def create(self, arguments={}, invert=False):
import numpy as np
if getValue(arguments, 'homography', 'None') == 'None':
if self.startIm.has_alpha():
img_array = np.asarray(self.startIm)
mask = np.copy(img_array[:, :, 3])
#accept the alpha channel as what is kept
mask[mask > 0] = 255
#invert since 0 in the donor mask indicates the donor pixels
return ImageWrapper(mask).invert()
# use the pre select mask (inverted) as the selection...invert what was removed to be what is kept
return _pre_select_mask(self.graph, self.donor_start, self.startIm)
mask = self.graph.get_edge_image(self.parent_of_end, self.donor_end,
'arguments.pastemask')
if mask is None:
mask = self.graph.get_edge_image(self.parent_of_end,
self.donor_end, 'maskname')
mask, analysis = interpolateMask(mask,
self.startIm,
self.destIm,
arguments=arguments,
invert=invert)
if mask is not None and mask.shape != (0, 0):
mask = ImageWrapper(mask)
else:
mask = None
return mask
def transform(img, source, target, **kwargs):
img_to_paste = openImageFile(kwargs['donor'])
pasteregionsize = kwargs['region size'] if 'region size' in kwargs else 1.0
approach = kwargs['approach'] if 'approach' in kwargs else 'simple'
segment_algorithm = kwargs[
'segment'] if 'segment' in kwargs else 'felzenszwalb'
if pasteregionsize < 1.0:
dims = (int(img.size[1] * pasteregionsize),
int(img.size[0] * pasteregionsize))
else:
dims = (img.size[1], img.size[0])
x = (img.size[1] - dims[0]) / 2
y = (img.size[0] - dims[1]) / 2
imgarray = np.asarray(img)
if len(imgarray.shape) > 2:
newimg = imgarray[x:dims[0] + x, y:dims[1] + y, :]
else:
newimg = imgarray[x:dims[0] + x, y:dims[1] + y]
transform_matrix, out = performPaste(ImageWrapper(newimg), img_to_paste,
approach, segment_algorithm)
if pasteregionsize < 1.0:
out2 = np.copy(imgarray)
if len(imgarray.shape) > 2:
out2[x:dims[0] + x, y:dims[1] + y, :] = out
else:
out2[x:dims[0] + x, y:dims[1] + y] = out
out = out2
ImageWrapper(out).save(target)
return {
'transform matrix': tool_set.serializeMatrix(transform_matrix)
} if transform_matrix is not None else None, None
def test_global_transform_analysis(self):
from maskgen.image_wrap import ImageWrapper
analysis = {}
mask = np.random.randint(0, 2, (1000, 1000), dtype=np.uint8)
mask[mask > 0] = 255
tool_set.globalTransformAnalysis(analysis,
ImageWrapper(mask),
ImageWrapper(mask),
mask=mask,
linktype='image.image',
arguments={},
directory='.')
self.assertEquals('yes', analysis['global'])
mask = np.zeros((1000, 1000), dtype=np.uint8)
mask[0:30, 0:30] = 255
tool_set.globalTransformAnalysis(analysis,
ImageWrapper(mask),
ImageWrapper(mask),
mask=mask,
linktype='image.image',
arguments={},
directory='.')
self.assertEquals('no', analysis['global'])
self.assertEquals('small', analysis['change size category'])
mask = np.zeros((1000, 1000), dtype=np.uint8)
mask[0:75, 0:75] = 255
tool_set.globalTransformAnalysis(analysis,
ImageWrapper(mask),
ImageWrapper(mask),
mask=mask,
linktype='image.image',
arguments={},
directory='.')
self.assertEquals('no', analysis['global'])
self.assertEquals('medium', analysis['change size category'])
mask[0:100, 0:100] = 255
tool_set.globalTransformAnalysis(analysis,
ImageWrapper(mask),
ImageWrapper(mask),
mask=mask,
linktype='image.image',
arguments={},
directory='.')
self.assertEquals('no', analysis['global'])
self.assertEquals('large', analysis['change size category'])
tool_set.globalTransformAnalysis(analysis,
ImageWrapper(mask),
ImageWrapper(mask),
mask=mask,
linktype='image.image',
arguments={},
directory='.')
def carveSeams(source,
target,
shape,
mask_filename,
approach='backward',
energy='Sobel',
keep_size=False):
"""
:param img:
:return:
@type img: ImageWrapper
"""
import traceback
import sys
try:
sc = SeamCarver(source,
shape=shape,
energy_function=SobelFunc()
if energy == 'Sobel' else ScharrEnergyFunc(),
mask_filename=mask_filename,
keep_size=keep_size,
seam_function=foward_base_energy_function
if approach == 'forward' else base_energy_function)
image, mask = sc.remove_seams()
except IndexError as ex:
texc_type, exc_value, exc_traceback = sys.exc_info()
traceback.print_tb(exc_traceback, limit=10, file=sys.stdout)
raise ex
maskname = os.path.join(
os.path.dirname(source),
shortenName(os.path.basename(source),
'_real_mask.png',
identifier=uniqueId()))
adjusternames = os.path.join(
os.path.dirname(source),
shortenName(os.path.basename(source), '.png', identifier=uniqueId()))
finalmaskname = os.path.join(
os.path.dirname(source),
shortenName(os.path.basename(source),
'_final_mask.png',
identifier=uniqueId()))
ImageWrapper(mask).save(os.path.join(os.path.dirname(source), maskname))
adjusternames_row, adjusternames_col = sc.mask_tracker.save_adjusters(
adjusternames)
sc.mask_tracker.save_neighbors_mask(finalmaskname)
ImageWrapper(image).save(target)
return {
'neighbor mask': finalmaskname,
'column adjuster': adjusternames_col,
'row adjuster': adjusternames_row,
'plugin mask': maskname
}
def transform(img, source, target, **kwargs):
finalimage = openImageFile(kwargs['Final Image'])
output = None
if 'inputmaskname' not in kwargs:
pastemask, analsys, error = createMask(img, finalimage)
if error:
logging.getLogger('maskgen').error("Error creating inputmask " +
error)
splits = os.path.split(source)
pastemask.invert()
pastemask.save(splits[0] + '_inputmask.png')
pastemask = pastemask.to_array()
output = {'inputmaskname': splits[0] + '_inputmask.png'}
else:
pastemask = openImageFile(kwargs['inputmaskname']).to_array()
finalimage = finalimage.to_array()
sourceimg = np.copy(img.to_array()).astype('float')
if len(pastemask.shape) > 2:
if pastemask.shape[2] > 3:
mult = pastemask[:, :, 3] / 255.0
else:
mult = pastemask[:, :, 1] / 255.0
else:
mult = pastemask / 255.0
for dim in range(sourceimg.shape[2]):
sourceimg[:,:,dim] = \
(sourceimg[:,:,dim]*(1.0-mult)).astype('uint8') + \
(finalimage[:,:,dim]*(mult)).astype('uint8')
ImageWrapper(sourceimg.astype('uint8')).save(target)
return output, None
def transform(img, source, target, **kwargs):
# NOTE: arguments passed on AS IS!!
im = openImageFile(source, args=kwargs)
imarray = np.array(im)
#deal with grayscale image
if len(imarray.shape) == 2:
w, h = imarray.shape
ret = np.empty((w, h, 3), dtype=np.uint8)
ret[:, :, :] = imarray[:, :, np.newaxis]
imarray = ret
analysis = {}
if 'Crop' in kwargs and kwargs['Crop'] == 'yes':
dims = getExifDimensions(source, crop=True)
if len(dims) > 0 and imarray.shape[0] != dims[0][0]:
h = int(imarray.shape[0] - dims[0]) / 2
w = int(imarray.shape[1] - dims[1]) / 2
imarray = imarray[h:-h, w:-w]
analysis['location'] = str((h, w))
if 'Image Rotated' in kwargs and kwargs['Image Rotated'] == 'yes':
orientation = exif.getOrientationFromExif(source)
if orientation is not None:
analysis.update(exif.rotateAnalysis(orientation))
imarray = exif.rotateAccordingToExif(imarray,
orientation,
counter=True)
ImageWrapper(imarray).save(target, format='PNG')
analysis['Image Rotated'] = 'yes' if 'rotation' in analysis else 'no'
return analysis, None
def transform(img, source, target, **kwargs):
pixelWidth = int(kwargs['width'])
pixelHeight = int(kwargs['height'])
ImageWrapper(
resizeImage(img.to_array(), (pixelHeight, pixelWidth),
kwargs['interpolation'])).save(target)
return None, None
def test_image_donor(self):
import numpy as np
from maskgen.image_wrap import ImageWrapper
graph = Mock()
def lkup_preds(x):
return {'b': ['a'], 'e': ['d']}[x]
def lkup_edge(x, y):
return \
{'ab': {'op': 'NoSelect'}, 'de': {'op': 'SelectRegion'}}[
x + y]
withoutalpha = ImageWrapper(np.zeros((400, 400, 3), dtype=np.uint8))
withAlpha = ImageWrapper(np.zeros((400, 400, 4), dtype=np.uint8))
mask = ImageWrapper(np.ones((400, 400), dtype=np.uint8) * 255)
mask.image_array[0:30, 0:30] = 0
withAlpha.image_array[0:30, 0:30, 3] = 255
graph.predecessors = lkup_preds
graph.get_edge = lkup_edge
graph.dir = '.'
graph.get_edge_image = Mock(return_value=mask)
donor = InterpolateDonor(
graph, 'e', 'f', 'x',
(withoutalpha, self.locateFile('tests/videos/sample1.mov')),
(withAlpha, self.locateFile('tests/videos/sample1.mov')))
mask = donor.create(arguments={})
self.assertTrue(np.all(mask.image_array[0:30, 0:30] == 255))
self.assertEquals(900, np.sum((mask.image_array / 255)))
donor = InterpolateDonor(
graph, 'b', 'c', 'x',
(withoutalpha, self.locateFile('tests/videos/sample1.mov')),
(withAlpha, self.locateFile('tests/videos/sample1.mov')))
mask = donor.create(arguments={})
self.assertIsNone(mask)
donor = InterpolateDonor(
graph, 'b', 'c', 'x',
(withAlpha, self.locateFile('tests/videos/sample1.mov')),
(withAlpha, self.locateFile('tests/videos/sample1.mov')))
mask = donor.create(arguments={})
self.assertTrue(np.all(mask.image_array[0:30, 0:30] == 0))
self.assertEquals(159100, np.sum((mask.image_array / 255)))
def transform(img, source, target, **kwargs):
areaConstraints = (int(kwargs['area.lower.bound']) if 'area.lower.bound'
in kwargs else 0, int(kwargs['area.upper.bound'])
if 'area.upper.bound' in kwargs else sys.maxint)
annotation, mask = createMaskImageWithParams(
np.asarray(img), source, kwargs, areaConstraint=areaConstraints)
ImageWrapper(mask).save(target)
return {'subject': annotation}, None
def transform(img, source, target, **kwargs):
pixelWidth = int(kwargs['pixel_width'])
pixelHeight = int(kwargs['pixel_height'])
x = int(kwargs['crop_x'])
y = int(kwargs['crop_y'])
cv_image = numpy.array(img)
new_img = cv_image[y:-(pixelHeight - y), x:-(pixelWidth - x), :]
ImageWrapper(new_img).save(target)
return None, None
def transform(img, source, target, **kwargs):
channel_map = {"red": 0, "green": 1, "blue": 2}
donor = kwargs['mask'] if 'mask' in kwargs else source
channel_name = kwargs['channel'] if 'channel' in kwargs else "green"
img = openImageFile(donor)
color_im = np.zeros((img.size[1], img.size[0], 3), dtype=np.uint8)
color_im[:, :, channel_map[channel_name]] = img.image_array
ImageWrapper(color_im).save(target)
return None, None
def xtest_mask_gen(self):
from maskgen import tool_set
from maskgen.image_wrap import ImageWrapper
aorig = image_wrap.openImageFile('tests/images/0c5a0bed2548b1d77717b1fb4d5bbf5a-TGT-17-CLONE.png')
a = aorig.convert('YCbCr')
borig = image_wrap.openImageFile('tests/images/0c5a0bed2548b1d77717b1fb4d5bbf5a-TGT-18-CARVE.png')
b = borig.convert('YCbCr')
mask = tool_set._tallySeam((a.to_array()[:, :, 0]),
(b.to_array()[20:, :, 0]))
ImageWrapper(mask).save('seam_mask.png')
def transform(img, source, target, **kwargs):
# source = zip of images
if 'Registration Type' in kwargs:
reg_type = kwargs['Registration Type']
else:
reg_type = 'ECC'
zipf = ZipCapture(source)
imgs = []
logger = logging.getLogger("maskgen")
retrieved, zip_image = zipf.read()
if not retrieved:
raise ValueError("Zip File {0} is empty".format(
os.path.basename(source)))
registrar = {
'ECC': OpenCVECCRegistration(os.path.join(zipf.dir, zipf.names[0]))
}
reg_tool = registrar[reg_type]
if 'Image Rotated' in kwargs and kwargs['Image Rotated'] == 'yes':
try:
orientation = getValue(zipf.get_exif(), 'Orientation', None)
except KeyError:
orientation = None
else:
orientation = None
logger.debug("Beginning image alignment for " + os.path.basename(source))
while retrieved:
aligned = reg_tool.align(zip_image)
imgs.append(aligned)
retrieved, zip_image = zipf.read()
logger.debug(os.path.basename(source) + " alignment complete")
if not imgs:
return None, False
stacks = np.stack(np.asarray(imgs))
median_img = np.median(stacks, 0)
analysis = {'Merge Operation': 'Median Pixel'}
if orientation is not None:
analysis.update(exif.rotateAnalysis(orientation))
median_img = exif.rotateAccordingToExif(median_img,
orientation,
counter=True)
ImageWrapper(median_img).save(target, format='PNG')
analysis['Image Rotated'] = 'yes' if 'rotation' in analysis else 'no'
return analysis, None
def open_heic(filename, isMask=False):
from wand.image import Image as WandImage
from PIL import Image
import numpy as np
from io import BytesIO
from maskgen.image_wrap import ImageWrapper
depthmap = {'8': 'uint8', '16':'uint16', '32':'uint32'}
with WandImage(filename=filename) as wand_img:
with wand_img.convert(format='bmp') as img:
img_buffer = np.asarray(bytearray(img.make_blob()), dtype=depthmap[str(img.depth)])
bytesio = BytesIO(img_buffer)
pilImage = Image.open(fp=bytesio)
return ImageWrapper(np.asarray(pilImage), mode=pilImage.mode, info=pilImage.info, to_mask=isMask, filename=filename)
def transform(img, source, target, **kwargs):
cv_image = img.to_array()
shape = cv_image.shape
percentageWidth = float(kwargs['percentage_width'])
percentageHeight = float(kwargs['percentage_height'])
pixelWidth = int(shape[1] * percentageWidth)
pixelHeight = int(shape[0] * percentageHeight)
pixelWidth = pixelWidth - pixelWidth % 8
pixelHeight = pixelHeight - pixelHeight % 8
ImageWrapper(
resizeImage(cv_image, (pixelHeight, pixelWidth),
kwargs['interpolation'])).save(target)
return None, None
def pasteAnywhere(img, img_to_paste, mask_of_image_to_paste, simple):
# get gravity center for rotation
w, h, area, cx_gra, cy_gra = minimum_bounding_box(mask_of_image_to_paste)
if not simple:
# use gravity center to rotate
rot_mat = build_random_transform(img_to_paste, mask_of_image_to_paste,
(cx_gra, cy_gra))
img_to_paste = cv2.warpAffine(
img_to_paste, rot_mat,
(img_to_paste.shape[1], img_to_paste.shape[0]))
mask_of_image_to_paste = cv2.warpAffine(
mask_of_image_to_paste, rot_mat,
(img_to_paste.shape[1], img_to_paste.shape[0]))
# x,y is the Geometry center(gravity center), which can't align to the crop center(bounding box center)
w, h, area, cx, cy = minimum_bounding_box(mask_of_image_to_paste)
else:
rot_mat = np.array([[1, 0, 0], [0, 1, 0]]).astype('float')
# To calculate the bbox center
x, y, w1, h1 = tool_set.widthandheight(mask_of_image_to_paste)
if img.size[0] < w + 4:
w = img.size[0] - 2
xplacement = w / 2 + 1
else:
xplacement = random.randint(w / 2 + 1, img.size[0] - w / 2 - 1)
if img.size[1] < h + 4:
h = img.size[1] - 2
yplacement = h / 2 + 1
else:
yplacement = random.randint(h / 2 + 1, img.size[1] - h / 2 - 1)
output_matrix = np.eye(3, dtype=float)
for i in range(2):
for j in range(2):
output_matrix[i, j] = rot_mat[i, j]
# That is the correct offset
output_matrix[0, 2] = rot_mat[0, 2] + xplacement - x - w1 / 2
output_matrix[1, 2] = rot_mat[1, 2] + yplacement - y - h1 / 2
return output_matrix, tool_set.place_in_image(
ImageWrapper(img_to_paste).to_mask().to_array(),
img_to_paste,
np.asarray(img),
(xplacement, yplacement),
# x,y have no use
rect=(x, y, w, h))
def test_two_images(self):
from maskgen import tool_set
from maskgen.image_wrap import ImageWrapper
aorig = image_wrap.openImageFile('tests/images/0c5a0bed2548b1d77717b1fb4d5bbf5a-TGT-17-CLONE.png')
a = aorig.convert('YCbCr')
borig = image_wrap.openImageFile('tests/images/0c5a0bed2548b1d77717b1fb4d5bbf5a-TGT-18-CARVE.png')
b = borig.convert('YCbCr')
index = CSHSingleIndexer()
index.init(number_of_tables=2, length_of_tables=6)
index.hash_images(ImageWrapper(a.to_array()[:, :, 0]),
ImageWrapper(b.to_array()[:, :, 0]))
collector = ImageLabel()
analysis = {}
src_dst_pts = tool_set.getMatchedSIFeatures()
data_set, labels = find_lines(src_dst_pts[0], src_dst_pts[1])
label_set = set(np.unique(labels))
label_set = set(label_set).difference(set([0, 1]))
dist = 125 / len(label_set)
label_map = {}
i = 0
for label in np.unique(labels):
if label >= 0:
label_map[label] = 124 + i * dist
i += 1
amask = np.zeros(a.to_array().shape, dtype=np.uint8)
bmask = np.zeros(b.to_array().shape, dtype=np.uint8)
for i in range(len(data_set)):
result = data_set[i]
if labels[i] >= 0:
amask[max(int(result[2][0][0]) - 5, 0):min(int(result[2][0][0]) + 5, amask.shape[0]),
max(int(result[2][0][1]) - 5, 0):min(int(result[2][0][1]) + 5, amask.shape[1]), :] = label_map[labels[i]]
bmask[max(int(result[3][0][0]) - 5, 0):min(int(result[3][0][0]) + 5, amask.shape[0]),
max(int(result[3][0][1]) - 5, 0):min(int(result[3][0][1]) + 5, amask.shape[1]), :] = label_map[labels[i]]
ImageWrapper(amask).save('amask.png')
ImageWrapper(bmask).save('bmask.png')
def test_wh_generation(self):
from maskgen.image_wrap import ImageWrapper
seq2 = gen_wh(4).astype(int)
self.assertEqual(seq2.shape[0], 16)
im = np.copy(seq2)
im[im < 0 ]= 0
im[im > 0] =255
ImageWrapper(im.astype('uint8')).save('foo.png')
for i in range(4):
for j in range(4):
quadsum = sum(sum(seq2[i*4:i*4+4,j*4:j*4+4]))
if (i,j)==(0,0):
self.assertEqual(16,quadsum, msg=str((i,j)))
else:
self.assertEqual(0, quadsum,msg=str((i,j)))
def transform(img, source, target, **kwargs):
cv_image = numpy.array(img)
shape = cv_image.shape
snapto8 = 'eightbit_boundary' in kwargs and kwargs[
'eightbit_boundary'] == 'yes'
percentageWidth = float(kwargs['percentage_width'])
percentageHeight = float(kwargs['percentage_height'])
pixelWidth = int(shape[1] * percentageWidth)
pixelHeight = int(shape[0] * percentageHeight)
# only percentages are too big
if pixelHeight > shape[0] / 2:
pixelHeight = shape[0] / 2 - 8
if pixelWidth > shape[1] / 2:
pixelWidth = shape[1] / 2 - 8
start_y = 0
end_y = shape[0]
start_x = 0
end_x = shape[1]
if snapto8:
pixelWidth = (pixelWidth + (8 - pixelWidth % 8))
pixelHeight = (pixelHeight + (8 - pixelHeight % 8))
# case where width is not cropped
if pixelWidth > 0:
if snapto8:
start_x = randint(1, pixelWidth / 8 -
1) * 8 if pixelWidth > 8 else 0
else:
start_x = randint(1, pixelWidth - 1) if pixelWidth > 1 else 0
end_x = -(pixelWidth - start_x)
if pixelHeight > 0:
if snapto8:
start_y = randint(1, pixelHeight / 8 -
1) * 8 if pixelHeight > 8 else 0
else:
start_y = randint(1, pixelHeight - 1) if pixelHeight > 1 else 0
end_y = -(pixelHeight - start_y)
new_img = cv_image[start_y:end_y, start_x:end_x, :]
ImageWrapper(new_img).save(target)
return {
'crop_x': start_x,
'crop_y': start_y,
'crop_width': pixelWidth,
'crop_height': pixelHeight
}, None
def test_SIFT(self):
from maskgen.image_wrap import ImageWrapper
img1 = ImageWrapper(
np.random.randint(0, 255, (4000, 5000, 3), dtype='uint8'))
img2 = ImageWrapper(
np.random.randint(0, 255, (8000, 8000, 3), dtype='uint8'))
img2.image_array[1000:2000,
1000:2000, :] = img1.image_array[2000:3000,
2000:3000, :]
mask1 = ImageWrapper(np.zeros((4000, 5000), dtype='uint8'))
mask1.image_array[2000:3000, 2000:3000] = 255
mask2 = ImageWrapper(np.zeros((8000, 8000), dtype='uint8'))
mask2.image_array[1000:2000, 1000:2000] = 255
features = tool_set.getMatchedSIFeatures(img1,
img2,
mask1=mask1,
mask2=mask2,
arguments={
'homography max matches':
'2000',
'homography': 'RANSAC-4'
})
img1 = ImageWrapper(
np.random.randint(0, 65535, (4000, 5000, 3), dtype='uint16'))
img2 = ImageWrapper(
np.random.randint(0, 65535, (8000, 8000, 3), dtype='uint16'))
img2.image_array[1000:2000,
1000:2000, :] = img1.image_array[2000:3000,
2000:3000, :]
mask1 = ImageWrapper(np.zeros((4000, 5000), dtype='uint8'))
mask1.image_array[2000:3000, 2000:3000] = 255
mask2 = ImageWrapper(np.zeros((8000, 8000), dtype='uint8'))
mask2.image_array[1000:2000, 1000:2000] = 255
features = tool_set.getMatchedSIFeatures(img1,
img2,
mask1=mask1,
mask2=mask2,
arguments={
'homography max matches':
'2000',
'homography': 'RANSAC-4'
})
def transform(img, source, target, **kwargs):
rotation = int(kwargs['rotation'])
imgdata = img.to_array()
#if len(imgdata.shape) > 2:
# imgdata = cv2.cvtColor(imgdata, cv2.COLOR_RGB2GRAY)
rows, cols, channels = imgdata.shape
M = cv2.getRotationMatrix2D((cols / 2, rows / 2), rotation, 1)
constant = int(imgdata.max())
rotated_img = cv2.warpAffine(imgdata,
M, (cols, rows),
flags=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_REPLICATE)
#rotated_img = rotateImage(rotation,(rows / 2,cols / 2),imgdata)
#out_img = cv2.cvtColor(rotated_img, cv2.COLOR_GRAY2RGB)
# display(out_img, 'rotation')
ImageWrapper(rotated_img).save(target)
return {'transform matrix': serializeMatrix(M)}, None
def transform(img, source, target, **kwargs):
donor = kwargs['donor'] # raise error if missing donor
im_source = openImageFile(source).image_array
im_donor_trace = openImageFile(donor).image_array
if np.shape(im_source)[0:2] != np.shape(im_donor_trace)[0:2]:
orientation_source = np.shape(im_source)[0] - np.shape(im_source)[1]
orientation_donor = np.shape(im_donor_trace)[0] - np.shape(
im_donor_trace)[1]
if sign(orientation_source) != sign(orientation_donor):
im_donor_trace = np.rot90(im_donor_trace, -1)
location, im_source = centeredCrop(im_source, im_donor_trace)
ImageWrapper(im_source).save(target, format='PNG')
else:
location = (0, 0)
return {'location': location}, None
def pca(i, component=0, normalize=False):
from sklearn.decomposition import PCA
import cv2
i1 = np.reshape(i.image_array[:, :, 0], i.size[1] * i.size[0])
i2 = np.reshape(i.image_array[:, :, 1], i.size[1] * i.size[0])
i3 = np.reshape(i.image_array[:, :, 2], i.size[1] * i.size[0])
X = np.stack([i1, i2, i3])
pca = PCA(3)
pca.fit_transform(X)
A = pca.components_[component] * pca.explained_variance_ratio_[component]
A1 = (A - min(A)) / (max(A) - min(A)) * 255
if normalize:
imhist, bins = np.histogram(A1, 256, normed=True)
cdf = imhist.cumsum() # cumulative distribution function
cdf = 255 * cdf / cdf[-1]
A1 = np.interp(A1, bins[:-1], cdf)
PCI = np.reshape(A1, i.image_array[:, :, 0].shape).astype('uint8')
return ImageWrapper(PCI)
def ela(i):
from PIL import Image
import time
import os
tmp = 't' + str(time.clock()) + '.jpg'
Image.fromarray(i.image_array).save(tmp, 'JPEG', quality=95)
with open(tmp, 'rb') as f:
i_qa = Image.open(f)
i_qa_array = np.asarray(i_qa)
i_qa.load()
os.remove(tmp)
ela_im = i.image_array - i_qa_array
maxdiff = np.max(ela_im)
mindiff = np.min(ela_im)
scale = 255.0 / (maxdiff - mindiff)
for channel in range(ela_im.shape[2]):
ela_im[:, :, channel] = histeq(ela_im[:, :,
channel]) # - mindiff) * scale
return ImageWrapper(ela_im.astype('uint8'))
def repairMask(graph, start, end):
"""
:param graph:
:param start:
:param end:
:return:
@type graph: ImageGraph
@type start: str
@type end: str
"""
edge = graph.get_edge(start, end)
startimage, name = graph.get_image(start)
finalimage, fname = graph.get_image(end)
mask = graph.get_edge_image(start, end, 'maskname')
inputmaskname = os.path.splitext(name)[0] + '_inputmask.png'
ImageWrapper(composeCloneMask(mask, startimage,
finalimage)).save(inputmaskname)
edge['inputmaskname'] = os.path.split(inputmaskname)[1]
graph.setDataItem('autopastecloneinputmask', 'yes')
def transform(img, source, target, **kwargs):
im_source = openImageFile(source).image_array
dimensionX, dimensionY = coordsFromString(kwargs['crop dimensions'])
eyePosX, eyePosY = coordsFromString(str(kwargs['right eye position'])) if 'right eye position' in kwargs else None,None
chinPosX, chinPosY = coordsFromString(str(kwargs['chin position'])) if 'chin position' in kwargs else None,None
filemap = kwargs['filemap'] if 'filemap' in kwargs else None
if filemap is not None:
face = selectfacefromdata(filemap, kwargs['donor'], im_source.shape, cropDimensions=(dimensionX,dimensionY))
#eyePosX = face['right_eye_x']
eyePosY = face['right_eye_y']
chinPosX = face['chin_x']
#chinPosY = face['chin_y']
faceCenter = chinPosX, eyePosY
top = (faceCenter[1] - dimensionY / 2) - (faceCenter[1] - dimensionY / 2) % 8
left = (faceCenter[0] - dimensionX / 2) - (faceCenter[0] - dimensionX / 2) % 8
im_source = im_source[top:top + dimensionY, left:left + dimensionX, :]
ImageWrapper(im_source).save(target, format='PNG')
return {"top":top, "left":left},None
def paste(self, video_file, object, codec):
vfi = cv2api_delegate.videoCapture(video_file)
width = int(vfi.get(cv2api_delegate.prop_frame_width))
height = int(vfi.get(cv2api_delegate.prop_frame_height))
fourcc = fourccs[codec]
video_prefix = video_file[:video_file.rfind('.')]
video_file_output = video_prefix + '_paste.' + suffices[codec]
video_file_output = os.path.split(video_file_output)[1]
vfo = cv2api_delegate.videoWriter(video_file_output, fourcc, (vfi.get(cv2api_delegate.prop_fps)), (width, height))
if not vfo.isOpened():
raise ValueError('VideoWriter failed to open.')
try:
while vfi.isOpened() and vfo.isOpened():
r,f = vfi.read()
if not r:
break
i = ImageWrapper(self.paste_in(f,object),mode='BGR')
vfo.write(i.image_array)
finally:
vfi.release()
vfo.release()
self.addFileToRemove(video_file_output)
return video_file_output
def transform(img, source, target, **kwargs):
mask = numpy.asarray(
tool_set.openImageFile(kwargs['inputmaskname']).to_mask())
img_array = img.to_array()
result = None
if len(img_array.shape) == 2:
# grey
result = numpy.zeros(
(img_array.shape[0], img_array.shape[1], 2)).astype('uint8')
result[:, :, 0] = img_array
result[:, :, 1] = mask
elif len(img_array.shape) == 3:
if img_array.shape[2] == 4:
result = img_array
result[:, :, 4] = mask
else:
result = numpy.zeros(
(img_array.shape[0], img_array.shape[1], 4)).astype('uint8')
result[:, :, 0] = img_array[:, :, 0]
result[:, :, 1] = img_array[:, :, 1]
result[:, :, 2] = img_array[:, :, 2]
result[:, :, 3] = mask
ImageWrapper(result).save(target)
return None, None
def create_zero(h, w):
return ImageWrapper(np.zeros((h, w), dtype='uint8'))
def copyFrames(in_file,
out_file,
start_time,
end_time,
paste_time,
codec=None):
"""
:param in_file: is the full path of the video file from which to drop frames
:param out_file: resulting video file
:param start_time: (milli,frame no) to start to fill
:param end_time: (milli,frame no) end fil
:param codec:
:return:
"""
import time
logger = logging.getLogger('maskgen')
frames_to_write = []
frames_to_copy = []
cap = cv2api_delegate.videoCapture(in_file)
fourcc = cv2api_delegate.get_fourcc(
str(codec)) if codec is not None else cap.get(
cv2api_delegate.fourcc_prop)
fps = cap.get(cv2api_delegate.prop_fps)
height = int(np.rint(cap.get(cv2api_delegate.prop_frame_height)))
width = int(np.rint(cap.get(cv2api_delegate.prop_frame_width)))
out_video = cv2api_delegate.videoWriter(out_file,
fourcc,
fps, (width, height),
isColor=1)
if not out_video.isOpened():
err = out_file + " fourcc: " + str(fourcc) + " FPS: " + str(fps) + \
" H: " + str(height) + " W: " + str(width)
raise ValueError('Unable to create video ' + err)
copy_time_manager = VidTimeManager(startTimeandFrame=start_time,
stopTimeandFrame=end_time)
paste_time_manager = VidTimeManager(startTimeandFrame=paste_time)
write_count = 0
try:
while (not copy_time_manager.isPastTime() and cap.grab()):
ret, frame = cap.retrieve()
elapsed_time = float(cap.get(cv2api_delegate.prop_pos_msec))
copy_time_manager.updateToNow(elapsed_time)
paste_time_manager.updateToNow(elapsed_time)
if not copy_time_manager.isBeforeTime(
) and not copy_time_manager.isPastTime():
frames_to_copy.append(frame)
if not paste_time_manager.isBeforeTime():
frames_to_write.append(frame)
else:
out_video.write(frame)
write_count += 1
if logger.isEnabledFor(logging.DEBUG):
logger.debug("First to copy {}".format(
hashlib.sha256(frames_to_copy[0]).hexdigest()))
logger.debug("Last to copy {}".format(
hashlib.sha256(frames_to_copy[-1]).hexdigest()))
ImageWrapper(frames_to_copy[0]).save('first_' + str(time.clock()) +
'.png')
ImageWrapper(frames_to_copy[-1]).save('last_' + str(time.clock()) +
'.png')
if len(frames_to_write) > 0:
# paste prior to copy
for copy_frame in frames_to_copy:
out_video.write(copy_frame)
for write_frame in frames_to_write:
out_video.write(write_frame)
else:
# paste after to copy
frame = None
while (paste_time_manager.isBeforeTime() and cap.grab()):
ret, frame = cap.retrieve()
elapsed_time = float(cap.get(cv2api_delegate.prop_pos_msec))
paste_time_manager.updateToNow(elapsed_time)
if paste_time_manager.isBeforeTime():
out_video.write(frame)
write_count += 1
for copy_frame in frames_to_copy:
out_video.write(copy_frame)
if frame is not None:
out_video.write(frame)
while (cap.grab()):
ret, frame = cap.retrieve()
out_video.write(frame)
finally:
cap.release()
out_video.release()
return write_count