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LICENSE

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+MIT License
+
+Copyright (c) 2024 Weichen Huang
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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----
-title: GeoIA
-emoji: 📈
-colorFrom: green
-colorTo: green
-sdk: streamlit
-sdk_version: 1.30.0
-app_file: app.py
-pinned: false
-license: mit
----
+# Streamlit - Drawable Canvas - Demo
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Streamlit Drawable Canvas demo on Streamlit Sharing
+
+[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://share.streamlit.io/andfanilo/streamlit-drawable-canvas-demo/master/app.py)

app.py

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+import streamlit as st
+from streamlit_drawable_canvas import st_canvas
+from PIL import Image
+import numpy as np
+import cv2
+import hashlib
+import time
+import os
+
+# Function to convert the canvas drawing to a binary mask
+def canvas_to_mask(canvas_result, img_shape):
+    if canvas_result is not None and canvas_result.image_data is not None:
+        canvas_image_data = np.array(canvas_result.image_data)
+        mask = cv2.cvtColor(canvas_image_data, cv2.COLOR_RGBA2GRAY)
+        mask = cv2.threshold(mask, 1, 255, cv2.THRESH_BINARY)[1]
+        mask = cv2.resize(mask, (img_shape[1], img_shape[0]))
+        return mask
+    else:
+        return None
+
+# Function to fill enclosed areas in the binary mask
+def fill_enclosed_areas(mask):
+    filled_mask = mask.copy()
+    h, w = filled_mask.shape[:2]
+    flood_fill_mask = np.zeros((h + 2, w + 2), np.uint8)
+    cv2.floodFill(filled_mask, flood_fill_mask, (0, 0), 255)
+    filled_mask_inv = cv2.bitwise_not(filled_mask)
+    filled_foreground = mask | filled_mask_inv
+    return filled_foreground
+
+def generate_short_hash():
+    current_time = str(time.time())
+    sha256_hash = hashlib.sha256(current_time.encode()).hexdigest()
+    short_hash = sha256_hash[:6]
+
+    return short_hash
+
+# Function to calculate the mean mask from all masks in the data folder
+def calculate_mean_mask(mask_folder):
+    mask_list = []
+    for filename in os.listdir(mask_folder):
+        if filename.endswith('.png'):
+            mask_path = os.path.join(mask_folder, filename)
+            mask_image = Image.open(mask_path).convert('L')  # convert to grayscale
+            mask_array = np.array(mask_image)
+            mask_list.append(mask_array)
+    if mask_list:
+        # Stack mask arrays and calculate the mean along the stack
+        mean_mask = np.mean(np.stack(mask_list), axis=0).astype(np.uint8)
+        return mean_mask
+    else:
+        return None
+
+# Function to overlay the mean mask onto the base image
+def overlay_mask(base_image_path, mean_mask):
+    base_image = Image.open(base_image_path).convert('RGBA')
+    mean_mask_image = Image.fromarray(mean_mask)
+    mean_mask_image = mean_mask_image.resize(base_image.size, resample=Image.BILINEAR)
+    mask_rgba = Image.merge('RGBA', (mean_mask_image, mean_mask_image, mean_mask_image, mean_mask_image))
+    final_image = Image.composite(mask_rgba, base_image, mean_mask_image)
+    return final_image
+
+st.title("IB Geo IA Survey")
+
+# Upload an image
+
+
+image = Image.open("img/map.png").convert("RGB")
+img_array = np.array(image)
+width = st.slider('Stroke width', 0, 20, 5)
+
+# Create a canvas for drawing
+st.subheader("Highlight the central business district area:")
+canvas_result = st_canvas(
+    fill_color="rgba(255, 165, 0, 0.7)",  # Use an orange, semi-transparent fill
+    stroke_width=width,
+    stroke_color="rgba(255, 165, 0, 0.7)",
+    background_image=Image.open("img/map.png"),
+    update_streamlit=True,
+    height=img_array.shape[0],
+    width=img_array.shape[1],
+    drawing_mode="freedraw",
+    key="canvas",
+)
+
+if st.button("Save"):
+
+    mask = canvas_to_mask(canvas_result, img_array.shape)
+    if mask is not None:
+        mask = fill_enclosed_areas(mask)
+        cv2.imwrite(f"data/{generate_short_hash()}.png", mask)
+    else:
+        st.warning("Please draw on the image.")
+
+if st.button("Aggregate data"):
+
+    mean_mask = calculate_mean_mask("data")
+
+    if mean_mask is not None:
+        final_image = overlay_mask("img/map.png", mean_mask)
+
+        st.image(final_image, caption='Where most people think the CBD is', use_column_width=True)
+
+        st.download_button(
+            label="Download Image",
+            data=final_image.tobytes(),
+            file_name="final_overlay.png",
+            mime="image/png"
+        )
+    else:
+        st.warning("No saved data found in the data folder.")

requirements.txt

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+streamlit>=0.88
+streamlit-drawable-canvas>=0.8
+svgpathtools
+svgwrite
+numpy
+pillow
+opencv-python