mirror of
https://github.com/tgorordo/pages.uoregon.edu.git
synced 2026-06-05 14:42:13 -07:00
basic course content added, more TODO
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commit
76468828e3
GPG key ID:
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94 changed files with 22022 additions and 0 deletions
401
courses/uoph410-510a_Image-Analysis/wk4/s4.py
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401
courses/uoph410-510a_Image-Analysis/wk4/s4.py
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import marimo
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__generated_with = "0.19.11"
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app = marimo.App(width="medium")
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@app.cell
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def _():
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import marimo as mo
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return (mo,)
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@app.cell
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def _():
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import numpy as np
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return (np,)
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@app.cell
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def _():
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from skimage import io, filters
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return filters, io
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@app.cell
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def _():
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import scipy as si
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import scipy.ndimage as nd
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return nd, si
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@app.cell
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def _():
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import matplotlib
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import matplotlib.pyplot as plt
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plt.ion();
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return (plt,)
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@app.cell
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def _(mo):
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from pathlib import Path
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mo.pdf(src=Path("Homework4.pdf"), width="100%", height="50vh")
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(r"""
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## Frequency Modulation
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""")
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return
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@app.cell
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def _(io):
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buster_keaton_general_train_512_png = io.imread("Buster_Keaton_General_Train_512.png", as_gray=True)
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buster_keaton_general_train_512_sinmod_png = io.imread("Buster_Keaton_General_Train_512_sineMod.png", as_gray=True)
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return (
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buster_keaton_general_train_512_png,
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buster_keaton_general_train_512_sinmod_png,
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)
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@app.cell
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def _(
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buster_keaton_general_train_512_png,
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buster_keaton_general_train_512_sinmod_png,
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plt,
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):
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fig1, (ax1a, ax1b) = plt.subplots(1, 2, figsize=(8, 4))
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ax1a.imshow(buster_keaton_general_train_512_png, cmap="gray")
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ax1b.imshow(buster_keaton_general_train_512_sinmod_png, cmap="gray")
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return
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@app.cell
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def _(
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buster_keaton_general_train_512_png,
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buster_keaton_general_train_512_sinmod_png,
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np,
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):
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buster_keaton_general_train_512_fft = np.abs(np.fft.fftshift(np.fft.fft2(buster_keaton_general_train_512_png)))
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buster_keaton_general_train_512_sinmod_fft = np.abs(np.fft.fftshift(np.fft.fft2(buster_keaton_general_train_512_sinmod_png)))
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return (
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buster_keaton_general_train_512_fft,
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buster_keaton_general_train_512_sinmod_fft,
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)
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@app.cell
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def _(
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buster_keaton_general_train_512_fft,
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buster_keaton_general_train_512_sinmod_fft,
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np,
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plt,
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):
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fig2, (ax2a, ax2b) = plt.subplots(1, 2, figsize=(8, 4))
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ax2a.imshow(np.log(buster_keaton_general_train_512_fft), cmap="gray")
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ax2b.imshow(np.log(buster_keaton_general_train_512_sinmod_fft), cmap="gray")
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(r"""
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## Quantized Aggregates
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""")
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return
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@app.cell
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def _(io):
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emitters_33px_100ph_png = io.imread("emitters_33px_100ph.png")
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return (emitters_33px_100ph_png,)
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@app.cell
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def _(emitters_33px_100ph_png, plt):
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fig3, ax3 = plt.subplots()
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ax3.imshow(emitters_33px_100ph_png, cmap="gray")
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return
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@app.cell
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def _(emitters_33px_100ph_png, plt):
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fig4, ax4 = plt.subplots()
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ax4.hist(emitters_33px_100ph_png.ravel(), bins="auto")
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fig4
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return
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@app.cell
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def _(emitters_33px_100ph_png, filters):
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emitters_33px_100ph_hflt = emitters_33px_100ph_png - 255 * filters.gaussian(emitters_33px_100ph_png, 8, mode="nearest")
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return (emitters_33px_100ph_hflt,)
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@app.cell
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def _(emitters_33px_100ph_hflt, plt):
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fig6, (ax6a, ax6b) = plt.subplots(1, 2, figsize=(8, 4))
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ax6a.imshow(emitters_33px_100ph_hflt, cmap="gray")
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ax6b.hist(emitters_33px_100ph_hflt.ravel(), bins=20)
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fig6
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return
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@app.cell
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def _(emitters_33px_100ph_hflt, np):
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ones = np.ones_like(emitters_33px_100ph_hflt, dtype=bool)
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zeros = np.zeros_like(ones, dtype=bool)
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x, y = np.arange(ones.shape[0]), np.arange(ones.shape[1])
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xs, ys = np.meshgrid(x, y)
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masks = [np.where((np.abs(xs - 33 * i) <= 2) & (np.abs(ys - 33 * j) <= 2), ones, zeros)
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for i in range(1, 11) for j in range(1, 11)]
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# mask = np.where((xs % (33 + 4) > (33 - 4)) & (ys % (33 + 4) > (33 - 4)), ones, zeros)
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mask = sum(masks)
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return mask, masks
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@app.cell
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def _(emitters_33px_100ph_hflt, masks, np):
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intensities = [np.sum(emitters_33px_100ph_hflt, where=m) for m in masks]
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return (intensities,)
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@app.cell
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def _(intensities, mask, plt):
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fig7, (ax7a, ax7b) = plt.subplots(1, 2, figsize=(8, 4))
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ax7a.imshow(mask, cmap="gray")
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counts, edges, _ = ax7b.hist(intensities, bins=25)
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fig7
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return counts, edges
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@app.cell
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def _(counts, edges, np):
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irange = (np.max(edges) - np.min(edges))
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imin = np.min(edges)
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(sum(counts[edges[1:] <= (irange/3) + imin]),
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sum(counts[((irange/3) + imin < edges[1:]) & (edges[1:] <= (2*irange/3) + imin)]),
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sum(counts[((2*irange/3) + imin < edges[1:])])
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)
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(r"""
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## A High-Resolution PSF
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""")
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return
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@app.cell
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def _(np, si):
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def psfker(l, NA=0.9, side=1.0, N=101, center=(0.0, 0.0)):
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xs, ys = np.meshgrid(np.linspace(0.0, side, N), np.linspace(0.0, side, N))
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r2s = np.square(xs - side / 2 - center[0]) + np.square(ys - side / 2 - center[1])
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v = 2 * np.pi * NA * np.sqrt(r2s) / l
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psf = 4 * np.square(si.special.j1(v) / v)
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nans = np.isnan(psf)
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psf[nans] = 1
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return psf / np.sum(psf)
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return (psfker,)
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@app.cell
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def _(plt, psfker):
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fig8, (ax8a, ax8b, ax8c) = plt.subplots(1, 3, figsize=(8, 2))
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ax8a.imshow(psfker(0.5), cmap="gray")
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ax8b.imshow(psfker(0.4), cmap="gray")
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ax8c.imshow(psfker(0.4, NA=0.5), cmap="gray")
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(r"""
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## A Worse Worm Image
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""")
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return
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@app.cell
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def _(io):
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fetter_celegans_cellfig10_png = io.imread("fetter_Celegans_cellfig10.jpg", as_gray=True)
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return (fetter_celegans_cellfig10_png,)
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@app.cell
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def _(fetter_celegans_cellfig10_png, plt):
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fig9, ax9 = plt.subplots()
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ax9.imshow(fetter_celegans_cellfig10_png, cmap="gray")
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return
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@app.cell
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def _(fetter_celegans_cellfig10_png, nd, psfker):
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fetter_celegans_cellfig10_psf = nd.convolve(fetter_celegans_cellfig10_png, psfker(0.53, NA=0.7))
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return (fetter_celegans_cellfig10_psf,)
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@app.cell
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def _(fetter_celegans_cellfig10_psf, plt):
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fig10, ax10 = plt.subplots()
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ax10.imshow(fetter_celegans_cellfig10_psf, cmap="gray")
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return
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@app.cell
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def _(mo):
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mo.md(r"""
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## SNR and Poisson Noise
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$$ N_\text{photon} \sim SNR^2 $$
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""")
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(r"""
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## Simulated Point Sources
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""")
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return
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@app.cell
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def _(np):
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def pixelate(image, inscale, shape=(15, 15)):
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outscale = image.shape[0] * inscale / shape[0]
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assert outscale == image.shape[1] * inscale / shape[1]
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assert outscale.is_integer()
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ii, jj = np.indices(image.shape)
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ones = np.ones_like(image, dtype=bool)
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zeros = np.zeros_like(image, dtype=bool)
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masks = [np.where((i * outscale <= ii * inscale) &
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(ii * inscale < (i + 1) * outscale)
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& (j * outscale <= jj * inscale) &
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(jj * inscale < (j + 1) * outscale),
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ones, zeros)
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for i in range(shape[0]) for j in range(shape[1])]
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outage = np.array([np.sum(image, where=m) for m in masks]).reshape(shape)
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return outage, outscale
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return (pixelate,)
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@app.cell
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def _(pixelate, psfker):
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simpoint, _ = pixelate(psfker(0.5, NA=0.9, side=15 * 0.1, N = 150), 0.1)
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return (simpoint,)
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@app.cell
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def _(plt, simpoint):
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fig11, ax11 = plt.subplots()
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ax11.imshow(simpoint, cmap="gray")
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return
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@app.cell
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def _(np):
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def fishify(image, N=1, rng=np.random.default_rng()):
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return rng.poisson(lam=N * (image / np.sum(image)))
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return (fishify,)
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@app.cell
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def _(fishify, pixelate, psfker):
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simpoint50 = fishify(pixelate(psfker(0.5, NA=0.9, side=15 * 0.1, N = 150), 0.1)[0], N=50)
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simpoint500 = fishify(pixelate(psfker(0.5, NA=0.9, side=15 * 0.1, N = 150), 0.1)[0], N=500)
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return simpoint50, simpoint500
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@app.cell
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def _(plt, simpoint50, simpoint500):
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fig12, (ax12a, ax12b) = plt.subplots(1, 2, figsize=(8, 4))
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ax12a.imshow(simpoint50, cmap="gray")
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ax12b.imshow(simpoint500, cmap="gray")
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return
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@app.cell
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def _(fishify, pixelate, psfker):
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simpoint50_shifted = fishify(pixelate(psfker(0.5, NA=0.9, side=15 * 0.1, N = 150, center=(0.3, 0.3)), 0.1)[0], N=50)
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simpoint500_shifted = fishify(pixelate(psfker(0.5, NA=0.9, side=15 * 0.1, N = 150, center=(0.3, 0.3)), 0.1)[0], N=500)
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return simpoint500_shifted, simpoint50_shifted
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@app.cell
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def _(plt, simpoint500_shifted, simpoint50_shifted):
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fig13, (ax13a, ax13b) = plt.subplots(1, 2, figsize=(8, 4))
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ax13a.imshow(simpoint50_shifted, cmap="gray")
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ax13b.imshow(simpoint500_shifted, cmap="gray")
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return
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@app.cell
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def _(fishify, np, pixelate, psfker):
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uniform = np.ones((15, 15)) / np.sum(np.ones((15, 15)))
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simpoint_i = fishify(pixelate(psfker(0.5, NA=0.9, side=15 * 0.1, N = 150), 0.1)[0], N=50) + fishify(np.ones((15, 15)), N=2 * 15**2)
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simpoint_ii = fishify(pixelate(psfker(0.5, NA=0.9, side=15 * 0.1, N = 150, center=(0.03, 0.03)), 0.1)[0], N=50) + fishify(np.ones((15, 15)), N=2 * 15**2)
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simpoint_iii = fishify(pixelate(psfker(0.5, NA=0.9, side=15 * 0.1, N = 150, center=(0.03, 0.03)), 0.1)[0], N=500) + fishify(np.ones((15, 15)), N=2 * 15**2)
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return simpoint_i, simpoint_ii, simpoint_iii
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@app.cell
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def _(plt, simpoint_i, simpoint_ii, simpoint_iii):
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fig14, (ax14a, ax14b, ax14c) = plt.subplots(1, 3, figsize=(8, 2))
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ax14a.imshow(simpoint_i, cmap="gray")
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ax14b.imshow(simpoint_ii, cmap="gray")
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ax14c.imshow(simpoint_iii, cmap="gray")
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(r"""
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## Simulating a Ring
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""")
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return
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@app.cell
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def _(np):
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def ring(inner, outer, center=(0, 0), scale=1, shape=(150, 150), rtol=1e-02, atol=1e-02):
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ii, jj = np.indices(shape)
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r2s = np.square((ii - shape[0] / 2) * scale - center[0]) + np.square((jj - shape[1] / 2) * scale - center[1])
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return ((r2s < np.square(outer)) & (r2s >= np.square(inner))).astype(np.float32)
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return (ring,)
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@app.cell
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def _(fishify, nd, plt, psfker, ring):
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fig15, (ax15a, ax15b, ax15c) = plt.subplots(1, 3, figsize=(8, 4))
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ax15a.imshow(ring(0.5, 0.6, scale=0.01), cmap="gray")
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ax15b.imshow(nd.convolve(ring(0.5, 0.6, scale=0.01), psfker(0.1), mode="nearest"), cmap="gray")
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ax15c.imshow(fishify(nd.convolve(ring(0.5, 0.6, scale=0.01), psfker(0.1), mode="nearest"), N=10_000), cmap="gray")
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return
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@app.cell
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def _():
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return
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if __name__ == "__main__":
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app.run()
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