In [38]:

    

%matplotlib inline


    

In [39]:

    

import cPickle as pickle
import numpy as np
import PIL

import json


    

In [40]:

    

received = None
with open('receivedImage.pkl', 'rb') as fin:
    received = pickle.load(fin)


    

In [41]:

    

encoded = received['the_file'].split(',')

imgData = encoded[1]
ext = encoded[0].split('/')[1].split(';')[0]

# or, more concisely using with statement
with open("imageToSave." + ext, "wb") as fh:
    fh.write(imgData.decode('base64'))


    

In [42]:

    

type(imgData.decode('base64'))


    

    
Out[42]:





str

In [43]:

    

import base64
import io
from matplotlib import pyplot as plt
import matplotlib.image as mpimg

i = base64.b64decode(imgData)
i = io.BytesIO(i)
i = mpimg.imread(i, format=ext)

plt.imshow(i, interpolation='nearest')
plt.show()


    

In [44]:

    

type(i)


    

    
Out[44]:





numpy.ndarray

In [45]:

    

i.shape


    

    
Out[45]:





(304L, 250L, 4L)

In [46]:

    

np.average(i)


    

    
Out[46]:





0.85505044

In [47]:

    

tmp = 'hello'
name = tmp if tmp else 'goodbye'
name


    

    
Out[47]:





'hello'

In [48]:

    

from laserCAM import Project, Image, Engraving, Laser


    

    




  File "laserCAM.py", line 108
    pixelX =
            ^
SyntaxError: invalid syntax

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project = None


    

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project = Project(name='TestProject')


    

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image = Image(image_data=i, original_extension=ext)


    

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project.image = image


    

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print type(project.image.image_data)


    

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del image


    

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print type(project.image.image_data)


    

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data = np.asarray([12, 240, 241, 242, 245, 250, 251, 253, 255])
data[data>=245] = 255
data


    

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np.where(data < 255)[0][0], np.where(data < 255)[0][-1], np.where(data < 255)[0]


    

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np.where(data[::-1] > 255)[0]


    

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from laserCAM import Project, Image, Preprocessor


    

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project = Project()


    

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image = Image(i)


    

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i.shape


    

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preproc = Preprocessor(ignore_white=True, split_white=False, white_cutoff=240)


    

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project.image = image
project.preprocessor = preproc


    

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project.generate_gcode()


    

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plt.imshow(project.image.normalized_data[::-1], interpolation='nearest')
plt.show()


    

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[1,2,3,4,5,6][:-1]


    

In [ ]:

    




    

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j = np.zeros_like(a=project.image.normalized_data)

for rowNum, row in enumerate(project.preprocessor._terminators):
    low, high = row
    
    if low is not None:
        j[rowNum, low] = 1.0
    if high is not None:
        j[rowNum, high] = 1.0

plt.imshow(j[::-1], interpolation='nearest')
plt.show()


    

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direction = -1

1 - int(direction == True)


    

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project.image


    

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project.image.normalize_image(255)


    

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project.image.normalized_data.shape


    

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project.image.normalized_data[0]


    

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i.shape


    

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tmp = i.copy()


    

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tmp = np.average(tmp, axis=-1)


    

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tmp.shape


    

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i[0]


    

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