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image_editing_app.py

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+import streamlit as st # for creating webapp
+import cv2 # image processing
+from PIL import Image, ImageEnhance
+import numpy as np # to deal with arrays
+
+def cartoonize_image(our_image):
+    new_img = np.array(our_image.convert('RGB'))
+    # mask
+    gray = cv2.cvtColor(new_img, cv2.COLOR_BGR2GRAY)
+    gray = cv2.medianBlur(gray, 5)
+    edges = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 9, 9)
+    color = cv2.bilateralFilter(new_img, 9, 300, 300)
+    cartoon = cv2.bitwise_and(color, color, mask=edges)
+
+    return cartoon
+
+def cannize_image(our_image):
+    new_img = np.array(our_image.convert('RGB'))
+    img = cv2.GaussianBlur(new_img, (9, 9), 0)
+    canny = cv2.Canny(img, 100, 150)
+
+    return canny
+
+def main():
+
+    st.title('Image Editing App')
+    st.text('Edit your images in a fast and simple way')
+
+    activities = ['Detection', 'About']
+    choice = st.sidebar.selectbox('Select Activity', activities)
+
+    if choice == 'Detection':
+        st.subheader('Face Detection')
+        image_file = st.file_uploader('Upload Image', type=['jpg', 'jpeg', 'png'])
+
+        if image_file is not None:
+            our_image = Image.open(image_file)
+            st.text('Original Image')
+            enhance_type = st.sidebar.radio('Enhance type', \
+                                            options=['Gray-scale', 'Contrast', 'Brightness', 'Blurring', 'Sharpness'])
+            st.sidebar.image(our_image)
+
+            if enhance_type == 'Gray-scale':
+                # convert image to RGB arrays
+                img = np.array(our_image.convert('RGB'))
+                gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+                st.image(gray)
+
+            elif enhance_type == 'Contrast':
+                rate = st.sidebar.slider('Contrast', 0.5, 6.0, 2.5)
+                enhancer = ImageEnhance.Contrast(our_image)
+                enhanced_img = enhancer.enhance(rate)
+                st.image(enhanced_img)
+
+            elif enhance_type == 'Brightness':
+                rate = st.sidebar.slider('Brightness', 0.5, 6.0, 1.5)
+                enhancer = ImageEnhance.Brightness(our_image)
+                enhanced_img = enhancer.enhance(rate)
+                st.image(enhanced_img)
+
+            elif enhance_type == 'Blurring':
+                rate = st.sidebar.slider('Blurring', 0.5, 6.0, 1.5)
+                blurred_img = cv2.GaussianBlur(np.array(our_image), (15, 15), rate)
+                st.image(blurred_img)
+
+            elif enhance_type == 'Sharpness':
+                rate = st.sidebar.slider('Sharpness', 0.5, 6.0, 1.5)
+                enhancer = ImageEnhance.Sharpness(our_image)
+                enhanced_img = enhancer.enhance(rate)
+                st.image(enhanced_img)
+
+            else:
+                st.image(our_image)
+
+        tasks = ['Cartoonize', 'Cannize']
+        feature_choice = st.sidebar.selectbox('Find features', tasks)
+
+        if st.sidebar.button('Process'):
+
+            if feature_choice == 'Cartoonize':
+                result_img = cartoonize_image(our_image)
+                st.image(result_img)
+
+            if feature_choice == 'Cannize':
+                result_img = cannize_image(our_image)
+                st.image(result_img)
+
+
+    if choice == 'About':
+        st.subheader('About the developer')
+        st.markdown('Built with streamlit by [Slender](https://huggingface.co/Slender)')
+        st.text('My name is Slender, I am computer science student with an experience of 3 years in python programming.')
+
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+streamlit==1.10.0
+opencv-python==4.6.0.66