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import marimo |
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__generated_with = "0.9.6" |
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app = marimo.App(width="medium") |
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@app.cell(hide_code=True) |
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def __(mo): |
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mo.md( |
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""" |
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# Seam Carving |
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_Example adapted from work by [Vincent Warmerdam](https://x.com/fishnets88)_. |
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## The seam carving algorithm |
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This marimo demonstration is partially an homage to [a great video by Grant |
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Sanderson](https://www.youtube.com/watch?v=rpB6zQNsbQU) of 3Blue1Brown, which demonstrates |
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the seam carving algorithm in [Pluto.jl](https://plutojl.org/): |
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<iframe width="560" height="315" src="https://www.youtube.com/embed/rpB6zQNsbQU?si=oiZclGIj2atJR47m" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe> |
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As Grant explains, the seam carving algorithm preserves the shapes of the main content in the image, while killing the "dead space": the image is resized, but the clocks and other content are not resized or deformed. |
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This notebook is a Python version of the seam carving algorithm, but it is also a |
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demonstration of marimo's [caching |
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feature](https://docs.marimo.io/guides/best_practices/performance.html#cache-computations-with-mo-cache), |
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which is helpful because the algorithm is compute intensive even when you |
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use [Numba](https://numba.pydata.org/). |
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Try it out by playing with the slider! |
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""" |
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) |
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return |
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@app.cell(hide_code=True) |
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def __(): |
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input_image = "https://upload.wikimedia.org/wikipedia/en/d/dd/The_Persistence_of_Memory.jpg" |
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return input_image, |
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@app.cell(hide_code=True) |
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def __(mo): |
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mo.md("""## Try it!""") |
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return |
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@app.cell |
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def __(): |
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import marimo as mo |
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slider = mo.ui.slider( |
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0.7, |
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1.0, |
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step=0.05, |
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value=1.0, |
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label="Amount of resizing to perform:", |
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show_value=True, |
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) |
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slider |
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return mo, slider |
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@app.cell |
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def __(efficient_seam_carve, input_image, mo, slider): |
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scale_factor = slider.value |
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result = efficient_seam_carve(input_image, scale_factor) |
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mo.hstack([mo.image(input_image), mo.image(result)], justify="start") |
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return result, scale_factor |
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@app.cell |
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def __(mo): |
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import numpy as np |
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from numba import jit |
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from skimage import io, filters, transform |
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import time |
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def rgb2gray(rgb): |
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return np.dot(rgb[..., :3], [0.2989, 0.5870, 0.1140]) |
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def compute_energy_map(gray): |
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return np.abs(filters.sobel_h(gray)) + np.abs(filters.sobel_v(gray)) |
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@jit(nopython=True) |
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def find_seam(energy_map): |
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height, width = energy_map.shape |
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dp = energy_map.copy() |
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backtrack = np.zeros((height, width), dtype=np.int32) |
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for i in range(1, height): |
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for j in range(width): |
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if j == 0: |
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idx = np.argmin(dp[i - 1, j : j + 2]) |
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backtrack[i, j] = idx + j |
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min_energy = dp[i - 1, idx + j] |
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elif j == width - 1: |
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idx = np.argmin(dp[i - 1, j - 1 : j + 1]) |
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backtrack[i, j] = idx + j - 1 |
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min_energy = dp[i - 1, idx + j - 1] |
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else: |
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idx = np.argmin(dp[i - 1, j - 1 : j + 2]) |
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backtrack[i, j] = idx + j - 1 |
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min_energy = dp[i - 1, idx + j - 1] |
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dp[i, j] += min_energy |
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return backtrack |
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@jit(nopython=True) |
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def remove_seam(image, backtrack): |
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height, width, _ = image.shape |
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output = np.zeros((height, width - 1, 3), dtype=np.uint8) |
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j = np.argmin(backtrack[-1]) |
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for i in range(height - 1, -1, -1): |
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for k in range(3): |
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output[i, :, k] = np.delete(image[i, :, k], j) |
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j = backtrack[i, j] |
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return output |
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def seam_carving(image, new_width): |
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height, width, _ = image.shape |
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while width > new_width: |
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gray = rgb2gray(image) |
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energy_map = compute_energy_map(gray) |
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backtrack = find_seam(energy_map) |
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image = remove_seam(image, backtrack) |
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width -= 1 |
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return image |
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@mo.cache |
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def efficient_seam_carve(image_path, scale_factor): |
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img = io.imread(image_path) |
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new_width = int(img.shape[1] * scale_factor) |
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start_time = time.time() |
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carved_img = seam_carving(img, new_width) |
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end_time = time.time() |
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print(f"Seam carving completed in {end_time - start_time:.2f} seconds") |
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return carved_img |
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return ( |
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compute_energy_map, |
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efficient_seam_carve, |
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filters, |
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find_seam, |
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io, |
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jit, |
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np, |
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remove_seam, |
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rgb2gray, |
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seam_carving, |
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time, |
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transform, |
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) |
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if __name__ == "__main__": |
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app.run() |
