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app.py

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+"""Application to demo inpainting using streamlit.
+
+Run using: streamlit run 10_03_image_inpaint_streamlit.py
+"""
+
+import streamlit as st
+import pathlib
+from streamlit_drawable_canvas import st_canvas
+import cv2
+import numpy as np
+import io
+import base64
+from PIL import Image
+
+STREAMLIT_STATIC_PATH = pathlib.Path(st.__path__[0]) / 'static'
+
+# We create a downloads directory within the streamlit static asset directory
+# and we write output files to it.
+DOWNLOADS_PATH = (STREAMLIT_STATIC_PATH / "downloads")
+if not DOWNLOADS_PATH.is_dir():
+    DOWNLOADS_PATH.mkdir()
+
+
+def get_image_download_link(img, filename, text):
+    """Generates a link to download a particular image file."""
+    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.sidebar.title('Image Inpaint')
+
+# Specify canvas parameters in application
+uploaded_file = st.sidebar.file_uploader("Upload Image to restore:", type=["png", "jpg"])
+image = None
+res = None
+
+if uploaded_file is not None:
+
+    # Convert the file to an opencv image.
+    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+    image = cv2.imdecode(file_bytes, 1)
+
+    stroke_width = st.sidebar.slider("Stroke width: ", 1, 25, 5)
+    h, w = image.shape[:2]
+    if w > 800:
+        h_, w_ = int(h * 800 / w), 800
+    else:
+        h_, w_ = h, w
+
+    # Create a canvas component.
+    canvas_result = st_canvas(
+        fill_color='gray',
+        stroke_width=stroke_width,
+        stroke_color='green',
+        background_image=Image.open(uploaded_file).resize((h_, w_)),
+        update_streamlit=True,
+        height=h_,
+        width=w_,
+        drawing_mode='freedraw',
+        key="canvas",
+    )
+    stroke = canvas_result.image_data
+
+    if stroke is not None:
+
+        if st.sidebar.checkbox('show mask'):
+            st.image(stroke)
+
+        mask = cv2.split(stroke)[3]
+        mask = np.uint8(mask)
+        mask = cv2.resize(mask, (w, h))
+
+    st.sidebar.caption('Happy with the selection?')
+    option = st.sidebar.selectbox('Mode', ['None', 'Telea', 'NS', 'Compare both'])
+
+    if option == 'Telea':
+        st.subheader('Result of Telea')
+        res = cv2.inpaint(src=image, inpaintMask=mask, inpaintRadius=3, flags=cv2.INPAINT_TELEA)[:, :, ::-1]
+        st.image(res)
+    elif option == 'Compare both':
+        col1, col2 = st.columns(2)
+        res1 = cv2.inpaint(src=image, inpaintMask=mask, inpaintRadius=3, flags=cv2.INPAINT_TELEA)[:, :, ::-1]
+        res2 = cv2.inpaint(src=image, inpaintMask=mask, inpaintRadius=3, flags=cv2.INPAINT_NS)[:, :, ::-1]
+        with col1:
+            st.subheader('Result of Telea')
+            st.image(res1)
+        with col2:
+            st.subheader('Result of NS')
+            st.image(res2)
+        if res1 is not None:
+            # Display link.
+            result1 = Image.fromarray(res1)
+            st.sidebar.markdown(
+                get_image_download_link(result1, 'telea.png', 'Download Output of Telea'),
+                unsafe_allow_html=True)
+        if res2 is not None:
+            # Display link.
+            result2 = Image.fromarray(res2)
+            st.sidebar.markdown(
+                get_image_download_link(result2, 'ns.png', 'Download Output of NS'),
+                unsafe_allow_html=True)
+
+    elif option == 'NS':
+        st.subheader('Result of NS')
+        res = cv2.inpaint(src=image, inpaintMask=mask, inpaintRadius=3, flags=cv2.INPAINT_NS)[:, :, ::-1]
+        st.image(res)
+    else:
+        pass
+
+    if res is not None:
+        # Display link.
+        result = Image.fromarray(res)
+        st.sidebar.markdown(
+            get_image_download_link(result, 'output.png', 'Download Output'),
+            unsafe_allow_html=True)

requirements.txt

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+numpy
+streamlit
+streamlit_drawable_canvas
+opencv-python-headless
+pillow