def test_yuv_reader(self):
yuv_reader = YuvReader(
filepath=VmafConfig.test_resource_path("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 _generate_result(self, asset):
# routine to 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)) \
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 _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)) as dis_yuv_reader:
scores_mtx_list = []
for dis_yuv in dis_yuv_reader:
dis_y = dis_yuv[0]
list_features = self.mscn_extract_niqe(dis_y, self.patch_size,
self.mode)
scores_mtx_list += list_features
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 test_iteration_manual(self):
y_1stmoments = []
y_2ndmoments = []
with YuvReader(
filepath=VmafConfig.test_resource_path("yuv", "src01_hrc01_576x324.yuv"),
width=576, height=324, yuv_type='yuv420p') as yuv_reader:
while True:
try:
y, u, v = yuv_reader.next()
y, u, v = y.astype(np.double), u.astype(np.double), v.astype(np.double)
y_1stmoments.append(y.mean())
y_2ndmoments.append(y.var() + y.mean() * y.mean())
except StopIteration:
break
self.assertEqual(len(y_1stmoments), 48)
self.assertEqual(len(y_2ndmoments), 48)
self.assertAlmostEqual(float(np.mean(y_1stmoments)), 61.332006624999984, places=4)
self.assertAlmostEqual(float(np.mean(y_2ndmoments)), 4798.659574041666, places=4)
def _open_dis_procfile(self, asset, fifo_mode):
# only need to open dis procfile if the path is different from dis path
assert asset.use_workpath_as_procpath is False and asset.dis_workfile_path != asset.dis_procfile_path
dis_proc_callback = asset.dis_proc_callback if asset.dis_proc_callback is not None else lambda x: x
if fifo_mode:
os.mkfifo(asset.dis_procfile_path)
quality_width, quality_height = self._get_quality_width_height(asset)
yuv_type = asset.workfile_yuv_type
with YuvReader(filepath=asset.dis_workfile_path, width=quality_width, height=quality_height,
yuv_type=yuv_type) as dis_yuv_reader:
with YuvWriter(filepath=asset.dis_procfile_path, width=quality_width, height=quality_height,
yuv_type=yuv_type) as dis_yuv_writer:
while True:
try:
y, u, v = dis_yuv_reader.next(format='float')
y, u, v = dis_proc_callback(y), u, v
dis_yuv_writer.next(y, u, v, format='float2uint')
except StopIteration:
break
def test_next_y_u_v_gray(self):
with YuvReader(filepath=VmafConfig.test_resource_path(
"yuv", "src01_hrc00_576x324.yuv"),
width=576,
height=324,
yuv_type='gray') as yuv_reader:
y, u, v = yuv_reader.next()
y = y.astype(np.double)
self.assertEqual(y[0][0], 87)
self.assertTrue(u is None)
self.assertTrue(v is None)
self.assertAlmostEqual(y.mean(), 61.928749785665296, places=4)
y, u, v = yuv_reader.next()
y = y.astype(np.double)
self.assertEqual(y[0][0], 92)
self.assertTrue(u is None)
self.assertTrue(v is None)
def test_iteration(self):
y_1stmoments = []
y_2ndmoments = []
with YuvReader(filepath=VmafConfig.test_resource_path(
"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.assertEqual(len(y_1stmoments), 48)
self.assertEqual(len(y_2ndmoments), 48)
self.assertAlmostEqual(np.mean(y_1stmoments),
61.332006624999984,
places=4)
self.assertAlmostEqual(np.mean(y_2ndmoments),
4798.659574041666,
places=4)
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.dis_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