17/02/2023 v1

Image_Filters_Streamlit_app.py

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+# Checkout this tutorial
+# https://blog.loginradius.com/engineering/guest-post/opencv-web-app-with-streamlit/
+# Online deployment:
+# https://towardsdatascience.com/3-easy-ways-to-deploy-your-streamlit-web-app-online-7c88bb1024b1
+# https://www.youtube.com/watch?v=4SO3CUWPYf0
+
+# Run: streamlit run Image_Filters_Streamlit_app.py
+
+import io
+import base64
+import cv2
+from PIL import Image
+from filters import *
+
+# Generating a link to download a particular image file.
+def get_image_download_link(img, filename, text):
+    buffered = io.BytesIO()
+    img.save(buffered, format = 'JPEG')
+    img_str = base64.b64encode(buffered.getvalue()).decode()
+    href = f'<a href="data:file/txt;base64,{img_str}" download="{filename}">{text}</a>'
+    return href
+
+# Set title.
+st.title('Artistic Image Filters')
+
+# Upload image.
+uploaded_file = st.file_uploader('Choose an image file:', type=['png','jpg'])
+
+if uploaded_file is not None:
+    # Convert the file to an opencv image.
+    raw_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+    img = cv2.imdecode(raw_bytes, cv2.IMREAD_COLOR)
+    input_col, output_col = st.columns(2)
+    with input_col:
+        st.header('Original')
+        # Display uploaded image.
+        st.image(img, channels='BGR', use_column_width=True)
+
+    st.header('Filter Examples:')
+    # Display a selection box for choosing the filter to apply.
+    option = st.selectbox('Select a filter:',
+                          ( 'None',
+                            'Black and White',
+                            'Sepia / Vintage',
+                            'Vignette Effect',
+                            'Pencil Sketch',
+                         ))
+
+    # Define columns for thumbnail images.
+    col1, col2, col3, col4 = st.columns(4)
+    with col1:
+        st.caption('Black and White')
+        st.image('filter_bw.jpg')
+    with col2:
+        st.caption('Sepia / Vintage')
+        st.image('filter_sepia.jpg')
+    with col3:
+        st.caption('Vignette Effect')
+        st.image('filter_vignette.jpg')
+    with col4:
+        st.caption('Pencil Sketch')
+        st.image('filter_pencil_sketch.jpg')
+
+    # Flag for showing output image.
+    output_flag = 1
+    # Colorspace of output image.
+    color = 'BGR'
+
+     # Generate filtered image based on the selected option.
+    if option == 'None':
+        # Don't show output image.
+        output_flag = 0
+    elif option == 'Black and White':
+        output = bw_filter(img)
+        color = 'GRAY'
+    elif option == 'Sepia / Vintage':
+        output = sepia(img)
+    elif option == 'Vignette Effect':
+        level = st.slider('level', 1, 5, 2)
+        output = vignette(img, level)
+    elif option == 'Pencil Sketch':
+        ksize = st.slider('Blur kernel size', 1, 11, 5, step=2)
+        output = pencil_sketch(img, ksize)
+        color = 'GRAY'
+
+    with output_col:
+        if output_flag == 1:
+            st.header('Output')
+            st.image(output, channels=color)
+            # fromarray convert cv2 image into PIL format for saving it using download link.
+            if color == 'BGR':
+                result = Image.fromarray(output[:,:,::-1])
+            else:
+                result = Image.fromarray(output)
+            # Display link.
+            st.markdown(get_image_download_link(result,'output.png','Download '+'Output'),
+                        unsafe_allow_html=True)
+    

filters.py

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+import cv2
+import numpy as np
+import streamlit as st
+
+
+# Refer to the application notebook implement the following filters
+
+@st.cache_data
+def bw_filter(img):
+    # Write your code here to convert img to a gray image
+	img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+	return img_gray
+
+@st.cache_data
+def vignette(img, level=2):
+	 # Write your code here to create the vignette effect
+    height, width = img.shape[:2]  
+
+    # Generate vignette mask using Gaussian kernels.
+    X_resultant_kernel = cv2.getGaussianKernel(width, width/level)
+    Y_resultant_kernel = cv2.getGaussianKernel(height, height/level)
+        
+    # Generating resultant_kernel matrix.
+    # H x 1 * 1 x W
+    kernel = Y_resultant_kernel * X_resultant_kernel.T 
+    mask = kernel / kernel.max()
+    
+    img_vignette = np.copy(img)
+        
+    # Applying the mask to each channel in the input image.
+    for i in range(3):
+        img_vignette[:,:,i] = img_vignette[:,:,i] * mask
+    
+
+    return img_vignette
+
+@st.cache_data
+def sepia(img):
+	# Write your code here to create the sepia effect
+	img_sepia = img.copy()
+	# Converting to RGB as sepia matrix below is for RGB.
+	img_sepia = cv2.cvtColor(img_sepia, cv2.COLOR_BGR2RGB) 
+	img_sepia = np.array(img_sepia, dtype = np.float64)
+	img_sepia = cv2.transform(img_sepia, np.matrix([[0.393, 0.769, 0.189],
+                                                    [0.349, 0.686, 0.168],
+                                                    [0.272, 0.534, 0.131]]))
+    # Clip values to the range [0, 255].
+	img_sepia = np.clip(img_sepia, 0, 255)
+	img_sepia = np.array(img_sepia, dtype = np.uint8)
+	img_sepia = cv2.cvtColor(img_sepia, cv2.COLOR_RGB2BGR)
+	return img_sepia
+
+@st.cache_data
+def pencil_sketch(img, ksize=5):
+	 # Write your code here to create the pencil sketch effect
+	img_blur = cv2.GaussianBlur(img, (ksize, ksize), 0, 0)
+	img_sketch, _ = cv2.pencilSketch(img_blur)
+	return img_sketch
+
+# Don't be constrained, add your own filters here

requirements.txt

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+numpy==1.24.2
+opencv_python==4.7.0.68
+Pillow==9.4.0
+streamlit==1.18.1