def input_symptoms():
if request.method == 'POST':
travel = str(request.form['travel'])
tiredcough = str(request.form['commonsym'])
breath = str(request.form['majorsym'])
exposure = str(request.form['exposure'])
try:
image = request.files['image']
image.save('beach1.bmp')
except:
return render_template('index.html', results="No File Found")
file_path = "beach1.bmp"
# to load pre-saved model
# test
pathogen = predict_pathogen("./image.png", loaded_model)
return render_template('index.html',
results=result,
result2=result2,
pathogen=pathogen)
return None
def runtests(opt, stream):
"""
Run *count* of tests, - images will be saved to the temp folder.
Side comparison.
"""
for _ in range(0, opt.test):
image1 = image.frombytes(stream.getimagebytes())
if opt.mask:
image.mask(image1, opt.mask)
time.sleep(opt.pause)
image2 = image.frombytes(stream.getimagebytes())
if opt.mask:
image.mask(image2, opt.mask)
difference = image.diff(image1, image2)
if difference >= opt.motionat:
image.save(
image.combine(
image1,
image2,
"%s // %s" % (time.strftime("%Y-%m-%d %H:%M:%S"), difference)
),
opt.storage
)
logging.info("!! Motion detected: %s" % difference)
else:
logging.debug("No motion detected: %s" % difference)
def saveImage(multi=False):
"""Save an image to file.
This will show the Save Image dialog, with the multisave mode checked if
multi = True. Then, depending on the user's selection, it will either:
- save the current Canvas/Window to file
- start the multisave/autosave mode
- do nothing
"""
pat = map(utils.fileDescription, ['img', 'icon', 'all'])
dia = widgets.SaveImageDialog(pf.cfg['workdir'], pat, multi=multi)
opt = dia.getResult()
if opt:
if opt.fm == 'From Extension':
if utils.fileTypeFromExt(opt.fn) == '':
opt.fn += '.png'
opt.fm = None
if opt.qu < 0:
opt.qu = -1
updateSettings({'workdir': os.path.dirname(opt.fn)}, save=True)
image.save(filename=opt.fn,
format=opt.fm,
quality=opt.qu,
size=opt.sz,
window=opt.wi,
multi=opt.mu,
hotkey=opt.hk,
autosave=opt.au,
border=opt.bo,
rootcrop=opt.rc)
def saveImage(multi=False):
"""Save an image to file.
This will show the Save Image dialog, with the multisave mode checked if
multi = True. Then, depending on the user's selection, it will either:
- save the current Canvas/Window to file
- start the multisave/autosave mode
- do nothing
"""
pat = map(utils.fileDescription, ['img','icon','all'])
dia = widgets.SaveImageDialog(GD.cfg['workdir'],pat,multi=multi)
opt = dia.getResult()
if opt:
if opt.fm == 'From Extension':
opt.fm = None
if opt.qu < 0:
opt.qu = -1
updateSettings({'workdir':os.path.dirname(opt.fn)},save=True)
image.save(filename=opt.fn,
format=opt.fm,
quality=opt.qu,
window=opt.wi,
multi=opt.mu,
hotkey=opt.hk,
autosave=opt.au,
border=opt.bo,
rootcrop=opt.rc
)
def result(self, image_path, results, expected, compare=True):
fullpath = path = os.path.abspath(os.path.join('data', image_path))
if self.resized_dir is not None:
im = image.max_size(image.load(path), (320, 240))
path = os.path.join(self.resized_dir, os.path.basename(image_path))
print os.path.dirname(image_path)
image.save(im, path)
del im
self.write('<tr>')
self.write('<td rowspan="%(len)d"><a href="%(fullpath)s"><img src="%(path)s" width="320" /><br />%(imgpath)s</a></td>',
fullpath=fullpath, path=path, imgpath=image_path, len=len(results))
for i,pair in enumerate(results.iteritems()):
name,result = pair
if i != 0:
self.write("</tr><tr>")
self.write('<td>%(name)s</td><td>%(score)r</td>',
name=name, score=result[1])
self.write('<td class="%(cls)s">%(confirmed)s</td><td>%(expected)s</td>',
cls="bad" if compare and result[2] != (name in expected) else "good",
confirmed="Yes" if result[2] else "",
expected="Yes" if name in expected else "")
self.write('<td class="%(cls)s">%(timing).2f second(s)</td>',
cls="bad" if compare and result[3] > 10 else "",
timing=result[3])
self.write("</tr>")
def make_preview(trainer):
with chainer.using_config('train', False):
with chainer.no_backprop_mode():
x_a = iterator_a.next()
x_a = convert.concat_examples(x_a, device)
x_a = chainer.Variable(x_a)
x_b = iterator_b.next()
x_b = convert.concat_examples(x_b, device)
x_b = chainer.Variable(x_b)
x_ab = g_a(x_a)
x_ba = g_b(x_b)
x_bab = g_a(x_ba)
x_aba = g_b(x_ab)
preview_dir = '{}/preview'.format(dst)
if not os.path.exists(preview_dir):
os.makedirs(preview_dir)
image_dir = '{}/image'.format(dst)
if not os.path.exists(image_dir):
os.makedirs(image_dir)
names = ['a', 'ab', 'aba', 'b', 'ba', 'bab']
images = [x_a, x_ab, x_aba, x_b, x_ba, x_bab]
for n, i in zip(names, images):
i = cp.asnumpy(i.data)[:,:,padding:-padding,:].reshape(1, -1, 128)
image.save(image_dir+'/{}{}.jpg'.format(trainer.updater.epoch,n), i)
w = np.concatenate([gla.inverse(_i) for _i in dataset.reverse(i)])
dataset.save(preview_dir+'/{}{}.wav'.format(trainer.updater.epoch,n), 16000, w)
def _shade_render_save(normals: Tensor, svbrdf: SVBRDF, lights: List[Light],
viewer: Viewer, camera: Camera, path: str) -> None:
'''
Shades, renders, and saves a picture to the given path from the provided normals, Lights, Viewer, Camera, and SVBRDF.
Args:
normals: Tensor [1, R, C, 3] of normals to use for shading.
svbrdf: SVBRDF to use for shading.
lights: Lights to use for shading.
viewer: Viewer to use for shading.
camera: Camera to use for rendering.
path: Path to use for saving the image.
'''
num_rows, num_cols = normals.size(1), normals.size(2)
num_size = max(num_rows, num_cols)
surface = (utils.create_grid(num_rows=num_rows, num_cols=num_cols) -
torch.tensor([0.5, 0.5, 0])) * torch.tensor(
[num_cols / num_size, num_rows / num_size, 1])
radiance = shader.shade(surface=surface,
normals=normals,
lights=lights,
viewer=viewer,
svbrdf=svbrdf)
picture = camera.render(surface=surface, radiance=radiance[0])
image.save(path=path, image=picture, encoding='sRGB')
def input_symptoms():
if request.method == 'POST':
travel = str(request.form['travel'])
tiredcough = str(request.form['commonsym'])
breath = str(request.form['majorsym'])
exposure = str(request.form['exposure'])
try:
# to load pre-saved model
loaded_model = load_model()
image = request.files['image']
image.save('temp_image.bmp')
pathogen = predict_pathogen("./temp_image.bmp", loaded_model)
current_labels = [
'No Finding', 'Effusion', 'Infiltration', 'Atelectasis',
'Edema', 'Consolidation', 'Mass', 'Nodule', 'Fibrosis'
]
pathogen = pathogen[0]
sorted_lists = [
x for _, x in sorted(zip(pathogen, current_labels))
]
result_dict = dict(zip(current_labels, pathogen))
print(sorted_lists)
filtered_list = []
top_two_results = sorted_lists[-2:]
print(top_two_results)
for condition in top_two_results:
if condition in diag_info_dict.keys():
filtered_list.append(
[condition, diag_info_dict[condition]])
else:
filtered_list.append([condition, 'No additional info'])
result = ','.join([travel, tiredcough, breath, exposure])
result2 = 'result 2'
return render_template('index.html',
results=result,
result2=result2,
pathogen=filtered_list)
except:
return render_template('index.html', results="No File Found")
def saveframe(frame, dest, filename,
caption="DATE: %s | DIFF: %s | FRAME: %s", params=()):
"""
Save image with watermark.
"""
filename = str(filename)
filename = filename.rjust(12, "0")
image.watermark(frame, caption % params)
image.save(frame, dest, filename)
def main():
filename = 'cat.png'
image = Image.open(filename)
size = width, height = image.size
enchancer = ImageEnhance.Bightness(image)
image = enchancer.enchance(0.5)
image.save("modified_" + filename)
del image
def input_symptoms():
if request.method == 'POST':
try:
travel = int(request.form['travel'])
except:
travel = 0
try:
tiredcough = int(request.form['commonsym'])
except:
tiredcough = 0
try:
breath = int(request.form['majorsym'])
except:
breath = 0
try:
exposure = int(request.form['exposure'])
except:
exposure = 0
try:
image = request.files['image']
image.save('beach1.bmp')
except:
return render_template('index.html', results="No File Found")
file_path = "beach1.bmp"
with open(file_path, "rb") as content_file:
content = content_file.read()
image = automl.Image(image_bytes=content)
payload = automl.ExamplePayload(image=image)
# params is additional domain-specific parameters.
# score_threshold is used to filter the result
# https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#predictrequest
params = {"score_threshold": "0.8"}
request2 = automl.PredictRequest(name=model_full_id,
payload=payload,
params=params)
# 'content' is base-64-encoded image data.
response = prediction_client.predict(request=request2)
for result in response.payload:
var1 = result.display_name
var2 = result.classification
result2 = 'Disease: ', var1, ' Probability of disease: ', var2
result = travel + tiredcough + breath + exposure
if result >= 3:
result = 'You have high chances of being covid positive'
else:
result = 'Please take care of yourself(wear a mask :D)'
return render_template('index.html', results=result, result2=result2)
return None
def on_btn_Save_Clicked(self):
save_path = QFileDialog.getSaveFileName(self, "Save Your Paint", ".\\",
"*.png")
print(save_path)
if save_path[0] == "":
print("Save cancel")
return
image = self.__paintBoard.GetContentAsQImage()
image.save(save_path[0])
print(save_path[0])
def compute_path_image(infilename, outfilename, start, end, threshold=10):
'''Altera l'immagine nel file infilename
colorando il percorso da start a end e
salvandola nel file outfilename.
Usa un grafo implicito.'''
img = load(infilename)
tree = visit_tree_image(img, start, end, threshold)
path = visit_path(tree, end)
draw_path(img, path)
save(outfilename, img)
def upload():
if request.files['image'].filename != '':
image = request.files['image']
file_path = './.tmp/{}'.format(image.filename)
image.save(file_path)
uploaded = bucket.upload(file_path, image.filename)
if uploaded:
return "uploaded"
return abort(400)
return abort(500)
def readfile():
"""
read the config file and execute the command
:return: nothing
"""
file = open("conf.txt", 'r')
for line in file:
isActive = line.split("=")
isActive[1] = isActive[1]
if line.find("input_dir=") >= 0 and isActive[1] != "\n":
if line.find("= ") >= 0:
command = line.split("= ")
else:
command = line.split("=")
i.path = command[1].rstrip("\n")
if line.find("output_dir=") >= 0 and isActive[1] != "\n":
print(isActive[1] + "s")
if line.find("= ") >= 0:
command = line.split("= ")
else:
command = line.split("=")
i.folder = command[1]
if line.find("filters=") >= 0 and isActive[1] != "":
i.dico = i.open_image()
if line.find("= ") >= 0:
command = line.split("= ")
else:
command = line.split("=")
filtre = command[1].split("|")
for c in filtre:
if c == "grayscale":
i.dico = i.nb()
if c.find("blur") >= 0:
sep = c.split(":")
i.dico = i.blur(int(sep[1]))
if c.find("dilate") >= 0:
sep = c.split(":")
i.dico = i.dilate(int(sep[1]))
if c.find("FilterZeTeam") >= 0:
i.dico = i.FilterZeTeam()
i.save()
def compute_path(infilename, outfilename, start, end, threshold=10):
'''Altera l'immagine nel file infilename
colorando il percorso da start a end e
salvandola nel file outfilename. Usa le
funzioni load() e save() dal capitolo sulle
immagini.'''
img = load(infilename)
g = image_to_graph(img, threshold)
tree = visit_tree(g, start)
path = visit_path(tree, end)
draw_path(img, path)
save(outfilename, img)
def f2i(path):
DPI = 200
OUTPUT_FOLDER = None
FIRST_PAGE = None
LAST_PAGE = None
FORMAT = 'jpg'
THREAD_COUNT = 1
USERPWD = None
USE_CROPBOX = False
STRICT = False
index = 0
# This method reads a pdf and converts it into a sequence of images
# PDF_PATH sets the path to the PDF file
# dpi parameter assists in adjusting the resolution of the image
# output_folder parameter sets the path to the folder to which the PIL images can be stored (optional)
# first_page parameter allows you to set a first page to be processed by pdftoppm
# last_page parameter allows you to set a last page to be processed by pdftoppm
# fmt parameter allows to set the format of pdftoppm conversion (PpmImageFile, TIFF)
# thread_count parameter allows you to set how many thread will be used for conversion.
# userpw parameter allows you to set a password to unlock the converted PDF
# use_cropbox parameter allows you to use the crop box instead of the media box when converting
# strict parameter allows you to catch pdftoppm syntax error with a custom type PDFSyntaxError
# path_1 = os.getcwd()
path_1 = os.path.join(os.getcwd(), 'images')
try:
os.makedirs(path_1, exist_ok=True)
print("Directory '%s' created successfully" % path_1)
except OSError as error:
os.remove(path_1)
os.makedirs(path_1)
print("Directory deleted and created")
images = [[]]
start_time = time.time()
dirs = os.listdir(path)
# print(len(dirs))
for file in dirs:
if file.endswith("pdf"):
path1 = path + "/" + file
pil_images = pdf2image.convert_from_path(path1, dpi=DPI, output_folder=OUTPUT_FOLDER, first_page=FIRST_PAGE,
last_page=LAST_PAGE, fmt=FORMAT, thread_count=THREAD_COUNT,
userpw=USERPWD, use_cropbox=USE_CROPBOX, strict=STRICT)
# images.append(pil_images)
print("Time taken : " + str(time.time() - start_time))
pathB = os.getcwd()
os.chdir(path_1)
# This method helps in converting the images in PIL Image file format to the required image format
# for image in pil_images:
for image in pil_images:
image.save("page" + str(index) + ".jpg")
index += 1
os.chdir(pathB)
def _extract_flow(config: Configuration) -> None:
'''
The "extract" flow renders a flash-lit picture of a texture from the dataset.
Args:
config: Configuration specifying the parameters of the flow.
'''
with torch.no_grad():
dataset, texture, output = config.load_extract_flow()
material = dataset.textures.index(texture)
batch, _ = dataset.sample(material)
picture = batch[0, :3].permute(1, 2, 0)
image.save(path=output, image=picture, encoding='sRGB')
def mosaic_average(fname_in, fname_out, s):
'''Ritorna una nuova immagine ottenuta dividendo
l'immagine img in quadrati di lato s e riempendo
ogni quadratino con la media dei suoi colori.'''
img = image.load(fname_in)
w, h = len(img[0]), len(img)
ret = image.create(w, h, (0,0,0))
# itera sui possibili quadrati
for jj in range(h//s):
for ii in range(w//s):
# colore medio dell'immagine
c = average(img,ii*s,jj*s,s,s)
draw_quad(ret, ii*s, jj*s, s, s, c)
image.save(fname_out, ret)
def mosaic_nearest(fname_in, fname_out, s):
"""Ritorna una nuova immagine ottenuta dividendo
l'immagine img in quadrati di lato s e riempendo
ogni quadrato con il colore del suo angolo in
alto a sinistra"""
img = image.load(fname_in)
w, h = len(img[0]), len(img)
ret = image.create(w, h, (0,0,0))
# itera sui possibili quadrati
for jj in range(h//s):
for ii in range(w//s):
# colore dell'angolo in alto-sinistra
c = img[jj*s][ii*s]
draw_quad(ret, ii*s, jj*s, s, s, c)
image.save(fname_out, ret)
def to_jpeg(self):
new_basename_root, old_ext = \
os.path.splitext(os.path.basename(self.filename))
new_basename = new_basename_root + '.jpg'
new_filename = tempfile.gettempdir() + os.path.sep + new_basename
image = Image.open(self.filename)
if image.mode != 'RGB':
image = image.convert('RGB')
image.save(new_filename, 'JPEG', quality=100)
new_file = JPEGFile(new_filename)
new_file.item = self.item
new_file.index = self.index
return new_file
def to_jpeg(self):
new_basename_root, old_ext = \
os.path.splitext(os.path.basename(self.filename))
new_basename = new_basename_root + '.jpg'
new_filename = tempfile.gettempdir() + os.path.sep + new_basename
image = Image.open(self.filename)
if image.mode != 'RGB':
image = image.convert('RGB')
image.save(new_filename, 'JPEG', quality=100)
new_file = JPEGFile(new_filename)
new_file.item = self.item
new_file.index = self.index
return new_file
def fit(input_path: str, output_path: str, optimizer: str,
svbrdf: SVBRDF) -> None:
'''
Fits the given SVBRDF to each material in the MERL 100 dataset using the provided hyperparameters.
Args:
input_path: Path to the MERL 100 BRDF slice image.
output_path: Path to the desired output image.
optimizer: SciPy optimizer to use to fit the SVBRDF.
svbrdf: SVBRDF model to fit.
'''
brdfs = _load_MERL_100_BRDF_slices(input_path)
# It is known a priori that each BRDF slice has a width and height of 60 pixels.
normals, incident_directions, outbound_directions = _generate_SVBRDF_directions(
num_rows=60, num_cols=60)
# There is a 10 pixel gap between each BRDF slice in a pair and a 30 pixel gap between BRDF slice pairs.
comparison = torch.zeros(30 + 10 * 90, 30 + 10 * 160, 3)
for i, want_brdf in tqdm.tqdm(enumerate(brdfs),
desc='SVBRDF Optimization',
total=100,
disable=logging.root.level == logging.DEBUG):
# Each SVBRDF parameter has a valid range of [0, 1]; however, the optimizers are bad at handling extremes.
guess = torch.rand(svbrdf.depth).numpy() / 5 + 0.4
bounds = scipy.optimize.Bounds(numpy.array([0.01] * svbrdf.depth),
numpy.array([0.99] * svbrdf.depth))
# Find an "optimal" set of parameters for the SVBRDF which minimizes the MSE loss with respect to the material BRDF slice.
solution = scipy.optimize.minimize(
fun=_evaluate_function,
jac=_evaluate_gradient,
x0=guess,
args=(svbrdf, normals, incident_directions, outbound_directions,
want_brdf),
method=optimizer,
bounds=bounds)
logging.debug('Material %d was fit with an MSE loss of %.6f', i,
solution.fun)
# Compute the BRDF slice associated with the optimized parameters.
svbrdf.parameters = torch.from_numpy(solution.x).expand(
1, normals.size(0), normals.size(1), -1)
have_brdf = svbrdf(normals, incident_directions,
outbound_directions).squeeze().clamp(0, 1)
# Copy the MERL 100 BRDF slice alongside the optimized BRDF slice into the output image.
row = 30 + 90 * (i // 10)
col = 30 + 160 * (i % 10)
comparison[row:row + 60, col:col + 60, :] = want_brdf
comparison[row:row + 60, col + 70:col + 130, :] = have_brdf
image.save(path=output_path, image=comparison, encoding='sRGB')
def polarize(fname_in, colors, fname_out):
inputImage = im.load(fname_in)
outputImage = []
for r in range(len(inputImage)):
outputImageRow = []
for c in range(len(inputImage[0])):
outputImageRow.append(ApproximateColor(inputImage[r][c], colors))
outputImage.append(outputImageRow)
im.save(fname_out, outputImage)
def mosaic_size(fname_in, fname_out, s):
'''Ritorna una nuova immagine ottenuta dividendo
l'immagine img in quadratini di lato s e
disegnando all'interno di ognuno di essi,
su sfondo nero, un quadratino centrale bianco di
lato proporzionale alla luminosità media del
corrispondente quadratino'''
img = image.load(fname_in)
w, h = len(img[0]), len(img)
ret = image.create(w, h, (0,0,0))
# itera sui possibili quadrati
for jj in range(h//s):
for ii in range(w//s):
# colore medio dell'immagine
c = average(img,ii*s,jj*s,s,s)
# lato del quadratino bianco
r = round(s*(c[0]+c[1]+c[2])/(3*255))
draw_quad(ret, ii*s+(s-r)//2,
jj*s+(s-r)//2, r, r, (255,255,255))
image.save(fname_out, ret)
def fill(img_in, boundaries, pp_cc, img_out):
'''Implementare qui la funzione'''
img = image.load(img_in)
w, h = len(img[0]), len(img)
for pc in pp_cc:
visited = set([pc[0]])
active = set([pc[0]])
while len(active) > 0:
newactive = set()
while len(active) > 0:
x, y = active.pop()
for dx, dy in ADJACENTS:
px, py = x + dx, y + dy
if px >= 0 and px < w and py >= 0 and py < h:
if (px, py) not in boundaries:
img[py][px] = pc[1]
if (px, py) not in visited:
visited.add((px, py))
newactive.add((px, py))
active = newactive
image.save(img_out, img)
def copy_to_offscreen(self, image):
global options, grabber, display
# Convert a PIL Image to a gtk.gdk.Pixbuf
if (platform.system() == 'Linux'):
file1 = io.BytesIO()
else:
file1 = StringIO()
if (file1 != False):
image.save(file1, "ppm")
contents = file1.getvalue()
loader = gtk.gdk.PixbufLoader("pnm")
loader.write(contents, len(contents))
pixbuf = loader.get_pixbuf()
loader.close()
file1.close()
# copy Pixbuf to offscreen area/Pixbuf
grabber.lock_offscreen.acquire()
gtk.gdk.threads_enter()
pixbuf.copy_area(0, 0, display.width, display.height, self.offscreen,
0, 0)
# Report status
if grabber.active:
display.bartext.set_text("Recording")
else:
display.bartext.set_text("")
# Force an 'expose' event for screen
display.expose_event(display.drawing_area, None)
gtk.gdk.threads_leave()
grabber.lock_offscreen.release()
return True
def _bakeAndSave(self, bake_type, out_name, cs):
log.info(f' {self.obj.name} start {bake_type} bake '
f'(size={self.img.generated_width}, samples={self.samples})')
self.img.colorspace_settings.name = cs
bpy.context.scene.render.engine = 'CYCLES'
bpy.context.scene.cycles.device = 'GPU'
bpy.context.scene.cycles.samples = self.samples
bpy.context.scene.cycles.use_denoising = True
bpy.context.scene.cycles.bake_type = bake_type
if bake_type == 'DIFFUSE':
bpy.context.scene.render.bake.use_pass_direct = False
bakeData = self.objData.setdefault('bake',
{}).setdefault(bake_type, {})
bakeData['res'] = self.img.generated_width
bakeData['samples'] = bpy.context.scene.cycles.samples
self.obj.select_set(state=True)
try:
t1 = time.time()
bpy.ops.object.bake('INVOKE_DEFAULT',
type=bake_type,
use_clear=True)
bakeData['bake_time'] = round(time.time() - t1, 2)
except Exception as err:
log.warning(err)
else:
# these should be named by content since some will be the same (dup object under 2 lights will have some of the same maps)
t2 = time.time()
image.save(
self.img,
dest /
f'stage/bake/render/{self.inst_name}/{self.obj.name}_{out_name}.png',
logPrefix=' ')
bakeData['save_time'] = round(time.time() - t2, 2)
def copy_to_offscreen(self, image):
global options, grabber, display
# Convert a PIL Image to a gtk.gdk.Pixbuf
if (platform.system() == 'Linux'):
file1 = io.BytesIO()
else:
file1 = StringIO()
if (file1 != False):
image.save(file1, "ppm")
contents = file1.getvalue()
loader = gtk.gdk.PixbufLoader("pnm")
loader.write(contents, len(contents))
pixbuf = loader.get_pixbuf()
loader.close()
file1.close()
# copy Pixbuf to offscreen area/Pixbuf
grabber.lock_offscreen.acquire()
gtk.gdk.threads_enter()
pixbuf.copy_area(0, 0, display.width, display.height,
self.offscreen, 0, 0)
# Report status
if grabber.active:
display.bartext.set_text("Recording")
else:
display.bartext.set_text("")
# Force an 'expose' event for screen
display.expose_event(display.drawing_area, None)
gtk.gdk.threads_leave()
grabber.lock_offscreen.release()
return True
def edging(fname_in, fname_out, n_iter):
img = image.load(fname_in)
width, height = len(img[0]), len(img)
img_out = image.create(width, height, (0, 0, 0))
pix_hood = ((-1, -1), (0, -1), (1, -1), (1, 0), (1, 1), (0, 1), (-1, 1), (-1, 0))
for _ in range(n_iter):
for row in range(height):
for col in range(width):
color = img[row][col]
max_distance = 0
max_color = color
for x, y in pix_hood:
row_hood = y + row
col_hood = x + col
if not (0 <= col_hood < width and 0 <= row_hood < height):
continue
color_hood = img[row_hood][col_hood]
distance = calc_dist(color, color_hood)
if distance > max_distance:
max_distance = distance
max_color = color_hood
img_out[row][col] = max_color
img, img_out = img_out, img
image.save(fname_out, img)
def save_spirals(training_set,
output,
filename,
size,
show,
descriptor=None,
drops=None):
colors = [utils.hex_to_tuple(x[2]) for x in output[1]]
image.save(positions=[(x[0], x[1]) for x in training_set],
colors=[utils.hex_to_tuple(x[2]) for x in training_set],
width=size,
height=size,
path=filename + "_original.png",
show=show)
image.save(positions=[(x[0], x[1]) for x in output[1]],
colors=colors,
width=size,
height=size,
path=filename + ".png",
show=show)
if descriptor != None:
image.save(positions=descriptor,
colors=[utils.hex_to_tuple("#00FF00") for x in descriptor],
width=size,
height=size,
path=filename + "_descriptor.png",
show=show)
if drops != None:
image.save(positions=drops,
colors=[utils.hex_to_tuple("#FF0000") for x in drops],
width=size,
height=size,
path=filename + "_drops.png",
show=show)
def save_cover_art(release, max_retries=3):
"""Save the front cover image for the release `release`. Return
the saved image's ID, or None if the image isn't available."""
if release['cover-art-archive']['front'] == 'false':
return None
for retry in range(max_retries):
try:
image_data = musicbrainzngs.get_image_front(release['id'],
size="500")
return image.save(image_data, 'catalog')
except ResponseError as err:
if err.cause.code == 404: # image doesn't exist
break
elif err.cause.code == 503:
print('rate limit exceeded: retrying in {} seconds.'.format(
RETRY_SECS))
time.sleep(RETRY_SECS)
except NetworkError:
break
return None
def stopMultiSave():
"""Stop multisave mode."""
image.save()
def save(self, filename):
image.save(filename, self.to_img())
#
"""
RGB color image
"""
from scipy import misc
import numpy as np
import image as img
class rgb_image(img.image):
"""
"""
def __init__(self, w=0, h=0, fileName=None):
"""
Construction d'une image de width x height pixels contenant trois channels R,G et B
"""
if None == fileName:
img.image.__init__(self, w, h, 3)
else:
data = misc.imread(fileName)
img.image.__init__(self, data.shape[0], data.shape[1], 3)
self.__pixels__ = data[:, :, 0:3] / 255.
if __name__ == '__main__':
img = rgb_image(fileName='lena_rgb.png')
img.pixels = np.ones((img.height, img.width, 3), np.double) - img.pixels
img.show()
img.save("inverse_rgb_lena.png")
def main(conf):
img = image.open(conf.file)
segments = image.segment(img, int(conf.cluster), it=5)
image.save('./output/' + str(time.time()), segments)
######################################
#### QR- CODE GENERATOR WITH PYTHON
####
#### First install 2 modules in CMD
#### -> qrcode -> pip install qrcode
#### -> image -> pip install image
####
######################################
import qrcode
import image
qr = qrcode.QRCode(
version=
15, # means the version of the qr code high the number bigger the code image and complicated pictuıre
box_size=10, # size of the box where qe code will be displayed
border=
5 # it is the white part of the image -- border in all 4 sides with white color
)
data = "https://fatihes1.github.io/"
# as I have given the path of my resume website
# if you do not want to redirect and create for normal text that write text in the quotes
qr.add_data(data)
qr.make(fit=True)
# The following lines of code will create your qr-code and save it in the same directory as your '.py' file
image = qr.make_image(fill='black', back_color='white')
image.save("qr_code.png")
def saveImage(image, target):
try:
image.save(target)
except IOError:
raise RuntimeError('Cannot write image file %s' % target)