From ab4c787b4fcf84b247cbf98a6626af86c8443494 Mon Sep 17 00:00:00 2001 From: will Date: Sat, 7 Sep 2024 14:53:14 +0100 Subject: [PATCH] Added .venv - removed unused loop.py dependencies - removed unused files + modified .gitignore to not include the calibration image + split loop.py into multiple functions + camera.py debugging tools + fixed persitant issue with scaling in fft.py + Keyboard exeption in multithreaded code + added main loop function to loop.py + added silence error correction in loop.py --- .gitignore | 2 + camera.py | 38 ++++++-- fft.py | 98 +++++++++---------- loop.py | 282 +++++++++++++++++++++++++++++++++++------------------ mum.wav | Bin 6375044 -> 0 bytes 5 files changed, 268 insertions(+), 152 deletions(-) mode change 100644 => 100755 camera.py mode change 100644 => 100755 loop.py delete mode 100644 mum.wav diff --git a/.gitignore b/.gitignore index 0d86721..7d6f707 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,6 @@ *.npy *.jpg +!calibration.jpg *.wav __pycache__/ +.venv/ diff --git a/camera.py b/camera.py old mode 100644 new mode 100755 index 4dc634f..d1684d0 --- a/camera.py +++ b/camera.py @@ -1,10 +1,10 @@ +#!./.venv/bin/python + import cv2 as cv import numpy as np import queue -import time import threading -import random -import matplotlib.pyplot as plt +import cProfile class VideoCapture: def __init__(self, device_id, backend): @@ -45,7 +45,7 @@ class camera(): window_height: int, display_size: tuple, device_id: int = 0, - debug: bool = True, + debug: bool = False, dummy: bool = False ): @@ -69,8 +69,8 @@ class camera(): self.last_recovered = None cv.namedWindow("display", cv.WINDOW_NORMAL + cv.WINDOW_GUI_NORMAL) - if self.show_debug == True: - cv.namedWindow("debug", cv.WINDOW_NORMAL) + if debug: + cv.namedWindow("debug", cv.WINDOW_NORMAL + cv.WINDOW_GUI_NORMAL) def calibrate( self @@ -114,14 +114,18 @@ class camera(): def display( self, - image: np.ndarray + image: np.ndarray, + debug: bool = False ) -> None: self.last_display = image + image = cv.cvtColor(image, cv.COLOR_HSV2BGR) image = cv.resize(image, self.display_size, interpolation=cv.INTER_NEAREST_EXACT) - image[...,0] = np.round(image[...,0] * 0.9) - cv.imshow("display", image) + if debug: + cv.imshow("debug", image) + else: + cv.imshow("display", image) cv.waitKey(1) def capture( @@ -139,6 +143,8 @@ class camera(): image = cv.warpPerspective(image, self.homography, self.display_size) image = cv.resize(image, (self.window_size, self.window_height)) + image = cv.cvtColor(image, cv.COLOR_BGR2HSV) + self.last_recovered = image if self.show_debug == True: @@ -164,3 +170,17 @@ class camera(): cv.imshow("debug", debug_image) cv.waitKey(1) +if __name__ == "__main__": + camera = camera( + 160, + 90, + (1920, 1080), + device_id = 0, + debug = False, + dummy = False + ) + #camera.calibrate() + + cProfile.run("camera.display(np.zeros((90, 160, 3), dtype=np.uint8))") + cProfile.run("camera.capture()") + diff --git a/fft.py b/fft.py index c45456b..fc14e25 100644 --- a/fft.py +++ b/fft.py @@ -1,6 +1,7 @@ +#!./.venv/bin/python + import numpy as np import cv2 as cv -import matplotlib.pyplot as plt """ Notes: @@ -19,20 +20,21 @@ class fft(): def __init__( self, window_size: int, - hop_size: int ): # calculate the window and hop size, use to calulate the cosine window self.window_size = window_size - self.hop_size = hop_size + self.hop_size = window_size // 2 self.window = np.hanning(window_size) + self.placeholder = np.full((window_size,), fill_value=60) # set the max and min numerical values for amplitude and angle to allow for easier combinations of them both self.amplitude_max = 254 self.amplitude_min = 0 + self.angle_max = 254 self.angle_min = 0 - # set the upper and lower limits (in dB) that are to be displayed on the screen + self.volume_max = 100 self.volume_min = -40 @@ -42,11 +44,12 @@ class fft(): self.volume_relative = self.volume_max - self.volume_min # generate lookup table for the converstion from decibels to power - a = self.volume_min - b = self.volume_max / self.amplitude_relative + self.a = a = self.volume_min + self.b = b = self.volume_relative / self.amplitude_relative + self.c = c = self.amplitude_min - # this is the parameterized inverted function of y = (20 * log10(x) - 40) * (255/140) - log_lookup = [10 ** (((x * b) + a) / 20) for x in range(0, 256)] + # this is the parameterized inverted function of (e.g.) y = (20 * log10(x) - 40) * (255/140) + log_lookup = [10 ** ((((x - c) * b) + a) / 20) for x in range(0, 256)] self.log_lookup = np.array(log_lookup) def stft( @@ -54,61 +57,58 @@ class fft(): data: np.ndarray ) -> np.ndarray: - # apply window and perform the fft - segment = data * self.window - spectrum = np.fft.fft(segment) / self.window_size - - # convert the vector length to decimals and confine - amplitude = np.abs(spectrum) - - amplitude = 20*np.log10(amplitude) - amplitude = np.clip(amplitude, self.volume_min, self.volume_max) + try: + # apply window and perform the fft + segment = data * self.window + spectrum = np.fft.fft(segment) / self.window_size - # confine the amplitude within the limits specified - a = self.volume_min - b = self.amplitude_relative / self.volume_relative # possibly change the vol_max to vol_rel ?? see [2] - c = self.amplitude_min - amplitude = ((amplitude - a) * b) + c + # convert the vector length to decimals and confine + amplitude = np.abs(spectrum) + amplitude = 20*np.log10(amplitude) + amplitude = np.clip(amplitude, self.volume_min, self.volume_max) + amplitude = ((amplitude - self.a) / self.b) + self.c - # convert x and y to the angle and confine - angle = np.angle(spectrum) + # convert x and y to the angle and confine + angle = np.angle(spectrum) + angle = ((angle + np.pi) * (self.angle_relative / (2 * np.pi))) + self.angle_min + angle = np.clip(angle, self.angle_min, self.angle_max) - # confine the angle within the limits specified - angle = ((angle + np.pi) * (self.angle_relative / (2 * np.pi))) + self.angle_min - angle = np.clip(angle, self.angle_min, self.angle_max) + # rearrange to image format + image = np.stack((amplitude * (180/255), angle, amplitude), axis=-1) + image = np.array([image], dtype=np.uint8) - # rearrange to image format - full = np.full(angle.shape, fill_value=60) - image = np.stack((amplitude * (180/255), angle, amplitude), axis=-1) - image = np.array([image], dtype=np.uint8) - image = cv.cvtColor(image, cv.COLOR_HSV2BGR) + return image - return image + except KeyboardInterrupt: + return None def istft( self, image: np.ndarray ) -> np.ndarray: - # split the image into constituant parts - image = cv.cvtColor(image, cv.COLOR_BGR2HSV) - amplitude = image[0][...,2] - angle = image[0][...,1].astype(np.float64) - #hue = image[0][...,0].astype(np.float64) * (255/180) + try: + # split the image into constituant parts + amplitude = image[0][...,2] + angle = image[0][...,1].astype(np.float64) - #amplitude = np.mean( np.array([ amplitude, hue ]), axis=0 ).astype(np.uint8) + # Use hue as seperate data point + #hue = image[0][...,0].astype(np.float64) * (255/180) + #amplitude = np.mean( np.array([ amplitude, hue ]), axis=0 ).astype(np.uint8) - # convert amplitude back into vector length - amplitude = self.log_lookup[amplitude] + # convert amplitude back into vector length + amplitude = self.log_lookup[amplitude] - # convert angle back into x and y - angle = ((angle - self.angle_min) / (self.angle_relative / (2 * np.pi))) - np.pi + # convert angle back into x and y + angle = ((angle - self.angle_min) / (self.angle_relative / (2 * np.pi))) - np.pi - # rearrange back into fft result - real = np.cos(angle) * amplitude - imag = np.sin(angle) * amplitude - segment = real + (1j * imag) + # rearrange back into fft result + real = np.cos(angle) * amplitude + imag = np.sin(angle) * amplitude + segment = real + (1j * imag) + data = np.fft.ifft(segment * self.window_size).real - data = np.fft.ifft(segment * self.window_size).real + return data.astype(np.int16) - return data.astype(np.int16) + except KeyboardInterrupt: + return None diff --git a/loop.py b/loop.py old mode 100644 new mode 100755 index b59d7f6..e0c534a --- a/loop.py +++ b/loop.py @@ -1,148 +1,242 @@ +#!./.venv/bin/python + 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 struct import pack from fft import fft import time import pyaudio import os import sys import wave -import matplotlib.pyplot as plt -import gi -from gi.repository import Gtk +from tqdm import tqdm -""" -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 -""" +def normalize(array: np.ndarray): + array = array.astype(np.float64) + array -= np.min(array) + array *= 255 / np.max(array) -sample_rate, data = wavfile.read("/home/will/Downloads/birdsong.wav") -#data = data[...,0] + return array.astype(np.uint8) -new_rate = 10_000. -sample_count = round(len(data) * new_rate / sample_rate) -data = sps.resample(data, sample_count) -sample_rate = int(new_rate) +def get_audio( + uri: str, + window_size: int, + window_height: int, + new_rate: int = 11_050 +) -> np.ndarray: -window_size = 170 -window_height = 80 + sample_rate, data = wavfile.read(uri) -hop_size = window_size // 2 -camera = camera( - window_size, - window_height, - (1920, 1080), - device_id = 2, - debug = True, - dummy = False -) - -file = wave.open("out.wav", "wb") -file.setparams(( - 1, # channels - 2, # sample width - sample_rate, - 0, - "NONE", # compression type - "NONE" # compression name -)) - -camera.calibrate() - -transform = fft(window_size, hop_size) - -segment_samples = window_height * hop_size -overflow_samples = segment_samples - (len(data) % segment_samples) + window_size -data = np.concatenate((data, data[0:overflow_samples])) - -segment_count = round(len(data) / segment_samples) -segment_index = 0 -audio = np.zeros((hop_size,), dtype=np.int16) + # ensure 1 channel + if len(data.shape) > 1: + data = data[...,0] -def callback(in_data, frame_count, time_info, status): + # resample + sample_count = round(len(data) * float(new_rate) / sample_rate) + data = sps.resample(data, sample_count) + + # make divisisible into screens + segment_samples = window_height * (window_height // 2) + overflow_samples = segment_samples - (len(data) % segment_samples) + window_size + data = np.concatenate((data, data[0:overflow_samples])) + + return data +def callback(in_data, frame_count, time_info, status): global audio + global caching + # handle not enough frames being available data = audio[:frame_count] if len(data) < frame_count: data = np.pad(data, [(0, frame_count - len(data))], mode='constant') audio = np.zeros((hop_size,), dtype=np.int16) + if not caching: print("Dropped frames!") + else: audio = np.delete(audio, np.s_[:frame_count]) + #handle buffer minimum + if len(audio) < hop_size: + audio = np.zeros((hop_size,), dtype=np.int16) + print("buffer minimum exceeded ") + return (data, pyaudio.paContinue) -pyaudio_object = pyaudio.PyAudio() -stream = pyaudio_object.open( - format = pyaudio.paInt16, - channels = 1, - rate = sample_rate, - frames_per_buffer = 1024, - output = True, - stream_callback = callback -) - -try: - while stream.is_active(): +def process_loop( + data: np.ndarray, + transform, + camera, + window_size: int, + window_height: int, + loop: bool = True, + correction: bool = False, + correction_array: np.ndarray = None +): + global audio + global caching + + hop_size = window_size // 2 + segment_samples = window_height * hop_size + + segment_count = round(len(data) / segment_samples) + + error_array = np.zeros((5, window_size)) + error_spectrum = np.zeros((window_height, window_size, 3)) + + print("caching data...") + caching = True + + for segment_index in tqdm(range(segment_count)): + # get the specturm of the current sample 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)] + segment_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) + mapping = p.map(transform.stft, segment_rows) spectrum = np.array(mapping)[:,0,...] + if correction_array is not None: + spectrum -= correction_array + spectrum = np.clip(spectrum, 0, 255) + + spectrum = spectrum.astype(np.uint8) - if segment_index == 10: cv.imwrite("sample.jpg", spectrum) + # print sample image + if segment_index == 10: + cv.imwrite("spectrum_sample.jpg", spectrum) + np.save(f"cache/frame{segment_index}.npy", spectrum) + + segment_index = 0 + print("cached!") + caching = False + + while segment_index < segment_count: + + spectrum = np.load(f"cache/frame{segment_index}.npy") + + # display and capture camera.display(spectrum) capture = camera.capture() - # plt.clf() - # plt.plot(rows[0]) - - rows = [np.array([i]) for i in capture] + # get the recovered sample data + recovered_rows = [np.array([i]) for i in capture] with Pool() as p: - recovered = np.array(p.map(transform.istft, rows)) - - if len(audio) < hop_size: - audio = np.zeros((hop_size,), dtype=np.int16) + recovered = np.array(p.map(transform.istft, recovered_rows)) - # plt.plot(recovered[0]) - # plt.pause(0.05) + if correction: + error_array[1:] = error_array[0:-1] + error_array[0] = np.mean(np.array(segment_rows) - recovered, axis=0) + # write the recovered data to the file and soundcard for row in recovered: audio[-hop_size:] += row[:hop_size] audio = np.append(audio, row[hop_size:]) - file.writeframes(row[hop_size:]) - - segment_index += 1 - if segment_index == segment_count: segment_index = 0 + # file.writeframes(row[hop_size:]) - slept = False + # sleep condition time.sleep(0.1) while len(audio) > 1 * segment_samples: - slept = True cv.waitKey(1) - if not slept: - print("Dropped frames!") + # move to next segment + segment_index += 1 + if loop and segment_index == segment_count: + segment_index = 0 + + error_spectrum = transform.stft(np.mean(error_array, axis=0)) + error_spectrum = np.tile(error_spectrum, (window_height, 1, 1)) + error_spectrum[...,2] *= 0 # correction of phase seems to cause stability issues + + return error_spectrum + +""" +notes: +- sample rate + due to nyquist, the maximum possible frequncy that can be recovered is half this + number. Lower can be dramatically better from a speed standpoint. +- 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. +- Window height + This is the height of the image, or, the number of ffts performed in one "batch". + This batching is multithreaded and therefore has some effect on process speed. +""" + +# define parameters +sample_rate = 22_050 +window_size = 150 +window_height = 80 -except KeyboardInterrupt: - stream.stop_stream() - stream.close() - pyaudio_object.terminate() - file.close() +caching = False + +if __name__ == "__main__": + + # get audio data + data = get_audio("/home/will/Downloads/Adducci - Around the Horn.wav", window_size, window_height, sample_rate) + + # setup fft + transform = fft(window_size) + + # setup and calibrate camera + camera = camera( + window_size, + window_height, + (1920, 1080), + device_id = 2, + debug = False, + dummy = True + ) + camera.calibrate() + + # setup output file + file = wave.open("out.wav", "wb") + file.setparams(( + 1, # channels + 2, # sample width + sample_rate, + 0, + "NONE", # compression type + "NONE" # compression name + )) + + # setup stream output + audio = np.zeros((window_size // 2,), dtype=np.int16) + pyaudio_object = pyaudio.PyAudio() + stream = pyaudio_object.open( + format = pyaudio.paInt16, + channels = 1, + rate = sample_rate, + frames_per_buffer = 1024, + output = True, + stream_callback = callback + ) try: - sys.exit() - except SystemExit: - os._exit(130) + print("performing error correction...") + silence = np.full(((10 * window_size * window_height) + window_size,), fill_value=1, dtype=np.int16) + correction_array = process_loop(silence, transform, camera, window_size, window_height, loop = False, correction = True) + + cv.imwrite("error.jpg", correction_array) + cv.imwrite("error_norm.jpg", normalize(correction_array)) + print("error correction complete!") + + process_loop(data, transform, camera, window_size, window_height, loop = True, correction = False, correction_array = correction_array) + + # elegantly handle interupt (intended but not succeded) + except KeyboardInterrupt: + stream.stop_stream() + stream.close() + pyaudio_object.terminate() + file.close() + + try: + sys.exit() + except SystemExit: + os._exit(130) diff --git a/mum.wav b/mum.wav deleted file mode 100644 index 0140a2b6ad9e4d7001936e225f345cf9457620e5..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6375044 zcmYhk1y~i&_xL}%_kySxSYUTx2ex8&VPJQ?P3*+(!tM_2?iNu|#BN0eMa4vHg}b}+ zd(D2n&;R+e&%JkJ=FFKhr)Oqq)u?{`3H5u3cD35p>pOU4p7cTpjU!)MA?ofELKo>p z?;az2w5N>x|Ng}P?_5gj|L$9y{qHcPDGff>|NW|S7l-;b?6YKIQf>}57bTI?1Ov{oX?zpfbamOm&hP$a8^X*VVtvqLspSdqyZlWQyv(lo9Xe`Q$f6`af6q&%-O9Y8JqMj%rYKaD-s&I%h;x$jz^G2c~ zy{bX|yuyc82Z`=tsAwp90;8yNZ_FDI{9+TY^#*z)hq5T;N}t`(?#QJ*`7MJ#+JII95G4oXy~{bJyfP5fn4b3s7|v^+0! z(vRozh1@Hz%BhT$JFraoN=7q=FXagtAx}ZmBl4s?D+kH-@-feD$duwCzu_`Wu9O?( zLODZDk{#rKa)F#9*DxvzWp6o8j+3EW&z2+PEIFRq)nq}LUzV1CEHA!&WEWXOHkEZ` z3E4x&S;b{#nO*wJn$k=9Ntabw2FT`|7m{UVb(vQ-l67SZSwZ%c-GLp%u^R}hXzdfc z`ULzx%T4m2+{x&>cs8GYUIml2aw)%CxqC(Kpce<}nUX3A??uBuvGRbtO)pQ$>-0Dk zJn)qA=j3s@9|`;*b$BzUNGo!SZ_wuqxR~q1@+dN( zgAl%SW3=sW9H+q#_jD?p9a<73HCVGgCqLmm9FP0H)#d^_R>=V0bsTXH`#SYO2 zPVOn1Az9v{4fR`zE@CwNJRBVS#SpO{j9P=`EK2tR*QsKum>@=R)mxkq(`jRwSPk!Q zpiX&kI40_f`JxHOYOzX;M1I=w-BBzM2gEh8Q_QCQ4PrTUx6sZ&`o2yq7Bj&!gzGBc zt?02(tQF&tzs4e1Oh%qU>C++6oTrQF!zQpP4z3%Jx-wwjlAhHR9jMWt@07@EdwQ;9 zHzg92SF`|!{zzv7s8A97>Oj>L!YLBaC|RlD$F(J^qa&22R$8e6I^hOqHP9dFf$|E? 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