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comparison mupdf-source/thirdparty/tesseract/src/training/degradeimage.cpp @ 2:b50eed0cc0ef upstream

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ADD: MuPDF v1.26.7: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.4. The directory name has changed: no version number in the expanded directory now.
author Franz Glasner <fzglas.hg@dom66.de>
date Mon, 15 Sep 2025 11:43:07 +0200
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1 /**********************************************************************
2 * File: degradeimage.cpp
3 * Description: Function to degrade an image (usually of text) as if it
4 * has been printed and then scanned.
5 * Authors: Ray Smith
6 *
7 * (C) Copyright 2013, Google Inc.
8 * Licensed under the Apache License, Version 2.0 (the "License");
9 * you may not use this file except in compliance with the License.
10 * You may obtain a copy of the License at
11 * http://www.apache.org/licenses/LICENSE-2.0
12 * Unless required by applicable law or agreed to in writing, software
13 * distributed under the License is distributed on an "AS IS" BASIS,
14 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15 * See the License for the specific language governing permissions and
16 * limitations under the License.
17 *
18 **********************************************************************/
19
20 #include "degradeimage.h"
21
22 #include <allheaders.h> // from leptonica
23 #include <cstdlib>
24 #include "helpers.h" // For TRand.
25 #include "rect.h"
26
27 namespace tesseract {
28
29 // A randomized perspective distortion can be applied to synthetic input.
30 // The perspective distortion comes from leptonica, which uses 2 sets of 4
31 // corners to determine the distortion. There are random values for each of
32 // the x numbers x0..x3 and y0..y3, except for x2 and x3 which are instead
33 // defined in terms of a single shear value. This reduces the degrees of
34 // freedom enough to make the distortion more realistic than it would otherwise
35 // be if all 8 coordinates could move independently.
36 // One additional factor is used for the color of the pixels that don't exist
37 // in the source image.
38 // Name for each of the randomizing factors.
39 enum FactorNames {
40 FN_INCOLOR,
41 FN_Y0,
42 FN_Y1,
43 FN_Y2,
44 FN_Y3,
45 FN_X0,
46 FN_X1,
47 FN_SHEAR,
48 // x2 = x1 - shear
49 // x3 = x0 + shear
50 FN_NUM_FACTORS
51 };
52
53 // Rotation is +/- kRotationRange radians.
54 const float kRotationRange = 0.02f;
55 // Number of grey levels to shift by for each exposure step.
56 const int kExposureFactor = 16;
57 // Salt and pepper noise is +/- kSaltnPepper.
58 const int kSaltnPepper = 5;
59 // Min sum of width + height on which to operate the ramp.
60 const int kMinRampSize = 1000;
61
62 // Degrade the pix as if by a print/copy/scan cycle with exposure > 0
63 // corresponding to darkening on the copier and <0 lighter and 0 not copied.
64 // Exposures in [-2,2] are most useful, with -3 and 3 being extreme.
65 // If rotation is nullptr, rotation is skipped. If *rotation is non-zero, the
66 // pix is rotated by *rotation else it is randomly rotated and *rotation is
67 // modified.
68 //
69 // HOW IT WORKS:
70 // Most of the process is really dictated by the fact that the minimum
71 // available convolution is 3X3, which is too big really to simulate a
72 // good quality print/scan process. (2X2 would be better.)
73 // 1 pixel wide inputs are heavily smeared by the 3X3 convolution, making the
74 // images generally biased to being too light, so most of the work is to make
75 // them darker. 3 levels of thickening/darkening are achieved with 2 dilations,
76 // (using a greyscale erosion) one heavy (by being before convolution) and one
77 // light (after convolution).
78 // With no dilation, after covolution, the images are so light that a heavy
79 // constant offset is required to make the 0 image look reasonable. A simple
80 // constant offset multiple of exposure to undo this value is enough to achieve
81 // all the required lighting. This gives the advantage that exposure level 1
82 // with a single dilation gives a good impression of the broken-yet-too-dark
83 // problem that is often seen in scans.
84 // A small random rotation gives some varying greyscale values on the edges,
85 // and some random salt and pepper noise on top helps to realistically jaggy-up
86 // the edges.
87 // Finally a greyscale ramp provides a continuum of effects between exposure
88 // levels.
89 Image DegradeImage(Image input, int exposure, TRand *randomizer, float *rotation) {
90 Image pix = pixConvertTo8(input, false);
91 input.destroy();
92 input = pix;
93 int width = pixGetWidth(input);
94 int height = pixGetHeight(input);
95
96 if (exposure >= 2) {
97 // An erosion simulates the spreading darkening of a dark copy.
98 // This is backwards to binary morphology,
99 // see http://www.leptonica.com/grayscale-morphology.html
100 pix = input;
101 input = pixErodeGray(pix, 3, 3);
102 pix.destroy();
103 }
104 // A convolution is essential to any mode as no scanner produces an
105 // image as sharp as the electronic image.
106 pix = pixBlockconv(input, 1, 1);
107 input.destroy();
108 // A small random rotation helps to make the edges jaggy in a realistic way.
109 if (rotation != nullptr) {
110 float radians_clockwise = 0.0f;
111 if (*rotation) {
112 radians_clockwise = *rotation;
113 } else if (randomizer != nullptr) {
114 radians_clockwise = randomizer->SignedRand(kRotationRange);
115 }
116
117 input = pixRotate(pix, radians_clockwise, L_ROTATE_AREA_MAP, L_BRING_IN_WHITE, 0, 0);
118 // Rotate the boxes to match.
119 *rotation = radians_clockwise;
120 pix.destroy();
121 } else {
122 input = pix;
123 }
124
125 if (exposure >= 3 || exposure == 1) {
126 // Erosion after the convolution is not as heavy as before, so it is
127 // good for level 1 and in addition as a level 3.
128 // This is backwards to binary morphology,
129 // see http://www.leptonica.com/grayscale-morphology.html
130 pix = input;
131 input = pixErodeGray(pix, 3, 3);
132 pix.destroy();
133 }
134 // The convolution really needed to be 2x2 to be realistic enough, but
135 // we only have 3x3, so we have to bias the image darker or lose thin
136 // strokes.
137 int erosion_offset = 0;
138 // For light and 0 exposure, there is no dilation, so compensate for the
139 // convolution with a big darkening bias which is undone for lighter
140 // exposures.
141 if (exposure <= 0) {
142 erosion_offset = -3 * kExposureFactor;
143 }
144 // Add in a general offset of the greyscales for the exposure level so
145 // a threshold of 128 gives a reasonable binary result.
146 erosion_offset -= exposure * kExposureFactor;
147 // Add a gradual fade over the page and a small amount of salt and pepper
148 // noise to simulate noise in the sensor/paper fibres and varying
149 // illumination.
150 l_uint32 *data = pixGetData(input);
151 for (int y = 0; y < height; ++y) {
152 for (int x = 0; x < width; ++x) {
153 int pixel = GET_DATA_BYTE(data, x);
154 if (randomizer != nullptr) {
155 pixel += randomizer->IntRand() % (kSaltnPepper * 2 + 1) - kSaltnPepper;
156 }
157 if (height + width > kMinRampSize) {
158 pixel -= (2 * x + y) * 32 / (height + width);
159 }
160 pixel += erosion_offset;
161 if (pixel < 0) {
162 pixel = 0;
163 }
164 if (pixel > 255) {
165 pixel = 255;
166 }
167 SET_DATA_BYTE(data, x, pixel);
168 }
169 data += pixGetWpl(input);
170 }
171 return input;
172 }
173
174 // Creates and returns a Pix distorted by various means according to the bool
175 // flags. If boxes is not nullptr, the boxes are resized/positioned according to
176 // any spatial distortion and also by the integer reduction factor box_scale
177 // so they will match what the network will output.
178 // Returns nullptr on error. The returned Pix must be pixDestroyed.
179 Image PrepareDistortedPix(const Image pix, bool perspective, bool invert, bool white_noise,
180 bool smooth_noise, bool blur, int box_reduction, TRand *randomizer,
181 std::vector<TBOX> *boxes) {
182 Image distorted = pix.copy();
183 // Things to do to synthetic training data.
184 if ((white_noise || smooth_noise) && randomizer->SignedRand(1.0) > 0.0) {
185 // TODO(rays) Cook noise in a more thread-safe manner than rand().
186 // Attempt to make the sequences reproducible.
187 srand(randomizer->IntRand());
188 Image pixn = pixAddGaussianNoise(distorted, 8.0);
189 distorted.destroy();
190 if (smooth_noise) {
191 distorted = pixBlockconv(pixn, 1, 1);
192 pixn.destroy();
193 } else {
194 distorted = pixn;
195 }
196 }
197 if (blur && randomizer->SignedRand(1.0) > 0.0) {
198 Image blurred = pixBlockconv(distorted, 1, 1);
199 distorted.destroy();
200 distorted = blurred;
201 }
202 if (perspective) {
203 GeneratePerspectiveDistortion(0, 0, randomizer, &distorted, boxes);
204 }
205 if (boxes != nullptr) {
206 for (auto &b : *boxes) {
207 b.scale(1.0f / box_reduction);
208 if (b.width() <= 0) {
209 b.set_right(b.left() + 1);
210 }
211 }
212 }
213 if (invert && randomizer->SignedRand(1.0) < -0) {
214 pixInvert(distorted, distorted);
215 }
216 return distorted;
217 }
218
219 // Distorts anything that has a non-null pointer with the same pseudo-random
220 // perspective distortion. Width and height only need to be set if there
221 // is no pix. If there is a pix, then they will be taken from there.
222 void GeneratePerspectiveDistortion(int width, int height, TRand *randomizer, Image *pix,
223 std::vector<TBOX> *boxes) {
224 if (pix != nullptr && *pix != nullptr) {
225 width = pixGetWidth(*pix);
226 height = pixGetHeight(*pix);
227 }
228 float *im_coeffs = nullptr;
229 float *box_coeffs = nullptr;
230 l_int32 incolor = ProjectiveCoeffs(width, height, randomizer, &im_coeffs, &box_coeffs);
231 if (pix != nullptr && *pix != nullptr) {
232 // Transform the image.
233 Image transformed = pixProjective(*pix, im_coeffs, incolor);
234 if (transformed == nullptr) {
235 tprintf("Projective transformation failed!!\n");
236 return;
237 }
238 pix->destroy();
239 *pix = transformed;
240 }
241 if (boxes != nullptr) {
242 // Transform the boxes.
243 for (auto &b : *boxes) {
244 int x1, y1, x2, y2;
245 const TBOX &box = b;
246 projectiveXformSampledPt(box_coeffs, box.left(), height - box.top(), &x1, &y1);
247 projectiveXformSampledPt(box_coeffs, box.right(), height - box.bottom(), &x2, &y2);
248 TBOX new_box1(x1, height - y2, x2, height - y1);
249 projectiveXformSampledPt(box_coeffs, box.left(), height - box.bottom(), &x1, &y1);
250 projectiveXformSampledPt(box_coeffs, box.right(), height - box.top(), &x2, &y2);
251 TBOX new_box2(x1, height - y1, x2, height - y2);
252 b = new_box1.bounding_union(new_box2);
253 }
254 }
255 lept_free(im_coeffs);
256 lept_free(box_coeffs);
257 }
258
259 // Computes the coefficients of a randomized projective transformation.
260 // The image transform requires backward transformation coefficient, and the
261 // box transform the forward coefficients.
262 // Returns the incolor arg to pixProjective.
263 int ProjectiveCoeffs(int width, int height, TRand *randomizer, float **im_coeffs,
264 float **box_coeffs) {
265 // Setup "from" points.
266 Pta *src_pts = ptaCreate(4);
267 ptaAddPt(src_pts, 0.0f, 0.0f);
268 ptaAddPt(src_pts, width, 0.0f);
269 ptaAddPt(src_pts, width, height);
270 ptaAddPt(src_pts, 0.0f, height);
271 // Extract factors from pseudo-random sequence.
272 float factors[FN_NUM_FACTORS];
273 float shear = 0.0f; // Shear is signed.
274 for (int i = 0; i < FN_NUM_FACTORS; ++i) {
275 // Everything is squared to make wild values rarer.
276 if (i == FN_SHEAR) {
277 // Shear is signed.
278 shear = randomizer->SignedRand(0.5 / 3.0);
279 shear = shear >= 0.0 ? shear * shear : -shear * shear;
280 // Keep the sheared points within the original rectangle.
281 if (shear < -factors[FN_X0]) {
282 shear = -factors[FN_X0];
283 }
284 if (shear > factors[FN_X1]) {
285 shear = factors[FN_X1];
286 }
287 factors[i] = shear;
288 } else if (i != FN_INCOLOR) {
289 factors[i] = fabs(randomizer->SignedRand(1.0));
290 if (i <= FN_Y3) {
291 factors[i] *= 5.0 / 8.0;
292 } else {
293 factors[i] *= 0.5;
294 }
295 factors[i] *= factors[i];
296 }
297 }
298 // Setup "to" points.
299 Pta *dest_pts = ptaCreate(4);
300 ptaAddPt(dest_pts, factors[FN_X0] * width, factors[FN_Y0] * height);
301 ptaAddPt(dest_pts, (1.0f - factors[FN_X1]) * width, factors[FN_Y1] * height);
302 ptaAddPt(dest_pts, (1.0f - factors[FN_X1] + shear) * width, (1 - factors[FN_Y2]) * height);
303 ptaAddPt(dest_pts, (factors[FN_X0] + shear) * width, (1 - factors[FN_Y3]) * height);
304 getProjectiveXformCoeffs(dest_pts, src_pts, im_coeffs);
305 getProjectiveXformCoeffs(src_pts, dest_pts, box_coeffs);
306 ptaDestroy(&src_pts);
307 ptaDestroy(&dest_pts);
308 return factors[FN_INCOLOR] > 0.5f ? L_BRING_IN_WHITE : L_BRING_IN_BLACK;
309 }
310
311 } // namespace tesseract