import scipy.ndimage as pyi
import matplotlib.pyplot as plt
import os
base_dir = os.path.dirname(os.path.realpath(__file__))
imgs_dir = os.path.join(base_dir, 'hand')
def read_hands_data():
imgs = np.ndarray((480, 512, 481))
for k in np.arange(481):
img_name = 'hand.seq{}.png'.format(k + 1)
img_dir = os.path.join(imgs_dir, img_name)
imgs[:, :, k] = pyi.imread(img_dir)
return imgs
imgs = read_hands_data()
resize_rate = 16
small_imgs = imgs[::resize_rate, ::resize_rate, :]
feats = (small_imgs.reshape((int(np.size(small_imgs) / 481), 481))).T
vs = get_eigenvalues(feats, NEIGHBOR=30, SAMPLE=20)
#plt.plot(vs[:30].T)
npzfile = np.load('large_data.npz')
X = npzfile['X']
y = npzfile['y']
SSVR = SVR(C=0.6)
SSVR.fit(X, y)
pred_y = SSVR.predict(vs.flatten()[:100])
row, col = [480, 512]
testdata = []
path = './hand/'
for i in range(1, 482):
testdata.append(
misc.imread(path + 'hand.seq' + str(i) + '.png').reshape(row * col))
testdata = np.array(testdata)
mu = testdata.mean(axis=0, keepdims=True)
testdata_ctr = testdata - mu
u, s, v = np.linalg.svd(testdata_ctr)
reduceddata = np.dot(testdata_ctr, v[0:100].T)
test_X = []
data = reduceddata.T
vs = get_eigenvalues(data)
test_X.append(vs)
test_X = np.array(test_X)
pred_y = svr.predict(test_X)
pred_y[np.where(pred_y <= 1)] = 1
pred_y = np.round(pred_y)
with open('ans.csv', 'w') as f:
print('SetId,LogDim', file=f)
for i, d in enumerate(pred_y):
print(f'{i},{np.log(d)}', file=f)
end = time.time()
print('elapsed time = ', end - start)
# X /= X.max(axis=0, keepdims=True)
svr = SVR(C=1)
svr.fit(X, y)
# svr.get_params() to save the parameters
# svr.set_params() to restore the parameters
# predict
#testdata = np.load(sys.argv[1]);
test_X = []
data_dir = "./hand"
for filename in os.listdir(data_dir):
loadfile = Image.open(os.path.join(data_dir, filename), 'r')
loadfile.load()
loadfile = loadfile.resize(
(int(loadfile.size[0] / 8), int(loadfile.size[1] / 8)))
data = np.asarray(loadfile, dtype="float32")
data = np.reshape(data, (1, 64 * 60))
test_X.append(data)
test_X = np.asarray(test_X)
test_X = np.reshape(test_X, (481, 64 * 60))
vs = get_eigenvalues(test_X, 1)
#test_X = np.array(test_X)
pred_y = svr.predict(vs)
pred_y = pred_y.astype(int)
print(pred_y)