def compress_frames(context: Context):
inputs_dir = context.input_frames_dir
frame_count = context.frame_count
compressed_dir = context.compressed_dir
quality_low = context.quality_low
extension_type = context.extension_type
for x in range(1, frame_count + 1):
if os.path.exists(compressed_dir + "compressed_" + str(x) + ".jpg"):
continue
frame = Frame()
frame.load_from_string(inputs_dir + "frame" + str(x) + extension_type)
frame.save_image_quality(
compressed_dir + "compressed_" + str(x) + ".jpg", quality_low)
def debug_image(block_size, frame_base, list_predictive, list_differences, output_location):
logger = logging.getLogger(__name__)
predictive_vectors = []
out_image = Frame()
out_image.create_new(frame_base.width, frame_base.height)
if not list_predictive and not list_differences:
logger.info("list_predictive and not list_differences: true")
logger.info("Saving inversion image..")
out_image.save_image(output_location)
return
if list_predictive and not list_differences:
logger.info("list_predictive and not list_differences")
logger.info("saving last image..")
out_image.copy_image(frame_base)
out_image.save_image(output_location)
return
# load list into vector displacements
for x in range(int(len(list_predictive) / 4)):
predictive_vectors.append(DisplacementVector(int(list_predictive[x * 4]),
int(list_predictive[x * 4 + 1]),
int(list_predictive[x * 4 + 2]),
int(list_predictive[x * 4 + 3])))
# copy over predictive vectors into new image
for vector in predictive_vectors:
out_image.copy_block(frame_base, block_size,
vector.x_2, vector.y_2,
vector.x_1, vector.y_1)
out_image.save_image_quality(output_location, 25)