DeepFaceLab/__dev/test.py

import os
os.environ['force_plaidML'] = '1'

import sys
import argparse
from utils import Path_utils
from utils import os_utils
from facelib import LandmarksProcessor
from pathlib import Path
import numpy as np
import cv2
import time
import multiprocessing
import traceback
from tqdm import tqdm
from utils.DFLPNG import DFLPNG
from utils.DFLJPG import DFLJPG
from utils.cv2_utils import *
from utils import image_utils
import shutil


    
def umeyama(src, dst, estimate_scale):
    """Estimate N-D similarity transformation with or without scaling.
    Parameters
    ----------
    src : (M, N) array
        Source coordinates.
    dst : (M, N) array
        Destination coordinates.
    estimate_scale : bool
        Whether to estimate scaling factor.
    Returns
    -------
    T : (N + 1, N + 1)
        The homogeneous similarity transformation matrix. The matrix contains
        NaN values only if the problem is not well-conditioned.
    References
    ----------
    .. [1] "Least-squares estimation of transformation parameters between two
            point patterns", Shinji Umeyama, PAMI 1991, DOI: 10.1109/34.88573
    """

    num = src.shape[0]
    dim = src.shape[1]

    # Compute mean of src and dst.
    src_mean = src.mean(axis=0)
    dst_mean = dst.mean(axis=0)

    # Subtract mean from src and dst.
    src_demean = src - src_mean
    dst_demean = dst - dst_mean

    # Eq. (38).
    A = np.dot(dst_demean.T, src_demean) / num

    # Eq. (39).
    d = np.ones((dim,), dtype=np.double)
    if np.linalg.det(A) < 0:
        d[dim - 1] = -1

    T = np.eye(dim + 1, dtype=np.double)

    U, S, V = np.linalg.svd(A)

    # Eq. (40) and (43).
    rank = np.linalg.matrix_rank(A)
    if rank == 0:
        return np.nan * T
    elif rank == dim - 1:
        if np.linalg.det(U) * np.linalg.det(V) > 0:
            T[:dim, :dim] = np.dot(U, V)
        else:
            s = d[dim - 1]
            d[dim - 1] = -1
            T[:dim, :dim] = np.dot(U, np.dot(np.diag(d), V))
            d[dim - 1] = s
    else:
        T[:dim, :dim] = np.dot(U, np.dot(np.diag(d), V.T))

    if estimate_scale:
        # Eq. (41) and (42).
        scale = 1.0 / src_demean.var(axis=0).sum() * np.dot(S, d)
    else:
        scale = 1.0

    T[:dim, dim] = dst_mean - scale * np.dot(T[:dim, :dim], src_mean.T)
    T[:dim, :dim] *= scale

    return T
    
def random_transform(image, rotation_range=10, zoom_range=0.5, shift_range=0.05, random_flip=0):
    h, w = image.shape[0:2]
    rotation = np.random.uniform(-rotation_range, rotation_range)
    scale = np.random.uniform(1 - zoom_range, 1 + zoom_range)
    tx = np.random.uniform(-shift_range, shift_range) * w
    ty = np.random.uniform(-shift_range, shift_range) * h
    mat = cv2.getRotationMatrix2D((w // 2, h // 2), rotation, scale)
    mat[:, 2] += (tx, ty)
    result = cv2.warpAffine(
        image, mat, (w, h), borderMode=cv2.BORDER_REPLICATE)
    if np.random.random() < random_flip:
        result = result[:, ::-1]
    return result

# get pair of random warped images from aligned face image
def random_warp(image, coverage=160, scale = 5, zoom = 1):
    assert image.shape == (256, 256, 3)
    range_ = np.linspace(128 - coverage//2, 128 + coverage//2, 5)
    mapx = np.broadcast_to(range_, (5, 5))
    mapy = mapx.T

    mapx = mapx + np.random.normal(size=(5,5), scale=scale)
    mapy = mapy + np.random.normal(size=(5,5), scale=scale)

    interp_mapx = cv2.resize(mapx, (80*zoom,80*zoom))[8*zoom:72*zoom,8*zoom:72*zoom].astype('float32')
    interp_mapy = cv2.resize(mapy, (80*zoom,80*zoom))[8*zoom:72*zoom,8*zoom:72*zoom].astype('float32')

    warped_image = cv2.remap(image, interp_mapx, interp_mapy, cv2.INTER_LINEAR)

    src_points = np.stack([mapx.ravel(), mapy.ravel() ], axis=-1)
    dst_points = np.mgrid[0:65*zoom:16*zoom,0:65*zoom:16*zoom].T.reshape(-1,2)
    mat = umeyama(src_points, dst_points, True)[0:2]

    target_image = cv2.warpAffine(image, mat, (64*zoom,64*zoom))

    return warped_image, target_image

def input_process(stdin_fd, sq, str):
    sys.stdin = os.fdopen(stdin_fd)
    try:
        inp = input (str)
        sq.put (True)
    except:
        sq.put (False)
        
def input_in_time (str, max_time_sec):
    sq = multiprocessing.Queue()
    p = multiprocessing.Process(target=input_process, args=( sys.stdin.fileno(), sq, str))
    p.start()
    t = time.time()
    inp = False
    while True:
        if not sq.empty():
            inp = sq.get()
            break
        if time.time() - t > max_time_sec:
            break
    p.terminate()
    sys.stdin = os.fdopen( sys.stdin.fileno() )
    return inp
    

 
def subprocess(sq,cq):   
    prefetch = 2
    while True:
        while prefetch > -1:
            cq.put ( np.array([1]) ) #memory leak numpy==1.16.0 , but all fine in 1.15.4
            #cq.put ( [1] )  #no memory leak
            prefetch -= 1
            
        sq.get() #waiting msg from serv to continue posting
        prefetch += 1 



def get_image_hull_mask (image_shape, image_landmarks):        
    if len(image_landmarks) != 68:
        raise Exception('get_image_hull_mask works only with 68 landmarks')
        
    hull_mask = np.zeros(image_shape[0:2]+(1,),dtype=np.float32)

    cv2.fillConvexPoly( hull_mask, cv2.convexHull( np.concatenate ( (image_landmarks[0:17], image_landmarks[48:], [image_landmarks[0]], [image_landmarks[8]], [image_landmarks[16]]))    ), (1,) )
    cv2.fillConvexPoly( hull_mask, cv2.convexHull( np.concatenate ( (image_landmarks[27:31], [image_landmarks[33]]) )                                                                    ), (1,) )
    cv2.fillConvexPoly( hull_mask, cv2.convexHull( np.concatenate ( (image_landmarks[17:27], [image_landmarks[0]], [image_landmarks[27]], [image_landmarks[16]], [image_landmarks[33]])) ), (1,) )
    
    return hull_mask
    

def umeyama(src, dst, estimate_scale):
    """Estimate N-D similarity transformation with or without scaling.
    Parameters
    ----------
    src : (M, N) array
        Source coordinates.
    dst : (M, N) array
        Destination coordinates.
    estimate_scale : bool
        Whether to estimate scaling factor.
    Returns
    -------
    T : (N + 1, N + 1)
        The homogeneous similarity transformation matrix. The matrix contains
        NaN values only if the problem is not well-conditioned.
    References
    ----------
    .. [1] "Least-squares estimation of transformation parameters between two
            point patterns", Shinji Umeyama, PAMI 1991, DOI: 10.1109/34.88573
    """

    num = src.shape[0]
    dim = src.shape[1]

    # Compute mean of src and dst.
    src_mean = src.mean(axis=0)
    dst_mean = dst.mean(axis=0)

    # Subtract mean from src and dst.
    src_demean = src - src_mean
    dst_demean = dst - dst_mean

    # Eq. (38).
    A = np.dot(dst_demean.T, src_demean) / num

    # Eq. (39).
    d = np.ones((dim,), dtype=np.double)
    if np.linalg.det(A) < 0:
        d[dim - 1] = -1

    T = np.eye(dim + 1, dtype=np.double)

    U, S, V = np.linalg.svd(A)

    # Eq. (40) and (43).
    rank = np.linalg.matrix_rank(A)
    if rank == 0:
        return np.nan * T
    elif rank == dim - 1:
        if np.linalg.det(U) * np.linalg.det(V) > 0:
            T[:dim, :dim] = np.dot(U, V)
        else:
            s = d[dim - 1]
            d[dim - 1] = -1
            T[:dim, :dim] = np.dot(U, np.dot(np.diag(d), V))
            d[dim - 1] = s
    else:
        T[:dim, :dim] = np.dot(U, np.dot(np.diag(d), V.T))

    if estimate_scale:
        # Eq. (41) and (42).
        scale = 1.0 / src_demean.var(axis=0).sum() * np.dot(S, d)
    else:
        scale = 1.0

    T[:dim, dim] = dst_mean - scale * np.dot(T[:dim, :dim], src_mean.T)
    T[:dim, :dim] *= scale

    return T
    
mean_face_x = np.array([
0.000213256, 0.0752622, 0.18113, 0.29077, 0.393397, 0.586856, 0.689483, 0.799124,
0.904991, 0.98004, 0.490127, 0.490127, 0.490127, 0.490127, 0.36688, 0.426036,
0.490127, 0.554217, 0.613373, 0.121737, 0.187122, 0.265825, 0.334606, 0.260918,
0.182743, 0.645647, 0.714428, 0.793132, 0.858516, 0.79751, 0.719335, 0.254149,
0.340985, 0.428858, 0.490127, 0.551395, 0.639268, 0.726104, 0.642159, 0.556721,
0.490127, 0.423532, 0.338094, 0.290379, 0.428096, 0.490127, 0.552157, 0.689874,
0.553364, 0.490127, 0.42689 ])

mean_face_y = np.array([
0.106454, 0.038915, 0.0187482, 0.0344891, 0.0773906, 0.0773906, 0.0344891,
0.0187482, 0.038915, 0.106454, 0.203352, 0.307009, 0.409805, 0.515625, 0.587326,
0.609345, 0.628106, 0.609345, 0.587326, 0.216423, 0.178758, 0.179852, 0.231733,
0.245099, 0.244077, 0.231733, 0.179852, 0.178758, 0.216423, 0.244077, 0.245099,
0.780233, 0.745405, 0.727388, 0.742578, 0.727388, 0.745405, 0.780233, 0.864805,
0.902192, 0.909281, 0.902192, 0.864805, 0.784792, 0.778746, 0.785343, 0.778746,
0.784792, 0.824182, 0.831803, 0.824182 ])

landmarks_2D = np.stack( [ mean_face_x, mean_face_y ], axis=1 )

def get_transform_mat (image_landmarks, output_size, scale=1.0):
    if not isinstance(image_landmarks, np.ndarray):
        image_landmarks = np.array (image_landmarks) 
        
    padding = (output_size / 64) * 12
        
    mat = umeyama(image_landmarks[17:], landmarks_2D, True)[0:2]
    mat = mat * (output_size - 2 * padding)
    mat[:,2] += padding        
    mat *= (1 / scale)
    mat[:,2] += -output_size*( ( (1 / scale) - 1.0 ) / 2 )
             
    return mat
    
#alignments = []
#        
#aligned_path_image_paths = Path_utils.get_image_paths("D:\\DeepFaceLab\\workspace issue\\data_dst\\aligned")
#for filepath in tqdm(aligned_path_image_paths, desc="Collecting alignments", ascii=True ):
#    filepath = Path(filepath)
#    
#    if filepath.suffix == '.png':
#        dflimg = DFLPNG.load( str(filepath), print_on_no_embedded_data=True )
#    elif filepath.suffix == '.jpg':
#        dflimg = DFLJPG.load ( str(filepath), print_on_no_embedded_data=True )
#    else:
#        print ("%s is not a dfl image file" % (filepath.name) ) 
#    
#    #source_filename_stem = Path( dflimg.get_source_filename() ).stem
#    #if source_filename_stem not in alignments.keys():
#    #    alignments[ source_filename_stem ] = []
#
#    #alignments[ source_filename_stem ].append (dflimg.get_source_landmarks())
#    alignments.append (dflimg.get_source_landmarks())
import mathlib
def main():

    def f ( *args, asd=True, **kwargs ):
        import code
        code.interact(local=dict(globals(), **locals()))
    
    f( 1, asd=True, bg=0)
    
    from nnlib import nnlib
    exec( nnlib.import_all( device_config=nnlib.device.Config() ), locals(), globals() )
    PMLTile = nnlib.PMLTile
    PMLK = nnlib.PMLK
    
    class DSSIMObjective:
        """Computes DSSIM index between img1 and img2.
        This function is based on the standard SSIM implementation from:
        Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004).
        """

        def __init__(self, k1=0.01, k2=0.03, max_value=1.0):
            self.__name__ = 'DSSIMObjective'
            self.k1 = k1
            self.k2 = k2
            self.max_value = max_value
            self.c1 = (self.k1 * self.max_value) ** 2
            self.c2 = (self.k2 * self.max_value) ** 2
            self.dim_ordering = K.image_data_format()
            self.backend = K.backend()

        def __int_shape(self, x):
            return K.int_shape(x) if self.backend == 'tensorflow' else K.shape(x)

        def __call__(self, y_true, y_pred):
            ch = K.shape(y_pred)[-1]

            def softmax(x, axis=-1):
                y = np.exp(x - np.max(x, axis, keepdims=True))
                return y / np.sum(y, axis, keepdims=True)
                    
            def _fspecial_gauss(size, sigma):
                #Function to mimic the 'fspecial' gaussian MATLAB function.
                coords = np.arange(0, size, dtype=K.floatx())
                coords -= (size - 1 ) / 2.0
                g = coords**2
                g *= ( -0.5 / (sigma**2) )
                g = np.reshape (g, (1,-1)) + np.reshape(g, (-1,1) )
                g = np.reshape (g, (1,-1))
                g = softmax(g)
                g = K.constant ( np.reshape (g, (size, size, 1, 1))  )
                g = K.tile (g, (1,1,ch,1))
                return g
                      
            kernel = _fspecial_gauss(11,1.5)

            def reducer(x):
                return K.depthwise_conv2d(x, kernel, strides=(1, 1), padding='valid')

            c1 = (self.k1 * self.max_value) ** 2
            c2 = (self.k2 * self.max_value) ** 2
            
            mean0 = reducer(y_true)
            mean1 = reducer(y_pred)
            num0 = mean0 * mean1 * 2.0
            den0 = K.square(mean0) + K.square(mean1)
            luminance = (num0 + c1) / (den0 + c1)
            
            num1 = reducer(y_true * y_pred) * 2.0
            den1 = reducer(K.square(y_true) + K.square(y_pred))
            c2 *= 1.0 #compensation factor
            cs = (num1 - num0 + c2) / (den1 - den0 + c2)

            ssim_val = K.mean(luminance * cs, axis=(-3, -2) )
            return K.mean( (1.0 - ssim_val ) / 2.0 )

    image = cv2.imread('D:\\DeepFaceLab\\test\\00000.png').astype(np.float32) / 255.0    
    image = np.expand_dims (image, 0)
    image_shape = image.shape
    
    image2 = cv2.imread('D:\\DeepFaceLab\\test\\00001.png').astype(np.float32) / 255.0    
    image2 = np.expand_dims (image2, 0)
    image2_shape = image2.shape
    
    #image = np.random.uniform ( size=(1,256,256,3) )
    #image2 = np.random.uniform ( size=(1,256,256,3) )
    
    t1 = K.placeholder ( (None,) + image_shape[1:], name="t1" )
    t2 = K.placeholder ( (None,) + image_shape[1:], name="t2" )
    
    l1_t = DSSIMObjective() (t1,t2 )
    l1, = K.function([t1, t2],[l1_t]) ([image, image2])
    
    print (l1)
    
    import code
    code.interact(local=dict(globals(), **locals()))
    
    '''
    >>> t[:,0:64,64::2,:].source.op.code
function (I[N0, N1, N2, N3]) -> (O) {

O[i0, i1, i2, i3: (1 + 1 - 1)/1, (64 + 1 - 1)/1, (64 + 2 - 1)/2, (1 + 1 - 1)/1] = 
       =(I[1*i0+0, 1*i1+0, 2*i2+64, 1*i3+0]);
       

        Status GetWindowedOutputSizeVerboseV2(int64 input_size, int64 filter_size,
                                          int64 dilation_rate, int64 stride,
                                          Padding padding_type, int64* output_size,
                                          int64* padding_before,
                                          int64* padding_after) {
      if (stride <= 0) {
        return errors::InvalidArgument("Stride must be > 0, but got ", stride);
      }
      if (dilation_rate < 1) {
        return errors::InvalidArgument("Dilation rate must be >= 1, but got ",
                                       dilation_rate);
      }

      // See also the parallel implementation in GetWindowedOutputSizeFromDimsV2.
      int64 effective_filter_size = (filter_size - 1) * dilation_rate + 1;
      switch (padding_type) {
        case Padding::VALID:
          *output_size = (input_size - effective_filter_size + stride) / stride;
          *padding_before = *padding_after = 0;
          break;
        case Padding::EXPLICIT:
          *output_size = (input_size + *padding_before + *padding_after -
                          effective_filter_size + stride) /
                         stride;
          break;
        case Padding::SAME:
          *output_size = (input_size + stride - 1) / stride;
          const int64 padding_needed =
              std::max(int64{0}, (*output_size - 1) * stride +
                                     effective_filter_size - input_size);
          // For odd values of total padding, add more padding at the 'right'
          // side of the given dimension.
          *padding_before = padding_needed / 2;
          *padding_after = padding_needed - *padding_before;
          break;
      }
      if (*output_size < 0) {
        return errors::InvalidArgument(
            "Computed output size would be negative: ", *output_size,
            " [input_size: ", input_size,
            ", effective_filter_size: ", effective_filter_size,
            ", stride: ", stride, "]");
      }
      return Status::OK();
    }
    '''
    class ExtractImagePatchesOP(PMLTile.Operation):
        def __init__(self, input, ksizes, strides, rates, padding='valid'):
        
            batch, in_rows, in_cols, depth = input.shape.dims

            ksize_rows = ksizes[1];
            ksize_cols = ksizes[2];

            stride_rows = strides[1];
            stride_cols = strides[2];

            rate_rows = rates[1];
            rate_cols = rates[2];

            ksize_rows_eff = ksize_rows + (ksize_rows - 1) * (rate_rows - 1);
            ksize_cols_eff = ksize_cols + (ksize_cols - 1) * (rate_cols - 1);
            
            #if padding == 'valid':
                
            out_rows = (in_rows - ksize_rows_eff + stride_rows) / stride_rows;
            out_cols = (in_cols - ksize_cols_eff + stride_cols) / stride_cols;
            
            out_sizes = (batch, out_rows, out_cols, ksize_rows * ksize_cols * depth);

            
        
            B, H, W, CI = input.shape.dims
            
            RATE = PMLK.constant ([1,rate,rate,1], dtype=PMLK.floatx() )
            
            #print (target_dims)
            code = """function (I[B, {H}, {W}, {CI} ], RATES[RB, RH, RW, RC] ) -> (O) {
                        
                        O[b, {wnd_size}, {wnd_size}, ] = =(I[b, h, w, ci]);
                        
                    }""".format(H=H, W=W, CI=CI, RATES=rates, wnd_size=wnd_size)
                    
            super(ExtractImagePatchesOP, self).__init__(code, [('I', input) ],
                    [('O', PMLTile.Shape(input.shape.dtype, out_sizes ) )])

    
    

    f = ExtractImagePatchesOP.function(t, [1,65,65,1], [1,1,1,1], [1,1,1,1])

    x, = K.function ([t],[f]) ([ image ])
    print(x.shape)
    
    import code
    code.interact(local=dict(globals(), **locals()))
    
    
    from nnlib import nnlib
    exec( nnlib.import_all(), locals(), globals() )
    
    #ch = 3
    #def softmax(x, axis=-1): #from K numpy backend
    #    y = np.exp(x - np.max(x, axis, keepdims=True))
    #    return y / np.sum(y, axis, keepdims=True)
    #    
    #def gauss_kernel(size, sigma):
    #    coords = np.arange(0,size, dtype=K.floatx() )                  
    #    coords -= (size - 1 ) / 2.0
    #    g = coords**2
    #    g *= ( -0.5 / (sigma**2) )
    #    g = np.reshape (g, (1,-1)) + np.reshape(g, (-1,1) )
    #    g = np.reshape (g, (1,-1))
    #    g = softmax(g)
    #    g = np.reshape (g, (size, size, 1, 1))  
    #    g = np.tile (g, (1,1,ch, size*size*ch))                
    #    return K.constant(g, dtype=K.floatx() )
    #
    ##kernel = gauss_kernel(11,1.5)                
    #kernel = K.constant( np.ones ( (246,246, 3, 1) ) , dtype=K.floatx() )
    ##g = np.eye(9).reshape((3, 3, 1, 9)) 
    ##g = np.tile (g, (1,1,3,1))               
    ##kernel = K.constant(g , dtype=K.floatx() )
    #
    #def reducer(x):
    #    shape = K.shape(x)
    #    x = K.reshape(x, (-1, shape[-3] , shape[-2], shape[-1]) )                  
    #
    #    y = K.depthwise_conv2d(x, kernel, strides=(1, 1), padding='valid')
    #    
    #    y_shape = K.shape(y)
    #    return y#K.reshape(y, (shape[0], y_shape[1], y_shape[2], y_shape[3] ) )
    
    image = cv2.imread('D:\\DeepFaceLab\\test\\00000.png').astype(np.float32) / 255.0    
    image = cv2.resize ( image, (128,128) )
    
    image = cv2.cvtColor (image, cv2.COLOR_BGR2GRAY)    
    image = np.expand_dims (image, -1)
    image_shape = image.shape
    
    image2 = cv2.imread('D:\\DeepFaceLab\\test\\00001.png').astype(np.float32) / 255.0    
    #image2 = cv2.cvtColor (image2, cv2.COLOR_BGR2GRAY)    
    #image2 = np.expand_dims (image2, -1)
    image2_shape = image2.shape

    image_tensor = K.placeholder(shape=[ 1, image_shape[0], image_shape[1], image_shape[2] ], dtype="float32" )  
    image2_tensor = K.placeholder(shape=[ 1, image_shape[0], image_shape[1], image_shape[2] ], dtype="float32" )  

    #loss = reducer(image_tensor)
    #loss = K.reshape (loss, (-1,246,246, 11,11,3) )
    tf = nnlib.tf
    
    sh = K.int_shape(image_tensor)[1]
    wnd_size = 16
    step_size = 8
    k = (sh-wnd_size) // step_size + 1
    
    loss = tf.image.extract_image_patches(image_tensor, [1,k,k,1], [1,1,1,1], [1,step_size,step_size,1], 'VALID')
    print(loss)
    
    f = K.function ( [image_tensor], [loss] )
    x = f ( [ np.expand_dims(image,0) ] )[0][0]
    
    import code
    code.interact(local=dict(globals(), **locals()))
    
    for i in range( x.shape[2] ):
        img = x[:,:,i:i+1]
    
        cv2.imshow('', (img*255).astype(np.uint8) )
        cv2.waitKey(0)
            
    #for i in range( len(x) ):
    #    for j in range ( len(x) ):
    #        img = x[i,j]
    #        import code
    #        code.interact(local=dict(globals(), **locals()))
    #
    #        cv2.imshow('', (x[i,j]*255).astype(np.uint8) )
    #        cv2.waitKey(0)
    
    import code
    code.interact(local=dict(globals(), **locals()))
    
    
    from nnlib import nnlib
    exec( nnlib.import_all(), locals(), globals() )
        
    PNet_Input = Input ( (None, None,3) )
    x = PNet_Input
    x = Conv2D (10, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv1")(x)
    x = PReLU (shared_axes=[1,2], name="PReLU1" )(x)
    x = MaxPooling2D( pool_size=(2,2), strides=(2,2), padding='same' ) (x)
    x = Conv2D (16, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv2")(x)
    x = PReLU (shared_axes=[1,2], name="PReLU2" )(x)
    x = Conv2D (32, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv3")(x)
    x = PReLU (shared_axes=[1,2], name="PReLU3" )(x)
    prob = Conv2D (2, kernel_size=(1,1), strides=(1,1), padding='valid', name="conv41")(x)
    prob = Softmax()(prob)    
    x = Conv2D (4, kernel_size=(1,1), strides=(1,1), padding='valid', name="conv42")(x)

    PNet_model = Model(PNet_Input, [x,prob] )        
    PNet_model.load_weights ( (Path(mtcnn.__file__).parent / 'mtcnn_pnet.h5').__str__() )
    
    RNet_Input = Input ( (24, 24, 3) )
    x = RNet_Input
    x = Conv2D (28, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv1")(x)
    x = PReLU (shared_axes=[1,2], name="prelu1" )(x)
    x = MaxPooling2D( pool_size=(3,3), strides=(2,2), padding='same' ) (x)
    x = Conv2D (48, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv2")(x)
    x = PReLU (shared_axes=[1,2], name="prelu2" )(x)
    x = MaxPooling2D( pool_size=(3,3), strides=(2,2), padding='valid' ) (x)    
    x = Conv2D (64, kernel_size=(2,2), strides=(1,1), padding='valid', name="conv3")(x)
    x = PReLU (shared_axes=[1,2], name="prelu3" )(x)
    x = Lambda ( lambda x: K.reshape (x, (-1, np.prod(K.int_shape(x)[1:]),) ), output_shape=(np.prod(K.int_shape(x)[1:]),) ) (x)
    x = Dense (128, name='conv4')(x)    
    x = PReLU (name="prelu4" )(x)
    prob = Dense (2, name='conv51')(x)
    prob = Softmax()(prob)  
    x = Dense (4, name='conv52')(x)        
    RNet_model = Model(RNet_Input, [x,prob] )        
    RNet_model.load_weights ( (Path(mtcnn.__file__).parent / 'mtcnn_rnet.h5').__str__() )
    
    ONet_Input = Input ( (48, 48, 3) )
    x = ONet_Input
    x = Conv2D (32, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv1")(x)
    x = PReLU (shared_axes=[1,2], name="prelu1" )(x)
    x = MaxPooling2D( pool_size=(3,3), strides=(2,2), padding='same' ) (x)
    x = Conv2D (64, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv2")(x)
    x = PReLU (shared_axes=[1,2], name="prelu2" )(x)
    x = MaxPooling2D( pool_size=(3,3), strides=(2,2), padding='valid' ) (x)    
    x = Conv2D (64, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv3")(x)
    x = PReLU (shared_axes=[1,2], name="prelu3" )(x)
    x = MaxPooling2D( pool_size=(2,2), strides=(2,2), padding='same' ) (x) 
    x = Conv2D (128, kernel_size=(2,2), strides=(1,1), padding='valid', name="conv4")(x)
    x = PReLU (shared_axes=[1,2], name="prelu4" )(x)
    x = Lambda ( lambda x: K.reshape (x, (-1, np.prod(K.int_shape(x)[1:]),) ), output_shape=(np.prod(K.int_shape(x)[1:]),) ) (x)    
    x = Dense (256, name='conv5')(x)
    x = PReLU (name="prelu5" )(x)
    prob = Dense (2, name='conv61')(x)
    prob = Softmax()(prob)    
    x1 = Dense (4, name='conv62')(x)
    x2 = Dense (10, name='conv63')(x)        
    ONet_model = Model(ONet_Input, [x1,x2,prob] )        
    ONet_model.load_weights ( (Path(mtcnn.__file__).parent / 'mtcnn_onet.h5').__str__() )
    
    pnet_fun = K.function ( PNet_model.inputs, PNet_model.outputs )
    rnet_fun = K.function ( RNet_model.inputs, RNet_model.outputs )
    onet_fun = K.function ( ONet_model.inputs, ONet_model.outputs )
        
    pnet_test_data = np.random.uniform ( size=(1, 64,64,3) )
    pnet_result1, pnet_result2 = pnet_fun ([pnet_test_data])
    
    rnet_test_data = np.random.uniform ( size=(1,24,24,3) )
    rnet_result1, rnet_result2 = rnet_fun ([rnet_test_data])

    onet_test_data = np.random.uniform ( size=(1,48,48,3) )
    onet_result1, onet_result2, onet_result3 = onet_fun ([onet_test_data])
    
    import code
    code.interact(local=dict(globals(), **locals()))
    
    from nnlib import nnlib
    #exec( nnlib.import_all( nnlib.device.Config(cpu_only=True) ), locals(), globals() )# nnlib.device.Config(cpu_only=True)
    exec( nnlib.import_all(), locals(), globals() )# nnlib.device.Config(cpu_only=True)
    
    #det1_Input = Input ( (None, None,3) )
    #x = det1_Input
    #x = Conv2D (10, kernel_size=(3,3), strides=(1,1), padding='valid')(x)
    #
    #import code
    #code.interact(local=dict(globals(), **locals()))
    
    tf = nnlib.tf
    tf_session = nnlib.tf_sess
    
    with tf.variable_scope('pnet2'):
        data = tf.placeholder(tf.float32, (None,None,None,3), 'input')
        pnet2 = mtcnn.PNet(tf, {'data':data})        
        pnet2.load( (Path(mtcnn.__file__).parent / 'det1.npy').__str__(), tf_session)
    with tf.variable_scope('rnet2'):
        data = tf.placeholder(tf.float32, (None,24,24,3), 'input')
        rnet2 = mtcnn.RNet(tf, {'data':data})
        rnet2.load( (Path(mtcnn.__file__).parent / 'det2.npy').__str__(), tf_session)
    with tf.variable_scope('onet2'):
        data = tf.placeholder(tf.float32, (None,48,48,3), 'input')
        onet2 = mtcnn.ONet(tf, {'data':data})
        onet2.load( (Path(mtcnn.__file__).parent / 'det3.npy').__str__(), tf_session)
    
    
    
    pnet_fun = K.function([pnet2.layers['data']],[pnet2.layers['conv4-2'], pnet2.layers['prob1']])
    rnet_fun = K.function([rnet2.layers['data']],[rnet2.layers['conv5-2'], rnet2.layers['prob1']])
    onet_fun = K.function([onet2.layers['data']],[onet2.layers['conv6-2'], onet2.layers['conv6-3'], onet2.layers['prob1']])

    det1_dict = np.load((Path(mtcnn.__file__).parent / 'det1.npy').__str__(), encoding='latin1').item()
    det2_dict = np.load((Path(mtcnn.__file__).parent / 'det2.npy').__str__(), encoding='latin1').item()
    det3_dict = np.load((Path(mtcnn.__file__).parent / 'det3.npy').__str__(), encoding='latin1').item()      
    
    PNet_Input = Input ( (None, None,3) )
    x = PNet_Input
    x = Conv2D (10, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv1")(x)
    x = PReLU (shared_axes=[1,2], name="PReLU1" )(x)
    x = MaxPooling2D( pool_size=(2,2), strides=(2,2), padding='same' ) (x)
    x = Conv2D (16, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv2")(x)
    x = PReLU (shared_axes=[1,2], name="PReLU2" )(x)
    x = Conv2D (32, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv3")(x)
    x = PReLU (shared_axes=[1,2], name="PReLU3" )(x)
    prob = Conv2D (2, kernel_size=(1,1), strides=(1,1), padding='valid', name="conv41")(x)
    prob = Softmax()(prob)    
    x = Conv2D (4, kernel_size=(1,1), strides=(1,1), padding='valid', name="conv42")(x)
    
    
    PNet_model = Model(PNet_Input, [x,prob] )
    
    #PNet_model.load_weights ( (Path(mtcnn.__file__).parent / 'mtcnn_pnet.h5').__str__() )
    PNet_model.get_layer("conv1").set_weights ( [ det1_dict['conv1']['weights'], det1_dict['conv1']['biases'] ] )
    PNet_model.get_layer("PReLU1").set_weights ( [ np.reshape(det1_dict['PReLU1']['alpha'], (1,1,-1)) ] )
    PNet_model.get_layer("conv2").set_weights ( [ det1_dict['conv2']['weights'], det1_dict['conv2']['biases'] ] )
    PNet_model.get_layer("PReLU2").set_weights ( [ np.reshape(det1_dict['PReLU2']['alpha'], (1,1,-1)) ] )
    PNet_model.get_layer("conv3").set_weights ( [ det1_dict['conv3']['weights'], det1_dict['conv3']['biases'] ] )
    PNet_model.get_layer("PReLU3").set_weights ( [ np.reshape(det1_dict['PReLU3']['alpha'], (1,1,-1)) ] )
    PNet_model.get_layer("conv41").set_weights ( [ det1_dict['conv4-1']['weights'], det1_dict['conv4-1']['biases'] ] )
    PNet_model.get_layer("conv42").set_weights ( [ det1_dict['conv4-2']['weights'], det1_dict['conv4-2']['biases'] ] )
    PNet_model.save ( (Path(mtcnn.__file__).parent / 'mtcnn_pnet.h5').__str__() )

    pnet_test_data = np.random.uniform ( size=(1, 64,64,3) )
    pnet_result1, pnet_result2 = pnet_fun ([pnet_test_data])
    pnet2_result1, pnet2_result2 =  K.function ( PNet_model.inputs, PNet_model.outputs ) ([pnet_test_data])   
    
    pnet_diff1 = np.mean ( np.abs(pnet_result1 - pnet2_result1) )
    pnet_diff2 = np.mean ( np.abs(pnet_result2 - pnet2_result2) )
    print ("pnet_diff1 = %f, pnet_diff2 = %f, "  % (pnet_diff1, pnet_diff2) )
    
    RNet_Input = Input ( (24, 24, 3) )
    x = RNet_Input
    x = Conv2D (28, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv1")(x)
    x = PReLU (shared_axes=[1,2], name="prelu1" )(x)
    x = MaxPooling2D( pool_size=(3,3), strides=(2,2), padding='same' ) (x)
    x = Conv2D (48, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv2")(x)
    x = PReLU (shared_axes=[1,2], name="prelu2" )(x)
    x = MaxPooling2D( pool_size=(3,3), strides=(2,2), padding='valid' ) (x)    
    x = Conv2D (64, kernel_size=(2,2), strides=(1,1), padding='valid', name="conv3")(x)
    x = PReLU (shared_axes=[1,2], name="prelu3" )(x)
    x = Lambda ( lambda x: K.reshape (x, (-1, np.prod(K.int_shape(x)[1:]),) ), output_shape=(np.prod(K.int_shape(x)[1:]),) ) (x)
    x = Dense (128, name='conv4')(x)
    x = PReLU (name="prelu4" )(x)
    prob = Dense (2, name='conv51')(x)
    prob = Softmax()(prob)  
    x = Dense (4, name='conv52')(x)
    
    RNet_model = Model(RNet_Input, [x,prob] )
    
    #RNet_model.load_weights ( (Path(mtcnn.__file__).parent / 'mtcnn_rnet.h5').__str__() )
    RNet_model.get_layer("conv1").set_weights ( [ det2_dict['conv1']['weights'], det2_dict['conv1']['biases'] ] )
    RNet_model.get_layer("prelu1").set_weights ( [ np.reshape(det2_dict['prelu1']['alpha'], (1,1,-1)) ] )
    RNet_model.get_layer("conv2").set_weights ( [ det2_dict['conv2']['weights'], det2_dict['conv2']['biases'] ] )
    RNet_model.get_layer("prelu2").set_weights ( [ np.reshape(det2_dict['prelu2']['alpha'], (1,1,-1)) ] )
    RNet_model.get_layer("conv3").set_weights ( [ det2_dict['conv3']['weights'], det2_dict['conv3']['biases'] ] )
    RNet_model.get_layer("prelu3").set_weights ( [ np.reshape(det2_dict['prelu3']['alpha'], (1,1,-1)) ] )
    RNet_model.get_layer("conv4").set_weights ( [ det2_dict['conv4']['weights'], det2_dict['conv4']['biases'] ] )
    RNet_model.get_layer("prelu4").set_weights ( [ det2_dict['prelu4']['alpha'] ] )
    RNet_model.get_layer("conv51").set_weights ( [ det2_dict['conv5-1']['weights'], det2_dict['conv5-1']['biases'] ] )
    RNet_model.get_layer("conv52").set_weights ( [ det2_dict['conv5-2']['weights'], det2_dict['conv5-2']['biases'] ] )
    RNet_model.save ( (Path(mtcnn.__file__).parent / 'mtcnn_rnet.h5').__str__() )
    
    #import code
    #code.interact(local=dict(globals(), **locals()))   

    rnet_test_data = np.random.uniform ( size=(1,24,24,3) )
    rnet_result1, rnet_result2 = rnet_fun ([rnet_test_data])
    rnet2_result1, rnet2_result2 =  K.function ( RNet_model.inputs, RNet_model.outputs ) ([rnet_test_data])   
    
    rnet_diff1 = np.mean ( np.abs(rnet_result1 - rnet2_result1) )
    rnet_diff2 = np.mean ( np.abs(rnet_result2 - rnet2_result2) )
    print ("rnet_diff1 = %f, rnet_diff2 = %f, "  % (rnet_diff1, rnet_diff2) )
    
    
    #################
    '''
    (self.feed('data') #pylint: disable=no-value-for-parameter, no-member
             .conv(3, 3, 32, 1, 1, padding='VALID', relu=False, name='conv1')
             .prelu(name='prelu1')
             .max_pool(3, 3, 2, 2, name='pool1')
             .conv(3, 3, 64, 1, 1, padding='VALID', relu=False, name='conv2')
             .prelu(name='prelu2')
             .max_pool(3, 3, 2, 2, padding='VALID', name='pool2')
             .conv(3, 3, 64, 1, 1, padding='VALID', relu=False, name='conv3')
             .prelu(name='prelu3')
             .max_pool(2, 2, 2, 2, name='pool3')
             .conv(2, 2, 128, 1, 1, padding='VALID', relu=False, name='conv4')
             .prelu(name='prelu4')
             .fc(256, relu=False, name='conv5')
             .prelu(name='prelu5')
             .fc(2, relu=False, name='conv6-1')
             .softmax(1, name='prob1'))

        (self.feed('prelu5') #pylint: disable=no-value-for-parameter
             .fc(4, relu=False, name='conv6-2'))

        (self.feed('prelu5') #pylint: disable=no-value-for-parameter
             .fc(10, relu=False, name='conv6-3'))
    '''
    ONet_Input = Input ( (48, 48, 3) )
    x = ONet_Input
    x = Conv2D (32, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv1")(x)
    x = PReLU (shared_axes=[1,2], name="prelu1" )(x)
    x = MaxPooling2D( pool_size=(3,3), strides=(2,2), padding='same' ) (x)
    x = Conv2D (64, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv2")(x)
    x = PReLU (shared_axes=[1,2], name="prelu2" )(x)
    x = MaxPooling2D( pool_size=(3,3), strides=(2,2), padding='valid' ) (x)    
    x = Conv2D (64, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv3")(x)
    x = PReLU (shared_axes=[1,2], name="prelu3" )(x)
    x = MaxPooling2D( pool_size=(2,2), strides=(2,2), padding='same' ) (x) 
    x = Conv2D (128, kernel_size=(2,2), strides=(1,1), padding='valid', name="conv4")(x)
    x = PReLU (shared_axes=[1,2], name="prelu4" )(x)
    x = Lambda ( lambda x: K.reshape (x, (-1, np.prod(K.int_shape(x)[1:]),) ), output_shape=(np.prod(K.int_shape(x)[1:]),) ) (x)    
    x = Dense (256, name='conv5')(x)
    x = PReLU (name="prelu5" )(x)
    prob = Dense (2, name='conv61')(x)
    prob = Softmax()(prob)    
    x1 = Dense (4, name='conv62')(x)
    x2 = Dense (10, name='conv63')(x)
    
    ONet_model = Model(ONet_Input, [x1,x2,prob] )
    
    #ONet_model.load_weights ( (Path(mtcnn.__file__).parent / 'mtcnn_onet.h5').__str__() )
    ONet_model.get_layer("conv1").set_weights ( [ det3_dict['conv1']['weights'], det3_dict['conv1']['biases'] ] )
    ONet_model.get_layer("prelu1").set_weights ( [ np.reshape(det3_dict['prelu1']['alpha'], (1,1,-1)) ] )
    ONet_model.get_layer("conv2").set_weights ( [ det3_dict['conv2']['weights'], det3_dict['conv2']['biases'] ] )
    ONet_model.get_layer("prelu2").set_weights ( [ np.reshape(det3_dict['prelu2']['alpha'], (1,1,-1)) ] )
    ONet_model.get_layer("conv3").set_weights ( [ det3_dict['conv3']['weights'], det3_dict['conv3']['biases'] ] )
    ONet_model.get_layer("prelu3").set_weights ( [ np.reshape(det3_dict['prelu3']['alpha'], (1,1,-1)) ] )
    ONet_model.get_layer("conv4").set_weights ( [ det3_dict['conv4']['weights'], det3_dict['conv4']['biases'] ] )
    ONet_model.get_layer("prelu4").set_weights ( [ np.reshape(det3_dict['prelu4']['alpha'], (1,1,-1)) ] )
    ONet_model.get_layer("conv5").set_weights ( [ det3_dict['conv5']['weights'], det3_dict['conv5']['biases'] ] )
    ONet_model.get_layer("prelu5").set_weights ( [ det3_dict['prelu5']['alpha'] ] )
    ONet_model.get_layer("conv61").set_weights ( [ det3_dict['conv6-1']['weights'], det3_dict['conv6-1']['biases'] ] )
    ONet_model.get_layer("conv62").set_weights ( [ det3_dict['conv6-2']['weights'], det3_dict['conv6-2']['biases'] ] )
    ONet_model.get_layer("conv63").set_weights ( [ det3_dict['conv6-3']['weights'], det3_dict['conv6-3']['biases'] ] )
    ONet_model.save ( (Path(mtcnn.__file__).parent / 'mtcnn_onet.h5').__str__() )
    
    onet_test_data = np.random.uniform ( size=(1,48,48,3) )
    onet_result1, onet_result2, onet_result3 = onet_fun ([onet_test_data])
    onet2_result1, onet2_result2, onet2_result3 =  K.function ( ONet_model.inputs, ONet_model.outputs ) ([onet_test_data])   
    
    onet_diff1 = np.mean ( np.abs(onet_result1 - onet2_result1) )
    onet_diff2 = np.mean ( np.abs(onet_result2 - onet2_result2) )
    onet_diff3 = np.mean ( np.abs(onet_result3 - onet2_result3) )
    print ("onet_diff1 = %f, onet_diff2 = %f, , onet_diff3 = %f "  % (onet_diff1, onet_diff2, onet_diff3) )
    
    
    import code
    code.interact(local=dict(globals(), **locals()))
    
    
    
    
    
    import code
    code.interact(local=dict(globals(), **locals()))
    
    
    
    
    
    
    #class MTCNNSoftmax(keras.Layer):
    #
    #    def __init__(self, axis=-1, **kwargs):
    #        super(MTCNNSoftmax, self).__init__(**kwargs)
    #        self.supports_masking = True
    #        self.axis = axis
    #
    #    def call(self, inputs):
    #    
    #    def softmax(self, target, axis, name=None):
    #        max_axis = self.tf.reduce_max(target, axis, keepdims=True)
    #        target_exp = self.tf.exp(target-max_axis)
    #        normalize = self.tf.reduce_sum(target_exp, axis, keepdims=True)
    #        softmax = self.tf.div(target_exp, normalize, name)
    #        return softmax
    #        #return activations.softmax(inputs, axis=self.axis)
    #
    #    def get_config(self):
    #        config = {'axis': self.axis}
    #        base_config = super(MTCNNSoftmax, self).get_config()
    #        return dict(list(base_config.items()) + list(config.items()))
    #
    #    def compute_output_shape(self, input_shape):
    #        return input_shape
    
    from nnlib import nnlib
    exec( nnlib.import_all(), locals(), globals() )
    
    
    
    
    image = cv2.imread('D:\\DeepFaceLab\\test\\00000.png').astype(np.float32) / 255.0    
    image = cv2.cvtColor (image, cv2.COLOR_BGR2GRAY)    
    image = np.expand_dims (image, -1)
    image_shape = image.shape
    
    image2 = cv2.imread('D:\\DeepFaceLab\\test\\00001.png').astype(np.float32) / 255.0    
    image2 = cv2.cvtColor (image2, cv2.COLOR_BGR2GRAY)    
    image2 = np.expand_dims (image2, -1)
    image2_shape = image2.shape
    
    #cv2.imshow('', image)

    
    image_tensor = K.placeholder(shape=[ 1, image_shape[0], image_shape[1], image_shape[2] ], dtype="float32" )  
    image2_tensor = K.placeholder(shape=[ 1, image_shape[0], image_shape[1], image_shape[2] ], dtype="float32" )  

    blurred_image_tensor = gaussian_blur(16.0)(image_tensor)   
    x, = nnlib.tf_sess.run ( blurred_image_tensor, feed_dict={image_tensor: np.expand_dims(image,0)} )
    cv2.imshow('', (x*255).astype(np.uint8) )
    cv2.waitKey(0)
    
    import code
    code.interact(local=dict(globals(), **locals()))
    
    
    #os.environ['plaidML'] = '1'
    from nnlib import nnlib
    
    dvc = nnlib.device.Config(force_gpu_idx=1)
    exec( nnlib.import_all(dvc), locals(), globals() )
    
    tf = nnlib.tf
    
    image = cv2.imread('D:\\DeepFaceLab\\test\\00000.png').astype(np.float32) / 255.0    
    image = cv2.cvtColor (image, cv2.COLOR_BGR2GRAY)    
    image = np.expand_dims (image, -1)
    image_shape = image.shape
    
    image2 = cv2.imread('D:\\DeepFaceLab\\test\\00001.png').astype(np.float32) / 255.0    
    image2 = cv2.cvtColor (image2, cv2.COLOR_BGR2GRAY)    
    image2 = np.expand_dims (image2, -1)
    image2_shape = image2.shape
    
    image1_tensor = K.placeholder(shape=[ 1, image_shape[0], image_shape[1], image_shape[2] ], dtype="float32" )  
    image2_tensor = K.placeholder(shape=[ 1, image_shape[0], image_shape[1], image_shape[2] ], dtype="float32" )  
    
    
    
    #import code
    #code.interact(local=dict(globals(), **locals()))
    def manual_conv(input, filter, strides, padding):
          h_f, w_f, c_in, c_out = filter.get_shape().as_list()
          input_patches = tf.extract_image_patches(input, ksizes=[1, h_f, w_f, 1 ], strides=strides, rates=[1, 1, 1, 1], padding=padding)
          return input_patches
          filters_flat = tf.reshape(filter, shape=[h_f*w_f*c_in, c_out])
          return tf.einsum("ijkl,lm->ijkm", input_patches, filters_flat)
          
    def extract_image_patches(x, ksizes, ssizes, padding='SAME',
                          data_format='channels_last'):
        """Extract the patches from an image.
        # Arguments
            x: The input image
            ksizes: 2-d tuple with the kernel size
            ssizes: 2-d tuple with the strides size
            padding: 'same' or 'valid'
            data_format: 'channels_last' or 'channels_first'
        # Returns
            The (k_w,k_h) patches extracted
            TF ==> (batch_size,w,h,k_w,k_h,c)
            TH ==> (batch_size,w,h,c,k_w,k_h)
        """
        kernel = [1, ksizes[0], ksizes[1], 1]
        strides = [1, ssizes[0], ssizes[1], 1]
        if data_format == 'channels_first':
            x = K.permute_dimensions(x, (0, 2, 3, 1))
        bs_i, w_i, h_i, ch_i = K.int_shape(x)
        patches = tf.extract_image_patches(x, kernel, strides, [1, 1, 1, 1],
                                           padding)
        # Reshaping to fit Theano
        bs, w, h, ch = K.int_shape(patches)
        reshaped = tf.reshape(patches, [-1, w, h, tf.floordiv(ch, ch_i), ch_i])
        final_shape = [-1, w, h, ch_i, ksizes[0], ksizes[1]]
        patches = tf.reshape(tf.transpose(reshaped, [0, 1, 2, 4, 3]), final_shape)
        if data_format == 'channels_last':
            patches = K.permute_dimensions(patches, [0, 1, 2, 4, 5, 3])
        return patches
    
    m = 32
    c_in = 3
    c_out = 16

    filter_sizes = [5, 11]
    strides = [1]
    #paddings = ["VALID", "SAME"]

    for fs in filter_sizes:
        h = w = 128
        h_f = w_f = fs
        str = 2
        #print "Testing for", imsize, fs, stri, pad

        #tf.reset_default_graph()
        X = tf.constant(1.0+np.random.rand(m, h, w, c_in), tf.float32)
        W = tf.constant(np.ones([h_f, w_f, c_in, h_f*w_f*c_in]), tf.float32)
        
        
        Z = tf.nn.conv2d(X, W, strides=[1, str, str, 1], padding="VALID")
        Z_manual = manual_conv(X, W, strides=[1, str, str, 1], padding="VALID")
        Z_2 = extract_image_patches (X, (fs,fs), (str,str),  padding="VALID")
        import code
        code.interact(local=dict(globals(), **locals()))
        #
        sess = tf.Session()
        sess.run(tf.global_variables_initializer())
        Z_, Z_manual_ = sess.run([Z, Z_manual])
        #self.assertEqual(Z_.shape, Z_manual_.shape)
        #self.assertTrue(np.allclose(Z_, Z_manual_, rtol=1e-05))
        sess.close()


        import code
        code.interact(local=dict(globals(), **locals()))
    
    
    
    
    
    #k_loss_t = keras_style_loss()(image1_tensor, image2_tensor)
    #k_loss_run = K.function( [image1_tensor, image2_tensor],[k_loss_t])
    #import code
    #code.interact(local=dict(globals(), **locals()))
    #image = np.expand_dims(image,0)
    #image2 = np.expand_dims(image2,0)
    #k_loss = k_loss_run([image, image2])
    #t_loss = t_loss_run([image, image2])
    
    
    
    
    #x, = tf_sess_run ([np.expand_dims(image,0)])
    #x = x[0]
    ##import code
    ##code.interact(local=dict(globals(), **locals()))
    
    
   
    image = cv2.imread('D:\\DeepFaceLab\\test\\00000.png').astype(np.float32) / 255.0    
    image = cv2.cvtColor (image, cv2.COLOR_BGR2GRAY)    
    image = np.expand_dims (image, -1)
    image_shape = image.shape
    
    image2 = cv2.imread('D:\\DeepFaceLab\\test\\00001.png').astype(np.float32) / 255.0    
    image2 = cv2.cvtColor (image2, cv2.COLOR_BGR2GRAY)    
    image2 = np.expand_dims (image2, -1)
    image2_shape = image2.shape
    
    image_tensor = tf.placeholder(tf.float32, shape=[1, image_shape[0], image_shape[1], image_shape[2] ])
    image2_tensor = tf.placeholder(tf.float32, shape=[1, image2_shape[0], image2_shape[1], image2_shape[2] ])
    
    blurred_image_tensor = sl(image_tensor, image2_tensor)        
    x = tf_sess.run ( blurred_image_tensor, feed_dict={image_tensor: np.expand_dims(image,0), image2_tensor: np.expand_dims(image2,0) } )
    
    cv2.imshow('', x[0])
    cv2.waitKey(0)
    import code
    code.interact(local=dict(globals(), **locals()))
    
    while True:
        image = cv2.imread('D:\\DeepFaceLab\\workspace\\data_src\\aligned\\00000.png').astype(np.float32) / 255.0
        image = cv2.resize(image, (256,256))
        image = random_transform( image )
        warped_img, target_img = random_warp( image )

        #cv2.imshow('', image)
        #cv2.waitKey(0)
    
        cv2.imshow('', warped_img)
        cv2.waitKey(0)
        cv2.imshow('', target_img)
        cv2.waitKey(0)
    
    import code
    code.interact(local=dict(globals(), **locals()))
    
    import code
    code.interact(local=dict(globals(), **locals()))

    return
    
    
    def keras_gaussian_blur(radius=2.0):
        def gaussian(x, mu, sigma):
            return np.exp(-(float(x) - float(mu)) ** 2 / (2 * sigma ** 2))

        def make_kernel(sigma):
            kernel_size = max(3, int(2 * 2 * sigma + 1))
            mean = np.floor(0.5 * kernel_size)
            kernel_1d = np.array([gaussian(x, mean, sigma) for x in range(kernel_size)])
            np_kernel = np.outer(kernel_1d, kernel_1d).astype(dtype=K.floatx())
            kernel = np_kernel / np.sum(np_kernel)
            return kernel
      
        gauss_kernel = make_kernel(radius)
        gauss_kernel = gauss_kernel[:, :,np.newaxis, np.newaxis]
        
        #import code
        #code.interact(local=dict(globals(), **locals()))
        def func(input):
            inputs = [ input[:,:,:,i:i+1]  for i in range( K.int_shape( input )[-1] ) ]

            outputs = []
            for i in range(len(inputs)):
                outputs += [ K.conv2d( inputs[i] , K.constant(gauss_kernel) , strides=(1,1), padding="same") ]

            return K.concatenate (outputs, axis=-1)
        return func
    
    def keras_style_loss(gaussian_blur_radius=0.0, loss_weight=1.0, epsilon=1e-5):
        if gaussian_blur_radius > 0.0:
            gblur = keras_gaussian_blur(gaussian_blur_radius)
        
        def sd(content, style):
            content_nc = K.int_shape(content)[-1]
            style_nc = K.int_shape(style)[-1]
            if content_nc != style_nc:
                raise Exception("keras_style_loss() content_nc != style_nc")
                
            axes = [1,2]
            c_mean, c_var = K.mean(content, axis=axes, keepdims=True), K.var(content, axis=axes, keepdims=True)
            s_mean, s_var = K.mean(style, axis=axes, keepdims=True), K.var(style, axis=axes, keepdims=True)
            c_std, s_std = K.sqrt(c_var + epsilon), K.sqrt(s_var + epsilon)

            mean_loss = K.sum(K.square(c_mean-s_mean))
            std_loss = K.sum(K.square(c_std-s_std))
            
            return (mean_loss + std_loss) * loss_weight
            
        def func(target, style):
            if gaussian_blur_radius > 0.0:
                return sd( gblur(target), gblur(style))
            else:
                return sd( target, style )
        return func
    
    data = tf.placeholder(tf.float32, (None,None,None,3), 'input')
    pnet2 = mtcnn.PNet(tf, {'data':data})
    filename = str(Path(mtcnn.__file__).parent/'det1.npy')
    pnet2.load(filename, tf_sess)
    
    pnet_fun = K.function([pnet2.layers['data']],[pnet2.layers['conv4-2'], pnet2.layers['prob1']])
    
    import code
    code.interact(local=dict(globals(), **locals()))

    return
    
    
    while True:
        img_bgr = np.random.rand ( 268, 640, 3 )
        img_size = img_bgr.shape[1], img_bgr.shape[0]

        mat = np.array( [[ 1.99319629e+00, -1.81504324e-01, -3.62479778e+02],
                         [ 1.81504324e-01,  1.99319629e+00, -8.05396709e+01]] )

        tmp_0 = np.random.rand ( 128,128 ) - 0.1
        tmp   = np.expand_dims (tmp_0, axis=-1)

        mask = np.ones ( tmp.shape, dtype=np.float32)        
        mask_border_size = int ( mask.shape[1] * 0.0625 )
        mask[:,0:mask_border_size,:] = 0
        mask[:,-mask_border_size:,:] = 0                        

        x = cv2.warpAffine( mask, mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
        
        if len ( np.argwhere( np.isnan(x) ) ) == 0:
            print ("fine")
        else:
            print ("wtf")
         
    import code
    code.interact(local=dict(globals(), **locals()))

    return
    
    aligned_path_image_paths = Path_utils.get_image_paths("E:\\FakeFaceVideoSources\\Datasets\\CelebA aligned")
    
    a = []
    r_vec = np.array([[0.01891013], [0.08560084], [-3.14392813]])
    t_vec = np.array([[-14.97821226], [-10.62040383], [-2053.03596872]])
        
    yaws = []
    pitchs = []
    for filepath in tqdm(aligned_path_image_paths, desc="test", ascii=True ):
        filepath = Path(filepath)
        
        if filepath.suffix == '.png':
            dflimg = DFLPNG.load( str(filepath), print_on_no_embedded_data=True )
        elif filepath.suffix == '.jpg':
            dflimg = DFLJPG.load ( str(filepath), print_on_no_embedded_data=True )
        else:
            print ("%s is not a dfl image file" % (filepath.name) ) 
        
        #source_filename_stem = Path( dflimg.get_source_filename() ).stem
        #if source_filename_stem not in alignments.keys():
        #    alignments[ source_filename_stem ] = []
        
        
        #focal_length = dflimg.shape[1]
        #camera_center = (dflimg.shape[1] / 2, dflimg.shape[0] / 2)
        #camera_matrix = np.array(
        #    [[focal_length, 0, camera_center[0]],
        #     [0, focal_length, camera_center[1]],
        #     [0, 0, 1]], dtype=np.float32)
        #
        landmarks = dflimg.get_landmarks()
        #
        #lm = landmarks.astype(np.float32)
        
        img = cv2_imread (str(filepath)) / 255.0
        
        LandmarksProcessor.draw_landmarks(img, landmarks, (1,1,1) )
        
        
        #(_, rotation_vector, translation_vector) = cv2.solvePnP(
        #    LandmarksProcessor.landmarks_68_3D,
        #    lm,
        #    camera_matrix,
        #    np.zeros((4, 1)) ) 
        #
        #rme = mathlib.rotationMatrixToEulerAngles( cv2.Rodrigues(rotation_vector)[0] )
        #import code
        #code.interact(local=dict(globals(), **locals()))
    
        #rotation_vector = rotation_vector / np.linalg.norm(rotation_vector)
        
        
        #img2 = image_utils.get_text_image ( (256,10, 3), str(rotation_vector) )
        pitch, yaw = LandmarksProcessor.estimate_pitch_yaw (landmarks)
        yaws += [yaw]
        #print(pitch, yaw)
        #cv2.imshow ("", (img * 255).astype(np.uint8) )
        #cv2.waitKey(0)
        #a += [ rotation_vector]
    yaws = np.array(yaws)       
    import code
    code.interact(local=dict(globals(), **locals()))
        
        
        
        
        
        
        #alignments[ source_filename_stem ].append (dflimg.get_source_landmarks())
        #alignments.append (dflimg.get_source_landmarks())


        
    
    
    
    
    o = np.ones ( (128,128,3), dtype=np.float32 )
    cv2.imwrite ("D:\\temp\\z.jpg", o)
    
    #DFLJPG.embed_data ("D:\\temp\\z.jpg", )
    
    dfljpg = DFLJPG.load("D:\\temp\\z.jpg")
    
    import code
    code.interact(local=dict(globals(), **locals()))

    return



    import sys, numpy; print(numpy.__version__, sys.version)
    sq = multiprocessing.Queue()
    cq = multiprocessing.Queue()

    p = multiprocessing.Process(target=subprocess, args=(sq,cq,))
    p.start()
    
    while True:
        cq.get() #waiting numpy array
        sq.put (1) #send message we are ready to get more
            
    #import code
    #code.interact(local=dict(globals(), **locals()))
    
    os.environ['TF_MIN_GPU_MULTIPROCESSOR_COUNT'] = '2'

    from nnlib import nnlib
    exec( nnlib.import_all(), locals(), globals() )

    
    
    
    #import tensorflow as tf
    #tf_module = tf
    #    
    #config = tf_module.ConfigProto()
    #config.gpu_options.force_gpu_compatible = True
    #tf_session = tf_module.Session(config=config)
    #
    #srgb_tensor = tf.placeholder("float", [None, None, 3])
    #
    #filename = Path(__file__).parent / '00050.png'
    #img = cv2.imread(str(filename)).astype(np.float32) / 255.0
    #
    #lab_tensor = rgb_to_lab (tf_module, srgb_tensor)
    #
    #rgb_tensor = lab_to_rgb (tf_module, lab_tensor)
    #
    #rgb = tf_session.run(rgb_tensor, feed_dict={srgb_tensor: img})
    #cv2.imshow("", rgb)
    #cv2.waitKey(0)    
    
    #from skimage import io, color
    #def_lab = color.rgb2lab(img)  
    #
    #t = time.time()
    #def_lab = color.rgb2lab(img)  
    #print ( time.time() - t )
    #
    #lab = tf_session.run(lab_tensor, feed_dict={srgb_tensor: img})
    #
    #t = time.time()
    #lab = tf_session.run(lab_tensor, feed_dict={srgb_tensor: img})
    #print ( time.time() - t )
    
    
    
    
    
    
    #lab_clr = color.rgb2lab(img_bgr)                         
    #lab_bw = color.rgb2lab(out_img)                          
    #tmp_channel, a_channel, b_channel = cv2.split(lab_clr)   
    #l_channel, tmp2_channel, tmp3_channel = cv2.split(lab_bw)
    #img_LAB = cv2.merge((l_channel,a_channel, b_channel))    
    #out_img = color.lab2rgb(lab.astype(np.float64))                         
    #
    #cv2.imshow("", out_img)
    #cv2.waitKey(0)    
    
    #import code
    #code.interact(local=dict(globals(), **locals()))



if __name__ == "__main__":
    #os.environ["KERAS_BACKEND"] = "plaidml.keras.backend"
    #os.environ["PLAIDML_DEVICE_IDS"] = "opencl_nvidia_geforce_gtx_1060_6gb.0"
    #import keras
    #K = keras.backend
    #
    #image = np.random.uniform ( size=(1,256,256,3) )
    #image2 = np.random.uniform ( size=(1,256,256,3) )
    #
    #y_true = K.placeholder ( (None,) + image.shape[1:] )
    #y_pred = K.placeholder ( (None,) + image2.shape[1:] )
    #
    #def reducer(x):
    #    shape = K.shape(x)        
    #    x = K.reshape(x, (-1, shape[-3] , shape[-2], shape[-1]) )      
    #    y = K.depthwise_conv2d(x, K.constant(np.ones( (11,11,3,1) )), strides=(1, 1), padding='valid' )
    #    y_shape = K.shape(y)
    #    return K.reshape(y, (shape[0], y_shape[1], y_shape[2], y_shape[3] ) )
    #        
    #mean0 = reducer(y_true)
    #mean1 = reducer(y_pred)
    #luminance = mean0 * mean1    
    #cs = y_true * y_pred
    #
    #result = K.function([y_true, y_pred],[luminance, cs]) ([image, image2])
    #
    #print (result)    
    #import code
    #code.interact(local=dict(globals(), **locals()))


    main()