()
+    # base DataFrame with predefined categories
+    output_df = pd.DataFrame(
+        {'category': ['ball-valve', 'butterfly-valve', 'centrifugal-pump', 'check-valve', 'gate-valve'],
+        'count': [0, 0, 0, 0, 0]
+        }
+    )
+    # extract relevant data into a new DataFrame
+    coco_df = pd.DataFrame(
+        [(item['category_name'], round(item['score'], 2)) for item in coco_annotations],
+        columns=['category', 'score']
+    )
+    # count occurrences of each category
+    category_counts = coco_df['category'].value_counts().reset_index()
+    category_counts.columns = ['category', 'count']
+    # update the `count` column in the base DataFrame
+    output_df['count'] = output_df['category'].map(category_counts.set_index('category')['count']).fillna(0).astype(int)
+    # calculate percentages
+    output_df['percentage'] = round((output_df['count'] / output_df['count'].sum()) * 100, 1)
 
-    output = Image.fromarray(visual_result["image"])
-
-    return output
+    return output_visual,coco_df,output_df