{{ message }}
forked from matplotlib/matplotlib
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy path_image_wrapper.cpp
More file actions
321 lines (267 loc) · 11.8 KB
/
Copy path_image_wrapper.cpp
File metadata and controls
321 lines (267 loc) · 11.8 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
#include <pybind11/pybind11.h>
#include <pybind11/numpy.h>
#include <algorithm>
#include "_image_resample.h"
#include "py_converters.h"
namespace py = pybind11;
using namespace pybind11::literals;
/**********************************************************************
* Free functions
* */
const char* image_resample__doc__ =
R"""(Resample input_array, blending it in-place into output_array, using an affine transform.
Parameters
----------
input_array : 2-d or 3-d NumPy array of float, double or `numpy.uint8`
If 2-d, the image is grayscale. If 3-d, the image must be of size 4 in the last
dimension and represents RGBA data.
output_array : 2-d or 3-d NumPy array of float, double or `numpy.uint8`
The dtype and number of dimensions must match `input_array`.
transform : matplotlib.transforms.Transform instance
The transformation from the input array to the output array.
interpolation : int, default: NEAREST
The interpolation method. Must be one of the following constants defined in this
module:
NEAREST, BILINEAR, BICUBIC, SPLINE16, SPLINE36, HANNING, HAMMING, HERMITE, KAISER,
QUADRIC, CATROM, GAUSSIAN, BESSEL, MITCHELL, SINC, LANCZOS, BLACKMAN
resample : bool, optional
When `True`, use a full resampling method. When `False`, only resample when the
output image is larger than the input image.
alpha : float, default: 1
The transparency level, from 0 (transparent) to 1 (opaque).
norm : bool, default: False
Whether to norm the interpolation function.
radius: float, default: 1
The radius of the kernel, if method is SINC, LANCZOS or BLACKMAN.
)""";
static py::array_t<double>
_get_transform_mesh(const py::object& transform, const py::ssize_t *dims)
{
/* TODO: Could we get away with float, rather than double, arrays here? */
/* Given a non-affine transform object, create a mesh that maps
every pixel center in the output image to the input image. This is used
as a lookup table during the actual resampling. */
// If attribute doesn't exist, raises Python AttributeError
auto inverse = transform.attr("inverted")();
py::ssize_t mesh_dims[2] = {dims[0]*dims[1], 2};
py::array_t<double> input_mesh(mesh_dims);
auto p = input_mesh.mutable_data();
for (auto y = 0; y < dims[0]; ++y) {
for (auto x = 0; x < dims[1]; ++x) {
// The convention for the supplied transform is that pixel centers
// are at 0.5, 1.5, 2.5, etc.
*p++ = (double)x + 0.5;
*p++ = (double)y + 0.5;
}
}
auto output_mesh = inverse.attr("transform")(input_mesh);
auto output_mesh_array =
py::array_t<double, py::array::c_style | py::array::forcecast>(output_mesh);
if (output_mesh_array.ndim() != 2) {
throw std::runtime_error(
"Inverse transformed mesh array should be 2D not {}D"_s.format(
output_mesh_array.ndim()));
}
return output_mesh_array;
}
// Using generic py::array for input and output arrays rather than the more usual
// py::array_t<type> as this function supports multiple array dtypes.
static void
image_resample(py::array input_array,
py::array& output_array,
const py::object& transform,
interpolation_e interpolation,
bool resample_, // Avoid name clash with resample() function
float alpha,
bool norm,
float radius)
{
// Validate input_array
auto dtype = input_array.dtype(); // Validated when determine resampler below
auto ndim = input_array.ndim();
if (ndim != 2 && ndim != 3) {
throw std::invalid_argument("Input array must be a 2D or 3D array");
}
if (ndim == 3 && input_array.shape(2) != 4) {
throw std::invalid_argument(
"3D input array must be RGBA with shape (M, N, 4), has trailing dimension of {}"_s.format(
input_array.shape(2)));
}
// Ensure input array is contiguous, regardless of dtype
input_array = py::array::ensure(input_array, py::array::c_style);
// Validate output array
auto out_ndim = output_array.ndim();
if (out_ndim != ndim) {
throw std::invalid_argument(
"Input ({}D) and output ({}D) arrays have different dimensionalities"_s.format(
ndim, out_ndim));
}
if (out_ndim == 3 && output_array.shape(2) != 4) {
throw std::invalid_argument(
"3D output array must be RGBA with shape (M, N, 4), has trailing dimension of {}"_s.format(
output_array.shape(2)));
}
if (!output_array.dtype().is(dtype)) {
throw std::invalid_argument("Input and output arrays have mismatched types");
}
if ((output_array.flags() & py::array::c_style) == 0) {
throw std::invalid_argument("Output array must be C-contiguous");
}
if (!output_array.writeable()) {
throw std::invalid_argument("Output array must be writeable");
}
resample_params_t params;
params.interpolation = interpolation;
params.transform_mesh = nullptr;
params.resample = resample_;
params.norm = norm;
params.radius = radius;
params.alpha = alpha;
// Only used if transform is not affine.
// Need to keep it in scope for the duration of this function.
py::array_t<double> transform_mesh;
// Validate transform
if (transform.is_none()) {
params.is_affine = true;
} else {
// Raises Python AttributeError if no such attribute or TypeError if cast fails
bool is_affine = py::cast<bool>(transform.attr("is_affine"));
if (is_affine) {
convert_trans_affine(transform, params.affine);
// If affine parameters will make subpixels visible, treat as nonaffine instead
if (params.affine.sx >= agg::image_subpixel_scale / 2 || params.affine.sy >= agg::image_subpixel_scale / 2) {
is_affine = false;
params.affine = agg::trans_affine(); // reset to identity affine parameters
}
}
if (!is_affine) {
transform_mesh = _get_transform_mesh(transform, output_array.shape());
params.transform_mesh = transform_mesh.data();
}
params.is_affine = is_affine;
}
if (auto resampler =
(ndim == 2) ? (
(dtype.equal(py::dtype::of<std::uint8_t>())) ? resample<agg::gray8> :
(dtype.equal(py::dtype::of<std::int8_t>())) ? resample<agg::gray8> :
(dtype.equal(py::dtype::of<std::uint16_t>())) ? resample<agg::gray16> :
(dtype.equal(py::dtype::of<std::int16_t>())) ? resample<agg::gray16> :
(dtype.equal(py::dtype::of<float>())) ? resample<agg::gray32> :
(dtype.equal(py::dtype::of<double>())) ? resample<agg::gray64> :
nullptr) : (
// ndim == 3
(dtype.equal(py::dtype::of<std::uint8_t>())) ? resample<agg::rgba8> :
(dtype.equal(py::dtype::of<std::int8_t>())) ? resample<agg::rgba8> :
(dtype.equal(py::dtype::of<std::uint16_t>())) ? resample<agg::rgba16> :
(dtype.equal(py::dtype::of<std::int16_t>())) ? resample<agg::rgba16> :
(dtype.equal(py::dtype::of<float>())) ? resample<agg::rgba32> :
(dtype.equal(py::dtype::of<double>())) ? resample<agg::rgba64> :
nullptr)) {
Py_BEGIN_ALLOW_THREADS
resampler(
input_array.data(), input_array.shape(1), input_array.shape(0),
output_array.mutable_data(), output_array.shape(1), output_array.shape(0),
params);
Py_END_ALLOW_THREADS
} else {
throw std::invalid_argument("arrays must be of dtype byte, short, float32 or float64");
}
}
// This is used by matplotlib.testing.compare to calculate RMS and a difference image.
static py::tuple
calculate_rms_and_diff(py::array_t<unsigned char> expected_image,
py::array_t<unsigned char> actual_image)
{
for (const auto & [image, name] : {std::pair{expected_image, "Expected"},
std::pair{actual_image, "Actual"}})
{
if (image.ndim() != 3) {
auto exceptions = py::module_::import("matplotlib.testing.exceptions");
auto ImageComparisonFailure = exceptions.attr("ImageComparisonFailure");
py::set_error(
ImageComparisonFailure,
"{name} image must be 3-dimensional, but is {ndim}-dimensional"_s.format(
"name"_a=name, "ndim"_a=image.ndim()));
throw py::error_already_set();
}
}
auto height = expected_image.shape(0);
auto width = expected_image.shape(1);
auto depth = expected_image.shape(2);
if (depth != 3 && depth != 4) {
auto exceptions = py::module_::import("matplotlib.testing.exceptions");
auto ImageComparisonFailure = exceptions.attr("ImageComparisonFailure");
py::set_error(
ImageComparisonFailure,
"Image must be RGB or RGBA but has depth {depth}"_s.format(
"depth"_a=depth));
throw py::error_already_set();
}
if (height != actual_image.shape(0) || width != actual_image.shape(1) ||
depth != actual_image.shape(2)) {
auto exceptions = py::module_::import("matplotlib.testing.exceptions");
auto ImageComparisonFailure = exceptions.attr("ImageComparisonFailure");
py::set_error(
ImageComparisonFailure,
"Image sizes do not match expected size: {expected_image.shape} "_s
"actual size {actual_image.shape}"_s.format(
"expected_image"_a=expected_image, "actual_image"_a=actual_image));
throw py::error_already_set();
}
auto expected = expected_image.unchecked<3>();
auto actual = actual_image.unchecked<3>();
py::ssize_t diff_dims[3] = {height, width, 3};
py::array_t<unsigned char> diff_image(diff_dims);
auto diff = diff_image.mutable_unchecked<3>();
double total = 0.0;
for (auto i = 0; i < height; i++) {
for (auto j = 0; j < width; j++) {
for (auto k = 0; k < depth; k++) {
auto pixel_diff = static_cast<double>(expected(i, j, k)) -
static_cast<double>(actual(i, j, k));
total += pixel_diff*pixel_diff;
if (k != 3) { // Hard-code a fully solid alpha channel by omitting it.
diff(i, j, k) = static_cast<unsigned char>(std::clamp(
abs(pixel_diff) * 10, // Expand differences in luminance domain.
0.0, 255.0));
}
}
}
}
total = total / (width * height * depth);
return py::make_tuple(sqrt(total), diff_image);
}
PYBIND11_MODULE(_image, m, py::mod_gil_not_used())
{
py::enum_<interpolation_e>(m, "_InterpolationType")
.value("NEAREST", NEAREST)
.value("BILINEAR", BILINEAR)
.value("BICUBIC", BICUBIC)
.value("SPLINE16", SPLINE16)
.value("SPLINE36", SPLINE36)
.value("HANNING", HANNING)
.value("HAMMING", HAMMING)
.value("HERMITE", HERMITE)
.value("KAISER", KAISER)
.value("QUADRIC", QUADRIC)
.value("CATROM", CATROM)
.value("GAUSSIAN", GAUSSIAN)
.value("BESSEL", BESSEL)
.value("MITCHELL", MITCHELL)
.value("SINC", SINC)
.value("LANCZOS", LANCZOS)
.value("BLACKMAN", BLACKMAN)
.export_values();
m.def("resample", &image_resample,
"input_array"_a,
"output_array"_a,
"transform"_a,
"interpolation"_a = interpolation_e::NEAREST,
"resample"_a = false,
"alpha"_a = 1,
"norm"_a = false,
"radius"_a = 1,
image_resample__doc__);
m.def("calculate_rms_and_diff", &calculate_rms_and_diff,
"expected_image"_a, "actual_image"_a);
}
You can’t perform that action at this time.