def zealous_crop(page_groups):
# Zealous crop all of the pages. Vertical margins can be cropped
# however, but be sure to crop all pages the same horizontally.
for idx in (0, 1):
# min horizontal extremes
minx = None
maxx = None
width = None
for grp in page_groups:
for pdf in grp[idx].values():
bbox = ImageOps.invert(pdf.convert("L")).getbbox()
if bbox is None: continue # empty
minx = min(bbox[0], minx) if minx is not None else bbox[0]
maxx = max(bbox[2], maxx) if maxx is not None else bbox[2]
width = max(width, pdf.size[0]) if width is not None else pdf.size[0]
if width != None:
minx = max(0, minx-int(.02*width)) # add back some margins
maxx = min(width, maxx+int(.02*width))
# do crop
for pg in grp[idx]:
im = grp[idx][pg]
bbox = ImageOps.invert(im.convert("L")).getbbox() # .invert() requires a grayscale image
if bbox is None: bbox = [0, 0, im.size[0], im.size[1]] # empty page
vpad = int(.02*im.size[1])
im = im.crop( (0, max(0, bbox[1]-vpad), im.size[0], min(im.size[1], bbox[3]+vpad) ) )
if os.environ.get("HORZCROP", "1") != "0":
im = im.crop( (minx, 0, maxx, im.size[1]) )
grp[idx][pg] = im
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 make_inverted(self, image):
if image == 1:
self.img1.image = ImageOps.invert(self.img1.image)
elif image == 2:
self.img2.image = ImageOps.invert(self.img2.image)
elif image == 3:
self.img3.image = ImageOps.invert(self.img3.image)
def invertimage(image):
if image.mode == 'RGBA':
r,g,b,a = image.split()
rgb_image = Image.merge('RGB', (r,g,b))
inverted_image = ImageOps.invert(rgb_image)
r2,g2,b2 = inverted_image.split()
final_image = Image.merge('RGBA', (r2,g2,b2,a))
else:
final_image = ImageOps.invert(image)
return final_image
def get_RGB_split_inverted(image):
if not is_RGB(image):
raise Exception("Not RGB mode")
image_r, image_g, image_b = image.split()
image_rn = ImageOps.invert(image_r)
image_gn = ImageOps.invert(image_g)
image_bn = ImageOps.invert(image_b)
return image_rn, image_gn, image_bn
def threshold(img, thresh): """Threshold an image""" pilIMG1 = Image.fromarray(img); pilInverted1 = ImageOps.invert(pilIMG1); inverted = numpy.asarray(pilInverted1); r, t = cv2.threshold(inverted, thresh, 0, type=cv.CV_THRESH_TOZERO); pilIMG2 = Image.fromarray(t); pilInverted2 = ImageOps.invert(pilIMG2); thresholded = numpy.asarray(pilInverted2); return thresholded;
def overlay_im_orig(images):
if not images:
return None
min_im = None
max_im = None
for im in images:
min_im = im if min_im == None else ImageChops.darker(im, min_im)
max_im = im if max_im == None else ImageChops.lighter(im, max_im)
overlay = Image.new("RGBA", images[0].size, (255,255,255,255))
overlay.paste(Image.new("RGB", images[0].size, COLOR1), None, ImageOps.invert(min_im))
overlay.paste(Image.new("RGB", images[0].size, COLOR2), None, ImageOps.invert(max_im))
return overlay
def make_all_night_image(day):
# Normalize to midnight
day_start = datetime(day.year, day.month, day.day, tzinfo=est)
# Find that day's sunset
obs = get_observer()
sunset_time = sunset(obs, day_start)
# And next day's sunrise
sunrise_time = sunrise(obs, sunset_time)
# One hour after and before to remove all light
start_time = normalize_time(sunset_time + timedelta(hours=1))
end_time = normalize_time(sunrise_time - timedelta(hours=1))
normals = Normal.objects.filter(timestamp__gte=start_time, timestamp__lte=end_time, info__isnull=False)
img_light = None
for normal in normals:
source = Image.open(normal.info.filepath)
# Make light image
if img_light is None:
img_light = source
else:
img_light = ImageChops.lighter(img_light, source)
# Put a date on the image
daystr = day.strftime('%Y-%m-%d')
canvas = ImageDraw.Draw(img_light)
canvas.text((20,1500), daystr)
# And save
filename = daystr + '.png'
filename_neg = daystr + '_neg.png'
imagepath = os.path.join(TIMELAPSE_DIR, APP_DIR, 'allnight')
# Make directory if it doesn't exist
if not os.path.exists(imagepath):
os.makedirs(imagepath)
img_filepath = os.path.join(imagepath, filename)
img_light.save(img_filepath)
image_record = get_image_product(day_start, start_time, end_time, Product.ALLNIGHT, imagepath, filename)
record_size(img_filepath, image_record)
img_neg_filepath = os.path.join(imagepath, filename_neg)
ImageOps.invert(img_light).save(img_neg_filepath)
image_record_neg = get_image_product(day_start, start_time, end_time, Product.ALLNIGHT_NEG, imagepath, filename_neg)
record_size(img_neg_filepath, image_record_neg)
def find_image_centroid(target_image, colour_threshold=166, use_mask=False, use_mask_gradient=False, verbose=False):
if verbose:
print('Converting image to greyscale, and detecting edges...')
edge_detected_image = ImageOps.invert(target_image.copy().filter(ImageFilter.FIND_EDGES))
# If we're using a mask (to try to direct detection to the edges of the image)
if use_mask == True:
# Gradient mask affects detection less, but is more "fair".
if use_mask_gradient == True:
mask = Image.new('L', (1,511))
for y in range(511):
mask.putpixel((0, 510-y), 254-int(fabs(254-y)))
mask = mask.resize(target_image.size, Image.NEAREST)
# Our normal mask is just a rectangle taking up twice the difference between the two image sizes
elif use_mask_gradient == False:
mask = Image.new('L', target_image.size)
drawmask = ImageDraw.Draw(mask)
drawmask.rectangle(
[
(fabs(target_image.size[0] - target_width)*2, fabs(target_image.size[1] - target_height)*2),
(edge_detected_image.size[0] - fabs(target_image.size[0] - target_width)*2, edge_detected_image.size[1] - fabs(target_image.size[1] - target_height)*2)
],
fill=(255)
)
del drawmask
# Overlaying the mask
draw = ImageDraw.Draw(edge_detected_image)
draw.bitmap([0,0], mask)
del draw
# Convert to greyscale, and apply a hard threshold to weed out less stark contrast
edge_detected_image = ImageOps.invert(edge_detected_image).convert("L").point(lambda i: i > colour_threshold and 255 or 0)
# Centroid logic contributed by Ben Stewart
if verbose:
print('Finding centroid...')
# Sum of colours in image
image_sum = sum([sum(row) for row in asarray(edge_detected_image)])
weighted_rows = sum([y * sum(row) for y, row in enumerate(asarray(edge_detected_image))])
centroid_y = weighted_rows / image_sum
weighted_cols = sum([sum([x * val for x, val in enumerate(row)]) for row in asarray(edge_detected_image)])
centroid_x = weighted_cols / image_sum
return (centroid_x, centroid_y)
def stitchedReverse(self, inputImageName, lookupImageNames):
matches = []
a = Image.open(self.getImagePathByName('A')).convert(mode='L', dither=Image.NONE)
originalWidth, originalHeight = a.size
halfWidth = originalWidth / 2
left = 0
top = 0
right = halfWidth
bottom = originalHeight
leftHalf = a.crop((left, top, right, bottom))
left = right
right = originalWidth
rightHalf = a.crop((left, top, right, bottom))
stitched = Image.new('RGB', (originalWidth, originalHeight))
stitched.paste(im=rightHalf, box=(0, 0))
stitched.paste(im=leftHalf, box=(halfWidth, 0))
stitched = stitched.convert(mode='L', dither=Image.NONE)
for problemImageName in lookupImageNames:
x = Image.open(self.getImagePathByName(problemImageName)).convert(mode='L', dither=Image.NONE)
stitched1 = ImageOps.invert(stitched)
try:
p1, q1, r1, s1 = stitched1.getbbox()
a1 = abs(p1 - r1) * abs(r1 - s1)
x1 = ImageOps.invert(x)
try:
p2, q2, r2, s2 = x1.getbbox()
a2 = abs(p2 - r2) * abs(r2 - s2)
p = q = r = s = None
if a1 <= a2:
p, q, r, s = p1, q1, r1, s1
else:
p, q, r, s = p2, q2, r2, s2
stitched1 = stitched.crop((p, q, r, s))
x1 = x.crop((p, q, r, s))
diff = self.getImageDifference(stitched1, x1)
log.info('{} diff: {}'.format(problemImageName, diff))
matches.append([problemImageName, diff])
except TypeError:
# empty image
pass
except TypeError:
# empty image
pass
matches.sort(lambda x, y: cmp(x[1], y[1]))
return matches
pass
def main():
im = Image.open('3.png')
new_im = normalize_image(im)
new_im.save('9_new.png')
for path in glob.glob('Photo*.jpg'):
user_im = Image.open(path)
user_im = ImageOps.invert(user_im)
user_im = user_im.crop(user_im.getbbox())
user_im = ImageOps.invert(user_im)
user_im = normalize_image(user_im)
if correct_pixels_ratio(new_im, user_im) > 0.1:
user_im.save('new_' + path)
def add_distortion(img, rotation=30, jitter=10, resize=0.05):
dx = random.randint(-jitter, +jitter)
dy = random.randint(-jitter, +jitter)
dr = random.uniform(-rotation, +rotation)
ds = random.uniform(1.0 - resize, 1.0 + resize)
width, height = img.width, img.height
img = ImageOps.invert(img)
img = img.rotate(dr)
img = ImageChops.offset(img, dx, dy)
img = ImageOps.invert(img)
return img
def test_sanity():
ImageOps.autocontrast(lena("L"))
ImageOps.autocontrast(lena("RGB"))
ImageOps.autocontrast(lena("L"), cutoff=10)
ImageOps.autocontrast(lena("L"), ignore=[0, 255])
ImageOps.colorize(lena("L"), (0, 0, 0), (255, 255, 255))
ImageOps.colorize(lena("L"), "black", "white")
ImageOps.crop(lena("L"), 1)
ImageOps.crop(lena("RGB"), 1)
ImageOps.deform(lena("L"), deformer)
ImageOps.deform(lena("RGB"), deformer)
ImageOps.equalize(lena("L"))
ImageOps.equalize(lena("RGB"))
ImageOps.expand(lena("L"), 1)
ImageOps.expand(lena("RGB"), 1)
ImageOps.expand(lena("L"), 2, "blue")
ImageOps.expand(lena("RGB"), 2, "blue")
ImageOps.fit(lena("L"), (128, 128))
ImageOps.fit(lena("RGB"), (128, 128))
ImageOps.fit(lena("RGB").resize((1, 1)), (35, 35))
ImageOps.flip(lena("L"))
ImageOps.flip(lena("RGB"))
ImageOps.grayscale(lena("L"))
ImageOps.grayscale(lena("RGB"))
ImageOps.invert(lena("L"))
ImageOps.invert(lena("RGB"))
ImageOps.mirror(lena("L"))
ImageOps.mirror(lena("RGB"))
ImageOps.posterize(lena("L"), 4)
ImageOps.posterize(lena("RGB"), 4)
ImageOps.solarize(lena("L"))
ImageOps.solarize(lena("RGB"))
success()
def doImage(path,rot):
try:
from PIL import Image, ImageOps
except:
err("please install Python Image Library")
return
try:
img = Image.open(path)
except:
err("file "+path+" not found!")
return
# convert to greyscale, invert
img=ImageOps.invert(img.convert('L'))
if rot:
img=img.rotate(90)
# figure out the desired W and H
if img.size[0]<=384:
# if the image is less than the max,
# round the width up to a multible of 8
img=img.crop((0,0,int(ceil(img.size[0]/8.)*8),img.size[1]))
else:
# if the image is larger than the max,
# scale it down to the max
img=img.resize((384,img.size[1]*384/img.size[0]))
# if verbose, show the image.
if myIO.verbose:
ImageOps.invert(img).convert('1').show()
# stringify
imgStr=img.convert('1').tostring()
info(path+' '+str(img.size))
# (GS v)
devSend('\x1D\x76\0\0'+twoBytes(img.size[0]/8)+twoBytes(img.size[1])+imgStr)
# I think we should wait for the data to send/print
time.sleep(6)
def startRecognition( infile, rotation=False, z=0 ):
global newImage
prefix = infile[:8]
im = Image.open(infile)
if rotation:
im = im.rotate(rotation)
width, height = im.size
resized = im.resize( ( width/25, height/30 ), Image.BICUBIC )
resized = ImageOps.invert(resized)
imgData = barImage( resized )
foundBarcode, newImage, newBar = False, False, False
for probable_barcode in imgData.findBarcode():
z+=1
# Try the first 20 barcodes, and see if one of them is legit.
if foundBarcode:
continue
try:
x1, y1 = (probable_barcode[1]-3)*25, (probable_barcode[2]) * 30
x2, y2 = x1+635, y1+265
if x2 > im.size[0] or y2 > im.size[1]:
x2,y2 = im.size[0], im.size[1]
x1,y1 = im.size[0]-800, im.size[1]-265
newImage = im.crop((x1,y1,x2,y2))
newBar = barImage(newImage)
foundBarcode = straightenBarcode ( newBar, "Hard", prefix=prefix )
if DEBUG and not foundBarcode:
smoo = im.crop( (x1,y1,x2,y2) )
smoo.save("%s.fail.%03d.barcode.png" % (prefix, z) )
print "Z: ", z
except:
foundBarcode = False
raise
if foundBarcode:
log.info("Found barcode for %s." % prefix )
newImage.putdata(newBar.im.reshape(newBar.stride*newBar.height))
newImage = ImageOps.invert(newImage)
newImage.save("%s.barcode.pre.png" % prefix )
try:
(x1, y1),(x2,y2) = newBar.findBarcodeLimits("Hard")
doPostBarcodeAnalysis(newImage.crop((x1-40,y1+1,x1+520,y1+90)), prefix )
except:
pass
elif not rotation:
startRecognition( infile, rotation=90, z=z )
else:
log.info("No barcode found for %s.", prefix)
def main():
if not os.path.exists('emoji'):
os.mkdir('emoji')
for font, glyphs in fonts.items():
for name, glyph in glyphs.items():
try:
colours = glyph.colours
font_size = glyph.size
glyph = glyph.letter
except AttributeError:
colours = [Icon.default_colour]
font_size = 128
imfont = ImageFont.truetype(font, font_size)
for colour_name, colour in colours:
im = Image.new("RGBA", (300, 300), (255, 255, 255, 0))
draw = ImageDraw.Draw(im)
draw.text((50, 50), glyph, font=imfont, fill=colour)
# remove unneccessory whitespaces if needed
im = im.crop(ImageOps.invert(im.convert('RGB')).getbbox())
# im = ImageOps.invert(im)
im.thumbnail(size, Image.ANTIALIAS)
background = Image.new('RGBA', size, (255, 255, 255, 0))
background.paste(
im,
((size[0] - im.size[0]) // 2, (size[1] - im.size[1]) // 2))
# write into file
background.save(f"emoji/{colour_name}{name}.png")
def update(self, speed):
result = None
if self.updateFlag:
self.im = None
self.updateFlag = False
self.loadGif(self.gifList[self.gifIndex])
if not self.im == None:
if self.newFlag:
self.newFlag = False
result = self.frame
else:
currentTime = int(round(time.time() * 1000))
elapsed = currentTime - self.lastTime
if elapsed >= self.delay and self.delay > 0:
self.lastTime = currentTime
self.nextFrame()
result = self.frame
if result != None:
result = result.convert("RGB")
if self.invertFlag:
result = ImageOps.invert(result)
if self.mirrorFlag:
result = result.transpose(Image.FLIP_LEFT_RIGHT)
result = result.getdata()
return result
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 _extract_percents(self, image, y_ticks, left_line_x) -> list:
percents = []
for tick in y_ticks:
# todo 25 here is hardcoded and bug-prone estimation.
# In fact we need to look for white strips from left to right.
# First such stripe will be border of image and second border of percents. Its right-x coordinate is what we need here
box = (25, tick - 5, left_line_x - 3, tick + 5)
region = image.crop(box)
text = region.crop(ImageOps.invert(region.convert('RGB')).getbbox())
# debug
# region.save(str(tick)+'.bmp')
word = ''
leftmost_unread_pixel = 0
while leftmost_unread_pixel < text.size[0]:
x_left = leftmost_unread_pixel
x_right = leftmost_unread_pixel + self.tesser.max_width
if x_right > text.size[0]:
x_right = text.size[0]
glyph = Image.new('RGBA', (x_right - x_left, 8), (255, 255, 255))
glyph.paste(text.crop((x_left, 0, x_right, 8)))
letter, width = self.tesser.tess(glyph)
word += letter
leftmost_unread_pixel += width
percents.append((float(word), tick))
return sorted(percents)
def createDigitsModel(fontfile, digitheight):
ttfont = ImageFont.truetype(fontfile, digitheight)
samples = np.empty((0,digitheight*(digitheight/2)))
responses = []
for n in range(10):
pil_im = Image.new("RGB", (digitheight, digitheight*2))
ImageDraw.Draw(pil_im).text((0, 0), str(n), font=ttfont)
pil_im = pil_im.crop(pil_im.getbbox())
pil_im = ImageOps.invert(pil_im)
#pil_im.save(str(n) + ".png")
# convert to cv image
cv_image = cv2.cvtColor(np.array( pil_im ), cv2.COLOR_RGBA2BGRA)
gray = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(5,5),0)
thresh = cv2.adaptiveThreshold(blur,255,1,1,11,2)
roi = cv2.resize(thresh,(digitheight,digitheight/2))
responses.append( n )
sample = roi.reshape((1,digitheight*(digitheight/2)))
samples = np.append(samples,sample,0)
samples = np.array(samples,np.float32)
responses = np.array(responses,np.float32)
model = cv2.KNearest()
model.train(samples,responses)
return model
def __init__(self, img_source):
"""
Load in an image
:param img_source: PIL.Image, or filename to load one from.
"""
if isinstance(img_source, Image.Image):
img_original = img_source
else:
img_original = Image.open(img_source)
# store image for eventual further processing (splitting)
self.img_original = img_original
# Convert to white RGB background, paste over white background
# to strip alpha.
img_original = img_original.convert('RGBA')
im = Image.new("RGB", img_original.size, (255, 255, 255))
im.paste(img_original, mask=img_original.split()[3])
# Convert down to greyscale
im = im.convert("L")
# Invert: Only works on 'L' images
im = ImageOps.invert(im)
# Pure black and white
self._im = im.convert("1")
def apply_invert(pixbuf):
'''
negative of an image (darkest-->lightest | lightest-->darkest)
'''
width,height = pixbuf.get_width(),pixbuf.get_height()
y = ImageOps.invert(Image.frombytes(K.ImageConstants.RGB_SHORT_NAME,(width,height),pixbuf.get_pixels() ))
return I.fromImageToPixbuf(y)
def invert_and_resize(self,newsize=600):
'''
Given a position and uncertainty, create a finding chart from the DSS
(Move this to another module)
Requires PIL
http://pythonmac.org/packages/py25-fat/index.html
http://www.p16blog.com/p16/2008/05/appengine-installing-pil-on-os-x-1053.html
'''
# SDSS - later http://casjobs.sdss.org/ImgCutoutDR5/getjpeg.aspx?ra=264.191&dec=-25.212&scale=0.8&width=750&height=750
self.img_size = newsize # in pixels
try:
im1 = Image.open(self.image_path)
except:
errstring = 'Could not open %s; image may be malformed.' % (self.image_path)
raise IOError(errstring)
# Invert Colors
im1 = ImageOps.invert(im1)
# Convert to color mode
im1 = im1.convert('RGB')
# Resize with a cubic spline - it looks nice.
# Other options are NEAREST, BILINEAR, ANTIALIAS
im1 = im1.resize((newsize,newsize),Image.BICUBIC)
self.save_str = storepath+'FCBase.png'
im1.save(self.save_str)
def process_rasters_in_dir(self, rootdir):
'''
descends through a TMS tile structure and converts the images
to a matrix of dimensions: num_images * width * height, dtype=numpy.uint8
'''
height = self.thumb_size
width = self.thumb_size
num_images = self.count_rasters_in_dir(rootdir)
images = numpy.zeros(num_images * width * height, dtype=numpy.uint8)
images = images.reshape(num_images, height, width)
index = 0
for folder, subs, files in os.walk(rootdir):
for filename in files:
if not filename.endswith('.jpg'):
continue
tile = self.tile_for_folder_and_filename(folder, filename, rootdir)
image_filename = os.path.join(folder, filename)
with open(image_filename, 'rb') as img_file:
with Image.open(img_file) as open_pil_img:
pil_image = open_pil_img.convert("L")
pil_image = ImageOps.invert(pil_image)
image_matrix = numpy.asarray(pil_image, dtype=numpy.uint8)
images[index] = image_matrix
index += 1
print("Packing {} images to a matrix of size num_images * width * height, dtype=numpy.uint8".format(index))
# Reshape to add a depth dimension
return images.reshape(num_images, width, height, 1)
def crop(image):
# Invert the image so the bounding box calculation works
inverted = ImageOps.invert(image.convert('L'))
# Found a bounding box for the non-white parts of the image
box = inverted.getbbox()
# Return the cropped image and bounding box
return image.crop(box), box
def convertImage(self, inputPath, outputPath):
r = inputPath
path = outputPath
for root, dirs, files in os.walk(r):
for f in files:
if f.endswith('.gif'):
if not os.path.exists(path): #Tworzony jest folder wyjsciowy
os.makedirs(path)
newroot = root.split(r)[1] #Te dwie linijki moga powodowac problemy, dlatego wazne jest, by obrazy do konwersji znajdowaly sie w podfolderach
newroot = newroot.split('/')[1] #Podfolder
f2 = f.split('.')[0]
orgImg = Image.open(root + "/" + f)
orgImg = orgImg.convert('L')
orgSize = orgImg.size
# niech beda tylko kwadratowe obrazy
assert orgSize[0] == orgSize[1]
assert self.newSize[0] == self.newSize[1]
borderSize = int((self.newSize[0] - orgSize[0]) / 2)
# dodanie bialej ramki
newImg = ImageOps.expand(orgImg, borderSize, 0)
# zapisanie wersji bez inwersji
newImg.save(path + newroot + f2 + ".pgm")
# dodanie wersji z inwersja
newImg = ImageOps.invert(newImg)
newImg.save(path + newroot + "n" + f2 + ".pgm")
def __call__(self, img_group):
if self.scale_worker is not None:
img_group = self.scale_worker(img_group)
image_w, image_h = img_group[0].size
crop_w, crop_h = self.crop_size
offsets = GroupMultiScaleCrop.fill_fix_offset(False, image_w, image_h, crop_w, crop_h)
oversample_group = list()
for o_w, o_h in offsets:
normal_group = list()
flip_group = list()
for i, img in enumerate(img_group):
crop = img.crop((o_w, o_h, o_w + crop_w, o_h + crop_h))
normal_group.append(crop)
flip_crop = crop.copy().transpose(Image.FLIP_LEFT_RIGHT)
if img.mode == 'L' and i % 2 == 0:
flip_group.append(ImageOps.invert(flip_crop))
else:
flip_group.append(flip_crop)
oversample_group.extend(normal_group)
oversample_group.extend(flip_group)
return oversample_group
def filter_contrastToAlpha(image, baseDir):
alpha = Image.new('L', image.size, 255)
alpha.paste(image, mask=get_alpha(image))
alpha = ImageOps.invert(alpha)
alpha = ImageOps.autocontrast(alpha)
return Image.merge('LA', [Image.new('L', image.size), alpha])
def tileify(image_data, num_blocks, max_shift):
"""
Breaks image up into rectangles and shifts them a random distance
Areas not covered by the moved rectangles are the negative of
the original image
"""
width = image_data.size[0]
height = image_data.size[1]
row_block_size = round(height / num_blocks)
col_block_size = round(width / num_blocks)
negative_image = ImageOps.invert(image_data)
pixels = array(image_data)
for row in xrange(num_blocks):
inner_row_val = int(row_block_size * row)
outer_row_val = int(row_block_size * row + row_block_size)
for col in xrange(num_blocks):
inner_col_val = int(col_block_size * col)
outer_col_val = int(col_block_size * col + col_block_size)
block = pixels[
inner_row_val:outer_row_val,
inner_col_val:outer_col_val
]
location = calculate_random_location(
inner_row_val,
inner_col_val,
max_shift
)
negative_image.paste(Image.fromarray(block), location)
return negative_image
def invert(img):
img = Image.fromarray(img)
inverted_img = ImageOps.invert(img)
inverted_img_array = np.asarray(inverted_img)
return inverted_img_array
def invert(img, magnitude):
img = ImageOps.invert(img)
return img
def invert(img, **__):
return ImageOps.invert(img)
import keras
import numpy as np
from PIL import Image, ImageOps
from keras.datasets import mnist
from keras.models import Sequential, load_model
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop
from keras.preprocessing import image
print('Loading model...')
model = keras.models.load_model('models')
img_width, img_height = 28, 28
source = Image.open('./images/0_2.png').convert('L').resize((img_width, img_height), 0)
source = ImageOps.invert(source)
# pil_image = Image.fromarray(color_coverted)
# pil_image = pil_image.convert('L')
# pil_image = pil_image.resize((28, 28))
# test_image = image.load_img('./images/5.png', color_mode='grayscale', target_size=(img_width, img_height))
test_image = image.img_to_array(source)
test_image = test_image.astype('float32')
test_image = test_image.reshape(28, 28)
test_image = 255 - test_image
test_image /= 255
test_image = test_image.reshape(1, img_width * img_height)
print(test_image)
# test_image = image.img_to_array(source)
def crop_by_coords(original, coords, inv=False):
img = original.crop(coords)
if inv:
img = ImageOps.invert(img)
return img.resize((3 * length(coords), 3 * height(coords)))
os.remove(os.path.join(angle_folder, file))
w = h = args.size
b = h / 2 # Height of the triangle
A = (w / 2, b) # Tip of the triangle
for angle in np.linspace(15, 160, 30):
angle_rad = np.deg2rad(angle / 2)
name = os.path.join(angle_folder, f'angle{angle:03.0f}-{{}}.{args.filetype}')
B = (w / 2 + b * np.tan(angle_rad), 0)
C = (w / 2 - b * np.tan(angle_rad), 0)
im = Image.new('L', (w, h), color=255)
draw = ImageDraw.Draw(im)
draw.polygon([C, B, A], fill='black')
im.save(name.format('0'))
if args.invert:
ImageOps.invert(im).save(name.format('0i'))
elif args.rotate and args.invert:
im = im.rotate(90)
im.save(name.format('1'))
ImageOps.invert(im).save(name.format('1i'))
im = im.rotate(90)
im.save(name.format('2'))
ImageOps.invert(im).save(name.format('2i'))
im = im.rotate(90)
im.save(name.format('3'))
ImageOps.invert(im).save(name.format('3i'))
elif args.rotate:
im = im.rotate(90)
im.save(name.format('1'))
im = im.rotate(90)
im.save(name.format('2'))
def invert(image: Image.Image, mode: str = "pillow") -> Image.Image:
if 'pil' in mode.lower():
return ImageOps.invert(image.convert('L')).convert('1')
else:
return Image.fromarray(obj=np.invert(np.array(image)), mode='1')
def main():
args = parse_args()
with open(args.font, 'r', encoding='utf-8') as f:
font = json.load(f)
if args.text == '-':
text = sys.stdin.read().rstrip('\n')
else:
text = args.text
if not text:
sys.exit('Text is empty.')
glyph_lookup = {}
for glyph in font['glyphs']:
glyph_lookup[glyph['codePoint']] = glyph
kerning_lookup = {}
for kerning_pair in font['kerningPairs']:
cp1 = kerning_pair['codePoint1']
cp2 = kerning_pair['codePoint2']
kerning_lookup[(cp1, cp2)] = kerning_pair['amount']
page_lookup = load_pages_for_text(
text, font, os.path.dirname(args.font), glyph_lookup)
min_canvas_size = get_image_size_for_text(
text, font, glyph_lookup, kerning_lookup)
padding_px = round(
font['bakingOptions']['fontPxSize'] * args.padding)
canvas_size = (
min_canvas_size[0] + padding_px * 2,
min_canvas_size[1] + padding_px * 2
)
grayscale_mode = all(p.mode == 'L' for p in page_lookup.values())
if grayscale_mode:
canvas_mode = 'L'
fill_color = 0
else:
canvas_mode = 'RGBA'
fill_color = (255, 255, 255, 0)
canvas = Image.new(canvas_mode, canvas_size, fill_color)
render_text(
text, canvas, (padding_px, padding_px),
font, glyph_lookup, page_lookup, kerning_lookup)
if grayscale_mode:
if args.transparent:
new_canvas = Image.new('LA', canvas.size, (0, 255))
new_canvas.putalpha(canvas)
canvas = new_canvas
else:
canvas = ImageOps.invert(canvas)
elif not args.transparent:
bg = Image.new('RGB', canvas.size, (255 ,255, 255))
bg.paste(canvas, mask=canvas.split()[-1])
canvas = bg
out_image_path = args.out_image
if os.path.splitext(out_image_path)[1].lower() != '.png':
out_image_path += '.png'
out_image_dir = os.path.dirname(out_image_path)
if out_image_dir:
os.makedirs(out_image_dir, exist_ok=True)
canvas.save(out_image_path, optimize=True)
def __init__(self,
mapfile,
camera=None,
light=None,
width=100.0,
depth=100.0,
height=10.0,
divx=0,
divy=0,
ntiles=1.0,
name="",
x=0.0,
y=0.0,
z=0.0,
rx=0.0,
ry=0.0,
rz=0.0,
sx=1.0,
sy=1.0,
sz=1.0,
cx=0.0,
cy=0.0,
cz=0.0,
smooth=True,
cubic=False):
"""uses standard constructor for Shape
Arguments:
*mapfile*
Greyscale image path/file, string.
Keyword arguments:
*width, depth, height*
Of the map in world units.
*divx, divy*
Number of divisions into which the map will be divided.
to create vertices
*ntiles*
Number of repeats for tiling the texture image.
*smooth*
Calculate normals with averaging rather than pointing
straight up, slightly faster if false.
"""
super(ElevationMap, self).__init__(camera, light, name, x, y, z, rx,
ry, rz, sx, sy, sz, cx, cy, cz)
if VERBOSE:
print("Loading height map ...", mapfile)
if divx > 200 or divy > 200:
print("... Map size can't be bigger than 200x200 divisions")
divx = 200
divy = 200
im = Image.open(mapfile)
im = ImageOps.invert(im)
ix, iy = im.size
if (ix > 200 and divx == 0) or (divx > 0):
if divx == 0:
divx = 200
divy = 200
im = im.resize((divx, divy), Image.ANTIALIAS)
ix, iy = im.size
if not im.mode == "P":
im = im.convert('P', palette=Image.ADAPTIVE)
im = im.transpose(Image.FLIP_TOP_BOTTOM)
im = im.transpose(Image.FLIP_LEFT_RIGHT)
self.pixels = im.load()
self.width = width
self.depth = depth
self.height = height
self.ix = ix
self.iy = iy
self.ttype = GL_TRIANGLE_STRIP
if VERBOSE:
print("Creating Elevation Map ...", ix, iy)
wh = width * 0.5
hh = depth * 0.5
ws = width / ix
hs = depth / iy
ht = height / 255.0
tx = 1.0 * ntiles / ix
ty = 1.0 * ntiles / iy
verts = []
norms = []
tex_coords = []
idx = []
for y in xrange(0, iy):
for x in xrange(0, ix):
hgt = (self.pixels[x, y]) * ht
this_x = -wh + x * ws
this_z = -hh + y * hs
if cubic:
""" this is a bit experimental. It tries to make the map either zero
or height high. Vertices are moved 'under' adjacent ones if there is
a step to make vertical walls. Goes wrong in places - mainly because
it doesn't check diagonals
"""
if hgt > height / 2:
hgt = height
else:
hgt = 0.0
if hgt == 0 and y > 0 and y < iy - 1 and x > 0 and x < ix - 1:
if self.pixels[x - 1, y] > 127:
this_x = -wh + (x - 1) * ws
elif self.pixels[x + 1, y] > 127:
this_x = -wh + (x + 1) * ws
elif self.pixels[x, y - 1] > 127:
this_z = -hh + (y - 1) * hs
elif self.pixels[x, y + 1] > 127:
this_z = -hh + (y + 1) * hs
elif self.pixels[x - 1, y - 1] > 127:
this_x = -wh + (x - 1) * ws
this_z = -hh + (y - 1) * hs
elif self.pixels[x - 1, y + 1] > 127:
this_x = -wh + (x - 1) * ws
this_z = -hh + (y + 1) * hs
elif self.pixels[x + 1, y - 1] > 127:
this_x = -wh + (x + 1) * ws
this_z = -hh + (y - 1) * hs
elif self.pixels[x + 1, y + 1] > 127:
this_x = -wh + (x + 1) * ws
this_z = -hh + (y + 1) * hs
verts.append((this_x, hgt, this_z))
tex_coords.append(((ix - x) * tx, (iy - y) * ty))
s = 0
#create one long triangle_strip by alternating X directions
for y in range(0, iy - 1):
for x in range(0, ix - 1):
i = (y * ix) + x
idx.append((i, i + ix, i + ix + 1))
idx.append((i + ix + 1, i + 1, i))
s += 2
self.buf = []
self.buf.append(Buffer(self, verts, tex_coords, idx, None, smooth))
def __init__(self, Numbers=None, max_Magnitude=None, **kwargs):
'''
Custom image data generator.
Behaves like ImageDataGenerator, but allows color augmentation.
'''
super().__init__(preprocessing_function=self.__call__, **kwargs)
self.transforms = ['autocontrast', 'equalize', 'rotate', 'solarize', 'color', 'posterize',
'contrast', 'brightness', 'sharpness', 'shearX', 'shearY', 'translateX', 'translateY']
if Numbers is None:
self.Numbers = len(self.transforms) // 2
else:
self.Numbers = Numbers
if max_Magnitude is None:
self.max_Magnitude = 10
else:
self.max_Magnitude = max_Magnitude
fillcolor = 128
self.ranges = {
# these Magnitude range , you must test it yourself , see what will happen after these operation ,
# it is no need to obey the value in autoaugment.py
"shearX": np.linspace(0, 0.3, 10),
"shearY": np.linspace(0, 0.3, 10),
"translateX": np.linspace(0, 0.2, 10),
"translateY": np.linspace(0, 0.2, 10),
"rotate": np.linspace(0, 360, 10),
"color": np.linspace(0.0, 0.9, 10),
"posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int),
"solarize": np.linspace(256, 231, 10),
"contrast": np.linspace(0.0, 0.5, 10),
"sharpness": np.linspace(0.0, 0.9, 10),
"brightness": np.linspace(0.0, 0.3, 10),
"autocontrast": [0] * 10,
"equalize": [0] * 10,
"invert": [0] * 10
}
self.func = {
"shearX": lambda img, magnitude: img.transform(
img.size, Image.AFFINE, (1, magnitude * random.choice([-1, 1]), 0, 0, 1, 0),
Image.BICUBIC, fill=fillcolor),
"shearY": lambda img, magnitude: img.transform(
img.size, Image.AFFINE, (1, 0, 0, magnitude * random.choice([-1, 1]), 1, 0),
Image.BICUBIC, fill=fillcolor),
"translateX": lambda img, magnitude: img.transform(
img.size, Image.AFFINE, (1, 0, magnitude * img.size[0] * random.choice([-1, 1]), 0, 1, 0),
fill=fillcolor),
"translateY": lambda img, magnitude: img.transform(
img.size, Image.AFFINE, (1, 0, 0, 0, 1, magnitude * img.size[1] * random.choice([-1, 1])),
fill=fillcolor),
"rotate": lambda img, magnitude: self.rotate_with_fill(img, magnitude),
# "rotate": lambda img, magnitude: img.rotate(magnitude * random.choice([-1, 1])),
"color": lambda img, magnitude: ImageEnhance.Color(img).enhance(1 + magnitude * random.choice([-1, 1])),
"posterize": lambda img, magnitude: ImageOps.posterize(img, magnitude),
"solarize": lambda img, magnitude: ImageOps.solarize(img, magnitude),
"contrast": lambda img, magnitude: ImageEnhance.Contrast(img).enhance(
1 + magnitude * random.choice([-1, 1])),
"sharpness": lambda img, magnitude: ImageEnhance.Sharpness(img).enhance(
1 + magnitude * random.choice([-1, 1])),
"brightness": lambda img, magnitude: ImageEnhance.Brightness(img).enhance(
1 + magnitude * random.choice([-1, 1])),
"autocontrast": lambda img, magnitude: ImageOps.autocontrast(img),
"equalize": lambda img, magnitude: img,
"invert": lambda img, magnitude: ImageOps.invert(img)
}
life -= ultra_ability_4
life -= ultra_ability_5
first_aibility_damage = first_aibility * 601 / (magic_resistance_true +
601)
life -= first_aibility_damage
return int(life)
os.system('adb shell screencap -p /sdcard/1.png')
print("截图完毕")
os.system('adb pull /sdcard/1.png .')
img = Image.open('C:\\Users\\wkz\\1.png').convert('RGB')
inverted_image = ImageOps.invert(img)
ability_power_range = np.zeros((6, 4))
enemy_life_range = np.zeros((6, 4))
enemy_magic_resistance_range = np.zeros((6, 4))
level_range = np.zeros((6, 4))
for i in range(1, 6):
ability_power_range[i] = np.array([
int(474 * screen_width / 1920),
int((133 * i + 210) * screen_height / 1080),
int(572 * screen_width / 1920),
int((133 * i + 273) * screen_height / 1080)
])
enemy_life_range[i] = np.array([
int(1220 * screen_width / 1920),
int((133 * i + 210) * screen_height / 1080),
def invertirImagen(imagen):
invertir = ImageOps.invert(imagen)
return invertir
import idx2numpy
from PIL import Image
from PIL import ImageEnhance
from PIL import ImageOps
coll=array([])
# TBD
# 1. Take a file list
# 2. Example Data Images
#
for n in range("""Nimber of Images"""):
img=Image.open("""Image Name"""{0}.format(n)).convert("L") #Opening the image in Monochrome version
img
contr = ImageEnhance.Contrast(img)
img = contr.enhance(3)
bright = ImageEnhance.Brightness(img)
img = bright.enhance(4) #Enhancement of Contrast and Brightness
img=ImageOps.invert(img) #Invert Image (White in Black)
img=img.resize((28,28)) #Conversion of Resolution to 28 X 28
img
arr=array(img) #Conversion of Image to 2D numpy array
for i in range(28):
for j in range(28):
if(arr[i][j]==0):
arr[i][j]=1
else:
arr[i][j]=255 #Concersion to MNIST Datset format
idx2numpy.convert_to_file("""File Name""",arr) #Conversion 2D numpy array to idx2 format
def imgmsg_to_pil(img_msg, rgba=False):
try:
if img_msg._type == 'sensor_msgs/CompressedImage':
pil_img = Image.open(BytesIO(img_msg.data))
if pil_img.mode.startswith('BGR'):
pil_img = pil_bgr2rgb(pil_img)
pil_mode = 'RGB'
else:
pil_mode = 'RGB'
if img_msg.encoding in ['mono8', '8UC1']:
mode = 'L'
elif img_msg.encoding == 'rgb8':
mode = 'RGB'
elif img_msg.encoding == 'bgr8':
mode = 'BGR'
elif img_msg.encoding in [
'bayer_rggb8', 'bayer_bggr8', 'bayer_gbrg8', 'bayer_grbg8'
]:
mode = 'L'
elif img_msg.encoding in [
'bayer_rggb16', 'bayer_bggr16', 'bayer_gbrg16',
'bayer_grbg16'
]:
pil_mode = 'I;16'
if img_msg.is_bigendian:
mode = 'I;16B'
else:
mode = 'I;16L'
elif img_msg.encoding == 'mono16' or img_msg.encoding == '16UC1':
pil_mode = 'F'
if img_msg.is_bigendian:
mode = 'F;16B'
else:
mode = 'F;16'
elif img_msg.encoding == '32FC1':
pil_mode = 'F'
if img_msg.is_bigendian:
mode = 'F;32BF'
else:
mode = 'F;32F'
elif img_msg.encoding == 'rgba8':
mode = 'BGR'
elif img_msg.encoding == 'bgra8':
mode = 'RGB'
else:
raise Exception("Unsupported image format: %s" %
img_msg.encoding)
pil_img = Image.frombuffer(pil_mode,
(img_msg.width, img_msg.height),
img_msg.data, 'raw', mode, 0, 1)
# 16 bits conversion to 8 bits
if pil_mode == 'I;16':
pil_img = pil_img.convert('I').point(lambda i: i *
(1. / 256.)).convert('L')
if pil_img.mode == 'F':
pil_img = pil_img.point(lambda i: i * (1. / 256.)).convert('L')
pil_img = ImageOps.autocontrast(pil_img)
pil_img = ImageOps.invert(pil_img)
if rgba and pil_img.mode != 'RGBA':
pil_img = pil_img.convert('RGBA')
return pil_img
except Exception as ex:
print('Can\'t convert image: %s' % ex, file=sys.stderr)
return None
def invert_alpha(self):
if self.alpha:
self.alpha = ImageOps.invert(self.alpha)
self.clear_cache()
def conv_image_to_module(name, scale_factor):
module = header % {"name": name.upper()}
front_image = Image.open("%s_front.png" % name).transpose(Image.FLIP_TOP_BOTTOM)
print("Reading image from \"%s_front.png\"" % name)
front_image_red, front_image_green, front_image_blue, front_image_alpha = front_image.split()
# Soldermask needs to be inverted
front_image_red = ImageOps.invert(front_image_red)
front_image_red = Image.composite(front_image_red, front_image_alpha, front_image_alpha)
front_image_red = front_image_red.point(lambda i: 0 if i < 127 else 1)
red_array = np.array(front_image_red)
bmp_red = potrace.Bitmap(red_array)
path_red = bmp_red.trace(alphamax = 0.0, opttolerance = 50)
# Soldermask needs to be inverted
front_image_green = ImageOps.invert(front_image_green)
front_image_green = Image.composite(front_image_green, front_image_alpha, front_image_alpha)
front_image_green = front_image_green.point(lambda i: 0 if i < 127 else 1)
green_array = np.array(front_image_green)
bmp_green = potrace.Bitmap(green_array)
path_green = bmp_green.trace(alphamax = 0.0, opttolerance = 50)
front_image_blue = front_image_blue.point(lambda i: 0 if i < 127 else 1)
blue_array = np.array(front_image_blue)
bmp_blue = potrace.Bitmap(blue_array)
path_blue = bmp_blue.trace(alphamax = 0.0, opttolerance = 50)
front_image_alpha = front_image_alpha.point(lambda i: 0 if i < 127 else 1)
front_image_alpha_array = np.array(front_image_alpha)
bmp_alpha = potrace.Bitmap(front_image_alpha_array)
path_alpha = bmp_alpha.trace(alphamax = 0.0, opttolerance = 50)
w, h = front_image.size
# print("Generating Outline layer from front alpha channel")
# module += render_path_to_layer(path_alpha, "line", "20", scale_factor)
print("Generating tKeepout layer from front red channel")
module += render_path_to_layer(path_red, "poly", "39", scale_factor)
print("Generating tStop layer from front green channel")
module += render_path_to_layer(path_green, "poly", "29", scale_factor)
print("Generating tPlace layer from front blue channel")
module += render_path_to_layer(path_blue, "poly", "21", scale_factor)
try:
back_image = Image.open("%s_back.png" % name).transpose(Image.FLIP_TOP_BOTTOM)
back_image = ImageOps.mirror(back_image)
print("Reading image from \"%s_back.png\"" % name)
back_image_red, back_image_green, back_image_blue, back_image_alpha = back_image.split()
back_image_red = back_image_red.point(lambda i: 0 if i < 127 else 1)
red_array = np.array(back_image_red)
bmp_red = potrace.Bitmap(red_array)
path_red = bmp_red.trace(alphamax = 0.0, opttolerance = 50)
# Soldermask needs to be inverted
back_image_green = ImageOps.invert(back_image_green)
back_image_green = back_image_green.point(lambda i: 0 if i < 127 else 1)
green_array = np.array(back_image_green)
bmp_green = potrace.Bitmap(green_array)
path_green = bmp_green.trace(alphamax = 0.0, opttolerance = 50)
back_image_blue = back_image_blue.point(lambda i: 0 if i < 127 else 1)
blue_array = np.array(back_image_blue)
bmp_blue = potrace.Bitmap(blue_array)
path_blue = bmp_blue.trace(alphamax = 0.0, opttolerance = 50)
print("Generating bKeepout layer from back red channel")
module += render_path_to_layer(path_red, "poly", "40", scale_factor)
print("Generating bStop layer from back green channel")
module += render_path_to_layer(path_green, "poly", "30", scale_factor)
print("Generating bPlace layer from back blue channel")
module += render_path_to_layer(path_blue, "poly", "22", scale_factor)
except IOError:
pass
module += footer % {"name": name.upper()}
return module, (w * 25.4 / scale_factor, h * 25.4 / scale_factor)
def trim(image):
inverted = ImageOps.invert(image.convert("RGB"))
return image.crop(inverted.getbbox())
def start():
startbutton.config(state='disabled')
previewbutton.config(state='disabled')
browsebutton.config(state='disabled')
mvar = 'Creating wallpaper. Please wait...'
messvar.set(mvar)
### CODE ###
print('>> Opening image...')
img = i.open(filename)
origimg = img # Original image to be used later
wid, hei = math.ceil(img.size[1]*(16/9)), img.size[1] # Working out 16:9 width for original height
# print(f'>> Orig size: {img.size}')
# print(f'>> New size: {wid, hei}')
mode = 'RGBA'
global bkg
bkg = i.new(mode, (wid, hei)) # Background
if cmvar.get() == 1: # Colour map
if invvar.get() == 1:
cmblack = cmwvar.get()
cmwhite = cmbvar.get()
else:
cmblack = cmbvar.get()
cmwhite = cmwvar.get()
img = img.convert('L') # Convert to greyscale
img = ImageOps.colorize(img, black=cmblack, white=cmwhite) # colour map
## Matplotlib colour map
if plbvar.get() == 0:
pass
else:
print(f'>> Applying {pltvar.get()} colour map...')
pltcm = matplotlib.cm.get_cmap(pltvar.get()) # color map
img = img.convert('L')
img = np.array(img)
img = pltcm(img)
img = np.uint8(img*255)
img = i.fromarray(img)
## Invert colours
if icvar.get() == 1:
print('>> Inverting image...')
img = img.convert('RGB')
# print(img.mode)
img = ImageOps.invert(img)
## Gaussian Blur
blurval = int(blurslider.get()) # Slider value
if blurval == 0: # Filter options
pass
else:
print('>> Applying Gaussian Blur...')
# blur = hei/()*8
img = img.filter(ImageFilter.GaussianBlur(radius=(blurval*0.1))) # Gaussian blur
## Darken
if fopt.get() == 2: # If radio button 2 not chosen
# https://stackoverflow.com/questions/43618910/pil-drawing-a-semi-transparent-square-overlay-on-image
img = i.eval(img, lambda x: x/1.5)
## Split image
def split(pos): # Splits image in 2 and puts top half on the left and bottom on the right
imgcent = math.ceil(hei/2) # Image center
global fopt
if pos == 't':
print('>> Creating left background...')
s = img.crop(box=(0,0,img.size[0],imgcent)) # Cropping image - 4 tuple: left,top,right,bottom
elif pos == 'b':
print('>> Creating right background...')
s = img.crop(box=(0,imgcent,img.size[0],img.size[1]))
sr = s.resize(tuple(z*2 for z in s.size)) # Scaling up by 2 because img was split in two
return sr
## Working out where top and bottom splits are to be pasted
print('>> Calculating dimensions...')
sw = math.ceil((wid-img.size[0])/2) # split width
tspos = (math.ceil((sw/2)-img.size[0]),0) # Top split position
bspos = (math.ceil((wid-sw/2) - img.size[0]),0) # Bottom split position
print('>> Creating background...')
bkg.paste(split('t'), box=tspos)
bkg.paste(split('b'), box=bspos)
bkg.paste(origimg, box=(math.ceil((wid/2)-(img.size[0]/2)), 0)) # Paste original image in the center of new image
# Add border
if bordvar.get() == 1:
bkg = ImageOps.expand(bkg,border=wid//100,fill='black')
elif bordvar.get() == 0:
pass
prevbkg = bkg.resize((800,450)) # Resizing preview image to fit on canvas
previmg = ImageTk.PhotoImage(image=prevbkg) # Creating a PhotoImage of image object to load on canvas
preview.create_image(0,0, image=previmg, anchor='nw') # Loading photoimage on canvas
preview.image = previmg # https://web.archive.org/web/20201111190625id_/http://effbot.org/pyfaq/why-do-my-tkinter-images-not-appear.htm
# preview.itemconfig(setprev)
# tkinter.Tk()
# master.mainloop()
# ~ Resetting GUI ~
startbutton.config(state='normal')
previewbutton.config(state='normal')
browsebutton.config(state='normal')
bordbutton.config(state='normal')
def inverse_color(image):
if np.random.random() < 0.4:
image = ImageOps.invert(image)
return image
lambda pil_img, level, _: pil_img.transpose(Image.FLIP_LEFT_RIGHT))
flip_ud = TransformT(
'FlipUD',
lambda pil_img, level, _: pil_img.transpose(Image.FLIP_TOP_BOTTOM))
# pylint:disable=g-long-lambda
auto_contrast = TransformT(
'AutoContrast',
lambda pil_img, level, _: ImageOps.autocontrast(
pil_img.convert('RGB')).convert('RGBA'))
equalize = TransformT(
'Equalize',
lambda pil_img, level, _: ImageOps.equalize(
pil_img.convert('RGB')).convert('RGBA'))
invert = TransformT(
'Invert',
lambda pil_img, level, _: ImageOps.invert(
pil_img.convert('RGB')).convert('RGBA'))
# pylint:enable=g-long-lambda
blur = TransformT(
'Blur', lambda pil_img, level, _: pil_img.filter(ImageFilter.BLUR))
smooth = TransformT(
'Smooth',
lambda pil_img, level, _: pil_img.filter(ImageFilter.SMOOTH))
def _rotate_impl(pil_img, level, _):
"""Rotates `pil_img` from -30 to 30 degrees depending on `level`."""
degrees = int_parameter(level, 30)
if random.random() > 0.5:
degrees = -degrees
return pil_img.rotate(degrees)
import struct
from PIL import Image, ImageOps
import os
target_name = 'ETL9B'
for root, dirs, files in os.walk(target_name):
for i, file in enumerate(sorted(files)):
filepath = os.path.join(root, file)
print(file)
sum_datasets = 40
sum_words = 3036
record_size = 576
with open(filepath, 'rb') as f:
for ds_idx in range(1, sum_datasets * sum_words + 1):
f.seek(ds_idx * record_size)
s = f.read(record_size)
r = struct.unpack('>2H4s504s64x', s)
i1 = Image.frombytes('1', (64, 63), r[3], 'raw')
img = ImageOps.invert(i1.convert('L'))
file_name = '{}_{}_{}_{}_{}.png'.format(
target_name, i, r[0],
hex(r[1])[2:], ds_idx)
dir_name = "./{}_img/{}".format(target_name, hex(r[1])[2:])
os.makedirs(dir_name, exist_ok=True)
img.save(os.path.join(dir_name, file_name), 'PNG')
def char_to_picture(text="",
font_name="宋体",
background_color=(255, 255, 255),
text_color=(0, 0, 0),
pictrue_size=400,
text_position=(0, 0),
in_meddium=False,
reverse_color=False,
smooth_times=0,
noise=0):
pictrue_shape = (pictrue_size, pictrue_size)
im = Image.new("RGB", pictrue_shape, background_color)
dr = ImageDraw.Draw(im)
# 由于系统内部不是使用汉字文件名，而是英文名，在此转换
if font_name == "宋体":
font_name = "SIMSUN.ttc"
if font_name == "楷体":
font_name = "SIMKAI.ttf"
if font_name == "黑体":
font_name = "SIMHEI.ttf"
if font_name == "等线":
font_name = "DENG.ttf"
if font_name == "仿宋":
font_name = "SIMFANG.ttf"
# 取得字体文件的位置
font_dir = "C:\Windows\Fonts\\" + font_name
font_size = int(pictrue_size * 0.8 / len(text)) # 设定文字的大小
font = ImageFont.truetype(font_dir, font_size)
# 开始绘图
# 如果设置了居中，那么就居中
# 英文字母的对齐方式并不一样
char_dict = []
for i in range(26):
char_dict.append(chr(i + ord('a')))
char_dict.append(chr(i + ord('A')))
if in_meddium:
char_num = len(text)
text_position = (pictrue_shape[0] / 2 - char_num * font_size / 2,
pictrue_shape[1] / 2 - font_size / 2) # 中文
if text in char_dict:
text_position = (pictrue_shape[0] / 2 - char_num * font_size / 4,
pictrue_shape[1] / 2 - font_size / 2) # 英文
# 开始绘制图像
dr.text(text_position, text, font=font, fill=text_color)
if reverse_color:
im = ImageOps.invert(im)
# 随机扰动
if noise > 0:
print("adding noise...")
im_array = np.array(im)
noise_num = noise * pictrue_size
for i in range(noise_num):
pos = (random.randint(0, pictrue_size - 1),
random.randint(0, pictrue_size - 1))
color = [
random.randint(0, 255),
random.randint(0, 255),
random.randint(0, 255)
]
im_array[pos[0], pos[1], :] = color
im = Image.fromarray(im_array)
# 模糊化图片
'''
for i in range(smooth_times):
im =im.filter(ImageFilter.GaussianBlur)
'''
im_array = np.array(im)
for i in range(smooth_times):
im_array = smooth(im_array)
im = Image.fromarray(im_array)
# 图片经过模糊后略有缩小
im = im.resize(pictrue_shape)
print("文字转换图片成功")
return im
def invert_color(image):
image = image.convert('L')
image = ImageOps.invert(image)
image = image.convert('1')
return image
def __init__(self,
dataset,
transformations=None,
n_classes=8000,
n_trans=100,
max_elms=10,
p=0.5):
"""ExemplarNet dataset.
Args:
dataset (torch.utils.data.Dataset): The dataset to train on.
transformations (list, optional): Type of elementar transformations to use.
n_classes (int, optional): Number of classes, i.e. the subset size of the dataset. Defaults to 8000.
n_trans (int, optional): Number of combined transformations. Defaults to 100.
max_elms (int, optional): Number of elementar transformations per combined transformation. Defaults to 10.
p (float, optional): Prob. of an elmentar transformation to be part of a combined transformation. Defaults to 0.5.
"""
pool = [
transforms.RandomRotation( # Rotation
30,
resample=False,
expand=False,
center=None,
fill=None),
transforms.RandomAffine( # Shearing
0,
translate=None,
scale=None,
shear=30,
resample=False,
fillcolor=0),
transforms.RandomAffine( # Translate
0,
translate=(0.3, 0.3),
scale=None,
shear=None,
resample=False,
fillcolor=0),
transforms.Lambda(lambda x: imo.autocontrast(x)), # Autocontrast
transforms.Lambda(lambda x: imo.invert(x)), # Invert
transforms.Lambda(lambda x: imo.equalize(x)), # Equalize
transforms.Lambda(lambda x: imo.solarize(x)), # Solarize
transforms.Lambda(lambda x: imo.posterize(
x, bits=int(np.random.randint(4, 8) + 1))), # Posterize
transforms.Lambda(
lambda x: ime.Color(x).enhance(np.random.uniform())), # Color
transforms.Lambda(lambda x: ime.Brightness(x).enhance(
np.random.uniform())), # Brightness
transforms.Lambda(lambda x: ime.Contrast(x).enhance(
np.random.uniform())), # Contrast
transforms.Lambda(lambda x: ime.Sharpness(x).enhance(
np.random.uniform())), # Sharpness
transforms.Compose( # Set black
[
transforms.ToTensor(),
transforms.RandomErasing(1.0),
transforms.ToPILImage()
]),
transforms.Lambda(
lambda x: transforms.functional.to_grayscale( # Grayscale
x, num_output_channels=3)),
transforms.Lambda(lambda x: elastic_transform(x, sigma=10))
]
# Processes full images and apply random cropping instead of gradient based sampling.
indices = torch.randint(len(dataset), (n_classes, )).long()
self.dataset = Subset(dataset, indices)
self.p = p
self.n_trans = n_trans
elm_transformations = transformations if transformations is not None else pool
self.transformations = []
for _ in range(self.n_trans):
transformation = []
for t in range(max_elms):
if random.random() < self.p:
transformation.append(
transforms.RandomChoice(elm_transformations))
self.transformations.append(transforms.Compose(transformation))
def read(location, source):
img = source.crop(location)
img = RGBtoBW(img)
img = ImageOps.invert(img)
return img
def updateDisplay(config, pricestack, whichcoin, fiat):
symbolstring = currency.symbol(fiat.upper())
if fiat == "jpy":
symbolstring = "¥"
pricenow = pricestack[-1]
currencythumbnail = 'currency/' + whichcoin + '.bmp'
tokenimage = Image.open(os.path.join(picdir, currencythumbnail))
sparkbitmap = Image.open(os.path.join(picdir, 'spark.bmp'))
pricechange = str("%+d" % round(
(pricestack[-1] - pricestack[0]) / pricestack[-1] * 100, 2)) + "%"
if pricenow > 1000:
pricenowstring = format(int(pricenow), ",")
else:
pricenowstring = str(float('%.5g' % pricenow))
if config['display']['orientation'] == 0 or config['display'][
'orientation'] == 180:
epd = epd2in7.EPD()
epd.Init_4Gray()
image = Image.new('L', (epd.width, epd.height),
255) # 255: clear the image with white
draw = ImageDraw.Draw(image)
draw.text((110, 80), "7day :", font=font_date, fill=0)
draw.text((110, 95), pricechange, font=font_date, fill=0)
# Print price to 5 significant figures
draw.text((15, 200), symbolstring + pricenowstring, font=font, fill=0)
draw.text((10, 10),
str(time.strftime("%a %H:%M %d %b %Y")),
font=font_date,
fill=0)
image.paste(tokenimage, (10, 25))
image.paste(sparkbitmap, (10, 125))
if config['display']['orientation'] == 180:
image = image.rotate(180, expand=True)
if config['display']['orientation'] == 90 or config['display'][
'orientation'] == 270:
epd = epd2in7.EPD()
epd.Init_4Gray()
image = Image.new('L', (epd.height, epd.width),
255) # 255: clear the image with white
draw = ImageDraw.Draw(image)
draw.text((100, 100), "7day : " + pricechange, font=font_date, fill=0)
# Print price to 5 significant figures
# draw.text((20,120),symbolstring,font =fonthiddenprice,fill = 0)
draw.text((10, 120),
symbolstring + pricenowstring,
font=fontHorizontal,
fill=0)
image.paste(sparkbitmap, (80, 50))
image.paste(tokenimage, (0, 0))
draw.text((85, 5),
str(time.strftime("%a %H:%M %d %b %Y")),
font=font_date,
fill=0)
if config['display']['orientation'] == 270:
image = image.rotate(180, expand=True)
# This is a hack to deal with the mirroring that goes on in 4Gray Horizontal
image = ImageOps.mirror(image)
# If the display is inverted, invert the image usinng ImageOps
if config['display']['inverted'] == True:
image = ImageOps.invert(image)
# Send the image to the screen
epd.display_4Gray(epd.getbuffer_4Gray(image))
epd.sleep()
def invert_img(imdir,outdir):
im = Image.open(imdir)
out_filename = outdir
ImageOps.invert(im).save(out_filename, 'JPEG', quality = 100)
def __init__(self,
p1,
operation1,
magnitude_idx1,
p2,
operation2,
magnitude_idx2,
fillcolor=(128, 128, 128),
magnitude_factor=1):
ranges = {
"shearX": np.linspace(0, 0.3, 10),
"shearY": np.linspace(0, 0.3, 10),
"translateX": np.linspace(0, 150 / 331, 10),
"translateY": np.linspace(0, 150 / 331, 10),
"rotate": np.linspace(0, 30, 10),
"color": np.linspace(0.0, 0.9, 10),
"posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int),
"solarize": np.linspace(256, 0, 10),
"contrast": np.linspace(0.0, 0.9, 10),
"sharpness": np.linspace(0.0, 0.9, 10),
"brightness": np.linspace(0.0, 0.9, 10),
"autocontrast": [0] * 10,
"equalize": [0] * 10,
"invert": [0] * 10
}
# from https://stackoverflow.com/questions/5252170/specify-image-filling-color-when-rotating-in-python-with-pil-and-setting-expand
def rotate_with_fill(img, magnitude):
rot = img.convert("RGBA").rotate(magnitude)
return Image.composite(rot, Image.new("RGBA", rot.size,
(128, ) * 4),
rot).convert(img.mode)
func = {
"shearX":
lambda img, magnitude: img.transform(img.size,
Image.AFFINE,
(1, magnitude * random.choice(
[-1, 1]), 0, 0, 1, 0),
Image.BICUBIC,
fillcolor=fillcolor),
"shearY":
lambda img, magnitude: img.transform(img.size,
Image.AFFINE,
(1, 0, 0, magnitude * random.
choice([-1, 1]), 1, 0),
Image.BICUBIC,
fillcolor=fillcolor),
"translateX":
lambda img, magnitude: img.transform(
img.size,
Image.AFFINE, (1, 0, magnitude * img.size[0] * random.choice(
[-1, 1]), 0, 1, 0),
fillcolor=fillcolor),
"translateY":
lambda img, magnitude: img.transform(
img.size,
Image.AFFINE, (1, 0, 0, 0, 1, magnitude * img.size[1] * random.
choice([-1, 1])),
fillcolor=fillcolor),
"rotate":
lambda img, magnitude: rotate_with_fill(img, magnitude),
"color":
lambda img, magnitude: ImageEnhance.Color(img).enhance(
1 + magnitude * random.choice([-1, 1])),
"posterize":
lambda img, magnitude: ImageOps.posterize(img, magnitude),
"solarize":
lambda img, magnitude: ImageOps.solarize(img, magnitude),
"contrast":
lambda img, magnitude: ImageEnhance.Contrast(img).enhance(
1 + magnitude * random.choice([-1, 1])),
"sharpness":
lambda img, magnitude: ImageEnhance.Sharpness(img).enhance(
1 + magnitude * random.choice([-1, 1])),
"brightness":
lambda img, magnitude: ImageEnhance.Brightness(img).enhance(
1 + magnitude * random.choice([-1, 1])),
"autocontrast":
lambda img, magnitude: ImageOps.autocontrast(img),
"equalize":
lambda img, magnitude: ImageOps.equalize(img),
"invert":
lambda img, magnitude: ImageOps.invert(img)
}
magnitude_1 = max(0, min(9, int(magnitude_factor * magnitude_idx1)))
magnitude_2 = max(0, min(9, int(magnitude_factor * magnitude_idx2)))
self.p1 = p1
self.operation1 = func[operation1]
self.magnitude1 = ranges[operation1][magnitude_1]
self.p2 = p2
self.operation2 = func[operation2]
self.magnitude2 = ranges[operation2][magnitude_2]
for l in range(LAYERS):
l0 = PARTICLES_PER_LAYER * l
l1 = min(PARTICLES_PER_LAYER * (l+1), PARTICLES)
pixelLayer = getPixelData(windData, nx, ny, scaledW, scaledH, particleProperties[l0:l1], POINTS_PER_PARTICLE, VELOCITY_MULTIPLIER, MAGNITUDE_RANGE, LINE_WIDTH_RANGE, ALPHA_RANGE, BRIGHTNESS)
pixelLayers[:,:,l] = pixelLayer
sys.stdout.write('\r')
sys.stdout.write("%s%%" % round(1.0*(l+1)/LAYERS*100,1))
sys.stdout.flush()
pixels = np.amax(pixelLayers, axis=2)
else:
pixels = getPixelData(windData, nx, ny, scaledW, scaledH, particleProperties, POINTS_PER_PARTICLE, VELOCITY_MULTIPLIER, MAGNITUDE_RANGE, LINE_WIDTH_RANGE, ALPHA_RANGE, BRIGHTNESS)
print("Building image...")
# add pixels and invert
im = Image.fromarray(pixels, mode="L")
im = ImageOps.invert(im)
if SMOOTH_FACTOR > 0.0:
im = im.resize((contentWidth, contentHeight), Image.LANCZOS)
if BLUR_RADIUS > 0:
im = im.filter(ImageFilter.GaussianBlur(BLUR_RADIUS))
# crop if necessary
if cropToWidth and cropToHeight:
im = im.crop((roundInt(OFFSETX), roundInt(OFFSETY), roundInt(OFFSETX+cropToWidth), roundInt(OFFSETY+cropToHeight)))
# add margin
base = Image.new('L', (roundInt(WIDTH), roundInt(HEIGHT)), 255)
cw, ch = im.size
base.paste(im, (contentX, contentY, contentX+cw, contentY+ch))
# add label
def invert(img: Image.Image) -> Image.Image:
if not _is_pil_image(img):
raise TypeError(f"img should be PIL Image. Got {type(img)}")
return ImageOps.invert(img)
def test_sanity(self):
ImageOps.autocontrast(hopper("L"))
ImageOps.autocontrast(hopper("RGB"))
ImageOps.autocontrast(hopper("L"), cutoff=10)
ImageOps.autocontrast(hopper("L"), ignore=[0, 255])
ImageOps.autocontrast_preserve(hopper("L"))
ImageOps.autocontrast_preserve(hopper("RGB"))
ImageOps.autocontrast_preserve(hopper("L"), cutoff=10)
ImageOps.autocontrast_preserve(hopper("L"), ignore=[0, 255])
ImageOps.colorize(hopper("L"), (0, 0, 0), (255, 255, 255))
ImageOps.colorize(hopper("L"), "black", "white")
ImageOps.crop(hopper("L"), 1)
ImageOps.crop(hopper("RGB"), 1)
ImageOps.deform(hopper("L"), self.deformer)
ImageOps.deform(hopper("RGB"), self.deformer)
ImageOps.equalize(hopper("L"))
ImageOps.equalize(hopper("RGB"))
ImageOps.expand(hopper("L"), 1)
ImageOps.expand(hopper("RGB"), 1)
ImageOps.expand(hopper("L"), 2, "blue")
ImageOps.expand(hopper("RGB"), 2, "blue")
ImageOps.fit(hopper("L"), (128, 128))
ImageOps.fit(hopper("RGB"), (128, 128))
ImageOps.flip(hopper("L"))
ImageOps.flip(hopper("RGB"))
ImageOps.grayscale(hopper("L"))
ImageOps.grayscale(hopper("RGB"))
ImageOps.invert(hopper("L"))
ImageOps.invert(hopper("RGB"))
ImageOps.mirror(hopper("L"))
ImageOps.mirror(hopper("RGB"))
ImageOps.posterize(hopper("L"), 4)
ImageOps.posterize(hopper("RGB"), 4)
ImageOps.solarize(hopper("L"))
ImageOps.solarize(hopper("RGB"))
