thumbnail analysis code

app.py

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+import streamlit as st
+import cv2
+from saliency_map import avg_saliency 
+import numpy as np
+from PIL import Image
+import io
+
+st.set_page_config(
+    page_title="Thumbnail Analyser",
+    page_icon="thumbnail_logo.png",
+    layout="wide",  # wide layout reduces left and right margins
+    initial_sidebar_state="expanded"
+)
+
+def saliency_analysis(image,saliency_image,col2):
+    with saliency_image:
+        with st.spinner('Loading...'):
+            img, saliency_output = avg_saliency(image)
+            if saliency_output is not None:
+                pil_image = Image.fromarray(saliency_output)
+                if pil_image.mode != 'RGB':
+                    pil_image = pil_image.convert('L')
+    saliency_image.image(pil_image, caption='Attention Spots', use_column_width=True)
+    return pil_image
+       
+
+def main():
+    st.markdown('<h1 style="text-align: center;">Thumbnail Analyser</h1>', unsafe_allow_html=True)
+
+
+    # Create two image containers
+    col1, col2 = st.columns(2)
+
+    # Placeholder images
+    image_placeholder = 'Upload_image.png'
+    original_image = col1.image(image_placeholder, use_column_width=True)
+    saliency_image = col2.image(image_placeholder, use_column_width=True)
+
+    # Upload images and replace placeholders
+    original_image_upload = col1.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
+    
+
+    if original_image_upload is not None:
+        original_image.image(original_image_upload, use_column_width=True)
+        file_bytes = np.asarray(bytearray(original_image_upload.read()), dtype=np.uint8)
+        cv2_image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
+        pil_image = saliency_analysis(cv2_image,saliency_image,col2)
+        buffered = io.BytesIO()
+        pil_image.save(buffered, format="PNG")
+        buffered.seek(0)
+    
+        col2.download_button(
+                label="Download Image",
+                data=buffered,
+                file_name="AttentionSpots.png",
+                mime="image/png",
+                key="download_button"
+            )
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit
+opencv-contrib-python
+saliency_map
+numpy
+PIL
+io

saliency_map.py

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+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def image_analysis(image, color):
+  
+  lab_image = cv2.cvtColor(image, color)
+
+  saliency_map = cv2.saliency.StaticSaliencyFineGrained_create()
+  channels = list(cv2.split(lab_image))
+  (success, saliency_map)=saliency_map.computeSaliency(channels[0])
+  saliency_map = (saliency_map*255).astype("uint8")
+  return saliency_map
+  
+def avg_saliency(image):
+  final_saliency_matrix = np.zeros((image.shape[:2]))
+
+  color_codes = [0,2,4,6,32,36,40,44,50,52,66,68,74,76,82]
+
+  for code in color_codes:
+    final_saliency_matrix += image_analysis(image,code)
+
+  avg_ed_saliency_map = final_saliency_matrix//(len(color_codes))
+
+  return image, avg_ed_saliency_map
+