@ccc Hmmmm, I thought PNG is not compressed so it would be safe already.

@cvp It worked! Thanks!

Hi all.
I crafted a little script to do stegnography in python. Here is the code: https://github.com/Xunius/py_stegnography.

The 3 files in the repo:

• steg.py: this does the core computations, also works as a CLI command.
• steg_ui.pyui: the pythonista ui design.
• steg_pythonista.py: script for use in pythonista.

In the pythonista ui, you are presented with an image selection button, from which you select a "carrier image" from camera roll.

The textview widget below shows the message you intend to hide, or the message read from the "carrier image". So if it's empty, it will try to read a hidden message. If non-empty, it will hide the message into the chosen image, and create a new png version of it.

This is achieved by converting the message into binary, and put that binary stream into the last bits in the color channels in the pixels. If the change is restricted to the last couple of bits, it is invisible to the eyes. The reading process is doing the opposite.

Optionally one can encrypt the message first. But it doesn't seem to work inside pythonista, the encryption part is throwing error at me.

Another issue I noticed is it often freezes after photos.pick_image(). Any idea why?

Improvements are welcome!

Hi all. I’m sharing a little script I crafted that turns an input image into gray scale, and then makes a spiral to approximate it.

When run, the script prompts you to choose an image to process or use the Lena demo.

''' convert image to a big spiral
'''

dr1=3.0  # delta radius for inner spiral
dr2=3    # delta radius for outter spiral
lseg=4   # length of spiral segment treated as a line
nlayers=4 # NO. of layers to segment gray scale img
sigma=3  # radius for Gaussian blur



#--------Import modules-------------------------
import numpy as np
from PIL import Image
from PIL import ImageFilter
import matplotlib.pyplot as plt
import dialogs
import photos

#-------------Main---------------------------------
if __name__=='__main__':
    
    #----------Read image-----
    i = dialogs.alert('Image', '', 'Demo Image', 'Select from Photos')
    if i == 1:
        img = Image.open('test:Lenna')
    else:
        img = photos.pick_image()
        
    img=img.convert('L')

    # resize
    newsize=(300, int(float(img.size[1])/img.size[0]*300))
    img=img.resize(newsize,Image.ANTIALIAS)
    print('Image size: %s' %str(img.size))

    # blur
    img=img.filter(ImageFilter.GaussianBlur(sigma))

    # convert to array
    img=np.array(img)
    img=img[::-1,:]

    # thresholding
    layers=np.linspace(np.min(img),np.max(img),nlayers+1)
    img_layers=np.zeros(img.shape)
    ii=1
    for z1,z2 in zip(layers[:-1],layers[1:]):
        img_layers=np.where((img>=z1) & (img<z2),ii,img_layers)
        ii+=1
    img_layers=img_layers.max()-img_layers+1

    # get diagonal length
    size=img.shape # ny,nx
    diag=np.sqrt(size[0]**2/4+size[1]**2/4)

    figure=plt.figure(figsize=(12,10),dpi=100)
    ax=figure.add_subplot(111)

    # create spiral
    rii=1.0
    line=np.zeros(img.shape)
    nc=0
    while True:
        if nc==0:
            rii2=rii+dr1
        elif nc<0:
            rii=rii2
            rii2=rii+dr1
        else:
            rii=rii2
            rii2=rii+dr2

        print('r = %.1f' %rii)
        nii=max(64,2*np.pi*rii//lseg)
        tii=np.linspace(0,2*np.pi,nii)
        riis=np.linspace(rii,rii2,nii)
        xii=riis*np.cos(tii)
        yii=riis*np.sin(tii)

        if np.all(riis>=diag):
            break

        # get indices
        xidx=np.around(xii,0).astype('int')+size[1]//2
        yidx=np.around(yii,0).astype('int')+size[0]//2
        idx=[jj for jj in range(len(yidx)) if xidx[jj]>=0 and yidx[jj]>=0\
                and xidx[jj]<=size[1]-1 and yidx[jj]<=size[0]-1]
        xidx=xidx[idx]
        yidx=yidx[idx]

        # skip diagonal jumps
        if len(yidx)>0 and yidx[0]*yidx[-1]<0:
            continue
        xii=xii[idx]
        yii=yii[idx]

        # pick line width from image
        lw=img_layers[yidx,xidx]

        # add random perturbation
        lwran=np.random.random(lw.shape)-0.5
        lw=lw+lwran

        # randomize color
        colorjj=np.random.randint(0,60)*np.ones(3)/float(255)

        for jj in range(len(xii)-1):

            # smooth line widths
            lwjjs=lw[max(0,jj-3):min(len(lw),jj+3)]
            if jj==0 and nc>0:
                lwjjs=np.r_[lwjjs_old,lwjjs]

            wjj=np.ones(len(lwjjs))
            wjj=wjj/len(wjj)
            lwjj=np.dot(lwjjs,wjj)

            ax.plot([xii[jj], xii[jj+1]],
                    [yii[jj], yii[jj+1]],
                    color=colorjj,
                    linewidth=lwjj)

            if jj==len(xii)-2:
                lwjjs_old=lwjjs

        nc+=1

    ax.axis('off')
    #ax.set_facecolor((1.,0.5,0.5))
    ax.set_aspect('equal')
    plt.show()

Update myself:

I've changed the script to load image from within pythonista rather than running as extension so we don't have the resource limitations, and also re-computed the model parameters. Now the script works, although the model itself is far from accurate.

Thanks for the reply.

You are probably right. If I shrink the image size down to about 150 in both dimension it works, but it becomes too small for the detector to actually work at least for now.

Hi all,

I'm trying to make a simple pedestrian detection script in numpy and port it to run on my phone. It consists of 2 scipts (hog_pythonista.py and pede_detect_pythonista.py). When using the sample image inside Image like this:

    if not appex.is_running_extension():
        print('Running in Pythonista app, using test image...')
        img=Image.open('test:Mandrill')
    else:
        img=appex.get_image()

It works OK, but if I go into the photo lib and run as extension, it will fail at the function call into hog_pythonista.py. Any idea what's going wrong?

hog_pythonista.py here: https://github.com/Xunius/HOG_python/blob/master/hog_pythonista.py
pede_detect_pythonista.py here: https://github.com/Xunius/HOG_python/blob/master/pede_detect_pythonista.py

Thanks