def correct_image(context, 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) block_size = context.correction_block_size for x in range(int(len(list_correction) / 4)): # / 4 because each there's 4 data points per block predictive_vectors.append( DisplacementVector(int(list_correction[x * 4 + 0]), 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 make_difference_image(context: Context, raw_frame: Frame, list_difference: list, list_predictive: list): 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) return out_image # 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) return out_image # 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)) return out_image
def make_merge_image(context: Context, frame_residual: Frame, frame_previous: Frame, list_predictive: list, list_residual: list, list_corrections: list, list_fade: list): """ This section can best be explained through pictures. A visual way of expressing what 'merging' is doing is this section in the wiki. https://github.com/aka-katto/dandere2x/wiki/How-Dandere2x-Works#part-2-using-observations-to-save-time Inputs: - frame(x) - frame(x+1)_residual - Residual vectors mapping frame(x+1)_residual -> frame(x+1) - Predictive vectors mapping frame(x) -> frame(x+1) Output: - frame(x+1) """ # Load context logger = logging.getLogger(__name__) out_image = Frame() out_image.create_new(frame_previous.width, frame_previous.height) # If list_predictive is empty, then the residual frame is simply the newly # produced image. if not list_predictive: out_image.copy_image(frame_residual) return out_image # By copying the image first as the first step, all the predictive elements of the form (x,y) -> (x,y) # are also copied. This allows us to ignore copying vectors (x,y) -> (x,y), which prevents redundant copying, # thus saving valuable computational time. out_image.copy_image(frame_previous) ################### # Plugins Section # ################### # Note: Run the plugins in the SAME order it was ran in dandere2x_cpp. If not, it won't work correctly. out_image = pframe_image(context, out_image, frame_previous, frame_residual, list_residual, list_predictive) out_image = fade_image(context, out_image, list_fade) out_image = correct_image(context, out_image, list_corrections) return out_image
def debug_image(block_size, frame_base, list_predictive, list_differences, output_location): logger = logging.getLogger(__name__) difference_vectors = [] predictive_vectors = [] out_image = Frame() out_image.create_new(frame_base.width, frame_base.height) out_image.copy_image(frame_base) black_image = Frame() black_image.create_new(frame_base.width, frame_base.height) if not list_predictive and not list_differences: out_image.save_image(output_location) return if list_predictive and not list_differences: 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]), 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)): if (int(list_predictive[x * 4 + 0]) != int(list_predictive[x * 4 + 1])) and \ (int(list_predictive[x * 4 + 2]) != int(list_predictive[x * 4 + 3])): 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 difference_vectors: out_image.copy_block(black_image, block_size, vector.x_1, vector.y_1, vector.x_1, vector.y_1) out_image.save_image_quality(output_location, 25)
def make_merge_image(context: Context, frame_residual: Frame, frame_previous: Frame, list_predictive: list, list_residual: list, list_corrections: list, list_fade: list): """ This section can best be explained through pictures. A visual way of expressing what 'merging' is doing is this section in the wiki. https://github.com/aka-katto/dandere2x/wiki/How-Dandere2x-Works#part-2-using-observations-to-save-time Inputs: - frame(x) - frame(x+1)_residual - Residual vectors mapping frame(x+1)_residual -> frame(x+1) - Predictive vectors mapping frame(x) -> frame(x+1) Output: - frame(x+1) """ # Load context logger = logging.getLogger(__name__) out_image = Frame() out_image.create_new(frame_previous.width, frame_previous.height) # If list_predictive and list_predictive are both empty, then the residual frame # is simply the new image. if not list_predictive and not list_predictive: out_image.copy_image(frame_residual) return out_image # by copying the image first as the first step, all the predictive elements like # (0,0) -> (0,0) are also coppied out_image.copy_image(frame_previous) # run the image through the same plugins IN ORDER it was ran in d2x_cpp out_image = pframe_image(context, out_image, frame_previous, frame_residual, list_residual, list_predictive) out_image = fade_image(context, out_image, list_fade) out_image = correct_image(context, out_image, list_corrections) return out_image
def make_merge_image(context: Context, frame_inversion: Frame, frame_base: Frame, list_predictive: list, list_differences: list, list_corrections: list, list_fade: list): # Load context logger = logging.getLogger(__name__) out_image = Frame() out_image.create_new(frame_base.width, frame_base.height) # assess the two cases where out images are either duplicates or a new frame completely if not list_predictive and not list_differences: out_image.copy_image(frame_inversion) return out_image if list_predictive and not list_differences: out_image.copy_image(frame_base) return out_image # by copying the image first as the first step, all the predictive elements like # (0,0) -> (0,0) are also coppied out_image.copy_image(frame_base) # run the image through the same plugins IN ORDER it was ran in d2x_cpp out_image = pframe_image(context, out_image, frame_base, frame_inversion, list_differences, list_predictive) out_image = fade_image(context, out_image, list_fade) out_image = correct_image(context, out_image, list_corrections) return out_image
def correct_image(context, frame_base: Frame, list_correction: list): """ Try and fix some artifact-residuals by using the same image as reference. Method Tasks: - Load all the vectors for blocks pointing to a block with lower MSE - Apply all the vectors to the image to produce a more 'correct' image """ logger = logging.getLogger(__name__) # load context scale_factor = context.scale_factor out_image = Frame() out_image.create_new(frame_base.width, frame_base.height) out_image.copy_image(frame_base) scale_factor = int(scale_factor) block_size = context.correction_block_size for x in range(int(len(list_correction) / 4)): # / 4 because each there's 4 data points per block # load vector vector = DisplacementVector(int(list_correction[x * 4 + 0]), int(list_correction[x * 4 + 1]), int(list_correction[x * 4 + 2]), int(list_correction[x * 4 + 3])) # apply vector 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 debug_image(block_size, frame_base, list_predictive, list_differences, output_location): """ Note: I haven't made an effort to maintain this method, as it's only for debugging. This section can best be explained through pictures. A visual way of expressing what 'debug' is doing is this section in the wiki. https://github.com/aka-katto/dandere2x/wiki/How-Dandere2x-Works#part-1-identifying-what-needs-to-be-drawn In other words, this method shows where residuals are, and is useful for finding good settings to use for a video. Inputs: - frame(x) - Residual vectors mapping frame(x)_residual -> frame(x) Output: - frame(x) minus frame(x)_residuals = debug_image """ logger = logging.getLogger(__name__) difference_vectors = [] predictive_vectors = [] out_image = Frame() out_image.create_new(frame_base.width, frame_base.height) out_image.copy_image(frame_base) black_image = Frame() black_image.create_new(frame_base.width, frame_base.height) if not list_predictive and not list_differences: out_image.save_image(output_location) return if list_predictive and not list_differences: 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]), 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)): if (int(list_predictive[x * 4 + 0]) != int(list_predictive[x * 4 + 1])) and \ (int(list_predictive[x * 4 + 2]) != int(list_predictive[x * 4 + 3])): 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 difference_vectors: out_image.copy_block(black_image, block_size, vector.x_1, vector.y_1, vector.x_1, vector.y_1) out_image.save_image_quality(output_location, 25)
def make_residual_image(context: Context, raw_frame: Frame, list_residual: list, list_predictive: list): """ This section can best be explained through pictures. A visual way of expressing what 'make_residual_image' is doing is this section in the wiki. https://github.com/aka-katto/dandere2x/wiki/How-Dandere2x-Works#observation_3 Inputs: - frame(x) - Residual vectors mapping frame(x)_residual -> frame(x) Output: - frame(x)_residual """ # Some conditions to check before making a residual image, in both cases, we don't need to do any actual # processing in the function call, if these conditions hold true. if not list_residual and list_predictive: """ If there are no items in 'list_residuals' but have list_predictives then the two frames are identical, so no residual image needed. """ residual_image = Frame() residual_image.create_new(1, 1) return residual_image if not list_residual and not list_predictive: """ 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. """ residual_image = Frame() residual_image.create_new(raw_frame.width, raw_frame.height) residual_image.copy_image(raw_frame) return residual_image buffer = 5 block_size = context.block_size bleed = context.bleed """ First make a 'bleeded' version of input_frame, as we need to create a buffer in the event the 'bleed' ends up going out of bounds. In other words, crop the image into an even larger image, so that if if we need to access out of bounds pixels, and place black pixels where it would be out of bounds. """ bleed_frame = raw_frame.create_bleeded_image(buffer) # size of output image is determined based off how many residuals there are image_size = int(math.sqrt(len(list_residual) / 4) + 1) * (block_size + bleed * 2) residual_image = Frame() residual_image.create_new(image_size, image_size) for x in range(int(len(list_residual) / 4)): # load every element in the list into a vector vector = DisplacementVector(int(list_residual[x * 4 + 0]), int(list_residual[x * 4 + 1]), int(list_residual[x * 4 + 2]), int(list_residual[x * 4 + 3])) # apply that vector to the image by copying over their respective blocks. residual_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)) return residual_image
def make_residual_image(context: Context, raw_frame: Frame, list_residual: list, list_predictive: list): """ This section can best be explained through pictures. A visual way of expressing what 'make_residual_image' is doing is this section in the wiki. https://github.com/aka-katto/dandere2x/wiki/How-Dandere2x-Works#observation_3 Inputs: - frame(x) - Residual vectors mapping frame(x)_residual -> frame(x) Output: - frame(x)_residual """ residual_vectors = [] buffer = 5 block_size = context.block_size bleed = context.bleed # first make a 'bleeded' version of input_frame, as we need to create a buffer in the event the 'bleed' # ends up going out of bounds. bleed_frame = raw_frame.create_bleeded_image(buffer) # if there are no items in 'list_residuals' but have list_predictives # then the two frames are identical, so no residual image needed. if not list_residual and list_predictive: out_image = Frame() out_image.create_new(1, 1) return out_image # 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_residual and not list_predictive: out_image = Frame() out_image.create_new(raw_frame.width, raw_frame.height) out_image.copy_image(raw_frame) return out_image # turn the list of residuals into a list of vectors for x in range(int(len(list_residual) / 4)): residual_vectors.append( DisplacementVector(int(list_residual[x * 4]), int(list_residual[x * 4 + 1]), int(list_residual[x * 4 + 2]), int(list_residual[x * 4 + 3]))) # size of output image is determined based off how many residuals there are image_size = int(math.sqrt(len(list_residual) / 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 residual frame using vectors. for vector in residual_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)) return out_image