def screenshotter():
im = pyautogui.screenshot(region=(350, 180, 625, 370))
im_left = im.crop((10, 210, 75, 350))
im_right = im.crop((505, 200, 565, 315))
im_left = im_left.rotate(-9.8)
im_right = im_right.rotate(10)
im_left_blurred = im_left.filter(filter=ImageFilter.BoxBlur(1))
im_right_blurred = im_right.filter(filter=ImageFilter.BoxBlur(1))
im.paste(im_left_blurred, (10, 210))
im.paste(im_right_blurred, (505, 200))
return im
def visualize(self, layer, filt, lr=1e-2, opt_steps=36):
sz = self.size
img = Image.fromarray(
np.uint8(np.random.uniform(150, 180, (sz, sz, 3))))
activations = SaveFeatures(list(self.target.children())[layer])
gaussian_filter = get_gaussian_kernel()
self.model.zero_grad()
for outer in tqdm(range(self.upscaling_steps), leave=False):
img_var = torch.unsqueeze(ToTensor()(img),
0).cuda(ORDINAL).requires_grad_(True)
img_var.requires_grad_(True).cuda(ORDINAL)
optimizer = torch.optim.Adam([img_var], lr=lr, weight_decay=1e-6)
pbar = tqdm(range(opt_steps), leave=False)
for n in pbar:
optimizer.zero_grad()
self.model(img_var)
loss = -activations.features[
0, filt].mean() + 0.00 * torch.norm(img_var)
loss.backward()
pbar.set_description(f'Loss: {loss.item()}')
optimizer.step()
sz = int(sz * self.upscaling_factor)
img = ToPILImage()(img_var.squeeze(0))
if outer != self.upscaling_steps:
img = img.resize((sz, sz))
img = img.filter(ImageFilter.BoxBlur(2))
self.output = img.copy()
self.save(layer, filt)
activations.close()
async def box_blur(client, message):
try:
userid = str(message.chat.id)
if not os.path.isdir(f"./DOWNLOADS/{userid}"):
os.makedirs(f"./DOWNLOADS/{userid}")
download_location = "./DOWNLOADS" + "/" + userid + "/" + userid + ".jpg"
if not message.reply_to_message.empty:
msg = await message.reply_to_message.reply_text(
"Downloading image", quote=True)
a = await client.download_media(message=message.reply_to_message,
file_name=download_location)
await msg.edit("Processing Image...")
im1 = Image.open(a)
im2 = im1.filter(ImageFilter.BoxBlur(0))
edit_img_loc = "./DOWNLOADS" + "/" + userid + "/" + "box_blur.jpg"
im2.save(edit_img_loc)
await message.reply_chat_action("upload_photo")
await message.reply_to_message.reply_photo(edit_img_loc,
quote=True)
await msg.delete()
else:
await message.reply_text("Why did you delete that??")
try:
shutil.rmtree(f"./DOWNLOADS/{userid}")
except Exception:
pass
except Exception as e:
print("box_blur-error - " + str(e))
if "USER_IS_BLOCKED" in str(e):
return
try:
await message.reply_to_message.reply_text("Something went wrong!",
quote=True)
except Exception:
return
def transform(self, frame):
frame = frame.resize((self._width, self._height), Image.BILINEAR)
frame = frame.rotate(self._rotation, Image.BILINEAR)
if self._blur_in_frames > 0 and self._count_frame_total < self._blur_in_frames and not self._deinit_started:
strength = int((self._blur_in_frames - self._count_frame_total) *
10 / self._blur_in_frames)
frame = frame.filter(ImageFilter.BoxBlur(strength))
if self.blur_out_frames > 0 and self._deinit_started:
strength = 10 - int(
(self._blur_out_frames - self._count_frame_total) * 10 /
self.blur_out_frames)
frame = frame.filter(ImageFilter.BoxBlur(strength))
return frame
def save(self, *args, **kwargs):
super().save(*args, **kwargs)
# Load Image from File
img_read = storage.open(self.img.name, 'rb')
img = Image.open(img_read)
# Convert Image to Array
pixels = asarray(img)
# Create the Detector Using the Default Weights
detector = MTCNN()
# Detect Faces in the Image
results = detector.detect_faces(pixels)
numfaces = len(results)
# Create For Loop to Blur All Faces in Photo
for i in range(numfaces):
# Extract the Bounding Box from the First Face
x1, y1, width, height = results[i]['box']
# Negative Coordinates Bug Fix
x1, y1 = abs(x1), abs(y1)
x2, y2 = x1 + width, y1 + height
# Extract the Face
face = pixels[y1:y2, x1:x2]
# Blur Out Face Pixels
faceimage = Image.fromarray(face)
faceimage = faceimage.filter(ImageFilter.BoxBlur(radius=100))
# Reattach Blurred Faces to Original Photo
img = img.copy()
img.paste(faceimage, (x1, y1))
# Show the New Image
# image.show()
# Save the New Image
in_mem_file = io.BytesIO()
img.save(in_mem_file, format='JPEG')
def __getitem__(self, idx):
img_number = np.random.randint(0, high=len(self.imgs))
x_coord = np.random.randint(0, high=self.x_dim - self.patch_size)
y_coord = np.random.randint(0, high=self.y_dim - self.patch_size)
if self.num_modalities > 1:
seed = np.random.randint(2147483647)
I_list = []
for m in self.modalities:
random.seed(seed)
I = Image.open(
os.path.join(self.data_dir, m, self.imgs[img_number]))
I_list.append(
self.transformation(
I.crop((x_coord, y_coord, x_coord + self.patch_size,
y_coord + self.patch_size))))
patch = torch.cat(I_list, dim=0)
else:
I = Image.open(os.path.join(
self.data_dir, self.imgs[img_number])).convert(mode='L')
if self.binary_data:
I = I.filter(ImageFilter.BoxBlur(1))
patch = self.transformation(
I.crop((x_coord, y_coord, x_coord + self.patch_size,
y_coord + self.patch_size)))
if self.binary_data: # clamp values away from boundaries if binarized data
patch = torch.clamp(patch, -0.35, 0.25)
return patch, torch.ones((1))
def upload_image_swarm(file: FileStorage, username: str,
is_public) -> (str, str):
image_format = DICTIONARY_FORMAT[secure_filename(
file.filename).split('.')[-1].lower()]
url = SWARM_URL_NODE
if is_public:
headers = {"content-type": f"image/{image_format}"}
else:
headers = {
"content-type": f"image/{image_format}",
"Swarm-Encrypt": "true"
}
result = requests.post(url, data=file, headers=headers)
# swarm_hash = json.loads(result.content.decode('utf8'))["reference"]
swarm_hash = result.content.decode('utf8')
image = Image.open(file)
output = io.BytesIO()
image.save(output, format=image_format)
hex_data = output.getvalue()
blob_client = BlobClient.from_connection_string(
BLOB_CONNECTION_STRING, IMAGES_CONTAINER,
f"{username.lower()}/{swarm_hash}.{image_format}")
blob_client.upload_blob(hex_data, overwrite=True)
blurry_image = Image.open(file).filter(ImageFilter.BoxBlur(30))
blurry_output = io.BytesIO()
blurry_image.save(blurry_output, format=image_format)
blurry_hex_data = blurry_output.getvalue()
blurry_blob_client = BlobClient.from_connection_string(
BLOB_CONNECTION_STRING, BLURRY_IMAGES_CONTAINER,
f"{username.lower()}/{swarm_hash}.{image_format}")
blurry_blob_client.upload_blob(blurry_hex_data, overwrite=True)
return swarm_hash
def box_blur(image, *params):
global box_blur_radius
if (not box_blur_radius):
box_blur_radius = 0
box_blur_radius += 2
print("From box blur")
return image.filter(ImageFilter.BoxBlur(box_blur_radius))
def blurFaces(self):
for top, right, bottom, left in self.faceLocations:
faceImage = self.image[top:bottom, left:right]
faceImage = Image.fromarray(faceImage)
faceImage = faceImage.filter(ImageFilter.BoxBlur(23))
self.image[top:bottom, left:right] = faceImage
def edit_image(img, width, height):
x, y = img.size
temp = img.resize((width, height), Image.ANTIALIAS)
temp = temp.filter(ImageFilter.BoxBlur(9))
enhancer = ImageEnhance.Color(temp)
temp = enhancer.enhance(.3)
print('baground size : ', temp.size)
print('required size : ', width, height)
w_dif = x - width
h_dif = y - height
print('width diff : ', x - width, 'height diff : ', y - height)
temp2 = temp
if w_dif < h_dif:
new_width = int(x / (y / height))
print('new_width : ', new_width)
temp2 = img.resize((new_width, height), Image.ANTIALIAS)
else:
new_height = int(y / (x / width))
print('new_height : ', new_height)
temp2 = img.resize((width, new_height), Image.ANTIALIAS)
#print('temp = ',temp.size)
#print('temp2 = ',temp2.size)
img_w, img_h = temp2.size
print('after editing size of image : ', temp2.size)
offset = ((width - img_w) // 2, (height - img_h) // 2)
temp.paste(temp2, offset)
return temp
def create_classifications(s3, folder, image_name, polygons, border, inside,
outside, window_width, window_height):
im = Image.open(folder + "/" + image_name)
im = im.filter(ImageFilter.BoxBlur(1))
px = im.load()
width, height = im.size
dim = len(px[0, 0])
assert (dim == 3)
increment = 5 # Don't need to use every pixel
for i in range(0, height * width, increment):
y = math.floor(i / width)
x = i % width
[r, g, b] = px[x, y]
window = create_window(px, x, y, window_width, window_height, width,
height)
if len(polygons) == 0:
write(outside, window, OUTSIDE)
else:
found = False
for polygon in polygons:
if on_border(polygon, x, y):
write(border, window, BORDER)
found = True
break
elif in_polygon(polygon, x, y):
write(inside, window, INSIDE)
found = True
if not found:
write(outside, window, OUTSIDE)
def test_sanity(self):
def filter(filter):
for mode in ["L", "RGB", "CMYK"]:
im = hopper(mode)
out = im.filter(filter)
self.assertEqual(out.mode, im.mode)
self.assertEqual(out.size, im.size)
filter(ImageFilter.BLUR)
filter(ImageFilter.CONTOUR)
filter(ImageFilter.DETAIL)
filter(ImageFilter.EDGE_ENHANCE)
filter(ImageFilter.EDGE_ENHANCE_MORE)
filter(ImageFilter.EMBOSS)
filter(ImageFilter.FIND_EDGES)
filter(ImageFilter.SMOOTH)
filter(ImageFilter.SMOOTH_MORE)
filter(ImageFilter.SHARPEN)
filter(ImageFilter.MaxFilter)
filter(ImageFilter.MedianFilter)
filter(ImageFilter.MinFilter)
filter(ImageFilter.ModeFilter)
filter(ImageFilter.GaussianBlur)
filter(ImageFilter.GaussianBlur(5))
filter(ImageFilter.BoxBlur(5))
filter(ImageFilter.UnsharpMask)
filter(ImageFilter.UnsharpMask(10))
self.assertRaises(TypeError, filter, "hello")
def add_random_alpha_variation(self, image: Image.Image) -> Image.Image:
"""
Randomly changes the alpha value of the image by applying a blur and max filter to a randomized array
and setting the result array as new alpha channel
# Arguments
image: Image. the image to augment
# Returns
Image. the augmented image
"""
if image.mode == "L": # skip mask images
return image
img_array = np.array(image.convert('RGBA'))
rand_alpha_array = np.random.randint(0, 255, img_array.shape[:2])
rand_alpha_img = Image.fromarray(rand_alpha_array, mode='L')
rand_alpha_img = rand_alpha_img.filter(ImageFilter.MaxFilter(self.max_filter_size))\
.filter(ImageFilter.BoxBlur(self.blur_size))
new_alpha_array = np.rint(img_array[:, :, 3] *
(np.asarray(rand_alpha_img) / 255))
img_array[:, :, 3] = new_alpha_array
return Image.fromarray(img_array, mode='RGBA')
def test_sanity():
def apply_filter(filter_to_apply):
for mode in ["L", "RGB", "CMYK"]:
im = hopper(mode)
out = im.filter(filter_to_apply)
assert out.mode == im.mode
assert out.size == im.size
apply_filter(ImageFilter.BLUR)
apply_filter(ImageFilter.CONTOUR)
apply_filter(ImageFilter.DETAIL)
apply_filter(ImageFilter.EDGE_ENHANCE)
apply_filter(ImageFilter.EDGE_ENHANCE_MORE)
apply_filter(ImageFilter.EMBOSS)
apply_filter(ImageFilter.FIND_EDGES)
apply_filter(ImageFilter.SMOOTH)
apply_filter(ImageFilter.SMOOTH_MORE)
apply_filter(ImageFilter.SHARPEN)
apply_filter(ImageFilter.MaxFilter)
apply_filter(ImageFilter.MedianFilter)
apply_filter(ImageFilter.MinFilter)
apply_filter(ImageFilter.ModeFilter)
apply_filter(ImageFilter.GaussianBlur)
apply_filter(ImageFilter.GaussianBlur(5))
apply_filter(ImageFilter.BoxBlur(5))
apply_filter(ImageFilter.UnsharpMask)
apply_filter(ImageFilter.UnsharpMask(10))
with pytest.raises(TypeError):
apply_filter("hello")
def output_mobile_images(image_file, destination, percent_viewport, quality):
widths = [math.ceil(w * (percent_viewport / 100.0)) \
for w in MOBILE_FULL_WIDTHS_TO_TARGET]
with Image.open(image_file) as image:
ratio = image.height / float(image.width)
for width in widths:
resized = image.resize((width, round(width * ratio)),
resample=Image.LANCZOS)
# For example: some/path/mansfield.jpg => mansfield_1440.jpg
filename = os.path.splitext(os.path.basename(image_file))[0] + \
"-" + str(width) + ".jpg"
save_to = os.path.join(destination, filename)
resized.save(save_to, "jpeg", quality=quality, optimize=True)
# Save a placeholder for lazy loading images.
placeholder_width = widths[-1] // 2
resized_for_placeholder = image.resize(
(placeholder_width, round(placeholder_width * ratio)),
resample=Image.LANCZOS)
placeholder = resized_for_placeholder.filter(ImageFilter.BoxBlur(3))
filename = os.path.splitext(os.path.basename(image_file))[0] + \
"-placeholder.jpg"
save_to = os.path.join(destination, filename)
placeholder.save(save_to, "jpeg", quality=quality, optimize=True)
def save_bblur():
if request.method == "POST":
image_src = 'static/uploads/img.png'
im = Image.open(image_src)
radius = int(request.get_data())
im = im.filter(ImageFilter.BoxBlur(radius))
im.save('static/uploads/img.png')
def show_box_blur(filename, r=1):
original = Image.open(os.path.join(INPUT_DIR, filename))
filtered = original.filter(ImageFilter.BoxBlur(r))
#show image side-by-side
show_horizontal(original, filtered)
filtered.save(
os.path.join(OUTPUT_DIR, '{}_boxblur_{}.jpg'.format(filename[:filename.index('.' ), r] ) )
)
def addBlur(self, pic: Image.Image):
if self.settings["GaussianBlurRadius"]:
pic = pic.filter(
ImageFilter.GaussianBlur(self.settings["GaussianBlurRadius"]))
if self.settings["BoxBlurRadiux"]:
pic = pic.filter(
ImageFilter.BoxBlur(self.settings["BoxBlurRadiux"]))
return pic
def NF_instance(parameter_blur, parameter_size):
boxImage = OriImage.filter(ImageFilter.BoxBlur(parameter_blur))
resizedImage = boxImage.resize(
[int(parameter_size * s) for s in boxImage.size])
mode = resizedImage.mode
data = resizedImage.tobytes()
size = resizedImage.size
return data, size, mode
def blur_and_print_image(image_full, image_name, output_path):
file, ext = os.path.splitext(image_full)
abrev_name, ext = os.path.splitext(image_name)
image = PI.open(image_full)
blur_ammount = random.randint(20, 60)
image_blurred = image.filter(ImageFilter.BoxBlur(blur_ammount))
image_blurred_final = image_blurred.filter(ImageFilter.DETAIL)
print('File Made: ' + output_path + '/' + abrev_name + ".jpeg")
image_blurred_final.save(output_path + '/' + abrev_name + ".jpeg", "JPEG")
def test_box_filter(self):
for radius in range(6):
pil_image = self.load_image(self.backends["PIL"])
pil_image = pil_image.filter(ImageFilter.BoxBlur(radius))
torchvision_image = self.load_image(self.backends["torchvision"])
torchvision_image = tif.box_blur(torchvision_image, radius)
self.assertImagesAlmostEqual(pil_image, torchvision_image)
def vhstext(image,
pos=(0, 0),
text="PLAY",
fill=0xffffff,
shadow=None,
shadowradius=3,
shadowfactor=5.0,
size=12,
align=C):
image = image.convert("RGBA")
fnt = font.font_variant(size=size)
text_layer = Image.new("RGBA", image.size)
shadow_layer = text_layer.copy()
text_draw = ImageDraw.Draw(text_layer)
if isinstance(fill, int):
fill = "#%.6x" % fill
if isinstance(shadow, int):
shadow = "#%.6x" % shadow
text_w, text_h = text_draw.textsize(text, font=fnt)
if align == L:
real_pos = (pos[0], pos[1])
if align == C:
real_pos = (pos[0] - text_w // 2, pos[1])
if align == R:
real_pos = (pos[0] - text_w, pos[1])
text_draw.multiline_text(real_pos, text, fill=fill, font=fnt, align=align)
if shadow is not None:
radius = shadowradius
fil = ImageFilter.BoxBlur(radius)
shadow_bbox = (text_w + radius * 2, text_h + radius * 2)
if align == L:
outpos = (pos[0] - radius, pos[1] - radius)
if align == C:
outpos = (pos[0] - shadow_bbox[0] // 2, pos[1] - radius)
if align == R:
outpos = (pos[0] - shadow_bbox[0] + radius, pos[1] - radius)
shadow_box = Image.new("RGBA", shadow_bbox)
shadow_draw = ImageDraw.Draw(shadow_box)
shadow_draw.multiline_text((radius, radius),
text,
fill=shadow,
font=fnt,
align=align)
shadow_box = shadow_box.filter(fil)
chans = list(shadow_box.split())
chans[3] = ImageEnhance.Brightness(chans[3]).enhance(shadowfactor)
shadow_box = Image.merge("RGBA", chans)
shadow_layer.paste(shadow_box, outpos)
text_with_shadow = Image.alpha_composite(shadow_layer, text_layer)
image = Image.alpha_composite(image, text_with_shadow)
return image.convert("RGB")
def do_work(parsed_image: dict,
image_filename='image.jpg',
metadata_filename='meta.json',
path=Path(Path.home(), Path('.bing-desk'))):
"""Does the work"""
image_path = Path(path, Path(image_filename))
split_image_path = os.path.splitext(image_path)
if BLUR and path.is_dir():
blurred_image_path = Path(split_image_path[0] + '_blur' +
split_image_path[1])
if blurred_image_path.is_file():
set_wallpaper(blurred_image_path)
if HIRES:
image = get_high_res(parsed_image)
else:
image = Image.open(BytesIO(r.get(parsed_image['image']).content))
if not isinstance(path, Path):
path = Path(path)
if not path.is_dir():
path.mkdir(parents=True, exist_ok=True)
if WINDOWS:
win.kernel32.SetFileAttributesW(str(path), 0x02)
if BLUR: # Excessive, but I like it
blurred_image = image.filter(ImageFilter.BoxBlur(radius=4)).filter(
ImageFilter.BoxBlur(radius=4))
blurred_image.save(str(blurred_image_path))
set_wallpaper(blurred_image_path)
time.sleep(.5)
image.save(str(image_path))
metadata_path = Path(path, Path(metadata_filename))
parsed_image.update({'fetched': str(datetime.now())})
with metadata_path.open('wb') as file:
file.write(json.dumps(parsed_image, ensure_ascii=False).encode('utf8'))
set_wallpaper(image_path)
return image_path, metadata_path
def glowFx(image, radius=0, brt=1.5):
if radius > 0:
base = image.copy()
image = image.filter(ImageFilter.BoxBlur(radius=radius))
enhancer = ImageEnhance.Brightness(image)
image = enhancer.enhance(brt)
base.paste(image, (0, 0), image)
return base
else:
return image
def test_main(xvfb, fs_with_data):
main()
# load and convert to gray
# reference
img_ref = Image.open("data/sitting.png").convert('LA')
# compute/render image
img_result = Image.open("output.png").convert('LA')
# using blur (box or gaussian) for reducing the antialiasing differentiation
img_ref = img_ref.filter(ImageFilter.BoxBlur(radius=4))
img_result = img_result.filter(ImageFilter.BoxBlur(radius=4))
diff = ImageChops.difference(img_ref, img_result)
# evaluate difference with some energy quantification
# not very precise and/or accurate, but enough for utest purpose
max_energy = compute_energy(img_ref)
diff_energy = compute_energy(diff)
coef_error = diff_energy / max_energy
assert coef_error <= 1e-3
def blurImage(self):
text = self.blurRadius.text()
if text and text.isnumeric() and self.img_pixmap:
radius = int(text)
if radius == 0:
self.img_pixmap = self.img_pixmap_orig
else:
img = Image.fromqpixmap(self.img_pixmap)
img = img.filter(ImageFilter.BoxBlur(radius))
self.img_pixmap = img.toqpixmap()
self.notifyImageChange()
def show_box_blur(filename, r=1):
'''Aplica um filtro BoxBlur à imagem, exibe e salva o resultado'''
original = Image.open(in_file(filename))
filtered = original.filter(ImageFilter.BoxBlur(r))
#Mostrar as imagens lado a lado
show_horizontal(original, filtered)
filtered.save(
out_file('{}_boxblur_{}.jpg'.format(filename[:filename.index('.')],
r)))
def face_gaussian_blur(image, image_cv, radius=20):
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
gray_image_cv = cv2.cvtColor(image_cv, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray_image_cv, 1.1, 4)
new_image = image
for (x, y, w, h) in faces:
box = (x, y, x+w, y+h)
cropped_image = image.crop(box)
cropped_image = cropped_image.filter(ImageFilter.BoxBlur(radius))
new_image.paste(cropped_image, box)
return new_image
def __call__(self, image):
'''
Arguments:
image (numpy array or PIL Image)
Returns:
image (numpy array or PIL Image)
'''
img = Image.fromarray(image)
img = img.filter(ImageFilter.BoxBlur(self.radius))
return (np.array(img))
def applyBlurPG(img_path,blur_radius,blur_type):
sharpimg = load_imgRGB(img_path)
blurredimg=[]
if blur_type is 'avg':
blurredimg = sharpimg.filter(ImageFilter.BoxBlur(radius=blur_radius))
elif blur_type is 'gaus':
blurredimg = sharpimg.filter(ImageFilter.GaussianBlur(radius=blur_radius))
else:
raise ValueError("Unknown blur type. Please check the blur options in applyBlurPG function")
return 0
return sharpimg,blurredimg
