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from __future__ import absolute_import, division, print_function, unicode_literals
import os
import cv2
import copy
import numpy as np
import scipy.io as sio
from tool.utils.common_utils import interp, BFconsistCheck, \
FBconsistCheck, consistCheck, get_KeySourceFrame_flowNN_gradient
def get_flowNN_gradient(args,
gradient_x,
gradient_y,
mask_RGB,
mask,
videoFlowF,
videoFlowB,
videoNonLocalFlowF,
videoNonLocalFlowB):
# gradient_x: imgH x (imgW - 1 + 1) x 3 x nFrame
# gradient_y: (imgH - 1 + 1) x imgW x 3 x nFrame
# mask_RGB: imgH x imgW x nFrame
# mask: imgH x imgW x nFrame
# videoFlowF: imgH x imgW x 2 x (nFrame - 1) | [u, v]
# videoFlowB: imgH x imgW x 2 x (nFrame - 1) | [u, v]
# videoNonLocalFlowF: imgH x imgW x 2 x 3 x nFrame
# videoNonLocalFlowB: imgH x imgW x 2 x 3 x nFrame
if args.Nonlocal:
num_candidate = 5
else:
num_candidate = 2
imgH, imgW, nFrame = mask.shape
numPix = np.sum(mask)
# |--------------------| |--------------------|
# | y | | v |
# | x * | | u * |
# | | | |
# |--------------------| |--------------------|
# sub: numPix * 3 | [y, x, t]
# flowNN: numPix * 3 * 2 | [y, x, t], [BN, FN]
# HaveFlowNN: imgH * imgW * nFrame * 2
# numPixInd: imgH * imgW * nFrame
# consistencyMap: imgH * imgW * 5 * nFrame | [BN, FN, NL2, NL3, NL4]
# consistency_uv: imgH * imgW * [BN, FN] * [u, v] * nFrame
# sub: numPix * [y, x, t] | position of mising pixels
sub = np.concatenate((np.where(mask == 1)[0].reshape(-1, 1),
np.where(mask == 1)[1].reshape(-1, 1),
np.where(mask == 1)[2].reshape(-1, 1)), axis=1)
# flowNN: numPix * [y, x, t] * [BN, FN] | flow neighbors
flowNN = np.ones((numPix, 3, 2)) * 99999 # * -1
HaveFlowNN = np.ones((imgH, imgW, nFrame, 2)) * 99999
HaveFlowNN[mask, :] = 0
numPixInd = np.ones((imgH, imgW, nFrame)) * -1
consistencyMap = np.zeros((imgH, imgW, num_candidate, nFrame))
consistency_uv = np.zeros((imgH, imgW, 2, 2, nFrame))
# numPixInd[y, x, t] gives the index of the missing pixel@[y, x, t] in sub,
# i.e. which row. numPixInd[y, x, t] = idx; sub[idx, :] = [y, x, t]
for idx in range(len(sub)):
numPixInd[sub[idx, 0], sub[idx, 1], sub[idx, 2]] = idx
# Initialization
frameIndSetF = range(1, nFrame)
frameIndSetB = range(nFrame - 2, -1, -1)
# 1. Forward Pass (backward flow propagation)
print('Forward Pass......')
NN_idx = 0 # BN:0
for indFrame in frameIndSetF:
# Bool indicator of missing pixels at frame t
holepixPosInd = (sub[:, 2] == indFrame)
# Hole pixel location at frame t, i.e. [y, x, t]
holepixPos = sub[holepixPosInd, :]
# Calculate the backward flow neighbor. Should be located at frame t-1
flowB_neighbor = copy.deepcopy(holepixPos)
flowB_neighbor = flowB_neighbor.astype(np.float32)
flowB_vertical = videoFlowB[:, :, 1, indFrame - 1] # t --> t-1
flowB_horizont = videoFlowB[:, :, 0, indFrame - 1]
flowF_vertical = videoFlowF[:, :, 1, indFrame - 1] # t-1 --> t
flowF_horizont = videoFlowF[:, :, 0, indFrame - 1]
flowB_neighbor[:, 0] += flowB_vertical[holepixPos[:, 0], holepixPos[:, 1]]
flowB_neighbor[:, 1] += flowB_horizont[holepixPos[:, 0], holepixPos[:, 1]]
flowB_neighbor[:, 2] -= 1
# Round the backward flow neighbor location
flow_neighbor_int = np.round(copy.deepcopy(flowB_neighbor)).astype(np.int32)
# Chen: I should combine the following two operations together
# Check the backward/forward consistency
IsConsist, _ = BFconsistCheck(flowB_neighbor,
flowF_vertical,
flowF_horizont,
holepixPos,
args.consistencyThres)
trueConsist = IsConsist[IsConsist == True]
BFdiff, BF_uv = consistCheck(videoFlowF[:, :, :, indFrame - 1],
videoFlowB[:, :, :, indFrame - 1])
# Check out-of-boundary
# Last column and last row does not have valid gradient
ValidPos = np.logical_and(
np.logical_and(flow_neighbor_int[:, 0] >= 0,
flow_neighbor_int[:, 0] < imgH - 1),
np.logical_and(flow_neighbor_int[:, 1] >= 0,
flow_neighbor_int[:, 1] < imgW - 1))
# Only work with pixels that are not out-of-boundary
holepixPos = holepixPos[ValidPos, :]
flowB_neighbor = flowB_neighbor[ValidPos, :]
flow_neighbor_int = flow_neighbor_int[ValidPos, :]
IsConsist = IsConsist[ValidPos]
# For each missing pixel in holepixPos|[y, x, t],
# we check its backward flow neighbor flowB_neighbor|[y', x', t-1].
# Case 1: If mask[round(y'), round(x'), t-1] == 0,
# the backward flow neighbor of [y, x, t] is known.
# [y', x', t-1] is the backward flow neighbor.
# KnownInd: Among all backward flow neighbors, which pixel is known.
KnownInd = mask[flow_neighbor_int[:, 0],
flow_neighbor_int[:, 1],
indFrame - 1] == 0
KnownIsConsist = np.logical_and(KnownInd, IsConsist)
holepixPos_valid = holepixPos[KnownIsConsist]
# We save backward flow neighbor flowB_neighbor in flowNN
flowNN[numPixInd[holepixPos[KnownIsConsist, 0],
holepixPos[KnownIsConsist, 1],
indFrame].astype(np.int32), :, NN_idx] = \
flowB_neighbor[KnownIsConsist, :]
# flowNN[np.where(holepixPosInd == 1)[0][ValidPos][KnownIsConsist], :, 0] = \
# flowB_neighbor[KnownIsConsist, :]
# We mark [y, x, t] in HaveFlowNN as 1
HaveFlowNN[holepixPos[KnownIsConsist, 0],
holepixPos[KnownIsConsist, 1],
indFrame,
NN_idx] = 1
# HaveFlowNN[:, :, :, 0]
# 0: Backward flow neighbor can not be reached
# 1: Backward flow neighbor can be reached
# -1: Pixels that do not need to be completed
consistency_uv[holepixPos[KnownIsConsist, 0], holepixPos[KnownIsConsist, 1], NN_idx, 0, indFrame] = np.abs(BF_uv[holepixPos[KnownIsConsist, 0], holepixPos[KnownIsConsist, 1], 0])
consistency_uv[holepixPos[KnownIsConsist, 0], holepixPos[KnownIsConsist, 1], NN_idx, 1, indFrame] = np.abs(BF_uv[holepixPos[KnownIsConsist, 0], holepixPos[KnownIsConsist, 1], 1])
# Case 2: If mask[round(y'), round(x'), t-1] == 1,
# the pixel@[round(y'), round(x'), t-1] is also occluded.
# We further check if we already assign a backward flow neighbor for the backward flow neighbor
# If HaveFlowNN[round(y'), round(x'), t-1] == 0,
# this is isolated pixel. Do nothing.
# If HaveFlowNN[round(y'), round(x'), t-1] == 1,
# we can borrow the value and refine it.
UnknownInd = np.invert(KnownInd)
# If we already assign a backward flow neighbor@[round(y'), round(x'), t-1]
HaveNNInd = HaveFlowNN[flow_neighbor_int[:, 0],
flow_neighbor_int[:, 1],
indFrame - 1,
NN_idx] == 1
# Unknown & IsConsist & HaveNNInd
Valid_ = np.logical_and.reduce((UnknownInd, HaveNNInd, IsConsist))
refineVec = np.concatenate((
(flowB_neighbor[:, 0] - flow_neighbor_int[:, 0]).reshape(-1, 1),
(flowB_neighbor[:, 1] - flow_neighbor_int[:, 1]).reshape(-1, 1),
np.zeros((flowB_neighbor[:, 0].shape[0])).reshape(-1, 1)), 1)
# Check if the transitive backward flow neighbor of [y, x, t] is known.
# Sometimes after refinement, it is no longer known.
flowNN_tmp = copy.deepcopy(flowNN[numPixInd[flow_neighbor_int[:, 0],
flow_neighbor_int[:, 1],
indFrame - 1].astype(np.int32), :, NN_idx] + refineVec[:, :])
flowNN_tmp = np.round(flowNN_tmp).astype(np.int32)
# Check out-of-boundary. flowNN_tmp may be out-of-boundary
ValidPos_ = np.logical_and(
np.logical_and(flowNN_tmp[:, 0] >= 0,
flowNN_tmp[:, 0] < imgH - 1),
np.logical_and(flowNN_tmp[:, 1] >= 0,
flowNN_tmp[:, 1] < imgW - 1))
# Change the out-of-boundary value to 0, in order to run mask[y,x,t]
# in the next line. It won't affect anything as ValidPos_ is saved already
flowNN_tmp[np.invert(ValidPos_), :] = 0
ValidNN = mask[flowNN_tmp[:, 0],
flowNN_tmp[:, 1],
flowNN_tmp[:, 2]] == 0
# Valid = np.logical_and.reduce((Valid_, ValidNN, ValidPos_))
Valid = np.logical_and.reduce((Valid_, ValidPos_))
# We save the transitive backward flow neighbor flowB_neighbor in flowNN
flowNN[numPixInd[holepixPos[Valid, 0],
holepixPos[Valid, 1],
indFrame].astype(np.int32), :, NN_idx] = \
flowNN[numPixInd[flow_neighbor_int[Valid, 0],
flow_neighbor_int[Valid, 1],
indFrame - 1].astype(np.int32), :, NN_idx] + refineVec[Valid, :]
# We mark [y, x, t] in HaveFlowNN as 1
HaveFlowNN[holepixPos[Valid, 0],
holepixPos[Valid, 1],
indFrame,
NN_idx] = 1
consistency_uv[holepixPos[Valid, 0], holepixPos[Valid, 1], NN_idx, 0, indFrame] = np.maximum(np.abs(BF_uv[holepixPos[Valid, 0], holepixPos[Valid, 1], 0]), np.abs(consistency_uv[flow_neighbor_int[Valid, 0], flow_neighbor_int[Valid, 1], NN_idx, 0, indFrame - 1]))
consistency_uv[holepixPos[Valid, 0], holepixPos[Valid, 1], NN_idx, 1, indFrame] = np.maximum(np.abs(BF_uv[holepixPos[Valid, 0], holepixPos[Valid, 1], 1]), np.abs(consistency_uv[flow_neighbor_int[Valid, 0], flow_neighbor_int[Valid, 1], NN_idx, 1, indFrame - 1]))
consistencyMap[:, :, NN_idx, indFrame] = (consistency_uv[:, :, NN_idx, 0, indFrame] ** 2 + consistency_uv[:, :, NN_idx, 1, indFrame] ** 2) ** 0.5
print("Frame {0:3d}: {1:8d} + {2:8d} = {3:8d}"
.format(indFrame,
np.sum(HaveFlowNN[:, :, indFrame, NN_idx] == 1),
np.sum(HaveFlowNN[:, :, indFrame, NN_idx] == 0),
np.sum(HaveFlowNN[:, :, indFrame, NN_idx] != 99999)))
# 2. Backward Pass (forward flow propagation)
print('Backward Pass......')
NN_idx = 1 # FN:1
for indFrame in frameIndSetB:
# Bool indicator of missing pixels at frame t
holepixPosInd = (sub[:, 2] == indFrame)
# Hole pixel location at frame t, i.e. [y, x, t]
holepixPos = sub[holepixPosInd, :]
# Calculate the forward flow neighbor. Should be located at frame t+1
flowF_neighbor = copy.deepcopy(holepixPos)
flowF_neighbor = flowF_neighbor.astype(np.float32)
flowF_vertical = videoFlowF[:, :, 1, indFrame] # t --> t+1
flowF_horizont = videoFlowF[:, :, 0, indFrame]
flowB_vertical = videoFlowB[:, :, 1, indFrame] # t+1 --> t
flowB_horizont = videoFlowB[:, :, 0, indFrame]
flowF_neighbor[:, 0] += flowF_vertical[holepixPos[:, 0], holepixPos[:, 1]]
flowF_neighbor[:, 1] += flowF_horizont[holepixPos[:, 0], holepixPos[:, 1]]
flowF_neighbor[:, 2] += 1
# Round the forward flow neighbor location
flow_neighbor_int = np.round(copy.deepcopy(flowF_neighbor)).astype(np.int32)
# Check the forawrd/backward consistency
IsConsist, _ = FBconsistCheck(flowF_neighbor,
flowB_vertical,
flowB_horizont,
holepixPos,
args.consistencyThres)
FBdiff, FB_uv = consistCheck(videoFlowB[:, :, :, indFrame],
videoFlowF[:, :, :, indFrame])
# Check out-of-boundary
# Last column and last row does not have valid gradient
ValidPos = np.logical_and(
np.logical_and(flow_neighbor_int[:, 0] >= 0,
flow_neighbor_int[:, 0] < imgH - 1),
np.logical_and(flow_neighbor_int[:, 1] >= 0,
flow_neighbor_int[:, 1] < imgW - 1))
# Only work with pixels that are not out-of-boundary
holepixPos = holepixPos[ValidPos, :]
flowF_neighbor = flowF_neighbor[ValidPos, :]
flow_neighbor_int = flow_neighbor_int[ValidPos, :]
IsConsist = IsConsist[ValidPos]
# Case 1:
KnownInd = mask[flow_neighbor_int[:, 0],
flow_neighbor_int[:, 1],
indFrame + 1] == 0
KnownIsConsist = np.logical_and(KnownInd, IsConsist)
flowNN[numPixInd[holepixPos[KnownIsConsist, 0],
holepixPos[KnownIsConsist, 1],
indFrame].astype(np.int32), :, NN_idx] = \
flowF_neighbor[KnownIsConsist, :]
HaveFlowNN[holepixPos[KnownIsConsist, 0],
holepixPos[KnownIsConsist, 1],
indFrame,
NN_idx] = 1
consistency_uv[holepixPos[KnownIsConsist, 0], holepixPos[KnownIsConsist, 1], NN_idx, 0, indFrame] = np.abs(FB_uv[holepixPos[KnownIsConsist, 0], holepixPos[KnownIsConsist, 1], 0])
consistency_uv[holepixPos[KnownIsConsist, 0], holepixPos[KnownIsConsist, 1], NN_idx, 1, indFrame] = np.abs(FB_uv[holepixPos[KnownIsConsist, 0], holepixPos[KnownIsConsist, 1], 1])
# Case 2:
UnknownInd = np.invert(KnownInd)
HaveNNInd = HaveFlowNN[flow_neighbor_int[:, 0],
flow_neighbor_int[:, 1],
indFrame + 1,
NN_idx] == 1
# Unknown & IsConsist & HaveNNInd
Valid_ = np.logical_and.reduce((UnknownInd, HaveNNInd, IsConsist))
refineVec = np.concatenate((
(flowF_neighbor[:, 0] - flow_neighbor_int[:, 0]).reshape(-1, 1),
(flowF_neighbor[:, 1] - flow_neighbor_int[:, 1]).reshape(-1, 1),
np.zeros((flowF_neighbor[:, 0].shape[0])).reshape(-1, 1)), 1)
# Check if the transitive backward flow neighbor of [y, x, t] is known.
# Sometimes after refinement, it is no longer known.
flowNN_tmp = copy.deepcopy(flowNN[numPixInd[flow_neighbor_int[:, 0],
flow_neighbor_int[:, 1],
indFrame + 1].astype(np.int32), :, NN_idx] + refineVec[:, :])
flowNN_tmp = np.round(flowNN_tmp).astype(np.int32)
# Check out-of-boundary. flowNN_tmp may be out-of-boundary
ValidPos_ = np.logical_and(
np.logical_and(flowNN_tmp[:, 0] >= 0,
flowNN_tmp[:, 0] < imgH - 1),
np.logical_and(flowNN_tmp[:, 1] >= 0,
flowNN_tmp[:, 1] < imgW - 1))
# Change the out-of-boundary value to 0, in order to run mask[y,x,t]
# in the next line. It won't affect anything as ValidPos_ is saved already
flowNN_tmp[np.invert(ValidPos_), :] = 0
ValidNN = mask[flowNN_tmp[:, 0],
flowNN_tmp[:, 1],
flowNN_tmp[:, 2]] == 0
# Valid = np.logical_and.reduce((Valid_, ValidNN, ValidPos_))
Valid = np.logical_and.reduce((Valid_, ValidPos_))
# We save the transitive backward flow neighbor flowB_neighbor in flowNN
flowNN[numPixInd[holepixPos[Valid, 0],
holepixPos[Valid, 1],
indFrame].astype(np.int32), :, NN_idx] = \
flowNN[numPixInd[flow_neighbor_int[Valid, 0],
flow_neighbor_int[Valid, 1],
indFrame + 1].astype(np.int32), :, NN_idx] + refineVec[Valid, :]
# We mark [y, x, t] in HaveFlowNN as 1
HaveFlowNN[holepixPos[Valid, 0],
holepixPos[Valid, 1],
indFrame,
NN_idx] = 1
consistency_uv[holepixPos[Valid, 0], holepixPos[Valid, 1], NN_idx, 0, indFrame] = np.maximum(np.abs(FB_uv[holepixPos[Valid, 0], holepixPos[Valid, 1], 0]), np.abs(consistency_uv[flow_neighbor_int[Valid, 0], flow_neighbor_int[Valid, 1], NN_idx, 0, indFrame + 1]))
consistency_uv[holepixPos[Valid, 0], holepixPos[Valid, 1], NN_idx, 1, indFrame] = np.maximum(np.abs(FB_uv[holepixPos[Valid, 0], holepixPos[Valid, 1], 1]), np.abs(consistency_uv[flow_neighbor_int[Valid, 0], flow_neighbor_int[Valid, 1], NN_idx, 1, indFrame + 1]))
consistencyMap[:, :, NN_idx, indFrame] = (consistency_uv[:, :, NN_idx, 0, indFrame] ** 2 + consistency_uv[:, :, NN_idx, 1, indFrame] ** 2) ** 0.5
print("Frame {0:3d}: {1:8d} + {2:8d} = {3:8d}"
.format(indFrame,
np.sum(HaveFlowNN[:, :, indFrame, NN_idx] == 1),
np.sum(HaveFlowNN[:, :, indFrame, NN_idx] == 0),
np.sum(HaveFlowNN[:, :, indFrame, NN_idx] != 99999)))
# Interpolation
gradient_x_BN = copy.deepcopy(gradient_x)
gradient_y_BN = copy.deepcopy(gradient_y)
gradient_x_FN = copy.deepcopy(gradient_x)
gradient_y_FN = copy.deepcopy(gradient_y)
for indFrame in range(nFrame):
# Index of missing pixel whose backward flow neighbor is from frame indFrame
SourceFmInd = np.where(flowNN[:, 2, 0] == indFrame)
print("{0:8d} pixels are from source Frame {1:3d}"
.format(len(SourceFmInd[0]), indFrame))
# The location of the missing pixel whose backward flow neighbor is
# from frame indFrame flowNN[SourceFmInd, 0, 0], flowNN[SourceFmInd, 1, 0]
if len(SourceFmInd[0]) != 0:
# |--------------------|
# | y |
# | x * |
# | |
# |--------------------|
# sub: numPix x 3 [y, x, t]
# img: [y, x]
# interp(img, x, y)
gradient_x_BN[sub[SourceFmInd[0], :][:, 0],
sub[SourceFmInd[0], :][:, 1],
:, sub[SourceFmInd[0], :][:, 2]] = \
interp(gradient_x_BN[:, :, :, indFrame],
flowNN[SourceFmInd, 1, 0].reshape(-1),
flowNN[SourceFmInd, 0, 0].reshape(-1))
gradient_y_BN[sub[SourceFmInd[0], :][:, 0],
sub[SourceFmInd[0], :][:, 1],
:, sub[SourceFmInd[0], :][:, 2]] = \
interp(gradient_y_BN[:, :, :, indFrame],
flowNN[SourceFmInd, 1, 0].reshape(-1),
flowNN[SourceFmInd, 0, 0].reshape(-1))
assert(((sub[SourceFmInd[0], :][:, 2] - indFrame) <= 0).sum() == 0)
for indFrame in range(nFrame - 1, -1, -1):
# Index of missing pixel whose forward flow neighbor is from frame indFrame
SourceFmInd = np.where(flowNN[:, 2, 1] == indFrame)
print("{0:8d} pixels are from source Frame {1:3d}"
.format(len(SourceFmInd[0]), indFrame))
if len(SourceFmInd[0]) != 0:
gradient_x_FN[sub[SourceFmInd[0], :][:, 0],
sub[SourceFmInd[0], :][:, 1],
:, sub[SourceFmInd[0], :][:, 2]] = \
interp(gradient_x_FN[:, :, :, indFrame],
flowNN[SourceFmInd, 1, 1].reshape(-1),
flowNN[SourceFmInd, 0, 1].reshape(-1))
gradient_y_FN[sub[SourceFmInd[0], :][:, 0],
sub[SourceFmInd[0], :][:, 1],
:, sub[SourceFmInd[0], :][:, 2]] = \
interp(gradient_y_FN[:, :, :, indFrame],
flowNN[SourceFmInd, 1, 1].reshape(-1),
flowNN[SourceFmInd, 0, 1].reshape(-1))
assert(((indFrame - sub[SourceFmInd[0], :][:, 2]) <= 0).sum() == 0)
# New mask
mask_tofill = np.zeros((imgH, imgW, nFrame)).astype(np.bool)
for indFrame in range(nFrame):
if args.Nonlocal:
consistencyMap[:, :, 2, indFrame], _ = consistCheck(
videoNonLocalFlowB[:, :, :, 0, indFrame],
videoNonLocalFlowF[:, :, :, 0, indFrame])
consistencyMap[:, :, 3, indFrame], _ = consistCheck(
videoNonLocalFlowB[:, :, :, 1, indFrame],
videoNonLocalFlowF[:, :, :, 1, indFrame])
consistencyMap[:, :, 4, indFrame], _ = consistCheck(
videoNonLocalFlowB[:, :, :, 2, indFrame],
videoNonLocalFlowF[:, :, :, 2, indFrame])
HaveNN = np.zeros((imgH, imgW, num_candidate))
if args.Nonlocal:
HaveKeySourceFrameFlowNN, gradient_x_KeySourceFrameFlowNN, gradient_y_KeySourceFrameFlowNN = \
get_KeySourceFrame_flowNN_gradient(sub,
indFrame,
mask,
videoNonLocalFlowB,
videoNonLocalFlowF,
gradient_x,
gradient_y,
args.consistencyThres)
HaveNN[:, :, 2] = HaveKeySourceFrameFlowNN[:, :, 0] == 1
HaveNN[:, :, 3] = HaveKeySourceFrameFlowNN[:, :, 1] == 1
HaveNN[:, :, 4] = HaveKeySourceFrameFlowNN[:, :, 2] == 1
HaveNN[:, :, 0] = HaveFlowNN[:, :, indFrame, 0] == 1
HaveNN[:, :, 1] = HaveFlowNN[:, :, indFrame, 1] == 1
NotHaveNN = np.logical_and(np.invert(HaveNN.astype(np.bool)),
np.repeat(np.expand_dims((mask[:, :, indFrame]), 2), num_candidate, axis=2))
if args.Nonlocal:
HaveNN_sum = np.logical_or.reduce((HaveNN[:, :, 0],
HaveNN[:, :, 1],
HaveNN[:, :, 2],
HaveNN[:, :, 3],
HaveNN[:, :, 4]))
else:
HaveNN_sum = np.logical_or.reduce((HaveNN[:, :, 0],
HaveNN[:, :, 1]))
gradient_x_Candidate = np.zeros((imgH, imgW, 3, num_candidate))
gradient_y_Candidate = np.zeros((imgH, imgW, 3, num_candidate))
gradient_x_Candidate[:, :, :, 0] = gradient_x_BN[:, :, :, indFrame]
gradient_y_Candidate[:, :, :, 0] = gradient_y_BN[:, :, :, indFrame]
gradient_x_Candidate[:, :, :, 1] = gradient_x_FN[:, :, :, indFrame]
gradient_y_Candidate[:, :, :, 1] = gradient_y_FN[:, :, :, indFrame]
if args.Nonlocal:
gradient_x_Candidate[:, :, :, 2] = gradient_x_KeySourceFrameFlowNN[:, :, :, 0]
gradient_y_Candidate[:, :, :, 2] = gradient_y_KeySourceFrameFlowNN[:, :, :, 0]
gradient_x_Candidate[:, :, :, 3] = gradient_x_KeySourceFrameFlowNN[:, :, :, 1]
gradient_y_Candidate[:, :, :, 3] = gradient_y_KeySourceFrameFlowNN[:, :, :, 1]
gradient_x_Candidate[:, :, :, 4] = gradient_x_KeySourceFrameFlowNN[:, :, :, 2]
gradient_y_Candidate[:, :, :, 4] = gradient_y_KeySourceFrameFlowNN[:, :, :, 2]
consistencyMap[:, :, :, indFrame] = np.exp( - consistencyMap[:, :, :, indFrame] / args.alpha)
consistencyMap[NotHaveNN[:, :, 0], 0, indFrame] = 0
consistencyMap[NotHaveNN[:, :, 1], 1, indFrame] = 0
if args.Nonlocal:
consistencyMap[NotHaveNN[:, :, 2], 2, indFrame] = 0
consistencyMap[NotHaveNN[:, :, 3], 3, indFrame] = 0
consistencyMap[NotHaveNN[:, :, 4], 4, indFrame] = 0
weights = (consistencyMap[HaveNN_sum, :, indFrame] * HaveNN[HaveNN_sum, :]) / ((consistencyMap[HaveNN_sum, :, indFrame] * HaveNN[HaveNN_sum, :]).sum(axis=1, keepdims=True))
# Fix the numerical issue. 0 / 0
fix = np.where((consistencyMap[HaveNN_sum, :, indFrame] * HaveNN[HaveNN_sum, :]).sum(axis=1, keepdims=True) == 0)[0]
weights[fix, :] = HaveNN[HaveNN_sum, :][fix, :] / HaveNN[HaveNN_sum, :][fix, :].sum(axis=1, keepdims=True)
# Fuse RGB channel independently
gradient_x[HaveNN_sum, 0, indFrame] = \
np.sum(np.multiply(gradient_x_Candidate[HaveNN_sum, 0, :], weights), axis=1)
gradient_x[HaveNN_sum, 1, indFrame] = \
np.sum(np.multiply(gradient_x_Candidate[HaveNN_sum, 1, :], weights), axis=1)
gradient_x[HaveNN_sum, 2, indFrame] = \
np.sum(np.multiply(gradient_x_Candidate[HaveNN_sum, 2, :], weights), axis=1)
gradient_y[HaveNN_sum, 0, indFrame] = \
np.sum(np.multiply(gradient_y_Candidate[HaveNN_sum, 0, :], weights), axis=1)
gradient_y[HaveNN_sum, 1, indFrame] = \
np.sum(np.multiply(gradient_y_Candidate[HaveNN_sum, 1, :], weights), axis=1)
gradient_y[HaveNN_sum, 2, indFrame] = \
np.sum(np.multiply(gradient_y_Candidate[HaveNN_sum, 2, :], weights), axis=1)
mask_tofill[np.logical_and(np.invert(HaveNN_sum), mask[:, :, indFrame]), indFrame] = True
return gradient_x, gradient_y, mask_tofill
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