def difference_loop(context, start_frame):
# load variables from context
workspace = context.workspace
differences_dir = context.differences_dir
inversion_data_dir = context.inversion_data_dir
pframe_data_dir = context.pframe_data_dir
input_frames_dir = context.input_frames_dir
frame_count = context.frame_count
block_size = context.block_size
extension_type = context.extension_type
bleed = context.bleed
debug = context.debug
logger = logging.getLogger(__name__)
logger.info((workspace, start_frame, frame_count, block_size))
# for every frame in the video, create a difference_frame given the text files.
for x in range(start_frame, frame_count):
f1 = Frame()
f1.load_from_string_wait(input_frames_dir + "frame" + str(x + 1) + extension_type)
logger.info("waiting on text")
logger.info(f1)
difference_data = get_list_from_file(inversion_data_dir + "inversion_" + str(x) + ".txt")
prediction_data = get_list_from_file(pframe_data_dir + "pframe_" + str(x) + ".txt")
make_difference_image(context, f1, difference_data, prediction_data,
differences_dir + "output_" + get_lexicon_value(6, x) + ".png")
output_file = workspace + "debug/debug" + str(x + 1) + extension_type
if debug == 1:
debug_image(block_size, f1, prediction_data, difference_data, output_file)
def verify_user_settings(context):
from wrappers.frame import Frame
input_frames_dir = context.input_frames_dir
extension_type = context.extension_type
block_size = context.block_size
f1 = Frame()
f1.load_from_string(input_frames_dir + "frame1" + extension_type)
valid = True
if f1.width % block_size != 0 and f1.height % block_size != 0:
print("----------------------ERROR---------------------------------------")
print("Your block size is incompatible with the resolution you provided. ")
print("Valid Block sizes are:")
print("------------------------------------------------------------------")
valid_sizes = []
larger_val = [f1.width, f1.height][f1.height > f1.width]
for x in range(1, larger_val):
if f1.width % x == 0 and f1.height % x == 0:
valid_sizes.append(x)
print(valid_sizes)
print("------------------------------------------------------------------")
new_block_size = int(input("Enter your value (recommended 25 or greater): "))
while new_block_size not in valid_sizes:
new_block_size = int(input("Invalid Choice! Re-Enter a correct value: "))
context.block_size = new_block_size
def fade_image(context, block_size, frame_base: Frame, list_correction: list):
logger = logging.getLogger(__name__)
# load context
scale_factor = int(context.scale_factor)
fade_list = []
out_image = Frame()
out_image.create_new(frame_base.width, frame_base.height)
out_image.copy_image(frame_base)
fade_data_size = 3
for x in range(int(len(list_correction) / fade_data_size)):
fade_list.append(FadeData(int(list_correction[x * fade_data_size + 0]),
int(list_correction[x * fade_data_size + 1]),
int(list_correction[x * fade_data_size + 2])))
# copy over predictive vectors into new image
for vector in fade_list:
out_image.fade_block(vector.x * scale_factor,
vector.y * scale_factor,
block_size * scale_factor,
vector.scalar)
#out_image.frame = np.clip(out_image.frame, 0, 255)
return out_image
def correct_image(context, block_size, frame_base: Frame,
list_correction: list):
logger = logging.getLogger(__name__)
# load context
scale_factor = context.scale_factor
predictive_vectors = []
out_image = Frame()
out_image.create_new(frame_base.width, frame_base.height)
out_image.copy_image(frame_base)
scale_factor = int(scale_factor)
for x in range(int(len(list_correction) / 4)):
predictive_vectors.append(
DisplacementVector(int(list_correction[x * 4]),
int(list_correction[x * 4 + 1]),
int(list_correction[x * 4 + 2]),
int(list_correction[x * 4 + 3])))
# copy over predictive vectors into new image
for vector in predictive_vectors:
out_image.copy_block(frame_base, block_size * scale_factor,
vector.x_2 * scale_factor,
vector.y_2 * scale_factor,
vector.x_1 * scale_factor,
vector.y_1 * scale_factor)
return out_image
def main():
block_size = 4
scale_factor = 2
frame_base = Frame()
frame_base.load_from_string(
"C:\\Users\\windwoz\\Desktop\\image_research\\shelter\\merged2x.jpg")
list_predictive = wait_on_text(
"C:\\Users\\windwoz\\Desktop\\image_research\\shelter\\correction.txt")
out_location = (
"C:\\Users\\windwoz\\Desktop\\image_research\\shelter\\new_correction.jpg"
)
correct_image(block_size, scale_factor, frame_base, list_predictive,
out_location)
def getFrame(uniq):
"""Returns a Frame object from the unique id.
@type uniq: a unique identifier.
@param uniq: id
@rtype: Frame
@return: A Frame object"""
return Frame(
Cuebot.getStub('frame').GetFrame(job_pb2.FrameGetFrameRequest(id=uniq),
timeout=Cuebot.Timeout).frame)
def getFrames(job, **options):
"""Finds frames in a job that match the search critieria
@type job: A unique job identifier.
@param: An id
@rtype: List<Frame>
@return: a list of matching frames"""
criteria = search.FrameSearch.criteriaFromOptions(**options)
framesSeq = Cuebot.getStub('frame').GetFrames(
job_pb2.FrameGetFramesRequest(job=job, r=criteria),
timeout=Cuebot.Timeout).frames
return [Frame(f) for f in framesSeq.frames]
def difference_loop(workspace, difference_dir, inversion_data_dir, pframe_data_dir,
input_frames_dir, start_frame, count, block_size, file_type):
logger = logging.getLogger(__name__)
bleed = 1
logger.info((workspace, start_frame, count, block_size))
for x in range(start_frame, count):
f1 = Frame()
f1.load_from_string_wait(input_frames_dir + "frame" + str(x + 1) + file_type)
logger.info("waiting on text")
logger.info(f1)
difference_data = wait_on_text(inversion_data_dir + "inversion_" + str(x) + ".txt")
prediction_data = wait_on_text(pframe_data_dir + "pframe_" + str(x) + ".txt")
make_difference_image(f1, block_size, bleed, difference_data, prediction_data,
difference_dir + "output_" + get_lexicon_value(6, x) + ".png")
debug(workspace, block_size, bleed, f1, prediction_data, difference_data,
workspace + "debug/debug" + str(x + 1) + file_type)
def set_mse(self):
print("calculating mse")
list = []
for x in range(1, int(math.sqrt(self.frame_count))):
print(str(x) + " out of " + str(int(math.sqrt(self.frame_count))))
num = random.randint(1, self.frame_count)
f1 = Frame()
f1.load_from_string(self.input_frames_dir + "frame" + str(num) +
".jpg")
list.append(
determine_sens(self.workspace, f1, self.quality_low,
self.quality_high))
output = [sum(y) / len(y) for y in zip(*list)]
self.mse_max = output[0]
self.mse_min = output[1]
print("mse is ")
print(output)
def findFrame(job, layer, number):
"""Finds and returns a layer from the specified pending job
@type job: str
@param job: the job name
@type layer: str
@param layer: the layer name
@type number: int
@param number: the frame number
@rtype: Frame
@return: the frame matching the query"""
return Frame(
Cuebot.getStub('frame').FindFrame(job_pb2.FrameFindFrameRequest(
job=job, layer=layer, frame=number),
timeout=Cuebot.Timeout).frame)
def merge_loop(context: Context, start_frame: int):
# load variables from context
workspace = context.workspace
upscaled_dir = context.upscaled_dir
merged_dir = context.merged_dir
inversion_data_dir = context.inversion_data_dir
pframe_data_dir = context.pframe_data_dir
correction_data_dir = context.correction_data_dir
fade_data_dir = context.fade_data_dir
frame_count = context.frame_count
extension_type = context.extension_type
logger = logging.getLogger(__name__)
for x in range(start_frame, frame_count):
logger.info("Upscaling frame " + str(x))
# load images required to merge this frame
f1 = Frame()
f1.load_from_string_wait(upscaled_dir + "output_" +
get_lexicon_value(6, x) + ".png")
base = Frame()
base.load_from_string_wait(merged_dir + "merged_" + str(x) +
extension_type)
# load vectors needed to piece image back together
prediction_data_list = get_list_from_file(pframe_data_dir + "pframe_" +
str(x) + ".txt")
difference_data_list = get_list_from_file(inversion_data_dir +
"inversion_" + str(x) +
".txt")
correction_data_list = get_list_from_file(correction_data_dir +
"correction_" + str(x) +
".txt")
fade_data_list = get_list_from_file(fade_data_dir + "fade_" + str(x) +
".txt")
output_file = workspace + "merged/merged_" + str(x +
1) + extension_type
make_merge_image(context, f1, base, prediction_data_list,
difference_data_list, correction_data_list,
fade_data_list, output_file)
def determine_sens(workspace, frame, lower_val, higher_val):
from wrappers.frame import Frame
frame.save_image_quality(workspace + "lower.jpg", lower_val)
frame.save_image_quality(workspace + "higher.jpg", higher_val)
lower_image = Frame()
lower_image.load_from_string(workspace + "lower.jpg")
higher_image = Frame()
higher_image.load_from_string(workspace + "higher.jpg")
lower_mse = frame.mean(lower_image)
higher_mse = frame.mean(higher_image)
os.remove(workspace + "lower.jpg")
os.remove(workspace + "higher.jpg")
return lower_mse, higher_mse
def correct_image(block_size, scale_factor, frame_base, list_correction):
predictive_vectors = []
out_image = Frame()
out_image.create_new(frame_base.width, frame_base.height)
out_image.copy_image(frame_base)
scale_factor = int(scale_factor)
for x in range(int(len(list_correction) / 4)):
predictive_vectors.append(
DisplacementVector(int(list_correction[x * 4]),
int(list_correction[x * 4 + 1]),
int(list_correction[x * 4 + 2]),
int(list_correction[x * 4 + 3])))
# copy over predictive vectors into new image
for vector in predictive_vectors:
out_image.copy_block(frame_base, block_size * scale_factor,
vector.x_2 * scale_factor,
vector.y_2 * scale_factor,
vector.x_1 * scale_factor,
vector.y_1 * scale_factor)
return out_image
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 merge_loop(workspace, upscaled_dir, merged_dir, inversion_data_dir, pframe_data_dir,
correction_data_dir, start_frame, count, block_size, scale_factor, file_type):
logger = logging.getLogger(__name__)
bleed = 1
for x in range(start_frame, count):
logger.info("Upscaling frame " + str(x))
# load images required to merge this frame
f1 = Frame()
f1.load_from_string_wait(upscaled_dir + "output_" + get_lexicon_value(6, x) + ".png")
base = Frame()
base.load_from_string_wait(merged_dir + "merged_" + str(x) + file_type)
# load vectors needed to piece image back together
difference_data = wait_on_text(inversion_data_dir + "inversion_" + str(x) + ".txt")
prediction_data = wait_on_text(pframe_data_dir + "pframe_" + str(x) + ".txt")
correction_data = wait_on_text(correction_data_dir + "correction_" + str(x) + ".txt")
make_merge_image(workspace, block_size, scale_factor, bleed, f1, base, prediction_data,
difference_data, correction_data, workspace + "merged/merged_" + str(x + 1) + file_type)
def make_difference_image(context: Context, raw_frame, list_difference, list_predictive, out_location):
difference_vectors = []
buffer = 5
block_size = context.block_size
bleed = context.bleed
# first make a 'bleeded' version of input_frame
# so we can preform numpy calculations w.o having to catch
bleed_frame = raw_frame.create_bleeded_image(buffer)
# if there are no items in 'differences' but have list_predictives
# then the two frames are identical, so no differences image needed.
if not list_difference and list_predictive:
out_image = Frame()
out_image.create_new(1, 1)
out_image.save_image(out_location)
return
# if there are neither any predictive or inversions
# then the frame is a brand new frame with no resemblence to previous frame.
# in this case copy the entire frame over
if not list_difference and not list_predictive:
out_image = Frame()
out_image.create_new(raw_frame.width, raw_frame.height)
out_image.copy_image(raw_frame)
out_image.save_image(out_location)
return
# turn the list of differences into a list of vectors
for x in range(int(len(list_difference) / 4)):
difference_vectors.append(DisplacementVector(int(list_difference[x * 4]), int(list_difference[x * 4 + 1]),
int(list_difference[x * 4 + 2]), int(list_difference[x * 4 + 3])))
# size of output image is determined based off how many differences there are
image_size = int(math.sqrt(len(list_difference) / 4) + 1) * (block_size + bleed * 2)
out_image = Frame()
out_image.create_new(image_size, image_size)
# move every block from the complete frame to the differences frame using vectors.
for vector in difference_vectors:
out_image.copy_block(bleed_frame, block_size + bleed * 2,
vector.x_1 + buffer - bleed, vector.y_1 + buffer + - bleed,
vector.x_2 * (block_size + bleed * 2), vector.y_2 * (block_size + bleed * 2))
out_image.save_image(out_location)
def make_merge_image(context: Context, frame_inversion: Frame,
frame_base: Frame, list_predictive: list,
list_differences: list, list_corrections: list,
list_fade: list, output_location: str):
# Load context
block_size = context.block_size
scale_factor = context.scale_factor
bleed = context.bleed
logger = logging.getLogger(__name__)
predictive_vectors = []
difference_vectors = []
out_image = Frame()
out_image.create_new(frame_base.width, frame_base.height)
scale_factor = int(scale_factor)
if not list_predictive and not list_differences:
logger.info("list_predictive and not list_differences: true")
logger.info("Saving inversion image..")
out_image.copy_image(frame_inversion)
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_differences) / 4)):
difference_vectors.append(
DisplacementVector(int(list_differences[x * 4 + 0]),
int(list_differences[x * 4 + 1]),
int(list_differences[x * 4 + 2]),
int(list_differences[x * 4 + 3])))
for x in range(int(len(list_predictive) / 4)):
predictive_vectors.append(
DisplacementVector(int(list_predictive[x * 4 + 0]),
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 * scale_factor,
vector.x_2 * scale_factor,
vector.y_2 * scale_factor,
vector.x_1 * scale_factor,
vector.y_1 * scale_factor)
# copy over inversion vectors (the difference images) into new image
for vector in difference_vectors:
out_image.copy_block(
frame_inversion, block_size * scale_factor,
(vector.x_2 *
(block_size + bleed * 2)) * scale_factor + (bleed * scale_factor),
(vector.y_2 *
(block_size + bleed * 2)) * scale_factor + (bleed * scale_factor),
vector.x_1 * scale_factor, vector.y_1 * scale_factor)
out_image = fade_image(context, block_size, out_image, list_fade)
out_image = correct_image(context, 2, out_image, list_corrections)
out_image.save_image(output_location)
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)
from wrappers.frame import Frame
# f1 = Frame()
# f1.load_from_string("C:\\Users\\windwoz\\Desktop\\workspace\\violetfade\\100\\frame20.png")
#
# f2 = Frame()
# f2.load_from_string("C:\\Users\\windwoz\\Desktop\\workspace\\violetfade\\100\\frame21.png")
#
f1 = Frame()
f1.load_from_string(
"C:\\Users\\windwoz\\Desktop\\workspace\\violetfade\\inputs\\frame30.jpg")
f1.fade_block(0, 0, 100, -100)
f1.save_image("C:\\Users\\windwoz\\Desktop\\workspace\\violetfade\\lmfao.jpg")