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import warnings |
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from io import BytesIO |
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import matplotlib as mpl |
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import matplotlib.patches as patches |
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import matplotlib.pyplot as plt |
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import numpy as np |
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from mpl_toolkits.axes_grid1 import make_axes_locatable |
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import streamlit as st |
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from img2cmap import ImageConverter |
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warnings.filterwarnings("ignore") |
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st.set_option("deprecation.showfileUploaderEncoding", False) |
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st.set_page_config( |
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page_title="img2cmap web", |
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layout="wide", |
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) |
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st.title("Convert images to a colormap") |
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st.markdown( |
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""" |
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This app converts images to colormaps using the Python |
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library [img2cmap](https://github.com/arvkevi/img2cmap). |
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Try your own image on the left. **Scroll down to generate an optimal colormap.** |
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""" |
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) |
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st.sidebar.markdown("### Image settings") |
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file_or_url = st.sidebar.radio("Upload an image file or paste an image URL", ("file", "url")) |
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if file_or_url == "file": |
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user_image = st.sidebar.file_uploader("Upload an image file") |
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if user_image is not None: |
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user_image = BytesIO(user_image.getvalue()) |
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elif file_or_url == "url": |
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user_image = st.sidebar.text_input("Paste an image URL", "https://static1.bigstockphoto.com/3/2/3/large1500/323952496.jpg") |
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else: |
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st.warning("Please select an option") |
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if user_image is None: |
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user_image = "https://raw.githubusercontent.com/arvkevi/img2cmap/main/tests/images/south_beach_sunset.jpg" |
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st.sidebar.markdown("### User settings") |
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n_colors = st.sidebar.number_input( |
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"Number of colors", min_value=2, max_value=20, value=5, help="The number of colors to return in the colormap" |
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) |
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n_colors = int(n_colors) |
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remove_transparent = st.sidebar.checkbox( |
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"Remove transparency", False, help="If checked, remove transparent pixels from the image before clustering." |
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) |
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random_state = st.sidebar.number_input("Random state", value=42, help="Random state for reproducibility") |
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random_state = int(random_state) |
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@st.cache(allow_output_mutation=True) |
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def get_image_converter(user_image, remove_transparent): |
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converter = ImageConverter(user_image, remove_transparent=remove_transparent) |
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return converter |
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converter = get_image_converter(user_image, remove_transparent) |
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with st.spinner("Generating colormap..."): |
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cmap = converter.generate_cmap(n_colors=n_colors, palette_name="", random_state=random_state) |
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fig1, ax1 = plt.subplots(figsize=(8, 8)) |
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ax1.axis("off") |
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img = converter.image |
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im = ax1.imshow(img, cmap=cmap) |
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divider = make_axes_locatable(ax1) |
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cax = divider.append_axes("right", size="10%", pad=0.05) |
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cb = fig1.colorbar(im, cax=cax, orientation="vertical", label=cmap.name) |
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cb.set_ticks([]) |
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st.pyplot(fig1) |
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colors1 = [mpl.colors.rgb2hex(c) for c in cmap.colors] |
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st.text("Hex Codes (click to copy on far right)") |
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st.code(colors1) |
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st.header("Detect optimal number of colors") |
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optimize = st.button("Optimize") |
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if optimize: |
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with st.spinner("Optimizing... (this takes about a minute)"): |
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cmaps, best_n_colors, ssd = converter.generate_optimal_cmap(max_colors=20, palette_name="", random_state=random_state) |
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figopt, ax = plt.subplots(figsize=(7, 5)) |
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ymax = 21 |
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xmax = 20 |
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ax.set_ylim(2, ymax) |
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ax.set_xlim(0, 20) |
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for y, cmap_ in cmaps.items(): |
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colors = sorted([mpl.colors.rgb2hex(c) for c in cmap_.colors]) |
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intervals, width = np.linspace(0, xmax, len(colors) + 1, retstep=True) |
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for j, color in enumerate(colors): |
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rect = patches.Rectangle((intervals[j], y), width, 1, facecolor=color) |
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ax.add_patch(rect) |
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ax.set_yticks(np.arange(2, ymax) + 0.5) |
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ax.set_yticklabels(np.arange(2, ymax)) |
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ax.set_ylabel("Number of colors") |
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ax.set_xticks([]) |
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rect = patches.Rectangle((0, best_n_colors), ymax, 1, linewidth=1, facecolor="none", edgecolor="black", linestyle="--") |
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ax.add_patch(rect) |
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ax.get_yticklabels()[best_n_colors - 2].set_color("red") |
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st.pyplot(figopt) |
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st.metric("Optimal number of colors", best_n_colors) |
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st.text("Hex Codes of optimal colormap (click to copy on far right)") |
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st.code(sorted([mpl.colors.rgb2hex(c) for c in cmaps[best_n_colors].colors])) |
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st.text("Sum of squared distances by number of colors:") |
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st.write(ssd) |
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