def to_dataframe(self):
"""
Export to pandas dataframe with columns: dataset, content_id, asset_id,
ref_name, dis_name, asset, executor_id, scores_key, scores
Example:
asset asset_id content_id \
0 {"asset_dict": {"height": 1080, "width": 1920}... 0 0
1 {"asset_dict": {"height": 1080, "width": 1920}... 0 0
2 {"asset_dict": {"height": 1080, "width": 1920}... 0 0
3 {"asset_dict": {"height": 1080, "width": 1920}... 0 0
4 {"asset_dict": {"height": 1080, "width": 1920}... 0 0
dataset dis_name executor_id \
0 test checkerboard_1920_1080_10_3_1_0.yuv VMAF_V0.1
1 test checkerboard_1920_1080_10_3_1_0.yuv VMAF_V0.1
2 test checkerboard_1920_1080_10_3_1_0.yuv VMAF_V0.1
3 test checkerboard_1920_1080_10_3_1_0.yuv VMAF_V0.1
4 test checkerboard_1920_1080_10_3_1_0.yuv VMAF_V0.1
ref_name \
0 checkerboard_1920_1080_10_3_0_0.yuv
1 checkerboard_1920_1080_10_3_0_0.yuv
2 checkerboard_1920_1080_10_3_0_0.yuv
3 checkerboard_1920_1080_10_3_0_0.yuv
4 checkerboard_1920_1080_10_3_0_0.yuv
scores scores_key
0 [0.798588, 0.84287, 0.800122] VMAF_adm_scores
1 [12.420815, 12.41775, 12.416308] VMAF_ansnr_scores
2 [0.0, 18.489031, 18.542355] VMAF_motion_scores
3 [42.1117149479, 47.6544689539, 40.6168118533] VMAF_scores
4 [0.156106, 0.156163, 0.156119] VMAF_vif_scores
[5 rows x 9 columns]
:return:
"""
import pandas as pd
asset = self.asset
executor_id = self.executor_id
list_scores_key = self.get_ordered_list_scores_key()
list_scores = map(lambda key: self.result_dict[key], list_scores_key)
rows = []
for scores_key, scores in zip(list_scores_key, list_scores):
row = [asset.dataset,
asset.content_id,
asset.asset_id,
get_file_name_with_extension(asset.ref_path),
get_file_name_with_extension(asset.dis_path),
repr(asset),
executor_id,
scores_key,
scores]
rows.append(row)
# zip rows into a dict, and wrap with df
df = pd.DataFrame(dict(zip(self.DATAFRAME_COLUMNS, zip(*rows))))
return df
def to_dataframe(self):
"""
Export to pandas dataframe with columns: dataset, content_id, asset_id,
ref_name, dis_name, asset, executor_id, scores_key, scores
Example:
asset asset_id content_id \
0 {"asset_dict": {"height": 1080, "width": 1920}... 0 0
1 {"asset_dict": {"height": 1080, "width": 1920}... 0 0
2 {"asset_dict": {"height": 1080, "width": 1920}... 0 0
3 {"asset_dict": {"height": 1080, "width": 1920}... 0 0
4 {"asset_dict": {"height": 1080, "width": 1920}... 0 0
dataset dis_name executor_id \
0 test checkerboard_1920_1080_10_3_1_0.yuv VMAF_V0.1
1 test checkerboard_1920_1080_10_3_1_0.yuv VMAF_V0.1
2 test checkerboard_1920_1080_10_3_1_0.yuv VMAF_V0.1
3 test checkerboard_1920_1080_10_3_1_0.yuv VMAF_V0.1
4 test checkerboard_1920_1080_10_3_1_0.yuv VMAF_V0.1
ref_name \
0 checkerboard_1920_1080_10_3_0_0.yuv
1 checkerboard_1920_1080_10_3_0_0.yuv
2 checkerboard_1920_1080_10_3_0_0.yuv
3 checkerboard_1920_1080_10_3_0_0.yuv
4 checkerboard_1920_1080_10_3_0_0.yuv
scores scores_key
0 [0.798588, 0.84287, 0.800122] VMAF_adm_scores
1 [12.420815, 12.41775, 12.416308] VMAF_ansnr_scores
2 [0.0, 18.489031, 18.542355] VMAF_motion_scores
3 [42.1117149479, 47.6544689539, 40.6168118533] VMAF_scores
4 [0.156106, 0.156163, 0.156119] VMAF_vif_scores
[5 rows x 9 columns]
:return:
"""
import pandas as pd
asset = self.asset
executor_id = self.executor_id
list_scores_key = self.get_ordered_list_scores_key()
list_scores = map(lambda key: self.result_dict[key], list_scores_key)
rows = []
for scores_key, scores in zip(list_scores_key, list_scores):
row = [
asset.dataset, asset.content_id, asset.asset_id,
get_file_name_with_extension(asset.ref_path),
get_file_name_with_extension(asset.dis_path),
repr(asset), executor_id, scores_key, scores
]
rows.append(row)
# zip rows into a dict, and wrap with df
df = pd.DataFrame(dict(zip(self.DATAFRAME_COLUMNS, zip(*rows))))
return df
def to_normalized_dict(self):
"""
Similar to self.__dict__ except for excluding workdir (which is random)
and dir part of ref_path/dis_path.
:return:
"""
d = {}
for key in self.__dict__:
if key == 'workdir':
d[key] = ""
elif key == 'ref_path' or key == 'dis_path':
d[key] = get_file_name_with_extension(self.__dict__[key])
else:
d[key] = self.__dict__[key]
return d
def to_normalized_dict(self):
"""
Similar to self.__dict__ except for excluding workdir (which is random)
and dir part of ref_path/dis_path.
:return:
"""
d = {}
for key in self.__dict__:
if key == 'workdir':
d[key] = ""
elif key == 'ref_path' or key == 'dis_path':
d[key] = get_file_name_with_extension(self.__dict__[key])
else:
d[key] = self.__dict__[key]
return d
