def get_bilinear_pixel_interpolation(self, img, posX, posY):
out = []
# Get integer parts of positions
modXi = int(posX)
modYi = int(posY)
# Get fractional parts of positions
modXf = posX - modXi
modYf = posY - modYi
# To avoid going over image bounderies
height, width = util.get_image_dimensions(img)
modXiPlusOneLim = min(modXi + 1, width - 1)
modYiPlusOneLim = min(modYi + 1, height - 1)
# Get pixels in four corners
for channel in range(img.shape[2]):
bottom_left = img[modYi, modXi, channel]
bottom_right = img[modYi, modXiPlusOneLim, channel]
top_left = img[modYiPlusOneLim, modXi, channel]
top_right = img[modYiPlusOneLim, modXiPlusOneLim, channel]
# Calculate interpolation
obtained_bottom = modXf * bottom_right + (1. - modXf) * bottom_left
obtained_top = modXf * top_right + (1. - modXf) * top_left
new_channel = modYf * obtained_top + (1. - modYf) * obtained_bottom
out.append(int(new_channel + 0.5))
return out
def apply_bilinear_interpolation(self, img, scale):
if scale <= 0:
return img
imHeight, imWidth = util.get_image_dimensions(img)
enlargedShape = list(
map(int, [imHeight * scale, imWidth * scale, img.shape[2]]))
enlargedImg = np.empty(enlargedShape, dtype=np.uint8)
enlargedHeight, enlargedWidth = util.get_image_dimensions(enlargedImg)
rowScale = float(imHeight) / float(enlargedHeight)
colScale = float(imWidth) / float(enlargedWidth)
for row in range(enlargedHeight):
for col in range(enlargedWidth):
oriRow = row * rowScale # Find position in original image
oriCol = col * colScale
enlargedImg[row, col] = self.get_bilinear_pixel_interpolation(
img, oriCol, oriRow)
return enlargedImg
def get_nearest_neighbour_pixel_interpolation(self, img, posX, posY):
out = []
# Get integer parts of positions
modXi = int(posX)
modYi = int(posY)
# Get fractional parts of positions
modXf = posX - modXi
modYf = posY - modYi
# To avoid going over image bounderies
height, width = util.get_image_dimensions(img)
modXiPlusOneLim = min(modXi + 1, width - 1)
modYiPlusOneLim = min(modYi + 1, height - 1)
for channel in range(img.shape[2]):
target = img[modYi, modXi, channel]
out.append(int(target + 0.5))
return out
def apply_sepia(img):
height, width = util.get_image_dimensions(img)
obtained, img = util.get_empty_image_with_same_dimensions(img)
r_matrix, g_matrix, b_matrix = rgb.get_rgb_layers(img)
for i in range(height):
for j in range(width):
r, g, b = r_matrix[i][j], g_matrix[i][j], b_matrix[i][j]
tr = int(0.393 * r + 0.769 * g + 0.189 * b)
tg = int(0.349 * r + 0.686 * g + 0.168 * b)
tb = int(0.272 * r + 0.534 * g + 0.131 * b)
r = ColorFilter.__normalize_max_value(tr, r)
g = ColorFilter.__normalize_max_value(tg, g)
b = ColorFilter.__normalize_max_value(tb, b)
obtained[i][j] = [r, g, b]
return obtained
def add_background(background, img, coord=(0, 0)):
img = img_as_ubyte(img)
x_size, y_size = util.get_image_dimensions(img)
(y_begin, x_begin) = coord
x_end = x_begin + x_size
y_end = y_begin + y_size
background_crop = background[
x_begin:x_end,
y_begin:y_end,
:]
pixel_preserve = (img[:, :, 3] > 10)
background_crop[pixel_preserve] = img[pixel_preserve]
background[x_begin:x_end, y_begin:y_end, :] = background_crop
return background
