Priyanka-Kumavat-At-TE commited on
Commit
ad52348
1 Parent(s): 4feb29a

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +21 -11
app.py CHANGED
@@ -13,14 +13,24 @@ import cv2
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  import numpy as np
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  from sklearn.ensemble import RandomForestClassifier
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- st.title("Image Bluriness Prediction")
 
 
 
 
 
 
 
 
 
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  # Load the saved random forest classifier model
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  with open('image_blur_model.pkl', 'rb') as f:
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  clf = pickle.load(f)
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  # For sample images as a sidebar
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- images = ["test2.jpg","test1.jpg","test3.jpg","test4.jpg","test5.jpg","test6.jpg","download1.jpg","download2.jpg","sample1.jpg","download3.jpg","download4.jpg","download.png","img1.jpg","img17.jpg"]
 
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  with st.sidebar:
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  st.write("Choose an image")
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  st.image(images)
@@ -39,15 +49,15 @@ def predict_bluriness(image):
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  return prediction, vol
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- # # CSS code for changing color of the button
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- # st.markdown("""
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- # <style>
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- # .stButton button {
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- # background-color: #668f45;
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- # color: white;
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- # }
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- # </style>
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- # """, unsafe_allow_html=True)
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  # File uploader
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  uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
 
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  import numpy as np
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  from sklearn.ensemble import RandomForestClassifier
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+ st.title("Image Blur Prediction System")
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+
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+ st.write("""Image Bluriness Prediction Model allows users to analyze the bluriness of images.
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+ It utilizes a pre-trained random forest classifier model to predict whether an image is blurry or not.
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+ The application provides two options for image selection:
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+ users can either upload their own image or choose from a set of sample images.
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+ Once an image is selected, the application calculates the Variance of Laplacian (VoL) score,
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+ a metric used to measure image bluriness. The classifier model then predicts whether the image is blurry or not based
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+ on the VoL score. The prediction result and the VoL score are displayed to the user.
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+ The application also includes a sidebar that showcases sample images for quick testing.""")
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  # Load the saved random forest classifier model
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  with open('image_blur_model.pkl', 'rb') as f:
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  clf = pickle.load(f)
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  # For sample images as a sidebar
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+ images = ["test2.jpg","test1.jpg","test4.jpg","test5.jpg","test6.jpg","download1.jpg","download2.jpg","sample1.jpg",
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+ "download3.jpg","download4.jpg","download.png","img1.jpg","img17.jpg"]
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  with st.sidebar:
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  st.write("Choose an image")
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  st.image(images)
 
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  return prediction, vol
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+ # CSS code for changing color of the button
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+ st.markdown("""
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+ <style>
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+ .stButton button {
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+ background-color: #668f45;
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+ color: white;
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+ }
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+ </style>
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+ """, unsafe_allow_html=True)
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  # File uploader
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  uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])