📑   Data Building. Practice Project 0_0: Artificial Image Data

In this project, we'll create artificial images, represent and process their examples.

✒️   Code Modules & Settings

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import os,warnings; warnings.filterwarnings('ignore')

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import h5py,pandas as pd,numpy as np

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from skimage.transform import resize

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img_size=int(1024); img_size_out=int(256)

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✒️   Image Creating

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def randint_coord(img_size_out,img_size=img_size):

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    a=(.5+.1**6*np.random.randint(1,999999))*\

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      np.random.choice([-1,1],1)[0]

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    b=np.random.randint(3,7)

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    c=.1**3*np.random.randint(1,99)*\

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      np.random.choice([-1,1],1)[0]

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    t=np.arange(0,12*np.pi,1/(1440*b))

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    fx=np.sin(t/6)+a*np.sin(b*t)*np.cos(t)-c*np.sin(16*b*t)

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    fy=np.cos(t/6)+a*np.sin(b*t)*np.sin(t)-c*np.cos(16*b*t)

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    fx=.951*(fx-1.051*fx.min())/(fx.max()-fx.min())

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    fy=.951*(fy-1.051*fy.min())/(fy.max()-fy.min())

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    fx=np.array(np.clip(fx*img_size,0,img_size),dtype='int32')

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    fy=np.array(np.clip(fy*img_size,0,img_size),dtype='int32')

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    f=np.array([[fx[i],fy[i]] for i in range(len(t))])

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    img=np.ones((img_size,img_size,3))

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    randcol=.9-.8*np.random.random(3)

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    for [x,y] in f: img[y,x,:]=randcol

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    img=resize(img,(img_size_out,img_size_out))

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    return img,np.around(a,6),b,np.around(c,3),randcol

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for i in range(2):

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    img,a,b,c,col=randint_coord(img_size_out)

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    matrix_plot(img,figsize=5).show()

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✒️   Data Storing

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num_images=50

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images=np.zeros((num_images,img_size_out,img_size_out,3),

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                dtype=np.float32)

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labels=np.zeros((num_images,),dtype=np.int32)

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targets=np.zeros((num_images,2),dtype=np.float32)

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colors=np.zeros((num_images,3),dtype=np.float32)

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for i in range(num_images):

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    if (i+1)%int(.2*num_images)==0: print('=>',end='',flush=True)

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    img,a,b,c,col=randint_coord(img_size_out)

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    images[i,:,:,:]=img

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    labels[i],targets[i,0],targets[i,1]=b-3,a,c

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    colors[i,:]=col

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h5f='ArtificialImages'+str(img_size)+'.h5'

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with h5py.File(h5f,'w') as f:

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    f.create_dataset('images',data=images,compression='gzip')

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    f.create_dataset('labels',data=labels,compression='gzip')

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    f.create_dataset('targets',data=targets,compression='gzip')

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    f.create_dataset('colors',data=colors,compression='gzip')

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    f.close()

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pretty_print('file size: %s'%list(os.stat(h5f))[6])

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with h5py.File(h5f,'r') as f:

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    keys=list(f.keys()); print(keys)

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    images=np.array(f[keys[1]],dtype='float32')

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    labels=np.array(f[keys[2]],dtype='int32')

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    targets=np.array(f[keys[3]],dtype='float32')

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    colors=np.array(f[keys[0]],dtype='float32')

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    f.close()

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✒️   Data Processing

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shuffle_ids=np.arange(num_images)

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np.random.RandomState(12).shuffle(shuffle_ids)

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images=images[shuffle_ids]; labels=labels[shuffle_ids]

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colors=colors[shuffle_ids]; targets=targets[shuffle_ids]

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n=int(.1*num_images)

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x_test,x_valid,x_train=images[:n],images[n:2*n],images[2*n:]

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y_test1,y_valid1,y_train1=labels[:n],labels[n:2*n],labels[2*n:]

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y_test2,y_valid2,y_train2=\

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targets[:,:n],targets[:,n:2*n],targets[:,2*n:]

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images.shape,labels.shape,targets.shape,colors.shape

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def pd_style():

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    return [dict(selector='th',

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                 props=[('font-size','12pt'),

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                        ('min-width','150px')]),

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            dict(selector='td',

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                 props=[('padding','0em 0em'),

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                        ('min-width','150px')]),

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            dict(selector='tr:hover th:hover',

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                 props=[('font-size','16pt'),

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                        ('max-width','150px'),

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                        ('text-shadow','3px 3px 3px #aaa')]),

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            dict(selector='tr:hover td:hover',

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                 props=[('font-size','13pt'),

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                        ('max-width','150px'),

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                        ('text-shadow','3px 3px 3px #aaa')])]

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pretty_print(html('<h2>data outputs: </h2>'))

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df=pd.DataFrame([[x_train.shape,x_valid.shape,x_test.shape],

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                 [x_train.dtype,x_valid.dtype,x_test.dtype],

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                 [y_train1.shape,y_valid1.shape,y_test1.shape],

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                 [y_train1.dtype,y_valid1.dtype,y_test1.dtype],

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                 [y_train2.shape,y_valid2.shape,y_test2.shape],

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                 [y_train2.dtype,y_valid2.dtype,y_test2.dtype]],

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                columns=['train','valid','test'],

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                index=['image shape','image type',

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                       'label shape','label type',

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                       'target shape','target type'])

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display(df.style.set_table_styles(pd_style()))

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✒️   Data Checking

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list_plot([[targets[i,0],targets[i,1]] 

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           for i in range(num_images)],color='#348ABD')\

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.show(frame=True,gridlines=True,figsize=4,title='targets')

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bar_chart(np.unique(labels,return_counts=True)[1],color='#348ABD')\

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.show(frame=True,axes=False,xmin=-1,figsize=4,title='labels')

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Messages

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def check_coord(a,b,c,img_size=img_size):

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    t=np.arange(0,12*np.pi,1/(1440*b))

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    fx=np.sin(t/6)+a*np.sin(b*t)*np.cos(t)-c*np.sin(16*b*t)

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    fy=np.cos(t/6)+a*np.sin(b*t)*np.sin(t)-c*np.cos(16*b*t)

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    fx=.951*(fx-1.051*fx.min())/(fx.max()-fx.min())

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    fy=.951*(fy-1.051*fy.min())/(fy.max()-fy.min())

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    fx=np.array(np.clip(fx*img_size,0,img_size),dtype='int32')

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    fy=np.array(np.clip(fy*img_size,0,img_size),dtype='int32')

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    f=np.array([[fx[i],fy[i]] for i in range(len(t))])

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    return f

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n=randint(0,num_images-1)

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b,a,c=labels[n]+3,targets[n,0],targets[n,1]

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f=check_coord(a,b,c)

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list_plot(f,size=1,color=list(colors[n]))\

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.show(frame=True,flip_y=True,figsize=(4,4))

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matrix_plot(images[n]).show(figsize=(4,4))

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