def add_result_to_cv( data, result, cv, LF_crosshair, mask_sum, bs_x, bs_y, bxx, dc ):
""" note: numpy arrays are passed by reference ... I think
"""
H_mask = hp.eval_res['h_mask']
W_mask = hp.eval_res['w_mask']
m = hp.eval_res['m']
print( 'x', end='', flush=True )
by = result[1]['py']
sv = result[0]['cv']
if len(sv.shape) !=4:
sv_v = result[0][ 'cv_v']
sv_h = result[0]['cv_h']
mask = im.get_mask(H_mask,W_mask,m)
mask3d = np.expand_dims(mask, axis = 2)
mask3d = np.tile(mask3d, (1, 1, 3))
maskLF = np.expand_dims(mask3d, axis = 3)
maskLF = np.transpose(np.tile(maskLF, (1, 1, 1,9)),[3,0,1,2])
# cv data is in the center of the result stack
# lazy, hardcoded the current fixed size
p = H_mask//2 - dc['SY']//2
q = H_mask//2 + dc['SY']//2
H_patch = dc['H']
W_patch = dc['W']
for bx in range(bxx):
px = bs_x * bx + dc['SX']
py = bs_y * by + dc['SY']
if len(sv.shape) == 4:
cv[py-p:py+q , px-p:px+q, :] = np.add(cv[py-p:py+q , px-p:px+q, : ],
np.multiply(sv[bx, H_patch//2 - H_mask//2 : H_patch//2 + H_mask//2 ,H_patch//2 - H_mask//2 : H_patch//2 + H_mask//2, :], mask3d))
else:
cv[py - p:py + q, px - p:px + q, :] = np.add(cv[py - p:py + q, px - p:px + q, :], np.multiply(sv[bx, 4,
H_patch // 2 - H_mask // 2: H_patch // 2 + H_mask // 2,
H_patch // 2 - H_mask // 2: H_patch // 2 + H_mask // 2,
:], mask3d))
LF_crosshair[0, :, py - p:py + q, px - p:px + q, :] = np.add(LF_crosshair[0, :, py - p:py + q, px - p:px + q, :]
, np.multiply(
sv_v[bx, :, H_patch // 2 - H_mask // 2: H_patch // 2 + H_mask // 2,
H_patch // 2 - H_mask // 2: H_patch // 2 + H_mask // 2, :], maskLF))
LF_crosshair[1, :, py - p:py + q, px - p:px + q, :] = np.add(LF_crosshair[1, :, py - p:py + q, px - p:px + q, :]
, np.multiply(
sv_h[bx, :, H_patch // 2 - H_mask // 2: H_patch // 2 + H_mask // 2,
H_patch // 2 - H_mask // 2: H_patch // 2 + H_mask // 2, :], maskLF))
mask_sum[py-p:py+q , px-p:px+q] = mask_sum[py-p:py+q , px-p:px+q] + mask
def add_result_to_cv(data, result, cv_interp, cv_raw, mask_sum, bs_x, bs_y,
bxx):
""" note: numpy arrays are passed by reference ... I think
"""
H_mask = hp.eval_res['h_mask']
W_mask = hp.eval_res['w_mask']
m = hp.eval_res['m']
print('x', end='', flush=True)
by = result[1]['py']
sv = result[0]['cv']
mask = im.get_mask(H_mask, W_mask, m)
mask3d = np.expand_dims(mask, axis=2)
num_channels = cv_interp.shape[-1]
mask3d = np.tile(mask3d, (1, 1, num_channels))
# cv data is in the center of the result stack
# lazy, hardcoded the current fixed size
p = H_mask // 2 - hp.sy_HR // 2
q = H_mask // 2 + hp.sy_HR // 2
H_patch = hp.H_HR
W_patch = hp.W_HR
for bx in range(bxx):
px = bs_x * bx + hp.sx_HR
py = bs_y * by + hp.sy_HR
cv_interp[py - p:py + q, px - p:px + q, :] = np.add(
cv_interp[py - p:py + q, px - p:px + q, :],
np.multiply(
sv[bx, H_patch // 2 - H_mask // 2:H_patch // 2 + H_mask // 2,
H_patch // 2 - H_mask // 2:H_patch // 2 + H_mask // 2, :],
mask3d))
cv_raw[py:py + 32, px:px + 32, :] = sv[bx, 32:64, 32:64, :]
mask_sum[py - p:py + q,
px - p:px + q] = mask_sum[py - p:py + q, px - p:px + q] + mask
def add_result_to_cv(data, result, LF_crosshair, mask_sum, bs_x, bs_y, bxx,
scale, colorspace):
""" note: numpy arrays are passed by reference ... I think
"""
H_mask = hp.eval_res['h_mask_' + scale]
W_mask = hp.eval_res['w_mask_' + scale]
m = hp.eval_res['m_' + scale]
print('x', end='', flush=True)
by = result[1]['py']
sv_v = result[0]['SR_v_' + scale]
sv_h = result[0]['SR_h_' + scale]
H_patch = sv_v.shape[-2]
# if colorspace == 'YCBCR':
# sv_v = np.clip(sv_v, 16.0 / 255.0, 240.0 / 255.0)
# sv_v[...,0] = np.clip(sv_v[...,0], 16.0 / 255.0, 235.0 / 255.0)
# sv_h = np.clip(sv_h, 16.0 / 255.0, 240.0 / 255.0)
# sv_h[...,0] = np.clip(sv_h[...,0], 16.0 / 255.0, 235.0 / 255.0)
# else:
# sv_v = np.clip(sv_v,0,1)
# sv_h = np.clip(sv_h, 0, 1)
# sv_v = np.clip(sv_v,0,1)
# sv_h = np.clip(sv_h, 0, 1)
num_channels = LF_crosshair.shape[-1]
mask = im.get_mask(H_mask, W_mask, m)
mask3d = np.expand_dims(mask, axis=2)
mask3d = np.tile(mask3d, (1, 1, num_channels))
maskLF = np.expand_dims(mask3d, axis=3)
maskLF = np.transpose(np.tile(maskLF, (1, 1, 1, 9)), [3, 0, 1, 2])
# cv data is in the center of the result stack
# lazy, hardcoded the current fixed size
if scale == 's2':
p = H_mask // 2 - hp.sy_s2 // 2
q = H_mask // 2 + hp.sy_s2 // 2
sx = hp.sx_s2
sy = hp.sy_s2
elif scale == 's4':
p = H_mask // 2 - hp.sy_HR // 2
q = H_mask // 2 + hp.sy_HR // 2
sx = hp.sx_HR
sy = hp.sy_HR
for bx in range(bxx):
px = bs_x * bx + sx
py = bs_y * by + sy
LF_crosshair[0, :, py - p:py + q, px - p:px + q, :] = np.add(
LF_crosshair[0, :, py - p:py + q, px - p:px + q, :],
np.multiply(
sv_v[bx, :,
H_patch // 2 - H_mask // 2:H_patch // 2 + H_mask // 2,
H_patch // 2 - H_mask // 2:H_patch // 2 + H_mask // 2, :],
maskLF))
LF_crosshair[1, :, py - p:py + q, px - p:px + q, :] = np.add(
LF_crosshair[1, :, py - p:py + q, px - p:px + q, :],
np.multiply(
sv_h[bx, :,
H_patch // 2 - H_mask // 2:H_patch // 2 + H_mask // 2,
H_patch // 2 - H_mask // 2:H_patch // 2 + H_mask // 2, :],
maskLF))
mask_sum[py - p:py + q,
px - p:px + q] = mask_sum[py - p:py + q, px - p:px + q] + mask