How to use the pyvips.Image.new_from_array function in pyvips
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libvips / pyvips / tests / test_convolution.py View on Github 
def setUp(self):
im = pyvips.Image.mask_ideal(100, 100, 0.5, reject=True, optical=True)
self.colour = im * [1, 2, 3] + [2, 3, 4]
self.colour = self.colour.copy(
interpretation=pyvips.Interpretation.SRGB)
self.mono = self.colour.extract_band(1)
self.mono = self.mono.copy(interpretation=pyvips.Interpretation.B_W)
self.all_images = [self.mono, self.colour]
self.sharp = pyvips.Image.new_from_array([[-1, -1, -1],
[-1, 16, -1],
[-1, -1, -1]], scale=8)
self.blur = pyvips.Image.new_from_array([[1, 1, 1],
[1, 1, 1],
[1, 1, 1]], scale=9)
self.line = pyvips.Image.new_from_array([[1, 1, 1],
[-2, -2, -2],
[1, 1, 1]])
self.sobel = pyvips.Image.new_from_array([[1, 2, 1],
[0, 0, 0],
[-1, -2, -1]])
self.all_masks = [self.sharp, self.blur, self.line, self.sobel]def setup_class(cls):
im = pyvips.Image.mask_ideal(100, 100, 0.5, reject=True, optical=True)
cls.colour = im * [1, 2, 3] + [2, 3, 4]
cls.colour = cls.colour.copy(interpretation=pyvips.Interpretation.SRGB)
cls.mono = cls.colour.extract_band(1)
cls.mono = cls.mono.copy(interpretation=pyvips.Interpretation.B_W)
cls.all_images = [cls.mono, cls.colour]
cls.sharp = pyvips.Image.new_from_array([[-1, -1, -1],
[-1, 16, -1],
[-1, -1, -1]], scale=8)
cls.blur = pyvips.Image.new_from_array([[1, 1, 1],
[1, 1, 1],
[1, 1, 1]], scale=9)
cls.line = pyvips.Image.new_from_array([[1, 1, 1],
[-2, -2, -2],
[1, 1, 1]])
cls.sobel = pyvips.Image.new_from_array([[1, 2, 1],
[0, 0, 0],
[-1, -2, -1]])
cls.all_masks = [cls.sharp, cls.blur, cls.line, cls.sobel]def test_buildlut(self):
M = pyvips.Image.new_from_array([[0, 0],
[255, 100]])
lut = M.buildlut()
assert lut.width == 256
assert lut.height == 1
assert lut.bands == 1
p = lut(0, 0)
assert p[0] == 0.0
p = lut(255, 0)
assert p[0] == 100.0
p = lut(10, 0)
assert p[0] == 100 * 10.0 / 255.0
M = pyvips.Image.new_from_array([[0, 0, 100],
[255, 100, 0],
[128, 10, 90]])
lut = M.buildlut()def test_buildlut(self):
M = pyvips.Image.new_from_array([[0, 0],
[255, 100]])
lut = M.buildlut()
assert lut.width == 256
assert lut.height == 1
assert lut.bands == 1
p = lut(0, 0)
assert p[0] == 0.0
p = lut(255, 0)
assert p[0] == 100.0
p = lut(10, 0)
assert p[0] == 100 * 10.0 / 255.0
M = pyvips.Image.new_from_array([[0, 0, 100],
[255, 100, 0],
[128, 10, 90]])
lut = M.buildlut()
assert lut.width == 256
assert lut.height == 1
assert lut.bands == 2
p = lut(0, 0)
assert_almost_equal_objects(p, [0.0, 100.0])
p = lut(64, 0)
assert_almost_equal_objects(p, [5.0, 95.0])def test_scale_offset(self):
im = pyvips.Image.new_from_array([1, 2], 8, 2)
assert im.width == 2
assert im.height == 1
assert im.bands == 1
assert im.scale == 8
assert im.offset == 2
assert im.avg() == 1.5def vips_bench(loops):
range_it = range(loops)
t0 = pyperf.perf_counter()
for loops in range_it:
im = pyvips.Image.new_from_file("tmp/x.tif", access='sequential')
im = im.crop(100, 100, im.width - 200, im.height - 200)
im = im.reduce(1.0 / 0.9, 1.0 / 0.9, kernel='linear')
mask = pyvips.Image.new_from_array([[-1, -1, -1],
[-1, 16, -1],
[-1, -1, -1]], scale=8)
im = im.conv(mask, precision='integer')
im.write_to_file("tmp/x2.tif")
return pyperf.perf_counter() - t0im = pyvips.Image.mask_ideal(100, 100, 0.5, reject=True, optical=True)
cls.colour = im * [1, 2, 3] + [2, 3, 4]
cls.colour = cls.colour.copy(interpretation=pyvips.Interpretation.SRGB)
cls.mono = cls.colour.extract_band(1)
cls.mono = cls.mono.copy(interpretation=pyvips.Interpretation.B_W)
cls.all_images = [cls.mono, cls.colour]
cls.sharp = pyvips.Image.new_from_array([[-1, -1, -1],
[-1, 16, -1],
[-1, -1, -1]], scale=8)
cls.blur = pyvips.Image.new_from_array([[1, 1, 1],
[1, 1, 1],
[1, 1, 1]], scale=9)
cls.line = pyvips.Image.new_from_array([[1, 1, 1],
[-2, -2, -2],
[1, 1, 1]])
cls.sobel = pyvips.Image.new_from_array([[1, 2, 1],
[0, 0, 0],
[-1, -2, -1]])
cls.all_masks = [cls.sharp, cls.blur, cls.line, cls.sobel]#!/usr/bin/python3
# use pyvips, but try to match the exact processing that the gegl code is
# doing, so everything is float, RGBA, and in linear light
import sys
import pyvips
im = pyvips.Image.new_from_file(sys.argv[1], access='sequential')
im = im.bandjoin(255)
im = im.colourspace('scrgb')
im = im.crop(100, 100, im.width - 200, im.height - 200)
im = im.reduce(1.0 / 0.9, 1.0 / 0.9, kernel='linear')
mask = pyvips.Image.new_from_array([[-1, -1, -1],
[-1, 16, -1],
[-1, -1, -1]], scale=8)
im = im.conv(mask, precision='integer')
im = im.colourspace('srgb')
im = im.extract_band(0, n=3)
im.write_to_file(sys.argv[2])#!/usr/bin/python3
import sys
import pyvips
im = pyvips.Image.new_from_file(sys.argv[1], access='sequential')
im = im.crop(100, 100, im.width - 200, im.height - 200)
im = im.reduce(1.0 / 0.9, 1.0 / 0.9, kernel='linear')
mask = pyvips.Image.new_from_array([[-1, -1, -1],
[-1, 16, -1],
[-1, -1, -1]], scale=8)
im = im.conv(mask, precision='integer')
im.write_to_file(sys.argv[2])#!/usr/bin/python
# import logging
# logging.basicConfig(level = logging.DEBUG)
import pyvips
a = pyvips.Image.new_from_array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], 8, 128)
print('scale =', a.get('scale'))
print('offset =', a.get('offset'))
a.write_to_file("x.v")pyvips
binding for the libvips image processing library, API mode
Package Health Score
82 / 100Popular pyvips functions
- pyvips._to_bytes
- pyvips._to_string
- pyvips.at_least_libvips
- pyvips.BandFormat
- pyvips.Error
- pyvips.ffi
- pyvips.ffi.cast
- pyvips.ffi.new
- pyvips.ffi.NULL
- pyvips.GValue
- pyvips.Image
- pyvips.Image.black
- pyvips.Image.identity
- pyvips.Image.new_from_array
- pyvips.Image.new_from_buffer
- pyvips.Image.new_from_file
- pyvips.Image.xyz
- pyvips.Operation.call
- pyvips.type_find
- pyvips.vips_lib