def test_swirl():
image = img_as_float(data.checkerboard())
swirl_params = {'radius': 80, 'rotation': 0, 'order': 2, 'mode': 'reflect'}
swirled = tf.swirl(image, strength=10, **swirl_params)
unswirled = tf.swirl(swirled, strength=-10, **swirl_params)
assert np.mean(np.abs(image - unswirled)) < 0.01
def test_swirl():
image = img_as_float(data.checkerboard())
swirl_params = {'radius': 80, 'rotation': 0, 'order': 2, 'mode': 'reflect'}
with expected_warnings(['Bi-quadratic.*bug']):
swirled = tf.swirl(image, strength=10, **swirl_params)
unswirled = tf.swirl(swirled, strength=-10, **swirl_params)
assert np.mean(np.abs(image - unswirled)) < 0.01
def swirl_images(self, data, strength, radius=100, center=(24, 24)):
if data.ndim == 3:
for i in xrange(data.shape[0]):
img_swirled = st.swirl(data[i], center=center, strength=strength, radius=radius)
data[i] = img_swirled
else:
img_swirled = st.swirl(data, center=center, strength=strength, radius=radius,
rotation=0, order=2)
data = img_swirled
return data
def apply(self, distort_name):
shape = self._image_array.shape
center = np.array(self._image_array.shape)[:2] / 2
warp_args = {'width': shape[1], 'height': shape[0]}
if distort_name == "SWIRL":
radius = max(shape[0], shape[1]) / 2
result = swirl(self._image_array, center=(shape[1]/2, shape[0]/2), strength=5,
radius=radius, order=1, mode='nearest')
elif distort_name == "FISH EYE LIGHT":
warp_args['radius_func'] = lambda radius_d: self.fish_eye(0.3, radius_d)
warp_args['zoom_factor'] = 1.6
result = warp(self._image_array, self.radius_distort, map_args=warp_args, mode='nearest')
elif distort_name == "FISH EYE HEAVY":
warp_args['radius_func'] = lambda radius_d: self.fish_eye(0.6, radius_d)
warp_args['zoom_factor'] = 2.2
result = warp(self._image_array, self.radius_distort, map_args=warp_args, mode='nearest')
elif distort_name == "BULGE":
warp_args['radius_func'] = lambda radius_d: self.bulge(radius_d)
warp_args['zoom_factor'] = 1.45
result = warp(self._image_array, self.radius_distort, map_args=warp_args, mode='nearest')
elif distort_name == "PINCH LIGHT":
warp_args['radius_func'] = lambda radius_d: self.pinch(0.8, radius_d)
warp_args['zoom_factor'] = 1
result = warp(self._image_array, self.radius_distort, map_args=warp_args, mode='nearest')
elif distort_name == "PINCH HEAVY":
warp_args['radius_func'] = lambda radius_d: self.pinch(0.5, radius_d)
warp_args['zoom_factor'] = 1
result = warp(self._image_array, self.radius_distort, map_args=warp_args, mode='nearest')
else:
result = self._image_array
return img_as_ubyte(result)
def __getitem__(self, idx):
global WARP_PROB, IMAGE_SIZE
file_path = self.path + self.files[idx]
img = cv2.imread(file_path)
# Resize image to reduce computation complexity
img = scipy.misc.imresize(img, (IMAGE_SIZE[0],IMAGE_SIZE[1],3))
warped = swirl(img,rotation=0, strength=random.uniform(0.6,1.5), radius=random.randint(300,600), mode = "constant")
img = np.rollaxis(img,2,0)
img = img.astype(float)
warped = np.rollaxis(warped, 2, 0)
warped = warped.astype(float)
# Image normalisation
img = img/255
warp_prob = random.uniform(0,1)
if(warp_prob < WARP_PROB):
return warped, img
else:
return img, img
def swirl(self): ''' implements skimage's swirl function ''' self.img = transform.swirl(self.img, rotation=0, strength=10, radius=1000, order=2) self.refreshimg()
def batch_unswirl(images):
from skimage.transform import swirl
images = np.array(images)
r = images.shape[1] * relative_swirl_radius
# from joblib import Parallel, delayed
# return np.array(Parallel(n_jobs=4)(delayed(swirl)(i, radius=r, **unswirl_args) for i in images))
return np.array([swirl(i, radius=r, **unswirl_args) for i in images])
def lokal_swirl(img_in, hi, wi, chan, n_swirls, radius, strength, positioning="random", directions="random", corr_size=3):
img = img_in.copy()
if not positioning in ["random", "center"]:
raise NotImplementedError("TODO!")
size = corr_size
for i in range(n_swirls):
sign = None
if directions == "random":
sign = np.sign(np.random.rand(1) - 0.5)[0]
elif directions == "left":
sign = -1
else:
sign = 1
xpos, ypos = None, None
if positioning == "random":
xpos = random.randint(0, hi - radius)
ypos = random.randint(0, wi - radius)
elif positioning == "center":
xpos = hi // 2
ypos = wi // 2
center = (xpos,ypos)
img = swirl(img, rotation=0, strength=sign*strength, radius=radius, center=center)
img[0:size] = img_in[0:size]
img[-(size+1):] = img_in[-(size+1):]
img[:,0:size] = img_in[:,0:size]
img[:,-(size+1):] = img_in[:,-(size+1):]
return img
def img_manipulation(img, mode=0):
# img = np.array(img, dtype='int8')
# add manipulation
if mode == 0: # gaussian噪声
return util.random_noise(img, mode='gaussian', mean=0.5, var=0.3)
elif mode == 1: # salt噪声
return util.random_noise(img, mode='salt')
elif mode == 2: # 直方图均衡化
img = np.transpose(img, axes=[2, 0, 1])
img1 = exposure.equalize_hist(img[0])
img2 = exposure.equalize_hist(img[1])
img3 = exposure.equalize_hist(img[2])
img = np.stack([img1, img2, img3], axis=0)
return np.transpose(img, [1, 2, 0])
elif mode == 3: # 改变对比度
return exposure.rescale_intensity(1 - filters.sobel(img))
elif mode == 4: # 改变伽马值
adjusted_gamma_image = exposure.adjust_gamma(img, gamma=0.4, gain=0.9)
return adjusted_gamma_image
elif mode == 5: # 扭曲
img = transform.swirl(img,
center=[100, 100],
rotation=0.3,
strength=10,
radius=120)
return img
elif mode == 6: # 改变尺寸
return transform.resize(img, (img.shape[0] * 1.5, img.shape[1] * 2))
elif mode == 7: # 反相
return util.invert(img)
def tourbillion(path, force) :
if path.endswith(".gif") :
img = Image.open(path)
nbFrames = img.n_frames
try :
duration = img.info['duration']
except :
duration = 40
if ":" in force :
_min=int(force.split(":")[0])
_max=int(force.split(":")[-1])
f=np.linspace(_min,_max,nbFrames)
else :
f=[int(force) for i in range(nbFrames)]
for frame in range(0, nbFrames) :
img.seek(frame)
img.save(str(frame)+".gif")
for frame in range(0, nbFrames) :
image = imread(str(frame)+".gif")
taille = max(image.shape)
image = swirl(image, rotation=0, strength=f[frame], radius=taille)
image = 255 * image
imsave(str(frame)+".gif", image.astype(np.uint8))
images = []
for frame in range(0, nbFrames) :
im = Image.open(str(frame)+".gif")
images.append(im)
images[0].save('anim.gif', save_all=True, append_images=images[1:], optimize=False, duration=duration, loop=0)
while os.path.getsize('anim.gif') > 8e+6 :
gifsize.resize_gif('anim.gif', duration=duration)
return nbFrames
else :
image = imread(path)
taille = max(image.shape)
image = swirl(image, rotation=0, strength=force, radius=taille)
image = 255 * image
imsave('img.png', image.astype(np.uint8))
return
def get_image11():
#noisy = denoise_tv_chambolle(io.imread(request.args.get('link')), weight=float(request.args.get('weight')), multichannel=True)
swirled = swirl(io.imread(request.args.get('link')), rotation=0, strength=10, radius=500, order=4)
skimage.io.imsave('test_noise.png', swirled)
filename = 'test_noise.png'
return send_file(filename, mimetype='image/gif')
def swirl(self):
filteredImage = tr.swirl(self.rawImage, (50, 50),
radius=150,
rotation=45)
self.showImage(filteredImage)
self.rawImage = filteredImage
print("Swirl")
def test_swirl():
image = img_as_float(data.checkerboard())
swirl_params = {'radius': 80, 'rotation': 0, 'order': 2, 'mode': 'reflect'}
with expected_warnings(['Bi-quadratic.*bug']):
swirled = tf.swirl(image, strength=10, **swirl_params)
unswirled = tf.swirl(swirled, strength=-10, **swirl_params)
assert np.mean(np.abs(image - unswirled)) < 0.01
swirl_params.pop('mode')
with expected_warnings(['Bi-quadratic.*bug']):
swirled = tf.swirl(image, strength=10, **swirl_params)
unswirled = tf.swirl(swirled, strength=-10, **swirl_params)
assert np.mean(np.abs(image[1:-1, 1:-1] - unswirled[1:-1, 1:-1])) < 0.01
def twist(T):
T = T.copy()
T = img_as_float(T)
height = T.shape[0]
width = T.shape[1]
full = swirl(T, center=(height/2, width/2),
rotation=0, strength=10, radius=max(height,width),
mode='wrap')
return img_as_ubyte(full)
def augmentation(dat):
num_cases = np.shape(dat)[0]
for i in range(num_cases):
swirl_stength = get_random_in_range(-.4, .4)
dat[i] = transform.swirl(dat[i], strength=swirl_stength)
rotation_angle = get_random_in_range(-30, 30)
dat[i] = transform.rotate(dat[i], angle=rotation_angle)
if np.random.random() > .5:
dat[i] = np.flipud(dat[i])
return dat
def swirl_image(image, prop=.5):
if prop:
if coin_flip(prop):
strength = np.random.uniform(0, 3)
radius = np.random.uniform(0, 250)
else:
return image
else:
strength = 3
radius = 250
return swirl(image, strength=strength, radius=radius)
def swirl_image(image, strength=10):
image = np.array(image)
w, h = image.shape[:2]
sw = swirl(image, rotation=0, strength=strength, radius=max(w, h))
with warnings.catch_warnings():
warnings.simplefilter('ignore')
sw = img_as_ubyte(sw)
pil_img = Image.fromarray(sw)
return pil_img
def __call__(self, image):
if self.level == 0: return image
pil = ToPILImage()(image)
swirled = transform.swirl(
np.array(pil),
center=(14, 14),
strength=10.0 * self.level,
radius=28,
rotation=0.0,
)
return ToTensor()(PIL.Image.fromarray(swirled))
def process(self, a: np.ndarray) -> np.ndarray:
""" Based on code taken from:
https://stackoverflow.com/questions/30448045
"""
# Determine the location of the center of the twirl effect.
center = [n / 2 + o for n, o in zip(a.shape[1:3], self.offset[1:3])]
# Run the swirl filter.
for i in range(a.shape[Z]):
a[i] = swirl(a[i], center[::-1], self.strength, self.radius)
return a
def swirl():
image = read_image_return_scikit()
swirled = transform.swirl(image, rotation=0, strength=10, radius=500)
fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(8, 3),
sharex=True, sharey=True)
setGlobalVar(swirled)
ax0.imshow(image, cmap=plt.cm.gray)
ax0.axis('off')
ax1.imshow(swirled, cmap=plt.cm.gray)
ax1.axis('off')
plt.show()
def swirled(image, map_args={}, output_shape=None, order=1, mode='constant', cval=0.0, clip=True, preserve_range=False):
#image = imageGlobal
swirled = swirl(image, rotation=0, strength=10, radius=120)
fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(8, 3),
sharex=True, sharey=True)
ax0.imshow(image, cmap=plt.cm.gray)
ax0.axis('off')
ax1.imshow(swirled, cmap=plt.cm.gray)
ax1.axis('off')
plt.show()
def augment_data(self, image, target):
images = [
image.ravel(),
]
targets = [
target,
]
image_modifiers = (lambda x: rotate(x, 90), lambda x: rotate(x, 180),
lambda x: rotate(x, 270), lambda x: rotate(x, 45),
lambda x: swirl(x))
for i in xrange(self.augmentation):
img = image_modifiers[i](image)
images.append(img.ravel())
targets.append(target)
return images, targets
def swirled(img):
swirled = transform.swirl(img, rotation=0, strength=20, radius=240)
fig, (ax0, ax1) = plt.subplots(nrows=1,
ncols=2,
figsize=(8, 3),
sharex=True,
sharey=True)
ax0.imshow(img, cmap=plt.cm.gray)
ax0.axis('off')
ax1.imshow(swirled, cmap=plt.cm.gray)
ax1.axis('off')
plt.show()
def augment_data(self, image, target):
images = [image.ravel(), ]
targets = [target, ]
image_modifiers = (
lambda x: rotate(x, 90),
lambda x: rotate(x, 180),
lambda x: rotate(x, 270),
lambda x: rotate(x, 45),
lambda x: swirl(x)
)
for i in xrange(self.augmentation):
img = image_modifiers[i](image)
images.append(img.ravel())
targets.append(target)
return images, targets
async def warp(self, ctx: commands.Context, help="", amount="0"):
img, amount, successful = await self.getImage(ctx, "warp", help,
amount)
if not successful:
if (amount == 0): #Error command
em = discord.Embed()
em.title = f'Error when running command'
em.description = f"Error: {img}."
em.color = 0xEE0000
await ctx.send(embed=em)
return
else: #Help Command
em = discord.Embed()
em.title = f'Usage: /warp [img|imgURL]'
em.description = f'Randomly warps the image attached, url in the message, or the image attached before the command'
em.add_field(
name="Examples",
value=
"/warp https://imgur.com/a/wUChw7w | /warp (imageAttachment)",
inline=False)
em.color = 0x22BBFF
await ctx.send(embed=em)
return ()
await ctx.send("Processing... (This may take a while)")
toDelete = await self.getMessages(ctx, 1)
# Processing
from skimage.transform import swirl #import works here but not at the beginning
img = np.array(img) #convert PIl image to Numpy
# Swirl
width, height = img.shape[:2]
for x in range(5):
img = swirl(img,
strength=((random.random() + 1) * -1**x),
radius=width / 1.4,
center=(random.randint(int(width / 4),
int(width - width / 4)),
random.randint(int(height / 4),
int(height - height / 4))))
img = (img * 255).astype('uint8')
#convert numpy array to Pillow img
im_pil = Image.fromarray(img)
# Convert to an attachable Discord Format
arr = io.BytesIO() #convert to bytes array
im_pil.save(arr, format='PNG')
arr.seek(0) #seek back to beginning of file
# Send
await ctx.send(file=discord.File(arr, 'warp.png'))
await ctx.channel.delete_messages(toDelete)
def swirled_with_checkerboard():
image = data.checkerboard()
swirled = transform.swirl(image, rotation=0, strength=10, radius=120)
fig, (ax0, ax1) = plt.subplots(nrows=1,
ncols=2,
figsize=(8, 3),
sharex=True,
sharey=True)
ax0.imshow(image, cmap=plt.cm.gray)
ax0.axis('off')
ax1.imshow(swirled, cmap=plt.cm.gray)
ax1.axis('off')
plt.show()
def swirl_imgs(self, set=[], labels=[]):
"""swirls each image 3 times with a random strength between 0.5 and 2.0"""
print('Swirling images...')
out_set = []
out_labels = []
for i in range(0, len(set)):
out_set.append(set[i])
out_labels.append(labels[i])
j = 0
while j < 3:
j += 1
str = np.random.uniform(0.5, 2.0)
img_swirled = swirl(set[i], strength=str)
out_set.append(img_swirled)
out_labels.append(labels[i])
return np.asarray(out_set), np.asarray(out_labels)
def swirl_rotate_augmentor(dataset):
'''
applies random swirl, followed by random rotation
inputs:
1. (n x m) n samples of images, each with m features
output:
1. (n x m) n samples of images, each with m features, after applying the augmentation
'''
img_width = int(math.sqrt(dataset.shape[-1]))
dataset = dataset.reshape(-1, img_width, img_width)
n_samples = dataset.shape[0]
augmented_dataset = np.zeros((n_samples, img_width, img_width))
for i in range(n_samples):
augmented_dataset[i] = transform.swirl(
dataset[i, :, :],
strength=random.uniform(-0.4, 0.4),
rotation=random.uniform(-0.4, 0.4))
return flatten_dataset(augmented_dataset)
async def swirl(self, ctx: commands.Context, help="", amount="10"):
img, amount, successful = await self.getImage(ctx, "radial", help,
amount)
if not successful:
if (amount == 0): #Error command
em = discord.Embed()
em.title = f'Error when running command'
em.description = f"Error: {img}."
em.color = 0xEE0000
await ctx.send(embed=em)
return
else: #Help Command
em = discord.Embed()
em.title = f'Usage: /swirl [img|imgURL] [amount]'
em.description = f'Swirls the image attached, url in the message, or the image attached before the command by [amount] from -100 to 100'
em.add_field(
name="Examples",
value=
"/swirl https://imgur.com/a/wUChw7w | /swirl (imageAttachment) 30",
inline=False)
em.color = 0x22BBFF
await ctx.send(embed=em)
return ()
await ctx.send("Processing... (This may take a while)")
toDelete = await self.getMessages(ctx, 1)
# Processing
from skimage.transform import swirl #import works here but not at the beginning
w, h = img.size
img = np.array(img) #convert PIl image to Numpy
# Swirl
img = swirl(img, strength=amount, radius=w / 1.3)
img = (img * 255).astype('uint8')
#convert numpy array to Pillow img
im_pil = Image.fromarray(img)
# Convert to an attachable Discord Format
arr = io.BytesIO() #convert to bytes array
im_pil.save(arr, format='PNG')
arr.seek(0) #seek back to beginning of file
# Send
await ctx.send(file=discord.File(arr, 'swirl.png'))
await ctx.channel.delete_messages(toDelete)
def apply_filter(self,img):
if self.filter == 0:
return img
elif self.filter == 1:
img = cv2.Laplacian(img,cv2.CV_8UC3)
return img
elif self.filter == 2:
img = np.array(Image.fromarray(img).filter(ImageFilter.GaussianBlur()))
return img
elif self.filter == 3:
if self.sin_transform is None:
h, w = img.shape[:2]
flow = np.zeros((h,w,2),np.float32)
flow[:,:,0] += np.arange(w) + np.sin(np.arange(w)/(2*np.pi*5))*10
flow[:,:,1] += np.arange(h)[:,np.newaxis]
self.sin_transform = flow
res = cv2.remap(img, self.sin_transform, None, cv2.INTER_LINEAR)
return res
else:
out1 = transform.swirl(img, rotation=0, strength=5, radius=200)
return out1
def apply_filter(self):
self.read_image()
if self.filter_type == 'edge':
self.filtered_image = roberts(self.image)
elif self.filter_type == 'red_tint':
rgb_image = color.gray2rgb(self.image)
red_multiplier = [1, 0, 0]
self.filtered_image = red_multiplier * rgb_image
elif self.filter_type == 'swirl':
self.filtered_image = swirl(self.image,
rotation=0,
strength=10,
radius=120)
else:
return None
self.save_image()
return self.result_file_path
def experiment2():
import matplotlib.pyplot as plt
from skimage import data
from skimage.transform import swirl
image = data.checkerboard()
swirled = swirl(image, rotation=0, strength=0.5, radius=180)
fig, (ax0, ax1) = plt.subplots(nrows=1,
ncols=2,
figsize=(8, 3),
sharex=True,
sharey=True,
subplot_kw={'adjustable': 'box-forced'})
ax0.imshow(image, cmap=plt.cm.gray, interpolation='none')
ax0.axis('off')
ax1.imshow(swirled, cmap=plt.cm.gray, interpolation='none')
ax1.axis('off')
plt.show()
def random_transform(self, image):
rotated = tf.rotate(image, self.angle)
if random.random() < 0.5:
rotated = horizontal_flip(rotated)
if random.random() < 0.2: # 20%
rotated = vertical_flip(rotated)
# Create Afine transform
afine_tf = tf.AffineTransform(scale=self.scale,
shear=self.shear,
translation=self.translate)
# Apply transform to image data
modified = tf.warp(rotated, afine_tf)
if random.random() < 0.1: # 20%
modified = tf.swirl(modified,
rotation=0,
strength=self.swirl,
radius=image.shape[1] / 2)
return tf.resize(modified, [self.reW, self.reH])
def swirl_(im, strength, radius):
return np.array([swirl(im_slice, rotation=0, strength=strength, radius=radius) for im_slice in im], dtype='float32')
def buttonSave(arg):
copy = arg[1]
imageTemp = arg[2]
filterTry = filterVar.get()
if (filterTry == 1):
copy = cv2.GaussianBlur(copy, (5, 5), 0)
elif (filterTry == 2):
copy = cv2.Canny(copy, 100, 150)
elif (filterTry == 3):
copy = filters.roberts(imageTemp)
elif (filterTry == 4):
copy = filters.sato(imageTemp)
elif (filterTry == 5):
copy = filters.scharr(imageTemp)
elif (filterTry == 6):
copy = filters.sobel(imageTemp)
elif (filterTry == 7):
copy = filters.unsharp_mask(copy, radius=30, amount=3)
elif (filterTry == 8):
#copy = filters.median(imageTemp, disk(5))
b, g, r = cv2.split(copy)
b = filters.median(b, disk(5))
g = filters.median(g, disk(5))
r = filters.median(r, disk(5))
copy = cv2.merge((b, g, r))
elif (filterTry == 9):
copy = filters.prewitt(imageTemp)
elif (filterTry == 10):
copy = filters.rank.modal(imageTemp, disk(5))
flag = 0
if (np.ndim(copy) == 2):
flag = 0
else:
flag = 1
if (hEsitleme.get() or hGrafik.get()):
if (flag):
copy = cv2.cvtColor(copy, cv2.COLOR_BGR2GRAY)
if (hGrafik.get()):
plt.hist(copy.ravel(), 256, [0, 256])
plt.show()
if (hEsitleme.get()):
copy = cv2.equalizeHist(copy)
if (uzaysalVars[0].get()):
reScaleRatio = float(uzaysalVarsInputs[0].get())
if (np.ndim(copy) == 3):
b, g, r = cv2.split(copy)
b = transform.rescale(b, reScaleRatio)
g = transform.rescale(g, reScaleRatio)
r = transform.rescale(r, reScaleRatio)
copy = cv2.merge((b, g, r))
else:
copy = transform.rescale(copy, reScaleRatio)
if (uzaysalVars[1].get()):
resizeY = float(uzaysalVarsInputs[1].get())
resizeX = float(uzaysalVarsInputs[2].get())
if (np.ndim(copy) == 3):
b, g, r = cv2.split(copy)
b = transform.resize(
b, (b.shape[0] // resizeX, b.shape[1] // resizeY),
anti_aliasing=True)
g = transform.resize(
g, (g.shape[0] // resizeX, g.shape[1] // resizeY),
anti_aliasing=True)
r = transform.resize(
r, (r.shape[0] // resizeX, r.shape[1] // resizeY),
anti_aliasing=True)
copy = cv2.merge((b, g, r))
else:
copy = transform.resize(
copy, (copy.shape[0] // resizeX, copy.shape[1] // resizeY),
anti_aliasing=True)
if (uzaysalVars[2].get()):
copy = transform.swirl(copy, rotation=0, strength=10, radius=120)
if (uzaysalVars[3].get()):
copy = transform.rotate(copy,
int(uzaysalVarsInputs[3].get()),
resize=True)
if (uzaysalVars[4].get()):
copy = copy[:, ::-1]
if (yogunlukVars[0].get() or yogunlukVars[1].get()):
if (yogunlukVars[0].get()):
startINX = int(yogunlukVars[2].get())
finishINX = int(yogunlukVars[3].get())
copy = exposure.rescale_intensity(copy,
in_range=(startINX,
finishINX))
if (yogunlukVars[1].get()):
startOUTX = int(yogunlukVars[4].get())
finishOUTX = int(yogunlukVars[5].get())
copy = exposure.rescale_intensity(copy,
out_range=(startOUTX,
finishOUTX))
morfoTry = morfVar.get()
morfoGirisN = 0
if (np.ndim(copy) == 3):
morfoGirisN = 1
if (morfoTry == 1):
if (morfoGirisN):
b, g, r = cv2.split(copy)
b = morphology.area_closing(b, 128, 9)
g = morphology.area_closing(g, 128, 9)
r = morphology.area_closing(r, 128, 9)
copy = cv2.merge((b, g, r))
else:
copy = morphology.area_closing(copy)
elif (morfoTry == 2):
if (morfoGirisN):
b, g, r = cv2.split(copy)
b = morphology.area_opening(b, 128, 9)
g = morphology.area_opening(g, 128, 9)
r = morphology.area_opening(r, 128, 9)
copy = cv2.merge((b, g, r))
else:
copy = morphology.area_opening(copy)
elif (morfoTry == 3):
if (morfoGirisN):
b, g, r = cv2.split(copy)
b = morphology.erosion(b, disk(6))
g = morphology.erosion(g, disk(6))
r = morphology.erosion(r, disk(6))
copy = cv2.merge((b, g, r))
else:
copy = morphology.erosion(copy, disk(6))
elif (morfoTry == 4):
if (morfoGirisN):
b, g, r = cv2.split(copy)
b = morphology.dilation(b, disk(6))
g = morphology.dilation(g, disk(6))
r = morphology.dilation(r, disk(6))
copy = cv2.merge((b, g, r))
else:
copy = morphology.dilation(copy, disk(6))
elif (morfoTry == 5):
if (morfoGirisN):
b, g, r = cv2.split(copy)
b = morphology.opening(b, disk(6))
g = morphology.opening(g, disk(6))
r = morphology.opening(r, disk(6))
copy = cv2.merge((b, g, r))
else:
copy = morphology.opening(copy, disk(6))
elif (morfoTry == 6):
if (morfoGirisN):
b, g, r = cv2.split(copy)
b = morphology.closing(b, disk(6))
g = morphology.opening(g, disk(6))
r = morphology.opening(r, disk(6))
copy = cv2.merge((b, g, r))
else:
copy = morphology.opening(copy, disk(6))
elif (morfoTry == 7):
if (morfoGirisN):
b, g, r = cv2.split(copy)
b = morphology.white_tophat(b, disk(6))
g = morphology.white_tophat(g, disk(6))
r = morphology.white_tophat(r, disk(6))
copy = cv2.merge((b, g, r))
else:
copy = morphology.white_tophat(copy, disk(6))
elif (morfoTry == 8):
if (morfoGirisN):
b, g, r = cv2.split(copy)
b = morphology.black_tophat(b, disk(6))
g = morphology.black_tophat(g, disk(6))
r = morphology.black_tophat(r, disk(6))
copy = cv2.merge((b, g, r))
else:
copy = morphology.black_tophat(copy, disk(6))
elif (morfoTry == 10):
if (morfoGirisN):
copy = cv2.cvtColor(copy, cv2.COLOR_BGR2GRAY)
copy = exposure.rescale_intensity(copy)
local_maxima = extrema.local_maxima(copy)
label_maxima = measure.label(local_maxima)
copy = color.label2rgb(label_maxima,
copy,
alpha=0.7,
bg_label=0,
bg_color=None,
colors=[(1, 0, 0)])
elif (morfoTry == 9):
if (morfoGirisN):
copy = cv2.cvtColor(copy, cv2.COLOR_BGR2GRAY)
copy = exposure.rescale_intensity(copy)
h = 0.05
h_maxima = extrema.h_maxima(copy, h)
label_h_maxima = measure.label(h_maxima)
copy = color.label2rgb(label_h_maxima,
copy,
alpha=0.7,
bg_label=0,
bg_color=None,
colors=[(1, 0, 0)])
arg[1] = copy
arg[2] = imageTemp
cv2.imshow("org", copy)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""
def swirl_(im, strength, radius):
from skimage.transform import swirl
return [swirl(im_slice, rotation=0, strength=strength, radius=radius) for im_slice in im]
async def fry(self, ctx: commands.Context, help="", amount="20"):
img, amount, successful = await self.getImage(ctx, "fry", help, amount)
if not successful:
if (amount == 0): #Error command
em = discord.Embed()
em.title = f'Error when running command'
em.description = f"Error: {img}."
em.color = 0xEE0000
await ctx.send(embed=em)
return
else: #Help Command
em = discord.Embed()
em.title = f'Usage: /fry [img|imgURL] [gamma]'
em.description = f'Deep-fries the image attached, url in the message, or the image attached before the command. Change gamma based on how dark the image is. Default gamma value is 20, range 1-500. Saturation default 3, range: 1-50'
em.add_field(name="Aliases", value="/deepfry", inline=False)
em.add_field(
name="Examples",
value=
"/fry https://imgur.com/a/wUChw7w | /deepfry (imageAttachment)",
inline=False)
em.color = 0x22BBFF
await ctx.send(embed=em)
return
await ctx.send("Processing... (This may take a while)")
toDelete = await self.getMessages(ctx, 1)
# Processing
img = np.array(
img) #convert PIL image to Numpy (CV2 works with numpy BRG arrays)
# Convert RGB to BGR
img = cv.cvtColor(np.array(img), cv.COLOR_RGB2BGR)
width, height = img.shape[:2]
#Increase brightness
value = int(
amount
) * 6 #default multiplier, change depending on dark/light images
hsv = cv.cvtColor(img, cv.COLOR_BGR2HSV)
h, s, v = cv.split(hsv)
lim = 255 - value
v[v > lim] = 255
v[v <= lim] += value
final_hsv = cv.merge((h, s, v))
img = cv.cvtColor(final_hsv, cv.COLOR_HSV2BGR)
#Sharpen
kernel = np.array(
[[-1, -1, -1], [-1, 9, -1],
[-1, -1, -1]]) #how to modify a pixel based on surrounding pixels
img = cv.filter2D(img, -1, kernel)
#Change gamma and make it crusty
gamma = int(amount)
table = np.array([((i / 255.0)**gamma) * 255
for i in np.arange(0, 256)]).astype("uint8")
img = cv.LUT(img, table)
#Now deform the image a bit
from skimage.transform import swirl #import works here but if at the beginning, breaks the code for some reason
#swirl the image at random places
for x in range(8):
img = swirl(img,
strength=((random.random() + 1) * -1**x),
radius=width / 1.4,
center=(random.randint(int(width / 4),
int(width - width / 4)),
random.randint(int(height / 4),
int(height - height / 4))))
img = (img * 255).astype('uint8')
#convert CV2 Numpy array to PIL
img = cv.cvtColor(img, cv.COLOR_BGR2RGB) #Change CV2 BGR to RGB
im_pil = Image.fromarray(img) #Convert to PIL
# Increase saturation
from PIL import ImageEnhance
from PIL import ImageFilter
converter = PIL.ImageEnhance.Color(im_pil)
im_pil = converter.enhance(0.4)
im_pil = im_pil.filter(
ImageFilter.EDGE_ENHANCE_MORE) #add edge detection
# Convert to an attachable Discord Format
arr = io.BytesIO() #convert to bytes array
im_pil.save(arr, format='PNG')
arr.seek(0) #seek back to beginning of file
# Send
await ctx.send(file=discord.File(arr, 'fry.png'))
await ctx.channel.delete_messages(toDelete)
def do_swirl(self): """ swirl image """ swirled = swirl(self.imgarray, rotation=0, strength=10, radius=150, order=2) self.imgarray = swirled
\phi = \mathtt{rotation}
s = \mathtt{strength}
\\theta' = \phi + s \, e^{-\\rho / r + \\theta}
where ``strength`` is a parameter for the amount of swirl, ``radius`` indicates
the swirl extent in pixels, and ``rotation`` adds a rotation angle. The
transformation of ``radius`` into :math:`r` is to ensure that the
transformation decays to :math:`\\approx 1/1000^{\mathsf{th}}` within the
specified radius.
"""
import matplotlib.pyplot as plt
from skimage import data
from skimage.transform import swirl
image = data.checkerboard()
swirled = swirl(image, rotation=0, strength=10, radius=120, order=2)
fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(8, 3), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
ax0.imshow(image, cmap=plt.cm.gray, interpolation='none')
ax0.axis('off')
ax1.imshow(swirled, cmap=plt.cm.gray, interpolation='none')
ax1.axis('off')
plt.show()
s = \mathtt{strength}
\\theta' = \phi + s \, e^{-\\rho / r + \\theta}
where ``strength`` is a parameter for the amount of swirl, ``radius`` indicates
the swirl extent in pixels, and ``rotation`` adds a rotation angle. The
transformation of ``radius`` into :math:`r` is to ensure that the
transformation decays to :math:`\\approx 1/1000^{\mathsf{th}}` within the
specified radius.
"""
import matplotlib.pyplot as plt
from skimage import data
from skimage.transform import swirl
image = data.checkerboard()
swirled = swirl(image, rotation=0, strength=-5, radius=120)
fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(8, 3),
sharex=True, sharey=True,
subplot_kw={'adjustable':'box-forced'})
ax0.imshow(image, cmap=plt.cm.gray, interpolation='none')
ax0.axis('off')
ax1.imshow(swirled, cmap=plt.cm.gray, interpolation='none')
ax1.axis('off')
plt.show()
