Loading images in OS

### Image loading... 
exp.imfolder = os.path.join(exp.experiment_path, '__pool__')
exp.all_img  = [scipy.misc.imread( os.path.join(exp.imfolder,im) ) 
	for im in os.listdir(exp.imfolder) if '.jpg' in im]
### "cut out center" of these images:

img = exp.all_img[0]
xc,yc= img.shape[0]//2, img.shape[0]//2
rr,cc = skimage.draw.circle(yc,xc, 45)

#### turn it into a PIL image:
for i in range(len(exp.all_img)):
	exp.all_img[i][rr,cc] = 255
	exp.all_img[i] = Image.fromarray(exp.all_img[i])

########## colors:
hs = np.linspace(0.00, 1.00, 7)
cols = []
for h in hs[0:-1:2]:
	# cols += [colorsys.hsv_to_rgb(h, 1.0, 0.5)]
	rgb = np.array(colorsys.hsv_to_rgb(h,1,.5))
	cols += [rgb] # for psychopy colors, : cols *2-1
exp.cols = cols




################################# And I'm doing the same thing with the masks ################################# 
# note that It's kind of misleading, because from the perspective of PIL.Image.composite

exp.maskpath = exp.experiment_path
exp.imsz = imsz = (768, 1024)
# for n in xrange(exp.n_unique_masks):
for n in range(5): # yields 5 x 4 x 2 = 40 unique masks
	msk = genmask(imsz)
	# create all permutations of it
	msk = msk[:, :, np.newaxis]
	mskset = np.concatenate((msk,
		np.flipud(msk), np.fliplr(msk), np.flipud(np.fliplr(msk)) ), axis=2)

	# and their 'negatives':
	mskset = np.concatenate((mskset, 1-mskset), axis=2)
	# try stacking it:
	try:
		allmsk = np.concatenate((allmsk, mskset), axis=2)
	except NameError, e: # because it doesn't exist yet.
		allmsk = mskset
np.save( exp.maskpath+'msks'+ str(exp.subject_nr), allmsk)

# Turn the masks into images:
exp.maskbank = []
cols = [exp.col2uint8(c) for c  in exp.cols]
for i in range(allmsk.shape[-1]):
	trial_mask = allmsk[:,:,i]
	mskIm = (trial_mask [:,:,np.newaxis] * cols[0] + 
		(1-trial_mask[:,:,np.newaxis]) * cols[1]).astype('uint8')
	exp.maskbank.append(Image.fromarray(mskIm))

############## and the bg is defined simply as ....
exp.bgim = Image.new('RGB', (imsz[1],imsz[0]), (0,0,0) )


################# Then all you have to do in the prep-phase of a trial is...
trial_mask = exp.maskbank[exp.get('mask_idx')]
img = exp.all_img[exp.get('img_idx')]
out = Image.composite(exp.bgim, trial_mask, img )

# create a canv out of it
stim = ImageStim(win=win, image=out)
cnv = canvas(exp)
cnv.stim_list.append(stim)

exp.imcnv = cnv