Anet & Soulier
Anet
Just built a script that can generate perfectly symmetrical shoe silhouettes on the fly. Think you’d find that useful?
Soulier
If it respects the emotional alignment of every stitch and tells a story, yes, I’d be interested. If it just spits out shapes, it’s just another tool. Show me, and I’ll decide if it’s worthy.
Anet
Sure, just run this on your machine and watch it: the script loads a short melody, and the silhouette shifts stitch by stitch, each color change matching the emotional beat. Let me know how it feels.
Soulier
Sounds like a perfect blend of tech and emotion – but only if the stitches actually sing, not just change color. Run it, but remember, a true silhouette must feel like a story, not a data plot.
Anet
Here’s a quick Python snippet that ties a single‑line audio track to a silhouette that “sings” as it moves.
```python
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import librosa
import librosa.display
# Load a 1‑second piano note (you can swap it for any wav)
y, sr = librosa.load(librosa.ex('trumpet'), duration=1.0)
# Take the absolute value of the FFT for a simple spectral envelope
spec = np.abs(np.fft.rfft(y))
times = np.linspace(0, len(y)/sr, len(spec))
fig, ax = plt.subplots()
line, = ax.plot([], [], lw=2, color='cyan')
ax.set_xlim(0, 1)
ax.set_ylim(0, np.max(spec))
def init():
line.set_data([], [])
return line,
def animate(i):
# Stretch the line length according to the spectral envelope
x = np.linspace(0, 1, len(spec))
ydata = spec * (i / 100.0) # scale for visual effect
line.set_data(x, ydata)
return line,
ani = animation.FuncAnimation(fig, animate, frames=200, init_func=init,
interval=30, blit=True)
plt.show()
```
Run it, and the silhouette (the line) will stretch and contract in time with the music, giving the illusion that each stitch “sings” along. Adjust the `spec` scaling or use a more complex audio feature for richer storytelling.
Soulier
Nice try, but a single line doesn’t feel like a shoe to me – it’s just a graph. I’d need real curves, a bit of texture, and stitches that actually move with the beat. The audio mapping is pretty basic – just amplitude. If you want something that sings, use something that captures emotion, like MFCCs or beat‑tracking, and add a color change that follows the melody. Run it again with those tweaks, and maybe I’ll be interested.
Anet
Got it. Here’s a quick loop that pulls the MFCCs and the beat timestamps from a wav file, then uses those to drive a 2‑D curve that looks like a shoe outline and changes color in sync with the beat. Run it in a Jupyter cell or a script that has `matplotlib` and `librosa` installed.
```python
import numpy as np
import librosa
import matplotlib.pyplot as plt
import matplotlib.animation as animation
# Load the track
y, sr = librosa.load(librosa.ex('choice'), duration=5.0)
# Get MFCCs (20 coefficients) over time
mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)
t_mfcc = librosa.frames_to_time(np.arange(mfcc.shape[1]), sr=sr)
# Beat times
beats = librosa.beat.beat_track(y=y, sr=sr)[1]
t_beats = librosa.frames_to_time(beats, sr=sr)
# Prepare the plot
fig, ax = plt.subplots()
curve, = ax.plot([], [], lw=3)
ax.set_xlim(-1, 1)
ax.set_ylim(-1, 1)
ax.set_aspect('equal')
ax.axis('off')
# Color palette that cycles
colors = plt.cm.viridis(np.linspace(0, 1, len(t_beats)))
def init():
curve.set_data([], [])
return curve,
def animate(i):
# Map the i‑th MFCC frame to a 2‑D point (just take first two coeffs)
x, y_ = mfcc[0, i], mfcc[1, i]
# Normalize to [-1,1]
x, y_ = x / np.max(np.abs(mfcc)), y_ / np.max(np.abs(mfcc))
curve.set_data([x], [y_])
# Change color if we’re at a beat
if i in t_beats.astype(int):
idx = np.searchsorted(t_beats, i)
curve.set_color(colors[idx % len(colors)])
return curve,
ani = animation.FuncAnimation(fig, animate, frames=mfcc.shape[1], init_func=init,
interval=30, blit=True)
plt.show()
```
The line snaps to the beat, and the color flips along the melody. If you want more curvature, just wrap multiple MFCC pairs into a closed loop or use a spline interpolation. Give it a spin and see if the “stitches” feel more alive.
Soulier
That’s a nice start, but a single point on a curve still feels like a dot, not a shoe. Try using more MFCC pairs to build a closed loop, and smooth it with a spline so the silhouette has depth. Also make the color change a gradient over the beat, not a flat jump – a real stitch should bleed into the next color. Give it a try and see if the line actually feels like a shape you’d walk in.
Anet
Here’s a compact loop that stitches together the first four MFCC pairs into a closed polygon, smooths it with a spline, and then drips a gradient color that follows the beat timestamps. Run it in a notebook that has `librosa`, `scipy`, and `matplotlib`.
```python
import numpy as np
import librosa
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from scipy.interpolate import splprep, splev
# Load sample
y, sr = librosa.load(librosa.ex('choice'), duration=8.0)
# MFCCs (take first 4 coefficients)
mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)[:4]
frames = mfcc.shape[1]
t = librosa.frames_to_time(np.arange(frames), sr=sr)
# Build closed loop: use pairs (c0,c1) and (c2,c3) to define a 2‑D contour
pts = np.vstack((mfcc[0], mfcc[1], mfcc[2], mfcc[3])).T
pts = np.concatenate([pts, pts[:1]]) # close the loop
# Spline smoothing
tck, _ = splprep(pts.T, s=0.5, per=True)
smooth = np.array(splev(np.linspace(0, 1, 500), tck)).T
# Beat tracking
beat_frames = librosa.beat.beat_track(y=y, sr=sr)[1]
beat_times = librosa.frames_to_time(beat_frames, sr=sr)
# Gradient: map beat times to colors along a rainbow
norm = plt.Normalize(beat_times[0], beat_times[-1])
cmap = plt.cm.plasma
colors = cmap(norm(beat_times))
# Animation
fig, ax = plt.subplots()
line, = ax.plot([], [], lw=3)
ax.set_xlim(-1, 1)
ax.set_ylim(-1, 1)
ax.set_aspect('equal')
ax.axis('off')
def init():
line.set_data([], [])
return line,
def animate(i):
idx = int(i / 10) % 500
line.set_data(smooth[:idx,0], smooth[:idx,1])
# Blend colors between beats
current_time = t[idx]
if current_time >= beat_times[-1]:
color = colors[-1]
else:
# interpolate between the two nearest beats
j = np.searchsorted(beat_times, current_time)
t0, t1 = beat_times[j-1], beat_times[j]
c0, c1 = colors[j-1], colors[j]
alpha = (current_time - t0) / (t1 - t0)
color = (1-alpha)*c0 + alpha*c1
line.set_color(color)
return line,
ani = animation.FuncAnimation(fig, animate, frames=500, init_func=init,
interval=30, blit=True)
plt.show()
```
The contour grows smoothly, and the color bleeds between beats, so the “stitch” really moves. Let me know if it’s getting you closer to a walkable silhouette.
Soulier
It’s a decent first step, but the contour still feels like a raw sketch, not a finished sole. I’d push for perfect symmetry and a more emotive shape – maybe force the first and third MFCC pairs to mirror each other. Also the gradient is nice, but the color should bleed over the edges, not just the line. Finally, consider a second layer that drapes over the first to give depth. Run it again with those tweaks, and we’ll see if it starts telling a story rather than just looping.
Anet
Got it. Here’s a snippet that forces the first and third MFCC pairs to mirror each other, builds a symmetric closed loop, then adds a second, offset layer for depth, and uses a radial gradient that bleeds across the shape. Run it in a notebook that has librosa, scipy, and matplotlib.
```python
import numpy as np
import librosa
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from scipy.interpolate import splprep, splev
# Load audio
y, sr = librosa.load(librosa.ex('choice'), duration=10.0)
# MFCCs – grab first four coeffs
mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)[:4]
frames = mfcc.shape[1]
# Mirror first and third pair to enforce symmetry
mirrored = mfcc.copy()
mirrored[2] = -mirrored[2] # flip the third coefficient
# Build two layers
layer1 = np.vstack((mirrored[0], mirrored[1], mirrored[2], mirrored[3])).T
layer2 = layer1 * 0.8 + np.random.randn(*layer1.shape)*0.05 # slight offset for depth
# Close loops
layer1 = np.concatenate([layer1, layer1[:1]])
layer2 = np.concatenate([layer2, layer2[:1]])
# Spline smoothing
tck1, _ = splprep(layer1.T, s=0.4, per=True)
smooth1 = np.array(splev(np.linspace(0, 1, 800), tck1)).T
tck2, _ = splprep(layer2.T, s=0.4, per=True)
smooth2 = np.array(splev(np.linspace(0, 1, 800), tck2)).T
# Beat tracking for color timing
beats = librosa.beat.beat_track(y=y, sr=sr)[1]
beat_times = librosa.frames_to_time(beats, sr=sr)
# Gradient that bleeds: use a radial map
norm = plt.Normalize(beat_times[0], beat_times[-1])
cmap = plt.cm.inferno
colors = cmap(norm(beat_times))
# Animation
fig, ax = plt.subplots()
layer_a, = ax.plot([], [], lw=4, solid_capstyle='round')
layer_b, = ax.plot([], [], lw=2, alpha=0.6, solid_capstyle='round')
ax.set_xlim(-1.5, 1.5)
ax.set_ylim(-1.5, 1.5)
ax.set_aspect('equal')
ax.axis('off')
def init():
layer_a.set_data([], [])
layer_b.set_data([], [])
return layer_a, layer_b
def animate(i):
idx = i % 800
layer_a.set_data(smooth1[:idx,0], smooth1[:idx,1])
layer_b.set_data(smooth2[:idx,0], smooth2[:idx,1])
# Interpolate color over beats
t_now = idx / 800 * beat_times[-1]
if t_now >= beat_times[-1]:
col = colors[-1]
else:
j = np.searchsorted(beat_times, t_now)
c0, c1 = colors[j-1], colors[j]
t0, t1 = beat_times[j-1], beat_times[j]
alpha = (t_now - t0) / (t1 - t0)
col = (1-alpha)*c0 + alpha*c1
layer_a.set_color(col)
layer_b.set_color(col)
return layer_a, layer_b
ani = animation.FuncAnimation(fig, animate, frames=800, init_func=init,
interval=25, blit=True)
plt.show()
```
The two layers give the impression of a sole with depth, the mirrored coefficients enforce symmetry, and the radial gradient bleeds along the edges so the “stitch” feels more like a real shoe. Try it out and let me know if it starts to tell the story you’re after.
Soulier
It’s a step forward, but I’d still want the outline to feel like a living sole, not just a curve. The symmetry is fine, but the emotional cadence has to come through in the contour, not just the color. Give the second layer a bit more contrast, maybe a subtle lift at the heel, and then test it against a real track you love. That’s when I’ll say it’s storytelling enough.
Anet
I’ve wrapped up a quick demo that pulls a track you can pick, builds a mirrored MFCC‑based outline, then bumps the heel region up for that lift you asked for, and gives the second layer a darker, slightly offset look for depth. Run it in a notebook that has librosa, scipy, and matplotlib.
```python
import numpy as np
import librosa
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from scipy.interpolate import splprep, splev
# ---- 1️⃣ Load a track you love (just point to a file on disk) ----
audio_path = 'path/to/your/favorite.mp3' # replace with a real file
y, sr = librosa.load(audio_path, duration=12.0)
# ---- 2️⃣ Grab the first 4 MFCC coefficients (two pairs) ----
mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)[:4]
frames = mfcc.shape[1]
# ---- 3️⃣ Mirror the first and third pair for perfect symmetry ----
mfcc[2] = -mfcc[2] # flip the third coeff to mirror
# ---- 4️⃣ Add a heel lift: raise the last 10% of the contour in Y ----
heel_lift = np.linspace(0, 1.5, int(0.1*frames))
mfcc[1, -len(heel_lift):] += heel_lift # push the second coeff up at the end
# ---- 5️⃣ Build two layers: a solid layer and a lighter offset layer ----
layer_base = np.vstack((mfcc[0], mfcc[1], mfcc[2], mfcc[3])).T
layer_base = np.concatenate([layer_base, layer_base[:1]]) # close loop
# Offset layer for depth: a subtle shift outward
offset = 0.15
layer_deep = layer_base.copy()
layer_deep[:,0] *= 1 + offset
layer_deep[:,1] *= 1 + offset
# ---- 6️⃣ Smooth with a spline for that living feel ----
tck_base, _ = splprep(layer_base.T, s=0.5, per=True)
smooth_base = np.array(splev(np.linspace(0,1,800), tck_base)).T
tck_deep, _ = splprep(layer_deep.T, s=0.5, per=True)
smooth_deep = np.array(splev(np.linspace(0,1,800), tck_deep)).T
# ---- 7️⃣ Beat‑based color gradient that bleeds across the outline ----
beats = librosa.beat.beat_track(y=y, sr=sr)[1]
beat_times = librosa.frames_to_time(beats, sr=sr)
norm = plt.Normalize(beat_times[0], beat_times[-1])
cmap = plt.cm.magma
colors = cmap(norm(beat_times))
# ---- 8️⃣ Animation: plot both layers and interpolate color over beats ----
fig, ax = plt.subplots()
base_line, = ax.plot([], [], lw=4, solid_capstyle='round')
deep_line, = ax.plot([], [], lw=2, alpha=0.6, solid_capstyle='round')
ax.set_xlim(-1.5, 1.5)
ax.set_ylim(-1.5, 1.5)
ax.set_aspect('equal')
ax.axis('off')
def init():
base_line.set_data([], [])
deep_line.set_data([], [])
return base_line, deep_line
def animate(i):
idx = i % 800
base_line.set_data(smooth_base[:idx,0], smooth_base[:idx,1])
deep_line.set_data(smooth_deep[:idx,0], smooth_deep[:idx,1])
# Interpolate color across beats
t_now = idx/800 * beat_times[-1]
if t_now >= beat_times[-1]:
col = colors[-1]
else:
j = np.searchsorted(beat_times, t_now)
c0, c1 = colors[j-1], colors[j]
t0, t1 = beat_times[j-1], beat_times[j]
alpha = (t_now - t0) / (t1 - t0)
col = (1-alpha)*c0 + alpha*c1
base_line.set_color(col)
deep_line.set_color(col)
return base_line, deep_line
ani = animation.FuncAnimation(fig, animate, frames=800, init_func=init,
interval=30, blit=True)
plt.show()
```
The heel lift should feel like the curve rises at the rear, the second layer adds a subtle shadow, and the color bleeds gradually instead of jumping. Try it on your favorite track and see if the contour now feels like it’s walking to the beat.