commit operation

This commit is contained in:
vivien
2025-12-10 16:59:49 +01:00
parent a3397074e1
commit 6a5e149f73
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# Un système UPIC pour un orchestre de gravures

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import cv2
import math
ring = cv2.imread('../circular.png')
size = ring.shape[0]
outer_radius = size // 2
inner_radius = 0
unwrapped = cv2.warpPolar(
ring,
(size, int(size * math.pi)),
(outer_radius, outer_radius),
outer_radius,
flags = 0
)
unwrapped = cv2.rotate(unwrapped, cv2.ROTATE_90_COUNTERCLOCKWISE)
unwrapped = unwrapped[inner_radius:, :]
cv2.imshow("original", ring)
cv2.imshow("Unwrapped", unwrapped)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite("output.png", unwrapped)

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from tqdm import tqdm
from scipy.ndimage import uniform_filter1d
from scipy.io.wavfile import write
import math, cv2, sys, getopt, wave
import numpy as np
def unwrap_img(input_file):
ring_img = cv2.imread(input_file)
size = ring_img.shape[0]
outer_radius = size // 2
inner_radius = 0
unwrapped_img = cv2.warpPolar(
ring_img,
(size, int(size * math.pi)),
(outer_radius, outer_radius),
outer_radius,
flags=0
)
rotated_unwrapped = cv2.rotate(unwrapped_img, cv2.ROTATE_90_COUNTERCLOCKWISE)
cropped_unwrapped = rotated_unwrapped[inner_radius:, :]
inverted_unwrapped = cv2.bitwise_not(cropped_unwrapped)
return inverted_unwrapped
def image_to_audio(input_img, out_wav, duration_seconds, sample_rate, vertical_res, amp_threshold):
max_freq = 10000
min_freq = 50
duration_seconds = float(duration_seconds)
# Downsample image vertically to reduce number of frequencies
input_img = input_img[::vertical_res] # Use one row every 20 pixels
height, width, _ = input_img.shape
num_samples = int(sample_rate * duration_seconds)
freqs = np.logspace(np.log10(min_freq), np.log10(max_freq), height)[::-1]
brightness = np.mean(input_img / 255.0, axis=2)
amplitudes = np.where(brightness >= 0.1, brightness, 0)
samples = np.zeros(num_samples, dtype=np.float32)
chunk_size = 10000
for start in tqdm(range(0, num_samples, chunk_size)):
end = min(start + chunk_size, num_samples)
t = np.linspace(start / sample_rate, end / sample_rate, end - start)
pixel_xs = (t * width / duration_seconds).astype(int)
pixel_xs = np.clip(pixel_xs, 0, width - 1)
amp_per_sample = amplitudes[:, pixel_xs]
amp_per_sample[amp_per_sample < amp_threshold] = 0
amp_per_sample = uniform_filter1d(amp_per_sample, size=7, axis=1) # Smooth
phases = 2 * np.pi * freqs[:, None] * t[None, :]
active = np.count_nonzero(amp_per_sample, axis=0)
chunk = np.sum(amp_per_sample * np.sin(phases), axis=0)
# Normalize chunk by number of active oscillators to avoid "snow"
chunk = np.where(active > 0, chunk / active, 0)
samples[start:end] = chunk
# Final normalization
samples /= np.max(np.abs(samples) + 1e-8)
# Convert to int16 for wav
wav_samples = (samples * 32767).astype(np.int16)
write(out_wav, sample_rate, wav_samples)
print(f"Audio saved to {out_wav}")
if __name__ == '__main__':
input_file = ''
output_file = ''
duration = 10
try:
opts, args = getopt.getopt(sys.argv[1:], "hi:o:d:yRes:thresh:")
except getopt.GetoptError:
print('error: img_to_freq.py -i <input_picture> -o <output_sound> -d <audio_duration>')
for opt, arg in opts:
if opt == '-h':
print('error: img_to_freq.py -i <input_picture> -o <output_sound> -d <audio_duration>')
sys.exit(2)
elif opt == '-i':
input_file = arg
elif opt == '-o':
output_file = arg
elif opt == '-d':
duration = arg
unwrapped_img = unwrap_img(input_file)
cv2.imwrite('output.png', unwrapped_img)
image_to_audio(unwrapped_img, output_file, duration, 44100, 20, 0.1)

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