Python cv2.COLOR_BGR2BGRA Examples
The following are 11
code examples of cv2.COLOR_BGR2BGRA().
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Example #1
| Source File: engine_cv3.py From opencv-engine with MIT License | 5 votes |
|
def enable_alpha(self):
if self.image_channels < 4:
with_alpha = np.zeros((self.size[1], self.size[0], 4), self.image.dtype)
if self.image_channels == 3:
cv2.cvtColor(self.image, cv2.COLOR_BGR2BGRA, with_alpha)
else:
cv2.cvtColor(self.image, cv2.COLOR_GRAY2BGRA, with_alpha)
self.image = with_alpha
Example #2
| Source File: utils.py From ad-versarial with MIT License | 5 votes |
|
def to_alpha(logo):
if has_alpha(logo):
return logo
if is_gray(logo):
return cv2.cvtColor(logo, cv2.COLOR_GRAY2BGRA)
else:
return cv2.cvtColor(logo, cv2.COLOR_BGR2BGRA)
Example #3
| Source File: EnStrToPng.py From Hidden-Eye with GNU General Public License v3.0 | 5 votes |
|
def HideStringIntoPng_8bit1pixel(img,DataArray,seed = 0):
# saving points where data is hidden
DataHidenX = []
DataHidenY = []
DataHidenXY = []
if(seed != 0):
rd.seed(seed)
h , w, c = img.shape
if c <= 3:
img = cv2.cvtColor(img,cv2.COLOR_BGR2BGRA)
# hiding data into image
counter = len(DataArray)
i = 0
while i < counter:
x = rd.randint(0,h -1)
y = rd.randint(0,w - 1)
while (x,y) in DataHidenXY:
x = rd.randint(0,h -1)
y = rd.randint(0,w - 1)
DataHidenXY.append((x,y))
DataHidenX.append(x)
DataHidenY.append(y)
img[x][y][0] |= 0x03
img[x][y][0] &= (0xfc | DataArray[i])
img[x][y][1] |= 0x03
img[x][y][1] &= (0xfc | DataArray[i + 1])
img[x][y][2] |= 0x03
img[x][y][2] &= (0xfc | DataArray[i + 2])
img[x][y][3] |= 0x03
img[x][y][3] &= (0xfc | DataArray[i + 3])
i += 4
return DataHidenX,DataHidenY,img
Example #4
| Source File: EnPdfToPng.py From Hidden-Eye with GNU General Public License v3.0 | 5 votes |
|
def HidePdfintoPng(img,DataArray,seed = 0):
# saving points where data is hidden
if(seed != 0):
rd.seed(seed)
h , w, c = img.shape
if c <= 3:
img = cv2.cvtColor(img,cv2.COLOR_BGR2BGRA)
# hiding data into image
counter = len(DataArray)
print (counter)
i = 0
x = 0
y = 0
while i < counter:
#print (i)
img[x][y][0] |= 0x03
img[x][y][0] &= (0xfc | DataArray[i])
img[x][y][1] |= 0x03
img[x][y][1] &= (0xfc | DataArray[i + 1])
img[x][y][2] |= 0x03
img[x][y][2] &= (0xfc | DataArray[i + 2])
img[x][y][3] |= 0x03
img[x][y][3] &= (0xfc | DataArray[i + 3])
i += 4
if(x == h -1):
break
if(y == w -1):
x += 1
y = 0
y += 1
return x,y-1,img
Example #5
| Source File: gui.py From PUBGIS with GNU General Public License v3.0 | 5 votes |
|
def __init__(self, parent, minimap_iterator, output_file, output_flags):
super(PUBGISWorkerThread, self).__init__(parent)
self.parent = parent
self.minimap_iterator = minimap_iterator
self.output_file = output_file
self.full_positions = []
self.timestamps = []
self.base_map_alpha = cv2.cvtColor(PUBGISMatch.full_map, cv2.COLOR_BGR2BGRA)
self.preview_map = cv2.cvtColor(PUBGISMatch.full_map, cv2.COLOR_BGR2BGRA)
self.output_flags = output_flags
Example #6
| Source File: image.py From surface-crack-detection with MIT License | 5 votes |
|
def overlay(image, layer):
if (len(layer.shape) == 2):
layer = cv2.cvtColor(layer, cv2.COLOR_GRAY2BGR)
image = cv2.cvtColor(image, cv2.COLOR_BGR2BGRA)
layer = cv2.cvtColor(layer, cv2.COLOR_BGR2BGRA)
layer[np.where((layer == [0,0,0,255]).all(axis=2))] = const.BACKGROUND_COLOR + [255]
layer[np.where((layer == [255,255,255,255]).all(axis=2))] = const.SEGMENTATION_COLOR + [255]
layer = cv2.addWeighted(image, 0.6, layer, 0.4, 0)
return layer
Example #7
| Source File: corruptions.py From robustness with Apache License 2.0 | 4 votes |
|
def spatter(x, severity=1):
c = [(0.65, 0.3, 4, 0.69, 0.6, 0),
(0.65, 0.3, 3, 0.68, 0.6, 0),
(0.65, 0.3, 2, 0.68, 0.5, 0),
(0.65, 0.3, 1, 0.65, 1.5, 1),
(0.67, 0.4, 1, 0.65, 1.5, 1)][severity - 1]
x = np.array(x, dtype=np.float32) / 255.
liquid_layer = np.random.normal(size=x.shape[:2], loc=c[0], scale=c[1])
liquid_layer = gaussian(liquid_layer, sigma=c[2])
liquid_layer[liquid_layer < c[3]] = 0
if c[5] == 0:
liquid_layer = (liquid_layer * 255).astype(np.uint8)
dist = 255 - cv2.Canny(liquid_layer, 50, 150)
dist = cv2.distanceTransform(dist, cv2.DIST_L2, 5)
_, dist = cv2.threshold(dist, 20, 20, cv2.THRESH_TRUNC)
dist = cv2.blur(dist, (3, 3)).astype(np.uint8)
dist = cv2.equalizeHist(dist)
ker = np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]])
dist = cv2.filter2D(dist, cv2.CV_8U, ker)
dist = cv2.blur(dist, (3, 3)).astype(np.float32)
m = cv2.cvtColor(liquid_layer * dist, cv2.COLOR_GRAY2BGRA)
m /= np.max(m, axis=(0, 1))
m *= c[4]
# water is pale turqouise
color = np.concatenate((175 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1])), axis=2)
color = cv2.cvtColor(color, cv2.COLOR_BGR2BGRA)
x = cv2.cvtColor(x, cv2.COLOR_BGR2BGRA)
return cv2.cvtColor(np.clip(x + m * color, 0, 1), cv2.COLOR_BGRA2BGR) * 255
else:
m = np.where(liquid_layer > c[3], 1, 0)
m = gaussian(m.astype(np.float32), sigma=c[4])
m[m < 0.8] = 0
# mud brown
color = np.concatenate((63 / 255. * np.ones_like(x[..., :1]),
42 / 255. * np.ones_like(x[..., :1]),
20 / 255. * np.ones_like(x[..., :1])), axis=2)
color *= m[..., np.newaxis]
x *= (1 - m[..., np.newaxis])
return np.clip(x + color, 0, 1) * 255
Example #8
| Source File: make_imagenet_c.py From robustness with Apache License 2.0 | 4 votes |
|
def spatter(x, severity=1):
c = [(0.65, 0.3, 4, 0.69, 0.6, 0),
(0.65, 0.3, 3, 0.68, 0.6, 0),
(0.65, 0.3, 2, 0.68, 0.5, 0),
(0.65, 0.3, 1, 0.65, 1.5, 1),
(0.67, 0.4, 1, 0.65, 1.5, 1)][severity - 1]
x = np.array(x, dtype=np.float32) / 255.
liquid_layer = np.random.normal(size=x.shape[:2], loc=c[0], scale=c[1])
liquid_layer = gaussian(liquid_layer, sigma=c[2])
liquid_layer[liquid_layer < c[3]] = 0
if c[5] == 0:
liquid_layer = (liquid_layer * 255).astype(np.uint8)
dist = 255 - cv2.Canny(liquid_layer, 50, 150)
dist = cv2.distanceTransform(dist, cv2.DIST_L2, 5)
_, dist = cv2.threshold(dist, 20, 20, cv2.THRESH_TRUNC)
dist = cv2.blur(dist, (3, 3)).astype(np.uint8)
dist = cv2.equalizeHist(dist)
# ker = np.array([[-1,-2,-3],[-2,0,0],[-3,0,1]], dtype=np.float32)
# ker -= np.mean(ker)
ker = np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]])
dist = cv2.filter2D(dist, cv2.CV_8U, ker)
dist = cv2.blur(dist, (3, 3)).astype(np.float32)
m = cv2.cvtColor(liquid_layer * dist, cv2.COLOR_GRAY2BGRA)
m /= np.max(m, axis=(0, 1))
m *= c[4]
# water is pale turqouise
color = np.concatenate((175 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1])), axis=2)
color = cv2.cvtColor(color, cv2.COLOR_BGR2BGRA)
x = cv2.cvtColor(x, cv2.COLOR_BGR2BGRA)
return cv2.cvtColor(np.clip(x + m * color, 0, 1), cv2.COLOR_BGRA2BGR) * 255
else:
m = np.where(liquid_layer > c[3], 1, 0)
m = gaussian(m.astype(np.float32), sigma=c[4])
m[m < 0.8] = 0
# m = np.abs(m) ** (1/c[4])
# mud brown
color = np.concatenate((63 / 255. * np.ones_like(x[..., :1]),
42 / 255. * np.ones_like(x[..., :1]),
20 / 255. * np.ones_like(x[..., :1])), axis=2)
color *= m[..., np.newaxis]
x *= (1 - m[..., np.newaxis])
return np.clip(x + color, 0, 1) * 255
Example #9
| Source File: make_cifar_c.py From robustness with Apache License 2.0 | 4 votes |
|
def spatter(x, severity=1):
c = [(0.62,0.1,0.7,0.7,0.5,0),
(0.65,0.1,0.8,0.7,0.5,0),
(0.65,0.3,1,0.69,0.5,0),
(0.65,0.1,0.7,0.69,0.6,1),
(0.65,0.1,0.5,0.68,0.6,1)][severity - 1]
x = np.array(x, dtype=np.float32) / 255.
liquid_layer = np.random.normal(size=x.shape[:2], loc=c[0], scale=c[1])
liquid_layer = gaussian(liquid_layer, sigma=c[2])
liquid_layer[liquid_layer < c[3]] = 0
if c[5] == 0:
liquid_layer = (liquid_layer * 255).astype(np.uint8)
dist = 255 - cv2.Canny(liquid_layer, 50, 150)
dist = cv2.distanceTransform(dist, cv2.DIST_L2, 5)
_, dist = cv2.threshold(dist, 20, 20, cv2.THRESH_TRUNC)
dist = cv2.blur(dist, (3, 3)).astype(np.uint8)
dist = cv2.equalizeHist(dist)
# ker = np.array([[-1,-2,-3],[-2,0,0],[-3,0,1]], dtype=np.float32)
# ker -= np.mean(ker)
ker = np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]])
dist = cv2.filter2D(dist, cv2.CV_8U, ker)
dist = cv2.blur(dist, (3, 3)).astype(np.float32)
m = cv2.cvtColor(liquid_layer * dist, cv2.COLOR_GRAY2BGRA)
m /= np.max(m, axis=(0, 1))
m *= c[4]
# water is pale turqouise
color = np.concatenate((175 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1])), axis=2)
color = cv2.cvtColor(color, cv2.COLOR_BGR2BGRA)
x = cv2.cvtColor(x, cv2.COLOR_BGR2BGRA)
return cv2.cvtColor(np.clip(x + m * color, 0, 1), cv2.COLOR_BGRA2BGR) * 255
else:
m = np.where(liquid_layer > c[3], 1, 0)
m = gaussian(m.astype(np.float32), sigma=c[4])
m[m < 0.8] = 0
# m = np.abs(m) ** (1/c[4])
# mud brown
color = np.concatenate((63 / 255. * np.ones_like(x[..., :1]),
42 / 255. * np.ones_like(x[..., :1]),
20 / 255. * np.ones_like(x[..., :1])), axis=2)
color *= m[..., np.newaxis]
x *= (1 - m[..., np.newaxis])
return np.clip(x + color, 0, 1) * 255
Example #10
| Source File: make_tinyimagenet_c.py From robustness with Apache License 2.0 | 4 votes |
|
def spatter(x, severity=1):
c = [(0.62,0.1,0.7,0.7,0.6,0),
(0.65,0.1,0.8,0.7,0.6,0),
(0.65,0.3,1,0.69,0.6,0),
(0.65,0.1,0.7,0.68,0.6,1),
(0.65,0.1,0.5,0.67,0.6,1)][severity - 1]
x = np.array(x, dtype=np.float32) / 255.
liquid_layer = np.random.normal(size=x.shape[:2], loc=c[0], scale=c[1])
liquid_layer = gaussian(liquid_layer, sigma=c[2])
liquid_layer[liquid_layer < c[3]] = 0
if c[5] == 0:
liquid_layer = (liquid_layer * 255).astype(np.uint8)
dist = 255 - cv2.Canny(liquid_layer, 50, 150)
dist = cv2.distanceTransform(dist, cv2.DIST_L2, 5)
_, dist = cv2.threshold(dist, 20, 20, cv2.THRESH_TRUNC)
dist = cv2.blur(dist, (3, 3)).astype(np.uint8)
dist = cv2.equalizeHist(dist)
# ker = np.array([[-1,-2,-3],[-2,0,0],[-3,0,1]], dtype=np.float32)
# ker -= np.mean(ker)
ker = np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]])
dist = cv2.filter2D(dist, cv2.CV_8U, ker)
dist = cv2.blur(dist, (3, 3)).astype(np.float32)
m = cv2.cvtColor(liquid_layer * dist, cv2.COLOR_GRAY2BGRA)
m /= np.max(m, axis=(0, 1))
m *= c[4]
# water is pale turqouise
color = np.concatenate((175 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1])), axis=2)
color = cv2.cvtColor(color, cv2.COLOR_BGR2BGRA)
x = cv2.cvtColor(x, cv2.COLOR_BGR2BGRA)
return cv2.cvtColor(np.clip(x + m * color, 0, 1), cv2.COLOR_BGRA2BGR) * 255
else:
m = np.where(liquid_layer > c[3], 1, 0)
m = gaussian(m.astype(np.float32), sigma=c[4])
m[m < 0.8] = 0
# m = np.abs(m) ** (1/c[4])
# mud brown
color = np.concatenate((63 / 255. * np.ones_like(x[..., :1]),
42 / 255. * np.ones_like(x[..., :1]),
20 / 255. * np.ones_like(x[..., :1])), axis=2)
color *= m[..., np.newaxis]
x *= (1 - m[..., np.newaxis])
return np.clip(x + color, 0, 1) * 255
Example #11
| Source File: make_imagenet_c_inception.py From robustness with Apache License 2.0 | 4 votes |
|
def spatter(x, severity=1):
c = [(0.65,0.3,4,0.69,0.9,0),
(0.65,0.3,3.5,0.68,0.9,0),
(0.65,0.3,3,0.68,0.8,0),
(0.65,0.3,1.2,0.65,1.8,1),
(0.67,0.4,1.2,0.65,1.8,1)][severity - 1]
x = np.array(x, dtype=np.float32) / 255.
liquid_layer = np.random.normal(size=x.shape[:2], loc=c[0], scale=c[1])
liquid_layer = gaussian(liquid_layer, sigma=c[2])
liquid_layer[liquid_layer < c[3]] = 0
if c[5] == 0:
liquid_layer = (liquid_layer * 255).astype(np.uint8)
dist = 255 - cv2.Canny(liquid_layer, 50, 150)
dist = cv2.distanceTransform(dist, cv2.DIST_L2, 5)
_, dist = cv2.threshold(dist, 20, 20, cv2.THRESH_TRUNC)
dist = cv2.blur(dist, (3, 3)).astype(np.uint8)
dist = cv2.equalizeHist(dist)
# ker = np.array([[-1,-2,-3],[-2,0,0],[-3,0,1]], dtype=np.float32)
# ker -= np.mean(ker)
ker = np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]])
dist = cv2.filter2D(dist, cv2.CV_8U, ker)
dist = cv2.blur(dist, (3, 3)).astype(np.float32)
m = cv2.cvtColor(liquid_layer * dist, cv2.COLOR_GRAY2BGRA)
m /= np.max(m, axis=(0, 1))
m *= c[4]
# water is pale turqouise
color = np.concatenate((175 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1]),
238 / 255. * np.ones_like(m[..., :1])), axis=2)
color = cv2.cvtColor(color, cv2.COLOR_BGR2BGRA)
x = cv2.cvtColor(x, cv2.COLOR_BGR2BGRA)
return cv2.cvtColor(np.clip(x + m * color, 0, 1), cv2.COLOR_BGRA2BGR) * 255
else:
m = np.where(liquid_layer > c[3], 1, 0)
m = gaussian(m.astype(np.float32), sigma=c[4])
m[m < 0.8] = 0
# m = np.abs(m) ** (1/c[4])
# mud brown
color = np.concatenate((63 / 255. * np.ones_like(x[..., :1]),
42 / 255. * np.ones_like(x[..., :1]),
20 / 255. * np.ones_like(x[..., :1])), axis=2)
color *= m[..., np.newaxis]
x *= (1 - m[..., np.newaxis])
return np.clip(x + color, 0, 1) * 255
