def from_text(cls, text, language='en-US', rate=0.4, videopath=None):
"""
Create a video from a text.
"""
speaker = GoogleSpeaker()
audio_filepath = speaker.speak(text, language=language, rate=rate)
if not videopath:
basename = os.path.splitext(os.path.basename(audio_filepath))[0]
filename = f'{basename}{cls.TARGET_EXTENSION}'
video_path = path_in_medialib(filename)
analyst = AudioAnalyst(audio_filepath, text)
analysis = analyst.analyse()
image_maker = ImageMaker(analysis)
pattern = image_maker.save_images()
outdict = {'vcodec': 'h264', 'shortest': None}
image = ffmpeg.input(pattern)
audio = ffmpeg.input(audio_filepath)
try:
ffmpeg.output(image, audio, video_path,
**outdict).run(capture_stdout=True,
capture_stderr=True)
return videopath
except ffmpeg.Error as e:
print('stdout:', e.stdout.decode('utf8'))
print('stderr:', e.stderr.decode('utf8'))
raise e
return None
def from_audio(filename, text=None):
wav_file = path_in_medialib(filename)
analyst = AudioAnalyst(wav_file, text)
analysis = analyst.analyse()
image_maker = ImageMaker(analysis)
pattern = image_maker.save_images()
outdict = {'vcodec': 'h264', 'shortest': None}
image = ffmpeg.input(pattern)
audio = ffmpeg.input(wav_file)
video_path = f"{wav_file}.mp4"
try:
ffmpeg.output(image, audio, video_path,
**outdict).run(capture_stdout=True,
capture_stderr=True)
except ffmpeg.Error as e:
print('stdout:', e.stdout.decode('utf8'))
print('stderr:', e.stderr.decode('utf8'))
raise e
parser.add_argument("-upload_loc", type=str, default="jacob.stanford.edu:/u/apache/htdocs/mkayser/reports/", help="Location to publish HTML doc to")
parser.add_argument("-url_loc", type=str, default="http://nlp.stanford.edu/mkayser/reports/", help="URL location where report will appear")
args = parser.parse_args()
output_file = "report.html"
workers = {}
worker_counts = {}
html_lines = []
work_times = []
total_work_time = 0
nrows = 0
imgmaker = ImageMaker(args.block_px, args.padding_px)
bmpmaker = BitmapMaker(args.bitmap_dim, args.bitmap_dim)
try:
os.mkdir(args.output_dir)
except OSError:
pass
with open(args.csv) as fin:
reader = csv.reader(fin)
header = next(reader)
image_index = header.index(args.image_field)
commands_index = header.index(args.commands_field)
draw_events_index = header.index(args.draw_events_field)
worker_id_index = header.index(args.worker_id_field)
def test_cairo_save_images(self):
"""
Test the frame generation to be used by the Video Maker
"""
generator = ImageMaker(self.analysis)
generator.cairo_save_images()
help="Dimensionality of grid")
parser.add_argument("-csv",
type=str,
required=True,
help="input CSV file with drawing task results")
parser.add_argument("-field",
type=str,
default="Answer.WritingTexts",
help="Name of CSV field containing draw events")
parser.add_argument("-output_dir",
type=str,
default="output/make_images_from_drawing_task_results",
help="output directory for images")
args = parser.parse_args()
imgmaker = ImageMaker(args.width, args.padding)
bmpmaker = BitmapMaker(args.width, args.width)
np.random.seed(0)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
with open(args.csv) as fin:
reader = csv.reader(fin)
header = next(reader)
colindex = header.index(args.field)
for i, row in enumerate(reader):
commands_str = row[colindex]
bmpmaker.clear()
event_sequence = AbsoluteEventSequence.from_mturk_string(
commands_str)
def test_make_images(self):
generator = ImageMaker(self.analysis)
images = generator.make_images()
images[0].write_to_png(path_in_medialib('test_cairo_frame_0.jpg'))
images[4].write_to_png(path_in_medialib('test_cairo_frame_4.jpg'))
from image import ImageMaker
from bitmap import BitmapMaker
from events import AbsoluteEventSequence, RelativeEventSequence
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-width", type=int, default=32, help="Width of blocks in pixels")
parser.add_argument("-padding", type=int, default=2, help="Padding of blocks in pixels")
parser.add_argument("-dim", type=int, default=5, help="Dimensionality of grid")
parser.add_argument("-csv", type=str, required=True, help="input CSV file with drawing task results")
parser.add_argument("-field", type=str, default="Answer.WritingTexts", help="Name of CSV field containing draw events")
parser.add_argument("-output_dir", type=str, default="output/make_images_from_drawing_task_results", help="output directory for images")
args = parser.parse_args()
imgmaker = ImageMaker(args.width, args.padding)
bmpmaker = BitmapMaker(args.width, args.width)
np.random.seed(0)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
with open(args.csv) as fin:
reader = csv.reader(fin)
header = next(reader)
colindex = header.index(args.field)
for i,row in enumerate(reader):
commands_str = row[colindex]
bmpmaker.clear()
event_sequence = AbsoluteEventSequence.from_mturk_string(commands_str)
default="http://nlp.stanford.edu/mkayser/reports/",
help="URL location where report will appear")
args = parser.parse_args()
output_file = "report.html"
workers = {}
worker_counts = {}
html_lines = []
work_times = []
total_work_time = 0
nrows = 0
imgmaker = ImageMaker(args.block_px, args.padding_px)
bmpmaker = BitmapMaker(args.bitmap_dim, args.bitmap_dim)
try:
os.mkdir(args.output_dir)
except OSError:
pass
with open(args.csv) as fin:
reader = csv.reader(fin)
header = next(reader)
image_index = header.index(args.image_field)
commands_index = header.index(args.commands_field)
draw_events_index = header.index(args.draw_events_field)
worker_id_index = header.index(args.worker_id_field)
type=int,
default=16,
help="Block width in pixels")
parser.add_argument("-padding_px",
type=int,
default=1,
help="Padding in pixels")
parser.add_argument("-bitmap_dim",
type=int,
default=25,
help="Width of bitmap in blocks (assumed square)")
# Output location
parser.add_argument("-output_dir",
type=str,
required=True,
help="Directory to write output to")
args = parser.parse_args()
output_file = "report.html"
imgmaker = ImageMaker(args.block_px, args.padding_px)
bmpmaker = BitmapMaker(args.bitmap_dim, args.bitmap_dim)
make_dir_unless_exists(args.output_dir)
convert_commands_to_image_sequence(bmpmaker, imgmaker, args.commands,
"{}/img_".format(args.output_dir),
args.mode)
parser.add_argument("-gridpadding", type=int, default=0, help="How many empty blocks to place on outside of bitmap")
parser.add_argument("-bc", type=int, default=32, help="Block color")
parser.add_argument("-nbc", type=int, default=255, help="Non block color")
parser.add_argument("-gc", type=int, default=128, help="Grid color")
parser.add_argument("-random_seed", type=int, default=0, help="Random seed")
parser.add_argument("-output_parent_dir", type=str, default="output", help="parent of output directory for images")
parser.add_argument("-batch_name", type=str, default=None, help="name of this batch")
parser.add_argument("-url_prefix", type=str, default="http://nlp.stanford.edu/mkayser/images", help="URL prefix for Mturk HIT's")
parser.add_argument("-html_folder", type=str, default="jacob.stanford.edu:/u/apache/htdocs/mkayser/images", help="Local name for folder containing HIT's")
parser.add_argument("-upload", action="store_true", default=False, help="Upload images to HTML folder")
args = parser.parse_args()
if not args.batch_name:
args.batch_name = get_unique_directory_name(args.output_parent_dir,"generate_images")
imgmaker = ImageMaker(args.width, args.padding, [args.nbc,args.bc,args.gc])
np.random.seed(args.random_seed)
output_dir = os.path.join(args.output_parent_dir, args.batch_name)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
write_text_to_file(str(args), os.path.join(output_dir,"args"))
url_file = os.path.join(output_dir,"image_urls.csv")
print("Output directory: {}".format(output_dir))
print("Output CSV file: {}".format(url_file))
bitmaps = set()
