From e91b29123a5e249b656c5ed8b0ac4936b38346b7 Mon Sep 17 00:00:00 2001 From: will Date: Wed, 3 Jul 2024 22:46:20 +0000 Subject: [PATCH] added live audio capability --- __pycache__/camera.cpython-312.pyc | Bin 0 -> 4245 bytes __pycache__/fft.cpython-312.pyc | Bin 0 -> 4152 bytes camera.py | 79 +++++++++++ fft.py | 77 ++++++++++ file.py | 69 +++++++++ main.py | 220 ----------------------------- out.wav | Bin 7001044 -> 1125044 bytes rec.wav | Bin 0 -> 950316 bytes stream.py | 102 +++++++++++++ test.py | 26 ---- test.wav | Bin 0 -> 441046 bytes test2.wav | Bin 0 -> 441046 bytes 12 files changed, 327 insertions(+), 246 deletions(-) create mode 100644 __pycache__/camera.cpython-312.pyc create mode 100644 __pycache__/fft.cpython-312.pyc create mode 100644 camera.py create mode 100644 fft.py create mode 100644 file.py delete mode 100644 main.py create mode 100644 rec.wav create mode 100644 stream.py delete mode 100644 test.py create mode 100644 test.wav create mode 100644 test2.wav diff --git 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z)x1F+dpA6{@8+gGt1^ODWsOlmIslqGnGkXE!{SmeS)5a=9#|t66vvoW=A>LOeAC-R zT;sJFqo%F;2-E%u^H4sA1n021-Y!uLkCt=sSj5I*}{rbV14*V6HO~V4n%;j4bkL3tsW~%9oHB zD0&@T8AMU}A}S4_{34vPl7Xjw#8ZQv2jjwo0jQsHL|ecOGlE^!S1W7PVkOrIB3eFL ze!TKEQfjV~qu*L+G&Sn#7x5L* zcn!LgYhT-y-@8^1PLyMZ?VZ(|J4@S3TKmYZ(ippE7@ zxcsL#fB&WppuLseD*KajwKJN3q#Qi}Nd3HKzdcmFQS08mV_YBzxzN6KwsN-mR!wP~ zpV8p!nW_6PHv9{kf1w;b^ask527E5X{P>#B@)>NxaqTU>=ZW`Tz$um4A>cTy)_l;-=W;33RtpfYgG0&h2e>IKQ{6MKoVI-4zK z@*Cy=>BRaaGq10: + src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2) + dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2) + self.homography, mask = cv.findHomography(dst_pts, src_pts, cv.RANSAC, 5.0) + + else: + print("calibration failed") + + def display( + self, + image: np.ndarray + ) -> None: + + image = cv.resize(image, self.display_size, cv.INTER_NEAREST) + cv.imshow("display", image) + cv.waitKey(1) + + def capture( + self + ) -> np.ndarray: + + image = self.camera.read() + if self.homography is not None: + image = cv.warpPerspective(image, self.homography, self.display_size) + image = cv.resize(image, (self.window_size, self.window_height), cv.INTER_NEAREST) + image = match_histograms(image, display, channel_axis=-1) + + return image + + diff --git a/fft.py b/fft.py new file mode 100644 index 0000000..2950929 --- /dev/null +++ b/fft.py @@ -0,0 +1,77 @@ +import numpy as np +import cv2 as cv + +class fft(): + def __init__( + self, + window_size: int, + hop_size: int + ): + self.window_size = window_size + self.hop_size = hop_size + self.window = np.hanning(window_size) + + self.lower_limit = -40 + self.upper_limit = 100 + + self.amplitude_max = 180 + self.amplitude_min = 0 + self.angle_max = 255 + self.angle_min = 100 + + self.amplitude_relative = self.amplitude_max - self.amplitude_min + self.angle_relative = self.angle_max - self.angle_min + + + def stft( + self, + data: np.ndarray + ) -> np.ndarray: + + segment = data * self.window + spectrum = np.fft.fft(segment) / self.window_size + + amplitude = np.abs(spectrum) + amplitude = 20*np.log10(amplitude) + amplitude = np.clip(amplitude, self.lower_limit, self.upper_limit) + amplitude -= self.lower_limit + amplitude *= (self.amplitude_relative / self.upper_limit) + self.amplitude_min + + angle = np.angle(spectrum) + angle = ((angle + np.pi) * (self.angle_relative / (2 * np.pi))) + self.angle_min + + full = np.full(angle.shape, fill_value=255) + + image = np.stack((amplitude, angle, full), axis=-1) + image = np.array([image], dtype=np.uint8) + + image = cv.cvtColor(image, cv.COLOR_HSV2BGR) + + return image + + def istft( + self, + image: np.ndarray + ) -> np.ndarray: + + image = cv.cvtColor(image, cv.COLOR_BGR2HSV) + + amplitude = image[0][...,0].astype(np.float64) + angle = image[0][...,1].astype(np.float64) + + amplitude -= self.amplitude_min + amplitude /= (self.amplitude_relative / self.upper_limit) + amplitude += self.lower_limit + amplitude = np.power(10, amplitude / 20) + + angle = ((angle - self.angle_min) / (self.angle_relative / (2 * np.pi))) - np.pi + + real = np.cos(angle) * amplitude + imag = np.sin(angle) * amplitude + segment = real + (1j * imag) + + data = np.fft.ifft(segment * self.window_size).real + + return data + + diff --git a/file.py b/file.py new file mode 100644 index 0000000..1ca680c --- /dev/null +++ b/file.py @@ -0,0 +1,69 @@ +import cv2 as cv +import numpy as np +from scipy.io import wavfile +import scipy.signal as sps +import matplotlib.pyplot as plt +from multiprocessing import Pool +from camera import camera +from fft import fft + +""" +notes: +- window size + the time to generate the spectrum is logaritmically related to the window size + bigger windows are exponentially better so you should prefer this if possible + obviously the biggest you can use is the size of your display unless you have + some way of arranging the pixles independant of the orrigional spectrogram +""" + +sample_rate, data = wavfile.read("/home/will/Downloads/number-station.wav") +new_rate = 22_050. + +sample_count = round(len(data) * new_rate / sample_rate) +data = sps.resample(data, sample_count) +sample_rate = int(new_rate) + +data = [data[i] for i in range(0, len(data), 2)] +sample_rate = sample_rate // 2 + +window_size = 250 +window_height = 125 + +hop_size = window_size // 2 +camera = camera(window_size, window_height, (1000, 1000)) +transform = fft(window_size, hop_size) + +segment_samples = window_height * hop_size + +padding = np.full((segment_samples - (len(data) % segment_samples) + window_height), fill_value=.1) +data = np.concatenate((data, padding)) + +recovered_data = np.zeros(data.shape) + +segment_count = round(len(data) / segment_samples) + +for segment_index in range(segment_count): + segment_start = segment_index * segment_samples + rows = [data[segment_start + i:segment_start + i + window_size] for i in range(0, segment_samples, hop_size)] + with Pool() as p: + mapping = p.map(transform.stft, rows) + + spectrum = np.array(mapping)[:,0,...] + + camera.display(spectrum) + + rows = [np.array([i]) for i in spectrum] + with Pool() as p: + recovered = np.array(p.map(transform.istft, rows)) + + for i, row in enumerate(recovered): + row_start = i * hop_size + recovered_data[segment_start + row_start:segment_start + row_start + window_size] += row + +wavfile.write("out.wav", sample_rate, recovered_data.astype(np.int16)) + +difference = (data - recovered_data)[1000:251000] + + + + diff --git a/main.py b/main.py deleted file mode 100644 index 229a797..0000000 --- a/main.py +++ /dev/null @@ -1,220 +0,0 @@ -import cv2 as cv -import numpy as np -from scipy.io import wavfile -import scipy.signal as sps -import matplotlib.pyplot as plt -from multiprocessing import Pool - -""" -notes: -- window size - the time to generate the spectrum is logaritmically related to the window size - bigger windows are exponentially better so you should prefer this if possible - obviously the biggest you can use is the size of your display unless you have - some way of arranging the pixles independant of the orrigional spectrogram -""" - -class camera(): - def __init__( - self, - window_size: int, - window_height: int, - device_id: int = 0 - ): - - self.window_size = window_size - self.window_height = window_height - - self.camera = cv.VideoCapture(device_id) - self.homography = None - - cv.namedWindow("display", cv.WINDOW_NORMAL) - - def calibrate( - self - ): - calibration_image = cv.imread("calibration/calibration.jpg") - calibration_image = cv.resize(calibration_image, (self.window_size, self.window_height), cv.INTER_NEAREST) - - cv.imshow("display", calibration_image) - cv.waitKey(0) - _, capture = camera.read() - - # detect SIFT keypoints - sift = cv.SIFT_create() - kp1, des1 = sift.detectAndCompute(calibration_image, None) - kp2, des2 = sift.detectAndCompute(capture, None) - - # get good matches between calibration image and the captured image - flann = cv.FlannBasedMatcher( - {"algorithm": 1, "trees": 5}, - {"checks": 50} - ) - matches = flann.knnMatch(des1, des2, k=2) - - #get good matches via ratio test - good = [] - for m,n in matches: - if m.distance < 0.7*n.distance: - good.append(m) - - if len(good)>10: - src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2) - dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2) - - self.homography, mask = cv.findHomography(dst_pts, src_pts, cv.RANSAC, 5.0) - - else: - print("calibration failed") - - def display( - self, - image: np.ndarray - ) -> None: - - cv.imshow("display", image) - cv.waitKey(1) - - def capture( - self - ) -> np.ndarray: - - image = self.camera.read() - if self.homography is not None: - image = cv.warpPerspective(image, self.homography, (self.display_size, self.display_height)) - image = match_histograms(image, display, channel_axis=-1) - - return image - -class fft(): - def __init__( - self, - window_size: int, - hop_size: int - ): - self.window_size = window_size - self.hop_size = hop_size - self.window = np.hanning(window_size) - - self.lower_limit = -40 - self.upper_limit = 100 - - self.amplitude_max = 180 - self.amplitude_min = 0 - self.angle_max = 255 - self.angle_min = 100 - - self.amplitude_relative = self.amplitude_max - self.amplitude_min - self.angle_relative = self.angle_max - self.angle_min - - - def stft( - self, - data: np.ndarray - ) -> np.ndarray: - - segment = data * self.window - spectrum = np.fft.fft(segment) / self.window_size - - amplitude = np.abs(spectrum) - amplitude = 20*np.log10(amplitude) - amplitude = np.clip(amplitude, self.lower_limit, self.upper_limit) - amplitude -= self.lower_limit - amplitude *= (self.amplitude_relative / self.upper_limit) + self.amplitude_min - - angle = np.angle(spectrum) - angle = ((angle + np.pi) * (self.angle_relative / (2 * np.pi))) + self.angle_min - - full = np.full(angle.shape, fill_value=255) - - image = np.stack((amplitude, angle, full), axis=-1) - image = np.array([image], dtype=np.uint8) - - image = cv.cvtColor(image, cv.COLOR_HSV2BGR) - - return image - - def istft( - self, - image: np.ndarray - ) -> np.ndarray: - - image = cv.cvtColor(image, cv.COLOR_BGR2HSV) - - amplitude = image[0][...,0].astype(np.float64) - angle = image[0][...,1].astype(np.float64) - - amplitude -= self.amplitude_min - amplitude /= (self.amplitude_relative / self.upper_limit) - amplitude += self.lower_limit - amplitude = np.power(10, amplitude / 20) - - angle = ((angle - self.angle_min) / (self.angle_relative / (2 * np.pi))) - np.pi - - real = np.cos(angle) * amplitude - imag = np.sin(angle) * amplitude - segment = real + (1j * imag) - - data = np.fft.ifft(segment * self.window_size).real - - return data - -sample_rate, data = wavfile.read("/home/will/Music/George Michael - Careless Whisper.wav") -new_rate = 22_050. - -sample_count = round(len(data) * new_rate / sample_rate) -data = sps.resample(data, sample_count) -sample_rate = int(new_rate) - -data = [data[i] for i in range(0, len(data), 2)] -sample_rate = sample_rate // 2 - -window_size = 1_000 -window_height = 500 - -hop_size = window_size // 2 -camera = camera(window_size, window_height) -transform = fft(window_size, hop_size) - -segment_samples = window_height * hop_size - -padding = np.full((segment_samples - (len(data) % segment_samples) + window_height), fill_value=.1) -data = np.concatenate((data, padding)) - -recovered_data = np.zeros(data.shape) - -segment_count = round(len(data) / segment_samples) - -for segment_index in range(segment_count): - segment_start = segment_index * segment_samples - rows = [data[segment_start + i:segment_start + i + window_size] for i in range(0, segment_samples, hop_size)] - with Pool() as p: - mapping = p.map(transform.stft, rows) - - spectrum = np.array(mapping)[:,0,...] - - # do the silly capture thing here - cv.imshow("display", spectrum) - cv.waitKey(1) - - rows = [np.array([i]) for i in spectrum] - with Pool() as p: - recovered = np.array(p.map(transform.istft, rows)) - - for i, row in enumerate(recovered): - row_start = i * hop_size - recovered_data[segment_start + row_start:segment_start + row_start + window_size] += row - -wavfile.write("out.wav", sample_rate, recovered_data.astype(np.int16)) - -difference = (data - recovered_data)[1000:251000] - -plt.style.use('dark_background') -fig, (ax1, ax2) = plt.subplots(nrows=2) -ax1.plot(difference) -Pxx, freqs, bins, im = ax2.specgram(difference, NFFT=1014, Fs=1/0.0005) -plt.show() - - - - diff --git a/out.wav b/out.wav index baf075e524d1e50868d1215e43557b5c46fb1ae6..b56a8b7086bf34b6a0577d41de3f509773bb7721 100644 GIT binary patch literal 1125044 zcmYhk3H(>X_xOKbyM3qZqR?U~WZz|9vZO+!ltL@ncS^Q4D($H+bhs!Mg58f6^$wmsyL*gam8U3 zM^qeHadgFT6(?4lQgK?vnHA?$oL_NKMT?5o6>Tc6uINzFxuR=D&x%_r?yR`4qEE%6 z6$2`URE($?S24Nbxr&(;uUEWX@j=CB6$>jCS1heqS+TleeMPyVP`P#G&Xs#q)~h_I z@`%dgE1OlGU)i$qs>)84Jt}Xne4z62$|03wDxayGSvkA%gUWf8-&HQJ{JXNUGGDcA z)oxXFs}89;w(9h%3#-~xb*j3l>Yl1ctAi4P*RfUZ^Y}|We 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