def test_yuv_reader(self):
yuv_reader = YuvReader(
filepath=config.ROOT + "/resource/yuv/src01_hrc00_576x324.yuv422p10le.yuv",
width=576,
height=324,
yuv_type='yuv422p10le'
)
self.assertEquals(yuv_reader.num_bytes, 35831808)
self.assertEquals(yuv_reader.num_frms, 48)
self.assertEquals(yuv_reader._get_uv_width_height_multiplier(), (0.5, 1.0))
def test_yuv_reader(self):
yuv_reader = YuvReader(filepath=config.ROOT +
"/resource/yuv/src01_hrc00_576x324.yuv",
width=576,
height=324,
yuv_type='yuv420p')
self.assertEquals(yuv_reader.num_bytes, 13436928)
self.assertEquals(yuv_reader.num_frms, 48)
self.assertEquals(yuv_reader._get_uv_width_height_multiplier(),
(0.5, 0.5))
def _generate_result(self, asset):
# routine to call the command-line executable and generate feature
# scores in the log file.
quality_w, quality_h = asset.quality_width_height
ref_scores_mtx = None
with YuvReader(filepath=asset.ref_workfile_path,
width=quality_w,
height=quality_h,
yuv_type=self._get_workfile_yuv_type(
asset.yuv_type)) as ref_yuv_reader:
scores_mtx_list = []
i = 0
for ref_yuv in ref_yuv_reader:
ref_y = ref_yuv[0]
firstm = ref_y.mean()
secondm = ref_y.var() + firstm**2
scores_mtx_list.append(np.hstack(([firstm], [secondm])))
i += 1
ref_scores_mtx = np.vstack(scores_mtx_list)
dis_scores_mtx = None
with YuvReader(filepath=asset.dis_workfile_path,
width=quality_w,
height=quality_h,
yuv_type=self._get_workfile_yuv_type(
asset.yuv_type)) as dis_yuv_reader:
scores_mtx_list = []
i = 0
for dis_yuv in dis_yuv_reader:
dis_y = dis_yuv[0]
firstm = dis_y.mean()
secondm = dis_y.var() + firstm**2
scores_mtx_list.append(np.hstack(([firstm], [secondm])))
i += 1
dis_scores_mtx = np.vstack(scores_mtx_list)
assert ref_scores_mtx is not None and dis_scores_mtx is not None
log_dict = {
'ref_scores_mtx': ref_scores_mtx.tolist(),
'dis_scores_mtx': dis_scores_mtx.tolist()
}
log_file_path = self._get_log_file_path(asset)
with open(log_file_path, 'wt') as log_file:
log_file.write(str(log_dict))
def test_with(self):
with YuvReader(filepath=config.ROOT +
"/resource/yuv/src01_hrc00_576x324.yuv",
width=576,
height=324,
yuv_type='yuv420p') as yuv_reader:
self.assertEquals(yuv_reader.file.__class__, file)
def _generate_result(self, asset):
quality_w, quality_h = asset.quality_width_height
# count number of frames
dis_ys = []
dis_us = []
dis_vs = []
with YuvReader(filepath=asset.dis_workfile_path,
width=quality_w,
height=quality_h,
yuv_type=self._get_workfile_yuv_type(
asset.yuv_type)) as dis_yuv_reader:
for dis_yuv in dis_yuv_reader:
dis_y, dis_u, dis_v = dis_yuv
dis_ys.append(dis_y)
dis_us.append(dis_u)
dis_vs.append(dis_v)
# Y
if 'y' in self.channels.lower():
h5py_cache_y = self.h5py_file.create_dataset(
str(asset) + '_y',
(len(dis_ys), dis_ys[0].shape[0], dis_ys[0].shape[1]),
dtype='float')
h5py_cache_y.dims[0].label = 'frame'
h5py_cache_y.dims[1].label = 'height'
h5py_cache_y.dims[2].label = 'width'
for idx, dis_y in enumerate(dis_ys):
h5py_cache_y[idx] = dis_y
# U
if 'u' in self.channels.lower():
h5py_cache_u = self.h5py_file.create_dataset(
str(asset) + '_u',
(len(dis_us), dis_us[0].shape[0], dis_us[0].shape[1]),
dtype='float')
h5py_cache_u.dims[0].label = 'frame'
h5py_cache_u.dims[1].label = 'height'
h5py_cache_u.dims[2].label = 'width'
for idx, dis_u in enumerate(dis_us):
h5py_cache_u[idx] = dis_u
# V
if 'v' in self.channels.lower():
h5py_cache_v = self.h5py_file.create_dataset(
str(asset) + '_v',
(len(dis_vs), dis_vs[0].shape[0], dis_vs[0].shape[1]),
dtype='float')
h5py_cache_v.dims[0].label = 'frame'
h5py_cache_v.dims[1].label = 'height'
h5py_cache_v.dims[2].label = 'width'
for idx, dis_v in enumerate(dis_vs):
h5py_cache_v[idx] = dis_v
def test_iteration(self):
y_1stmoments = []
y_2ndmoments = []
with YuvReader(
filepath=config.ROOT + "/resource/yuv/src01_hrc01_576x324.yuv",
width=576, height=324, yuv_type='yuv420p') as yuv_reader:
for y, u, v in yuv_reader:
y_1stmoments.append(y.mean())
y_2ndmoments.append(y.var() + y.mean() * y.mean())
self.assertEquals(len(y_1stmoments), 48)
self.assertEquals(len(y_2ndmoments), 48)
self.assertAlmostEquals(np.mean(y_1stmoments), 61.332006624999984, places=4)
self.assertAlmostEquals(np.mean(y_2ndmoments), 4798.659574041666, places=4)
def test_next_y_u_v(self):
with YuvReader(
filepath=config.ROOT + "/resource/yuv/src01_hrc00_576x324.yuv",
width=576,
height=324,
yuv_type='yuv420p'
) as yuv_reader:
y, u, v = yuv_reader.next_y_u_v()
self.assertEquals(y[0][0], 87)
self.assertEquals(y[0][1], 131)
self.assertEquals(y[1][0], 95)
self.assertEquals(u[0][0], 92)
self.assertEquals(u[0][1], 97)
self.assertEquals(u[1][0], 90)
self.assertEquals(v[0][0], 121)
self.assertEquals(v[0][1], 126)
self.assertEquals(v[1][0], 122)
self.assertAlmostEquals(y.mean(), 61.928749785665296, places=4)
self.assertAlmostEquals(u.mean(), 114.6326517489712, places=4)
self.assertAlmostEquals(v.mean(), 122.05084019204389, places=4)
y, u, v = yuv_reader.next_y_u_v()
self.assertEquals(y[0][0], 142)
self.assertEquals(y[0][1], 128)
self.assertEquals(y[1][0], 134)
self.assertEquals(u[0][0], 93)
self.assertEquals(u[0][1], 102)
self.assertEquals(u[1][0], 91)
self.assertEquals(v[0][0], 128)
self.assertEquals(v[0][1], 126)
self.assertEquals(v[1][0], 124)
self.assertAlmostEquals(y.mean(), 61.265260631001375, places=4)
self.assertAlmostEquals(u.mean(), 114.72515860768175, places=4)
self.assertAlmostEquals(v.mean(), 122.12022033607681, places=4)
def test_with(self):
with YuvReader(
filepath=config.ROOT + "/resource/yuv/src01_hrc00_576x324.yuv422p10le.yuv",
width=576,
height=324,
yuv_type='yuv422p10le'
) as yuv_reader:
y, u, v = yuv_reader.next_y_u_v()
self.assertEquals(y[0][0], 87)
self.assertEquals(y[0][1], 131)
self.assertEquals(y[1][0], 95)
self.assertEquals(u[0][0], 92.25)
self.assertEquals(u[0][1], 97.5)
self.assertEquals(u[1][0], 91.75)
self.assertEquals(v[0][0], 121)
self.assertEquals(v[0][1], 126.25)
self.assertEquals(v[1][0], 121.25)
self.assertAlmostEquals(y.mean(), 61.928749785665296, places=4)
self.assertAlmostEquals(u.mean(), 114.63283661265432, places=4)
self.assertAlmostEquals(v.mean(), 122.05113490226337, places=4)
y, u, v = yuv_reader.next_y_u_v()
self.assertEquals(y[0][0], 142)
self.assertEquals(y[0][1], 128)
self.assertEquals(y[1][0], 134)
self.assertEquals(u[0][0], 93.25)
self.assertEquals(u[0][1], 102.75)
self.assertEquals(u[1][0], 92.75)
self.assertEquals(v[0][0], 128.25)
self.assertEquals(v[0][1], 126.5)
self.assertEquals(v[1][0], 127.25)
self.assertAlmostEquals(y.mean(), 61.265260631001375, places=4)
self.assertAlmostEquals(u.mean(), 114.72527917095336, places=4)
self.assertAlmostEquals(v.mean(), 122.12047217935527, places=4)
def _generate_result(self, asset):
# routine to call the command-line executable and generate feature
# scores in the log file.
quality_w, quality_h = asset.quality_width_height
with YuvReader(filepath=asset.dis_workfile_path, width=quality_w,
height=quality_h,
yuv_type=self._get_workfile_yuv_type(asset.yuv_type)) \
as dis_yuv_reader:
scores_mtx_list = []
i = 0
for dis_yuv in dis_yuv_reader:
dis_y = dis_yuv[0]
firstm = dis_y.mean()
secondm = dis_y.var() + firstm**2
scores_mtx_list.append(np.hstack(([firstm], [secondm])))
i += 1
scores_mtx = np.vstack(scores_mtx_list)
# write scores_mtx to log file
log_file_path = self._get_log_file_path(asset)
with open(log_file_path, "wb") as log_file:
np.save(log_file, scores_mtx)
def plot_explanations(cls, exps, assets=None, ys=None, ys_pred=None):
# asserts
N = cls.assert_explanations(exps, assets, ys, ys_pred)
figs = []
for n in range(N):
weights = exps['feature_weights'][n]
features = exps['features'][n]
normalized = exps['features_normalized'][n]
asset = assets[n] if assets is not None else None
y = ys['label'][n] if ys is not None else None
y_pred = ys_pred[n] if ys_pred is not None else None
img = None
if asset is not None:
w, h = asset.dis_width_height
with YuvReader(filepath=asset.dis_path,
width=w,
height=h,
yuv_type=asset.yuv_type) as yuv_reader:
for yuv in yuv_reader:
img, _, _ = yuv
break
assert img is not None
title = ""
if asset is not None:
title += "{}\n".format(
get_file_name_without_extension(asset.ref_path))
if y is not None:
title += "ground truth: {:.3f}\n".format(y)
if y_pred is not None:
title += "predicted: {:.3f}\n".format(y_pred)
if title != "" and title[-1] == '\n':
title = title[:-1]
assert len(weights) == len(features)
M = len(weights)
fig = plt.figure()
ax_top = plt.subplot(2, 1, 1)
ax_left = plt.subplot(2, 3, 4)
ax_mid = plt.subplot(2, 3, 5, sharey=ax_left)
ax_right = plt.subplot(2, 3, 6, sharey=ax_left)
if img is not None:
ax_top.imshow(img, cmap='Greys_r')
ax_top.get_xaxis().set_visible(False)
ax_top.get_yaxis().set_visible(False)
ax_top.set_title(title)
pos = np.arange(M) + 0.1
ax_left.barh(pos, features, color='b', label='feature')
ax_left.set_xticks(np.arange(0, 1.1, 0.2))
ax_left.set_yticks(pos + 0.35)
ax_left.set_yticklabels(exps['feature_names'])
ax_left.set_title('feature')
ax_mid.barh(pos, normalized, color='g', label='fnormal')
ax_mid.get_yaxis().set_visible(False)
ax_mid.set_title('fnormal')
ax_right.barh(pos, weights, color='r', label='weight')
ax_right.get_yaxis().set_visible(False)
ax_right.set_title('weight')
plt.tight_layout()
figs.append(fig)
return figs