def test_exception_raised_when_param_missing():
data = np.random.rand(5)
x_node = Array()
x_node.addfilter(Missing("prob", "m_val"))
try:
x_node.generate_error(data, {"probb": .5, "m_val": np.nan})
except Exception as e:
assert "prob" in str(e)
def visualize(title, ind, data, flt, params):
root_node = Array()
root_node.addfilter(flt)
result = root_node.generate_error(data, params)
sub = fig.add_subplot(3, 4, ind + 1)
sub.imshow(result)
sub.set_title(title)
def main():
data = cv2.imread("data/landscape.png")
x_node = Array()
# Some filters, e.g. Rotation, expect the data to have a specific data type, e.g. uint8.
#
# This example takes an image, sums the original image and 180 degrees rotated version,
# and then takes the average of each pixel's value.
#
# cv2's rotation requires data to be uint8, but summing them needs datatype with larger
# precision, and thus type conversions are required.
const = Constant("c")
rot1 = Rotation("deg1")
mod1 = ModifyAsDataType("rotation_dtype", rot1)
rot2 = Rotation("deg2")
mod2 = ModifyAsDataType("rotation_dtype", rot2)
add = Addition(mod1, mod2)
avg = Division(add, const)
x_node.addfilter(ModifyAsDataType("avg_dtype", avg))
params = {}
params['c'] = 2
params['rotation_dtype'] = np.uint8
params['avg_dtype'] = np.uint16
params['deg1'] = 0
params['deg2'] = 180
result = x_node.generate_error(data, params)
cv2.imshow("Rotated", result)
cv2.waitKey(0)
def test_array_works_with_numpy_arrays():
a = np.array([0.])
x_node = Array()
x_node.addfilter(Missing("prob", "m_val"))
params = {"prob": 1., "m_val": np.nan}
out = x_node.generate_error(a, params)
assert np.isnan(out[0])
def test_modify_as_datatype():
a = np.array([256 + 42])
params = {}
params['dtype'] = np.int8
x_node = Array()
x_node.addfilter(ModifyAsDataType('dtype', Identity()))
out = x_node.generate_error(a, params)
assert np.array_equal(out, np.array([42]))
def test_seed_determines_result_for_fastrain_filter_two():
a = np.zeros((10, 10, 3), dtype=int)
x_node = Array()
x_node.addfilter(Rain("probability", "range"))
params = {"probability": 0.03, "range": 1}
out1 = x_node.generate_error(a, params, np.random.RandomState(seed=42))
out2 = x_node.generate_error(a, params, np.random.RandomState(seed=42))
assert np.array_equal(out1, out2)
def test_seed_determines_result_for_gap_filter():
a = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16])
x_node = Array()
x_node.addfilter(Gap("prob_break", "prob_recover", "missing_value"))
params = {"prob_break": 0.1, "prob_recover": 0.1, "missing_value": 1337}
out1 = x_node.generate_error(a, params, np.random.RandomState(seed=42))
out2 = x_node.generate_error(a, params, np.random.RandomState(seed=42))
assert np.array_equal(out1, out2)
def test_seed_determines_result_for_gaussian_noise_filter():
a = np.array([0., 1., 2., 3., 4.])
x_node = Array()
x_node.addfilter(GaussianNoise("mean", "std"))
params = {"mean": .5, "std": .5}
out1 = x_node.generate_error(a, params, np.random.RandomState(seed=42))
out2 = x_node.generate_error(a, params, np.random.RandomState(seed=42))
assert np.allclose(out1, out2, equal_nan=True)
def main():
img = Image.open("data/landscape.png")
data = np.array(img)
root_node = Array()
root_node.addfilter(JPEG_Compression('quality'))
result = root_node.generate_error(data, {'quality': 5})
filtered_img = Image.fromarray(result.astype('uint8'), 'RGB')
filtered_img.show()
def test_seed_determines_result_for_missing_filter():
a = np.array([0., 1., 2., 3., 4.])
x_node = Array()
x_node.addfilter(Missing("prob", "m_val"))
params = {"prob": .5, "m_val": np.nan}
out1 = x_node.generate_error(a, params, np.random.RandomState(seed=42))
out2 = x_node.generate_error(a, params, np.random.RandomState(seed=42))
assert np.allclose(out1, out2, equal_nan=True)
def test_seed_determines_result_for_uppercase_filter():
a = np.array(["hello world"])
x_node = Array()
x_node.addfilter(Uppercase("prob"))
params = {"prob": .5}
out1 = x_node.generate_error(a, params, np.random.RandomState(seed=42))
out2 = x_node.generate_error(a, params, np.random.RandomState(seed=42))
assert np.alltrue(out1 == out2)
def main():
img = Image.open("data/yellow_circle.jpg")
data = img_to_pixel_data(img)
root_node = Array()
root_node.addfilter(LensFlare())
result = root_node.generate_error(data, {})
filtered_img = Image.fromarray(result.astype('uint8'), 'RGB')
filtered_img.show()
def test_clip():
a = np.arange(5)
params = {}
params['min'] = 2
params['max'] = 3
x_node = Array()
x_node.addfilter(Clip('min', 'max'))
out = x_node.generate_error(a, params, np.random.RandomState(seed=42))
assert np.array_equal(out, np.array([2, 2, 2, 3, 3]))
def main():
img = Image.open("data/landscape.png")
data = np.array(img)
root_node = Array()
# root_node.addfilter(filters.Blur_Gaussian('std'))
# result = root_node.generate_error(data, {'std': 10.0})
root_node.addfilter(Blur('repeats', 'radius'))
result = root_node.generate_error(data, {'repeats': 1, 'radius': 20})
filtered_img = Image.fromarray(result.astype('uint8'), 'RGB')
filtered_img.show()
def test_seed_determines_result_for_strange_behaviour_filter():
def f(data, random_state):
return data * random_state.randint(2, 4)
a = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16])
x_node = Array()
x_node.addfilter(StrangeBehaviour("f"))
params = {"f": f}
out1 = x_node.generate_error(a, params, np.random.RandomState(seed=42))
out2 = x_node.generate_error(a, params, np.random.RandomState(seed=42))
assert np.array_equal(out1, out2)
def main():
d = {"resolution": 10}
data = plt.imread("data/landscape.png")
x_node = Array()
r = Resolution("resolution")
x_node.addfilter(r)
start = time.time()
result = x_node.generate_error(data, d)
end = time.time()
print(f"Time vectorized: {end-start}")
plt.imshow(result)
plt.show()
def test_seed_determines_result_for_snow_filter():
a = np.zeros((10, 10, 3), dtype=int)
x_node = Array()
x_node.addfilter(
Snow("snowflake_probability", "snowflake_alpha", "snowstorm_alpha"))
params = {
"snowflake_probability": 0.04,
"snowflake_alpha": 0.4,
"snowstorm_alpha": 1
}
out1 = x_node.generate_error(a, params, np.random.RandomState(seed=42))
out2 = x_node.generate_error(a, params, np.random.RandomState(seed=42))
assert np.array_equal(out1, out2)
def test_seed_determines_result_for_ocr_error_filter():
a = np.array(["hello world"])
x_node = Array()
x_node.addfilter(OCRError("probs", "p"))
params = {
"probs": {
"e": (["E", "i"], [.5, .5]),
"g": (["q", "9"], [.2, .8])
},
"p": 1
}
out1 = x_node.generate_error(a, params, np.random.RandomState(seed=42))
out2 = x_node.generate_error(a, params, np.random.RandomState(seed=42))
assert np.array_equal(out1, out2)
def test_seed_determines_result_for_missing_area_filter_with_gaussian_radius_generator(
):
a = np.array(["hello world\n" * 10])
x_node = Array()
x_node.addfilter(
MissingArea("probability", "radius_generator", "missing_value"))
params = {
"probability": 0.05,
"radius_generator": radius_generators.GaussianRadiusGenerator(1, 1),
"missing_value": "#"
}
out1 = x_node.generate_error(a, params, np.random.RandomState(seed=42))
out2 = x_node.generate_error(a, params, np.random.RandomState(seed=42))
assert np.array_equal(out1, out2)
def main():
d = {"tar": 1, "rat": 0.5, "range": 1}
img_path = "data/landscape.png"
data = plt.imread(img_path)
x_node = Array()
b = Brightness("tar", "rat", "range")
x_node.addfilter(b)
start = time.time()
result = x_node.generate_error(data, d)
end = time.time()
print(f"Time vectorized: {end-start}")
plt.imshow(result)
plt.show()
def main():
img_path = "data/landscape.png"
d = {"tar": 1, "rat": 0.55, "range": 255}
img2 = Image.open(img_path)
data = np.array(img2)
x_node = Array()
b2 = Brightness("tar", "rat", "range")
x_node.addfilter(b2)
start = time.time()
result = x_node.generate_error(data, d)
end = time.time()
print(f"Time vectorized: {end-start}")
plt.imshow(result)
plt.show()
def main():
img_path = "data/landscape.png"
d = {"tar": 0, "rat": 0.8, "range": 255}
img = Image.open(img_path)
data = np.array(img)
x_node = Array()
s = Saturation("tar", "rat", "range")
x_node.addfilter(s)
start = time.time()
result = x_node.generate_error(data, d)
end = time.time()
print(f"Time vectorized: {end-start}")
plt.imshow(result)
plt.show()
def main():
img_path = "data/landscape.png"
img = Image.open(img_path)
data = np.array(img)
# data = plt.imread(img_path)
root_node = Array()
# root_node.addfilter(Snow("p", "flake_alpha", "storm_alpha"))
root_node.addfilter(Rain("p", "r"))
before = time.time()
result = root_node.generate_error(data, {'p': .01, 'r': 255})
end = time.time()
print(f"{end - before} faster time")
plt.imshow(result)
plt.show()
def test_seed_determines_result_for_stain_filter():
def f(data, random_state):
return data * random_state.randint(2, 4)
a = np.random.RandomState(seed=42).randint(0, 255, size=300).reshape(
(10, 10, 3))
x_node = Array()
x_node.addfilter(
StainArea("probability", "radius_generator",
"transparency_percentage"))
params = {
"probability": .005,
"radius_generator": radius_generators.GaussianRadiusGenerator(10, 5),
"transparency_percentage": 0.5
}
out1 = x_node.generate_error(a, params, np.random.RandomState(seed=42))
out2 = x_node.generate_error(a, params, np.random.RandomState(seed=42))
assert np.array_equal(out1, out2)
"laboris nisi ut aliquid ex ea commodi consequat. \n" +
"Quis aute iure reprehenderit in voluptate\n" +
"velit esse cillum dolore eu fugiat nulla pariatur.\n" +
"Excepteur sint obcaecat cupiditat non proident,\n" +
"sunt in culpa qui officia\n" + "deserunt mollit anim id est laborum.",
"Hello\n" + # the next string starts here
"Hello\n" + "Hello\n" + "Hello\n" + "Hello"
])
root_node = Array()
root_node.addfilter(MissingArea("p", "radius_gen", "value"))
params = {}
params['value'] = " "
params['p'] = .03
params['radius_gen'] = radius_generators.GaussianRadiusGenerator(1, 1)
out = root_node.generate_error(data, params)
for elem in out:
print(elem, end="\n\n")
params['p'] = .04
params['radius_gen'] = radius_generators.ProbabilityArrayRadiusGenerator(
[0, 0.6, 0.4])
out = root_node.generate_error(data, params)
for elem in out:
print(elem, end="\n\n")
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import numpy as np
from dpemu.nodes import Array
from dpemu.filters.text import Uppercase
data = np.array(["hello world", "all your Bayes' theorems are belong to us"])
root_node = Array()
root_node.addfilter(Uppercase("prob"))
out = root_node.generate_error(data, {'prob': .45})
print(out)
print("output shape:", out.shape, ", output dtype:", out.dtype)
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""This example raises an exception (on purpose).
The parameter identifier "mean" is misspelled in the
params dictionary. This should result in an exception
with a helpful error message.
"""
import numpy as np
from dpemu.filters.common import GaussianNoise
from dpemu.nodes import Array
xs = np.random.rand(100, 200)
array_node = Array()
array_node.addfilter(GaussianNoise("mean", "std"))
params = {"meany": 0.0, "std": 20.0}
print("""
---
This example should terminate with a helpful error message.
---
""")
errorified = array_node.generate_error(xs, params)
import numpy as np
from dpemu.nodes import Array
from dpemu.filters.time_series import Gap
y = np.arange(100.0, 200.0)
print("Original y:\n", y)
data = y
root_node = Array()
print(f"input shape: {data.shape}")
"""
Every increase in time results in drift increasing by 0.1
root_node.addfilter(SensorDrift(magnitude=0.1))
"""
"""
def strange(a):
if a <= 170 and a >= 150:
return 1729
return a
"""
# y_node.addfilter(StrangeBehaviour(strange))
root_node.addfilter(Gap("prob_break", "prob_recover", "missing_value"))
output = root_node.generate_error(data, {'prob_break': .3, 'prob_recover': .3, 'missing_value': np.nan})
print("output:\n", output)
print(f"output dtype: {output.dtype}")
from dpemu.filters import Constant, Identity, Subtraction
# generate image with bitwise operations
data = []
for y in range(0, 512):
data.append([])
for x in range(0, 512):
data[y].append((x ^ y, x & y, 0))
data = np.array(data, dtype=np.uint8)
# show original image
img_original = Image.fromarray(data, "RGB")
img_original.show()
# generate error
root_node = Array()
# add filter which subtracts each pixel value from 255
root_node.addfilter(Subtraction("const", "identity"))
out = root_node.generate_error(data, {
'c': 255,
'const': Constant("c"),
'identity': Identity()
})
# show modified image
img_modified = Image.fromarray(out, "RGB")
img_modified.show()
print(out)
print("output shape:", out.shape, ", output dtype:", out.dtype)
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import numpy as np
from dpemu.nodes import Array
from dpemu.filters.text import OCRError
from dpemu import pg_utils
data = np.array([
"shambler", "shub-niggurath", "ogre", "difficulty: nightmare",
"quad damage", "health 100, health 99, health 0"
])
root_node = Array()
params = pg_utils.load_ocr_error_params("data/example_ocr_error_config.json")
normalized_params = pg_utils.normalize_ocr_error_params(params)
root_node.addfilter(OCRError("params", "p"))
out = root_node.generate_error(data, {'params': normalized_params, 'p': .5})
for c in ["a", "q", "1", ":"]:
print(c, params[c], normalized_params[c])
print(out)
print("output shape:", out.shape, ", output dtype:", out.dtype)
