def calibrate(): #Take a known sound, take a recording from a microphone, and figure out how to Transform #the recording to the known sound. #Will finish at some time, I guess now I'll just equalize. threads = [] #Array of threads. #Initialize the mic streams for i in range(0,len(micChannels)): t = threading.Thread(target=recordThread, args=(i,)) threads.append(t) #Run the threads in parallel for j in range(0,len(threads)): threads[j].start() #Streams should autoclose after completion. for k in range(0,len(threads)): threads[k].join() #Wait until all threads are finished. equalize() if len(streamData) == 2: streamData[1] = np.asArray(streamData[1]) streamData[1] = np.toList(np.multiply(streamData[1], eqRatio)) streamAnalysis()
def butter_filter(freq, fs=RATE, order=5): nyq = 0.5 * fs low = freq / nyq b, a = butter(order, low, 'lowpass') for i in streamData: i = np.asArray(i) i = lfilter(b, a, i) i = np.tolist(i)
def imageToNumpy(image):
image = np.asArray(image)
userUpload = Image.open(image)
userUpload.show()
w, h = userUpload.size
print(userUpload.size)
width = w
height = h
if w > 1500:
width = 1499
height = h * (1499 / w)
if h > 1500:
height = 1499
width = w * (1499 / h)
userUpload = userUpload.resize((int(width), int(height)))
userUpload.show()
def worldToPixels(world):
'''
TODO
Compute the pixel coordinates given the world coordinates
and an inverse transformation matrix. You have access to the
transformationMatrixInv variable above.
'''
world_y, world_x = world
world_arr = np.asArray([world_x, world_y, 1])
scaled_pixel_arr = np.dot(transformationMatrixInv, world_arr)
scaled_pixel_list = np.toList(scaled_pixel_arr)
w_inverse = scaled_pixel_list[2]
pixel_x = scaled_pixel_list[0] / w_inverse
pixel_y = scaled_pixel_list[1] / w_inverse
pixelCoord = (pixel_y, pixel_x)
return pixelCoord
