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 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 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 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 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 _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 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 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 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 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 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 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 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 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 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 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 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 createImages(a, b, c, d, chenPhoto, cls):
info = []
dir_path = os.path.dirname(os.path.realpath(__file__)) + '/PNGFiles'
numSimToConsider = 30
simPhotosLookedAt = 0
if (cls == 'tball'): cls = 'tennisball'
if (cls == 'gball'): cls = 'GolfBall'
if isinstance(a, tuple): a = a[0]
if isinstance(b, tuple): b = b[0]
if isinstance(c, tuple): c = c[0]
if isinstance(d, tuple): d = d[0]
if (c - a <= 40 or d - b <= 40): return -100
for filename in os.listdir(dir_path):
if (simPhotosLookedAt > numSimToConsider): break
debris = filename.split('.')
if (not debris[-1] == 'png'): continue
if (not re.search(cls, filename, re.IGNORECASE)): continue
im = Image.open(
dir_path + '/' +
filename) # might be png, gif etc, for instance test1.png
imageCrop = im.crop(
(a, b, c, d)
) # old one im.crop((100,0,550,280))#im.crop((100,150,550,380)) # ' 2 3 4 7 8 9 10 - no 5 6
maxsize = (40, 40)
imageCrop = ImageOps.fit(imageCrop, maxsize, Image.ANTIALIAS)
im2 = ImageMath.eval('im/256', {'im': imageCrop}).convert('L')
xLen, yLen = im2.size
if (not (xLen == 40 and yLen == 40)): return -100
#im2.save(filename.replace('png','jpeg'), 'JPEG')
shifts = [i for i in range(10)]
for shift in shifts:
lIm, rIm, uIm, dIm = bootstrapSample(im2, shift, 240, -65, 12)
for imToModify in [lIm, rIm, uIm, dIm]:
imageNew = ImageOps.fit(imToModify, maxsize, Image.ANTIALIAS)
info.append(mutual_information(imageNew, chenPhoto))
simPhotosLookedAt += 1
return max(info)
def up_icon_image(sublist):
# 检查是否有图标，没有图标下载保存到本地
id = sublist["id"]
icon_url = sublist["icon"]
icon_path = os.path.join(FILE_PATH, "icon", f"{id}.png")
if not os.path.exists(icon_path):
schedule = request.Request(icon_url)
schedule.add_header('User-Agent', header)
with request.urlopen(schedule) as f:
icon = Image.open(f)
icon = icon.resize((150, 150))
box_alpha = Image.open(
os.path.join(FILE_PATH, "icon",
"box_alpha.png")).getchannel("A")
box = Image.open(os.path.join(FILE_PATH, "icon", "box.png"))
try:
icon_alpha = icon.getchannel("A")
icon_alpha = ImageMath.eval("convert(a*b/256, 'L')",
a=icon_alpha,
b=box_alpha)
except ValueError:
# 米游社的图有时候会没有alpha导致报错，这时候直接使用box_alpha当做alpha就行
icon_alpha = box_alpha
icon2 = Image.new("RGBA", (150, 150), "#00000000")
icon2.paste(icon, (0, -10))
bg = Image.new("RGBA", (150, 150), "#00000000")
bg.paste(icon2, mask=icon_alpha)
bg.paste(box, mask=box)
with open(icon_path, "wb") as icon_file:
bg.save(icon_file)
def set_sat(c, s):
"""
:type c: tuple(ImageMath._Operand)
:type s: ImageMath._Operand
:rtype: tuple(ImageMath._Operand)
"""
x = ImageMath.imagemath_max(ImageMath.imagemath_max(c[0], c[1]), c[2])
n = ImageMath.imagemath_min(ImageMath.imagemath_min(c[0], c[1]), c[2])
cs = x - n
not_even_area = ImageMath.imagemath_int(x != n)
result = []
for cc in c:
max_area = ImageMath.imagemath_int(x == cc)
min_area = ImageMath.imagemath_int(n == cc)
mid_area = (max_area ^ 1) & (min_area ^ 1)
mid = (((cc - n) * s) / cs) * mid_area
cc = ((s * max_area) + mid) * not_even_area
result.append(cc)
return tuple(result)
def createAnaglyph(self, images):
'''Create an anaglyph image from two images.
Args:
images: Two PIL images.
Returns:
The anaglyph PIL image.
'''
left, right = self.process_images(images)
left_bands = left.split()
right_bands = right.split()
output_bands = list()
for i in range(3):
expression = (
"(float(lr)*{lm[0]}+float(lg)*{lm[1]}+float(lb)*{lm[2]}+" +
"float(rr)*{rm[0]}+float(rg)*{rm[1]}+float(rb)*{rm[2]})"
).format(lm=self.matrices[0][i], rm=self.matrices[1][i])
expression = self.process_expression(i, expression)
output_bands.append(
ImageMath.eval("convert(" + expression + ", 'L')",
lr=left_bands[0],
lg=left_bands[1],
lb=left_bands[2],
rr=right_bands[0],
rg=right_bands[1],
rb=right_bands[2]))
if len(left_bands) > 3 and len(right_bands) > 3:
output_bands.append(
ImageChops.lighter(left_bands[3], right_bands[3]))
return Image.merge("RGBA", output_bands)
return Image.merge("RGB", output_bands)
def mse(imageA, imageB):
"""
Name:
mse
Description:
the 'Mean Squared Error' between the two images is the
sum of the squared difference between the two images.
NOTE: the two images must have the same dimension
Params:
imageA, imageB - the images to be compared
Returns:
err - the % of error in MSE
"""
imgA = Image.open(imageA)
imgB = Image.open(imageB)
err = np.sum(ImageMath.eval(
"(convert(imgA, 'F') - convert(imgB, 'F'))**2", imgA=imgA, imgB=imgB))
err /= float(imgA.size[0] * imgA.size[1])
# return the MSE, the lower the error, the more "similar"
# the two images are
return err
def color_aug(image):
# 色相いじる
h, s, v = image.convert("HSV").split()
_h = ImageMath.eval("(h + {}) % 255".format(np.random.randint(-25, 25)),
h=h).convert("L")
img = Image.merge("HSV", (_h, s, v)).convert("RGB")
# 彩度を変える
saturation_converter = ImageEnhance.Color(img)
img = saturation_converter.enhance(np.random.uniform(0.9, 1.1))
# コントラストを変える
contrast_converter = ImageEnhance.Contrast(img)
img = contrast_converter.enhance(np.random.uniform(0.9, 1.1))
# 明度を変える
brightness_converter = ImageEnhance.Brightness(img)
img = brightness_converter.enhance(np.random.uniform(0.9, 1.1))
# シャープネスを変える
sharpness_converter = ImageEnhance.Sharpness(img)
img = sharpness_converter.enhance(np.random.uniform(0.9, 1.1))
return img
def change_background(img, mask, bg):
# oh = img.height
# ow = img.width
ow, oh = img.size
bg = bg.resize((ow, oh)).convert('RGB')
imcs = list(img.split())
bgcs = list(bg.split())
maskcs = list(mask.split())
fics = list(Image.new(img.mode, img.size).split())
for c in range(len(imcs)):
negmask = maskcs[c].point(lambda i: 1 - i / 255)
posmask = maskcs[c].point(lambda i: i / 255)
#a是真正图片的当前通道，b是背景的当前通道，c是正掩码，d是负掩码
fics[c] = ImageMath.eval("a * c + b * d",
a=imcs[c],
b=bgcs[c],
c=posmask,
d=negmask).convert('L')
out = Image.merge(img.mode, tuple(fics))
return out
def getdims(pdf):
tmpdir=mkdtemp()
gs('-q',
'-dQUIET',
'-dSAFER',
'-dBATCH',
'-dNOPAUSE',
'-dNOPROMPT',
'-sDEVICE=pngmono',
'-r72x72',
'-sOutputFile=%s/%%08d' % tmpdir,
'-f%s' % pdf)
mask=None
for fname in os.listdir(tmpdir):
page=Image.open(tmpdir+'/'+fname) #.convert("1",dither=Image.NONE)
if not mask:
mask=page
mask=ImageMath.eval("a & b", a=page, b=mask)
rmtree(tmpdir)
data = np.array(mask)
data_slices = find_paws(255-data, smooth_radius = 5, threshold = 5)
bboxes = remove_overlaps(slice_to_bbox(data_slices))
m=max(bboxes,key=lambda x: (x.x2 - x.x1)*(x.y2 - x.y1))
return (m.x1,m.y1,m.y2-m.y1,m.x2-m.x1)
def assert_compare_images(a, b, max_average_diff, max_diff=255):
assert a.mode == b.mode, "got mode %r, expected %r" % (a.mode, b.mode)
assert a.size == b.size, "got size %r, expected %r" % (a.size, b.size)
a, b = convert_to_comparable(a, b)
bands = ImageMode.getmode(a.mode).bands
for band, ach, bch in zip(bands, a.split(), b.split()):
ch_diff = ImageMath.eval("convert(abs(a - b), 'L')", a=ach, b=bch)
ch_hist = ch_diff.histogram()
average_diff = sum(i * num for i, num in enumerate(ch_hist)) / (
a.size[0] * a.size[1]
)
msg = "average pixel value difference {:.4f} > expected {:.4f} "
"for '{}' band".format(average_diff, max_average_diff, band)
assert max_average_diff >= average_diff, msg
last_diff = [i for i, num in enumerate(ch_hist) if num > 0][-1]
assert (
max_diff >= last_diff
), "max pixel value difference {} > expected {} for '{}' band".format(
last_diff, max_diff, band
)
def MakeColorTransparent(image, color, thresh2=0):
"""
将指定颜色转为透明
https://stackoverflow.com/questions/765736/using-pil-to-make-all-white-pixels-transparent/765829"""
from PIL import ImageMath
def Distance2(a, b):
return (a[0] - b[0]) * (a[0] - b[0]) + (a[1] - b[1]) * (
a[1] - b[1]) + (a[2] - b[2]) * (a[2] - b[2])
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 diffImages(imgA, imgB):
"""Subtract two images (r-r, g-g, b-b). Also add 128 to reduce negative values
If a pixel is exactly same in both images, then the result will be 128,128,128 gray
Out of range values (<0 and > 255) are moved to 0 and 255 by the convert('L') function
Args:
imgA: Pillow image object to subtract from
imgB: Pillow image object to subtract
Returns:
Pillow image object containing the results of the subtraction with 128 mean
"""
bandsImgA = imgA.split()
bandsImgB = imgB.split()
bandsImgOut = []
for bandNum in range(len(bandsImgA)):
# out = ImageMath.eval("convert((128+a/2)-b/2,'L')", a=bandsImgA[bandNum], b=bandsImgB[bandNum])
out = ImageMath.eval("convert(128+a-b,'L')",
a=bandsImgA[bandNum],
b=bandsImgB[bandNum])
bandsImgOut.append(out)
return Image.merge('RGB', bandsImgOut)
def clip_color(c):
"""
:type c: tuple(ImageMath._Operand)
:rtype: tuple(ImageMath._Operand)
"""
l = lum(c)
x = ImageMath.imagemath_max(ImageMath.imagemath_max(c[0], c[1]), c[2])
n = ImageMath.imagemath_min(ImageMath.imagemath_min(c[0], c[1]), c[2])
n_l_0 = ImageMath.imagemath_int(n < 0)
x_b_1 = ImageMath.imagemath_int(x > 1.0)
l_m_n = l - n
x_m_l = x - l
m_l = (1.0 - l)
if bool(n_l_0):
c = [(_c * (n_l_0 ^ 1)) + ((l + ((_c - l) * l / l_m_n)) * n_l_0)
for _c in c]
if bool(x_b_1):
c = [(_c * (x_b_1 ^ 1)) + ((l + ((_c - l) * m_l / x_m_l)) * x_b_1)
for _c in c]
return c
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(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 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 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)
# Convert 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:
# TODO Replace with C routine
arrbuf = array.array('B', self.empty_buf)
width, height = self.get_size()
max_byte_offset = 0
for x in range(0, width):
for y in range(0, height):
pixel_offset = y * width + x
byte_offset = pixel_offset / 8
max_byte_offset = max(max_byte_offset, byte_offset)
bit_offset = 7 - (pixel_offset % 8)
val = ord(buf[x + (y * 160)])
pv = arrbuf[byte_offset]
if val > 0:
arrbuf[byte_offset] = pv | 1 << bit_offset
else:
arrbuf[byte_offset] = pv & ~(1 << bit_offset)
buf = arrbuf.tostring()
try:
logger.debug("Writing buffer of %d bytes", len(buf))
pylibg15.write_pixmap(buf)
except IOError as e:
logger.error("Failed to send buffer.", exc_info=e)
self.disconnect()
finally:
self.lock.release()
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")
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 _show_picture(self):
if (1 == self._fb.bits_per_pixel):
self._write_image(ImageMath.eval('convert(img, "1")', img=Image.open(self._filename)))
else:
self._write_image(img=Image.open(self._filename))
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_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 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")
for x in range(0, width, 32):
draw.rectangle(
(x + 0, y + 0, x + 29, y + 29), outline=255, fill=x // 4 + y // 4
)
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 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_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_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_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")
# Pavel Tisnovsky
#
from PIL import Image
from PIL import ImageMath
filename = "Lenna.png"
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)
# aplikace operace
result_image = ImageMath.eval("(~first) & 255", first=grayscale_image)
# další převod výsledku na stupně šedi
result_image = ImageMath.eval("convert(src, 'L')", src=result_image)
# zobrazení výsledného obrázku uživateli
result_image.show()
# uložení výsledného obrázku
result_image.save("40_image_math_not.png")
except Exception as e:
print("Vyjimka: " + e.__str__())
def gen_captcha(text, fontname, fontsize, file_name, old):
"""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]
if old:
k = 3
wob = 0.20*dim[1]/k
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, old)
im = wobbly_copy(im, wob, bgcolor, i*2+1, rot+45, old)
im = wobbly_copy(im, wob, bgcolor, i*2+2, rot+90, old)
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))
###
center = (bbox[0] + (bbox[2]-bbox[0])/2,
bbox[1] + (bbox[3]-bbox[1])/2)
bord = 20
im = im.crop((center[0] - (bord + dim[0]/2),
center[1] - (bord + dim[0]/len(text)/2),
center[0] + (bord + dim[0]/2),
center[1] + (bord + dim[0]/len(text)/2)))
###
if not old:
# 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)
