├── .gitignore ├── C └── devernay_1.0 │ ├── COPYING │ ├── Makefile │ ├── README.txt │ ├── devernay │ ├── devernay.c │ ├── devernay.h │ ├── devernay_cmd.c │ ├── image.asc │ ├── image.pgm │ ├── image_out.pdf │ ├── image_out.svg │ ├── image_out.txt │ ├── io.c │ └── io.h ├── LICENSE ├── README.md ├── apply_tophat.py ├── compare.py ├── data ├── 50SQE_2018_12_10_0_012.jpeg ├── 50SQE_2018_12_10_0_012.json ├── 50SQE_2018_12_10_0_012.pgm └── gt.npz ├── hough_devernay.py ├── interpolated_shapes.zip ├── iso_th_devernay.py ├── results ├── HCT │ ├── output │ │ └── detection_mask_zoom_0_tophat_0_autoth.png │ └── score │ │ ├── best_f1_zoom_0_tophat_0_autoth.json │ │ ├── best_prec_zoom_0_tophat_0_autoth.json │ │ └── best_rec_zoom_0_tophat_0_autoth.json ├── edges │ ├── h_15_l_5_sig_0_zoom_0_tophat_0.pdf │ ├── h_15_l_5_sig_0_zoom_0_tophat_0.txt │ ├── h_41_l_20.5_sig_0_zoom_0_tophat_0.pdf │ └── h_41_l_20.5_sig_0_zoom_0_tophat_0.txt ├── iso_th │ └── output │ │ └── detection_mask_zoom_0_tophat_0_autoth.png └── tmp │ └── im_tophat_False_zoom_False.jpeg └── utils.py /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__ 2 | __pycache__/* 3 | -------------------------------------------------------------------------------- /C/devernay_1.0/COPYING: -------------------------------------------------------------------------------- 1 | GNU AFFERO GENERAL PUBLIC LICENSE 2 | Version 3, 19 November 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU Affero General Public License is a free, copyleft license for 11 | software and other kinds of works, specifically designed to ensure 12 | cooperation with the community in the case of network server software. 13 | 14 | The licenses for most software and other practical works are designed 15 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 611 | 612 | If the disclaimer of warranty and limitation of liability provided 613 | above cannot be given local legal effect according to their terms, 614 | reviewing courts shall apply local law that most closely approximates 615 | an absolute waiver of all civil liability in connection with the 616 | Program, unless a warranty or assumption of liability accompanies a 617 | copy of the Program in return for a fee. 618 | 619 | END OF TERMS AND CONDITIONS 620 | 621 | How to Apply These Terms to Your New Programs 622 | 623 | If you develop a new program, and you want it to be of the greatest 624 | possible use to the public, the best way to achieve this is to make it 625 | free software which everyone can redistribute and change under these terms. 626 | 627 | To do so, attach the following notices to the program. It is safest 628 | to attach them to the start of each source file to most effectively 629 | state the exclusion of warranty; and each file should have at least 630 | the "copyright" line and a pointer to where the full notice is found. 631 | 632 | 633 | Copyright (C) 634 | 635 | This program is free software: you can redistribute it and/or modify 636 | it under the terms of the GNU Affero General Public License as published by 637 | the Free Software Foundation, either version 3 of the License, or 638 | (at your option) any later version. 639 | 640 | This program is distributed in the hope that it will be useful, 641 | but WITHOUT ANY WARRANTY; without even the implied warranty of 642 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 643 | GNU Affero General Public License for more details. 644 | 645 | You should have received a copy of the GNU Affero General Public License 646 | along with this program. If not, see . 647 | 648 | Also add information on how to contact you by electronic and paper mail. 649 | 650 | If your software can interact with users remotely through a computer 651 | network, you should also make sure that it provides a way for users to 652 | get its source. For example, if your program is a web application, its 653 | interface could display a "Source" link that leads users to an archive 654 | of the code. There are many ways you could offer source, and different 655 | solutions will be better for different programs; see section 13 for the 656 | specific requirements. 657 | 658 | You should also get your employer (if you work as a programmer) or school, 659 | if any, to sign a "copyright disclaimer" for the program, if necessary. 660 | For more information on this, and how to apply and follow the GNU AGPL, see 661 | . 662 | -------------------------------------------------------------------------------- /C/devernay_1.0/Makefile: -------------------------------------------------------------------------------- 1 | devernay: devernay_cmd.c io.c io.h devernay.c devernay.h 2 | $(CC) -O3 -o devernay devernay_cmd.c io.c devernay.c -lm 3 | 4 | test: devernay 5 | ./devernay image.pgm -t output.txt -p output.pdf -g output.svg 6 | 7 | clean: 8 | rm -f devernay output.txt output.pdf output.svg 9 | -------------------------------------------------------------------------------- /C/devernay_1.0/README.txt: -------------------------------------------------------------------------------- 1 | The Canny/Devernay algorithm 2 | ============================ 3 | 4 | Version 1.0 - October 10, 2017 5 | by Rafael Grompone von Gioi 6 | and Gregory Randall 7 | 8 | 9 | Introduction 10 | ------------ 11 | 12 | This is an implementation of Canny/Devernay's sub-pixel edge detector. This 13 | code is part of the following publication and was subject to peer review: 14 | 15 | "A Sub-Pixel Edge Detector: an Implementation of the Canny/Devernay Algorithm" 16 | by Rafael Grompone von Gioi and Gregory Randall, 17 | Image Processing On Line, 2017. DOI:10.5201/ipol.2017.216 18 | http://dx.doi.org/10.5201/ipol.2017.216 19 | 20 | 21 | Files 22 | ----- 23 | 24 | README.txt - This file 25 | COPYING - GNU AFFERO GENERAL PUBLIC LICENSE Version 3 26 | Makefile - Compilation instructions for 'make' 27 | devernay.c - Devernay module ANSI C89 code (peer reviewed) 28 | devernay.h - Devernay module ANSI C89 header (peer reviewed) 29 | devernay_cmd.c - Command line interface for Devernay, ANSI C89 30 | io.c - Input/Output functions for command interface, ANSI C89 31 | io.h - Input/Output functions header, ANSI C89 32 | image.pgm - Test image in PGM format 33 | image.asc - Test image in ASC format 34 | image_out.txt - Expected result for the test image as an TXT file 35 | image_out.pdf - Expected result for the test image as a PDF file 36 | image_out.svg - Expected result for the test image as a SVG file 37 | 38 | The files 'devernay.c' and 'devernay.h' were subject to peer review as part of 39 | the acceptance process of the IPOL article and are the official version of 40 | Devernay. 41 | 42 | 43 | Compiling 44 | --------- 45 | 46 | Devernay is an ANSI C89 Language program and can be used as a module to 47 | be called from a C language program or as an independent command. 48 | 49 | In the distribution is included a Makefile file with instructions to build the 50 | command line program 'devernay'. A C compiler must be installed on your system 51 | as well as the program 'make'. Devernay only uses the standard C library so it 52 | should compile in any ANSI C89 Language environment. In particular, it should 53 | compile in a Unix like system. 54 | 55 | The compiling instruction is just 56 | 57 | make 58 | 59 | from the directory where the source codes and the Makefile are located. If the 60 | compilation needs to be made manually, the usual command to do it is: 61 | 62 | cc -o devernay devernay_cmd.c io.c devernay.c -lm 63 | 64 | To verify a correct compilation you can apply Devernay to the test image 65 | 'image.pgm' and compare the result to the provided ones. This can be done by 66 | executing: 67 | 68 | make test 69 | 70 | 71 | Running the Command Line Interface 72 | ---------------------------------- 73 | 74 | The simplest Devernay command execution is just 75 | 76 | devernay 77 | 78 | (use ./devernay if the command is not included in the current path). That 79 | should print Devernay version number and a description of the command line 80 | interface, including the available options. The input image formats handled are 81 | PGM (in its two versions, ASCII and Binary) and the ASC format (as defined by 82 | the CImg Library and as described below). A typical execution would be: 83 | 84 | devernay image.pgm -t output.txt 85 | 86 | That should give the result as an TXT file 'output.txt' which consists of two 87 | columns of real numbers in ASCII format (the numbers are separated by a 88 | space). Each row corresponds to a contour point, the first column gives the x 89 | coordinate and the second row gives the y coordinate. Contour points of 90 | consecutive rows are part of the same curve, they are chained. Each curve is 91 | ended by a row "-1 -1", which mark the end of the curve or chain. A new chain 92 | may start on the next row. Then end of the file indicates that no more curves 93 | are present. For closed curves, the first point of the curve is repeated again 94 | as the last point of the chain. The following is an example of output: 95 | 96 | 9 5.57116 97 | 10 5.73102 98 | 11 6.64051 99 | 11.9352 8 100 | 12.0939 9 101 | 11.9844 10 102 | 11.4029 11 103 | 10 12.0179 104 | 9 12.119 105 | 8 11.9399 106 | 6.62612 11 107 | 5.65646 10 108 | 5.51349 9 109 | 5.62126 8 110 | 7 6.54667 111 | 8 5.68361 112 | 9 5.57116 113 | -1 -1 114 | 2.09193 5 115 | 2.17282 4 116 | 3 2.7223 117 | 4 2.13559 118 | 5 1.99507 119 | -1 -1 120 | 121 | It corresponds to two curves, each one ended by a "-1 -1" row. The first one is 122 | a closed curve described by 16 points. Please note that the list has 17 points, 123 | but the first and last are the same, "9 5.57116", indicating a closed 124 | curve. The second curve is an open curve described by five points. Note that 125 | one of the two coordinates is always an integer. This is due the modified 126 | Devernay sub-pixel correction, in which the sub-pixel interpolation is always 127 | performed along the vertical or horizontal axis, but not both; thus, one of the 128 | two coordinates is not interpolated and remain an integer. 129 | 130 | For easy visualization of the result, the command line interface can also 131 | provide the output in PDF and SVG file formats: 132 | 133 | devernay image.pgm -p output.pdf 134 | 135 | will produce the PDF file 'output.pdf' and 136 | 137 | devernay image.pgm -g output.svg 138 | 139 | will produce the SVG file 'output.svg'. Using all these options will generate 140 | TXT, PDF and SVG outputs: 141 | 142 | devernay image.pgm -p output.pdf -t output.txt -g output.svg 143 | 144 | Note that the line width used in the PDF and SVG output is arbitrary. The 145 | default value is 1.3 (in pixel units) but can be modified with the -w 146 | option. By using a smaller value, the sub-pixel accuracy of the result is 147 | better appreciated: 148 | 149 | devernay image.pgm -p output.pdf -w 0.5 150 | 151 | The width value can be set to zero: 152 | 153 | devernay image.pgm -p output.pdf -g output.svg -w 0 154 | 155 | In this case, the PDF and SVG standards determine different behavior. In PDF, 156 | a zero width means that the software rendering the PDF will choose the smallest 157 | possible line width that can be rendered in the current device. This is useful 158 | to see details but it is not recommended for figures to be distributed as the 159 | rendering is device dependent. In SVG, however, line with zero width are not 160 | drawn. 161 | 162 | The Canny/Devernay algorithm depends on three parameters: sigma, th_low, 163 | th_high. The default value of these parameters is zero for the three of them. 164 | Their value can be modified individually using the -s, -l and -h options of the 165 | command line interface. For example: 166 | 167 | devernay image.pgm -p output.pdf -s 1 -l 5 -h 15 168 | 169 | will set the standard deviation of the Gaussian filtering to 1, the low 170 | gradient threshold to 5 and the high gradient threshold to 15. 171 | 172 | 173 | The ASC file format 174 | ------------------- 175 | 176 | The ASC image file format was defined by the CImg Library (http://cimg.eu/). It 177 | is a simple ASCII format allowing to store 4D arrays of floating point values 178 | (and not just integer as in PGM). In addition, multiple channels and multiple 179 | frames are possible; the command line interface of Devernay, however, only 180 | handles a single gray level frame. The main reason to include this file format 181 | as a possible input is to be able to use floating point images in a simple 182 | way. As an example, the file 'image.asc' in this distribution contains the same 183 | image as 'image.pgm' but in ASC file format. 184 | 185 | To explain the ASC file format, the following is an example of a 6x6 floating 186 | point gray-level in ASC format: 187 | 188 | 6 6 1 1 189 | 0.0022 0.1119 0.9455 1.7084 0.9455 0.1119 190 | 0.1119 1.7270 5.8871 7.4892 5.8871 1.7270 191 | 0.9455 5.8871 9.6757 9.9586 9.6757 5.8888 192 | 1.7084 7.4892 9.9586 9.9999 9.9586 7.4934 193 | 0.9455 5.8871 9.6757 9.9586 9.6757 5.8888 194 | 0.1119 1.7270 5.8888 7.4934 5.8888 1.7271 195 | 196 | An ASC file consists of a simple header followed by the data, all written as 197 | numbers in ASCII format. The header is just four integer numbers separated by 198 | spaces and ending in a new line. The four numbers of the header correspond to 199 | the size of each of the 4 dimensions X, Y, Z and C and in that order: X is the 200 | width of the image; Y is the height of the image; Z is the number of frames; C 201 | is the number of channels. For single frame and single channel (as in the 202 | example above and as required by Devernay), Z=1 and C=1. 203 | 204 | The header is followed by the data written in standard ASCII floating point 205 | notation. Any format accepted by the standard C function scanf is valid; for 206 | example: 0, 692315, 0.34230295282, -30.3423, +45, 1e10, 5E4, 3.14e-34. The 207 | numbers should be separated by any combination of spaces, tabs, or end-of-line. 208 | The order of the numbers is important. The pixels have coordinates (x,y,z,c), 209 | with 0 <= x < X and 0 <= y < Y and 0 <= z < Z and 0 <= c < C. The pixel 210 | (x,y,z,c) will be stored at position x + y*X + z*X*Y + c*X*Y*Z. Thus, the first 211 | number following the header corresponds to pixel (0,0,0,0) which is the upper 212 | left pixel of the first channel of the first frame. The second value is the 213 | second pixel on the same row (1,0,0,0) and the following values complete the 214 | first row (up to X-1,0,0,0). Then comes the second row of the first channel of 215 | the first frame, from (0,1,0,0) to (X-1,1,0,0). Then the following rows and so 216 | on until the last row of the frame, (0,Y-1,0,0) to (X-1,Y-1,0,0). This would 217 | complete the image if only one frame and one channel are present (as required 218 | in Devernay and as in the example above). 219 | 220 | In multiple frame and multiple channel (Z>1 and/or C>1), the next values will 221 | store, in the same way, the first channel of the second frame. When all the 222 | frames are complete, the same procedure will be done for the values of the 223 | second channel, and so on. 224 | 225 | 226 | Copyright and License 227 | --------------------- 228 | 229 | Copyright (c) 2016-2017 Rafael Grompone von Gioi and Gregory Randall 230 | 231 | This program is free software: you can redistribute it and/or modify 232 | it under the terms of the GNU Affero General Public License as 233 | published by the Free Software Foundation, either version 3 of the 234 | License, or (at your option) any later version. 235 | 236 | This program is distributed in the hope that it will be useful, 237 | but WITHOUT ANY WARRANTY; without even the implied warranty of 238 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 239 | GNU Affero General Public License for more details. 240 | 241 | You should have received a copy of the GNU Affero General Public License 242 | along with this program. If not, see . 243 | 244 | 245 | Thanks 246 | ------ 247 | 248 | We would be grateful to receive any comment, especially about errors, bugs, 249 | or strange results. 250 | -------------------------------------------------------------------------------- /C/devernay_1.0/devernay: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anttad/SubpixelCircleDetection/912252d4a305d71f800ed39b3ce87946251cb675/C/devernay_1.0/devernay -------------------------------------------------------------------------------- /C/devernay_1.0/devernay.c: -------------------------------------------------------------------------------- 1 | /*---------------------------------------------------------------------------- 2 | 3 | Implementation of Canny/Devernay's sub-pixel edge detector. This code is part 4 | of the following publication and was subject to peer review: 5 | 6 | "A Sub-Pixel Edge Detector: an Implementation of the Canny/Devernay 7 | Algorithm" by Rafael Grompone von Gioi and Gregory Randall, 8 | Image Processing On Line, 2017. DOI:10.5201/ipol.2017.216 9 | http://dx.doi.org/10.5201/ipol.2017.216 10 | 11 | Copyright (c) 2016-2017 rafael grompone von gioi , 12 | Gregory Randall 13 | 14 | This program is free software: you can redistribute it and/or modify 15 | it under the terms of the GNU Affero General Public License as 16 | published by the Free Software Foundation, either version 3 of the 17 | License, or (at your option) any later version. 18 | 19 | This program is distributed in the hope that it will be useful, 20 | but WITHOUT ANY WARRANTY; without even the implied warranty of 21 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 22 | GNU Affero General Public License for more details. 23 | 24 | You should have received a copy of the GNU Affero General Public License 25 | along with this program. If not, see . 26 | 27 | ----------------------------------------------------------------------------*/ 28 | #include 29 | #include 30 | #include 31 | #include 32 | 33 | /*----------------------------------------------------------------------------*/ 34 | #ifndef FALSE 35 | #define FALSE 0 36 | #endif /* !FALSE */ 37 | 38 | #ifndef TRUE 39 | #define TRUE 1 40 | #endif /* !TRUE */ 41 | 42 | /*----------------------------------------------------------------------------*/ 43 | /* PI */ 44 | #ifndef M_PI 45 | #define M_PI 3.14159265358979323846 46 | #endif /* !M_PI */ 47 | 48 | /*----------------------------------------------------------------------------*/ 49 | /* fatal error, print a message to standard error and exit 50 | */ 51 | static void error(char * msg) 52 | { 53 | fprintf(stderr,"error: %s\n",msg); 54 | exit(EXIT_FAILURE); 55 | } 56 | 57 | /*----------------------------------------------------------------------------*/ 58 | /* memory allocation, print an error and exit if fail 59 | */ 60 | static void * xmalloc(size_t size) 61 | { 62 | void * p; 63 | if( size == 0 ) error("xmalloc: zero size"); 64 | p = malloc(size); 65 | if( p == NULL ) error("xmalloc: out of memory"); 66 | return p; 67 | } 68 | 69 | /*----------------------------------------------------------------------------*/ 70 | /* compute a > b considering the rounding errors due to the representation 71 | of double numbers 72 | */ 73 | static int greater(double a, double b) 74 | { 75 | if( a <= b ) return FALSE; /* trivial case, return as soon as possible */ 76 | 77 | if( (a-b) < 1000 * DBL_EPSILON ) return FALSE; 78 | 79 | return TRUE; /* greater */ 80 | } 81 | 82 | /*----------------------------------------------------------------------------*/ 83 | /* Euclidean distance between x1,y1 and x2,y2 84 | */ 85 | static double dist(double x1, double y1, double x2, double y2) 86 | { 87 | return sqrt( (x2-x1)*(x2-x1) + (y2-y1)*(y2-y1) ); 88 | } 89 | 90 | /*----------------------------------------------------------------------------*/ 91 | /* compute a Gaussian kernel of length n, standard deviation sigma, 92 | and centered at value mean. 93 | 94 | for example, if mean=0.5, the Gaussian will be centered in the middle point 95 | between values kernel[0] and kernel[1]. 96 | 97 | kernel must be allocated to a size n. 98 | */ 99 | static void gaussian_kernel(double * kernel, int n, double sigma, double mean) 100 | { 101 | double sum = 0.0; 102 | double val; 103 | int i; 104 | 105 | /* check input */ 106 | if( kernel == NULL ) error("gaussian_kernel: kernel not allocated"); 107 | if( sigma <= 0.0 ) error("gaussian_kernel: sigma must be positive"); 108 | 109 | /* compute Gaussian kernel */ 110 | for(i=0; i 0.0 ) for(i=0; i=nx2) j -= nx2; 172 | if( j >= X ) j = nx2-1-j; 173 | 174 | val += image[j+y*X] * kernel[i]; 175 | } 176 | tmp[x+y*X] = val; 177 | } 178 | 179 | /* y axis convolution */ 180 | for(x=0; x=ny2) j -= ny2; 191 | if( j >= Y ) j = ny2-1-j; 192 | 193 | val += tmp[x+j*X] * kernel[i]; 194 | } 195 | out[x+y*X] = val; 196 | } 197 | 198 | /* free memory */ 199 | free( (void *) kernel ); 200 | free( (void *) tmp ); 201 | 202 | return out; 203 | } 204 | 205 | /*----------------------------------------------------------------------------*/ 206 | /* return a score for chaining pixels 'from' to 'to', favoring closet point: 207 | = 0.0 invalid chaining 208 | > 0.0 valid forward chaining; the larger the value, the better the chaining 209 | < 0.0 valid backward chaining; the smaller the value, the better the chaining 210 | 211 | input: 212 | from, to the two pixel IDs to evaluate their potential chaining 213 | Ex[i], Ey[i] the sub-pixel position of point i, if i is an edge point; 214 | they take values -1,-1 if i is not an edge point 215 | Gx[i], Gy[i] the image gradient at pixel i 216 | X, Y the size of the image 217 | */ 218 | static double chain( int from, int to, double * Ex, double * Ey, 219 | double * Gx, double * Gy, int X, int Y ) 220 | { 221 | double dx,dy; 222 | 223 | /* check input */ 224 | if( Ex == NULL || Ey == NULL || Gx == NULL || Gy == NULL ) 225 | error("chain: invalid input"); 226 | if( from < 0 || to < 0 || from >= X*Y || to >= X*Y ) 227 | error("chain: one of the points is out the image"); 228 | 229 | /* check that the points are different and valid edge points, 230 | otherwise return invalid chaining */ 231 | if( from == to ) return 0.0; /* same pixel, not a valid chaining */ 232 | if( Ex[from] < 0.0 || Ey[from] < 0.0 || Ex[to] < 0.0 || Ey[to] < 0.0 ) 233 | return 0.0; /* one of them is not an edge point, not a valid chaining */ 234 | 235 | /* in a good chaining, the gradient should be roughly orthogonal 236 | to the line joining the two points to be chained: 237 | 238 | Gx,Gy 239 | | ------> dx,dy 240 | | thus 241 | from x-------x to ---> Gy,-Gx (orthogonal to the gradient) 242 | 243 | when Gy * dx - Gx * dy > 0, it corresponds to a forward chaining, 244 | when Gy * dx - Gx * dy < 0, it corresponds to a backward chaining. 245 | (this choice is arbitrary) 246 | 247 | first check that the gradient at both points to be chained agree 248 | in one direction, otherwise return invalid chaining. 249 | */ 250 | dx = Ex[to] - Ex[from]; 251 | dy = Ey[to] - Ey[from]; 252 | if( (Gy[from] * dx - Gx[from] * dy) * (Gy[to] * dx - Gx[to] * dy) <= 0.0 ) 253 | return 0.0; /* incompatible gradient angles, not a valid chaining */ 254 | 255 | /* return the chaining score: positive for forward chaining, 256 | negative for backwards. the score is the inverse of the distance 257 | to the chaining point, to give preference to closer points */ 258 | if( (Gy[from] * dx - Gx[from] * dy) >= 0.0 ) 259 | return 1.0 / dist(Ex[from],Ey[from],Ex[to],Ey[to]); /* forward chaining */ 260 | else 261 | return -1.0 / dist(Ex[from],Ey[from],Ex[to],Ey[to]); /* backward chaining */ 262 | } 263 | 264 | /*----------------------------------------------------------------------------*/ 265 | /* compute the image gradient, giving its x and y components as well as the 266 | modulus. Gx, Gy, and modG must be already allocated. 267 | */ 268 | static void compute_gradient( double * Gx, double * Gy, double * modG, 269 | double * image, int X, int Y ) 270 | { 271 | int x,y; 272 | 273 | /* check input */ 274 | if( Gx == NULL || Gy == NULL || modG == NULL || image == NULL ) 275 | error("compute_gradient: invalid input"); 276 | 277 | /* approximate image gradient using centered differences */ 278 | for(x=1; x<(X-1); x++) 279 | for(y=1; y<(Y-1); y++) 280 | { 281 | Gx[x+y*X] = image[(x+1)+y*X] - image[(x-1)+y*X]; 282 | Gy[x+y*X] = image[x+(y+1)*X] - image[x+(y-1)*X]; 283 | modG[x+y*X] = sqrt( Gx[x+y*X] * Gx[x+y*X] + Gy[x+y*X] * Gy[x+y*X] ); 284 | } 285 | } 286 | 287 | /*----------------------------------------------------------------------------*/ 288 | /* compute sub-pixel edge points using adapted Canny and Devernay methods. 289 | 290 | input: Gx, Gy, and modG are the x and y components and modulus of the image 291 | gradient, respectively. X,Y is the image size. 292 | 293 | output: Ex and Ey will have the x and y sub-pixel coordinates of the edge 294 | points found, or -1 and -1 when not an edge point. Ex and Ey must be 295 | already allocated. 296 | 297 | a modified Canny non maximal suppression [1] is used to select edge points, 298 | and a modified Devernay sub-pixel correction [2] is used to improve the 299 | position accuracy. in both cases, the modification boils down to using only 300 | vertical or horizontal non maximal suppression and sub-pixel correction. 301 | no threshold is used on the gradient. 302 | 303 | [1] J.F. Canny, "A computational approach to edge detection", 304 | IEEE Transactions on Pattern Analysis and Machine Intelligence, 305 | vol.8, no.6, pp.679-698, 1986. 306 | 307 | [2] F. Devernay, "A Non-Maxima Suppression Method for Edge Detection 308 | with Sub-Pixel Accuracy", Rapport de recherche 2724, INRIA, Nov. 1995. 309 | 310 | the reason for this modification is that Devernay correction is inconsistent 311 | for some configurations at 45, 225, -45 or -225 degree. in edges that should 312 | go exactly in the middle of a pixels like (5 pixels drawn): 313 | 314 | ___ 315 | | 316 | ___| 317 | | 318 | ___| 319 | | 320 | ___| 321 | | 322 | ___| 323 | 324 | the correction terms of both sides of the perfect edge are not compatible, 325 | leading to edge points with "oscillations" like: 326 | 327 | . 328 | . 329 | 330 | . 331 | . 332 | 333 | . 334 | . 335 | 336 | but the Devernay correction works very well and is very consistent when used 337 | to interpolate only along horizontal or vertical direction. this modified 338 | version requires that a pixel, to be an edge point, must be a local maximum 339 | horizontally or vertically, depending on the gradient orientation: if the 340 | x component of the gradient is larger than the y component, Gx > Gy, this 341 | means that the gradient is roughly horizontal and a horizontal maximum is 342 | required to be an edge point. 343 | */ 344 | static void compute_edge_points( double * Ex, double * Ey, double * modG, 345 | double * Gx, double * Gy, int X, int Y ) 346 | { 347 | int x,y,i; 348 | 349 | /* check input */ 350 | if( Ex == NULL || Ey == NULL || modG == NULL || Gx == NULL || Gy == NULL ) 351 | error("compute_edge_points: invalid input"); 352 | 353 | /* initialize Ex and Ey as non-edge points for all pixels */ 354 | for(i=0; i= |Gy|), 372 | => a "horizontal" (H) edge found 373 | else, if local vertical maxima of the gradient modulus and the 374 | gradient direction is more vertical (|Gx| <= |Gy|), 375 | => a "vertical" (V) edge found */ 376 | /* it can happen that two neighbor pixels have equal value and are both 377 | maxima, for example when the edge is exactly between both pixels. in 378 | such cases, as an arbitrary convention, the edge is marked on the 379 | left one when an horizontal max or below when a vertical max. for 380 | this the conditions are L < mod >= R and D < mod >= U, 381 | respectively. the comparisons are done using the function greater() 382 | instead of the operators > or >= so numbers differing only due to 383 | rounding errors are considered equal */ 384 | if( greater(mod,L) && !greater(R,mod) && gx >= gy ) Dx = 1; /* H */ 385 | else if( greater(mod,D) && !greater(U,mod) && gx <= gy ) Dy = 1; /* V */ 386 | 387 | /* Devernay sub-pixel correction [2] 388 | 389 | the edge point position is selected as the one of the maximum of a 390 | quadratic interpolation of the magnitude of the gradient along a 391 | unidimensional direction. the pixel must be a local maximum. so we 392 | have the values: 393 | . b 394 | a . | 395 | x = -1, |Gx| = a | | . c 396 | x = 0, |Gx| = b | | | 397 | x = 1, |Gx| = c ------------------> x 398 | -1 0 1 399 | 400 | the x position of the maximum of the parabola passing through 401 | (-1,a), (0,b), and (1,c) is 402 | 403 | offset = (a - c) / 2(a - 2b + c) 404 | 405 | and because b >= a and b >= c, -0.5 <= offset <= 0.5 406 | */ 407 | if( Dx > 0 || Dy > 0 ) 408 | { 409 | /* offset value is in [-0.5, 0.5] */ 410 | double a = modG[ x-Dx + (y-Dy) * X ]; 411 | double b = modG[ x + y * X ]; 412 | double c = modG[ x+Dx + (y+Dy) * X ]; 413 | double offset = 0.5 * (a - c) / (a - b - b + c); 414 | 415 | /* store edge point */ 416 | Ex[x+y*X] = x + offset * Dx; 417 | Ey[x+y*X] = y + offset * Dy; 418 | } 419 | } 420 | } 421 | 422 | /*----------------------------------------------------------------------------*/ 423 | /* chain edge points 424 | 425 | input: Ex and Ey are the sub-pixel coordinates when an edge point is present 426 | or -1,-1 otherwise. Gx, Gy and modG are the x and y components and the 427 | modulus of the image gradient, respectively. X,Y is the image size. 428 | 429 | output: next and prev will contain the number of next and previous edge 430 | points in the chain. when not chained in one of the directions, the 431 | corresponding value is set to -1. next and prev must be allocated 432 | before calling. 433 | */ 434 | static void chain_edge_points( int * next, int * prev, double * Ex, double * Ey, 435 | double * Gx, double * Gy, int X, int Y ) 436 | { 437 | int x,y,i,j,alt; 438 | 439 | /* check input */ 440 | if( next==NULL || prev==NULL || Ex==NULL || Ey==NULL || Gx==NULL || Gy==NULL ) 441 | error("chain_edge_points: invalid input"); 442 | 443 | /* initialize next and prev as non linked */ 444 | for(i=0; i= 0.0 && Ey[x+y*X] >= 0.0 ) /* must be an edge point */ 450 | { 451 | int from = x+y*X; /* edge point to be chained */ 452 | double fwd_s = 0.0; /* score of best forward chaining */ 453 | double bck_s = 0.0; /* score of best backward chaining */ 454 | int fwd = -1; /* edge point of best forward chaining */ 455 | int bck = -1; /* edge point of best backward chaining */ 456 | 457 | /* try all neighbors two pixels apart or less. 458 | 459 | looking for candidates for chaining two pixels apart, in most 460 | such cases, is enough to obtain good chains of edge points that 461 | accurately describes the edge. 462 | */ 463 | for(i=-2; i<=2; i++) 464 | for(j=-2; j<=2; j++) 465 | { 466 | int to = x+i + (y+j)*X; /* candidate edge point to be chained */ 467 | double s = chain(from,to,Ex,Ey,Gx,Gy,X,Y); /* score from-to */ 468 | 469 | if( s > fwd_s ) /* a better forward chaining found */ 470 | { 471 | fwd_s = s; /* set the new best forward chaining */ 472 | fwd = to; 473 | } 474 | if( s < bck_s ) /* a better backward chaining found */ 475 | { 476 | bck_s = s; /* set the new best backward chaining */ 477 | bck = to; 478 | } 479 | } 480 | 481 | /* before making the new chain, check whether the target was 482 | already chained and in that case, whether the alternative 483 | chaining is better than the proposed one. 484 | 485 | x alt x alt 486 | \ / 487 | \ / 488 | from x---------x fwd bck x---------x from 489 | 490 | we know that the best forward chain starting at from is from-fwd. 491 | but it is possible that there is an alternative chaining arriving 492 | at fwd that is better, such that alt-fwd is to be preferred to 493 | from-fwd. an analogous situation is possible in backward chaining, 494 | where an alternative link bck-alt may be better than bck-from. 495 | 496 | before making the new link, check if fwd/bck are already chained, 497 | and in such case compare the scores of the proposed chaining to 498 | the existing one, and keep only the best of the two. 499 | 500 | there is an undesirable aspect of this procedure: the result may 501 | depend on the order of exploration. consider the following 502 | configuration: 503 | 504 | a x-------x b 505 | / 506 | / 507 | c x---x d with score(a-b) < score(c-b) < score(c-d) 508 | or equivalently ||a-b|| > ||b-c|| > ||c-d|| 509 | 510 | let us consider two possible orders of exploration. 511 | 512 | order: a,b,c 513 | we will first chain a-b when exploring a. when analyzing the 514 | backward links of b, we will prefer c-b, and a-b will be unlinked. 515 | finally, when exploring c, c-d will be preferred and c-b will be 516 | unlinked. the result is just the chaining c-d. 517 | 518 | order: c,b,a 519 | we will first chain c-d when exploring c. then, when exploring 520 | the backward connections of b, c-b will be the preferred link; 521 | but because c-d exists already and has a better score, c-b 522 | cannot be linked. finally, when exploring a, the link a-b will 523 | be created because there is no better backward linking of b. 524 | the result is two chainings: c-d and a-b. 525 | 526 | we did not found yet a simple algorithm to solve this problem. by 527 | simple, we mean an algorithm without two passes or the need to 528 | re-evaluate the chaining of points where one link is cut. 529 | 530 | for most edge points, there is only one possible chaining and this 531 | problem does not arise. but it does happen and a better solution 532 | is desirable. 533 | */ 534 | if( fwd >= 0 && next[from] != fwd && 535 | ((alt=prev[fwd]) < 0 || chain(alt,fwd,Ex,Ey,Gx,Gy,X,Y) < fwd_s) ) 536 | { 537 | if( next[from] >= 0 ) /* remove previous from-x link if one */ 538 | prev[next[from]] = -1; /* only prev requires explicit reset */ 539 | next[from] = fwd; /* set next of from-fwd link */ 540 | if( alt >= 0 ) /* remove alt-fwd link if one */ 541 | next[alt] = -1; /* only next requires explicit reset */ 542 | prev[fwd] = from; /* set prev of from-fwd link */ 543 | } 544 | if( bck >= 0 && prev[from] != bck && 545 | ((alt=next[bck]) < 0 || chain(alt,bck,Ex,Ey,Gx,Gy,X,Y) > bck_s ) ) 546 | { 547 | if( alt >= 0 ) /* remove bck-alt link if one */ 548 | prev[alt] = -1; /* only prev requires explicit reset */ 549 | next[bck] = from; /* set next of bck-from link */ 550 | if( prev[from] >= 0 ) /* remove previous x-from link if one */ 551 | next[prev[from]] = -1; /* only next requires explicit reset */ 552 | prev[from] = bck; /* set prev of bck-from link */ 553 | } 554 | } 555 | } 556 | 557 | /*----------------------------------------------------------------------------*/ 558 | /* apply Canny thresholding with hysteresis 559 | 560 | next and prev contain the number of next and previous edge points in the 561 | chain or -1 when not chained. modG is modulus of the image gradient. X,Y is 562 | the image size. th_h and th_l are the high and low thresholds, respectively. 563 | 564 | this function modifies next and prev, removing chains not satisfying the 565 | thresholds. 566 | */ 567 | static void thresholds_with_hysteresis( int * next, int * prev, 568 | double * modG, int X, int Y, 569 | double th_h, double th_l ) 570 | { 571 | int * valid; 572 | int i,j,k; 573 | 574 | /* check input */ 575 | if( next == NULL || prev == NULL || modG == NULL ) 576 | error("thresholds_with_hysteresis: invalid input"); 577 | 578 | /* get memory */ 579 | valid = (int *) xmalloc( X * Y * sizeof(int) ); 580 | for(i=0; i=0 or next[i]>=0 implies an edge point */ 584 | if( (prev[i] >= 0 || next[i] >= 0) && !valid[i] && modG[i] >= th_h ) 585 | { 586 | valid[i] = TRUE; /* mark as valid the new point */ 587 | 588 | /* follow the chain of edge points forwards */ 589 | for(j=i; j>=0 && (k=next[j])>=0 && !valid[k]; j=next[j]) 590 | if( modG[k] < th_l ) 591 | { 592 | next[j] = -1; /* cut the chain when the point is below th_l */ 593 | prev[k] = -1; /* j must be assigned to next[j] and not k, 594 | so the loop is chained in this case */ 595 | } 596 | else 597 | valid[k] = TRUE; /* otherwise mark the new point as valid */ 598 | 599 | /* follow the chain of edge points backwards */ 600 | for(j=i; j>=0 && (k=prev[j])>=0 && !valid[k]; j=prev[j]) 601 | if( modG[k] < th_l ) 602 | { 603 | prev[j] = -1; /* cut the chain when the point is below th_l */ 604 | next[k] = -1; /* j must be assigned to prev[j] and not k, 605 | so the loop is chained in this case */ 606 | } 607 | else 608 | valid[k] = TRUE; /* otherwise mark the new point as valid */ 609 | } 610 | 611 | /* remove any remaining non-valid chained point */ 612 | for(i=0; i=0 or next[i]>=0 implies edge point */ 613 | if( (prev[i] >= 0 || next[i] >= 0) && !valid[i] ) 614 | prev[i] = next[i] = -1; 615 | 616 | /* free memory */ 617 | free( (void *) valid ); 618 | } 619 | 620 | /*----------------------------------------------------------------------------*/ 621 | /* create a list of chained edge points composed of 3 lists 622 | x, y and curve_limits; it also computes N (the number of edge points) and 623 | M (the number of curves). 624 | 625 | x[i] and y[i] (0<=i=0 or next[i]>=0 implies an edge point */ 671 | if( prev[i] >= 0 || next[i] >= 0 ) 672 | { 673 | /* a new chain found, set chain starting index to the current point 674 | and then increase the curve counter */ 675 | (*curve_limits)[*M] = *N; 676 | ++(*M); 677 | 678 | /* set k to the beginning of the chain, or to i if closed curve */ 679 | for(k=i; (n=prev[k])>=0 && n!=i; k=n); 680 | 681 | /* follow the chain of edge points starting on k */ 682 | do 683 | { 684 | /* store the current point coordinates in the output lists */ 685 | (*x)[*N] = Ex[k]; 686 | (*y)[*N] = Ey[k]; 687 | ++(*N); 688 | 689 | n = next[k]; /* save the id of the next point in the chain */ 690 | 691 | next[k] = -1; /* unlink chains from k so it is not used again */ 692 | prev[k] = -1; 693 | 694 | /* for closed curves, the initial point is included again as 695 | the last point of the chain. actually, testing if the first 696 | and last points are equal is the only way to know that it is 697 | a closed curve. 698 | 699 | to understand that this code actually repeats the first point, 700 | consider a closed chain as follows: a--b 701 | | | 702 | d--c 703 | 704 | let us say that the algorithm starts by point a. it will store 705 | the coordinates of point a and then unlink a-b. then, will store 706 | point b and unlink b-c, and so on. but the link d-a is still 707 | there. (point a is no longer pointing backwards to d, because 708 | both links are removed at each step. but d is indeed still 709 | pointing to a.) so it will arrive at point a again and store its 710 | coordinates again as last point. there, it cannot continue 711 | because the link a-b was removed, there would be no next point, 712 | k would be -1 and the curve is finished. 713 | */ 714 | 715 | k = n; /* set the current point to the next in the chain */ 716 | } 717 | while( k >= 0 ); /* continue while there is a next point in the chain */ 718 | } 719 | (*curve_limits)[*M] = *N; /* store end of the last chain */ 720 | } 721 | 722 | /*----------------------------------------------------------------------------*/ 723 | /* chained, sub-pixel edge detector. based on a modified Canny non-maximal 724 | suppression and a modified Devernay sub-pixel correction. 725 | 726 | input: 727 | 728 | image : the input image 729 | X,Y : the size of the input image 730 | sigma : standard deviation sigma for the Gaussian filtering 731 | (if sigma=0 no filtering is performed) 732 | th_h : high gradient threshold in Canny's hysteresis 733 | th_l : low gradient threshold in Canny's hysteresis 734 | 735 | output: 736 | 737 | x,y : lists of sub-pixel coordinates of edge points 738 | curve_limits : the limits of each curve in lists x and y 739 | N : number of edge points 740 | M : number of curves 741 | 742 | the input is a XxY graylevel image given as a pointer to an array of doubles 743 | such that image[x+y*X] is the value at coordinates x,y 744 | (for 0 <= x < X and 0 <= y < Y). 745 | 746 | the output are the chained edge points given as 3 allocated lists: x, y and 747 | curve_limits. also the numbers N (size of lists x and y) and M (number of 748 | curves). 749 | 750 | x[i] and y[i] (0<=i, 12 | Gregory Randall 13 | 14 | This program is free software: you can redistribute it and/or modify 15 | it under the terms of the GNU Affero General Public License as 16 | published by the Free Software Foundation, either version 3 of the 17 | License, or (at your option) any later version. 18 | 19 | This program is distributed in the hope that it will be useful, 20 | but WITHOUT ANY WARRANTY; without even the implied warranty of 21 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 22 | GNU Affero General Public License for more details. 23 | 24 | You should have received a copy of the GNU Affero General Public License 25 | along with this program. If not, see . 26 | 27 | ----------------------------------------------------------------------------*/ 28 | #ifndef DEVERNAY_HEADER 29 | #define DEVERNAY_HEADER 30 | 31 | /*----------------------------------------------------------------------------*/ 32 | #define DEVERNAY_VERSION "1.0 (October 10, 2017)" 33 | 34 | /*----------------------------------------------------------------------------*/ 35 | /* chained, sub-pixel edge detector. based on a modified Canny non-maximal 36 | suppression and a modified Devernay sub-pixel correction. 37 | 38 | input: 39 | 40 | image : the input image 41 | X,Y : the size of the input image 42 | sigma : standard deviation sigma for the Gaussian filtering 43 | (if sigma=0 no filtering is performed) 44 | th_h : high gradient threshold in Canny's hysteresis 45 | th_l : low gradient threshold in Canny's hysteresis 46 | 47 | output: 48 | 49 | x,y : lists of sub-pixel coordinates of edge points 50 | curve_limits : the limits of each curve in lists x and y 51 | N : number of edge points 52 | M : number of curves 53 | 54 | the input is a XxY graylevel image given as a pointer to an array of doubles 55 | such that image[x+y*X] is the value at coordinates x,y 56 | (for 0 <= x < X and 0 <= y < Y). 57 | 58 | the output are the chained edge points given as 3 allocated lists: x, y and 59 | curve_limits. also the numbers N (size of lists x and y) and M (number of 60 | curves). 61 | 62 | x[i] and y[i] (0<=i, 6 | Gregory Randall 7 | 8 | This program is free software: you can redistribute it and/or modify 9 | it under the terms of the GNU Affero General Public License as 10 | published by the Free Software Foundation, either version 3 of the 11 | License, or (at your option) any later version. 12 | 13 | This program is distributed in the hope that it will be useful, 14 | but WITHOUT ANY WARRANTY; without even the implied warranty of 15 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 16 | GNU Affero General Public License for more details. 17 | 18 | You should have received a copy of the GNU Affero General Public License 19 | along with this program. If not, see . 20 | 21 | ----------------------------------------------------------------------------*/ 22 | #include 23 | #include 24 | #include 25 | #include "io.h" 26 | #include "devernay.h" 27 | 28 | /*----------------------------------------------------------------------------*/ 29 | /* print usage and exit 30 | */ 31 | static void usage(void) 32 | { 33 | fprintf(stderr,"devernay %s\n",DEVERNAY_VERSION); 34 | fprintf(stderr,"Copyright (c) 2016-2017 "); 35 | fprintf(stderr,"Rafael Grompone von Gioi and Gregory Randall\n\n"); 36 | fprintf(stderr,"usage: devernay [-s S] [-h H] [-l L] [-w W] [-t T] "); 37 | fprintf(stderr,"[-p P] [-g G]\n\n"); 38 | fprintf(stderr,"image PGM or ASC file formats are handled\n"); 39 | fprintf(stderr,"-s S set the blur standard deviation "); 40 | fprintf(stderr,"(default S=0.0 -> no blurring)\n"); 41 | fprintf(stderr,"-h H set high threshold (default H=0.0)\n"); 42 | fprintf(stderr,"-l L set low threshold (default L=0.0)\n"); 43 | fprintf(stderr,"-w W set line width in PDF and SVG to W (default W=1.3)\n"); 44 | fprintf(stderr,"-t T write TXT output to file T\n"); 45 | fprintf(stderr,"-p P write PDF output to file P\n"); 46 | fprintf(stderr,"-g G write SVG output to file G\n\n"); 47 | fprintf(stderr,"examples: devernay image.pgm -p output.pdf\n"); 48 | fprintf(stderr," devernay image.pgm -t output.txt -p output.pdf "); 49 | fprintf(stderr,"-g output.svg\n"); 50 | fprintf(stderr," devernay image.pgm -p output.pdf "); 51 | fprintf(stderr,"-s 1.0 -l 5.0 -h 15.0 -w 0.5\n"); 52 | 53 | exit(EXIT_FAILURE); 54 | } 55 | 56 | /*----------------------------------------------------------------------------*/ 57 | /* get an optional parameter from arguments 58 | 59 | if found, the value is returned and it is removed from the list of arguments. 60 | adapted from pick_option by Enric Meinhardt-Llopis. 61 | 62 | example: if arguments are "command -p 123 input.txt", 63 | char * p = get_option(&argc,&argv,"-p","0"); 64 | will give "123" in p and leave arguments as "command input.txt" 65 | */ 66 | static char * get_option(int * argc, char *** argv, char * opt, char * def) 67 | { 68 | int i,j; 69 | 70 | for(i=0; i<(*argc-1); i++) /* last argument cannot have an optional value */ 71 | if( strcmp( (*argv)[i], opt ) == 0 ) /* option opt found */ 72 | { 73 | char * r = (*argv)[i+1]; /* save the optional value to return */ 74 | for(j=i; j < (*argc-2); j++) /* shift arguments to remove opt+value */ 75 | (*argv)[j] = (*argv)[j+2]; 76 | *argc -= 2; /* decrease the number of arguments in 2, opt+value */ 77 | return r; /* return the value found for option opt */ 78 | } 79 | return def; /* option not found, return the default value */ 80 | } 81 | 82 | /*----------------------------------------------------------------------------*/ 83 | /* main */ 84 | /*----------------------------------------------------------------------------*/ 85 | int main(int argc, char ** argv) 86 | { 87 | double * image; /* image of size X,Y */ 88 | double * x; /* x[n] y[n] coordinates of result contour point n */ 89 | double * y; 90 | int * curve_limits; /* limits of the curves in the x[] and y[] */ 91 | int X,Y,N,M; /* result: N contour points, forming M curves */ 92 | char * txt_out = get_option(&argc,&argv,"-t",NULL); /* txt filename */ 93 | char * pdf_out = get_option(&argc,&argv,"-p",NULL); /* pdf filename */ 94 | char * svg_out = get_option(&argc,&argv,"-g",NULL); /* svg filename */ 95 | double S = atof(get_option(&argc,&argv,"-s","0.0")); /* default sigma=0 */ 96 | double H = atof(get_option(&argc,&argv,"-h","0.0")); /* default th_h=0 */ 97 | double L = atof(get_option(&argc,&argv,"-l","0.0")); /* default th_l=0 */ 98 | double W = atof(get_option(&argc,&argv,"-w","1.3")); /* default W=1.3 */ 99 | 100 | /* read input */ 101 | if( argc != 2 ) usage(); 102 | image = read_image(argv[1],&X,&Y); 103 | 104 | /* call Canny/Devernay algorithm */ 105 | devernay(&x, &y, &N, &curve_limits, &M, image, X, Y, S, H, L); 106 | 107 | /* write required outputs, TXT and/or PDF */ 108 | if( txt_out != NULL ) write_curves_txt(x,y,curve_limits,M,txt_out); 109 | if( pdf_out != NULL ) write_curves_pdf(x,y,curve_limits,M,pdf_out,X,Y,W); 110 | if( svg_out != NULL ) write_curves_svg(x,y,curve_limits,M,svg_out,X,Y,W); 111 | 112 | /* free memory */ 113 | free( (void *) image ); 114 | free( (void *) curve_limits ); 115 | free( (void *) x ); 116 | free( (void *) y ); 117 | 118 | return EXIT_SUCCESS; 119 | } 120 | /*----------------------------------------------------------------------------*/ 121 | -------------------------------------------------------------------------------- /C/devernay_1.0/image_out.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anttad/SubpixelCircleDetection/912252d4a305d71f800ed39b3ce87946251cb675/C/devernay_1.0/image_out.pdf -------------------------------------------------------------------------------- /C/devernay_1.0/io.c: -------------------------------------------------------------------------------- 1 | /*---------------------------------------------------------------------------- 2 | 3 | I/O functions: read PGM or ASC images and curve output to PDF or TXT files. 4 | 5 | Copyright (c) 2016-2017 rafael grompone von gioi , 6 | Gregory Randall 7 | 8 | This program is free software: you can redistribute it and/or modify 9 | it under the terms of the GNU Affero General Public License as 10 | published by the Free Software Foundation, either version 3 of the 11 | License, or (at your option) any later version. 12 | 13 | This program is distributed in the hope that it will be useful, 14 | but WITHOUT ANY WARRANTY; without even the implied warranty of 15 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 16 | GNU Affero General Public License for more details. 17 | 18 | You should have received a copy of the GNU Affero General Public License 19 | along with this program. If not, see . 20 | 21 | ----------------------------------------------------------------------------*/ 22 | #include 23 | #include 24 | #include 25 | #include 26 | #include 27 | 28 | /*----------------------------------------------------------------------------*/ 29 | #ifndef FALSE 30 | #define FALSE 0 31 | #endif /* !FALSE */ 32 | 33 | #ifndef TRUE 34 | #define TRUE 1 35 | #endif /* !TRUE */ 36 | 37 | /*----------------------------------------------------------------------------*/ 38 | /* fatal error, print a message to standard error and exit 39 | */ 40 | static void error(char * msg) 41 | { 42 | fprintf(stderr,"error: %s\n",msg); 43 | exit(EXIT_FAILURE); 44 | } 45 | 46 | /*----------------------------------------------------------------------------*/ 47 | /* memory allocation, print an error and exit if fail 48 | */ 49 | static void * xmalloc(size_t size) 50 | { 51 | void * p; 52 | if( size == 0 ) error("xmalloc input: zero size"); 53 | p = malloc(size); 54 | if( p == NULL ) error("out of memory"); 55 | return p; 56 | } 57 | 58 | /*----------------------------------------------------------------------------*/ 59 | /* open file, print an error and exit if fail 60 | */ 61 | static FILE * xfopen(const char * path, const char * mode) 62 | { 63 | FILE * f = fopen(path,mode); 64 | if( f == NULL ) 65 | { 66 | fprintf(stderr,"error: unable to open file '%s'\n",path); 67 | exit(EXIT_FAILURE); 68 | } 69 | return f; 70 | } 71 | 72 | /*----------------------------------------------------------------------------*/ 73 | /* close file, print an error and exit if fail 74 | */ 75 | static int xfclose(FILE * f) 76 | { 77 | if( fclose(f) == EOF ) error("unable to close file"); 78 | return 0; 79 | } 80 | 81 | /*----------------------------------------------------------------------------*/ 82 | /* skip white characters and comments in a PGM file 83 | */ 84 | static void skip_whites_and_comments(FILE * f) 85 | { 86 | int c; 87 | do 88 | { 89 | while(isspace(c=getc(f))); /* skip spaces */ 90 | if(c=='#') /* skip comments */ 91 | while( c!='\n' && c!='\r' && c!=EOF ) 92 | c=getc(f); 93 | } 94 | while( c == '#' || isspace(c) ); 95 | if( c != EOF && ungetc(c,f) == EOF ) 96 | error("unable to 'ungetc' while reading PGM file."); 97 | } 98 | 99 | /*----------------------------------------------------------------------------*/ 100 | /* read a number in ASCII from a PGM file 101 | */ 102 | static int get_num(FILE * f) 103 | { 104 | int num,c; 105 | 106 | while(isspace(c=getc(f))); 107 | if(!isdigit(c)) error("corrupted PGM or PPM file."); 108 | num = c - '0'; 109 | while( isdigit(c=getc(f)) ) num = 10 * num + c - '0'; 110 | if( c != EOF && ungetc(c,f) == EOF ) 111 | error("unable to 'ungetc' while reading PGM file."); 112 | 113 | return num; 114 | } 115 | 116 | /*----------------------------------------------------------------------------*/ 117 | /* read a PGM image file 118 | */ 119 | double * read_pgm_image(char * name, int * X, int * Y) 120 | { 121 | FILE * f; 122 | int i,n,depth,bin=FALSE; 123 | double * image; 124 | 125 | /* open file */ 126 | f = xfopen(name,"rb"); /* open to read as a binary file (b option). otherwise, 127 | in some systems, it may behave differently */ 128 | 129 | /* read header */ 130 | if( getc(f) != 'P' ) error("not a PGM file!"); 131 | if( (n=getc(f)) == '2' ) bin = FALSE; 132 | else if( n == '5' ) bin = TRUE; 133 | else error("not a PGM file!"); 134 | skip_whites_and_comments(f); 135 | *X = get_num(f); /* X size */ 136 | skip_whites_and_comments(f); 137 | *Y = get_num(f); /* Y size */ 138 | skip_whites_and_comments(f); 139 | depth = get_num(f); /* pixel depth */ 140 | if( depth < 0 ) error("pixel depth < 0, unrecognized PGM file"); 141 | if( bin && depth > 255 ) error("pixel depth > 255, unrecognized PGM file"); 142 | /* white before data */ 143 | if(!isspace(getc(f))) error("corrupted PGM file."); 144 | 145 | /* get memory */ 146 | image = (double *) xmalloc( *X * *Y * sizeof(double) ); 147 | 148 | /* read data */ 149 | for(i=0; i<(*X * *Y); i++) 150 | image[i] = (double) (bin ? getc(f) : get_num(f)); 151 | 152 | /* close file */ 153 | xfclose(f); 154 | 155 | /* return image */ 156 | return image; 157 | } 158 | 159 | /*----------------------------------------------------------------------------*/ 160 | /* read a 2D ASC format file 161 | */ 162 | double * read_asc_file(char * name, int * X, int * Y) 163 | { 164 | FILE * f; 165 | int i,n,Z,C; 166 | double val; 167 | double * image; 168 | 169 | /* open file */ 170 | f = xfopen(name,"rb"); /* open to read as a binary file (b option). otherwise, 171 | in some systems, it may behave differently */ 172 | 173 | /* read header */ 174 | n = fscanf(f,"%d%*c%d%*c%d%*c%d",X,Y,&Z,&C); 175 | if( n!=4 || *X<=0 || *Y<=0 || Z<=0 || C<=0 ) error("invalid ASC file"); 176 | 177 | /* only gray level images are handled */ 178 | if( Z!=1 || C!=1 ) error("only single channel ASC files are handled"); 179 | 180 | /* get memory */ 181 | image = (double *) xmalloc( *X * *Y * Z * C * sizeof(double) ); 182 | 183 | /* read data */ 184 | for(i=0; i<(*X * *Y * Z * C); i++) 185 | { 186 | n = fscanf(f,"%lf",&val); 187 | if( n!=1 ) error("invalid ASC file"); 188 | image[i] = val; 189 | } 190 | 191 | /* close file */ 192 | xfclose(f); 193 | 194 | return image; 195 | } 196 | 197 | /*----------------------------------------------------------------------------*/ 198 | /* read an image from a file in ASC or PGM formats 199 | */ 200 | double * read_image(char * name, int * X, int * Y) 201 | { 202 | int n = (int) strlen(name); 203 | char * ext = name+n-4; 204 | 205 | if( n>=4 && ( strcmp(ext,".asc")==0 || strcmp(ext,".ASC")==0) ) 206 | return read_asc_file(name,X,Y); 207 | 208 | return read_pgm_image(name,X,Y); 209 | } 210 | 211 | /*----------------------------------------------------------------------------*/ 212 | /* write curves into a PDF file. the output is PDF version 1.4 as described in 213 | "PDF Reference, third edition" by Adobe Systems Incorporated, 2001 214 | */ 215 | void write_curves_pdf( double * x, double * y, int * curve_limits, int M, 216 | char * filename, int X, int Y, double width ) 217 | { 218 | FILE * pdf; 219 | long start1,start2,start3,start4,start5,startxref,stream_len; 220 | int i,j,k; 221 | 222 | /* check input */ 223 | if( filename == NULL ) error("invalid filename in write_curves_pdf"); 224 | if( M > 0 && ( x == NULL || y == NULL || curve_limits == NULL ) ) 225 | error("invalid curves data in write_curves_pdf"); 226 | if( X <= 0 || Y <= 0 ) error("invalid image size in write_curves_pdf"); 227 | 228 | /* open file */ 229 | pdf = xfopen(filename,"wb"); /* open to write as a binary file (b option). 230 | otherwise, in some systems, 231 | it may behave differently */ 232 | 233 | /* PDF header */ 234 | fprintf(pdf,"%%PDF-1.4\n"); 235 | /* The following PDF comment contains characters with ASCII codes greater 236 | than 128. This helps to classify the file as containing 8-bit binary data. 237 | See "PDF Reference" p.63. */ 238 | fprintf(pdf,"%%%c%c%c%c\n",0xe2,0xe3,0xcf,0xd3); 239 | 240 | /* Catalog, Pages and Page objects */ 241 | start1 = ftell(pdf); 242 | fprintf(pdf,"1 0 obj\n<>\n"); 243 | fprintf(pdf,"endobj\n"); 244 | start2 = ftell(pdf); 245 | fprintf(pdf,"2 0 obj\n<> /MediaBox [0 0 %d %d]>>\nendobj\n",X,Y); 247 | start3 = ftell(pdf); 248 | fprintf(pdf,"3 0 obj\n"); 249 | fprintf(pdf,"<>\n"); 250 | fprintf(pdf,"endobj\n"); 251 | 252 | /* Contents object - graphic contents */ 253 | start4 = ftell(pdf); 254 | fprintf(pdf,"4 0 obj\n<>\n"); /* indirect length in obj 5 */ 255 | fprintf(pdf,"stream\n"); 256 | stream_len = ftell(pdf); 257 | fprintf(pdf,"%.4f w\n",width); /* set line width */ 258 | for(k=0; k>\n"); 306 | fprintf(pdf,"startxref\n"); 307 | fprintf(pdf,"%ld\n",startxref); 308 | fprintf(pdf,"%%%%EOF\n"); 309 | 310 | /* close file */ 311 | xfclose(pdf); 312 | } 313 | 314 | /*----------------------------------------------------------------------------*/ 315 | /* write curves into a TXT file 316 | */ 317 | void write_curves_txt( double * x, double * y, int * curve_limits, int M, 318 | char * filename ) 319 | { 320 | FILE * txt; 321 | int i,k; 322 | 323 | /* check input */ 324 | if( filename == NULL ) error("invalid filename in write_curves_txt"); 325 | if( M > 0 && ( x == NULL || y == NULL || curve_limits == NULL ) ) 326 | error("invalid curves data in write_curves_txt"); 327 | 328 | /* open file */ 329 | txt = xfopen(filename,"wb"); /* open to write as a binary file (b option). 330 | otherwise, in some systems, 331 | it may behave differently */ 332 | 333 | /* write curves */ 334 | for(k=0; k 0 && ( x == NULL || y == NULL || curve_limits == NULL ) ) 357 | error("invalid curves data in write_curves_svg"); 358 | if( X <= 0 || Y <= 0 ) error("invalid image size in write_curves_svg"); 359 | 360 | /* open file */ 361 | svg = xfopen(filename,"wb"); /* open to write as a binary file (b option). 362 | otherwise, in some systems, 363 | it may behave differently */ 364 | 365 | /* write SVG header */ 366 | fprintf(svg,"\n"); 367 | fprintf(svg,"\n"); 369 | fprintf(svg,"\n"); 372 | 373 | /* write curves */ 374 | for(k=0; k\n"); /* end of chain */ 381 | } 382 | 383 | /* close SVG file */ 384 | fprintf(svg,"\n"); 385 | xfclose(svg); 386 | } 387 | /*----------------------------------------------------------------------------*/ 388 | -------------------------------------------------------------------------------- /C/devernay_1.0/io.h: -------------------------------------------------------------------------------- 1 | /*---------------------------------------------------------------------------- 2 | 3 | I/O functions: read PGM or ASC images and curve output to PDF or TXT files. 4 | 5 | Copyright (c) 2016-2017 rafael grompone von gioi , 6 | Gregory Randall 7 | 8 | This program is free software: you can redistribute it and/or modify 9 | it under the terms of the GNU Affero General Public License as 10 | published by the Free Software Foundation, either version 3 of the 11 | License, or (at your option) any later version. 12 | 13 | This program is distributed in the hope that it will be useful, 14 | but WITHOUT ANY WARRANTY; without even the implied warranty of 15 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 16 | GNU Affero General Public License for more details. 17 | 18 | You should have received a copy of the GNU Affero General Public License 19 | along with this program. If not, see . 20 | 21 | ----------------------------------------------------------------------------*/ 22 | #ifndef IO_HEADER 23 | #define IO_HEADER 24 | 25 | /*----------------------------------------------------------------------------*/ 26 | /* read a PGM image file 27 | */ 28 | double * read_pgm_image(char * name, int * X, int * Y); 29 | 30 | /*----------------------------------------------------------------------------*/ 31 | /* read a 2D ASC format file 32 | */ 33 | double * read_asc_file(char * name, int * X, int * Y); 34 | 35 | /*----------------------------------------------------------------------------*/ 36 | /* read an image from a file in ASC or PGM formats 37 | */ 38 | double * read_image(char * name, int * X, int * Y); 39 | 40 | /*----------------------------------------------------------------------------*/ 41 | /* write curves into a PDF file. the output is PDF version 1.4 as described in 42 | "PDF Reference, third edition" by Adobe Systems Incorporated, 2001 43 | */ 44 | void write_curves_pdf( double * x, double * y, int * curve_limits, int M, 45 | char * filename, int X, int Y, double width ); 46 | 47 | /*----------------------------------------------------------------------------*/ 48 | /* write curves into a TXT file 49 | */ 50 | void write_curves_txt( double * x, double * y, int * curve_limits, int M, 51 | char * filename ); 52 | 53 | /*----------------------------------------------------------------------------*/ 54 | /* write curves into a SVG file 55 | */ 56 | void write_curves_svg( double * x, double * y, int * curve_limits, int M, 57 | char * filename, int X, int Y, double width ); 58 | 59 | #endif /* !IO_HEADER */ 60 | /*----------------------------------------------------------------------------*/ 61 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 Antoine Tadros 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # SubpixelCircleDetection 2 | Code for ISPRS 2020 article "Circular-Shaped Object Detection in Low Resolution Satellite Images" 3 | 4 | 5 | (still formating the code) 6 | 7 | 8 | python code: 9 | `iso_th_devernay.py`: Isoperimetric thresholding with Canny-Devernay's edge extraction code 10 | `hough_devernay.py`: Isoperimetric thresholding with Canny-Devernay's edge extraction code 11 | 12 | 13 | The ELSDc code is available here : **https://github.com/viorik/ELSDc** 14 | 15 | 16 | 17 | The level-line extraction code is available in **interpolated_shapes.zip** 18 | 19 | -------------------------------------------------------------------------------- /apply_tophat.py: -------------------------------------------------------------------------------- 1 | import tifffile 2 | import skimage.draw 3 | import imageio 4 | import numpy as np 5 | import scipy.ndimage 6 | 7 | def load_image(filepath, bands=['B02', 'B03', 'B04']): 8 | fim_np = [] 9 | for band in bands: 10 | im_np = tifffile.imread(filepath.replace('(:band)', band)) 11 | fim_np.append(im_np) 12 | 13 | fim_np = np.array(fim_np) 14 | fim_np = np.moveaxis(fim_np, 0, 2) 15 | return fim_np 16 | 17 | def minimum_of_directional_tophat_bottomhat(im_np, size, method='tophat'): # method='tophat', 'bottomhat' 18 | x = list(range(0, size)) + [size - 1] * size 19 | y = [0] * size + list(range(0, size)) 20 | 21 | fims_np = [] 22 | for i in range(len(x)): 23 | se_np = np.zeros((size, size), dtype=bool) 24 | rr, cc = skimage.draw.line(y[i], x[i], size - 1 - y[i], size - 1 - x[i]) 25 | se_np[rr, cc] = True 26 | 27 | 28 | #imageio.imsave('tmp/' + str(i) + '.png', se_np.astype(float)) 29 | 30 | filtered_np = np.zeros(im_np.shape) 31 | for j in range(im_np.shape[2]): 32 | if (method == 'tophat'): 33 | filtered_np[:,:,j] = im_np[:,:,j] - scipy.ndimage.grey_opening(im_np[:,:,j], size=(size,size), footprint=se_np) 34 | else: 35 | filtered_np[:,:,j] = scipy.ndimage.grey_closing(im_np[:,:,j], size=(size,size), footprint=se_np) - im_np[:,:,j] 36 | # tifffile.imsave('tmp/f_' + str(i) + '.tif', filtered_np) 37 | fims_np.append(filtered_np) 38 | 39 | fims_np = np.array(fims_np) 40 | fims_np = np.min(fims_np, axis=0) 41 | 42 | fims_grey_np = np.min(fims_np, axis=2) 43 | #tifffile.imsave('out_grey.tif', fims_np) 44 | 45 | return fims_np, fims_grey_np 46 | 47 | #im_np = load_image('data/2019-01-20_S2B_orbit_032_tile_50SNJ_L1C_band_(:band).tif') 48 | #fims_np, fims_grey_np = minimum_of_directional_tophat_bottomhat(im_np, 11) 49 | #tifffile.imsave('tophat_per_channel.tif', fims_np) 50 | #tifffile.imsave('tophat_per_channel_min_all_channels.tif', fims_grey_np) -------------------------------------------------------------------------------- /compare.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import scipy.spatial.distance 3 | 4 | # listes [[x, y], [x, y], [x, y]...] 5 | def precision_recall(dets, gts, tolerance=3): 6 | dists = scipy.spatial.distance.cdist(dets, gts) 7 | idx = np.argsort(dists.flatten()) 8 | ys = (idx / gts.shape[0]).astype(int) 9 | xs = (idx % gts.shape[0]) 10 | 11 | affected_np = -np.ones(dets.shape[0], dtype=int) 12 | used_np = -np.ones(gts.shape[0], dtype=int) 13 | for i in range(ys.shape[0]): 14 | y = ys[i] 15 | x = xs[i] 16 | if (dists[y, x] > tolerance): 17 | break 18 | if (used_np[x] >= 0): 19 | continue 20 | 21 | affected_np[y] = x 22 | used_np[x] = y 23 | 24 | tp = np.sum(affected_np >= 0) 25 | precision = tp / dets.shape[0] 26 | recall = tp / gts.shape[0] 27 | 28 | return precision, recall 29 | 30 | 31 | 32 | 33 | # 34 | #gts = np.load('gt.npz')['points'] 35 | # 36 | # 37 | #print (precision_recall(np.random.random((100,2)) * 500, gts)) -------------------------------------------------------------------------------- /data/50SQE_2018_12_10_0_012.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anttad/SubpixelCircleDetection/912252d4a305d71f800ed39b3ce87946251cb675/data/50SQE_2018_12_10_0_012.jpeg -------------------------------------------------------------------------------- /data/50SQE_2018_12_10_0_012.json: -------------------------------------------------------------------------------- 1 | {"images":[{"id":1,"date_captured":"2019-07-04 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-------------------------------------------------------------------------------- /data/50SQE_2018_12_10_0_012.pgm: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anttad/SubpixelCircleDetection/912252d4a305d71f800ed39b3ce87946251cb675/data/50SQE_2018_12_10_0_012.pgm -------------------------------------------------------------------------------- /data/gt.npz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anttad/SubpixelCircleDetection/912252d4a305d71f800ed39b3ce87946251cb675/data/gt.npz -------------------------------------------------------------------------------- /hough_devernay.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | 5 | 6 | 7 | import os 8 | import numpy as np 9 | import cv2 10 | import json 11 | from skimage import color #, data 12 | from skimage.transform import hough_circle, hough_circle_peaks,hough_ellipse 13 | from skimage.feature import canny 14 | from skimage.draw import circle_perimeter, rectangle_perimeter,ellipse_perimeter,circle 15 | from skimage.util import img_as_ubyte 16 | 17 | import scipy.misc 18 | import scipy.ndimage 19 | import skimage.morphology 20 | from skimage.morphology import disk 21 | from skimage.viewer import ImageViewer 22 | from utils import * 23 | 24 | from skimage.draw import rectangle 25 | 26 | from skimage.morphology import binary_closing, binary_dilation, erosion, dilation 27 | from skimage.morphology import square 28 | import skimage.io 29 | 30 | import scipy.misc 31 | import scipy.ndimage 32 | from skimage.viewer import ImageViewer 33 | from skimage.color import rgb2gray 34 | 35 | import argparse 36 | 37 | from utils import * 38 | 39 | from skimage.filters import threshold_otsu 40 | 41 | from apply_tophat import minimum_of_directional_tophat_bottomhat 42 | 43 | 44 | 45 | 46 | """ 47 | Formule pour calculer l'air d'un polygone connaissant les coordonnées de ses sommets. "shoelace forumula" 48 | """ 49 | def PolyArea(x,y): 50 | return 0.5*np.abs(np.dot(x,np.roll(y,1))-np.dot(y,np.roll(x,1))) 51 | 52 | 53 | """ 54 | A partir d'une liste de edge point obtenu par l'algorithme canny-devernay, renvoie 55 | l'image binaire des contours 56 | """ 57 | def get_edge_map(txt,im_dim, width): 58 | 59 | coord = np.loadtxt(txt) 60 | coord = coord[coord[:,0]!=-1,:] # on élimine la délimitation 61 | A = np.uint(np.round(coord/width)) # on augmente la valeur des coordonnées par 2 et on arrondie pour pouvoir les plascer 62 | 63 | new_dim = ( int(im_dim[0]/width), int(im_dim[1]/width)) 64 | edge_map = np.zeros(new_dim) 65 | 66 | y,x= tuple(A.T) # corrige l'inversion des coordonnées x et y 67 | edge_map[(x,y)]=1 68 | 69 | return edge_map 70 | 71 | 72 | 73 | """ 74 | determine si un segment de contours est fermé 75 | """ 76 | def is_closed(segment): 77 | 78 | x_o, y_o = segment[0,:] 79 | x_f, y_f = segment[-1,:] 80 | 81 | if (x_o == x_f) and (y_o == y_f): 82 | return True 83 | else: 84 | return False 85 | 86 | """ 87 | renvoie la liste des contours fermés 88 | """ 89 | def get_closed_contour_map(txt,im_dim, width): 90 | 91 | coord = np.loadtxt(txt)/width 92 | list_of_segment = np.split(coord,np.argwhere(coord[:,0]<0).reshape(-1)) 93 | list_of_segment = [x[1:,:] if x[0,0] <0 else x for x in list_of_segment[:-1]] # le dernier terme de la liste ne sert à rien 94 | 95 | 96 | closed_edge_list = [ np.uint(np.round(segment)) for segment in list_of_segment if is_closed(segment)] 97 | #coord = coord[coord[:,0]!=-1,:] # on élimine la délimitation 98 | # on augmente la valeur des coordonnées par 2 et on arrondie pour pouvoir les placer 99 | # découpage des segments de contours 100 | new_dim = ( int(im_dim[0]/width), int(im_dim[1]/width)) 101 | 102 | edge_map = np.zeros(new_dim) 103 | 104 | for seg in closed_edge_list: 105 | y,x= tuple(seg.T) # corrige l'inversion des coordonnées x et y 106 | edge_map[(x,y)]=1 107 | return edge_map 108 | 109 | 110 | def svg2png(in_svg, out_png, out_shape): 111 | import cairo 112 | import rsvg 113 | nrow, ncol = out_shape 114 | img = cairo.ImageSurface(cairo.FORMAT_ARGB32, ncol,nrow) 115 | 116 | ctx = cairo.Context(img) 117 | 118 | ## handle = rsvg.Handle() 119 | # or, for in memory SVG data: 120 | handle= rsvg.Handle(in_svg) 121 | 122 | handle.render_cairo(ctx) 123 | 124 | img.write_to_png(out_png) 125 | 126 | 127 | 128 | def fftzoom(img, factor=2): 129 | 130 | if len(img.shape) == 2: 131 | nrow,ncol = img.shape 132 | r_alpha, c_alpha= nrow*(factor-1),ncol*(factor-1) 133 | r_step, c_step = int(np.ceil(r_alpha/2)), int(np.ceil(c_alpha/2)) 134 | fft_im = np.fft.fftshift(np.fft.fft2(img)) 135 | fft_pad = np.pad(fft_im,((r_step,r_step), (c_step, c_step))) 136 | res = np.abs(np.fft.ifft2(np.fft.ifftshift(fft_pad))) 137 | # res = np.uint8(255*(res-res.min())/(res.max()-res.min())) 138 | 139 | elif len(img.shape)==3: 140 | nrow,ncol,_ = img.shape 141 | r_alpha, c_alpha= nrow*(factor-1),ncol*(factor-1) 142 | r_step, c_step = int(np.ceil(r_alpha/2)), int(np.ceil(c_alpha/2)) 143 | res_list =[] 144 | for i in range(3): 145 | fft_im = np.fft.fftshift(np.fft.fft2(img[:,:,i])) 146 | fft_pad = np.pad(fft_im,((r_step,r_step), (c_step, c_step))) 147 | res_list.append(np.abs(np.fft.ifft2(np.fft.ifftshift(fft_pad)))) 148 | res= np.stack(res_list, axis=2) 149 | # res = np.uint8(255*(res-res.min())/(res.max()-res.min())) 150 | 151 | return res 152 | 153 | 154 | #%% test 155 | 156 | import matplotlib.pyplot as plt 157 | 158 | from compare import precision_recall 159 | 160 | 161 | 162 | if __name__ == '__main__': 163 | 164 | parser = argparse.ArgumentParser() 165 | 166 | parser.add_argument('-i', '--input', default="./data/50SQE_2018_12_10_0_012.jpeg", type=str,help="path to the input image") 167 | parser.add_argument('-z',"--zoom", action='store_true', help="(preprocessing) apply 2x fft zoom to the image") 168 | parser.add_argument('-t',"--top_hat", action='store_true', help="(preprocessing) apply the top-hat procedure to the image") 169 | parser.add_argument('-k',"--top_hat_size", default=5,type=int , help="(preprocessing) top-hat parmameter") 170 | parser.add_argument('-o','--output',default="./results/", type=str,help="output folder") 171 | parser.add_argument('-a',"--auto_th", action='store_true', help="set automatically the threshold using Otsu's histogram method") 172 | parser.add_argument('-lt','--low',default=5, type=int,help="low threshold for Canny-Devernay's edge extraction") 173 | parser.add_argument('-ht','--high',default=15, type=int,help="high threshold for Canny-Devernay's edge extraction") 174 | parser.add_argument('-s','--std',default=0, type=float,help="std Gaussian kernel for Canny-Devernay's edge extraction pre-processing") 175 | parser.add_argument('-e', '--eval', action="store_true", help="Use only if ground truth is avaible to evaluate the method performance") 176 | 177 | args = parser.parse_args() 178 | 179 | gts = np.load('./data/gt.npz')['points'] 180 | path = "./data/" 181 | 182 | im_name = args.input.split('/')[-1].split('.')[0] + '.pgm' 183 | #im_name= '50SQE_2018_12_10_0_012.pgm' 184 | 185 | im_path = os.path.join(path, im_name) 186 | test_path = "./results/edges" 187 | 188 | jpeg_file = os.path.join(path,'50SQE_2018_12_10_0_012.jpeg') 189 | 190 | img = skimage.io.imread(args.input) 191 | 192 | # os.listdir() 193 | 194 | 195 | devernay = "./C/devernay_1.0/devernay" 196 | 197 | tophat=args.top_hat 198 | zoom = args.zoom 199 | th_size = args.top_hat_size 200 | 201 | # zoom=False 202 | std = args.std 203 | l_th = args.low 204 | h_th = args.high 205 | th_size = args.top_hat_size 206 | width = 1 + zoom 207 | 208 | # std_list = [0,0.1,0.3,0.5,0.8,1] 209 | # l_th_list = [0,2,5,7,10,12,15] 210 | # h_th_list = [5,7,10,12,15,20,30,50] 211 | # iso_th_list = np.linspace(0.7,0.99,50) # [0.7,0.75,0.8,0.85,0.9,0.91,0.92,0.93,0.94,0.95] 212 | # zoom_list = [False,True] 213 | # tophat_list = [True,False] 214 | # th_size_list = [7,9,11,15,19,21,23] 215 | 216 | 217 | # th_size_list = [7,9,11,15,19,21,23] 218 | 219 | #json_file = os.path.join(json_path,'50SQE_2018_12_10_0_012.json') 220 | #data = read_json(json_file) 221 | # TPR = true_positive_rate(circles, data) 222 | 223 | 224 | prec_list = [] 225 | rec_list = [] 226 | f1_list = [] 227 | best_f1 = 0 228 | im_dim=(img.shape[0],img.shape[1]) 229 | 230 | 231 | 232 | prec_list = [] 233 | rec_list = [] 234 | f1_list = [] 235 | best_f1 = 0 236 | best_prec =0 237 | best_rec= 0 238 | 239 | # 240 | # for zoom in zoom_list: 241 | # for l_th in l_th_list: 242 | # for h_th in h_th_list: 243 | # if l_th< h_th : 244 | # for th_size in th_size_list: 245 | # print(zoom,l_th,h_th,th_size) 246 | if zoom : 247 | im_path = "./results/tmp/zoom_50SQE_2018_12_10_0_012.pgm" 248 | img_zoom = fftzoom(img) 249 | print(img_zoom.shape) 250 | if tophat : 251 | img_zoom,img_zoom_gr = minimum_of_directional_tophat_bottomhat(img_zoom,th_size) 252 | skimage.io.imsave(im_path,img_zoom_gr) 253 | else : 254 | img_zoom_gr = skimage.color.rgb2gray(img_zoom) 255 | skimage.io.imsave(im_path,img_zoom_gr) 256 | else : 257 | if tophat : 258 | 259 | im_path = "./results/tmp/top_hat_50SQE_2018_12_10_0_012.pgm" 260 | img_zoom,img_zoom_gr = minimum_of_directional_tophat_bottomhat(img,th_size) 261 | skimage.io.imsave(im_path,img_zoom_gr) 262 | else : 263 | im_path = os.path.join(path, im_name) 264 | img_zoom = img.copy() 265 | gr_img = rgb2gray(img_zoom) 266 | skimage.io.imsave(im_path,gr_img) 267 | 268 | 269 | isprs_img = "./results/tmp/im_tophat_{}_zoom_{}.jpeg".format(tophat,zoom) 270 | skimage.io.imsave(isprs_img,img_zoom) 271 | 272 | 273 | 274 | if args.auto_th: 275 | h_th = int(threshold_otsu(skimage.io.imread(im_path))) 276 | l_th = 0.5*h_th 277 | txt_path = os.path.join(test_path,'h_{}_l_{}_sig_{}_zoom_{}_tophat_{}.txt'.format(h_th,l_th,std,int(zoom),int(tophat))) 278 | pdf_path = os.path.join(test_path,'h_{}_l_{}_sig_{}_zoom_{}_tophat_{}.pdf'.format(h_th,l_th,std,int(zoom), int(tophat))) 279 | svg_path = os.path.join(test_path,'h_{}_l_{}_sig_{}_zoom_{}_tophat_{}.pdf'.format(h_th,l_th,std,int(zoom), int(tophat))) 280 | os.system('{} {} -t {} -p {} -s {} -l {} -h {} -w 0.5 '.format(devernay,im_path, txt_path,pdf_path,std,l_th, h_th)) 281 | 282 | im_dim = (img_zoom.shape[0], img_zoom.shape[1]) 283 | edge_map = get_edge_map(txt_path,im_dim,width) 284 | 285 | hough_radii = [i for i in range(5,int(20/width))] 286 | 287 | 288 | plt.close('all') 289 | 290 | 291 | #cv2 292 | ################################# Houghs circles ############################### 293 | hough_radii = [i for i in range(5,int(20/width))] 294 | 295 | output = img.copy() 296 | X,Y,_= img.shape 297 | newX, newY = X/width, Y/width 298 | output = cv2.resize(output,(int(newX),int(newY))) 299 | hough_res = hough_circle(edge_map, hough_radii,full_output = False) 300 | accums, cx, cy, radii = hough_circle_peaks(hough_res, hough_radii, total_num_peaks=30) 301 | 302 | output = img_zoom.copy() 303 | centers = np.zeros((img.shape[0],img.shape[1])) 304 | mask = np.zeros((img.shape[0],img.shape[1])) 305 | list_center=[] 306 | 307 | if zoom : 308 | for center_y, center_x, radius in zip(cy,cx,radii): 309 | circy, circx = circle_perimeter(center_y,center_x, radius, shape=output.shape) 310 | cy, cx = circle(center_y/2,center_x/2, int(np.ceil(radius/2)), shape=output.shape) 311 | output[circy,circx] = (0,255,0) 312 | centers[int(center_y/2),int(center_x/2)] = 1 313 | mask[cy,cx] = 1 314 | list_center.append((int(center_x/2),int(center_y/2))) 315 | else : 316 | for center_y, center_x, radius in zip(cy,cx,radii): 317 | circy, circx = circle_perimeter(center_y,center_x, radius, shape=output.shape) 318 | cy, cx = circle(center_y,center_x, radius, shape=output.shape) 319 | output[circy,circx] = (0,255,0) 320 | centers[int(center_y),int(center_x)] = 1 321 | mask[cy,cx] = 1 322 | list_center.append((center_x,center_y)) 323 | 324 | 325 | res_img = "./results/HCT/output/detection_mask_zoom_{}_tophat_{}_autoth.png".format(int(zoom), int(tophat)) 326 | skimage.io.imsave(res_img,mask) 327 | print('image saved !') 328 | 329 | 330 | 331 | -------------------------------------------------------------------------------- /interpolated_shapes.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anttad/SubpixelCircleDetection/912252d4a305d71f800ed39b3ce87946251cb675/interpolated_shapes.zip -------------------------------------------------------------------------------- /iso_th_devernay.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Thu Apr 30 21:24:34 2020 5 | 6 | @author: antoine 7 | """ 8 | 9 | #!/usr/bin/env python3 10 | # -*- coding: utf-8 -*- 11 | """ 12 | Created on Tue Oct 22 17:21:12 2019 13 | 14 | @author: antoine 15 | 16 | Code pour devernay avec rapport isopérimétrique 17 | """ 18 | 19 | import os 20 | import numpy as np 21 | import cv2 22 | from skimage.filters import threshold_otsu 23 | from skimage import color #, data 24 | from skimage.transform import hough_circle, hough_circle_peaks,hough_ellipse 25 | from skimage.feature import canny 26 | from skimage.draw import circle_perimeter, rectangle_perimeter,rectangle,circle 27 | from skimage.util import img_as_ubyte 28 | 29 | from skimage.morphology import binary_closing, binary_dilation, erosion, dilation 30 | from skimage.morphology import square,disk 31 | import skimage.io 32 | from skimage.color import rgb2gray 33 | 34 | import scipy.misc 35 | import scipy.ndimage 36 | from skimage.viewer import ImageViewer 37 | 38 | import argparse 39 | 40 | from utils import * 41 | 42 | from apply_tophat import minimum_of_directional_tophat_bottomhat 43 | 44 | """ 45 | A partir d'une liste de edge point obtenu par l'algorithme canny-devernay, renvoie 46 | l'image binaire des contours 47 | """ 48 | def get_edge_map(txt,im_dim, width): 49 | 50 | coord = np.loadtxt(txt) 51 | coord = coord[coord[:,0]!=-1,:] # on élimine la délimitation 52 | A = np.uint(np.round(coord/width)) # on augmente la valeur des coordonnées par 2 et on arrondie pour pouvoir les plascer 53 | 54 | new_dim = ( int(im_dim[0]/width), int(im_dim[1]/width)) 55 | edge_map = np.zeros(new_dim) 56 | 57 | y,x= tuple(A.T) # corrige l'inversion des coordonnées x et y 58 | edge_map[(x,y)]=1 59 | 60 | return edge_map 61 | 62 | 63 | 64 | """ 65 | determine si un segment de contours est fermé 66 | """ 67 | def is_closed(segment): 68 | 69 | x_o, y_o = segment[0,:] 70 | x_f, y_f = segment[-1,:] 71 | 72 | if (x_o == x_f) and (y_o == y_f): 73 | return True 74 | else: 75 | return False 76 | 77 | """ 78 | renvoie la liste des contours fermés 79 | """ 80 | def get_closed_contour_map(txt,im_dim, width): 81 | 82 | coord = np.loadtxt(txt)/width 83 | list_of_segment = np.split(coord,np.argwhere(coord[:,0]<0).reshape(-1)) 84 | list_of_segment = [x[1:,:] if x[0,0] <0 else x for x in list_of_segment[:-1]] # le dernier terme de la liste ne sert à rien 85 | 86 | 87 | closed_edge_list = [ np.uint(np.round(segment)) for segment in list_of_segment if is_closed(segment)] 88 | #coord = coord[coord[:,0]!=-1,:] # on élimine la délimitation 89 | # on augmente la valeur des coordonnées par 2 et on arrondie pour pouvoir les placer 90 | # découpage des segments de contours 91 | new_dim = ( int(im_dim[0]/width), int(im_dim[1]/width)) 92 | 93 | edge_map = np.zeros(new_dim) 94 | 95 | for seg in closed_edge_list: 96 | y,x= tuple(seg.T) # corrige l'inversion des coordonnées x et y 97 | edge_map[(x,y)]=1 98 | return edge_map 99 | 100 | """ 101 | fonction qui cherche à determiner si un segment fermé est un cercle, en regardant sont rapport d'isopérimétrie, 102 | le seuil doit être inférieur à 1 (égalité pour les cercles parfaits) 103 | """ 104 | def convert2pgm(im_name,folder): 105 | 106 | img = scipy.misc.imread(os.path.join(folder,im_name)) 107 | gr_img = skimage.color.rgb2gray(img) 108 | name = im_name.split('.')[0] 109 | pgm_name = name + '.pgm' 110 | 111 | scipy.misc.imsave(os.path.join(folder,pgm_name),gr_img) 112 | 113 | return True 114 | 115 | 116 | def get_canny_spx_points(f_name,edges_path = None, std=0,l_th=5, h_th=15,keep_closed=True): 117 | """ 118 | input: 119 | f_name: Nom de fichier de l'image à traiter 120 | std: écart-type du noyau gaussien 121 | l_th: low threshold pour le detecteur de Canny 122 | h_th: high threshold pour le detecteur de Canny 123 | edges_path: lieu où sont stockés les contours au format .txt 124 | keep_closed: True si on ne veut que les contours fermés 125 | 126 | output: 127 | E: Liste des contours détectés (faire attention aux) 128 | """ 129 | 130 | # "/home/antoine/Documents/THESE_CMLA/Images/Training_sample/edges" 131 | 132 | if edges_path is None: 133 | edges_path='../edges' 134 | 135 | try: 136 | os.mkdir(edges_path) 137 | except : 138 | pass 139 | 140 | 141 | im_name = f_name.split('.')[0] 142 | txt_name = im_name+'.txt' 143 | txt_path = os.path.join(edges_path,txt_name) 144 | if not txt_name in os.listdir(edges_path): 145 | os.system('devernay {} -t {} -s {} -l {} -h {} -w 0.5 '.format(im_path, txt_path,std,l_th, h_th)) 146 | 147 | 148 | E = get_list_of_edge(txt_path,closed=keep_closed) 149 | 150 | return E 151 | 152 | 153 | 154 | def fftzoom(img, factor=2): 155 | 156 | if len(img.shape) == 2: 157 | nrow,ncol = img.shape 158 | r_alpha, c_alpha= nrow*(factor-1),ncol*(factor-1) 159 | r_step, c_step = int(np.ceil(r_alpha/2)), int(np.ceil(c_alpha/2)) 160 | fft_im = np.fft.fftshift(np.fft.fft2(img)) 161 | fft_pad = np.pad(fft_im,((r_step,r_step), (c_step, c_step))) 162 | res = np.abs(np.fft.ifft2(np.fft.ifftshift(fft_pad))) 163 | # res = np.uint8(255*(res-res.min())/(res.max()-res.min())) 164 | 165 | elif len(img.shape)==3: 166 | nrow,ncol,_ = img.shape 167 | r_alpha, c_alpha= nrow*(factor-1),ncol*(factor-1) 168 | r_step, c_step = int(np.ceil(r_alpha/2)), int(np.ceil(c_alpha/2)) 169 | res_list =[] 170 | for i in range(3): 171 | fft_im = np.fft.fftshift(np.fft.fft2(img[:,:,i])) 172 | fft_pad = np.pad(fft_im,((r_step,r_step), (c_step, c_step))) 173 | res_temp = np.abs(np.fft.ifft2(np.fft.ifftshift(fft_pad))) 174 | per_inf = np.percentile(res_temp,q=1) 175 | per_sup = np.percentile(res_temp,q=99) 176 | res_temp= np.clip(res_temp,per_inf,per_sup) 177 | res_temp = np.uint8(255*(res_temp-res_temp.min())/(res_temp.max()-res_temp.min())) 178 | res_list.append(res_temp) 179 | res= np.stack(res_list, axis=2) 180 | # res = np.uint8(255*(res-res.min())/(res.max()-res.min())) 181 | 182 | return res 183 | 184 | 185 | def img_dyn_enhancement(img,q_inf=1,q_sup=99): 186 | 187 | if len(img.shape)==3: 188 | for i in range(img.shape[-1]): 189 | per_inf = np.percentile(img[:,:,i],q=q_inf) 190 | per_sup = np.percentile(img[:,:,i],q=q_sup) 191 | new_chan =np.clip(img[:,:,i],per_inf,per_sup) 192 | img[:,:,i] = (new_chan-new_chan.min())/(new_chan.max()-new_chan.min()) 193 | 194 | return img 195 | #%% test 196 | 197 | import matplotlib.pyplot as plt 198 | from compare import precision_recall 199 | 200 | if __name__ == '__main__': 201 | 202 | 203 | parser = argparse.ArgumentParser() 204 | 205 | parser.add_argument('-i', '--input', default="./data/50SQE_2018_12_10_0_012.jpeg", type=str,help="path to the input image") 206 | parser.add_argument('-z',"--zoom", action='store_true', help="(preprocessing) apply 2x fft zoom to the image") 207 | parser.add_argument('-t',"--top_hat", action='store_true', help="(preprocessing) apply the top-hat procedure to the image") 208 | parser.add_argument('-k',"--top_hat_size", default=5,type=int , help="(preprocessing) top-hat parmameter") 209 | parser.add_argument('-o','--output',default="./results/", type=str,help="output folder") 210 | parser.add_argument('-a',"--auto_th", action='store_true', help="set automatically the threshold using Otsu's histogram method") 211 | parser.add_argument('-lt','--low',default=5, type=int,help="low threshold for Canny-Devernay's edge extraction") 212 | parser.add_argument('-ht','--high',default=15, type=int,help="high threshold for Canny-Devernay's edge extraction") 213 | parser.add_argument('-s','--std',default=0, type=float,help="std Gaussian kernel for Canny-Devernay's edge extraction pre-processing") 214 | parser.add_argument('-e', '--eval', action="store_true", help="Use only if ground truth is avaible to evaluate the method performance") 215 | parser.add_argument('-it', '--iso_th',type=float, default=0.9, help="Isoperimetric threshold") 216 | 217 | args = parser.parse_args() 218 | 219 | 220 | gts = np.load('./data/gt.npz')['points'] 221 | path = "./data/" 222 | 223 | im_name = args.input.split('/')[-1].split('.')[0] + '.pgm' 224 | #im_name= '50SQE_2018_12_10_0_012.pgm' 225 | 226 | im_path = os.path.join(path, im_name) 227 | test_path = "./results/edges" 228 | 229 | jpeg_file = os.path.join(path,'50SQE_2018_12_10_0_012.jpeg') 230 | 231 | img = skimage.io.imread(args.input) 232 | 233 | # os.listdir() 234 | 235 | 236 | devernay = "./C/devernay_1.0/devernay" 237 | 238 | tophat=args.top_hat 239 | zoom = args.zoom 240 | th_size = args.top_hat_size 241 | 242 | # zoom=False 243 | std = args.std 244 | l_th = args.low 245 | h_th = args.high 246 | th_size = args.top_hat_size 247 | iso_th = args.iso_th 248 | width = 1 + zoom 249 | 250 | # 251 | # gts = np.load('/home/antoine/Documents/THESE_CMLA/ISPRS2020/gt.npz')['points'] 252 | # json_path = "/home/antoine/Documents/THESE_CMLA/Images/Training_sample/training" 253 | # path = "/home/antoine/Documents/THESE_CMLA/Images/Training_sample/pgm_images" 254 | # 255 | # 256 | # 257 | # im_name='50SQE_2018_12_10_0_012.pgm' 258 | # 259 | # im_path = os.path.join(path, im_name) 260 | # test_path = '/home/antoine/Documents/THESE_CMLA/Images/test/' 261 | # edges_path = "/home/antoine/Documents/THESE_CMLA/Images/Training_sample/edges" 262 | # 263 | # isprs_fold = "/home/antoine/Documents/THESE_CMLA/ISPRS2020/" 264 | # jpeg_file = os.path.join(isprs_fold,'50SQE_2018_12_10_0_012.jpeg') 265 | # 266 | # img = skimage.io.imread(jpeg_file) 267 | # 268 | ## os.listdir() 269 | # 270 | # 271 | # devernay = "/home/antoine/Documents/THESE_CMLA/CodeV0/Devernay_Ipol/devernay_1.0/devernay" 272 | # 273 | # tophat=True 274 | # zoom=True 275 | # std = 0 276 | # auto_th= True 277 | # l_th = 2 278 | # h_th = 7 279 | # iso_th =0.9 280 | # th_size = 11 281 | # 282 | # std_list = [0,0.1,0.3,0.5,0.8,1] 283 | # l_th_list = [2,5,7,10,12,15] 284 | # h_th_list = [5,7,10,12,15,20,30,50] 285 | # iso_th_list = np.linspace(0.7,0.99,10) # 38 286 | # zoom_list = [True,False] 287 | # tophat_list = [True,False] 288 | # th_size_list = [5,11,15,19,23] 289 | # 290 | # json_file = os.path.join(json_path,'50SQE_2018_12_10_0_012.json') 291 | # data = read_json(json_file) 292 | ## TPR = true_positive_rate(circles, data) 293 | # 294 | 295 | prec_list = [] 296 | rec_list = [] 297 | f1_list = [] 298 | best_f1 = 0 299 | best_prec = 0 300 | best_rec = 0 301 | # 302 | # for tophat in tophat_list : 303 | 304 | if zoom : 305 | im_path = "./results/tmp/zoom_50SQE_2018_12_10_0_012.pgm" 306 | img_zoom = fftzoom(img) 307 | print(img_zoom.shape) 308 | if tophat : 309 | img_zoom,img_zoom_gr = minimum_of_directional_tophat_bottomhat(img_zoom,th_size) 310 | skimage.io.imsave(im_path,img_zoom_gr) 311 | else : 312 | img_zoom_gr = skimage.color.rgb2gray(img_zoom) 313 | skimage.io.imsave(im_path,img_zoom_gr) 314 | else : 315 | if tophat : 316 | 317 | im_path = "./results/tmp/top_hat_50SQE_2018_12_10_0_012.pgm" 318 | img_zoom,img_zoom_gr = minimum_of_directional_tophat_bottomhat(img,th_size) 319 | skimage.io.imsave(im_path,img_zoom_gr) 320 | else : 321 | im_path = os.path.join(path, im_name) 322 | img_zoom = img.copy() 323 | gr_img = rgb2gray(img_zoom) 324 | skimage.io.imsave(im_path,gr_img) 325 | 326 | 327 | isprs_img = "./results/tmp/im_tophat_{}_zoom_{}.jpeg".format(tophat,zoom) 328 | skimage.io.imsave(isprs_img,img_zoom) 329 | 330 | 331 | 332 | if args.auto_th: 333 | h_th = int(threshold_otsu(skimage.io.imread(im_path))) 334 | l_th = 0.5*h_th 335 | txt_path = os.path.join(test_path,'h_{}_l_{}_sig_{}_zoom_{}_tophat_{}.txt'.format(h_th,l_th,std,int(zoom),int(tophat))) 336 | pdf_path = os.path.join(test_path,'h_{}_l_{}_sig_{}_zoom_{}_tophat_{}.pdf'.format(h_th,l_th,std,int(zoom), int(tophat))) 337 | svg_path = os.path.join(test_path,'h_{}_l_{}_sig_{}_zoom_{}_tophat_{}.pdf'.format(h_th,l_th,std,int(zoom), int(tophat))) 338 | os.system('{} {} -t {} -p {} -s {} -l {} -h {} -w 0.5 '.format(devernay,im_path, txt_path,pdf_path,std,l_th, h_th)) 339 | 340 | 341 | output = img_zoom.copy() 342 | 343 | im_dim=img.shape 344 | 345 | # 346 | list_of_edge = get_list_of_edge(txt_path,closed=True) 347 | # list_of_edge = get_canny_spx_points('h_{}_l_{}_sig_{}_zoom_{}_tophat_{}.txt'.format(h_th,l_th,std,int(zoom),int(tophat)),test_path) 348 | circles = select_circles(list_of_edge,threshold=iso_th) 349 | 350 | 351 | # centers=np.array(circles)[:,:-1] 352 | # gt_img, gt_mask, nb_tanks_gt = get_ground_truth(img,data) 353 | # mask_centers = centers2mask(centers,gt_mask.shape) 354 | # TPR = true_positive_rate(mask_centers, gt_mask, nb_tanks_gt) 355 | 356 | 357 | 358 | centers = np.zeros((img.shape[0],img.shape[1])) 359 | mask = np.zeros((img.shape[0],img.shape[1])) 360 | list_center=[] 361 | if zoom : 362 | for center_y, center_x, radius in circles: 363 | circy, circx = circle_perimeter(int(center_y),int(center_x), int(np.ceil(radius)), shape=output.shape) 364 | cy, cx = circle(int(center_y),int(center_x), int(np.ceil(radius)), shape=output.shape) 365 | output[circy,circx] = (0,255,0) 366 | centers[int(center_y/2),int(center_x/2)] = 1 367 | mask[np.uint16(np.ceil(cy/2)),np.uint16(np.ceil(cx/2))] = 1 368 | list_center.append((int(center_x/2),int(center_y/2))) 369 | 370 | else: 371 | 372 | for center_y, center_x, radius in circles: 373 | circy, circx = circle_perimeter(int(center_y),int(center_x), int(np.ceil(radius)), shape=output.shape) 374 | cy, cx = circle(int(center_y),int(center_x), int(np.ceil(radius)), shape=output.shape) 375 | output[circy,circx] = (0,255,0) 376 | centers[int(center_y),int(center_x)] = 1 377 | mask[cy,cx] = 1 378 | list_center.append((center_x,center_y)) 379 | 380 | 381 | # if len(list_center) == 0: 382 | # prec=0 383 | # rec=0 384 | # f1_score=0 385 | # else: 386 | # prec, rec = precision_recall(np.array(list_center),gts) 387 | # f1_score = 2*prec*rec/(prec+rec) 388 | 389 | 390 | # cv2.imwrite("/home/antoine/Documents/THESE_CMLA/ISPRS2020/iso_devernay/h_{}_l_{}_sig_{}_th_{}.png".format(h_th,l_th,std,iso_th),output) 391 | res_img = "./results/iso_th/output/detection_mask_zoom_{}_tophat_{}_autoth.png".format(int(zoom), int(tophat)) 392 | skimage.io.imsave(res_img,mask) 393 | print('image saved !') 394 | 395 | 396 | 397 | 398 | 399 | -------------------------------------------------------------------------------- /results/HCT/output/detection_mask_zoom_0_tophat_0_autoth.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anttad/SubpixelCircleDetection/912252d4a305d71f800ed39b3ce87946251cb675/results/HCT/output/detection_mask_zoom_0_tophat_0_autoth.png -------------------------------------------------------------------------------- /results/HCT/score/best_f1_zoom_0_tophat_0_autoth.json: -------------------------------------------------------------------------------- 1 | {"l_th": 20.5, "h_th": 41, "std": 0, "zoom": false, "tophat": false, "prec": 0.26666666666666666, "recall": 0.03463203463203463, "f1": 0.06130268199233717, "tophat_size": 7} -------------------------------------------------------------------------------- /results/HCT/score/best_prec_zoom_0_tophat_0_autoth.json: -------------------------------------------------------------------------------- 1 | {"l_th": 20.5, "h_th": 41, "std": 0, "zoom": false, "tophat": false, "prec": 0.26666666666666666, "recall": 0.03463203463203463, "f1": 0.06130268199233717, "tophat_size": 7} -------------------------------------------------------------------------------- /results/HCT/score/best_rec_zoom_0_tophat_0_autoth.json: -------------------------------------------------------------------------------- 1 | {"l_th": 20.5, "h_th": 41, "std": 0, "zoom": false, "tophat": false, "prec": 0.26666666666666666, "recall": 0.03463203463203463, "f1": 0.06130268199233717, "tophat_size": 7} -------------------------------------------------------------------------------- /results/edges/h_15_l_5_sig_0_zoom_0_tophat_0.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anttad/SubpixelCircleDetection/912252d4a305d71f800ed39b3ce87946251cb675/results/edges/h_15_l_5_sig_0_zoom_0_tophat_0.pdf -------------------------------------------------------------------------------- /results/edges/h_41_l_20.5_sig_0_zoom_0_tophat_0.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anttad/SubpixelCircleDetection/912252d4a305d71f800ed39b3ce87946251cb675/results/edges/h_41_l_20.5_sig_0_zoom_0_tophat_0.pdf -------------------------------------------------------------------------------- /results/iso_th/output/detection_mask_zoom_0_tophat_0_autoth.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anttad/SubpixelCircleDetection/912252d4a305d71f800ed39b3ce87946251cb675/results/iso_th/output/detection_mask_zoom_0_tophat_0_autoth.png -------------------------------------------------------------------------------- /results/tmp/im_tophat_False_zoom_False.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anttad/SubpixelCircleDetection/912252d4a305d71f800ed39b3ce87946251cb675/results/tmp/im_tophat_False_zoom_False.jpeg -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Mon Sep 30 14:51:50 2019 5 | 6 | @author: antoine 7 | """ 8 | 9 | 10 | import numpy as np 11 | import cv2 12 | import os 13 | import json 14 | import matplotlib.pyplot as plt 15 | import scipy.misc 16 | import scipy.ndimage 17 | import skimage.morphology 18 | from skimage.morphology import disk 19 | from skimage.draw import rectangle, rectangle_perimeter 20 | 21 | 22 | 23 | 24 | 25 | """ 26 | fonction qui cherche à determiner si un segment fermé est un cercle, en regardant sont rapport d'isopérimétrie, 27 | le seuil doit être inférieur à 1 (égalité pour les cercles parfaits) 28 | """ 29 | 30 | #def get_list_of_edge(txt,closed=True): 31 | # coord = np.loadtxt(txt) 32 | # list_of_segment = np.split(coord,np.argwhere(coord[:,0]<0).reshape(-1)) 33 | # list_of_segment = [x[1:,:] if x[0,0] <0 else x for x in list_of_segment[:-1]] 34 | # if closed: 35 | # closed_edge_list = [segment for segment in list_of_segment if is_closed(segment)] 36 | # return closed_edge_list 37 | # else: 38 | # return list_of_segment 39 | 40 | 41 | 42 | def get_list_of_edge(txt,closed=True): 43 | coord = np.loadtxt(txt) 44 | if len(coord) != 0 : 45 | list_of_segment = np.split(coord,np.argwhere(coord[:,0]<0).reshape(-1)) 46 | list_of_segment = [x[1:,:] if x[0,0] <0 else x for x in list_of_segment[:-1]] 47 | 48 | if closed: 49 | closed_edge_list = [segment for segment in list_of_segment if is_closed(segment)] 50 | else: 51 | closed_edge_list = [segment if is_closed(segment) else close_segment(segment) for segment in list_of_segment] 52 | 53 | return closed_edge_list 54 | 55 | else : 56 | return [] 57 | 58 | 59 | def stretch_im(im_np): 60 | im_np = im_np.astype(int) 61 | im_np *= 3 62 | im_np[im_np > 255] = 255 63 | return im_np 64 | 65 | 66 | def tophat(im_np): 67 | tophat_np = [] 68 | if len(im_np.shape) >= 3: 69 | for i in range(im_np.shape[2]): 70 | tophat_np.append(im_np[:,:,i] - skimage.morphology.opening(im_np[:,:,i], selem=disk(10))) 71 | else : 72 | tophat_np.append(im_np - skimage.morphology.opening(im_np, selem=disk(10))) 73 | return np.max(tophat_np, axis=0) 74 | 75 | 76 | def bottomhat(im_np): 77 | bottomhat_np = [] 78 | if len(im_np.shape) >= 3: 79 | for i in range(im_np.shape[2]): 80 | bottomhat_np.append(skimage.morphology.closing(im_np[:,:,i], selem=disk(10)) - im_np[:,:,i]) 81 | else : 82 | bottomhat_np.append(skimage.morphology.closing(im_np, selem=disk(10)) - im_np[:,:,i]) 83 | return np.max(bottomhat_np, axis=0) 84 | 85 | 86 | def get_other_version(im_name,version='th'): 87 | return im_name[:-5]+'_{}.png'.format(version) 88 | 89 | 90 | 91 | def read_json(json_file): 92 | with open(json_file) as f : 93 | data = json.load(f) 94 | 95 | return data 96 | 97 | """ 98 | Place les pixels de l'image sur l'interval 0-255 99 | """ 100 | def to_255_pxl(img): 101 | """ 102 | Retourne une image dont les pixels sont entre 0 et 255 103 | """ 104 | img = img.astype(float) 105 | return np.round(255*(img - img.min(axis=(0,1)))/(img.max(axis=(0,1))-img.min(axis=(0,1)))).astype(int) 106 | 107 | 108 | 109 | """ 110 | Formule pour calculer l'air d'un polygone connaissant les coordonnées de ses sommets. "shoelace forumula" 111 | """ 112 | def PolyArea(x,y): 113 | return 0.5*np.abs(np.dot(x,np.roll(y,1))-np.dot(y,np.roll(x,1))) 114 | 115 | 116 | 117 | """ 118 | Formule pour calculer l'air d'un polygone connaissant les coordonnées de ses sommets. "shoelace forumula" 119 | """ 120 | def PolyPerimeter(coord): 121 | coord_roll = np.roll(coord,shift=1, axis=0) 122 | p = np.sum(np.sqrt(np.sum((coord - coord_roll)**2,axis=1))) 123 | return p 124 | 125 | 126 | 127 | """ 128 | determine si un segment de contours est fermé 129 | """ 130 | def is_closed(segment): 131 | 132 | x_o, y_o = segment[0,:] 133 | x_f, y_f = segment[-1,:] 134 | 135 | if (x_o == x_f) and (y_o == y_f): 136 | return True 137 | else: 138 | return False 139 | 140 | 141 | """ 142 | Ferme un contour s'il n'est pas fermé 143 | """ 144 | def close_segment(segment): 145 | 146 | x_o, y_o = segment[0,:] 147 | x_f, y_f = segment[-1,:] 148 | 149 | if (x_o == x_f) and (y_o == y_f): 150 | return True 151 | else: 152 | final_point = np.array([[x_o,y_o]]) 153 | return np.concatenate([segment,final_point]) 154 | 155 | 156 | def select_circles(list_of_edge,threshold=0.8,rad_th=0): 157 | 158 | circles = [] 159 | for i in range(len(list_of_edge)): 160 | segment = list_of_edge[i] 161 | p = PolyPerimeter(segment[:,:]) 162 | x,y = segment[:,1], segment[:,0] # correction de l'inversion des coordonnées, on ne prend pas le dernier terme 163 | a = PolyArea(x,y) 164 | 165 | q = 4*np.pi*a/(p**2) 166 | # print('q is equal to ', q) 167 | m_x, m_y = np.mean(x), np.mean(y) 168 | # radius = (np.max(x)-np.min(x) + np.max(y) - np.min(y))/4 169 | radius = np.sqrt(a/np.pi) 170 | # if q > 1 : 171 | # print(" Warning !!! q is greater than 1 at {} !!!".format(i)) 172 | if q >= threshold and radius>rad_th: # and q <=1 173 | circles.append((m_x,m_y,radius)) 174 | 175 | 176 | return circles 177 | 178 | 179 | 180 | def get_ground_truth(img,data): 181 | """ 182 | input: 183 | img: np.array, image 184 | data: dict, json correspondant à l'image 185 | """ 186 | gt = data['annotations'] # on extrait les données de détections 187 | output = img.copy() 188 | mask = np.zeros_like(output)[:,:,0] 189 | 190 | for tank_data in gt: 191 | bbox = tank_data['bbox'] 192 | rx, ry = rectangle(start=(bbox[1], bbox[0]),extent=(bbox[2], bbox[3])) 193 | rpx, rpy = rectangle_perimeter(start=(bbox[1], bbox[0]),extent=(bbox[2], bbox[3])) 194 | output[rpx,rpy] = (250,200,20) 195 | mask[np.int64(rx),np.int64(ry)] = 1 196 | 197 | nb_tanks =len(list(gt)) 198 | 199 | return output, mask, nb_tanks 200 | 201 | 202 | def get_bbox_mask(data,N=513): 203 | """ 204 | input: 205 | img: np.array, image 206 | data: dict, json correspondant à l'image 207 | """ 208 | 209 | try : 210 | gt = data['annotations'] # on extrait les données de détections 211 | N = gt[0]['height'] 212 | except : 213 | gt = [] 214 | 215 | mask = np.zeros((N,N)) 216 | 217 | for tank_data in gt: 218 | bbox = tank_data['bbox'] 219 | rx, ry = rectangle(start=(bbox[1], bbox[0]),extent=(bbox[2], bbox[3]),shape=(N,N)) 220 | mask[np.int64(rx),np.int64(ry)] = 1 221 | 222 | nb_tanks =len(list(gt)) 223 | 224 | return mask, nb_tanks 225 | 226 | 227 | 228 | def centers2mask(centers,size): 229 | """ 230 | input : 231 | centers: np.array (N,2) avec N le nombre de centres de tanks détectés 232 | size: tuple, taille de l'image (2D) 233 | """ 234 | mask=np.zeros(size) 235 | for i in range(centers.shape[0]): 236 | y, x = centers[i] 237 | mask[int(y), int(x)] = True 238 | 239 | return mask 240 | 241 | def create_folders(res_folder): 242 | 243 | try : 244 | os.mkdir(res_folder) 245 | except : 246 | pass 247 | 248 | 249 | res_edges_path = os.path.join(res_folder,"edges") 250 | 251 | try : 252 | os.mkdir(res_edges_path) 253 | except : 254 | pass 255 | 256 | res_out_path = os.path.join(res_folder,"output") 257 | res_in_path = os.path.join(res_folder,"input") 258 | res_svg_tanks_path = os.path.join(res_folder,"output_svg_tanks") 259 | res_svg_path = os.path.join(res_folder,"output_svg") 260 | res_svg_clusters_path = os.path.join(res_folder,"output_svg_clusters") 261 | edges_path = "/home/antoine/Documents/THESE_CMLA/Images/Training_sample/edges" 262 | 263 | 264 | try : 265 | os.mkdir(res_edges_path) 266 | except : 267 | pass 268 | 269 | edge_name_list = os.listdir(res_edges_path) 270 | 271 | try : 272 | os.mkdir(res_folder) 273 | except : 274 | pass 275 | 276 | try : 277 | os.mkdir(res_svg_tanks_path) 278 | except : 279 | pass 280 | 281 | try : 282 | os.mkdir(res_svg_path) 283 | except : 284 | pass 285 | 286 | try : 287 | os.mkdir(res_svg_clusters_path) 288 | except : 289 | pass 290 | 291 | try : 292 | os.mkdir(pgm_path) 293 | except : 294 | pass 295 | try : 296 | os.mkdir(res_in_path) 297 | except : 298 | pass 299 | try : 300 | os.mkdir(res_out_path) 301 | except : 302 | pass 303 | try : 304 | os.mkdir(edges_path) 305 | except : 306 | pass 307 | 308 | def create_edges(name,devernay,res_edges_path,res_in_path,pgm_path="/home/antoine/Documents/THESE_CMLA/Images/Training_sample/pgm_images"): 309 | 310 | 311 | std = 0 312 | l_th = 5 313 | h_th = 15 314 | th_iso = 0.9 315 | 316 | from shutil import copy2 317 | edge_name_list = os.listdir(res_edges_path) 318 | 319 | if name+'.txt' not in edge_name_list: 320 | im_path = os.path.join(pgm_path,name+'.pgm') 321 | copy2(im_path, res_in_path) 322 | edges_path = "/home/antoine/Documents/THESE_CMLA/Images/Training_sample/edges" 323 | text_file = os.path.join(edges_path,name+'.txt') 324 | result_png = os.path.join(res_edges_path,name+'.png') 325 | result_pkl = os.path.join(res_edges_path,name+'.pkl') 326 | result_pdf = os.path.join(res_edges_path,name+'.pdf') 327 | # result_svg = os.path.join(res_edges_path,name+'.svg') 328 | os.system('{} {} -p {} -t {} -s {} -l {} -h {} -w 0.5 '.format(devernay,im_path,result_pdf,text_file, std, l_th,h_th)) 329 | print("{} Edge computed".format(name)) --------------------------------------------------------------------------------