lightfield/code/train/util.py [9:128]:
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def np_space_to_depth(x, block_size):
    x = np.asarray(x)
    batch, height, width, depth = x.shape #NHWC
    reduced_height = height // block_size
    reduced_width = width // block_size
    y = x.reshape(batch, reduced_height, block_size,
                         reduced_width, block_size, depth)
    z = np.swapaxes(y, 2, 3).reshape(batch, reduced_height, reduced_width, -1)
    return z

# interleave numpy array
def interleave_np(r,x):
    #NCHW
    if (r==1):
        return x
    #elif (r==2):
    #    H,W = x.shape[2], x.shape[3]   
    #    return np.concatenate((x[:,:,0:H:r,0:W:r],x[:,:,0:H:r,1:W:r],x[:,:,1:H:r,0:W:r],x[:,:,1:H:r,1:W:r]),axis=1) 
    else:        
        return np.transpose(np_space_to_depth(np.transpose(x, (0,2,3,1)), r), (0,3,1,2))

# interleave tensor
def interleave(r,x):
    #NCHW
    if (r==1):
        return x
    else:
        return tf.space_to_depth(x, r, data_format='NCHW')

# deinterleave tensor
def deinterleave(r,x):
    if (r==1):
        return x
    else:
        return tf.depth_to_space(x,r,data_format='NCHW')

def init_weights(shape, init_method='xavier', xavier_params = (None, None), r = 0.5):
    #xavier
    (fan_in, fan_out) = xavier_params        
    filtersize = shape[1]*shape[2]
    high = np.sqrt(r*2.0/(fan_in+fan_out))
    low = -high
    return tf.Variable(tf.random_uniform(shape, minval=low, maxval=high, dtype=tf.float32))

def arcsin_hack(x):
    x = np.minimum(x, 1)
    x = np.maximum(x, -1)
    return np.arcsin(x)

def circ_filter(w): # reimplement from matlab function fspecial('disk', rad)
    rad = w/2
    rad = np.maximum(rad, 1e-6) # prevent zero-divide
    rad2 = rad**2
    crad  = np.ceil(rad-0.5) 
    l = np.linspace(-crad,crad,2*crad+1)
    [x,y] = np.meshgrid(l,l)
    maxxy = np.maximum(np.abs(x), np.abs(y))
    minxy = np.minimum(np.abs(x), np.abs(y))
    m1 = (rad2 < (maxxy+0.5)**2 + (minxy-0.5)**2)*(minxy-0.5) + (rad2 >= (maxxy+0.5)**2 + (minxy-0.5)**2)*np.sqrt(np.abs(rad2 - (maxxy + 0.5)**2))
    m2 = (rad2 > (maxxy-0.5)**2 + (minxy+0.5)**2)*(minxy+0.5) + (rad2 <= (maxxy-0.5)**2 + (minxy+0.5)**2)*np.sqrt(np.abs(rad2 - (maxxy - 0.5)**2))
    sgrid = (rad2*(0.5*(arcsin_hack(m2/rad) - arcsin_hack(m1/rad)) + 0.25*(np.sin(2*arcsin_hack(m2/rad)) - np.sin(2*arcsin_hack(m1/rad)))) - 
            (maxxy-0.5)*(m2-m1) + (m1-minxy+0.5))*((np.logical_or(np.logical_and((rad2 < (maxxy+0.5)**2 + (minxy+0.5)**2) , (rad2 > (maxxy-0.5)**2 + (minxy-0.5)**2)),
	        (np.logical_and(np.logical_and((minxy==0),(maxxy-0.5 < rad)),(maxxy+0.5>=rad))))))
    sgrid = sgrid + ((maxxy+0.5)**2 + (minxy+0.5)**2 < rad2)
    cradint = np.int(crad)
    sgrid[cradint,cradint] = np.minimum(np.pi*rad2,np.pi/2)
    if ((crad>0) and (rad > crad-0.5) and (rad2 < (crad-0.5)**2+0.25)): 
        m1  = np.sqrt(rad2 - (crad - 0.5)**2)
        m1n = m1/rad
        sg0 = 2*(rad2*(0.5*arcsin_hack(m1n) + 0.25*np.sin(2*arcsin_hack(m1n)))-m1*(crad-0.5))
        sgrid[2*cradint,cradint] = sg0
        sgrid[cradint,2*cradint] = sg0
        sgrid[cradint,0]        = sg0
        sgrid[0,cradint]        = sg0
        sgrid[2*cradint-1,cradint]   = sgrid[2*cradint-1,cradint] - sg0
        sgrid[cradint,2*cradint-1]   = sgrid[cradint,2*cradint-1] - sg0
        sgrid[cradint,1]        = sgrid[cradint,1]      - sg0
        sgrid[1,cradint]        = sgrid[1,cradint]      - sg0

    sgrid[cradint,cradint] = np.minimum(sgrid[cradint,cradint],1)
    kernel = (sgrid/np.sum(sgrid)).astype(dtype=np.float32)

    return kernel.shape[0], kernel



def cal_psnr_focalstack(recon_fs, true_fs, crop_width):
    C = recon_fs.shape[0]
    H = recon_fs.shape[1]
    W = recon_fs.shape[2]
    N = recon_fs.shape[3]
    PSNRs = np.zeros((N))
    for n in range(N):
        diff = recon_fs[:,crop_width:H-crop_width,crop_width:W-crop_width,n] - true_fs[:,crop_width:H-crop_width,crop_width:W-crop_width,n]
        rmse = math.sqrt(np.mean(diff**2.))
        PSNRs[n] = 20*math.log10(1.0/rmse)
    return PSNRs

def log10(x):
  numerator = tf.log(x)
  denominator = tf.log(tf.constant(10, dtype=numerator.dtype))
  return numerator / denominator

def cal_ssim_focalstack(recon_fs, true_fs, crop_width):
    #C-H-W-N
    C = recon_fs.shape[0]
    H = recon_fs.shape[1]
    W = recon_fs.shape[2]
    N = recon_fs.shape[3]
    SSIMs = np.zeros((N))
    y_pred = tf.transpose(recon_fs[:,crop_width:H-crop_width,crop_width:W-crop_width,:], [3,1,2,0]) #NHWC
    y_true = tf.transpose(true_fs[:,crop_width:H-crop_width,crop_width:W-crop_width,:], [3,1,2,0]) #NHWC
    SSIMs = tf.image.ssim(y_true, y_pred, max_val = 1.0)
    return SSIMs

def calImageGradients(images):
    # x is a 4-D tensor
    dx = images[:, :, 1:, :] - images[:, :, :-1, :]
    dy = images[:, 1:, :, :] - images[:, :-1, :, :]
    return dx, dy
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multifocal/code/multifocal_decomposition_from_rgbd/train/util.py [10:130]:
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def np_space_to_depth(x, block_size):
    x = np.asarray(x)
    batch, height, width, depth = x.shape #NHWC
    reduced_height = height // block_size
    reduced_width = width // block_size
    y = x.reshape(batch, reduced_height, block_size,
                         reduced_width, block_size, depth)
    z = np.swapaxes(y, 2, 3).reshape(batch, reduced_height, reduced_width, -1)
    return z

# interleave numpy array
def interleave_np(r,x):
    #NCHW
    if (r==1):
        return x
    #elif (r==2):
    #    H,W = x.shape[2], x.shape[3]   
    #    return np.concatenate((x[:,:,0:H:r,0:W:r],x[:,:,0:H:r,1:W:r],x[:,:,1:H:r,0:W:r],x[:,:,1:H:r,1:W:r]),axis=1) 
    else:        
        return np.transpose(np_space_to_depth(np.transpose(x, (0,2,3,1)), r), (0,3,1,2))

# interleave tensor
def interleave(r,x):
    #NCHW
    if (r==1):
        return x
    else:
        return tf.space_to_depth(x, r, data_format='NCHW')

# deinterleave tensor
def deinterleave(r,x):
    if (r==1):
        return x
    else:
        return tf.depth_to_space(x,r,data_format='NCHW')

def init_weights(shape, init_method='xavier', xavier_params = (None, None), r = 0.5):
    #xavier
    (fan_in, fan_out) = xavier_params        
    filtersize = shape[1]*shape[2]
    high = np.sqrt(r*2.0/(fan_in+fan_out))
    low = -high
    return tf.Variable(tf.random_uniform(shape, minval=low, maxval=high, dtype=tf.float32))

def arcsin_hack(x):
    x = np.minimum(x, 1)
    x = np.maximum(x, -1)
    return np.arcsin(x)

def circ_filter(w): # reimplement from matlab function fspecial('disk', rad)
    rad = w/2
    rad = np.maximum(rad, 1e-6) # prevent zero-divide
    rad2 = rad**2
    crad  = np.ceil(rad-0.5) 
    l = np.linspace(-crad,crad,2*crad+1)
    [x,y] = np.meshgrid(l,l)
    maxxy = np.maximum(np.abs(x), np.abs(y))
    minxy = np.minimum(np.abs(x), np.abs(y))
    m1 = (rad2 < (maxxy+0.5)**2 + (minxy-0.5)**2)*(minxy-0.5) + (rad2 >= (maxxy+0.5)**2 + (minxy-0.5)**2)*np.sqrt(np.abs(rad2 - (maxxy + 0.5)**2))
    m2 = (rad2 > (maxxy-0.5)**2 + (minxy+0.5)**2)*(minxy+0.5) + (rad2 <= (maxxy-0.5)**2 + (minxy+0.5)**2)*np.sqrt(np.abs(rad2 - (maxxy - 0.5)**2))
    sgrid = (rad2*(0.5*(arcsin_hack(m2/rad) - arcsin_hack(m1/rad)) + 0.25*(np.sin(2*arcsin_hack(m2/rad)) - np.sin(2*arcsin_hack(m1/rad)))) - 
            (maxxy-0.5)*(m2-m1) + (m1-minxy+0.5))*((np.logical_or(np.logical_and((rad2 < (maxxy+0.5)**2 + (minxy+0.5)**2) , (rad2 > (maxxy-0.5)**2 + (minxy-0.5)**2)),
	        (np.logical_and(np.logical_and((minxy==0),(maxxy-0.5 < rad)),(maxxy+0.5>=rad))))))
    sgrid = sgrid + ((maxxy+0.5)**2 + (minxy+0.5)**2 < rad2)
    cradint = np.int(crad)
    sgrid[cradint,cradint] = np.minimum(np.pi*rad2,np.pi/2)
    if ((crad>0) and (rad > crad-0.5) and (rad2 < (crad-0.5)**2+0.25)): 
        m1  = np.sqrt(rad2 - (crad - 0.5)**2)
        m1n = m1/rad
        sg0 = 2*(rad2*(0.5*arcsin_hack(m1n) + 0.25*np.sin(2*arcsin_hack(m1n)))-m1*(crad-0.5))
        sgrid[2*cradint,cradint] = sg0
        sgrid[cradint,2*cradint] = sg0
        sgrid[cradint,0]        = sg0
        sgrid[0,cradint]        = sg0
        sgrid[2*cradint-1,cradint]   = sgrid[2*cradint-1,cradint] - sg0
        sgrid[cradint,2*cradint-1]   = sgrid[cradint,2*cradint-1] - sg0
        sgrid[cradint,1]        = sgrid[cradint,1]      - sg0
        sgrid[1,cradint]        = sgrid[1,cradint]      - sg0

    sgrid[cradint,cradint] = np.minimum(sgrid[cradint,cradint],1)
    kernel = (sgrid/np.sum(sgrid)).astype(dtype=np.float32)

    return kernel.shape[0], kernel



def cal_psnr_focalstack(recon_fs, true_fs, crop_width):
    C = recon_fs.shape[0]
    H = recon_fs.shape[1]
    W = recon_fs.shape[2]
    N = recon_fs.shape[3]
    PSNRs = np.zeros((N))
    for n in range(N):
        diff = recon_fs[:,crop_width:H-crop_width,crop_width:W-crop_width,n] - true_fs[:,crop_width:H-crop_width,crop_width:W-crop_width,n]
        rmse = math.sqrt(np.mean(diff**2.))
        PSNRs[n] = 20*math.log10(1.0/rmse)
    return PSNRs

def log10(x):
  numerator = tf.log(x)
  denominator = tf.log(tf.constant(10, dtype=numerator.dtype))
  return numerator / denominator

def cal_ssim_focalstack(recon_fs, true_fs, crop_width):
    #C-H-W-N
    C = recon_fs.shape[0]
    H = recon_fs.shape[1]
    W = recon_fs.shape[2]
    N = recon_fs.shape[3]
    SSIMs = np.zeros((N))
    y_pred = tf.transpose(recon_fs[:,crop_width:H-crop_width,crop_width:W-crop_width,:], [3,1,2,0]) #NHWC
    y_true = tf.transpose(true_fs[:,crop_width:H-crop_width,crop_width:W-crop_width,:], [3,1,2,0]) #NHWC
    SSIMs = tf.image.ssim(y_true, y_pred, max_val = 1.0)
    return SSIMs


def calImageGradients(images):
    # x is a 4-D tensor
    dx = images[:, :, 1:, :] - images[:, :, :-1, :]
    dy = images[:, 1:, :, :] - images[:, :-1, :, :]
    return dx, dy
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