def highFrequencyConv(img, tmpl):
Q = metrics.slidingWindowH(img,inner=7,maxM=50,norm=True)[20:32, :]
mf = cv2.matchTemplate(Q.astype('Float32'), tmpl, cv2.TM_CCOEFF_NORMED)
print mf.shape
return mf
import metrics
import fileio
import numpy
from matplotlib import mlab, pyplot as plt
import pandas
df = pandas.DataFrame(
numpy.zeros(30000 * 5901).reshape(30000, 5901), columns=(["label"] + map(lambda a: str(a), range(100 * 59)))
)
labels = pandas.read_csv("../data/train.csv")
for i in range(30000):
img = fileio.ReadAIFF("../data/train/train{}.aiff".format(i + 1))
P, freqs, bins = mlab.specgram(img, NFFT=256, Fs=2000, noverlap=192)
Q = metrics.slidingWindowV(P, inner=3, maxM=40)
W = metrics.slidingWindowH(P, inner=3, outer=32, maxM=60)
s = numpy.vstack((Q, W))
df.ix[i] = numpy.append(labels["label"][i], s.reshape(100 * 59))
if i % 100 == 0:
print i
df.to_csv("total.csv", index=False)
def slidingWindowH(img):
w = metrics.slidingWindowH(img, inner=3, outer=32, maxM=60)
print w.shape
return w
