def test_method_calc_points_no_point_structure(self):
driver_name = 'Kobernulf_Monnur'
position = 1
driver_best_lap = 42.0
race_best_lap = 11.0
mock_driver = MagicMock(spec=Driver)
type(mock_driver).laps_complete = PropertyMock(return_value=6)
type(mock_driver).race_time = PropertyMock(return_value=42.0)
type(mock_driver).stops = PropertyMock(return_value=0)
mock_classification_entry = MagicMock(spec=ClassificationEntry)
type(mock_classification_entry).driver = PropertyMock(
return_value=mock_driver)
type(mock_classification_entry).driver_name = PropertyMock(
return_value=driver_name)
type(mock_classification_entry).position = PropertyMock(
return_value=position)
type(mock_classification_entry).best_lap = PropertyMock(
return_value=driver_best_lap)
mock_starting_grid_entry = MagicMock(spec=StartingGridEntry)
type(mock_starting_grid_entry).driver_index = PropertyMock(
return_value=0)
type(mock_starting_grid_entry).driver_name = PropertyMock(
return_value=driver_name)
type(mock_starting_grid_entry).position = PropertyMock(return_value=2)
instance = RaceResultsWithChange([mock_classification_entry],
[mock_starting_grid_entry])
expected_result = '0'
self.assertEqual(
instance.calc_points((driver_name, position, race_best_lap)),
expected_result)
def test_method_calc_points_best_lap(self):
driver_name = 'Kobernulf_Monnur'
position = 1
best_lap = 42.0
mock_driver = MagicMock(spec=Driver)
type(mock_driver).laps_complete = PropertyMock(return_value=6)
type(mock_driver).race_time = PropertyMock(return_value=42.0)
type(mock_driver).stops = PropertyMock(return_value=0)
mock_classification_entry = MagicMock(spec=ClassificationEntry)
type(mock_classification_entry).driver = PropertyMock(
return_value=mock_driver)
type(mock_classification_entry).driver_name = PropertyMock(
return_value='Kobernulf Monnur')
type(mock_classification_entry).position = PropertyMock(return_value=1)
type(mock_classification_entry).best_lap = PropertyMock(
return_value=best_lap)
mock_starting_grid_entry = MagicMock(spec=StartingGridEntry)
type(mock_starting_grid_entry).driver_index = PropertyMock(
return_value=0)
type(mock_starting_grid_entry).driver_name = PropertyMock(
return_value='Kobernulf Monnur')
type(mock_starting_grid_entry).position = PropertyMock(return_value=2)
configuration = {'point_structure': [5, 15, 12, 10, 8, 6, 4, 2, 1]}
instance = RaceResultsWithChange([mock_classification_entry],
[mock_starting_grid_entry],
**configuration)
expected_result = '20'
self.assertEqual(
instance.calc_points((driver_name, position, best_lap),
**configuration), expected_result)
def test_method_to_frame_with_changes(self):
mock_driver_1 = MagicMock(spec=Driver)
type(mock_driver_1).laps_complete = PropertyMock(return_value=6)
type(mock_driver_1).race_time = PropertyMock(return_value=42.0)
type(mock_driver_1).stops = PropertyMock(return_value=0)
mock_driver_2 = MagicMock(spec=Driver)
type(mock_driver_2).laps_complete = PropertyMock(return_value=6)
type(mock_driver_2).race_time = PropertyMock(return_value=45.0)
type(mock_driver_2).stops = PropertyMock(return_value=0)
mock_classification_entry_1 = MagicMock(spec=ClassificationEntry)
type(mock_classification_entry_1).driver = PropertyMock(
return_value=mock_driver_1)
type(mock_classification_entry_1).driver_name = PropertyMock(
return_value='Kobernulf Monnur')
type(mock_classification_entry_1).position = PropertyMock(
return_value=1)
mock_classification_entry_2 = MagicMock(spec=ClassificationEntry)
type(mock_classification_entry_2).driver = PropertyMock(
return_value=mock_driver_2)
type(mock_classification_entry_2).driver_name = PropertyMock(
return_value='Testy McTest')
type(mock_classification_entry_2).position = PropertyMock(
return_value=2)
mock_starting_grid_entry_1 = MagicMock(spec=StartingGridEntry)
type(mock_starting_grid_entry_1).driver_index = PropertyMock(
return_value=0)
type(mock_starting_grid_entry_1).driver_name = PropertyMock(
return_value='Kobernulf Monnur')
type(mock_starting_grid_entry_1).position = PropertyMock(
return_value=2)
mock_starting_grid_entry_2 = MagicMock(spec=StartingGridEntry)
type(mock_starting_grid_entry_2).driver_index = PropertyMock(
return_value=1)
type(mock_starting_grid_entry_2).driver_name = PropertyMock(
return_value='Testy McTest')
type(mock_starting_grid_entry_2).position = PropertyMock(
return_value=1)
instance = RaceResultsWithChange(
[mock_classification_entry_1, mock_classification_entry_2],
[mock_starting_grid_entry_2, mock_starting_grid_entry_1])
expected_result = numpy.ndarray
self.assertIsInstance(instance.to_frame(), expected_result)
def test_init_config(self):
configuration = {
'participant_config': {
'Kobernulf Monnur': {
'display': 'Senor Pez',
'car': '125cc Shifter Kart',
'team': 'DarkNitro',
}
},
'point_structure': [5, 15, 12, 10, 8, 6, 4, 2, 1]
}
mock_driver = MagicMock(spec=Driver)
type(mock_driver).laps_complete = PropertyMock(return_value=6)
type(mock_driver).race_time = PropertyMock(return_value=42.0)
type(mock_driver).stops = PropertyMock(return_value=0)
mock_classification_entry = MagicMock(spec=ClassificationEntry)
type(mock_classification_entry).driver = PropertyMock(
return_value=mock_driver)
type(mock_classification_entry).driver_name = PropertyMock(
return_value='Kobernulf Monnur')
type(mock_classification_entry).position = PropertyMock(return_value=1)
mock_starting_grid_entry = MagicMock(spec=StartingGridEntry)
type(mock_starting_grid_entry).driver_index = PropertyMock(
return_value=0)
type(mock_starting_grid_entry).driver_name = PropertyMock(
return_value='Kobernulf Monnur')
type(mock_starting_grid_entry).position = PropertyMock(return_value=2)
instance = RaceResultsWithChange([mock_classification_entry],
[mock_starting_grid_entry],
**configuration)
expected_result = RaceResultsWithChange
self.assertIsInstance(instance, expected_result)
def test_init_no_config(self):
mock_driver = MagicMock(spec=Driver)
type(mock_driver).laps_complete = PropertyMock(return_value=6)
type(mock_driver).race_time = PropertyMock(return_value=42.0)
type(mock_driver).stops = PropertyMock(return_value=0)
mock_classification_entry = MagicMock(spec=ClassificationEntry)
type(mock_classification_entry).driver = PropertyMock(
return_value=mock_driver)
type(mock_classification_entry).driver_name = PropertyMock(
return_value='Kobernulf Monnur')
type(mock_classification_entry).position = PropertyMock(return_value=1)
mock_starting_grid_entry = MagicMock(spec=StartingGridEntry)
type(mock_starting_grid_entry).driver_index = PropertyMock(
return_value=0)
type(mock_starting_grid_entry).driver_name = PropertyMock(
return_value='Kobernulf Monnur')
type(mock_starting_grid_entry).position = PropertyMock(return_value=2)
instance = RaceResultsWithChange([mock_classification_entry],
[mock_starting_grid_entry])
expected_result = RaceResultsWithChange
self.assertIsInstance(instance, expected_result)
def test_property_row_colors(self):
mock_driver = MagicMock(spec=Driver)
type(mock_driver).laps_complete = PropertyMock(return_value=6)
type(mock_driver).race_time = PropertyMock(return_value=42.0)
type(mock_driver).stops = PropertyMock(return_value=0)
mock_classification_entry = MagicMock(spec=ClassificationEntry)
type(mock_classification_entry).driver = PropertyMock(
return_value=mock_driver)
type(mock_classification_entry).driver_name = PropertyMock(
return_value='Kobernulf Monnur')
type(mock_classification_entry).position = PropertyMock(return_value=1)
mock_starting_grid_entry = MagicMock(spec=StartingGridEntry)
type(mock_starting_grid_entry).driver_index = PropertyMock(
return_value=0)
type(mock_starting_grid_entry).driver_name = PropertyMock(
return_value='Kobernulf Monnur')
type(mock_starting_grid_entry).position = PropertyMock(return_value=2)
instance = RaceResultsWithChange([mock_classification_entry],
[mock_starting_grid_entry])
expected_result = [(192, 192, 192, 255), (255, 255, 255, 255)]
self.assertListEqual(instance.row_colors, expected_result)
def make_video(config_file):
"""
Test of race data.
"""
configuration = json.load(open(config_file))
race_data = RaceData(configuration['source_telemetry'])
result_data = RaceData(configuration['source_telemetry'])
output_prefix = re.match(r'(.*)\.json$',
os.path.basename(config_file)).group(1)
if os.environ.get('HEADINGFONTOVERRIDE') is not None:
configuration['heading_font'] = \
os.environ['HEADINGFONTOVERRIDE']
if os.environ.get('DISPLAYFONTOVERRIDE') is not None:
configuration['font'] = os.environ['DISPLAYFONTOVERRIDE']
framerate = 30
source_video = mpy.VideoFileClip(configuration['source_video']).subclip(
configuration['video_skipstart'], configuration['video_skipend'])
pcre_standings = GTStandings(race_data, ups=framerate, **configuration)
standings_clip_mask = mpy.VideoClip(
make_frame=pcre_standings.make_mask_frame, ismask=True)
standings_clip = mpy.VideoClip(
make_frame=pcre_standings.make_frame).set_mask(standings_clip_mask)
try:
if sys.argv[2] == "sync":
def timecode_frame(time):
"""
Custom make frame for timecode.
"""
timecode_image = Image.new('RGB', (200, 50))
draw = ImageDraw.Draw(timecode_image)
draw.text((50, 25), "%.02f" % (time))
return PIL_to_npimage(timecode_image)
timecode_clip = mpy.VideoClip(
timecode_frame, duration=source_video.duration).set_position(
('center', 'top'))
first_lap_data = RaceData(configuration['source_telemetry'])
while first_lap_data.drivers_by_index[0].laps_complete < 1:
_ = first_lap_data.get_data()
main_event = mpy.CompositeVideoClip(
[source_video, standings_clip,
timecode_clip]).set_duration(source_video.duration).subclip(
first_lap_data.elapsed_time - 5,
first_lap_data.elapsed_time + 5)
else:
raise IndexError
except IndexError:
main_event = mpy.CompositeVideoClip(
[source_video, standings_clip]).set_duration(source_video.duration)
pcre_starting_grid = StartingGrid(sorted(race_data.starting_grid,
key=lambda x: x.position),
size=source_video.size,
**configuration)
Image.fromarray(pcre_starting_grid.to_frame()).save(output_prefix +
"_starting_grid.png")
starting_grid = mpy.ImageClip(
pcre_starting_grid.to_frame()).set_duration(5)
while True:
try:
result_data.get_data()
except StopIteration:
break
pcre_results = RaceResultsWithChange(sorted(result_data.classification,
key=lambda x: x.position),
result_data.starting_grid,
size=source_video.size,
**configuration)
Image.fromarray(pcre_results.to_frame()).save(output_prefix +
'_results.png')
results = mpy.ImageClip(pcre_results.to_frame()).set_duration(20)
if not any(
[x['points'] for x in configuration['participant_config'].values()]):
pcre_series_standings = SeriesStandings(result_data.classification,
size=source_video.size,
**configuration)
else:
pcre_series_standings = SeriesStandingsWithChange(
result_data.classification,
size=source_video.size,
**configuration)
Image.fromarray(
pcre_series_standings.to_frame()).save(output_prefix +
'_series_standings.png')
series_standings = mpy.ImageClip(
pcre_series_standings.to_frame()).set_duration(20)
output = mpy.concatenate_videoclips([
starting_grid.fadeout(1), main_event,
results.fadein(1).fadeout(1),
series_standings.fadein(1)
],
method="compose")
output.write_videofile(configuration['output_video'], fps=framerate)
def test_method_format_time_passed_string(self):
time = "ERROR"
expected_result = ""
self.assertEqual(RaceResultsWithChange.format_time(time),
expected_result)
def test_method_format_time_passed_none(self):
time = None
expected_result = ""
self.assertEqual(RaceResultsWithChange.format_time(time),
expected_result)
def test_method_format_time_round(self):
time = 3702.9876
expected_result = '1:01:42.988'
self.assertEqual(RaceResultsWithChange.format_time(time),
expected_result)
def test_method_format_time_truncate(self):
time = 3702.1234
expected_result = '1:01:42.123'
self.assertEqual(RaceResultsWithChange.format_time(time),
expected_result)
def test_method_format_time(self):
time = 3702
expected_result = '1:01:42.000'
self.assertEqual(RaceResultsWithChange.format_time(time),
expected_result)
def test_method_format_time_below_hour_round(self):
time = 84.9876
expected_result = '1:24.988'
self.assertEqual(RaceResultsWithChange.format_time(time),
expected_result)
def test_method_format_time_below_hour_truncate(self):
time = 84.1234
expected_result = '1:24.123'
self.assertEqual(RaceResultsWithChange.format_time(time),
expected_result)
def test_method_format_time_below_min(self):
time = 42
expected_result = '42.000'
self.assertEqual(RaceResultsWithChange.format_time(time),
expected_result)
def make_video(config_file, *, framerate=None, sync=False):
configuration = json.load(open(config_file))
try:
race_data = RaceData(configuration['source_telemetry'])
result_data = RaceData(configuration['source_telemetry'])
except KeyError:
sys.exit("Configuration Error: Source Telemetry not found.")
try:
output_prefix = os.path.splitext(configuration['output_video'])[0]
except KeyError:
sys.exit("Configuration Error: Output Video not found.")
try:
champion = configuration['show_champion']
except KeyError:
champion = False
if os.environ.get('HEADINGFONTOVERRIDE') is not None:
configuration['heading_font'] = \
os.environ['HEADINGFONTOVERRIDE']
if os.environ.get('DISPLAYFONTOVERRIDE') is not None:
configuration['font'] = os.environ['DISPLAYFONTOVERRIDE']
try:
video_skipstart = configuration['video_skipstart']
except KeyError:
video_skipstart = 0
try:
video_skipend = configuration['video_skipend']
except KeyError:
video_skipend = None
if 'source_video' in configuration \
and configuration['source_video'] is not None:
source_video = mpy.VideoFileClip(
configuration['source_video']
).subclip(
video_skipstart,
video_skipend)
if framerate is None:
framerate = source_video.fps
else:
time_data = RaceData(configuration['source_telemetry'])
with tqdm(desc="Detecting Telemetry Duration") as progress:
while True:
try:
_ = time_data.get_data()
progress.update()
except StopIteration:
break
source_video = mpy.ColorClip((1280, 1024)).set_duration(
time_data.elapsed_time)
if framerate is None:
framerate = 30
pcre_standings = GTStandings(
race_data,
ups=framerate,
**configuration)
standings_clip_mask = mpy.VideoClip(
make_frame=pcre_standings.make_mask_frame,
ismask=True)
standings_clip = mpy.VideoClip(
make_frame=pcre_standings.make_frame
).set_mask(standings_clip_mask)
if sync:
def timecode_frame(time):
"""
Custom make frame for timecode.
"""
timecode_image = Image.new('RGB', (100, 40))
draw = ImageDraw.Draw(timecode_image)
draw.text((10, 10), "%.02f" % time)
return PIL_to_npimage(timecode_image)
timecode_clip = mpy.VideoClip(
timecode_frame,
duration=source_video.duration
).set_position(('center', 'top'))
first_lap_data = RaceData(configuration['source_telemetry'])
with tqdm(desc="Detecting Video Start") as progress:
while not any(
[x.laps_complete > 0
for x in first_lap_data.drivers.values()]):
_ = first_lap_data.get_data()
progress.update()
start_time = first_lap_data.elapsed_time - 10
end_time = None
with tqdm(desc="Detecting Video End") as progress:
while not all(
[x.laps_complete > 0
for x in first_lap_data.drivers.values()]):
try:
_ = first_lap_data.get_data()
progress.update()
except StopIteration:
end_time = start_time + 60
break
if end_time is None:
end_time = first_lap_data.elapsed_time + 10
main_event = mpy.CompositeVideoClip(
[source_video, standings_clip, timecode_clip]
).set_duration(
source_video.duration
).subclip(start_time, end_time)
else:
main_event = mpy.CompositeVideoClip(
[source_video, standings_clip]
).set_duration(source_video.duration)
pcre_starting_grid = StartingGrid(
sorted(race_data.starting_grid, key=lambda x: x.position),
size=source_video.size,
**configuration)
Image.fromarray(pcre_starting_grid.to_frame()).save(
output_prefix + "_starting_grid.png")
starting_grid = mpy.ImageClip(
pcre_starting_grid.to_frame()).set_duration(5)
while True:
try:
result_data.get_data()
except StopIteration:
break
end_titles = list()
if 'car_classes' in configuration and len(configuration['car_classes']):
no_points_config = copy(configuration)
try:
del no_points_config['point_structure']
except KeyError:
pass
pcre_results = RaceResultsWithChange(
sorted(result_data.all_driver_classification,
key=lambda x: x.position),
result_data.starting_grid,
size=source_video.size,
**no_points_config)
Image.fromarray(pcre_results.to_frame()).save(
output_prefix + '_results.png')
results = mpy.ImageClip(pcre_results.to_frame()).set_duration(20)
end_titles.append(results)
for car_class_filter in [
car_class for car_class in configuration['car_classes']
if car_class != ""]:
class_cars = [
car for car_class, car_class_data
in configuration['car_classes'].items()
if car_class == car_class_filter
for car in car_class_data['cars']]
class_drivers = [
driver for driver, data
in configuration['participant_config'].items()
if data['car'] in class_cars]
class_classification = [
classification for classification
in result_data.all_driver_classification
if classification.driver_name in class_drivers]
for position, classification in enumerate(sorted(
class_classification,
key=lambda x: x.position), 1):
classification.position = position
class_starting_grid = [
grid for grid
in result_data.starting_grid
if grid.driver_name in class_drivers]
for position, grid in enumerate(sorted(
class_starting_grid,
key=lambda x: x.position), 1):
grid.position = position
pcre_results = RaceResultsWithChange(
sorted(class_classification, key=lambda x: x.position),
class_starting_grid,
size=source_video.size,
**configuration)
Image.fromarray(pcre_results.to_frame()).save(
output_prefix + '_' + car_class_filter + '_results.png')
results = mpy.ImageClip(pcre_results.to_frame()).set_duration(20)
end_titles.append(results)
else:
pcre_results = RaceResultsWithChange(
sorted(result_data.all_driver_classification,
key=lambda x: x.position),
result_data.starting_grid,
size=source_video.size,
**configuration)
Image.fromarray(pcre_results.to_frame()).save(
output_prefix + '_results.png')
results = mpy.ImageClip(pcre_results.to_frame()).set_duration(20)
end_titles.append(results)
try:
if any(configuration['point_structure']):
if not any([
x['points'] for x
in configuration['participant_config'].values()]):
if 'car_classes' in configuration \
and len(configuration['car_classes']):
for car_class_filter in [car_class for car_class in
configuration['car_classes'] if
car_class != ""]:
class_cars = [
car for car_class, car_class_data
in configuration['car_classes'].items()
if car_class == car_class_filter
for car in car_class_data['cars']]
class_drivers = [
driver for driver, data
in configuration['participant_config'].items()
if data['car'] in class_cars]
class_classification = [
classification for classification
in result_data.all_driver_classification
if classification.driver_name in class_drivers]
for position, classification in enumerate(sorted(
class_classification,
key=lambda x: x.position), 1):
classification.position = position
pcre_series_standings = SeriesStandings(
class_classification,
size=source_video.size,
**configuration)
Image.fromarray(pcre_series_standings.to_frame()).save(
output_prefix
+ '_'
+ car_class_filter
+ '_series_standings.png')
series_standings = mpy.ImageClip(
pcre_series_standings.to_frame()).set_duration(20)
end_titles.append(series_standings)
else:
pcre_series_standings = SeriesStandings(
result_data.all_driver_classification,
size=source_video.size,
**configuration)
Image.fromarray(pcre_series_standings.to_frame()).save(
output_prefix + '_series_standings.png')
series_standings = mpy.ImageClip(
pcre_series_standings.to_frame()).set_duration(20)
end_titles.append(series_standings)
else:
if 'car_classes' in configuration \
and len(configuration['car_classes']):
for car_class_filter in [car_class for car_class in
configuration['car_classes'] if
car_class != ""]:
class_cars = [
car for car_class, car_class_data
in configuration['car_classes'].items()
if car_class == car_class_filter
for car in car_class_data['cars']]
class_drivers = [
driver for driver, data
in configuration['participant_config'].items()
if data['car'] in class_cars]
class_classification = [
classification for classification
in result_data.all_driver_classification
if classification.driver_name in class_drivers]
for position, classification in enumerate(sorted(
class_classification,
key=lambda x: x.position), 1):
classification.position = position
pcre_series_standings = SeriesStandingsWithChange(
class_classification,
size=source_video.size,
**configuration)
Image.fromarray(pcre_series_standings.to_frame()).save(
output_prefix
+ '_'
+ car_class_filter
+ '_series_standings.png')
series_standings = mpy.ImageClip(
pcre_series_standings.to_frame()).set_duration(20)
end_titles.append(series_standings)
else:
pcre_series_standings = SeriesStandingsWithChange(
result_data.all_driver_classification,
size=source_video.size,
**configuration)
Image.fromarray(pcre_series_standings.to_frame()).save(
output_prefix + '_series_standings.png')
series_standings = mpy.ImageClip(
pcre_series_standings.to_frame()).set_duration(20)
end_titles.append(series_standings)
except KeyError:
try:
_ = configuration['point_structure']
pcre_series_standings = SeriesStandings(
result_data.all_driver_classification,
size=source_video.size,
**configuration)
Image.fromarray(pcre_series_standings.to_frame()).save(
output_prefix + '_series_standings.png')
series_standings = mpy.ImageClip(
pcre_series_standings.to_frame()).set_duration(20)
end_titles.append(series_standings)
except KeyError:
pass
if champion:
if 'car_classes' in configuration and len(configuration['car_classes']):
for car_class_filter in [car_class for car_class in
configuration['car_classes'] if
car_class != ""]:
class_cars = [
car for car_class, car_class_data
in configuration['car_classes'].items()
if car_class == car_class_filter
for car in car_class_data['cars']]
class_drivers = [
driver for driver, data
in configuration['participant_config'].items()
if data['car'] in class_cars]
class_classification = [
classification for classification
in result_data.all_driver_classification
if classification.driver_name in class_drivers]
for position, classification in enumerate(sorted(
class_classification,
key=lambda x: x.position), 1):
classification.position = position
pcre_series_standings = SeriesChampion(
class_classification,
size=source_video.size,
**configuration)
Image.fromarray(pcre_series_standings.to_frame()).save(
output_prefix
+ '_'
+ car_class_filter
+ '_series_champion.png')
series_standings = mpy.ImageClip(
pcre_series_standings.to_frame()).set_duration(20)
end_titles.append(series_standings)
else:
pcre_series_champion = SeriesChampion(
result_data.all_driver_classification,
size=source_video.size,
**configuration)
Image.fromarray(pcre_series_champion.to_frame()).save(
output_prefix + '_series_champion.png')
series_champion = mpy.ImageClip(
pcre_series_champion.to_frame()).set_duration(20)
end_titles.append(series_champion)
output = mpy.concatenate_videoclips(
[starting_grid.fadeout(1), main_event]
+ [clip.fadein(1).fadeout(1) for clip in end_titles[:-1]]
+ [end_titles[-1].fadein(1)], method="compose")
output.write_videofile(configuration['output_video'], fps=float(framerate))