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make the script test_kerasmodel_to_pb.py compatible with my docker …
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…image mpkuse/kusevisionkit:tfgpu-1.12-tensorrt-5.1
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mpkuse committed May 27, 2019
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267 changes: 267 additions & 0 deletions CustomDataProc.py
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from keras import backend as K
from keras.engine.topology import Layer
import keras
import code
import numpy as np

import cv2
import code

from imgaug import augmenters as iaa
import imgaug as ia


#--------------------------------------------------------------------------------
# Data
#--------------------------------------------------------------------------------
# TODO : removal
def dataload_( n_tokyoTimeMachine, n_Pitssburg, nP, nN ):
D = []
if n_tokyoTimeMachine > 0 :
TTM_BASE = '/Bulk_Data/data_Akihiko_Torii/Tokyo_TM/tokyoTimeMachine/' #Path of Tokyo_TM
pr = TimeMachineRender( TTM_BASE )
print 'tokyoTimeMachine:: nP=', nP, '\tnN=', nN
for s in range(n_tokyoTimeMachine):
a,_ = pr.step(nP=nP, nN=nN, return_gray=False, resize=(320,240), apply_distortions=False, ENABLE_IMSHOW=False)
if s%100 == 0:
print 'get a sample Tokyo_TM #%d of %d\t' %(s, n_tokyoTimeMachine),
print a.shape
D.append( a )

if n_Pitssburg > 0 :
PTS_BASE = '/Bulk_Data/data_Akihiko_Torii/Pitssburg/'
pr = PittsburgRenderer( PTS_BASE )
print 'Pitssburg nP=', nP, '\tnN=', nN
for s in range(n_Pitssburg):
a,_ = pr.step(nP=nP, nN=nN, return_gray=False, resize=(240,320), apply_distortions=False, ENABLE_IMSHOW=False)
if s %100 == 0:
print 'get a sample Pitssburg #%d of %d\t' %(s, n_Pitssburg),
print a.shape
D.append( a )

return D


def do_augmentation( D ):
""" D : Nx(n+p+1)xHxWx3. Return N1x(n+p+1)xHxWx3 """

n_samples = D.shape[0]
n_images_per_sample = D.shape[1]

im_rows = D.shape[2]
im_cols = D.shape[3]
im_chnl = D.shape[4]

E = D.reshape( n_samples*n_images_per_sample, im_rows,im_cols,im_chnl )


sometimes = lambda aug: iaa.Sometimes(0.5, aug)

# Very basic
if True:
seq = iaa.Sequential([
sometimes( iaa.Crop(px=(0, 50)) ), # crop images from each side by 0 to 16px (randomly chosen)
# iaa.Fliplr(0.5), # horizontally flip 50% of the images
iaa.GaussianBlur(sigma=(0, 3.0)), # blur images with a sigma of 0 to 3.0
sometimes( iaa.Affine(
scale={"x": (0.8, 1.2), "y": (0.8, 1.2)},
translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)},
rotate=(-25, 25),
shear=(-8, 8)
) )
])
seq_vbasic = seq

# Sometimes(0.5, ...) applies the given augmenter in 50% of all cases,
# e.g. Sometimes(0.5, GaussianBlur(0.3)) would blur roughly every second image.

# Typical
if True:
seq = iaa.Sequential([
iaa.Fliplr(0.5), # horizontal flips
iaa.Crop(percent=(0, 0.1)), # random crops
# Small gaussian blur with random sigma between 0 and 0.5.
# But we only blur about 50% of all images.
iaa.Sometimes(0.5,
iaa.GaussianBlur(sigma=(0, 0.5))
),
# Strengthen or weaken the contrast in each image.
iaa.ContrastNormalization((0.75, 1.5)),
# Add gaussian noise.
# For 50% of all images, we sample the noise once per pixel.
# For the other 50% of all images, we sample the noise per pixel AND
# channel. This can change the color (not only brightness) of the
# pixels.
iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5),
# Make some images brighter and some darker.
# In 20% of all cases, we sample the multiplier once per channel,
# which can end up changing the color of the images.
iaa.Multiply((0.8, 1.2), per_channel=0.2),
# Apply affine transformations to each image.
# Scale/zoom them, translate/move them, rotate them and shear them.
iaa.Affine(
scale={"x": (0.8, 1.2), "y": (0.8, 1.2)},
translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)},
rotate=(-25, 25),
shear=(-8, 8)
)
], random_order=True) # apply augmenters in random order
# seq = sometimes( seq )
seq_typical = seq

# Heavy
if True:
# Define our sequence of augmentation steps that will be applied to every image
# All augmenters with per_channel=0.5 will sample one value _per image_
# in 50% of all cases. In all other cases they will sample new values
# _per channel_.
seq = iaa.Sequential(
[
# apply the following augmenters to most images
iaa.Fliplr(0.2), # horizontally flip 20% of all images
iaa.Flipud(0.2), # vertically flip 20% of all images
# crop images by -5% to 10% of their height/width
sometimes(iaa.CropAndPad(
percent=(-0.05, 0.1),
pad_mode=ia.ALL,
pad_cval=(0, 255)
)),
sometimes(iaa.Affine(
scale={"x": (0.8, 1.2), "y": (0.8, 1.2)}, # scale images to 80-120% of their size, individually per axis
translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)}, # translate by -20 to +20 percent (per axis)
rotate=(-45, 45), # rotate by -45 to +45 degrees
shear=(-16, 16), # shear by -16 to +16 degrees
order=[0, 1], # use nearest neighbour or bilinear interpolation (fast)
cval=(0, 255), # if mode is constant, use a cval between 0 and 255
mode=ia.ALL # use any of scikit-image's warping modes (see 2nd image from the top for examples)
)),
# execute 0 to 5 of the following (less important) augmenters per image
# don't execute all of them, as that would often be way too strong
iaa.SomeOf((0, 5),
[
sometimes(iaa.Superpixels(p_replace=(0, 1.0), n_segments=(20, 200))), # convert images into their superpixel representation
iaa.OneOf([
iaa.GaussianBlur((0, 3.0)), # blur images with a sigma between 0 and 3.0
iaa.AverageBlur(k=(2, 7)), # blur image using local means with kernel sizes between 2 and 7
#iaa.MedianBlur(k=(3, 11)), # blur image using local medians with kernel sizes between 2 and 7
]),
iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)), # sharpen images
iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)), # emboss images
# search either for all edges or for directed edges,
# blend the result with the original image using a blobby mask
iaa.SimplexNoiseAlpha(iaa.OneOf([
iaa.EdgeDetect(alpha=(0.5, 1.0)),
iaa.DirectedEdgeDetect(alpha=(0.5, 1.0), direction=(0.0, 1.0)),
])),
iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5), # add gaussian noise to images
iaa.OneOf([
iaa.Dropout((0.01, 0.1), per_channel=0.5), # randomly remove up to 10% of the pixels
iaa.CoarseDropout((0.03, 0.15), size_percent=(0.02, 0.05), per_channel=0.2),
]),
iaa.Invert(0.05, per_channel=True), # invert color channels
iaa.Add((-10, 10), per_channel=0.5), # change brightness of images (by -10 to 10 of original value)
iaa.AddToHueAndSaturation((-20, 20)), # change hue and saturation
# either change the brightness of the whole image (sometimes
# per channel) or change the brightness of subareas
iaa.OneOf([
iaa.Multiply((0.5, 1.5), per_channel=0.5),
iaa.FrequencyNoiseAlpha(
exponent=(-4, 0),
first=iaa.Multiply((0.5, 1.5), per_channel=True),
second=iaa.ContrastNormalization((0.5, 2.0))
)
]),
iaa.ContrastNormalization((0.5, 2.0), per_channel=0.5), # improve or worsen the contrast
iaa.Grayscale(alpha=(0.0, 1.0)),
sometimes(iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)), # move pixels locally around (with random strengths)
sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.05))), # sometimes move parts of the image around
sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.1)))
],
random_order=True
)
],
random_order=True
)
seq_heavy = seq

print 'Add data'
L = [E]
print 'seq_vbasic'
L.append( seq_vbasic.augment_images(E) )
print 'seq_typical'
L.append( seq_typical.augment_images(E) )
print 'seq_typical'
L.append( seq_typical.augment_images(E) )
print 'seq_heavy'
L.append( seq_heavy.augment_images(E) )

G = [ l.reshape(n_samples, n_images_per_sample, im_rows,im_cols,im_chnl) for l in L ]
G = np.concatenate( G )
print 'Input.shape ', D.shape, '\tOutput.shape ', G.shape
return G

# for j in range(n_times):
# images_aug = seq.augment_images(E)
# # L.append( images_aug.reshape( n_samples, n_images_per_sample, im_rows,im_cols,im_chnl ) )
# L.append( images_aug )

# code.interact( local=locals() )
return L



def do_typical_data_aug( D ):
""" D : Nx(n+p+1)xHxWx3. Return N1x(n+p+1)xHxWx3 """
D = np.array( D )
assert( len(D.shape) == 5 )
print '[do_typical_data_aug]', 'D.shape=', D.shape

n_samples = D.shape[0]
n_images_per_sample = D.shape[1]

im_rows = D.shape[2]
im_cols = D.shape[3]
im_chnl = D.shape[4]

E = D.reshape( n_samples*n_images_per_sample, im_rows,im_cols,im_chnl )


sometimes = lambda aug: iaa.Sometimes(0.5, aug)
sometimes_2 = lambda aug: iaa.Sometimes(0.2, aug)


seq = iaa.Sequential( [
#iaa.Fliplr(0.5), # horizontal flips
#iaa.Crop(percent=(0, 0.1)), # random crops
# Small gaussian blur with random sigma between 0 and 0.5.
# But we only blur about 50% of all images.
iaa.Sometimes(0.5,
iaa.GaussianBlur(sigma=(0, 0.5))
),
# Strengthen or weaken the contrast in each image.
iaa.ContrastNormalization((0.75, 1.5)),
# Add gaussian noise.
# For 50% of all images, we sample the noise once per pixel.
# For the other 50% of all images, we sample the noise per pixel AND
# channel. This can change the color (not only brightness) of the
# pixels.
iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5),
# Make some images brighter and some darker.
# In 20% of all cases, we sample the multiplier once per channel,
# which can end up changing the color of the images.
iaa.Multiply((0.8, 1.2), per_channel=0.2),
# Apply affine transformations to each image.
# Scale/zoom them, translate/move them, rotate them and shear them.
iaa.Affine(
scale={"x": (0.8, 1.2), "y": (0.8, 1.2)},
translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)},
rotate=(-45, 45),
shear=(-8, 8)
)
], random_order=True) # apply augmenters in random order

D = seq.augment_images(E)
D = D.reshape(n_samples, n_images_per_sample, im_rows,im_cols,im_chnl)
print '[do_typical_data_aug] Done...!', 'D.shape=', D.shape

return D
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