Add application file

app.py

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

requirements.txt

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+streamlit
+git+https://github.com/arvkevi/img2cmap@main#egg=img2cmap