def test_get_filtered(self):
shutil.copyfile("./untagged_cow.csv", "init_totag.csv")
json_fn = "init_classes_map.json"
json_config = None
with open(json_fn, "r") as read_file:
json_config = json.load(read_file)
classmap = json_config["classmap"]
ideal_balance_list = []
new_tag_names = []
init_tag_names = []
class_map_dict = {}
for m in classmap:
ideal_balance_list.append(m['balance'])
new_tag_names.append(m['map'])
init_tag_names.append(m['initclass'])
class_map_dict[m['initclass']] = m['map']
ideal_balance = ','.join(ideal_balance_list)
unmapclass_list = json_config["unmapclass"]
default_class = json_config["default_class"]
file_location_totag = Path('.')/"init_totag.csv"
new_tag_names = add_bkg_class_name(new_tag_names)
ideal_class_balance = parse_class_balance_setting(ideal_balance, len(new_tag_names))
rows, _, _ = get_top_rows(file_location_totag, 10, True, False,
init_tag_names, ideal_class_balance,
filter_top,
unmapclass_list, init_tag_names, class_map_dict, default_class)
expected_rows = np.load("init_class_get_rows_min.npy")
self.assertEqual((rows == expected_rows).all(), True)
print("")
def test_get_top_rows_no_folder(self):
# prepare file
shutil.copyfile("./totag_no_folder_source.csv",
str(self.totag_csv_file_loc))
N_ROWS = 3
N_FILES = 3
EXPECTED = [[[
'st1840.png', 'knot', '0.12036637', '0.18497443', '0.7618415',
'0.8283344', '512', '488', '0.986', '0.986'
],
[
'st1840.png', 'knot', '0.7297609', '0.7755673',
'0.62443626', '0.6670296', '512', '488', '0.986',
'0.986'
],
[
'st1840.png', 'defect', '0.76513', '0.9952971',
'0.6075407', '0.6546806', '512', '488', '0.986',
'0.986'
]],
[[
'st1578.png', 'knot', '0.594302', '0.6663906',
'0.35276932', '0.43525606', '512', '488', '0.98448783',
'0.98448783'
]],
[[
'st1026.png', 'knot', '0.2674017', '0.35383838',
'0.39859554', '0.50976944', '512', '488', '0.9884343',
'0.96366304'
],
[
'st1026.png', 'knot', '0.69417506', '0.744075',
'0.34379873', '0.39051458', '512', '488',
'0.97863936', '0.96366304'
],
[
'st1026.png', 'defect', '0.70078284', '0.9907891',
'0.5857268', '0.6470487', '512', '488', '0.96366304',
'0.96366304'
]]]
class_balance = "0.7,0.3,0"
user_folders = False
tag_names = add_bkg_class_name(self.tag_names)
ideal_class_balance = parse_class_balance_setting(
class_balance, len(tag_names))
all_rows, _, _ = get_top_rows(self.totag_csv_file_loc, N_ROWS,
user_folders, self.pick_max, tag_names,
ideal_class_balance)
self.assertEqual(len(all_rows), N_FILES, 'number of rows')
self.assertEqual(all_rows, EXPECTED, 'raw values')
def test_get_top_rows_invalid_class_balance2(self):
# prepare file
shutil.copyfile("./totag_source.csv", str(self.totag_csv_file_loc))
N_ROWS = 3
N_FILES = 3
EXPECTED = [[[
'st1840.png', 'knot', '0.12036637', '0.18497443', '0.7618415',
'0.8283344', '512', '488', 'board_images_png', '0.986', '0.986'
],
[
'st1840.png', 'knot', '0.7297609', '0.7755673',
'0.62443626', '0.6670296', '512', '488',
'board_images_png', '0.986', '0.986'
],
[
'st1840.png', 'defect', '0.76513', '0.9952971',
'0.6075407', '0.6546806', '512', '488',
'board_images_png', '0.986', '0.986'
]],
[[
'st1578.png', 'knot', '0.594302', '0.6663906',
'0.35276932', '0.43525606', '512', '488',
'board_images_png', '0.98448783', '0.98448783'
]],
[[
'st1611.png', 'knot', '0.6326234', '0.7054164',
'0.86741334', '0.96444726', '512', '488',
'board_images_png', '0.99616516', '0.9843567'
],
[
'st1611.png', 'knot', '0.07399843', '0.11282173',
'0.32572043', '0.36819047', '512', '488',
'board_images_png', '0.9843567', '0.9843567'
]]]
class_balance = '0.1, 0.2, 0.3'
tag_names = add_bkg_class_name(self.tag_names)
ideal_class_balance = parse_class_balance_setting(
class_balance, len(tag_names))
all_rows, _, _ = get_top_rows(self.totag_csv_file_loc, N_ROWS,
self.user_folders, self.pick_max,
tag_names, ideal_class_balance)
self.assertEqual(len(all_rows), N_FILES, 'number of rows')
self.assertEqual(all_rows, EXPECTED, 'raw values')
def test_get_top_rows_with_bkg(self):
# prepare file
shutil.copyfile("./totag_source.csv", str(self.totag_csv_file_loc))
N_ROWS = 5
N_FILES = 5
EXPECTED = [[[
'st1840.png', 'knot', '0.12036637', '0.18497443', '0.7618415',
'0.8283344', '512', '488', 'board_images_png', '0.986', '0.986'
],
[
'st1840.png', 'knot', '0.7297609', '0.7755673',
'0.62443626', '0.6670296', '512', '488',
'board_images_png', '0.986', '0.986'
],
[
'st1840.png', 'defect', '0.76513', '0.9952971',
'0.6075407', '0.6546806', '512', '488',
'board_images_png', '0.986', '0.986'
]],
[[
'st1578.png', 'knot', '0.594302', '0.6663906',
'0.35276932', '0.43525606', '512', '488',
'board_images_png', '0.98448783', '0.98448783'
]],
[[
'st1611.png', 'knot', '0.6326234', '0.7054164',
'0.86741334', '0.96444726', '512', '488',
'board_images_png', '0.99616516', '0.9843567'
],
[
'st1611.png', 'knot', '0.07399843', '0.11282173',
'0.32572043', '0.36819047', '512', '488',
'board_images_png', '0.9843567', '0.9843567'
]],
[[
'st1026.png', 'knot', '0.2674017', '0.35383838',
'0.39859554', '0.50976944', '512', '488',
'board_images_png', '0.9884343', '0.96366304'
],
[
'st1026.png', 'knot', '0.69417506', '0.744075',
'0.34379873', '0.39051458', '512', '488',
'board_images_png', '0.97863936', '0.96366304'
],
[
'st1026.png', 'defect', '0.70078284', '0.9907891',
'0.5857268', '0.6470487', '512', '488',
'board_images_png', '0.96366304', '0.96366304'
]],
[[
'st1524.png', 'NULL', '0', '0', '0', '0', '512', '488',
'board_images_png', '0', '0.05'
]]]
class_balance = "0.6, 0.29, 0.11"
tag_names = add_bkg_class_name(self.tag_names)
ideal_class_balance = parse_class_balance_setting(
class_balance, len(tag_names))
all_rows, _, _ = get_top_rows(self.totag_csv_file_loc, N_ROWS,
self.user_folders, self.pick_max,
tag_names, ideal_class_balance)
self.assertEqual(len(all_rows), N_FILES, 'number of rows')
self.assertEqual(all_rows, EXPECTED, 'raw values')
def test_get_top_rows_class_balance_min(self):
# prepare file
shutil.copyfile("./totag_source.csv", str(self.totag_csv_file_loc))
N_ROWS = 3
EXPECTED = [[[
'st1091.png', 'knot', '0.20989896', '0.251748', '0.34986168',
'0.3921352', '512', '488', 'board_images_png', '0.99201256',
'0.70161'
],
[
'st1091.png', 'knot', '0.696119', '0.7461088',
'0.27078417', '0.33086362', '512', '488',
'board_images_png', '0.9827361', '0.70161'
],
[
'st1091.png', 'knot', '0.89531857', '0.93743694',
'0.4605299', '0.5066802', '512', '488',
'board_images_png', '0.9794672', '0.70161'
],
[
'st1091.png', 'defect', '0.7629506', '1.0',
'0.6205898', '0.67307687', '512', '488',
'board_images_png', '0.74762243', '0.70161'
],
[
'st1091.png', 'knot', '0.14214082', '0.247842',
'0.7355515', '0.8967391', '512', '488',
'board_images_png', '0.7072498', '0.70161'
],
[
'st1091.png', 'defect', '0.0', '0.1281265',
'0.55038965', '0.59755194', '512', '488',
'board_images_png', '0.70161', '0.70161'
]],
[[
'st1185.png', 'knot', '0.6978268', '0.7582275',
'0.66821593', '0.7535644', '512', '488',
'board_images_png', '0.97257924', '0.7035888'
],
[
'st1185.png', 'defect', '0.35780182', '0.60781866',
'0.27580062', '0.32093963', '512', '488',
'board_images_png', '0.9720861', '0.7035888'
],
[
'st1185.png', 'knot', '0.5183983', '0.57071316',
'0.84764653', '0.91617334', '512', '488',
'board_images_png', '0.9241496', '0.7035888'
],
[
'st1185.png', 'knot', '0.55567926', '0.5904746',
'0.51832056', '0.5461106', '512', '488',
'board_images_png', '0.7035888', '0.7035888'
]],
[[
'st1192.png', 'knot', '0.39846605', '0.45543727',
'0.36765742', '0.4488806', '512', '488',
'board_images_png', '0.99612194', '0.7127546'
],
[
'st1192.png', 'defect', '0.07790943', '0.44866413',
'0.5975798', '0.640683', '512', '488',
'board_images_png', '0.80447847', '0.7127546'
],
[
'st1192.png', 'defect', '0.47953823', '0.7499259',
'0.5517361', '0.59940904', '512', '488',
'board_images_png', '0.7127546', '0.7127546'
]]]
pick_max = False
class_balance = "0.7,0.3,0"
tag_names = add_bkg_class_name(self.tag_names)
ideal_class_balance = parse_class_balance_setting(
class_balance, len(tag_names))
all_rows, _, _ = get_top_rows(self.totag_csv_file_loc, N_ROWS,
self.user_folders, pick_max, tag_names,
ideal_class_balance)
#self.assertEqual(len(all_rows), N_FILES, 'number of rows')
self.assertEqual(all_rows, EXPECTED, 'raw values')