def image_diff(im1, im2):
"""Return the diff of two images"""
from PIL from PIL import Image, ImageMath
r1, g1, b1, a1 = im1.convert('RGBA').split()
r2, g2, b2, a2 = im2.convert('RGBA').split()
diff_image = ImageMath.eval(
"""convert(
max(
max(
max(abs(r1 - r2), abs(g1 - g2)),
abs(b1 - b2)
),
abs(a1 - a2)
), 'L')""",
r1=r1,
r2=r2,
g1=g1,
g2=g2,
b1=b1,
b2=b2,
a1=a1,
a2=a2)
if ImageMath.eval('not(image)', image=diff_image):
return
return diff_image
def main(args):
coverImg = open_image(args.cover_image, "rb") # read only in binary
secretImg = open_image(args.secret_image, "rb")
# check the size, for now just make secret image size equals cover image
secretImg = secretImg.resize(coverImg.size)
red, green, blue = coverImg.split()
sred, sgreen, sblue = secretImg.split()
red2 = ImageMath.eval("convert(a&0xFE|b&0x1,'L')", a=red, b=sred)
green2 = ImageMath.eval("convert(a&0xFE|b&0x1,'L')", a=green, b=sgreen)
blue2 = ImageMath.eval("convert(a&0xFE|b&0x1,'L')", a=blue, b=sblue)
out = Image.merge("RGB", (red2, green2, blue2))
out.save("merged.bmp")
exit(0)
def paint(self, image):
if self.bpp != 0:
width = self.lcd_size[0]
height = self.lcd_size[1]
if self.bpp == 1:
# Paint to 565 image provided into an ARGB image surface for PIL's benefit. PIL doesn't support 565?
argb_surface = cairo.ImageSurface(cairo.FORMAT_ARGB32, width, height)
argb_context = cairo.Context(argb_surface)
argb_context.set_source_surface(image)
argb_context.paint()
# Now convert the ARGB to a PIL image so it can be converted to a 1 bit monochrome image, with all
# colours dithered. It would be nice if Cairo could do this :( Any suggestions?
pil_img = Image.frombuffer("RGBA", self.lcd_size, argb_surface.get_data(), "raw", "RGBA", 0, 1)
pil_img = ImageMath.eval("convert(pil_img,'1')",pil_img=pil_img)
pil_img = ImageMath.eval("convert(pil_img,'P')",pil_img=pil_img)
pil_img = pil_img.point(lambda i: i >= 250,'1')
invert_control = self.get_control("invert_lcd")
if invert_control and invert_control.value == 1:
pil_img = pil_img.point(lambda i: 1^i)
# Create drawable message
pil_img = pil_img.convert("RGB")
self.image = pil_img
else:
# Take a copy of the image to prevent flickering
argb_surface = cairo.ImageSurface(cairo.FORMAT_ARGB32, width, height)
argb_context = cairo.Context(argb_surface)
argb_context.set_source_surface(image)
argb_context.paint()
self.image = argb_surface
gobject.timeout_add(0, self.redraw)
def put_highlight(image, highlight, resample_highlight, opacity, cache=None):
if cache is None:
cache = {}
resample_highlight = getattr(Image, resample_highlight)
id = 'highlight_%s_w%d_h%d_o%d'\
% (highlight, image.size[0], image.size[1], opacity)
try:
highlight = cache[id]
except KeyError:
highlight = open_image(highlight)\
.convert('RGBA').resize(image.size, resample_highlight)
if opacity < 100:
#apply opacity
highlight_alpha = imtools.get_alpha(highlight)
opacity = (255 * opacity) / 100
highlight.putalpha(ImageMath.eval("convert((a * o) / 255, 'L')",
a=highlight_alpha, o=opacity))
#store in cache
cache[id] = highlight
if not has_transparency(image):
image = image.convert('RGBA')
else:
if has_transparency(image):
image = image.convert('RGBA')
alpha = imtools.get_alpha(image)
highlight = highlight.copy()
highlight_alpha = imtools.get_alpha(highlight)
highlight.putalpha(ImageMath.eval("convert(min(a, b), 'L')",
a=alpha, b=highlight_alpha))
overlay = highlight.convert('RGB')
paste(image, overlay, mask=highlight)
return image
def test_convert(self):
self.assertEqual(pixel(
ImageMath.eval("convert(A+B, 'L')", images)), "L 3")
self.assertEqual(pixel(
ImageMath.eval("convert(A+B, '1')", images)), "1 0")
self.assertEqual(pixel(
ImageMath.eval("convert(A+B, 'RGB')", images)), "RGB (3, 3, 3)")
def _lsb_pixel_decode(image):
r, g, b, *_ = image.split()
r = ImageMath.eval("convert(c&0x1, 'L')", c=r)
g = ImageMath.eval("convert(c&0x1, 'L')", c=g)
b = ImageMath.eval("convert(c&0x1, 'L')", c=b)
data = roundrobin(iter_pixels(r), iter_pixels(g), iter_pixels(b))
return bytes([reduce(lambda byte, bit: byte << 1 | bit, eight_bits)
for eight_bits in grouper(data, 8, fillvalue=0)])
def color_to_alpha(image, color=None):
image = image.convert('RGBA')
width, height = image.size
color = map(float, color)
img_bands = [band.convert("F") for band in image.split()]
# Find the maximum difference rate between source and color. I had to use two
# difference functions because ImageMath.eval only evaluates the expression
# once.
alpha = ImageMath.eval(
"""float(
max(
max(
max(
difference1(red_band, cred_band),
difference1(green_band, cgreen_band)
),
difference1(blue_band, cblue_band)
),
max(
max(
difference2(red_band, cred_band),
difference2(green_band, cgreen_band)
),
difference2(blue_band, cblue_band)
)
)
)""",
difference1=difference1,
difference2=difference2,
red_band = img_bands[0],
green_band = img_bands[1],
blue_band = img_bands[2],
cred_band = color[0],
cgreen_band = color[1],
cblue_band = color[2]
)
# Calculate the new image colors after the removal of the selected color
new_bands = [
ImageMath.eval(
"convert((image - color) / alpha + color, 'L')",
image = img_bands[i],
color = color[i],
alpha = alpha
)
for i in xrange(3)
]
# Add the new alpha band
new_bands.append(ImageMath.eval(
"convert(alpha_band * alpha, 'L')",
alpha = alpha,
alpha_band = img_bands[3]
))
return Image.merge('RGBA', new_bands)
def getColorfulness(im): #OK
"""return (cf, sigma_rgyb, mu_rgyb)"""
r, g, b = im.split()
im_rg = ImageMath.eval("a - b", a=r, b=g)
im_yb = ImageMath.eval("(a + b)/2 - c", a=r, b=g, c=b)
rg = list(im_rg.getdata())
yb = list(im_yb.getdata())
sigma = math.sqrt(numpy.std(rg) ** 2 + numpy.std(yb) ** 2)
mu = math.sqrt(numpy.average(rg) ** 2 + numpy.average(yb) ** 2)
return sigma + 0.3 * mu
def normalizeImage(im):
# split RGB images into 3 channels
rr, gg, bb = im.split()
rTmp = ImageMath.eval("int(a*((float(255/max(a,b)))))", a=rr, b=rr).convert('L')
gTmp = ImageMath.eval("int(a*((float(255/max(a,b)))))", a=gg, b=gg).convert('L')
bTmp = ImageMath.eval("int(a*((float(255/max(a,b)))))", a=bb, b=bb).convert('L')
# merge channels into RGB image
imgOut = Image.merge("RGB", (rTmp, gTmp, bTmp))
return imgOut
def applyOutline(a, o):
w, h = a.size
c = a.crop((o, o, w - o, h - o))
t = Image.new("L", a.size)
for n in ((o, 0), (0, o), (o + o, o), (o, o + o)):
t.paste(c, n)
a = ImageMath.eval("convert(max(a,b),'L')", a=a, b=t)
for n in ((0, 0), (o + o, 0), (0, o + o), (o + o, o + o)):
t.paste(c, n)
a = ImageMath.eval("convert(max(a,b/1.414),'L')", a=a, b=t)
return a
def substractImage(imgA,imgB):
rA, gA, bA = imgA.split()
rB, gB, bB = imgB.split()
rTmp = ImageMath.eval("a-b", a=rA, b=rB).convert('L')
gTmp = ImageMath.eval("a-b", a=gA, b=gB).convert('L')
bTmp = ImageMath.eval("a-b", a=bA, b=bB).convert('L')
# merge channels into RGB image
imgOut = Image.merge("RGB", (rTmp, gTmp, bTmp))
return imgOut
def divideImage(imgA,imgB):
# split RGB images into 3 channels
rA, gA, bA = imgA.split()
rB, gB, bB = imgB.split()
# divide each channel (image1/image2)
rTmp = ImageMath.eval("int(a/((float(b))/256))", a=rA, b=rB).convert('L')
gTmp = ImageMath.eval("int(a/((float(b))/256))", a=gA, b=gB).convert('L')
bTmp = ImageMath.eval("int(a/((float(b))/256))", a=bA, b=bB).convert('L')
# merge channels into RGB image
imgOut = Image.merge("RGB", (rTmp, gTmp, bTmp))
return imgOut
def optim_obj_xor(x):
draw(x)
image = get_image()
xor = imath.eval("a^b", a=image, b=image_obj)
res = sum(xor.getdata())
print x, res
return res
def encode_data(cover_rgb, secret_rgb, bits):
encoded_rgb = []
for k in range(3):
encoded_rgb.append(ImageMath.eval("convert((cover_rgb & (256 - 2**bits)) + ((secret_rgb & (256 - 2**(8 - bits)) - 1) >> 8 - bits), 'L')", cover_rgb = cover_rgb[k], secret_rgb = secret_rgb[k], bits = bits))
return tuple(encoded_rgb)
def segment(image_filepath, **kwargs):
input_img = Image.open(image_filepath)
# make a '1' bit image with the same size as the input_img
mask_img = Image.new('1', input_img.size)
# instantiate an ImageDraw object using the mask_img object
mask_drawer = ImageDraw.Draw(mask_img)
try:
json_poly_data = json.loads(kwargs['JSON'])
except ValueError:
# There's a problem in the JSON - it may be malformed, or empty
json_poly_data = []
for polygon in json_poly_data:
flattened_poly = [j for i in polygon for j in i]
mask_drawer.polygon(flattened_poly, outline=1, fill=1)
output_img = ImageMath.eval('b - a', a=input_img, b=mask_img)
output_img = ImageOps.invert(output_img.convert('RGB'))
encoded = json.dumps(json_poly_data)
return {
'tiff': output_img,
'json': encoded
}
def run(self):
while True:
try:
camera = WebCamera.objects.get(pk = self._camera.id)
if camera.motion_control:
now = datetime.now()
request = get_pool().request("GET", "%s?action=snapshot" % camera.internal_url)
try:
source = Image.open(BytesIO(request.data))
img = ImageOps.equalize(ImageOps.grayscale(source))
if self._previous is not None:
out = ImageMath.eval("convert(a - b, 'L')", a = img, b = self._previous)
out = out.filter(MedianFilter())
total = 0
for idx, val in enumerate(out.histogram()):
total += val * idx
if total > 3000000:
camera.last_motion = now
camera.save()
filename = os.path.join(camera.motion_folder, "{:%Y%m%d-%H%M%S}.jpg".format(now))
source.save(filename)
filesize = os.path.getsize(filename)
if filesize < 6700:
os.remove(filename)
self._previous = img
finally:
request.close()
else:
self._previous = None
except:
print("Ignore Exception")
sleep(1)
def decode_data(rgb, bits):
decoded_rgb = []
for k in range(3):
decoded_rgb.append(ImageMath.eval("convert((rgb & 2**bits - 1) << 8 - bits, 'L')", rgb = rgb[k], bits = bits))
return tuple(decoded_rgb)
def warmup(image, midtone, brighten, amount=100):
"""Apply a toning filter. Move the midtones to the desired
color while preserving blacks and whites with optional mixing
with original image - amount: 0-100%"""
mode = image.mode
info = image.info
if image.mode != "L":
im = imtools.convert(image, "L")
else:
im = image
if image.mode != "RGBA" and imtools.has_transparency(image):
image = imtools.convert(image, "RGBA")
luma = imtools.convert(imtools.split(im)[0], "F")
o = []
m = ImageColor.getrgb(midtone)
b = brighten / 600.0
# Calculate channels separately
for l in range(3):
o.append(ImageMath.eval("m*(255-i)*i+i", i=luma, m=4 * ((m[l] / 255.0) - 0.5 + b) / 255.0).convert("L"))
colorized = Image.merge("RGB", tuple(o))
if imtools.has_alpha(image):
imtools.put_alpha(colorized, imtools.get_alpha(image))
if amount < 100:
colorized = imtools.blend(image, colorized, amount / 100.0)
return colorized
def strip_processing(path, blur_radius=7, iter_steps=1, binarize_threshold=10):
"""Strip precessing function.
Inputs:
string: path of the image
blur_radius: radius of gaussian blur
iter_steps: execution times of denoise
binarize_threshold: used as function lut's input
Outputs:
Image: the processed image."""
gray=ImageOps.invert(ImageOps.grayscale(Image.open("stripes.jpg")))
gray_blur=gray.filter(ImageFilter.GaussianBlur(blur_radius))
high_freq=ImageMath.eval("25*(a-b)",a=gray,b=gray_blur).convert("L")
binarized=ImageOps.invert(high_freq.point(lut(binarize_threshold))).convert("1")
binarized_data=binarized.load()
#binarized.show()
height=binarized.height
width=binarized.width
pix=np.array([[np.uint8(binarized_data[j,i]) for j in range(width)] for i in range(height)])
for i in range(iter_steps):
pix=denoise(pix)
answer=Image.fromarray(pix)
#answer.show()
#answer.save("test.bmp")
return answer
def makeColorTransparent(image, color, thresh2=0):
from PIL import ImageMath
image = image.convert("RGBA")
red, green, blue, alpha = image.split()
image.putalpha(ImageMath.eval("""convert(((((t - d(c, (r, g, b))) >> 31) + 1) ^ 1) * a, 'L')""",
t=thresh2, d=distance2, c=color, r=red, g=green, b=blue, a=alpha))
return image
def generate_animated_images_old(front_image, back_image, scale_factor):
animated_images = []
for x in xrange (0, front_image.size[0], scale_factor):
temp_image = Image.new('1', front_image.size, "white")
temp_image.paste(front_image.crop((front_image.size[0] - x, 0, front_image.size[0], front_image.size[1])), (0,0))
animated_image = ImageMath.eval("convert((a & b), 'L')", a=temp_image, b=back_image)
#animated_image.save(file_name + "_" + str(x) + file_ext)
animated_images.append(animated_image)
for x in xrange (0, front_image.size[0] + 1, scale_factor):
temp_image = Image.new('1', front_image.size, "white")
temp_image.paste(front_image.crop((0, 0, front_image.size[0] - x, front_image.size[1])), (x, 0))
animated_image = ImageMath.eval("convert((a & b), 'L')", a=temp_image, b=back_image)
#animated_image.save(file_name + "_" + str(x+front_image.size[0]) + file_ext)
animated_images.append(animated_image)
return animated_images
def assert_image_similar(self, a, b, epsilon, msg=None):
epsilon = float(epsilon)
self.assertEqual(
a.mode, b.mode,
msg or "got mode %r, expected %r" % (a.mode, b.mode))
self.assertEqual(
a.size, b.size,
msg or "got size %r, expected %r" % (a.size, b.size))
a, b = convert_to_comparable(a, b)
diff = 0
for ach, bch in zip(a.split(), b.split()):
chdiff = ImageMath.eval("abs(a - b)", a=ach, b=bch).convert('L')
diff += sum(i * num for i, num in enumerate(chdiff.histogram()))
ave_diff = float(diff)/(a.size[0]*a.size[1])
try:
self.assertGreaterEqual(
epsilon, ave_diff,
(msg or '') +
" average pixel value difference %.4f > epsilon %.4f" % (
ave_diff, epsilon))
except Exception as e:
if HAS_UPLOADER:
try:
url = test_image_results.upload(a, b)
logger.error("Url for test images: %s" % url)
except:
pass
raise e
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
base, alpha = image.split()
if self.scaleTowardCenter:
scale = float(self.factor)
assert base.mode == "L"
maxValue = 255 # TODO: Determine how to get maximum value __allowed__.
offset = ((1.0 - self.factor) / 2.0) * maxValue
newImage = ImageMath.eval(
"convert(convert(gray, 'F') * scale + offset, mode)",
gray=base,
scale=scale,
offset=offset,
mode=base.mode,
)
else:
contrastEnhancer = ImageEnhance.Contrast(image.split()[0])
newImage = contrastEnhancer.enhance(self.factor)
newImage.putalpha(alpha)
return newImage
def getContrast(im): #OK
"""return contrast"""
im = im.convert('L')
stat = ImageStat.Stat(im)
mean = stat.mean[0]
im_diff = ImageMath.eval("abs(a - b)", a=im, b=mean)
diff = list(im_diff.getdata())
return math.sqrt(1.0 / (len(diff) - 1) * sum(pix ** 2 for pix in diff))
def xor_closure(target, path):
'''
target shall be a single-band Image instance
'''
target = imath.eval("x>>7", x=target)
def _get_xor(image):
xor_img = imath.eval("(a>>7)^b", a=image, b=target)
return sum(xor_img.getdata())
return _get_xor
def paint(self, img):
if not self.is_connected():
return
# Just return if the device has no LCD
if self.device.bpp == 0:
return None
self.lock.acquire()
try :
size = self.get_size()
# Paint to 565 image provided into an ARGB image surface for PIL's benefit. PIL doesn't support 565?
argb_surface = cairo.ImageSurface(cairo.FORMAT_ARGB32, size[0], size[1])
argb_context = cairo.Context(argb_surface)
argb_context.set_source_surface(img)
argb_context.paint()
# Now convert the ARGB to a PIL image so it can be converted to a 1 bit monochrome image, with all
# colours dithered. It would be nice if Cairo could do this :( Any suggestions?
pil_img = Image.frombuffer("RGBA", size, argb_surface.get_data(), "raw", "RGBA", 0, 1)
pil_img = ImageMath.eval("convert(pil_img,'1')",pil_img=pil_img)
pil_img = ImageMath.eval("convert(pil_img,'P')",pil_img=pil_img)
pil_img = pil_img.point(lambda i: i >= 250,'1')
invert_control = self.get_control("invert_lcd")
if invert_control.value == 0:
pil_img = pil_img.point(lambda i: 1^i)
# Covert image buffer to string
buf = ""
for x in list(pil_img.getdata()):
buf += chr(x)
if len(buf) != self.device.lcd_size[0] * self.device.lcd_size[1]:
logger.warning("Invalid buffer size")
else:
try :
self.send(buf)
except IOError as e:
logger.error("Failed to send buffer.", exc_info = e)
self.disconnect()
finally:
self.lock.release()
def _on_snapshot_clicked(self):
img = self.optimizer.rendis.acquire().acquire_snapshot()
filename = QFileDialog.getSaveFileName(self,
"save the binary shadow snapshot",
"../img/",
"image file (*.png)")
if filename != '':
img = ImageMath.eval("a/127 * 255", a=img)
img.save(str(filename), format='png')
pass
def generate_animated_images(front_image, back_image, scale_factor):
animated_images = []
for x in xrange (-scale_factor, front_image.size[0], scale_factor):
left_image = Image.new('1', (front_image.size[0] * 2, front_image.size[1]), "white")
left_image.paste(front_image, (x, 0))
right_image = Image.new('1', (front_image.size[0] * 2, front_image.size[1]), "white")
right_image.paste(back_image, (front_image.size[0] - x, 0))
animated_image = ImageMath.eval("convert((a & b), 'L')", a=left_image, b=right_image)
#animated_image.save(str(x) + ".png")
animated_images.append(animated_image)
return animated_images
def sketch(image, details_degree=1):
im1 = image.convert('L')
im2 = im1.copy()
im2 = ImageOps.invert(im2)
for i in xrange(details_degree):
im2 = im2.filter(ImageFilter.BLUR)
im1 = ImageMath.eval('convert(min(a * 255/ (256 - b), 255), "L")',
a=im1,
b=im2)
return im1
def gen_captcha(text, fontname, fontsize, file_name):
"""Generate a captcha image"""
# white text on a black background
bgcolor = 0x0
fgcolor = 0xffffff
# create a font object
font = ImageFont.truetype(fontname,fontsize)
# determine dimensions of the text
dim = font.getsize(text)
# create a new image significantly larger that the text
edge = max(dim[0], dim[1]) + 2*min(dim[0], dim[1])
im = Image.new('RGB', (edge, edge), bgcolor)
d = ImageDraw.Draw(im)
x, y = im.size
# add the text to the image
d.text((x/2-dim[0]/2, y/2-dim[1]/2), text, font=font, fill=fgcolor)
k = 2
wob = 0.09*dim[1]
rot = 45
# Apply lots of small stirring operations, rather than a few large ones
# in order to get some uniformity of treatment, whilst
# maintaining randomness
for i in range(k):
im = wobbly_copy(im, wob, bgcolor, i*2+3, rot+0)
im = wobbly_copy(im, wob, bgcolor, i*2+1, rot+45)
im = wobbly_copy(im, wob, bgcolor, i*2+2, rot+90)
rot += 30
# now get the bounding box of the nonzero parts of the image
bbox = im.getbbox()
bord = min(dim[0], dim[1])/4 # a bit of a border
im = im.crop((bbox[0]-bord, bbox[1]-bord, bbox[2]+bord, bbox[3]+bord))
# Create noise
nblock = 4
nsize = (im.size[0] / nblock, im.size[1] / nblock)
noise = Image.new('L', nsize, bgcolor)
data = noise.load()
for x in range(nsize[0]):
for y in range(nsize[1]):
r = random.randint(0, 65)
gradient = 70 * x / nsize[0]
data[x, y] = r + gradient
# Turn speckles into blobs
noise = noise.resize(im.size, Image.BILINEAR)
# Add to the image
im = ImageMath.eval('convert(convert(a, "L") / 3 + b, "RGB")', a=im, b=noise)
# and turn into black on white
im = ImageOps.invert(im)
# save the image, in format determined from filename
im.save(file_name)
def test_logical(self):
self.assertEqual(pixel(ImageMath.eval("not A", images)), 0)
self.assertEqual(pixel(ImageMath.eval("A and B", images)), "L 2")
self.assertEqual(pixel(ImageMath.eval("A or B", images)), "L 1")
def test_logical_ge(self):
self.assertEqual(pixel(ImageMath.eval("A>=A", A=A)), "I 1")
self.assertEqual(pixel(ImageMath.eval("B>=B", B=B)), "I 1")
self.assertEqual(pixel(ImageMath.eval("A>=B", A=A, B=B)), "I 0")
self.assertEqual(pixel(ImageMath.eval("B>=A", A=A, B=B)), "I 1")
def test_ops(self):
self.assertEqual(pixel(ImageMath.eval("-A", images)), "I -1")
self.assertEqual(pixel(ImageMath.eval("+B", images)), "L 2")
self.assertEqual(pixel(ImageMath.eval("A+B", images)), "I 3")
self.assertEqual(pixel(ImageMath.eval("A-B", images)), "I -1")
self.assertEqual(pixel(ImageMath.eval("A*B", images)), "I 2")
self.assertEqual(pixel(ImageMath.eval("A/B", images)), "I 0")
self.assertEqual(pixel(ImageMath.eval("B**2", images)), "I 4")
self.assertEqual(pixel(ImageMath.eval("B**33", images)),
"I 2147483647")
self.assertEqual(pixel(ImageMath.eval("float(A)+B", images)), "F 3.0")
self.assertEqual(pixel(ImageMath.eval("float(A)-B", images)), "F -1.0")
self.assertEqual(pixel(ImageMath.eval("float(A)*B", images)), "F 2.0")
self.assertEqual(pixel(ImageMath.eval("float(A)/B", images)), "F 0.5")
self.assertEqual(pixel(ImageMath.eval("float(B)**2", images)), "F 4.0")
self.assertEqual(pixel(ImageMath.eval("float(B)**33", images)),
"F 8589934592.0")
plt.ylabel('# of cells')
plt.axes().yaxis.grid()
plt.savefig(directory + '/neighbourhist' + info + '.png')
# Overlay image
if options.overlay:
if len(overlay) == 1:
image = Image.open(overlay[0])
else:
image0 = Image.open(overlay[0])
image1 = Image.open(overlay[1])
r0, g0, b0 = image0.split()[0], image0.split()[1], image0.split()[2]
r1, g1, b1 = image1.split()[0], image1.split()[1], image1.split()[2]
r, g, b = ImageMath.eval('convert(max(a, b), "L")', a=r0, b=r1), \
ImageMath.eval('convert(max(a, b), "L")', a=g0, b=g1), \
ImageMath.eval('convert(max(a, b), "L")', a=b0, b=b1)
image = Image.merge('RGB', (r, g, b))
image.save(directory + '/merged_image' + info + '.png')
graph = Image.open(directory + '/cellgraph' + info + '.gv.png')
ratio = float(image.size[0]) / image.size[1]
image = image.resize((int(round(graph.size[1] * ratio)), graph.size[1]))
bands = list(graph.split())
if len(bands) == 3:
mask = Image.new('L', graph.size, color=128)
graph.putalpha(mask)
bands = list(graph.split())
def test_logical_lt(self):
self.assertEqual(pixel(ImageMath.eval("A<A", A=A)), "I 0")
self.assertEqual(pixel(ImageMath.eval("B<B", B=B)), "I 0")
self.assertEqual(pixel(ImageMath.eval("A<B", A=A, B=B)), "I 1")
self.assertEqual(pixel(ImageMath.eval("B<A", A=A, B=B)), "I 0")
def test_one_image_larger(self):
self.assertEqual(pixel(ImageMath.eval("A+B", A=A2, B=B)), "I 3")
self.assertEqual(pixel(ImageMath.eval("A+B", A=A, B=B2)), "I 3")
def test_compare():
assert_equal(pixel(ImageMath.eval("min(A, B)", images)), "I 1")
assert_equal(pixel(ImageMath.eval("max(A, B)", images)), "I 2")
assert_equal(pixel(ImageMath.eval("A == 1", images)), "I 1")
assert_equal(pixel(ImageMath.eval("A == 2", images)), "I 0")
def watercolorize(mask,
edge_texture,
display_texture,
texture_offsets,
buffer_size,
edge_multiplier=.2,
edge_gauss=6,
edge_threshold=90,
outline_gauss=15,
outline_multiplier=.7,
aa_mult=2):
## print "using the right watercolorize:", buffer_size, edge_multiplier, edge_gauss, edge_threshold, outline_gauss, outline_multiplier, aa_mult
## edge_texture = edge_texture.point(lambda k:int(k * edge_multiplier))
## tile the edger and texture as necessary to fit the mask
edger_layer = Image.new("L", mask.size)
for i in range(-1,
(edger_layer.size[1] - buffer_size) / edge_texture.size[1] +
1):
for j in range(
-1,
(edger_layer.size[0] - buffer_size) / edge_texture.size[1] + 1):
edger_layer.paste(
edge_texture,
(edge_texture.size[0] * j - texture_offsets[0] - buffer_size,
edge_texture.size[1] * i - texture_offsets[1] - buffer_size))
tex_layer = Image.new("RGBA", mask.size)
for i in range(
-1,
(tex_layer.size[1] - buffer_size) / display_texture.size[1] + 1):
for j in range(
-1,
(tex_layer.size[0] - buffer_size) / display_texture.size[1] + 1):
tex_layer.paste(display_texture,
(display_texture.size[0] * j - texture_offsets[0] -
buffer_size, display_texture.size[1] * i -
texture_offsets[1] - buffer_size))
artwork = mask.convert("L")
dat = img2arr(artwork)
edg = img2arr(edger_layer)
gaussian(dat, edge_gauss)
dat = add(dat * (1 - edge_multiplier), edg * edge_multiplier)
dat = (dat > edge_threshold).astype(uint8) * 255
#blur, then unsharp mask to smooth pixelated edges
gaussian(dat, 3)
dat = usm(dat, 4, 15, 0)
new_outline = arr2img(255 - dat)
if outline_multiplier > 0:
outlined = get_outlines_even(ImageOps.invert(new_outline),
outline_gauss, outline_multiplier)
old_bands = tex_layer.split()
new_bands = []
for i in range(3):
band1 = ImageOps.invert(old_bands[i])
band2 = outlined
temp = ImageMath.eval("int(a/((float(b)+1)/256))",
a=band1,
b=band2).convert("L")
new_bands.append(ImageOps.invert(temp))
outlined = Image.merge("RGB", new_bands)
else:
outlined = tex_layer
alpha_test = outlined.convert("RGBA")
alpha_test.putalpha(new_outline)
return alpha_test
from PIL import Image, ImageFilter, ImageMath
# Load an image from the hard drive
original = Image.open("original.jpg")
secret = Image.open("secret.jpg").resize(original.size)
# Convert to black and white
secret = secret.convert('1')
# Split channels
r, g, b = original.split()
# Encode the secret in the last bit of the red channel
out = ImageMath.eval("convert(a&254|b&1,'L')", a=r, b=secret)
stegano = Image.merge("RGB", (out, g, b))
stegano.show()
stegano.save("stegano.png")
#!/usr/bin/python
from PIL import Image, ImageMath
# beide Bilder laden
Bild1 = Image.open("beuth-original.png")
Bild2 = Image.open("beuth-verarbeitet.png")
# Bild in einen Layer pro Farbkanal zerlegen
(r1,g1,b1) = Bild1.split()
(r2,g2,b2) = Bild2.split()
# Bilder voneinander abziehen
secret = ImageMath.eval("(b-a)*255", a=r1, b=r2).convert("P")
secret.show()
def test_convert():
assert_equal(pixel(ImageMath.eval("convert(A+B, 'L')", images)), "L 3")
assert_equal(pixel(ImageMath.eval("convert(A+B, '1')", images)), "1 0")
assert_equal(pixel(ImageMath.eval("convert(A+B, 'RGB')", images)), "RGB (3, 3, 3)")
def test_bitwise_and(self):
self.assertEqual(pixel(ImageMath.eval("Z&Z", A=A, Z=Z)), "I 0")
self.assertEqual(pixel(ImageMath.eval("Z&A", A=A, Z=Z)), "I 0")
self.assertEqual(pixel(ImageMath.eval("A&Z", A=A, Z=Z)), "I 0")
self.assertEqual(pixel(ImageMath.eval("A&A", A=A, Z=Z)), "I 1")
def test_bitwise_invert(self):
self.assertEqual(pixel(ImageMath.eval("~Z", Z=Z)), "I -1")
self.assertEqual(pixel(ImageMath.eval("~A", A=A)), "I -2")
self.assertEqual(pixel(ImageMath.eval("~B", B=B)), "I -3")
def test_abs(self):
self.assertEqual(pixel(ImageMath.eval("abs(A)", A=A)), "I 1")
self.assertEqual(pixel(ImageMath.eval("abs(B)", B=B)), "I 2")
def test_compare(self):
self.assertEqual(pixel(ImageMath.eval("min(A, B)", images)), "I 1")
self.assertEqual(pixel(ImageMath.eval("max(A, B)", images)), "I 2")
self.assertEqual(pixel(ImageMath.eval("A == 1", images)), "I 1")
self.assertEqual(pixel(ImageMath.eval("A == 2", images)), "I 0")
def diferenca_view(index):
"""Home."""
index = int(index)
pontos = json.loads(DB.get("pontos_perspectiva"))
b = Image.open(IMAGE_DIR + os.listdir(IMAGE_DIR)[index]).convert("L")
b = perspectiva(b, pontos, (594, 420))
# b.show()
a = Image.open(IMAGE_DIR + os.listdir(IMAGE_DIR)[index - 1]).convert("L")
a = perspectiva(a, pontos, (594, 420))
# a.show()
c = ImageMath.eval("a - b", a=a, b=b)
e = Image.new("RGB", c.size, 0)
f = e.load()
d = c.load()
for x in xrange(c.width):
for y in xrange(c.height):
cor = d[x, y]
f[x, y] = (0, 0, 0) if cor < 0 else (cor, cor, cor)
# e.show()
arquivo = StringIO()
e = ImageOps.invert(e)
e = e.filter(ImageFilter.BLUR)
enhancer = ImageEnhance.Brightness(e)
e = enhancer.enhance(2)
enhancer = ImageEnhance.Contrast(e)
e = enhancer.enhance(8)
e.save(arquivo, "png")
e.save("/Users/nano/Desktop/teste.png")
arquivo.seek(0)
im = cv2.imread("/Users/nano/Desktop/teste.png")
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 50, 150, apertureSize=3)
lines = cv2.HoughLines(edges, 1, numpy.pi/180, 150)
# lines = cv2.HoughLinesP(edges, 1, numpy.pi/180, 150)
if lines is not None:
for rho, theta in lines[0]:
a = numpy.cos(theta)
b = numpy.sin(theta)
x0 = a*rho
y0 = b*rho
x1 = int(x0 + 1000*(-b))
y1 = int(y0 + 1000*(a))
x2 = int(x0 - 1000*(-b))
y2 = int(y0 - 1000*(a))
cv2.line(im, (x1, y1), (x2, y2), (0, 0, 255), 2)
# if lines is not None:
# for x1, y1, x2, y2 in lines[0]:
# cv2.line(im, (x1, y1), (x2, y2), (0, 0, 255), 2)
# frame_string = im.tostring()
# e = Image.frombytes('RGB', (im.shape[1], im.shape[0]), frame_string)
# # Create a detector with the parameters
params = cv2.SimpleBlobDetector_Params()
# # Change thresholds
# params.minThreshold = 10
# params.maxThreshold = 200
# # Filter by Area.
params.filterByArea = True
params.maxArea = 400
# # Filter by Circularity
params.filterByCircularity = True
params.minCircularity = 0.1
# # Filter by Convexity
params.filterByConvexity = True
params.minConvexity = 0.1
# # Filter by Inertia
params.filterByInertia = True
params.minInertiaRatio = 0.01
# # Set up the detector with default parameters.
# detector = cv2.SimpleBlobDetector()
detector = cv2.SimpleBlobDetector(params)
# # Detect blobs.
keypoints = detector.detect(im)
# print keypoints
# # Draw detected blobs as red circles.
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob
im = cv2.drawKeypoints(im, keypoints, numpy.array([]), (0,255,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# frame_string = im_with_keypoints.tostring()
# e = Image.frombytes('RGB', (im_with_keypoints.shape[1], im_with_keypoints.shape[0]), frame_string)
ret, thresh = cv2.threshold(gray, 127, 255, 0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# cv2.drawContours(im, contours, -1, (0,255,0), 3)
cores = [(255, 0, 0), # vermelho
(0, 255, 0), # verde
(0, 0, 255),
(127, 0, 0),
(0, 127, 0),
(0, 0, 127),
(255, 255, 0),
(0, 255, 255),
(255, 0, 255)]
for i in range(len(contours))[1:]:
cnt = contours[i]
cv2.drawContours(im, [cnt], 0, (255, 255, 255), cv2.cv.CV_FILLED)
frame_string = im.tostring()
e = Image.frombytes('RGB', (im.shape[1], im.shape[0]), frame_string)
arquivo = StringIO()
e.save(arquivo, "png")
arquivo.seek(0)
response = send_file(arquivo, as_attachment=False, attachment_filename="diferenca.png")
return response
def im_not(im1):
return ImageMath.eval("~B", B=im1)
def __init__(self,
testImage,
refImage,
allowDifferentImageMode=False,
enhance=10):
self.testImage = Image.open(testImage)
self.refImage = Image.open(refImage)
self.maskImage = None
self.diffImage = None
self.difference = 100
self.numberOfDifferentPixels = None
self.maxDifference = None
if self.testImage.size != self.refImage.size:
return
if self.testImage.mode != self.refImage.mode:
if not allowDifferentImageMode:
return
self.refImage = self.refImage.convert(self.testImage.mode)
# ImageChops.difference does not work for signed integers
if self.testImage.mode == 'I':
self.testImage = self.testImage.convert('L')
self.refImage = self.refImage.convert('L')
numPixels = self.testImage.size[0] * self.testImage.size[1]
self.diffImage = ImageChops.difference(self.testImage, self.refImage)
channels = len(self.testImage.getbands())
if channels == 1:
normImage = self.diffImage
elif channels == 2:
(a, b) = self.diffImage.split()
normImage = ImageMath.eval("convert((a+b), 'L')", a=a, b=b)
elif channels == 3:
(a, b, c) = self.diffImage.split()
normImage = ImageMath.eval("convert((a+b+c), 'L')", a=a, b=b, c=c)
elif channels == 4:
(a, b, c, d) = self.diffImage.split()
normImage = ImageMath.eval("convert((a+b+c+d), 'L')",
a=a,
b=b,
c=c,
d=d)
self.maskImage = normImage.point(lambda p: 0 if p > 0 else 255, 'L')
stats = ImageStat.Stat(normImage)
self.maxDifference = stats.extrema[0][1] / (channels * 255)
self.difference = (sum(stats.sum) /
(255 * channels)) * 100.0 / numPixels
if enhance != 1:
self.diffImage = self.diffImage.point(lambda i: i * (enhance))
self.diffImage = ImageChops.invert(self.diffImage)
self.numberOfDifferentPixels = int(
numPixels - ImageStat.Stat(self.maskImage).sum[0] / 255)
draw.rectangle((0, 0, width / 2, height / 2), fill=0)
draw.rectangle((width / 2, 0, width, height / 2), fill=127)
draw.rectangle((0, height / 2, width / 2, height), fill=128)
draw.rectangle((width / 2, height / 2, width, height), fill=255)
del draw
return second_image
try:
# načtení originálního obrázku Leny
original_image = Image.open(filename)
original_image.load()
# převod na úrovně šedi
grayscale_image = ImageMath.eval("convert(src, 'L')", src=original_image)
# vytvoření druhého obrázku s maskou
second_image = create_second_image(512, 512)
# aplikace operace
result_image = ImageMath.eval("first & second",
first=grayscale_image,
second=second_image)
# další převod výsledku na stupně šedi
result_image = ImageMath.eval("convert(src, 'L')", src=result_image)
# zobrazení výsledného obrázku uživateli
result_image.show()
def chromeKeyBluescreen(r, g, b):
# return an alpha image with mode 'F'
# the r,g,b and white should be 'F' mode
# we use 'F' mode in case of negative value
global white
return ImageMath.eval('r+g-b+d', r=r, g=g, b=b, d=white)
def im_and(im1, im2):
return ImageMath.eval("A & B", A=im1, B=im2)
def test_bitwise_or(self):
self.assertEqual(pixel(ImageMath.eval("Z|Z", A=A, Z=Z)), "I 0")
self.assertEqual(pixel(ImageMath.eval("Z|A", A=A, Z=Z)), "I 1")
self.assertEqual(pixel(ImageMath.eval("A|Z", A=A, Z=Z)), "I 1")
self.assertEqual(pixel(ImageMath.eval("A|A", A=A, Z=Z)), "I 1")
def im_xor(im1, im2):
return ImageMath.eval("A ^ B", A=im1, B=im2)
def test_bitwise_xor(self):
self.assertEqual(pixel(ImageMath.eval("Z^Z", A=A, Z=Z)), "I 0")
self.assertEqual(pixel(ImageMath.eval("Z^A", A=A, Z=Z)), "I 1")
self.assertEqual(pixel(ImageMath.eval("A^Z", A=A, Z=Z)), "I 1")
self.assertEqual(pixel(ImageMath.eval("A^A", A=A, Z=Z)), "I 0")
def test_bitwise_leftshift(self):
self.assertEqual(pixel(ImageMath.eval("Z<<0", Z=Z)), "I 0")
self.assertEqual(pixel(ImageMath.eval("Z<<1", Z=Z)), "I 0")
self.assertEqual(pixel(ImageMath.eval("A<<0", A=A)), "I 1")
self.assertEqual(pixel(ImageMath.eval("A<<1", A=A)), "I 2")
def test_bitwise_rightshift(self):
self.assertEqual(pixel(ImageMath.eval("Z>>0", Z=Z)), "I 0")
self.assertEqual(pixel(ImageMath.eval("Z>>1", Z=Z)), "I 0")
self.assertEqual(pixel(ImageMath.eval("A>>0", A=A)), "I 1")
self.assertEqual(pixel(ImageMath.eval("A>>1", A=A)), "I 0")
def _color_to_alpha(image: Image, color=None) -> Image:
"""
Implements GIMP's color to alpha algorithm.
See https://stackoverflow.com/a/1617909
GPLv3: http://bazaar.launchpad.net/~stani/phatch/trunk/annotate/head:/phatch/actions/color_to_alpha.py#L50
INPUTS
image: A PIL image to work on
color: A 4-tuple (R, G, B, A) value as the color to change to alpha
OUTPUTS
A string of XML representing the new SVG
"""
image = image.convert("RGBA")
color = list(map(float, color))
img_bands = [band.convert("F") for band in image.split()]
# Find the maximum difference rate between source and color. I had to use two
# difference functions because ImageMath.eval only evaluates the expression
# once.
alpha = ImageMath.eval("""float(
max(
max(
max(
difference1(red_band, cred_band),
difference1(green_band, cgreen_band)
),
difference1(blue_band, cblue_band)
),
max(
max(
difference2(red_band, cred_band),
difference2(green_band, cgreen_band)
),
difference2(blue_band, cblue_band)
)
)
)""",
difference1=lambda source, color: (source - color) /
(255.0 - color),
difference2=lambda source, color:
(color - source) / color,
red_band=img_bands[0],
green_band=img_bands[1],
blue_band=img_bands[2],
cred_band=color[0],
cgreen_band=color[1],
cblue_band=color[2])
# Calculate the new image colors after the removal of the selected color
new_bands = [
ImageMath.eval("convert((image - color) / alpha + color, 'L')",
image=img_bands[i],
color=color[i],
alpha=alpha) for i in range(3)
]
# Add the new alpha band
new_bands.append(
ImageMath.eval("convert(alpha_band * alpha, 'L')",
alpha=alpha,
alpha_band=img_bands[3]))
new_image = Image.merge("RGBA", new_bands)
background = Image.new("RGB", new_image.size, (0, 0, 0, 0))
background.paste(new_image.convert("RGB"), mask=new_image)
# SE addition: Lastly, convert transparent pixels to rgba(0, 0, 0, 0) so that Pillow's
# crop function can detect them.
# See https://stackoverflow.com/a/14211878
pixdata = new_image.load()
width, height = new_image.size
for image_y in range(height):
for image_x in range(width):
if pixdata[image_x, image_y] == (255, 255, 255, 0):
pixdata[image_x, image_y] = (0, 0, 0, 0)
return new_image
def get_mask(image):
r, g, b, _ = image.split()
r_mask = r.point(lambda i: i > 0 and 255)
g_mask = g.point(lambda i: i > 0 and 255)
b_mask = b.point(lambda i: i > 0 and 255)
return ImageMath.eval("convert(a | b | c, 'L')", a=r_mask, b=g_mask, c=b_mask)
#!/usr/bin/env python3
# vim: set fileencoding=utf-8
from PIL import Image
from PIL import ImageMath
filename = "Lenna.png"
try:
test_image = Image.open(filename)
test_image.load()
modified_image = ImageMath.eval("convert(src, 'L')", src=test_image)
modified_image.save("Lenna_grayscale.png")
except Exception as e:
print(e)
def test_logical():
assert_equal(pixel(ImageMath.eval("not A", images)), 0)
assert_equal(pixel(ImageMath.eval("A and B", images)), "L 2")
assert_equal(pixel(ImageMath.eval("A or B", images)), "L 1")