hahattpro / crowd_counting_framework (public) (License: Unspecified) (since 2020-12-23) (hash sha1)
backupfrom https://github.com/ttpro1995/crowd_counting_framework
Clone URLs: https://rocketgit.com/user/hahattpro/crowd_counting_framework ssh://rocketgit@ssh.rocketgit.com/user/hahattpro/crowd_counting_framework git://git.rocketgit.com/user/hahattpro/crowd_counting_framework
debug/a-1 develop feature/add_can_adcrowdnet feature/add_context_aware_network feature/attn_can_adcrowd_simple feature/cache_dataloader feature/compact_cnn feature/compare_with_original_count feature/dataloader_not_divide_8 feature/debug_viz feature/documentation feature/improve_density_gen feature/improve_loss_fn feature/loader_full_and_augment feature/refactor-dataflow feature/refactor_ccnn_basecode feature/save_best feature/simple_cometml feature/train_context_aware_network_on_shtA feature/train_dev_test_split hotfix/fix_visualize_data_loader master meow_exp server-training with_apex
List of commits:
Subject Hash Author Date (UTC)
notebook 5b8b840bc1fd0d806a13646477988534f4982fac Thai Thien 2020-11-22 07:10:42
l2_adamw2_bigtail13i_t13_sha 7fd22510845dec808100058190a202b805bf6a02 Thai Thien 2020-09-23 14:17:00
l2_adamw2_bigtail13i_t12_sha bf67476db55f55f348068bc7683f68834659cc96 Thai Thien 2020-09-23 14:14:35
l2_adamw2_bigtail13i_t11_sha 1365b305bf541a3d941d173b169fa14aa811beb1 Thai Thien 2020-09-22 19:31:17
l2_adamw2_bigtail13i_t10_sha 4f6f2b8d3ac4654cc2ed94ca6900643f3ca07be9 Thai Thien 2020-09-22 19:27:24
l2_adamw2_bigtail13i_t9_sha 576b424688dff640a0a7d448cbd615cfb4f33d54 Thai Thien 2020-09-22 19:21:17
t8 9e00ee3a50c93bbd726ebd8554a68f535d836691 Thai Thien 2020-09-22 19:11:37
split a a7b2115bee8ab76497528b84aee09c79631575b1 Thai Thien 2020-09-22 17:48:37
load_data_shanghaitech_256_v2 and l2_adamw2_bigtail13i_t7_sha 07bca1a460c30ed7ec7269d72dad99eef6fc96b3 Thai Thien 2020-09-22 17:04:45
adamw1_ccnnv7_t7_jhu c5f106103aa3696827627f5e815105d9432e2acb Thai Thien 2020-09-16 18:21:22
adamw1_ccnnv7_t6_jhu.sh 5c8644da7445b3dd4f6d2611d564592e8e8ed45c Thai Thien 2020-09-16 18:19:07
adamw1_CompactCNNV7_t6_jhu 1fa1d544a254cd042bcdd36e8de2984fe3d5244f Thai Thien 2020-09-16 17:56:15
adamw1_CompactCNNV7_t5_jhu 40720162c9e9b731fb852ed7e3e191228e421ced Thai Thien 2020-09-16 17:46:20
test_data cache 73633cbd6d70448268a3e1534440601ddcf75276 Thai Thien 2020-09-16 17:12:57
fix test data not exist 474f506204251e26825801499f6bc503f44f8410 Thai Thien 2020-09-16 16:41:00
fix test data not exist 6377e18d87e0fabbf9b4ed143e26ae5912c0b872 Thai Thien 2020-09-16 16:40:06
jhucrowd_downsample_512 30b8b9a63e93a5631f003858ef2ab63dc62c6563 Thai Thien 2020-09-16 16:17:02
fix 77d74f09874a7be0d8fcec141597be4ce331bf0c Thai Thien 2020-09-16 16:08:39
fix 897856c014a517da679a8a1c46c6d0b90c965876 Thai Thien 2020-09-16 16:05:29
jhucrowd_downsample_256 b992d3acc7f1e1aa0ed6265c48e9f065fa1d3085 Thai Thien 2020-09-16 16:02:19
Commit 5b8b840bc1fd0d806a13646477988534f4982fac - notebook
Author: Thai Thien
Author date (UTC): 2020-11-22 07:10
Committer name: Thai Thien
Committer date (UTC): 2020-11-22 07:10
Parent(s): 7fd22510845dec808100058190a202b805bf6a02
Signer:
Signing key:
Signing status: N
Tree: f180d8df16e122e921f9b5ce8b28fe9f312302d8
File Lines added Lines deleted
.gitignore 2 0
notebook/evaluation_shb_3_BigTail13i_t1.ipynb 400 0
notebook/evaluation_shb_3_CompactCNNV7i_t2.ipynb 400 0
notebook/explain_ground_truth.ipynb 413 0
File .gitignore changed (mode: 100644) (index a704682..54a0bfa)
... ... visualize/
10 10 saved_model/ saved_model/
11 11
12 12 debug/data_info.txt debug/data_info.txt
13
14 notebook/.ipynb_checkpoints/
File notebook/evaluation_shb_3_BigTail13i_t1.ipynb added (mode: 100644) (index 0000000..5b5fa50)
1 {
2 "cells": [
3 {
4 "cell_type": "code",
5 "execution_count": 1,
6 "metadata": {},
7 "outputs": [],
8 "source": [
9 "import pandas as pd"
10 ]
11 },
12 {
13 "cell_type": "code",
14 "execution_count": 2,
15 "metadata": {},
16 "outputs": [],
17 "source": [
18 "data_path = \"../log/evaluation_shb_BigTail13i_t1.txt\""
19 ]
20 },
21 {
22 "cell_type": "code",
23 "execution_count": 3,
24 "metadata": {},
25 "outputs": [],
26 "source": [
27 "df = pd.read_csv(data_path, header=None, sep=\" \")\n",
28 "df.columns=[\"name\", \"gt_density\", \"gt_count\", \"pred\"]"
29 ]
30 },
31 {
32 "cell_type": "code",
33 "execution_count": 4,
34 "metadata": {},
35 "outputs": [
36 {
37 "data": {
38 "text/html": [
39 "<div>\n",
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47 " }\n",
48 "\n",
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51 " }\n",
52 "</style>\n",
53 "<table border=\"1\" class=\"dataframe\">\n",
54 " <thead>\n",
55 " <tr style=\"text-align: right;\">\n",
56 " <th></th>\n",
57 " <th>name</th>\n",
58 " <th>gt_density</th>\n",
59 " <th>gt_count</th>\n",
60 " <th>pred</th>\n",
61 " </tr>\n",
62 " </thead>\n",
63 " <tbody>\n",
64 " <tr>\n",
65 " <th>0</th>\n",
66 " <td>IMG_1</td>\n",
67 " <td>21.938055</td>\n",
68 " <td>23</td>\n",
69 " <td>15.121191</td>\n",
70 " </tr>\n",
71 " <tr>\n",
72 " <th>1</th>\n",
73 " <td>IMG_10</td>\n",
74 " <td>168.015335</td>\n",
75 " <td>181</td>\n",
76 " <td>185.331696</td>\n",
77 " </tr>\n",
78 " <tr>\n",
79 " <th>2</th>\n",
80 " <td>IMG_100</td>\n",
81 " <td>154.466904</td>\n",
82 " <td>157</td>\n",
83 " <td>131.892746</td>\n",
84 " </tr>\n",
85 " <tr>\n",
86 " <th>3</th>\n",
87 " <td>IMG_101</td>\n",
88 " <td>34.326912</td>\n",
89 " <td>37</td>\n",
90 " <td>30.035917</td>\n",
91 " </tr>\n",
92 " <tr>\n",
93 " <th>4</th>\n",
94 " <td>IMG_102</td>\n",
95 " <td>65.542725</td>\n",
96 " <td>70</td>\n",
97 " <td>66.501656</td>\n",
98 " </tr>\n",
99 " </tbody>\n",
100 "</table>\n",
101 "</div>"
102 ],
103 "text/plain": [
104 " name gt_density gt_count pred\n",
105 "0 IMG_1 21.938055 23 15.121191\n",
106 "1 IMG_10 168.015335 181 185.331696\n",
107 "2 IMG_100 154.466904 157 131.892746\n",
108 "3 IMG_101 34.326912 37 30.035917\n",
109 "4 IMG_102 65.542725 70 66.501656"
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112 "execution_count": 4,
113 "metadata": {},
114 "output_type": "execute_result"
115 }
116 ],
117 "source": [
118 "df.head()"
119 ]
120 },
121 {
122 "cell_type": "code",
123 "execution_count": 5,
124 "metadata": {},
125 "outputs": [],
126 "source": [
127 "df[\"gt_generate_error\"] = abs(df[\"gt_count\"]-df[\"gt_density\"])"
128 ]
129 },
130 {
131 "cell_type": "code",
132 "execution_count": 6,
133 "metadata": {},
134 "outputs": [],
135 "source": [
136 "df[\"pred_error\"] = abs(df[\"gt_count\"]-df[\"pred\"])\n",
137 "df[\"pred_error2\"] = abs(df[\"gt_density\"]-df[\"pred\"])"
138 ]
139 },
140 {
141 "cell_type": "code",
142 "execution_count": 7,
143 "metadata": {},
144 "outputs": [
145 {
146 "name": "stdout",
147 "output_type": "stream",
148 "text": [
149 "7.338098435462276\n"
150 ]
151 }
152 ],
153 "source": [
154 "gt_generate_mae = df[\"gt_generate_error\"].mean()\n",
155 "print(gt_generate_mae)"
156 ]
157 },
158 {
159 "cell_type": "code",
160 "execution_count": 8,
161 "metadata": {},
162 "outputs": [
163 {
164 "name": "stdout",
165 "output_type": "stream",
166 "text": [
167 "12.229519746160205\n"
168 ]
169 }
170 ],
171 "source": [
172 "pred_mae = df[\"pred_error\"].mean()\n",
173 "print(pred_mae)"
174 ]
175 },
176 {
177 "cell_type": "code",
178 "execution_count": 9,
179 "metadata": {},
180 "outputs": [
181 {
182 "name": "stdout",
183 "output_type": "stream",
184 "text": [
185 "11.066216304521017\n"
186 ]
187 }
188 ],
189 "source": [
190 "pred_mae2 = df[\"pred_error2\"].mean()\n",
191 "print(pred_mae2)"
192 ]
193 },
194 {
195 "cell_type": "code",
196 "execution_count": 10,
197 "metadata": {},
198 "outputs": [],
199 "source": [
200 "df[\"gt_generate_error_percentage\"] = abs(df[\"gt_count\"]-df[\"gt_density\"])/df[\"gt_count\"] * 100\n",
201 "df[\"pred_error_percentage\"] = abs(df[\"gt_count\"]-df[\"pred\"])/df[\"gt_count\"] * 100"
202 ]
203 },
204 {
205 "cell_type": "code",
206 "execution_count": 11,
207 "metadata": {},
208 "outputs": [
209 {
210 "data": {
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228 " <tr style=\"text-align: right;\">\n",
229 " <th></th>\n",
230 " <th>name</th>\n",
231 " <th>gt_density</th>\n",
232 " <th>gt_count</th>\n",
233 " <th>pred</th>\n",
234 " <th>gt_generate_error</th>\n",
235 " <th>pred_error</th>\n",
236 " <th>pred_error2</th>\n",
237 " <th>gt_generate_error_percentage</th>\n",
238 " <th>pred_error_percentage</th>\n",
239 " </tr>\n",
240 " </thead>\n",
241 " <tbody>\n",
242 " <tr>\n",
243 " <th>0</th>\n",
244 " <td>IMG_1</td>\n",
245 " <td>21.938055</td>\n",
246 " <td>23</td>\n",
247 " <td>15.121191</td>\n",
248 " <td>1.061945</td>\n",
249 " <td>7.878809</td>\n",
250 " <td>6.816864</td>\n",
251 " <td>4.617152</td>\n",
252 " <td>34.255691</td>\n",
253 " </tr>\n",
254 " <tr>\n",
255 " <th>1</th>\n",
256 " <td>IMG_10</td>\n",
257 " <td>168.015335</td>\n",
258 " <td>181</td>\n",
259 " <td>185.331696</td>\n",
260 " <td>12.984665</td>\n",
261 " <td>4.331696</td>\n",
262 " <td>17.316360</td>\n",
263 " <td>7.173848</td>\n",
264 " <td>2.393202</td>\n",
265 " </tr>\n",
266 " <tr>\n",
267 " <th>2</th>\n",
268 " <td>IMG_100</td>\n",
269 " <td>154.466904</td>\n",
270 " <td>157</td>\n",
271 " <td>131.892746</td>\n",
272 " <td>2.533096</td>\n",
273 " <td>25.107254</td>\n",
274 " <td>22.574158</td>\n",
275 " <td>1.613437</td>\n",
276 " <td>15.991882</td>\n",
277 " </tr>\n",
278 " <tr>\n",
279 " <th>3</th>\n",
280 " <td>IMG_101</td>\n",
281 " <td>34.326912</td>\n",
282 " <td>37</td>\n",
283 " <td>30.035917</td>\n",
284 " <td>2.673088</td>\n",
285 " <td>6.964083</td>\n",
286 " <td>4.290995</td>\n",
287 " <td>7.224562</td>\n",
288 " <td>18.821845</td>\n",
289 " </tr>\n",
290 " <tr>\n",
291 " <th>4</th>\n",
292 " <td>IMG_102</td>\n",
293 " <td>65.542725</td>\n",
294 " <td>70</td>\n",
295 " <td>66.501656</td>\n",
296 " <td>4.457275</td>\n",
297 " <td>3.498344</td>\n",
298 " <td>0.958931</td>\n",
299 " <td>6.367536</td>\n",
300 " <td>4.997635</td>\n",
301 " </tr>\n",
302 " </tbody>\n",
303 "</table>\n",
304 "</div>"
305 ],
306 "text/plain": [
307 " name gt_density gt_count pred gt_generate_error pred_error \\\n",
308 "0 IMG_1 21.938055 23 15.121191 1.061945 7.878809 \n",
309 "1 IMG_10 168.015335 181 185.331696 12.984665 4.331696 \n",
310 "2 IMG_100 154.466904 157 131.892746 2.533096 25.107254 \n",
311 "3 IMG_101 34.326912 37 30.035917 2.673088 6.964083 \n",
312 "4 IMG_102 65.542725 70 66.501656 4.457275 3.498344 \n",
313 "\n",
314 " pred_error2 gt_generate_error_percentage pred_error_percentage \n",
315 "0 6.816864 4.617152 34.255691 \n",
316 "1 17.316360 7.173848 2.393202 \n",
317 "2 22.574158 1.613437 15.991882 \n",
318 "3 4.290995 7.224562 18.821845 \n",
319 "4 0.958931 6.367536 4.997635 "
320 ]
321 },
322 "execution_count": 11,
323 "metadata": {},
324 "output_type": "execute_result"
325 }
326 ],
327 "source": [
328 "df.head()"
329 ]
330 },
331 {
332 "cell_type": "code",
333 "execution_count": 12,
334 "metadata": {},
335 "outputs": [
336 {
337 "data": {
338 "text/plain": [
339 "5.966772023225838"
340 ]
341 },
342 "execution_count": 12,
343 "metadata": {},
344 "output_type": "execute_result"
345 }
346 ],
347 "source": [
348 "df[\"gt_generate_error_percentage\"].mean()"
349 ]
350 },
351 {
352 "cell_type": "code",
353 "execution_count": 13,
354 "metadata": {},
355 "outputs": [
356 {
357 "data": {
358 "text/plain": [
359 "11.322604763421914"
360 ]
361 },
362 "execution_count": 13,
363 "metadata": {},
364 "output_type": "execute_result"
365 }
366 ],
367 "source": [
368 "df[\"pred_error_percentage\"].mean()"
369 ]
370 },
371 {
372 "cell_type": "code",
373 "execution_count": null,
374 "metadata": {},
375 "outputs": [],
376 "source": []
377 }
378 ],
379 "metadata": {
380 "kernelspec": {
381 "display_name": "Python 3",
382 "language": "python",
383 "name": "python3"
384 },
385 "language_info": {
386 "codemirror_mode": {
387 "name": "ipython",
388 "version": 3
389 },
390 "file_extension": ".py",
391 "mimetype": "text/x-python",
392 "name": "python",
393 "nbconvert_exporter": "python",
394 "pygments_lexer": "ipython3",
395 "version": "3.8.1"
396 }
397 },
398 "nbformat": 4,
399 "nbformat_minor": 4
400 }
File notebook/evaluation_shb_3_CompactCNNV7i_t2.ipynb added (mode: 100644) (index 0000000..424ee68)
1 {
2 "cells": [
3 {
4 "cell_type": "code",
5 "execution_count": 1,
6 "metadata": {},
7 "outputs": [],
8 "source": [
9 "import pandas as pd"
10 ]
11 },
12 {
13 "cell_type": "code",
14 "execution_count": 2,
15 "metadata": {},
16 "outputs": [],
17 "source": [
18 "data_path = \"../log/evaluation_shb_CompactCNNV7i_t2.txt\""
19 ]
20 },
21 {
22 "cell_type": "code",
23 "execution_count": 3,
24 "metadata": {},
25 "outputs": [],
26 "source": [
27 "df = pd.read_csv(data_path, header=None, sep=\" \")\n",
28 "df.columns=[\"name\", \"gt_density\", \"gt_count\", \"pred\"]"
29 ]
30 },
31 {
32 "cell_type": "code",
33 "execution_count": 4,
34 "metadata": {},
35 "outputs": [
36 {
37 "data": {
38 "text/html": [
39 "<div>\n",
40 "<style scoped>\n",
41 " .dataframe tbody tr th:only-of-type {\n",
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48 "\n",
49 " .dataframe thead th {\n",
50 " text-align: right;\n",
51 " }\n",
52 "</style>\n",
53 "<table border=\"1\" class=\"dataframe\">\n",
54 " <thead>\n",
55 " <tr style=\"text-align: right;\">\n",
56 " <th></th>\n",
57 " <th>name</th>\n",
58 " <th>gt_density</th>\n",
59 " <th>gt_count</th>\n",
60 " <th>pred</th>\n",
61 " </tr>\n",
62 " </thead>\n",
63 " <tbody>\n",
64 " <tr>\n",
65 " <th>0</th>\n",
66 " <td>IMG_1</td>\n",
67 " <td>21.938055</td>\n",
68 " <td>23</td>\n",
69 " <td>27.056337</td>\n",
70 " </tr>\n",
71 " <tr>\n",
72 " <th>1</th>\n",
73 " <td>IMG_10</td>\n",
74 " <td>168.015335</td>\n",
75 " <td>181</td>\n",
76 " <td>171.466995</td>\n",
77 " </tr>\n",
78 " <tr>\n",
79 " <th>2</th>\n",
80 " <td>IMG_100</td>\n",
81 " <td>154.466904</td>\n",
82 " <td>157</td>\n",
83 " <td>107.567741</td>\n",
84 " </tr>\n",
85 " <tr>\n",
86 " <th>3</th>\n",
87 " <td>IMG_101</td>\n",
88 " <td>34.326912</td>\n",
89 " <td>37</td>\n",
90 " <td>41.111763</td>\n",
91 " </tr>\n",
92 " <tr>\n",
93 " <th>4</th>\n",
94 " <td>IMG_102</td>\n",
95 " <td>65.542725</td>\n",
96 " <td>70</td>\n",
97 " <td>74.637390</td>\n",
98 " </tr>\n",
99 " </tbody>\n",
100 "</table>\n",
101 "</div>"
102 ],
103 "text/plain": [
104 " name gt_density gt_count pred\n",
105 "0 IMG_1 21.938055 23 27.056337\n",
106 "1 IMG_10 168.015335 181 171.466995\n",
107 "2 IMG_100 154.466904 157 107.567741\n",
108 "3 IMG_101 34.326912 37 41.111763\n",
109 "4 IMG_102 65.542725 70 74.637390"
110 ]
111 },
112 "execution_count": 4,
113 "metadata": {},
114 "output_type": "execute_result"
115 }
116 ],
117 "source": [
118 "df.head()"
119 ]
120 },
121 {
122 "cell_type": "code",
123 "execution_count": 5,
124 "metadata": {},
125 "outputs": [],
126 "source": [
127 "df[\"gt_generate_error\"] = abs(df[\"gt_count\"]-df[\"gt_density\"])"
128 ]
129 },
130 {
131 "cell_type": "code",
132 "execution_count": 6,
133 "metadata": {},
134 "outputs": [],
135 "source": [
136 "df[\"pred_error\"] = abs(df[\"gt_count\"]-df[\"pred\"])\n",
137 "df[\"pred_error2\"] = abs(df[\"gt_density\"]-df[\"pred\"])"
138 ]
139 },
140 {
141 "cell_type": "code",
142 "execution_count": 7,
143 "metadata": {},
144 "outputs": [
145 {
146 "name": "stdout",
147 "output_type": "stream",
148 "text": [
149 "7.338098435462276\n"
150 ]
151 }
152 ],
153 "source": [
154 "gt_generate_mae = df[\"gt_generate_error\"].mean()\n",
155 "print(gt_generate_mae)"
156 ]
157 },
158 {
159 "cell_type": "code",
160 "execution_count": 8,
161 "metadata": {},
162 "outputs": [
163 {
164 "name": "stdout",
165 "output_type": "stream",
166 "text": [
167 "15.029132263569892\n"
168 ]
169 }
170 ],
171 "source": [
172 "pred_mae = df[\"pred_error\"].mean()\n",
173 "print(pred_mae)"
174 ]
175 },
176 {
177 "cell_type": "code",
178 "execution_count": 9,
179 "metadata": {},
180 "outputs": [
181 {
182 "name": "stdout",
183 "output_type": "stream",
184 "text": [
185 "14.812069554872151\n"
186 ]
187 }
188 ],
189 "source": [
190 "pred_mae2 = df[\"pred_error2\"].mean()\n",
191 "print(pred_mae2)"
192 ]
193 },
194 {
195 "cell_type": "code",
196 "execution_count": 10,
197 "metadata": {},
198 "outputs": [],
199 "source": [
200 "df[\"gt_generate_error_percentage\"] = abs(df[\"gt_count\"]-df[\"gt_density\"])/df[\"gt_count\"] * 100\n",
201 "df[\"pred_error_percentage\"] = abs(df[\"gt_count\"]-df[\"pred\"])/df[\"gt_count\"] * 100"
202 ]
203 },
204 {
205 "cell_type": "code",
206 "execution_count": 11,
207 "metadata": {},
208 "outputs": [
209 {
210 "data": {
211 "text/html": [
212 "<div>\n",
213 "<style scoped>\n",
214 " .dataframe tbody tr th:only-of-type {\n",
215 " vertical-align: middle;\n",
216 " }\n",
217 "\n",
218 " .dataframe tbody tr th {\n",
219 " vertical-align: top;\n",
220 " }\n",
221 "\n",
222 " .dataframe thead th {\n",
223 " text-align: right;\n",
224 " }\n",
225 "</style>\n",
226 "<table border=\"1\" class=\"dataframe\">\n",
227 " <thead>\n",
228 " <tr style=\"text-align: right;\">\n",
229 " <th></th>\n",
230 " <th>name</th>\n",
231 " <th>gt_density</th>\n",
232 " <th>gt_count</th>\n",
233 " <th>pred</th>\n",
234 " <th>gt_generate_error</th>\n",
235 " <th>pred_error</th>\n",
236 " <th>pred_error2</th>\n",
237 " <th>gt_generate_error_percentage</th>\n",
238 " <th>pred_error_percentage</th>\n",
239 " </tr>\n",
240 " </thead>\n",
241 " <tbody>\n",
242 " <tr>\n",
243 " <th>0</th>\n",
244 " <td>IMG_1</td>\n",
245 " <td>21.938055</td>\n",
246 " <td>23</td>\n",
247 " <td>27.056337</td>\n",
248 " <td>1.061945</td>\n",
249 " <td>4.056337</td>\n",
250 " <td>5.118282</td>\n",
251 " <td>4.617152</td>\n",
252 " <td>17.636249</td>\n",
253 " </tr>\n",
254 " <tr>\n",
255 " <th>1</th>\n",
256 " <td>IMG_10</td>\n",
257 " <td>168.015335</td>\n",
258 " <td>181</td>\n",
259 " <td>171.466995</td>\n",
260 " <td>12.984665</td>\n",
261 " <td>9.533005</td>\n",
262 " <td>3.451660</td>\n",
263 " <td>7.173848</td>\n",
264 " <td>5.266853</td>\n",
265 " </tr>\n",
266 " <tr>\n",
267 " <th>2</th>\n",
268 " <td>IMG_100</td>\n",
269 " <td>154.466904</td>\n",
270 " <td>157</td>\n",
271 " <td>107.567741</td>\n",
272 " <td>2.533096</td>\n",
273 " <td>49.432259</td>\n",
274 " <td>46.899162</td>\n",
275 " <td>1.613437</td>\n",
276 " <td>31.485515</td>\n",
277 " </tr>\n",
278 " <tr>\n",
279 " <th>3</th>\n",
280 " <td>IMG_101</td>\n",
281 " <td>34.326912</td>\n",
282 " <td>37</td>\n",
283 " <td>41.111763</td>\n",
284 " <td>2.673088</td>\n",
285 " <td>4.111763</td>\n",
286 " <td>6.784851</td>\n",
287 " <td>7.224562</td>\n",
288 " <td>11.112873</td>\n",
289 " </tr>\n",
290 " <tr>\n",
291 " <th>4</th>\n",
292 " <td>IMG_102</td>\n",
293 " <td>65.542725</td>\n",
294 " <td>70</td>\n",
295 " <td>74.637390</td>\n",
296 " <td>4.457275</td>\n",
297 " <td>4.637390</td>\n",
298 " <td>9.094666</td>\n",
299 " <td>6.367536</td>\n",
300 " <td>6.624843</td>\n",
301 " </tr>\n",
302 " </tbody>\n",
303 "</table>\n",
304 "</div>"
305 ],
306 "text/plain": [
307 " name gt_density gt_count pred gt_generate_error pred_error \\\n",
308 "0 IMG_1 21.938055 23 27.056337 1.061945 4.056337 \n",
309 "1 IMG_10 168.015335 181 171.466995 12.984665 9.533005 \n",
310 "2 IMG_100 154.466904 157 107.567741 2.533096 49.432259 \n",
311 "3 IMG_101 34.326912 37 41.111763 2.673088 4.111763 \n",
312 "4 IMG_102 65.542725 70 74.637390 4.457275 4.637390 \n",
313 "\n",
314 " pred_error2 gt_generate_error_percentage pred_error_percentage \n",
315 "0 5.118282 4.617152 17.636249 \n",
316 "1 3.451660 7.173848 5.266853 \n",
317 "2 46.899162 1.613437 31.485515 \n",
318 "3 6.784851 7.224562 11.112873 \n",
319 "4 9.094666 6.367536 6.624843 "
320 ]
321 },
322 "execution_count": 11,
323 "metadata": {},
324 "output_type": "execute_result"
325 }
326 ],
327 "source": [
328 "df.head()"
329 ]
330 },
331 {
332 "cell_type": "code",
333 "execution_count": 12,
334 "metadata": {},
335 "outputs": [
336 {
337 "data": {
338 "text/plain": [
339 "5.966772023225838"
340 ]
341 },
342 "execution_count": 12,
343 "metadata": {},
344 "output_type": "execute_result"
345 }
346 ],
347 "source": [
348 "df[\"gt_generate_error_percentage\"].mean()"
349 ]
350 },
351 {
352 "cell_type": "code",
353 "execution_count": 13,
354 "metadata": {},
355 "outputs": [
356 {
357 "data": {
358 "text/plain": [
359 "12.624878158989976"
360 ]
361 },
362 "execution_count": 13,
363 "metadata": {},
364 "output_type": "execute_result"
365 }
366 ],
367 "source": [
368 "df[\"pred_error_percentage\"].mean()"
369 ]
370 },
371 {
372 "cell_type": "code",
373 "execution_count": null,
374 "metadata": {},
375 "outputs": [],
376 "source": []
377 }
378 ],
379 "metadata": {
380 "kernelspec": {
381 "display_name": "Python 3",
382 "language": "python",
383 "name": "python3"
384 },
385 "language_info": {
386 "codemirror_mode": {
387 "name": "ipython",
388 "version": 3
389 },
390 "file_extension": ".py",
391 "mimetype": "text/x-python",
392 "name": "python",
393 "nbconvert_exporter": "python",
394 "pygments_lexer": "ipython3",
395 "version": "3.8.1"
396 }
397 },
398 "nbformat": 4,
399 "nbformat_minor": 4
400 }
File notebook/explain_ground_truth.ipynb added (mode: 100644) (index 0000000..9342b8a)
1 {
2 "cells": [
3 {
4 "cell_type": "code",
5 "execution_count": 1,
6 "metadata": {},
7 "outputs": [],
8 "source": [
9 "import numpy as np\n",
10 "import scipy\n",
11 "import scipy.ndimage.filters\n",
12 "import matplotlib.pyplot as plt\n"
13 ]
14 },
15 {
16 "cell_type": "code",
17 "execution_count": null,
18 "metadata": {},
19 "outputs": [],
20 "source": []
21 },
22 {
23 "cell_type": "code",
24 "execution_count": 2,
25 "metadata": {},
26 "outputs": [],
27 "source": [
28 "xtick_marks = list(range(0, 181, 15))\n",
29 "xtick_label = list(range(0, 13, 1))"
30 ]
31 },
32 {
33 "cell_type": "code",
34 "execution_count": 3,
35 "metadata": {},
36 "outputs": [],
37 "source": [
38 "def plot_gaussian_blur_with_center_x(m, m_blur= None, name=None):\n",
39 " \n",
40 " plt.figure(figsize=(10, 10))\n",
41 " \n",
42 " # plt.xticks([0, 15, 30, 45, 60, 75, 90], size=20)\n",
43 " if m_blur is not None: # there are blur\n",
44 " mxy = np.argwhere(m == 1).T\n",
45 " plt.scatter(mxy[0], mxy[1], marker='x', color=\"black\")\n",
46 " plt.imshow(m_blur.T, cmap='jet', interpolation='nearest')\n",
47 " plt.xticks(xtick_marks, xtick_label, size=20)\n",
48 " plt.yticks(xtick_marks, reversed(xtick_label), size=20)\n",
49 " else: # there are no blur\n",
50 " m = np.flip(m, 1)\n",
51 " mxy = np.argwhere(m == 1).T\n",
52 " plt.scatter(mxy[0], mxy[1], marker='x', color=\"black\")\n",
53 " plt.xticks(xtick_marks, xtick_label, size=20)\n",
54 " plt.yticks(xtick_marks, xtick_label, size=20)\n",
55 " if name is not None:\n",
56 " plt.savefig(\"./crowd_counting/\" + name+\".png\", dpi=300)\n",
57 " plt.show()"
58 ]
59 },
60 {
61 "cell_type": "code",
62 "execution_count": 4,
63 "metadata": {},
64 "outputs": [],
65 "source": [
66 "def create_point_with_blur(x, y):\n",
67 " m = np.zeros((181, 181))\n",
68 " m[x*15, (12-y)*15] = 1\n",
69 " m_blur = scipy.ndimage.filters.gaussian_filter(m, 15)\n",
70 " return m, m_blur"
71 ]
72 },
73 {
74 "cell_type": "code",
75 "execution_count": 5,
76 "metadata": {},
77 "outputs": [],
78 "source": [
79 "# def plot(x, y):\n",
80 "# # for plot point only\n",
81 "# p1, _ = create_point_with_blur(x, y)\n",
82 "# # for point and gaussian \n",
83 "# p2, b2 = create_point_with_blur(x, 12 - y)\n",
84 "# plot_gaussian_blur_with_center_x(p1, b2)"
85 ]
86 },
87 {
88 "cell_type": "code",
89 "execution_count": null,
90 "metadata": {},
91 "outputs": [],
92 "source": []
93 },
94 {
95 "cell_type": "code",
96 "execution_count": 6,
97 "metadata": {},
98 "outputs": [],
99 "source": [
100 "m1 = np.zeros((181, 181))\n",
101 "m1[60, 60] = 1\n",
102 "# get the indices where data is 1\n",
103 "m1_blur = scipy.ndimage.filters.gaussian_filter(m1, 15)\n",
104 "\n",
105 "m2 = np.zeros((181, 181))\n",
106 "m2[90, 90] = 1\n",
107 "m2_blur = scipy.ndimage.filters.gaussian_filter(m2, 15)\n",
108 "\n",
109 "m3 = np.zeros((181, 181))\n",
110 "m3[150, 150] = 1\n",
111 "m3_blur = scipy.ndimage.filters.gaussian_filter(m3, 15)"
112 ]
113 },
114 {
115 "cell_type": "code",
116 "execution_count": 7,
117 "metadata": {},
118 "outputs": [],
119 "source": [
120 "m1, m1_blur = create_point_with_blur(3, 8)\n",
121 "m2, m2_blur = create_point_with_blur(6, 5)\n",
122 "m3, m3_blur = create_point_with_blur(3, 5)\n",
123 "\n"
124 ]
125 },
126 {
127 "cell_type": "markdown",
128 "metadata": {},
129 "source": [
130 "# Vẽ các điểm "
131 ]
132 },
133 {
134 "cell_type": "markdown",
135 "metadata": {},
136 "source": [
137 "Giả sử ta có 3 điểm đánh nhãn "
138 ]
139 },
140 {
141 "cell_type": "code",
142 "execution_count": 8,
143 "metadata": {},
144 "outputs": [
145 {
146 "data": {
147 "image/png": 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\n",
148 "text/plain": [
149 "<Figure size 720x720 with 1 Axes>"
150 ]
151 },
152 "metadata": {
153 "needs_background": "light"
154 },
155 "output_type": "display_data"
156 }
157 ],
158 "source": [
159 "plot_gaussian_blur_with_center_x(m1+m2+m3, None, \"p123noblur\")"
160 ]
161 },
162 {
163 "cell_type": "markdown",
164 "metadata": {},
165 "source": [
166 "Giả sử ta tạo ra 3 ảnh, mỗi ảnh chỉ có 1 label "
167 ]
168 },
169 {
170 "cell_type": "code",
171 "execution_count": 13,
172 "metadata": {},
173 "outputs": [
174 {
175 "data": {
176 "image/png": 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\n",
177 "text/plain": [
178 "<Figure size 720x720 with 1 Axes>"
179 ]
180 },
181 "metadata": {
182 "needs_background": "light"
183 },
184 "output_type": "display_data"
185 }
186 ],
187 "source": [
188 "plot_gaussian_blur_with_center_x(m1, m1_blur, \"p1\")"
189 ]
190 },
191 {
192 "cell_type": "code",
193 "execution_count": 14,
194 "metadata": {},
195 "outputs": [
196 {
197 "data": {
198 "image/png": 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+dpwYtIToEBsTR7IAAAASMMgCAABIQFwIjGUtRoQ162dss69qxJUshHZNjNU1UizFizWRYtd4blLbnFQFZQnRITYOjmQBAAAkYJAFAACQgLgQ6KyviLCvWHCcKHBTx3VKJhkRlnSNDksxWc1z7hoplrY/qWspRl2rI4kOgRKOZAEAACRgkAUAAJCAuBCoknF9vUlVDmasU+pb1/VL62RfD/F8xKhrobBOKdKKj+0a+ZUis9LrlREjdq2CrHlsNKnrQgJrC0eyAAAAEjDIAgAASEBcCBTNQkQ4qaq9jFhwnOU1+yrJqjTsWlFYEx3eUVheiuS6ThaaXWkYTXPyUqJDrA8cyQIAAEjAIAsAACABcSEwZJoR4TiTi06qcrBrzLdQsc44E5lOasLVWqXoqmsUOE50GJefKKzTVyxY8/OQPXkp0SHWLo5kAQAAJEgbZJnZ88zMV/k6k7V/AACAPmXGhZ+R9JrCfd8t6TGS3p+4f6BSRrXaNCPCWYsFt47RTxXWscI6WTy0ayLFmljweGF5zcSkNTFi6bHjRI01UWDN5KWTivmIDrG2pA2y3P0zagZa38TMPtE2D2btHwAAoE9TPyfLzO4v6SGS/lnSn0x7/wAAANPQR3Xhj7S3b3N3zsnCGjSpCrhJVQiWlneN/BYK65S2U/PY0n47xn+llyeruPB07F/NhK2leDFGWvE1OlFYpxQRXlhYXjOpabZxosNpVk0C0zfVI1lmtiDp2ZLOSnrrNPcNAAAwTdM+kvXvJO2W9Cfu/tXSSmZ2QNKB5rtdU+kYAADAJE17kNUOnPTrK63k7gfVnhRvtt9XWhc4P5OadHQWIsKaar6uUWDN8tI2K6LA+cJDa9qqWF6rGBd2bC+W4sVSpFiKDrtGhKWqw5K4zePFtUYrxXw162REh1QaYvZNLS40s38r6TJJX5N09bT2CwAA0IdpnpPFCe8AAGDDmEpcaGbzkp6j5oT3t01jn8A3y7guYdf9jhMR1lQLjhPzbS2sU9pmQSkK7Lp81uPCxa7LayLFUoxYivlKkWJUihfj+901busaHXZFdIj1YVpHsq6QdCdJV690wjsAAMB6Ma1B1tIJ78zwDgAANoT0uNDM7ivp4eKEd/QiIyLsep3Bmu1MavLPmpivtE6pXagWnB+jPU5cWBMj1upaRdg1LuzaLn4mSu2uEWFUE8PVbKcUz5V+Brpe35DoEGtX+iDL3f9O07+6KwAAQK+mfu1CAACAjaCPaxcCa9A4EWHXaw5OKiKM7TEqB0uR3/aKdcaJEUuRogrrnI9S+lSKAicVC8b2sdI6MQCI71/NZ6VmotEY/y0U1xroem3BUixYsw4xH9YHjmQBAAAkYJAFAACQgLgQ61Bf1yUsrTPORKOTigh3FNYJkVTcfCkKnNTyjKrD85FdRViKAuPy+cLy0vqnayoQu+oaHXZVihq5viHWN45kAQAAJGCQBQAAkIC4EBtU9nUJx1k+TkS4s7A8RodBKdqradc8dlLViJOcmHScSUe7RoTbC8vjc1ssLI/rbyosL1YgRl0/6zXR4aSit2le3xCYPo5kAQAAJGCQBQAAkIC4EOtE14rCrtuZVBQYl28tLE+OCLvGgrG9e4zHZkeHKixfKUnKiAhLy2vaXWPR0naK0WFXMTosfXZrJj7tKmOSUioNMX0cyQIAAEjAIAsAACABcSE2kK4f90n9eNREhzWx4IQiwprIr7RO1+XjRIqdKw1dVU6Hirxx4sKuUWBsHwntjMlYU6LDKMaIpYh9mpWDVBpiNnEkCwAAIAGDLAAAgATEhVjDsisKS+t0rSisuUZhaZ1SFFgREY4T+dW0ax7bNVKcH0RMF8yfOtfePH/yXHtLWD636YxGmZsbLD9zZm7kOpJ05vTgvpOLm8+1Ty1uOdc+G5ZrMbxPNVFgTURYmqQ0rt81LozL43aK0WFNxFbKVmvW76oUNVJpiLWFI1kAAAAJGGQBAAAkIC7EOlf6iHeddLRm++NEh6UYMcY7OwrLg5pqwXHae8d47PZB9d+Fu4+ea2/dPpjMcmHLoL1FIS7UyZHL53QmtAvx1LK08Ex4b87MhbhwS4gLdw3iwpMaLD9xcvC6Hz82aN9xJLw3u0P1YozqSu9N11iw6/Ua48vSueowPrh0HcPS+jUxXE0sWNp+KfKj0hCzgyNZAAAACRhkAQAAJCAuxBozqYrCrmpixJrocJxrFBbimknFgqUocG/HdXaPjgV3xPZciAvDte8WQntrmPByrLhwmaG4MGSJMRY8pUFceDy87ie2hLgwtI/uHsSFR0N0eMf2ECNuDzFi54lWNXlD0WH8bNVEfqV4rubn5I6KdaYZ81FpiDwcyQIAAEjAIAsAACABcSHWoXGuUThORWHXdoxoaqLDQtxUc03AmohwjPYFe28fbH7voFwuxoK7QxndDo2OC+PyzSEWjOtsHooLB7HSJo2emHS50yEijNHhqRAXHg+Vd3H50VDhGdc5OjdYfuSiwQscY8QjhwbLz85vG3QoIy4szRtavFZj+GwNfebuKLRL8WLNhKWl5eNUGpZQaYh+cSQLAAAgAYMsAACABMSF2EC6ViZ2rSgsLS9FgaVJRwvrxM1kR4T7SssHlYPze78x2PyuQRR4pxAL7i60YyxYai+E6sItobowViBuGqourIsLY0VhjA5PhPfgZKguPBHejxgXltox2jwyN3jhN9958ByOzA+WL87fadC5TSG2G+e3c9e4cKhdirRLDyjFi5s6Lh8nFqRCELOJI1kAAAAJ0gdZZvbdZvb7ZvZ1MzvZ3n7IzJ6UvW8AAIC+pMaFZvZKST8v6ZCkP5b0dTXhw4MkPUrS1Zn7x3qRMQHppCoQM6oIKyYdndQ1CksR4VBcOIgIt+87dK590bZbBm0NlsdYcK9uGbm8Ji6sqTScGzMujO2aisKa9nDkOXqi1S27Bs/nlk2Dfh+Lb0iMDkvGigUL7XgtxapJSrtWHapieU3kl1E5SOyIyUobZJnZFWoGWH8q6enufnTZ/X1N3Q0AAJAuJS40swskvV7ScUk/sHyAJUnuzr8JAABg3co6knWZpLtLeq+kb5jZkyXdX9KipE+6+yeS9osNq/RRrjlgWrrO4KQqCkvRYWl5xTXu0icgHUSEu77tpnPti7bcElYP0aFuGdneW4gRa6oOh69peGLk8tK1C2PVYawglMrXLoxxYawoHJp0NMSCR8KLGtuliVPjNReHqiK3hfa3Ddq36s6DTp8On4nOE40W2qV14uesOEnpicLyuNHSz8M4lYZRzeSlpX0xMSmmI2uQ9Z3t7U2S/lrSt8c7zexaSc90939J2j8AAECvsqoLL25vf1TNvznfI2mHmqNZH5T0CEm/V3qwmR0ws0+Z2acU/isEAABYK7KOZC0dgzc1R6z+pv3+b83saZK+IOmRZvbQUdGhux+UdFCSzPb78vuB6SvFF10rCmuiw6AmIuxaRVicgLRQRRgiwjsrRIdafXmsOixVGtZUHcbKvK1xktIzgwk+505XVhduGkSEJ+cGk44eD+/HiUJEWKoiLPV1OC4MfS1VRQ66ozP7Bv0cqjrsGh2WIsKaSDG2i5/dUiRXU2lYigiJ87A+ZB3JWpoK+rowwJIkufsJNUezJOnBSfsHAADoVdYg6x/a2yOF+5cGYYUJgQAAANa2rEHWtWqO997TzDaPuP/+7e31SfsHAADoVco5We5+yMzeLek/SHqVpFcu3Wdmj5P0vZJulfSBjP1jPcie5b3mHKuaaSFqpm2oOW8rKJ2HlTydQ7zgc5zNvTRVQzwP62LdPHJ5aTqHi2rOz7r92KBvt4c+x/bJ0K49jWfT2UF7SzgnaNtgf4vbBouPbhu8kHGqhtJ0E6VpG2pmpB+ajX5buJD13kF7cXHP4AGl86eOhfb2inVKn6fi+Vml6RxK51XV/JyU1ldhnXEuKF2D2d8xvszL6rxM0ndJ+hkze4SkT0q6q6SnSToj6YXuXooTAQAA1rS0QZa732xm36XmKNbTJD1E0lFJfyLpv7r7X2btGwAAoG+pF4h298Nqjmi9LHM/2KjGmeW9ZjuldWqiwFI8UpraIciIBQsR4QV7B9nb7l2DA8sXjRER7tcNqz52KC68/dZz7fnDoZ+3FtoxLowRVt0MDhqaAD6+viEinN8V24Ncbcee0N5WExfGPHN1MS6MM9Wf3DU4tfXmxcE8D2djrlmKAkuRX00sOF9YXvxMHy+sU4oFay46XdpvTSzI7O/oV9aJ7wAAABsagywAAIAEqXEhsLaNEzvGx24trFNxIeiU6HAws/vuvYOI8E6FKr/SxZ9LEWEpRrw4tPfcHLKnweoKm5didFiqLowRVm0yF2ZVL8WFQ+1QzDcfYst9Fw++2XxxxWzuBTEiPBk6d1KDiDDOQH9q72Cdw8fCZ+tY+DzFuLBUabi9sE7N52/owtHxMx0/6zXVgiXM/o71gSNZAAAACRhkAQAAJCAuxDrX9SM+qQlIa9YprFITEY4RHV64O1zYeO5oWGUQEe4tTiI6uuqwGCOeGcSIO28I1V2DVYbjwtiuqS6M7erJSEO7FBGG6sKaysY9JwffbNo/eM6xkrEmFjxVaMe48PhcuHh1eC/vOLZzsLM4A2HpM1H6DJWiw+KPUsbPRilqrJkUlMpBzA6OZAEAACRgkAUAAJCAuBAzJON6haXt11zHcJwJSGv2G4xTXVhaHisKtw8qCnfsHh0R1rRLMWKpinAoIgwp2lA7RoQxRixNTDqN6sIYF4bqwqF9xHYoItwZIq3Td7kprDKIC6tiwdA+qh2jl+8eLD98ZNDW7lD9F6PDrhFhqR3XH+tnqevEpDW/IzKuM8h1DHF+OJIFAACQgEEWAABAAuJCrDGTul7hpHSNCAsTkJYKriY0MWmponCHVm+XosNSdeHQRKMx/ivFhbEdJyMtVRrWVBfGOUDjtQql7tWFpUiyVLgW9rdny+DBpy4ePLnjFbFgbBffp/BeDlUaHgmVhjXVhaV2zeeyODFp/Bk4UVhnmriOIaaPI1kAAAAJGGQBAAAkIC4EhtTEjjURYald2EzX6sKO0eHW7ccHbY1ud40Lh9q3hzyvNLlobNdUF5biwtsGTQ9R3onK6sKFUF1o8bULCVtVDBnF97JQvbh72+BJHN02OhaM7SOhPDS+H6X3L77Ht24vxIWT+pxVTUxaaneNDrmOIdYujmQBAAAkYJAFAACQgLgQGGcS0aprslXstmu7Kt4ZVFAtbBlESQsVEWHXqsP5OHFoqULwpsI6cfnXR69zR9j+bWEizNtCfFeaynJ5WLspRIE7QyXgzrD8wsJEo0Ni1eKWQjtULM6HisXd2wavXSkWrGnH9zK+x7eG917z4RXoWkVY0y6quUZhKTqc1HUMgX5xJAsAACABgywAAIAExIVAusKPWUpEOGheMH/qXHuLBu2tIaLZHJYvhOWlarah2Or2kNvF6r/SNQfj8lLVYYgIj4flh0OUFzcTCg3r48LQPhqiwBOhr3vCxgZThWo4Iiy9N3Ei09jZiwbNHXsGr92Obau/1guF9yy+l/E9ju/92Zq4cJzocAh/UoCII1kAAAAJGGQBAAAk4Ngu1qGuExx2VfPYuE64tts4EWHHGHHz/KBEbrNGt2M8tWVondExYqxmm48TdpauLXhrt3asIowRYSxAjAnciUJ7pbhwoeIx8TlcGHZ4YaFysPjcCq9FfO0WtsXKz9Gx4JbCe1Z6X+N7vzgfSxxDf8b5zJXap+N1DLv+nJworrX6Y0uTlHJdQvSLI1kAAAAJGGQBAAAkIC4EhtRMOlpaf5wIsqILHaObLYXqwtgejgVXv75hrGYbisLGaYcSwTjRaIwFSxWFR0O7NmyKceGOwjrxnVwIfboo7jxWEY7x/ONrWnrdF4YiwtXf1/jeL45TLTixvxBdJ/YtTVLKBKRYWziSBQAAkIBBFgAAQALiQqxzNTFFzWO7mtAEpGO05zYNZtqcU6k9qLjaVFxn0N5yJlzUL17fb7HQvn315R6Wx2sRxmQuhkcxIiyts5JS4BTf7bjdHaFPe0JfreK5FV+X8NrF13RubvTrvqnwnhXf1/DeT/MzN1zAN86fl/hu1ESEpX0RL6JfHMkCAABIkDbIMrPrzY42hYcAACAASURBVMwLXzdm7RcAAGAWZMeFt0p644jlx0YsA1CrWA3mIxcPR0zdIsKhx54OMVSMhsLioRjx9OrLT5wcuXioXZp0NLaX1vdlj92koelghx5Tqmcr9iP0dWvFcxtafmb0OvE1nZtbPQosR4eFyTaHPhOFiXEBpMj+MTvi7q9O3gcASJL+TM2pT49TM5xwSVermcT8sT32C8DGxDlZANYFVzPA+oSkD7bff7D9frH9HgCmKftI1hYze7akf62m5uazkq519zMrPwxAtcJPcaxUG2/zCT+uhU3eUdEuOa3mCNZZSX/VfknSd7XLl8K0mjq0qn0nvCyTeq2L7z0RITBV2T9y+yS9Y9myL5vZ8939z0sPMrMDkg403+0qrQYAQ0zS92owwFL7vY1eHQBSZcaFb1dzGsQ+Nder/3ZJvy7pbpLeb2YPKD3Q3Q+6+6Xufqm0NbGLANaTpYgwWooOAWDa0o5kuftrli36nKQfNbNjkn5S0qslPS1r/8CGUSgqO3NmbvDN3Oh16jY/xoNLCpu8sKJdsknNSe5/pSYi/F41A6y/UvPf5JO08hGtzvtOeFkm9VoPvffDOwAwRX2c+P6W9vYRPewbwDplaqoIH6pBRPi97ffzIjIEMH19nAZ5c3u7rYd9A1jHHqvhebJMqx/BAoAsfQyyHtreXtfDvoH1oRT7nB49nDgTftRjO8ZTZ4rt8NhNIYbadHbQjunUltDetPryhbB80+0jV9FCoR0r/0qTjC5X2lZsly7Nt9DxuQ0tnxu9TnxNh9+n0e/H6dJ7U/p1XvhMEB0C+VLiQjO7n5ntGbH8rpJ+tf32nRn7BgAAmAVZR7KukPQKM7tG0pclHZV0D0lPVnN6xNWSfilp3wAAAL3LGmRdI+nekh6kJh7cJumIpI+rmTfrHe5OVTWmoDSVZE29WnzsQnGt0QpZTOmieAntM6dDrDS3ehRYEx2enAv515bw+sxrdHvb6sstLN8ZIrWjYT7NGAXu0Gg176g0/E7uKCzfGduhT9bxuRVfl/Ayxte0e0RYeM/Cez/Nz9ywcfLImulnS/vq+lggT8ogq51otDjZKAAAwHrHtQsBAAAScCUrYEiMHWIAVYoOE2KKYuVgt/bJxc2D9pbQ1qB9KrRPhKsrHC+2w3Pfdiy0df7tkM3tDNWFJ24dtGtC3xgp3lFYRypXFO4ptHduj9+E9jjPObTja1p63eN7E9+zk6V2eO/HivwmVoEY35HSRkvvMmWQWLs4kgUAAJCAQRYAAEAC4kKsQzFeKH3E76hYp6Sm6jCuEwpp48SQNdHNYsd1QvvU4qBq7dSu0A6lbTGSOqm4TowRF0J7sP5iiLzmd4U+xFhsV0U7RIQXnhy095wurBMW3xbapeB2eVwY3+2Y/g3FheE5XBjvqHk+Fa9FfO1ODMWCg9d6OBYc/Z6d0uj3Nb73pc/HWJ+5YtQYi8Zr4vNxIvaaxxI1ol8cyQIAAEjAIAsAACABcSGQrlCx2DWKKcU+hfbZxdGVZ8cLkVSMqo6GaTpjPBWXH902KLub3xUqDWO8FioEh5aXIqkwAenWsPjCw4P2QtjVjrD+ecWFYc7OWEU4FBFeFNoXF9qlSLGwPL52Na91KUaM72V8j+N73/Vz098EpMD6w5EsAACABAyyAAAAEhAXAlUTkJaujFeaZLEiNhnnGnFVEdCgzydOhgq2LYX4r2P7iHafa+/YM8jw5mNEGKoCdbLQDpHfkBDlXRiK5S4KJYV7wvM9Ebe5goWwraFrEcZSwxj5xVjwzqF9UWH5xaPXWQzRYXztxnkPhioTw3sc3/uUiLAqFSw9oGbSUSYmxfrAkSwAAIAEDLIAAAASEBcCQ2omKa259lqpXagu7FoBVmofG90+fixcE2/L6OvjVcWCOjq6vW3Q3ndxyAtj32KcV3oJQ0Q49PIXJvW0EEduLVQpDm1zpe2WJlEtxH/aX1in0D6ybbDR+JqW2qX3o3hdyfAelz4HE/ucFVO7mp+BqCb+m9A1QYEecCQLAAAgAYMsAACABMSFWGNK1yWsqQTMUBMdxnXCtd0WK65jOEZEGNt3HAnR0+7QnutYRRgiwq06PrK9+eJBLrjnZOhoqYowvo2h8k+x8q9UsViKI1dS2kcpLowTipaqCPePXufwxYMd3BKyxtjuGhcOtc8M2vE9rvlMjFV1GNcZul7hLFcIzlp/sBFwJAsAACABgywAAIAExIWYIdmRX02VUvyRKEUfmwrtrtFhxwkjSxFhafmR0N49iCaPhljpyEWDeCpGfqVYcCHGgjo1sj0XcsFN+284194ZX4dY8bel0I6RXSkujO3a1KdrdWGp0rAwSelt+wfv683hjptC+5D2hvbq0WExUhyKCEP8HN/7ms9K18/fkHEmGi21u/4sZVcgUuGI88ORLAAAgAQMsgAAABIQF2KdK1Uj1qxfcx3DUmRRc922jhOTxnhne2F5qR3iozu2j640PDI3iKFqYsEtQ+1BaV+MC2MsePouN51r79kSnlipwu9waJciwqzqwtiO1YVxMtIQF8YqwlJEWKouvGUoOlw9RixWFJYiwprqwprosGoC0prP/TjXNKxBtSBmB0eyAAAAEjDIAgAASEBcCBTVXMcwKkUfx0N7IbQLE5NOaALSofgoxovbB/s6cmgQQ22+8yBvi3FhVSxYcCbkhbF96uJbzrV3bxuUDs7HSr4YzdVMRrp6dxqxsrFmMtLQXgzRYbwWYYz/ShHhDWHG0poYMUaE34jVheE905FCReE40WFVdWFpAtL4Wa+pFiyhmg/rA0eyAAAAEjDIAgAASEBciDVsUtcxLFUUltbpOjFpbJ8I7RgdVkxMGuOd+cLyI4V1to9efnZ+kJEdmR/EUFt2DSLCTSGHq4kIo5OhlO+UNp9rH9fWc+2j2wYVcru3DZ7Ajj2DJzZfqi6MFYXnMxlprDQMceFiaB/dNnjxSpOCxpgvVgiWYsSbQ2liOTocbOfIrYN9nT0UOnco9L8UF04qOhxS+kzXVA52nYC0tN8aXK8Q/eJIFgAAQIKpDrLM7Dlm5u3XC6a5bwAAgGmaWlxoZneR9GY1B6a3r7I6NryM6xiWIoLSpKM169RMTFqKMmPMUogLSxHhpGLE0F6cv9O59i2bQkS4rVtcGKsIT4aIsBgXKkyIGiK4HdsG109c2DaoWtsaXrctZ0K14+m6KPPMptC/uUFeeDzEtycK/Sv1NbYPFSK/mgrEoajx9sHyxUOD96YqIhwnFiytMyR+drtGgeNc61CF5RnxHxWOGN9UjmSZmUl6u6RbJL1lGvsEAADo07TiwhdLeoyk52v4tFUAAIB1KT0uNLP7SnqdpF9x92vN7DHZ+wQmryb+i8sLUWAxLqyoNCzFgqUCx5p1htqDiS2Phdhq7ttCDBeq8UoTjZYqCmsiuB0aRIRbw8SWC+G1isvn5kKUOTeIjGJF5Omh2UelM+FJx37HCPNEeD9qos1ydDg6LixGhCcHy4/dOFiuG8OkozEuLLXHqTQsRoSlz24ptquJFEvbB9aH1EGWmW2S9A5J/yTppzs87oCkA813u1ZcFwAAYBZlH8l6laQHSXq4u59YbeUl7n5Q0kFJMtvvq6wOAAAwc9IGWWb2YDVHr97g7p/I2g/QGGdi0pqYojTRaKm6sFQdVYoR4/Kwr9I1DWO8U4oIjxTW6Rgd3hoq4c7sCxHhtkH79KQqCkNcGNubw/UTh+LC85gotRRzxv7FfpeizZqqw0OFaxHeUqgiHIoID00oIhxrAtL4P24p/quJDkvVf6Xlpe3U/KwyASlmR8qJ7yEm/IKkn83YBwAAwCzLqi7cLuleku4raTFMQOqSfq5d5zfaZW9M6gMAAEBvsuLCk5LeVrjvO9Scp/VxSf8giSgRq8iemLTmx6AUNZQeW1NpWFoeHzuIsIoR4aTaJadHVx2e3hsiwl2jqwhLFYWlWDC2Y0XhlnCRwoUQF57PdRVjRBhjztjXWCEZKw27xoWx/Y24PFyLcGii0VIV4Y0avbymirBrdDjkREW7a0XhONcujJiAFLMvZZDVnuQ+8rI5ZvZqNYOs33L3t2bsHwAAoG9cIBoAACDB1K5dCPSva+xYExF2rTQsVBGGCGx4edjm6dAeJzrsauipDOKsxcU959o3L4YJSPeG6wHODSK4WL1XmnS0pqIwLp/T6AlIV3J6qLpw8MKUKiFLlYY11ZJHz4TlhwbLzx7aNuhQTeVgdnXh0Ee9VDl4vGKdjIrCGsR8mE1TP5Ll7q92dyMqBAAA6xlxIQAAQALiQqxDk6ocLEWK41QaTqjqME5S2jU6rFFKfWI7TFp5dnEQfx0+FmK03UdDO1QXzpWuUXg8LB+8DptDdeGWobgwVhfWRUylaxcOT6IaIs9QXRgrEIfiwhALHj0yaN8R2jpSqByM0V5NFJgxAWlx0tGMKsKuseCsVSAC9TiSBQAAkIBBFgAAQALiQqwxGROT1uhaaRh1rTosPbZiktJJ6RgXDl9XcRCL3XFs57n24RCdxRhx6/YQF24ZtGMs2GdcGJefOBniwhCLDsWC4fl3nix0nIlGu05GOqRm0tGaKsLSdQYzKgonhcpE5OFIFgAAQAIGWQAAAAmIC7HOlSoNS7HjpCoNo7ivmh+50iSlJRXXNyypSX1K7ZoKthhV7Q4x4pFBjHjr9tCeH7xWF8yHuHA+xIVh+dym0ROQzs0Nlp85MzdyHUk6czrEhYshLgyTq54Ny7VYmBC29JxLy7uu0/WxVdcljJ+to4XlpUlHa6oLu0aHUdeoMaKiELODI1kAAAAJGGQBAAAkIC7EGjapSsOa7YxTgdQ1IixVGtZsM0SHMT6quYxc1yrC7YXlxwrrHCksj+35wet/NrQX5weTnS4WJ1yNE2quIFx/sfNzLj3Pru2ukV9NFFhVRRijwNJ1CbvGgpOKDmt+xiZVCUhFIaaDI1kAAAAJGGQBAAAkIC7EBjLONQ3H+VGpqVisiQi7qpiwtGtc2DVGHIoCK5bXtIvXZLTRy1cqNhsnLixFh+NEil2vP9i5ijB+zm4rLO86GWnGhKJcoxDrA0eyAAAAEjDIAgAASEBciHUiu9IwI46IMU6I9qquadhVITqc1GSkfUWExehwBV2fZ3Z0OE68GB87ZFIRYelFOV5YPk5FYddJR7uiohDTx5EsAACABAyyAAAAEhAXYoOaVOVgdpSxENpdo8NS/BK2uTjGxJzzheWlGDEur4kFu8aF5yOjurJrdNh1+dDbGidgjZ+P0rUIu0aEpYlGJxURjoOKQsw+jmQBAAAkYJAFAACQgLgQ61DXSsNSdFhTaVj6EcqIMsaJDqPYt7DN0+E5xkkxS7Fg6dqFcf2aiLAmFozrqLDO+Si9TaWIcJyqw66RYrFysHTdwK7XIuwaEY4THZZ0rSjs+nNFRSH6xZEsAACABAyyAAAAEhAXAlW6TlI6zkSmNT+WXaPDUqQT2wuj27ECsabSsGssOM6ko9OoLsyoOqyKBUuVgzXtmkixJiIstceZUDR70lFgdnAkCwAAIAGDLAAAgATEhVjnJlVpWLPNmuiwtJ2oFOmU1FQ4LhTWKcU4pceG51ITHdbEguNMOppVXTipuLC0vKhr5WAp8quJDkuRX9cqwpJJRYRUFGLt4kgWAABAgtRBlpm93sz+zMy+amYnzOywmX3azH7OzC7K3DcAAECfzN1XX+t8N252StJfS/q8pJslbZP0EEmXSrpB0kPc/asrb2O/SwfS+oiNqiY6jEq5VGk7Net3Xae0fGvF+guFdQpRYNXy0jZDNWLJOBWFqlhea5y4sHMUGH/XjhPz1cR/NZFfKXbsGhF2XSdi0lHMqoNyv6Hil9nKss/J2unu3/Trx8x+QdJPS/q/JP3H5D4AAABMXWpcOGqA1XpPe3vPzP0DAAD0pa/qwu9vbz/b0/6x4fV1fcNSrFGzTkmMfWqeS9xXqYqwNElpKaoqRYeFiHOx41H4PuPCKqUosBS91Uz+OU4UOKlrDhIRAuOYyiDLzK5UcznZXWrOx3q4mgHW6wrrH9C5E7F2TaOLAAAAEzWtI1lXSrpz+P4Dkp7n7v8yamV3PyjpoLR04jsAAMDaMpVBlrvvkyQzu7Oky9Qcwfq0mX2fu//1NPoATE52dJih1IdS7LNQWKcUBcbIsqbasbRco9c5XYgXO8d6tUrxX9Q1ehunmq9mEtGa61N2feykIr9JRYTA2jLVyUjd/SZ3f5+kx0u6SNJvT3P/AAAA09LLjO/u/hU1c2fdz8z29tEHAACATH1eu3B/e3umxz4A6l5pOM42x4kau+63FAHV9Ke0vCZGrLn+44WF9qQmfa01TrxViuRq1u8rFuzaNxXWKW2nZv1xUFGItSXtSJaZ3cfM9o1YfkE7GenFkv7C3b+R1QcAAIC+ZB7JeoKkXzSzayV9SdItaioMHynpEkk3Snph4v4BAAB6kznI+lM10zA8TNIDJO2WdLukL0h6h6Q3ufvhxP0D52FSk5TWbLMmChwnOuyqFBnVRIFdr7d4omKdkklFusvVRFGTiuEmNSnopCoEx1mntH4Jk45i40gbZLn75yS9KGv7AAAAs6yX6kIAAID1rs/qQmDGTTM6LG0nQ00s2HX9Up/HiQ5Lsn5tjROHTSqqGycWrOnPOOuUEBECJRzJAgAASMAgCwAAIAFxIVBlnOgwqrl2YcZ1D2v6XxMLdq2U7Bopniisk1VRWDJOBNY1Luwr8suYXHScqJuIEOsPR7IAAAASMMgCAABIQFwIdDbOtQ6zJy/tqmuMWOpP1+izpPSadK1APB+TisO6RoE1fZhmLFjz2K7bGWf7wNrFkSwAAIAEDLIAAAASEBcCY+krOqzZzjixY81ju+6rJhqqeQ0nGR2OU6VZkl3NN6lJU7s+tut2xtk+sD5wJAsAACABgywAAIAExIXAxEwzOozGqUDMqF7sOoHqOBWIk4yeMq6v11e0N6lYsOs2J7UvYH3gSBYAAEACBlkAAAAJiAuBFJOKDqNpxog1sqsjS/qMnqZZnTfN7XTdZg0iQoAjWQAAAAkYZAEAACQgLgTS1cR5NcaJ1WqiuiijKrDGpF6rcY0TdfVVpdh1m+Nsf5x9ARsHR7IAAAASMMgCAABIQFwI9GYWKhCj7GrEcXR9vuNudxzZUd00o8yMPgAbB0eyAAAAEjDIAgAASEBcCMyEcaLDaJxYrWtlX5/XGZyU7D71VbU3CzEoAI5kAQAAJGCQBQAAkIC4EJg5GRNyZsSI0TiRYo1xqibPR3Z15aT6TSwIzDKOZAEAACRIGWSZ2UVm9gIze5+Z/aOZnTCzW83s42b2w2bG4A4AAKxrWXHhFZJ+TdLXJV0j6Z8k3VnS0yW9VdITzewKd/ek/QPr0DRjxGhSkWI0qclXZ0VGxLZWIksAJVmDrC9IeoqkP3H3s0sLzeynJX1S0jPUDLh+P2n/AAAAvUqJ7dz9I+7+P+MAq11+o6S3tN8+KmPfAAAAs6CP6sKlY9SzfvwfWCMyYsRoUpFitNGjqmn++tvorzXQn6kOssxsk6QfbL/9wArrHZB0oPluV3q/AAAAJm3aVX6vk3R/SVe7+wdLK7n7QXe/1N0vlbZOr3cAAAATMrUjWWb2Ykk/KenvJT1nWvsFNq7sGDHqGn+tt3mQZ+HsB2JBYNZM5UiWmb1I0q9I+rykR7v74WnsFwAAoC/pgywze6mkX5X0OTUDrBuz9wkAANC31GP2ZvZTas7D+oykx7n7ocz9AagxzrUIJ2UW4rW1hCgQWIvSjmSZ2c+qGWD9b0mPZYAFAAA2kpQjWWb2XEmvlXRG0sckvdjMlq92vbtflbF/AACAvmXFhXdvb+ckvbSwzp9Luipp/wDGMguR4kZBFAisV1mX1Xm1u9sqX4/K2DcAAMAsmPZkpAAAABvCepsREMDUdI25Nkq8SPwHoMGRLAAAgAQMsgAAABIQFwKYEmI0ABsLR7IAAAASMMgCAABIwCALAAAgAYMsAACABAyyAAAAEjDIAgAASMAgCwAAIAGDLAAAgAQMsgAAABIwyAIAAEjAIAsAACABgywAAIAEDLIAAAASMMgCAABIwCALAAAgAYMsAACABAyyAAAAEjDIAgAASMAgCwAAIAGDLAAAgAQMsgAAABIwyAIAAEjAIAsAACABgywAAIAEDLIAAAASpA2yzOyZZvZmM/uYmd1mZm5m78zaHwAAwCzZlLjtV0p6gKRjkr4m6T6J+wIAAJgpmXHhT0i6l6Sdkn4scT8AAAAzJ+1Ilrtfs9Q2s6zdAAAAzCROfAcAAEiQeU7WeTOzA5IONN/t6rUvAAAA52Mmj2S5+0F3v9TdL5W29t0dAACAzmZykAUAALDWMcgCAABIwCALAAAgAYMsAACABAyyAAAAEqRN4WBml0u6vP12X3v7UDO7qm0fcvcrs/YPAADQp8x5sh4o6bnLll3SfknSVyQxyAIAAOtSWlzo7q92d1vh625Z+wYAAOgb52QBAAAkYJAFAACQgEEWAABAAgZZAAAACRhkAQAAJGCQBQAAkIBBFgAAQAIGWQAAAAkYZAEAACRgkAUAAJCAQRYAAEACBlkAAAAJGGQBAAAkYJAFAACQgEEWAABAAgZZAAAACRhkAQAAJGCQBQAAkIBBFgAAQAIGWQAAAAkYZAEAACRgkAUAAJCAQRYAAEACBlkAAAAJGGQBAAAkYJAFAACQgEEWAABAAgZZAAAACRhkAQAAJGCQBQAAkCB1kGVm32Zmv2lmN5jZSTO73szeaGZ3ytwvAABA3zZlbdjM7iHpLyRdLOkPJf29pAdLeomkJ5jZw9z9lqz9AwAA9CnzSNZ/VzPAerG7X+7ur3D3x0j6ZUn3lvQLifsGAADolbn75DdqdomkL0m6XtI93P1suG+HpK9LMkkXu/vtK29rv0sHJt5HAACA0Q7K/QYbdytZR7Ie095+KA6wJMndj0r6X5K2SnpI0v4BAAB6lTXIund7+4XC/V9sb+816k4zO2BmnzKzT0nHJ945AACAbFmDrF3t7a2F+5eW7x51p7sfdPdL3f3S5oAXAADA2pJWXbiKpZyz4oSwrx+SXnO7pEOZHUqwV/R5GujzdNDn6VmL/abP00Gfp2OvpG2T2FDWIGvpSNWuwv07l61X5O7fYmafao5qrR30eTro83TQ5+lZi/2mz9NBn6ej7fPdJrGtrLjwH9rbkedcSbpne1s6ZwsAAGBNyxpkXdPePt7MhvbRTuHwMEknJP1l0v4BAAB6lTLIcvcvSfqQpLtJetGyu1+jJuv87dXmyAoOTq53U0Ofp4M+Twd9np612G/6PB30eTom1ueUyUilkZfV+TtJ3yXp0Wpiwsu4rA4AAFiv0gZZkmRmd5H0WklPkHSRmpne/0DSa9z9cNqOAQAAepY6yAIAANioMi8QDQAAsGHN7CDLzL7NzH7TzG4ws5Nmdr2ZvdHM7tR330Yxs2ea2ZvN7GNmdpuZuZm9s+9+lZjZRWb2AjN7n5n9o5mdMLNbzezjZvbDy6tCZ4mZvd7M/szMvtr2+7CZfdrMfs7MLuq7fzXM7DntZ8TN7AV992eU9mfOC1839t2/lZjZd5vZ75vZ19vfH183sw+Z2ZP67ltkZs9b4TVe+jrTdz9HMbMnt6/p19qfw+vM7PfM7KF9920Ua/yQmf2lmR01s+Pt740Xm9lcj/3q/LfDzC4zs6vb333HzeyzZvbSaT2PLn02swvN7CVm9nYz+4yZnerj917HPt/TzH7KzD7S/p05ZWY3mdkfmtmju+y3rxnfVzTipPm/l/RgSS+R9AQze9gMnjT/SkkPkHRM0tck3aff7qzqCkm/puY8uWsk/ZOkO0t6uqS3SnqimV3hs5kn/4Skv5b0YUk3q6lWfYikV0s6YGYPcfev9te9lbXnKr5ZzWdle8/dWc2tkt44YvmxaXeklpm9UtLPq5ll+o/VfMb3SnqQpEdJurq3zn2zz6ipuB7luyU9RtL7p9edOmb2ekkvl3SLmvNsD0n6N5KeKukZZvaD7j5r/2T+lqTnqPmd8W5Jt0v6Hkm/IukRPf6+6/S3w8yeKun3JS2qeR6HJX2/pF9WMz3SFZmdbXXp8zYNfofcJOlGSXdJ7d1oXfr885L+vaTPq/l9cVjNNZmfIukpZvYSd39T1V7dfea+JH1QzSV3fnzZ8v/WLn9L330c0edHq5lk1dT8IndJ7+y7Xyv09zFqfjAvWLZ8n5oBl0t6Rt/9LPR9vrD8F9p+//e++7hC303Sn0r6kqRfbPv7gr77Vejr9ZKu77sfHft8RfuafljSjhH3X9h3Hzs8l0+0z+UpffdlWb/2STqj5o/lxcvue3Tb5+v67ueyfl2+1C9Je+PnQdL72vue11Pfqv92qLlays2STkq6NCyfV3NgwiU9a8b6vFnSEyV9a/v9q/v4vdexz8+T9KARyx8p6VT7+n9rzX5nLhIys0skPV7NL/j/Z9ndP6fmv4/nmNlEris0Ke5+jbt/0dt3Yta5+0fc/X+6+9lly2+U9Jb220dNvWMV3H2xcNd72tt7Fu6fBS9WM8B9vprPMiakjbhfL+m4pB9w96PL13H3O6besfNgZvdXc3T2nyX9Sc/dWe6uak41+St3vzne4e7XSDoq6Vv66NgKnt7evsHdz11Hr/08/Gz77Y9PvVfq/LfjmWpe23e5+6fCNhbVHKmRpB9L6OaQLn1291Pu/n53/3p2v1bpR5c+X+Xunx6x/M8lfVTNwPGymv3OYlz4mPb2QyMGAEfN7H+pGYQ9RNKfTbtzG8TSH6LTvfaiu+9vbz/bay8KzOy+kl4n6Vfc/Voze8xqj5kBW8zs2ZL+tZpB4WclXevus3ie0GWS7i7pvZK+YWZPlnR/NbHKJ939E312rqMfaW/fNoOv9RfV/Df/YDPbGwctZvYISTvURIizZF97e92I+5aWfYeZ7Xb3I1Pq0/lY+p3xgRH3XavmkCzgpgAAB0tJREFUH4zLzGyLu5+cXrc2lE5/H2dxkHXv9rZ0XcMvqhlk3UsMsibOzDZJ+sH221E/yDPDzK5Uc07TLkmXSnq4mkHA6/rs1yjt6/oONVHsT/fcnS72qel39GUze377X90s+c729iY15+x9e7zTzK6V9Ex3/5dpd6wLM1uQ9GxJZ9WcHzlT3P2wmf2UmtM3Pm9mf6Dm3Kx7qDln5cMaDBJnxdJA8O4j7rsktO+j2b7cW/Hvo7ufNrMvS7qfmuf0d9Ps2EZgZneV9Fg1g9lrax4zc3Ghmj+YUnPC7ShLy3dPoS8b0evU/Pd/tbt/sO/OrOJKNRHyS9UMsD4g6fEz+kf0VWpOvH6eu5/ouzOV3q7mF8o+NSevfrukX1dzuaz3m9kD+uvaSBe3tz8qaUHNSc071HyePyjpEZJ+r5+udfLv1Px+e7/PaAGHu79RTQS3SdILJb1CzflwX5V01fIYcQb8cXv7MjPbs7Sw/ecnFh7MZPV6wN/HnpjZFkm/I2mLpFe7+zdqHjeLg6zVWHu7Js59WkvM7MWSflJNNedzeu7Oqtx9n7ubmkHA09X89/ZpM/uOfns2zMwerObo1RvWUmTl7q9pz927yd2Pu/vn3P1H1RzBWFBzAussWSpfNzVHrP7M3Y+5+99KepqaiqJHzuoUA8GB9vbXe+3FCszs5Wpi2avUHMHaJun/VBO9/Y6Z/d/99W6kd6mp0ryHmqNvB83sjWqqO5+kJiGRmhP61zL+PiZop8Z4h5rqzXdL+qXax87iIGtpJL6rcP/OZethAszsRWpKmT8v6dG+hi571A4C3qcmRr5I0m/33KVzQkz4BQ1OsF3rlgojHtFrL77Z0n+W17n738Q72qOHS0dmHzzVXnVgZv9WzbllX9NsTTVxjpk9Sk2BwR+5+8vc/bp2EP7Xagaz/yzpJ9sippnQnt/7FDVHv29U80/kD6l5nR+uJu6Umsq9WcbfxylrB1jvVHOk9j2Snt2lwG0WB1n/0N7eq3D/UuVY6ZwtdGRmL5X0q5I+p2aANdMTTZa4+1fUDBLvZ2Z7++5Pa7uaz/J9JS3GSSbVRJ2S9BvtslHzUc2ipT9EM1Xhq8HvjtKJy0uDsIUp9OV8zfIJ70u+r729Zvkd7n5c0ifV/G150DQ7tRp3P+3ub3D3B7r7grvvdPcnqPmd8UBJJyT9bb+9XFXx72P7D93d1ZyQPeoEf3TUvqa/K+lZkv6HmqrlTgVhs3ji+9IP7uPN7IJYYWhmO9Qcrjuh2T45cc1oT2B9nZrD5o+LlUJr1P72dlb+QJ2U9LbCfd+h5g/Rx9X88lwrUeJS3DZrv8ivVfMH5p5mttndTy27//7t7fVT7VUlM5tXc4TlrMqfmVmwpb0tTdOwtHz56z+rnqNmnqnfWgNTfHxE0n+Q9AQ1f/yjR0jaqqb6l8rCMZnZZjVHrp6qJh15/vIZD2rM3JEsd/+SpA+pObn2Rcvufo2a/55/292ZY2hMZvazagZY/1vSY9fCAMvM7mNm+0Ysv8DMfkHNyc9/UXtSYjZ3P+HuLxj1JemP2tV+q1327j77GpnZ/eIJwmH5XdUc9ZSaQ+gzo/38vltNlPKqeJ+ZPU7S96qJUWa1avYKNSdeXz2rJ7y3PtbeHjCzfxXvMLMnqvlHeFHN5Jgzw8x2jlj2nWp+Bx6T9Nqpd6q796qplHyWmV26tLAdoP/n9ttf66Nj60l7kvv71Ayw3qbzHGBJs3kkS5L+o5of0DeZ2WPVlKJ+l5oZW78g6Wd67NtIZna5mlmFpcGcLA81s6va9iF3v3LqHSsws+eq+aVyRs0vzReb2fLVrnf3q6bctdU8QdIvtuX4X1JzLsWd1czEe4ma8y1e2F/31o0rJL3CzK6R9GU1E0zeQ9KT1fzXf7U6nPw5RS9T87viZ9o5mz6pZvLMp6n5rL9whudBWjrh/WCvvVjde9VcteB7JP2dmb1Pzc/dfdVEiSbpFT57lz77sJmdUHNaxFE1Ux08Sc3R5qe7ey9HZrv87XD328zshWreg4+a2bvUXPLlKWqmd3ivmn80ZqbP7fqv0OAyNg9sb59vZg9v2x9399TpSjr2+S1qPhuH1Jxj+KoRfx8/6u4fXXXH3sNlBGq+1Fzb6O1qrjt2StJX1JyYvafvvhX6+2o1FR2lr+v77mPH/nr7Ieq9r8v6fX81VwL4TPsDcFrN0Yn/t31OM/n5WOU9mLnL6qgZtP6umkrTI2om4PsXNXMg/aAk67uPK/R9j5oKyC+3vztuUXMN1If03bcV+nzf9rPwVUlzffenor8Xqpk65S8l3db+HN6sZqqEx/fdv0Kf/5Oao/ZH1Aysvqzmj+ndeu5X578dao4WXq3mPMMTkv4/Ndd0ncpnp2uf1cySvtL6V81Snyv662qmcVh1v9ZuEAAAABM0c+dkAQAArAcMsgAAABIwyAIAAEjAIAsAACABgywAAIAEDLIAAAASMMgCAABIwCALAAAgAYMsAACABP8/EI3M7EOA10QAAAAASUVORK5CYII=\n",
199 "text/plain": [
200 "<Figure size 720x720 with 1 Axes>"
201 ]
202 },
203 "metadata": {
204 "needs_background": "light"
205 },
206 "output_type": "display_data"
207 }
208 ],
209 "source": [
210 "plot_gaussian_blur_with_center_x(m2, m2_blur, \"p2\")"
211 ]
212 },
213 {
214 "cell_type": "code",
215 "execution_count": 15,
216 "metadata": {},
217 "outputs": [
218 {
219 "data": {
220 "image/png": 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\n",
221 "text/plain": [
222 "<Figure size 720x720 with 1 Axes>"
223 ]
224 },
225 "metadata": {
226 "needs_background": "light"
227 },
228 "output_type": "display_data"
229 }
230 ],
231 "source": [
232 "plot_gaussian_blur_with_center_x(m3, m3_blur, \"p3\")"
233 ]
234 },
235 {
236 "cell_type": "markdown",
237 "metadata": {},
238 "source": [
239 "Kết hợp lại theo phép cộng "
240 ]
241 },
242 {
243 "cell_type": "code",
244 "execution_count": 12,
245 "metadata": {},
246 "outputs": [
247 {
248 "data": {
249 "image/png": 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ORRhFfiWxYNjuYpatx7rSOI8FLypqdyV5Q+LCpSA6dB4dFsWFNgCst2/awb25eXFiuxcjXrcDuhacx5LLrUTJ/Iy9tl+Hfr05j9FqN1BbsRih0hDA+cGTLAAAgAa4yQIAAGjgnMSFkdq3Vzt4ackIkh4LbgTLg+22iAhL2k92zUceu9Wt3iLCK0vW1oet3S2vjQvXrXLNByBdGhAXetsHJt21418UF9o8kjfsQN/YfPSkfcHmXryx1vW5u7bZ7dxYg47Wpnk+8GkYYZdEgSWVtu5OQZ+ov2MuQgCLhydZAAAADTS7yUpH/mBK6V+nlG6mlHZSSj+dUvqalNLkQY0AAADOiZZx4d+R9OWSnpX0PZJuSfpsSX9V0meklN6cc84Pvvp5riiMBiCNYkQzJCKsHYz0ycnLV5588aR97dr1k/ajljld03O2+hsT2x4X+nKPCL2PVxSu9ioNfU7DsyvIDu04exVhb75Ciw49CvS40GPBm724c3die9XmYbzwRNd+brk7uHe8si+6DKsrByvbPvdi7zr0gUaja7W20jAasLREq8pBokcA09HkJiul9AYd3WD9iqRPyTlfP16+IukfSPoSSW+R9PYW2wcAAJi1VnHhFx9//eZ7N1iSlHO+I+nPH3/7PzbaNgAAwMy1igvvBVHvn/Cze8t+e0rpSs75xoQ+JqkuGpxVRWFULRhVFNq8cr6aaI7CFtWFFheuPfl81+VyFwV6LHhNXXT4WEFc2G93FYhjDUwaGWsAUo8yPTqM99niQtv/5WvdPl9f7mKvveWrvtOT2wqW9+YoDNpFfaL5DaONRXMglkTtrec0ZGBSAPOl1ZOse/81fumEn73M2p/QaPsAAAAz1eom6weOv35tSunkf9dTSsuSvsH6PaoJUkpPpZTel1J639Hn5QEAABZLq7jwuyV9maTPk/TzKaV/ImlHR9WFv1nSL0n6OGly9pNzflrS05KU0sfMqAIxWh61S2LEBhWFtdGhRYQrj3VVhFFE+ISembg8ig7jqsPJcaFHclH0ttqrLpw8MGk8AOnkisJ+RGjzEvbiwrr99P0JY83LXfOZg+46uXNgVYe1saCf65LXhoOURnHhjrWjWLB1pWErVBoCaKfJk6yc811JXyjprZI+pKNKwz8o6QOSPl06+S/xsy22DwAAMGvNxsnKOR9I+ubjfydSSuuSXqGjQXn+XavtAwAAzNIs5i78ch3V0P2d4yEdRlDyNkqqBVXQp2Tuto2gj1VyeRVhSXuk6kKfi9AHGo0iwsftYeNjFhFGy6+F0aHHhZPnNLx4q2uvdimckkddnsJ54uSH2eYTyHYM97u0UDc3fV7CyXMURlWQq1EVYeUciwfXuh199qBr392zuQ5L4sKo7YOOllxje15p6AfUr+fooJf8XkSvVdAniu9LokYqDQHMXstpdS5NWPY7JH2jjv78/8VW2wYAAJi1lk+y/nlKaVfSz0m6Kem3Snq9pH1JX5xznjSGFgAAwLnQ8ibreyV9qY6qDNclfVDSd0j6xpzzrw5ffUnMN2SdJRWFtdWFpjYWHDJ34WNdKnvlsa6c7LGgWtBjPo8CP1ofPGk/YXFhSaWh97n4QpdprXRjoPZH6/B2FBdGLC5MdmzXNr3dZWlXrnbtm5d9jkWfo/DsQUd9XsWo8vEgqIK8/ViXZT6/11VEas+uMY//SuLCrYI+a8HyojkNS6oLo0rD6JMCtTHfmNWBVBoCGFfLD77/ZUl/udX6AQAA5lmzz2QBAAA8zGZRXThA7e5G/UvmWStZZ0m8GMxRGM1XWBsdFsSIWxYRXlmaPLdgFB16FeETvfbkCsTe4KW3uixwzWPB56ztyz0O87hwP2hHVoO2xYV+3FZe6NpXr3WZ2cbVD3TtzS4uLBl09NBOsA+IehjMq7iz1FXw3X6s67+9/VHdSv34eLvk2iipNOwlddGchlF0OGTO0NrqPyoHASwGnmQBAAA0wE0WAABAAwsQFyaNs5slsWBJtWDU39uVFYVRRFjSJ4gIfdDRrc2b1uXD1j47OuwPNBrFiBYdvtDlfyu/Yfv2jLU9InwhaEdxYa8CLuDHKooLbQ7BaB/WbB8e/2jbaX+tOehFgReC9uT5E3fsmtnZtPZjXZ+72/YGSqLD6Jrx/lFs7X2KqmtLBimtHZg0ElX+RdWBp9c/7apFAA8rnmQBAAA0wE0WAABAAwsQF5ZoMQBptP7aOQ2DxUPmKyyoLtzY6qrhfK7AKCKM5hzsz2lYGRF+0NoeFz5r7Sg6jAYjra0u9OMZxYVXg21Z4eCKJUyPa3J0GFUO7gftKC7cteW7V7r2C1fsDXSnrj4iLKku7IkG19053fFYySClJQOTzguiQwAPhidZAAAADXCTBQAA0MA5iQtdyVuqrTSM1l85AGlJRDgoOuyyLq8o9LiwHx1+OGhHMaJVGt4qiAijdjQYqS9vXV14LVinbyuYJ3HFzum15e4N7G92VYT9KHByLHjTTp63t+0cra92kdwLdn61ZW8yuh6GXG/hwKTR9b9IA5MymCmA6eBJFgAAQAPcZAEAADSwwHHhkMFFS9ZTO0dhQUVh7dyFlTHimlUUbtgccxettCxu18WIa14h6JWDUUQY9S+JC73t6Y7HeUvW9mO7GbSjCDKICHtsW2sWR155aXd8buqitSfHgtG5uNE7d915uWHnd8/jwrFiwdEGJi2Z37B2YFLvU1Lt16oikEpDAOV4kgUAANAAN1kAAAANLHBc2MKQwxHEjiVxYW2M2KsmyyfNja0uptnQzpntftXh5OjKI8JHn7dcraRCsCRSDAYjzdbetWhvt6C6cN2O1brFeckHIK2tWIyqFK396OVuRTevTo4Ob+jRk3ZU9entDRth1s/vnp13bVn1X21EWD0wqSuJAj06LFlPFMG1qggcst7oowbEiACO8CQLAACgAW6yAAAAGnhI48IhgyUOmK8wimkGVRp2udeFpa5dEhHG0eHkdvK5BV+0dm10GPTZseXPW0WhB04l4dP6rcntqxYRbkTJUHS+oipFiyD9+Fy86sd5crVgdI5WLcv0tp9fP+9aK6g0LLn2isYTLZnr09XOYzhEFP2d3s/WkSQViACO8CQLAACgAW6yAAAAGliwuLAknpjmtgr61FYRVlYgrqzdPmmvqmtfsPa6xVW+vChSvGWldx4XDmkHEeEzFu15olgdFwbtO7b+J2z5hrXDKkLf/0vBcmv7cbu4eXZEGJ0vb3vs6Of9ThQXjnSNxfMYupKBf0vUVuy1iuZaRIeOGBF4GPAkCwAAoAFusgAAABpYsLgwUvI2osf20fxr0fqjPhajVFdsFfQPqsaWlrso44JXogUVar1qtSBe9NeuRXMIRu2CuDB7LBhEhC3iQrfiFYg+YKnvcxQXXrV2cBz8uF3YnHzM+1Hg2dWFSxZbrVp14Z0GMXRPmJzVzulZO4+hgj6upEz0dJ/aiLHFQKjEiMDDgCdZAAAADXCTBQAA0MACxIVJ060qnJIhFV7WXl4+tMVd2ysNl2x5FAv2I6quT2+OP29HcWFB/5vW9jFNb2ry8tq48E7Q9qvIY8RLtya3x3rvfjyj+C86R0vRObXzPta11DOzvwwl8xhGohjw9JuJBiqtjQ6jdQ5BjAicJzzJAgAAaKDpTVZK6fNTSv8spfSBlNJuSun9KaV/mFJ6VcvtAgAAzFqzUCCl9E2S/pSOhp78x5KuS/ovJX2RpC9JKf3+nPM7Wm1/OgrmK2yx3Hhs5NFS3O5ijeWgj8eIsrFIe21PRw6DPkF8tuttPXjbd6HkQg7XaftzKYoI/X35+z0I+lj7QlDRWRsj9tolcWGk5HorWmdJFWHJ3IXRPIa1HxMojQFL5ziMXj/JrGJER6QIzJsmN1kppSclvVXSM5L+65zzs/azz5T0I5L+oqQFv8nCrOQJ30djkQMAMAut4sKPPV73v/EbLEnKOb9LR59x/qhG28Y594OSvk/djVY+/v4HZ7ZHAAB8pFZx4S9Jui3pU1JKj+Wcr9/7QUrpMyRd1FGE2MBYc6iNpEV0aO0oLnQlEWH02lBJ8mPtbO07k7uEy0/3uSXpPcfLv0DSD0j6CUmvOf55FFCVbNf3M1W+xyhCXT60b5ZUZakkblq253rLBYPiqmD5zJQMTBopjQGjysPagVCHxIiOykTgvGryJzbn/HxK6U9L+j8k/XxK6R/r6LNZv1nSF0r655L+cItt43xLOvpQn3R0o/UTx+3XHC8nMgQAzItm/x+bc/6WlNKvSvpbkr7KfvTLkt5+OkZ0KaWnJD119N3VqBseUvdutN5jy7jBAgDMm2ZDOKSU/pSk75X0dh09wdqU9N9Ier+kv5dS+t+j1+acn845vzLn/Eppq9UuYkFlSd9/atn36yM/DD+PlnRw8m/uLNu/kuXxD4I+Jf+i167Yv2h56b6VvKbkva0U/CtRu91aQ/YNwBBNbrJSSq+T9E2S/knO+Wtzzu/POe/knH9K0hsl/UdJfzKl9LIW28f5de8G6z2SPk3SXzr++h4tzo0WAODh0OpJ1hccf33X6R/knHck/eTxtj+50fZxTiUdTYnzGh1dZOn462uOlxMZAgDmRavPZK0ef42Gabi3/Hbw8wGiSpoZPR6PUqGRlh8edOVqh0uTS9cO7TQfWHnbYdAuUjkvXrJ2VP9ZUhe6LOnzdfTEyushv0STb7BK1u/LU8n7ingfO5wHS36c56Ccb8iApaGVoF0y22S0HlcyaKh7kLkLS18/ySJWJlKNCLTW6knWvc8kP5VS+s/8Bymlz9NRwrMn6V812j7OudM3VDzBAgDMm1b/W/29kv6FpM+W9O9TSt8n6UOSPlFdyvNncs7PNdo+AADATLUaJ+tuSun1kv6opC/V0YfdNyQ9L+mdkr415/zPWmx7uoJH9Y0jQl/eiwtXz44CS6LD2ydpr6Q1m4RvzfYhiMbC5bbKZVu+bpnfuia3ozkKo+UegkTr9PZysJ/h+/I+0TGxth/z6LwcBOeoKF48sOd4JenRaIWNJc8Pa+cudCWDho75MYAxI8mx1x9pMWfi/eZ9BFCj5ThZdyR9y/E/AACAh0qzcbIAAAAeZnNQ6nSWrO6R9TkaQK8kIixoHxxMjpz2deGkPSQ6jCK/MDLbDNrW55KNL/viC137onWPQoqSWjWPAi8Fy3t9fLzbtaAdvd/omFj7tp0LPy9xRFgQ+x5UVoMOEVVZHkSdauMmf633L/l9bxUdjrW9kkhxmpWJtTEiFYjAEDzJAgAAaICbLAAAgAYWIC4cS8lje+8T1bdFj8l9Qpeg2mukiLBfXTi5Es3b+5ZdeVzlMdauNk7aO9a+s/n8SXvF47AoFozal7vmyq2ufc2KFw/2rW0vjYa2jIKlKBb0qcav2XtZifb5UrC8oJ0tUuwf89WgPTneDWNfjwtrr5/m7jeU7Fl9hsRZQyOsMAtttL0atQOoRkreYwkqEIESPMkCAABogJssAACABs5JXBgNS1kiihGj6DB6bbA7Y7X3Jrfv7E2O/+J2F1F5LBjGiFtdLLCyZe83iALD5RYRartrXrL3eMfG/1+2QUpv2ktrqwt7caElbJd83zwW9OVbwfIoLrT+O5vd/79EseCO7V0/0u3aUex7UBsXNheFuiWDkbrod80NjfXGisyGGGv+xCExIhWIQGs8yQIAAGiAmywAAIAGFiwurB2oMDJkAMOSEsGCdY4WKXol2uQqwv0govK2VxfetGFBd5a65Zcu2cihHp/ZYl2z9q2gbVWEzl+6YuvcsP47k1/as2Htdasi7EWE3vYNewliFCMWRIc7q8HxtL2LYsSi9p69sSEVrLWqByONKnNL/vT0ZpiMNlzQ5/TvY/R3ZEg8VxuNDXntIsaIRId4OPEkCwAAoAFusgAAABpYsLhwVkoGKQ00qCjstbe7R/K3960q0OIqjwKjdhRp+fIrl7sMby2Kz160tsdwHhEWHLZLloZdtKjxwKoO79h6VuxKXrbCu1QwOGp/lNKgXVCNeMeW37RSw51goNeSdjTX4V2vLvTroUUsWPRXwgbgDV8cxVYlNaPO1/Mg0Vb0mpJYrSReHCtSrNU67hxSlUl0iIcTT7IAAAAa4CYLAACggQWOC8eqNIzWEz0Ojx51B+66NcwAACAASURBVOsZEgtWRoc72xYzrU4edLSk7RFhr73ZRWBrV21E0aiKsOR9Ob8abT3JNrViceFKNAatJWmyOQTDyC+qLozaQaR483K3MT9uu70qzsnVnd6OBo+9fWgZakl1YYtBSov+YpT8HkWik1py0UTrGfqaIfHiPESKtfsQvdYRHQIleJIFAADQADdZAAAADSxwXFgiehQ91gCkd87uUxsLlvTfnrzc5zGMoihvh7Fg0L6hKyftjWvdsKCbt+52OxENOlqSxHi05+vxqsDoWEUFbUOqC58IllvbKwr9+JQcz91gYNIoxr1t51d7Vs03JCIsSXqiYxsmRrW/X0MGBJ1lXFjSf6xIsaTPWGqjTKJDIMKTLAAAgAa4yQIAAGjgHMaFJVFC7Rxq0fqjR/7ZuhfEOiWDjhYNTNrlbbv7NuhoOI/ejYnLfSDNXkRoMwdurFr72vMn7VQ56Gjv8FvBXBg7+vuNeOzo66yNC33545OX37jcHSs/httBRBhXd0bVhTYHZVRROM3BSIviQhfFZdGLfXk08G80OHBp3FQb/5dsY0hVZOtIzpVEgUOqIIkOAceTLAAAgAa4yQIAAGjgHMaFQ0SPzKMIIxI8Yh9rvsKo0tDaPjDpTjCPYT/SumltX749cbnHiOtXu+jw6p7tXO0ApCVxYW11YUlc6G2PBYOKwhevdee0tqKwpNKwP3dh9wbu9KoLbd+GRIS1sWBUDerCSHclaFu8HsaCCvq4MePCkm3XxoslkWJtjOjGitvGGryU6BDgSRYAAEAD3GQBAAA0sABxYVb3qLmkWnDIQKMlr40e/0cxQmVcWFI1VhAj3tm2AUivnT1H4VYvCnz0pL2hXWvvTGyvW2S2eu3Zk/bmoQ1S6qJ5Bj3Oi+ZDHFKxuGVtn7vQ40KPCC063LOKwg8vTY4IS6LDnXAeQ48Irbpw3yPCxvMV1kaEB8FyBX3CfbAK3DBSjORg+f3e8JAYsqS6uDYWHCtGnGasFh2r2orFEkSHWFw8yQIAAGig2U1WSukPpJTyGf8OW20fAABgllrGhT8j6RuCn71G0mdJ+sGG2z+lZADSKHasrUYaMI9hbUQYVBdqu9v/3Vs2X+FmF11dtIrC3V6MOHmAzSgau6DbJ+3V1a69dLUbpLSXJkVRlO9/FBeW3JZ7BOkbjuYxDAYjveODjm52meJ2dUQ4ubpzvzdfoQ9G2i3f94rCkoFtaw2pLvT4dS/o02IuxV6fFHS6X9RYG0PWxoi7QZ+S2DFaz5AYsWQ+xEjtxymGDHZagugQi6XZTVbO+Wd0dKP1EVJK7z1uPt1q+wAAALM09c9kpZQ+SdKnSvqPkv7ptLcPAAAwDbOoLvzDx1+/M+dc+ZmsknkJS/rXPgKP1lmich7DBnMa9gYm3fSKNosRNTlGjCPCfWtbXGjLlza703tFL5y0w+jQKwGjmM+vmOj0RnGhrz8YjPROwbyEHw4iwu0wIpxcRRhGhF5d6BWFJQOQlgz6OtagoyW/XmNFhLX979enZD8OoopHBcs9XowGI/X4zze2Eyz3gxq9NurTQot5D8eakxGYT1N9kpVSWpf0ZZLuSvqOaW4bAABgmqYdF/53kq5I+sGc869HnVJKT6WU3pdSel//U88AAACLYdpx4VPHX//G/TrlnJ/W8YfiU/qYaLTBkdRWHXoUUDswYcHApAMGII2qDu9aXLh7xdqrZw9Suh4MQNpvdzGFx4hLnu1Z5NeLDj3ai6LDaO7CSMlgpLY/d2xg0puXu2zMqyw9Kt0uqCKMlhdFhIfd8rsHdoCmWVEYRYS1seDQaK9G6XpqI8yi39WSAVX9b8duwXKPEVeCPtHfrNqPVtQqiQJr1UaHVBpi/k3tSVZK6b+S9GpJH5D0zmltFwAAYBamGRcO+MA7AADAYplKXJhSWpP05Tr6wPt3jrPW6HH4kHkMXRQjlkSHBY+6G89d2G93UYYPbrmzenYF3K4mR4pRNObVhatWdejRoVcdeq65VhsXRpdAtB5r37G4sB8Rnh0FllQO3rb4r798dWKfQ9vpw15E6HlqgSGVgyWViWNFhK0Nri4M2iW/k0WR4oa1/RMR0YkqqSiMIsWI998Je52tpFq7Nl4kOsT5MK0nWW+W9Kikd97vA+8AAADnxbRusu594J0R3gEAwEOheVyYUvpESZ+uhfjAe+0gpVF0GPUpqC6sjSkqY0Qf3HL/8uRIayeMDicv90pDX34higt9py22O1y2isXluyft5O+lcu7CbHHYvsWFO5uTI0KvHIzmc4yiw+i4eSy432sH0WEvLrRf0doYrjYWjF471uCisxj6WCqPDltE+EW/n1GMGFUdRvMVlkSEUdTo2x0St81DdAjMj+Z/9nLO/17xLK4AAADn0tTnLgQAAHgYzOoB/oIpeURdMhhpwepbtC2auLs3edDL/SVbHsRYHm9F0dh6MDDphV7VYTBgqcV5h8v22tXueK56pWHA07bba93/R+yvdu9lt7KCMj4mk4/boWWW/fbyxOW9/Z9mRWHJOltUDkZ/eWr/Ij1IktQ6IhzStkGE42rE2qrpkvkNW8x7WFuuOlaFIJWGmB88yQIAAGiAmywAAIAGiAurDclLbNBBjwJqq7QGVSB2j9JvW3R4uNldClFEeDuIxvqRWRQ7etTYRR9LUbmgJ2YWbx0uW5XiwV1b/oi1uxcfLHXtaJ+jdlwVGAwiajvtfQ6C6NAdBMu1bCdv2WKQ2sFFS5KbkuhtSOVgtK2S9Qz5S/Ug1YUN5hAtWr4cLPf+vTjsUrB8yECjUXQ4VvQWDfQ8pOw1QnSI2eJJFgAAQAPcZAEAADTwEMWFtTlHyRyIDQbGGzKvWkl/X3wwOcaK4q39sHpu2fpcmNjndrDc+3sFovf3JG1pqXszy8uTo8aSiLBfCbg8sX0QVgVOjgijKLDEssWm0ftqEhF6n9oBS6N1RstL2iWvrVUaF7aICLeC5X6s94Ll3n85WH6wEnQa6097NGDpkLkOXRThEfPhfOBJFgAAQAPcZAEAADTwEMWFD4EB88pFUVfJ8ig6jCrmouX91/pch0vW7t6Ax4LRekrivHj5+L8e/Tkcg7bFhY+sdfM/3l2zCGXeqghrY8Eh7ZL9caffS+0ApGNVEZa0a49Fb7s+eGk0l+oQHh2WDIhaW/0XvTaKDqk0xPzjSRYAAEAD3GQBAAA0QFyIcy2K5KI+/eV1JXa+nuWg7evst7s+F1a7iNCjw7trFnF4dFgbEY5VRVg7T+KQPmFcmIMfmN4cgBoWF44VC96wdvT+a49Lb1vRvIe1hkxc+SBZ7qTXthikFJgOnmQBAAA0wE0WAABAA8SF58mAaqz6KC2qkrPBQoPXlkRpy8H6lw+DgTr7O3fmdqN9jvehW75qg6Ze6LVvT2xHMeKq9VkNBmLd2OoGfbxpg8fe3fITadHQPEeEaxbtrXXv9xGLRC/Ych+UdSloP4hDH4TX2vt73QC1d2xeT9nyXgxXGwtGg5FG0aEvr41RXW/ew5KqQ79AavuXLC/RepBSKg0xHTzJAgAAaICbLAAAgAYeorjQ32rJQHolfRocviGDPZb098XLk+Oz1SDq8kgritui10bxWbRd7796aNs9sNce3P3IN6V+nHTo0ZLFiNH8jLdtu7d7cd7Zy/34eHvd5nnz+RN9Lsh+H1u+2i0/3Or202eOu7vs0ZZdt9FcfEMGGu1FgZPjvxUbQHXVll+w5ReWLBYMrpP4ehsYF9ogtvurNqfmZvdGdywmu33YnY+d7W753rZV7W3bgfGYz+cujGLBIRFh7Z+g7WjAUr8oSuKzaFDQqI8bkldTaYjFwpMsAACABrjJAgAAaOAhigvHMuSQFVSBlWxq0MCP3SN8j2+igTFXw+o5j8Ymx3wXgiq8DQu7PCbz127s2/K9Lhb09C8FSUBe7vrvr1qk6MfHokOPCzfCGNFjvq5dUl3ox2fD5n+7bbFgf1tWzeY2bfftQOxYbHXocx16hZw7iPJjO6A+f2JQ/bfq8d/q5Jivd0575/rs66ckhi4ZMPb0HJTRfJYe0+5alLa/ZHHh5W75zcsXu/773Tm4eaNbfve6nTSPDr0dRYdDYsFIb8DVKDqMYj5fHlUdRvMb+oZr3wyVhlhcPMkCAABogJssAACABogLi5QcppWgXfDa2iqiAYNDPhJUeHmMFUVgq5rc32Ogfrvrc1E3T9rrQR+PCDdudTFf8iq5fZ0p2Xtfs9eu7ndRwNKm5Y5BOucViFHbo6h+e3IkFUWQUZ/I8urkuQ5v79s6baDNg4PJ6ywZ8NOvk9WCyHgjioArl5dUF9bOLyn1z9PtoNqzFxfa8puyiNBKAW+udss//MSVk/b2Vrd8+3q3vDf35JCPDkRK5mfstf1v1qVgpR6l1W6gtmIxQqUhFgtPsgAAABrgJgsAAKAB4sKe2sMxYFDTseaDi/oE7V51WGUUuF653CPCfnTYTaZ28VbXXnvB9nk/aEcDbEanzt57svVs2kCmS5s2uZsVg0WGRISHQewY6Q/o2r2BXW3Y8u48Hq4uTWz7tqI5JUsq+y4UXSeT4+CS10ZxYbRv0Xu5n/2gqnPHjqnHiL7c48KbViK4Zcv9/d/YfPSk7dW8N9a66PDuml10Yw06Wpvm+SCovb9Z0YClURQY7XT0t/JOQZ+ov6NCEPOJJ1kAAAANNL/JSim9JqX0j1JKv5FS2j/++s9SSq9vvW0AAIBZaRoXppS+TtL/Ium6pB+Q9BuSHpP0yZJeJ+mdD772aNdLHjmXiNYTLa98tj8kIiyIBfvtbo45H0CyLAqM4r/Jy7es7cuvWB7Riwift/28ZW1L8MLosMRq0Lb1WIKqpYNuw0uXJ1cjjRUR1saF0TyP+70qxcn7VrL+koFnPebrz884uZK0JEaMBimNosbl4JiUio6XVxR6RFgSF95QFwv235u1l+x9PtG9n+eWHztp31m2yr7oz0h15WBl23/3enGhDzS6HPSprTSMBiwt0aJykNgR42p2k5VSerOObrD+haQvzjnfPPXzse6GAAAA5k6TuDCl9Iikb5K0I+n3nr7BkqScM/+bAAAAzq1WT7JeLemlkr5X0odTSp8v6ZN0VBv2kznn9zba7gMYUCEYVs8UDEbaOiK0udEe2bLIZjWKbybHf14J6H168Z/1f9SWF0WEz1n7VkE7SiBcdGy9ctDXeblrrtg6Hz20UsarvfKr0XkEttyrKJwc23l14Xo4OOrkuDAazHOsKsKSWDmKp6NKw2jg0+i9nJ6v0EUDkNZWFPry6PckikJ9v5evde3rNn/k3sHVbqdLrvvevIQF7ZL+B9H8htEOlXwOYlZzGjIwKaav1U3W7zj++oykn5L02/yHKaV3S3pTzvk/Ndo+AADATLWqLnz8+Osf0dH//ny2pIs6epr1w5I+Q9I/jF6cUnoqpfS+lNL7+o8cAAAAFkOrJ1n3coqkoydW//b4+3+XUnqjpF+U9NqU0qsmRYc556clPS1JKX1MVxpXvbtjTQQ25LX2uH3IAKQD2htbUVXX2XMLhhWCwfJe+1Y3uujas7ZvHhG+aG0fjHSsuNArCn09Hh0G1YvJ1n/1oMtQlq/ZGzi7gK9IVDEXDUbqkVntAKfRtqL5BGsHni1pl6wner/hwKSHkysND5b6x+S2XRReaXg7mKNwx2KyG7pi7ckVhf0qzSAijKoiLbp+5qC7kO/4fILR70BtLFiyvJeSt640VNAHWFytnmR9+Pjr++0GS5KUc97V0dMsSfqURtsHAACYqVY3Wb9w/DX6tPC9m7D14OcAAAALrdVN1rt19Lz341JKFyb8/JOOv/5qo+0DAADMVJPPZOWcr6eUvkfS75P09ZK+7t7PUkqfI+l36+gTOD/UYvuT1Q7VUDKC+4BhG0o+V1XSfytqd591WN+sG427N4FzwQjuV04eTEpX9rvl4VAN/vks/xyWfz7LR532z1L5R1lKPpPlH8fxz2FtBcujz6bYdi/550hG+nxW9Dksb/sQA/4ZIF9e8pmsaAiEaPiEkiEZonY0A0A4TMi+fRbsVjeJd/Lz4p+hC85Rz9Kpz/2s2crsM3t7dh3sbHa/ZP2hGs4e3iQaOT867pGDa925fPaga9/1Ha0dtuFKsLzk79HerIZzKBlSkdHfMZ9aTqvztZJ+p6Q/l1L6DEk/KeljJb1RR38Ovyrn3HbwIQAAgBlpdpOVc342pfQ7dfQU642SPlXSTUn/VNJfyjn/61bbBgAAmLWmE0TnnJ/X0ROtr33wtSSdvZtDpkGsPQQlkWKwP0OGcKiMGle2zh5RO44CLf6LhmcI2pvPdRGPnrF984gwihGj6LAkKnLREA5+rDwitOr43raiaNLaY0WHtXFhNFJ52Sjvk4eFiEZ293bRBOAlfV7oDvSKn+toknBfHl0DJdeD1L8O7PpYs2ti7XK3f49a++LVye8tGtk9Gm6iZAJxP6+3H+t29Pk9+5jrnv2t8eNVEh1uFfQJ/zyWDOcQRYHe30URYcnf99o4j9HfMR2tPvgOAADwUOMmCwAAoIGmceHiiaoFa+PIykmhR5oI2tv9Ud4nVxFuBZVSJVVjHv08+rzlC1EsGI34XjtZdFE1mbWjCaKjikKPoqK4MNjukOgwjgu7DZeMVF62/smTQkcjuxfFf1Hl6QvdddWLBaOYuGSScD9Hfu4ip38dS64JG3k9WZx81WLOjcc/dNJe3ZxcORiN7O4TWB8E0WHvfC9ZPPxYd+63tz+qW6nHhd4OK5CD/mF1obV7E0eXVFmXxIhRRDgkzqNCELPFkywAAIAGuMkCAABo4BzGhSVvaaxqxOgxeeWk0CNNBK2t7nn++mo00KhXREWRUJcdRFWEF/e7/imK/54N2kPiwiHVhd62OGhQ5VqgNjqM4rwoLowqDSNRRFgyKbTHyo/aNdBbftgtv/Scvffo/A6ZJDyKdyOn/yREA9QGcaGuTd6PNdv24x9tb9Rfa8LKwSD63dGGtbtqvp3Nrr19pRsoVdv2xyCKDsf6W+PrDCsNV4LlrQcmBeYHT7IAAAAa4CYLAACggXMYF7qC+QSLlMxdOGC+wtoYcWvy8pU1j352z2yXVBFGVWO9QUdLIqFo7sIoOiyJikqqC4OBJ3vr9HinsqIw3K6dR48ODx6fPJNUFBF6dZrHR15duB4O7jh5/f24sMu8orn4wirCW122VzRPZXSuXyhoT2Ew0t51EA2QGsyjuWLbflyTo8OwcjCMCCfHhT6X4vaV7nxs37A3VlJRWFtpGP4JHTJH4dnXbl9J1SHzGGJ+8CQLAACgAW6yAAAAGljguHBIhWC0nuhRd8l2B8xXWBIjFlT8+ACkq7056SZXjZXMY9hr37IcIYqHSqoLK+PCO9betbjwTpAErNhxW/c4NYqGCuYo7IkGO43a1v/RZXsDVydHh265N89gF/N5RVp/MMtuw1HsGFUURlWovWpTH1y0drDZZ4LlUaXhdrC8ZH5Jd/pXuaTitKT6NODX37Xl7o3ubEZRYNf2KNDbFy3P83Nzc9PiQq80LIkOS6oIS+ZSrR6YVEGfaGDSKJ6L1kmch/nBkywAAIAGuMkCAABoYIHjwmmKIsKCw1cSJw0ZpHSrezR+YbWLgdaDGChql8SIa1HlVxT9RP2DOCnb8uet/4sWCXktUpgOWf91i3cuWWx3yZav9OZki1Za0F4K2tYnWfviUrfhw8u90R3tpZPnNPTqwsOC67BkvsKtKCa2+fqKIsIhg9BGVYcFg5Hm4Nyl+8WFPgBpyXyZkeDcr9n6r21eP2n3qwW3gnYUHXoVaPcbsWYfF9jbsj8StbFgSUQYFvBFFYVRnxKt5zFsUY0IHOFJFgAAQAPcZAEAADSwYHHhWBWFLQYmrVznkBix1+4iJK8o9EioHw9NHpg0nN/wsFsezjFXEiMGMZBHhM9aH0+Qblrb48Kohsivkt6sah472j5ctYSgq/NSWSwYVapFsYz18Sq0i8sWF3qE1dusVwhOjgt9wMtoPkS/HsKKwigirB1gtnZg0qDS0CtMX7RDdWDntOR6kKR1OwcX7Vin2jkyo+vAz7dVKV681B3Ti5cnV29GA8IWtbe63469seYoLPkb1FNSfR3NYxj1J8LD4uJJFgAAQAPcZAEAADSwYHFhZMjbqBxctKTPaFGgtYPH+T5f4QWVtKNBSncn9l/fthDGC+CiSqySGNFiIK8ijBIkjwstvCwqBPT4L6xMtP33CG/Fj7lHQx7n+fuK5peMjo8XgFncdLhs79KW7wSVhoe9nZvsQhAl9+JCjwijaLhkzsEoJo6iw2CdL/q1YfGdn8eiatNT3/cqTq191dq92NiVRGw+kKm9txWPDi97RGjxfBTbF8SFF5bsDazZ7+2a/Z2qHWi0Oi50JfMYRtFhyQCkDDqK+ceTLAAAgAa4yQIAAGjgnMSFJQbEf0UD7KXJi0sesQ+IEZeWu5DEK8j61WSTo6KSGHElqriK4sKC9o7FQM9bdViUMnlyVRsPef9LBf3XLRK9FlUOejQWDWa5HfTZDPrY+jeW7560D5ftoFsq6IOU9ucunFxduBqd61sWEZZEwL68ttq0oP9zPiBtcG1EcWFxdWH0entvT9jyDb9AomsiipCDiN3nAd3YDOI/O0/rQZx/IagifsQ+RnDX48KxIsLwvxwP0X9SgAI8yQIAAGiAmywAAIAGFuDZbtJ4g5DOkZGiw2UbjHQ5iIeWgqo0jxSXewNddq/tRYRRO5hLrrfcopJdWx5FP1HbKw1L4qEoUvQ+vs51i6h68xtG7z2aU69kuUdMts5k/S+sepWY9V/y5tlxYa+6cN8ipuh9bQfLo0i0JDIOYscXC+LjF4P2A1UXWju8hmz/1m3/UslchwVtP+4XNrtvovjPI/8LQbt3vte65XvLttOjxYJBn95JqP2Ixm7Y6+zXlpx9BjXF9PEkCwAAoAFusgAAABpYgLhwngWHr3Z6wwHLl5ajKPDBI0Lv03vCvhe0S6JDW09vDkHVtT3eOShY7vxw+qCmUbWZx5orBe+rt/wwWB719/VbuuOx0qGf66XJ7zI814d2rve66sUUva9o30r6F8Sp2ePjgoFGPSKM5rKM3O+PXBRW9do+SGlUbVt5HP24R5WfF4LIfzloe6ToHyMYFP9Vx4hBlXXvSNf+Z4cBSLG4eJIFAADQQLObrJTSr6aUcvDvQ622CwAAMA9ax4UvSPqWCcu3JywbyZDH0g00jg6juLDXJ4gIo/493iXK4QqWZ2v7A/+S9sGEdj61PKsLKvy1UW1RSdR4x7+JjkO0vKQdsT7J2ksHXczXi4OCqQs9Ilw6sIjJ97lk/2v77wXL7bW2O73otjYynnTu/FqQjg5PSYgVbtvez0b0Pkuiw+A4RlFgf3Dhyt9bN9pchK0NqTQE5lPrX60bOee3Nd4GHjL/Ukf/HfscHf3HM0v6YR2NcPC62e0W5sAP6+g/z1+k7tr4Ph197u7zZrhfAB5OfCYLCyXr6AbrvTr6D+q9G6x/c7w8z27XMGNZRzdY75H0/cfff7+kHztezrUBYNpaP8laTSl9maT/Qkd1Rj8r6d0558rn3ZgLtRFnA0nS64/b79XRzZUk/U5Jv1sLPHRt43ESPWqs3ofKiDNc7pFxwTqjOrL7JbGvl3RXRzda7zle/hpJv0eqDdkmG+svl8fAA1Y65LUA2mv9n8cnJX3XqWW/klL6ipzzj0UvSik9Jempo++uNts5LKZ7N1rvtWX3brDwcEuSvkDST9iye9EhAExby7jwb0v6XTq60dqU9Nsk/Q1Jv0nSD6aUXh69MOf8dM75lTnnV0pbDXcRiyhLeuepZfeiQzzcsqQfOLXsXnQIANPW7ElWzvkbTi36OUl/JKW0LelPSnqbpDeOv+WSGe2maEBFXrjc2ocHNm/d0uQys0M7zQfBPHeHUYlasLh27sVkbT8TJW1f5ZKO/iP6Xkmv0tETrXcef//I8ffeP1pPSZ+V0xuOduisDZRUcQXLsy0/XK77f6IDux68ClX7QXQ4JA6uvB5WCtYZ/cZGh/PeZ7B+QkcR4Rcdf/8eHV0bb9QIT7Si34dafl4HrHTIawG0N4vC3W/X0U3WZ8xg21hwSUdVhJ8m6XPV/4zWmoiFHmZJR1WE926w0vHXR46Xc20AmLZZ3GQ9e/x18769gMDn6Oipxb1nM/dutPiPKH63+uNkJY30BAsAHsAsbrJedfz1/TPY9siCbK9FRBjoxYWrZ0eBJdHhba12G1izkRXXbMN+5awWLLf2uiUc65Zi+RyC3i6pMnO+C9E6Nwr6rPv79XbJ+y2JEb1/sH47vTpc7r7ZX+o6RZGRV575vIf7q11c6Kc33M+loB31j64Te629lbJzYe2ourAkXizdRngdRO+z4FxGx9HP30Hwu1oU7UfGGiS3OeYlxPnT5IPvKaXfmlL6iLLAlNLHSvprx9++o8W2AQAA5kGrJ1lvlvRnUkrvkvQrkm5K+s2SPl9H/5/3Tkl/pdG2AQAAZq7VTda7JP0WSZ+so3hwU9INST+uo3GzvivnXFhVndU9Rl7IYSYnq32EH7QPDiZHDfu6cNLuR4EXrL1q/b3d9Ykiv94n6kraFqFcslE5Xnyha/tsZVF64VdASR1pFAFdDNqXLIlZGfAei/oHxzbbem6vdQ+bPSL083gQxEfLCuY3tPWv7ndHMfk8e1H8Vfleen3svKdbXXvd2hdtH/x6uKTJorkpoz7SA8SF0fspiQiDPn6O94PfydvB7/BB0PbfYf+7MGTuzPp4MfqzHs0QWmIuskzggTS5yToeaDQcbBQAAOC8Y+5CAACABmZRXdiAP06O3tKdoE+0PKpnikIqe0x+YAXjYz2q35vcPjzo9rkfNURR4OQ4IooR9yweWauNz7x9uWuu2P5fs/aBx1XGz4pHSEPiQq/KuObLbT+r36NnWltBnyA+81hpZ7P7f5+d1a72zs9RPw6efM0fnGoUWwAAIABJREFU2gXUixS9udlFips+v6GfC2/7dTjkerD2JTvmd57r2geWdkaDx9ZeD1IcG/s1cdX2Lw25JoL2vp3vKCLc77Un/257u/exgD0vUdU47UjYp6RacEhFYclriRoxWzzJAgAAaICbLAAAgAYWLC70x8OtKw19W+sFfQIlj+H3CvoE7Tt7k+MFjx12bf99+Y4NA+ntmxai7Gx2ZVBrl21HPULxtmdvVjUWRU6XLBKSRUXL1t+P/mhxoUVmHhGmXnYYtK8FbT8Ol4LlQXy0Z308Ityxvd61cxRVm0VWdTv6wYmlze2T9lp07fl59PN7OegTRY1BiuOHc8UqT0uuhwepLvTT5BHhhp/v6PqOzr23g9j45mb3g+j30KPA6Hc4ihfv2t+F8LgP+LsTp3DEc4DjSRYAAEAD3GQBAAA0sGBx4VhKZjxz0UB6BbOo1UaEQRVh3LZKo8OuvbNUEAWGy9cnLn/U4sLkOYvHJi8Eyz02ChIFHwh0/cWu/WKXYmnX4sWiuNDXaQNAbpREe1E0VBIjRuu3Pnv2Wo+PblrGtBtESSVxoQ9GGlWh9ebB603ZbtGhR7rR9VzSpzJJumR/nXzAUr8eDiqvB6k/uOhFryL0919yvqP25cnL7/j5lp/vkt/JyddBL16033/t2buO/naMFR2GohfUzkZa2weYHzzJAgAAaICbLAAAgAYe0rgwMuRRdFD5OKRqJ3rMv+3tbuDTne0u5tu5fHa84HGExxc3dOWkfVE3u/bVrn31BdshrzLzdhQhOb8CLc5bsfY1j9ssdszBOpOv09sFA6WGsc/j1i6JEYP+UUToxzyKa6NBK0tcsOrCaJBLH9T0cLOLEa9YBuxT9A2JAhWdI5/rzyrzvNJw0PVwehvRdRCd+yesfS3oE0TINy93Ry/63fN2SUTYa9vvf/3HDoJ2dVwYRYG1EeGQQUqB+cGTLAAAgAa4yQIAAGhggePCkoFJS+qNovWUVMZEVYcFqxlUUWhtiw73ti06uOyR0+Q4IqpwuqFHT9oXbQMeHW48/qGT9lo0+GRJRLgatD0SCgY1TbXr96xrrLgwioksVvKI8MZmt1KPCPvx0eRKz6hCsKS6cMnaF+wg1lYdhtFhf8NnL/cXR/FdNC9kEEkXXQ+nt+ftkgF2/Xy/xNpPTG77uX9Oj520/Xcsbvv1EUWK3fXhv/+9jxQMiQVL+vSMVUXoxooOx9ofoBxPsgAAABrgJgsAAKCBBY4LI/7oN3p7dyr7+GxnJdFhtu5d9d9YsWDc7jKY7VsWP21OjqKiWHDDZoTrRYTaOWmvbnbVao9/9PMn7ZWSyMbTrSgeiioWayvaSmKpkpgoGnQ0qCi8da37/5cbq13s4xHQh629XTDwZDRP3WFwDS/ZAfK5C73SMKpY7K/fYkSvOly+cdLeXLrbbdiPeW10a/NX9s7FkOvh9OGJronoOojO/Usmt+98dNd+brN78XP2Ym/7NVESIXvbf891w95Yyd+L2hgxnHvS/t7Vfpyiur8riSaB2eJJFgAAQAPcZAEAADRwTuLCkkrDSPTIeblg+YB5DEeLCK3dpTfauWKDjm56RNjFER4L9qJAqz7z5R4/ebXa0uWufc2qz1aiqCiKh6IKsqC6sCdKiUsiqpJKwyAizLb8w1e7DfQjoKhibHIEtGvxtMeFUUR4GFQX9s5REB3G2/IYcXXi8v3Vbn+uvKS7+B5dszkuaysH/fj7PJh+DfjvSO2At1JZXF0SG1sVoUeEz17uOj1jna4HcWFJdBhdN9s3LC6s/XtRGyMWVRTWVmXXKnlti2pB4kg8GJ5kAQAANMBNFgAAQAMLEBdmdY9/h+xubUVhNDBp1N/bu9a29dRGhFsFfSwi9P53r3fZx/aWxYWbXXQVxYXe9ko0j5+WezlN5/Byl8VcW+6qDtdKKvuieKh1dWFJpaHFRF45eHN1chRbEvX05yuM5qY7ezDSsrhw8mCkG7YtP+/9GNH34cLE9q6tZ/dqd1Fe2bQKxE2rQPRj/kLQ9mguiowfpLqwdj5LjzN9HkqrJvUqQo8In7WSUx+M9HqvXRIXdr+3YUWh/y3wdm1FYUm7x//elcSCJZFi1B9YLDzJAgAAaICbLAAAgAYWIC50JQONlvQvzRXOWueAgUlLKg0HVBd6dLh9vYsdLqx18d/GkseCXQYTxUxLwXE7CKKr/U2rPnupVZ9dtuqzKCqK4qEosohOdRQXettjWYuG9iwyurnZdaqNAksiwqi6cD8cLNTfwNm8YtQj4G1bvh7Ex/0BUVcnLr9iF18v7ly1PlaBePFyd+Gu+Xl/0dp+bUfzY4YR1n1UDlCbrR1VkHoU+EwQEfryOEYMqg4PbeBa+30uighbDExaPQBpbaVh6/kKgengSRYAAEAD3GQBAAA0sGBxoYvyodYDk0bt6NG4V9500UlRRLgWLL8R9PG2R2Br3XG4sWbR4ROTI8LlIC500WCYvjyKw25a9dnFqzY34q3uQKyVxIWTd60/2GQwMOkdi4Z2t7rjs7MU7HPQro0L++3uJO0WzVc4TnVhFB16RLgexIXe9nktS5b7e7+4eXNie+MJ24ft7ndwxa8HP+8jxoV7dk3sbHadovPqFX/R4KJeRegRoS/vv7Zb7nNb3vCI8Lr9XYviwiExYtEApLtBpyEDkJZUcY81XyEVi5gOnmQBAAA0MNWbrJTSl6eU8vG/r5zmtgEAAKZpanFhSuljJH2bjh5Mb53RvVJJ1WHJ42GPF0sGJo0eXe9Ye93aVpGzZ5WGtYOUetxREh3aIbm71mUizy130cTytSh760QRlUdaYURop9yX+4CoHhtd2OwiLZ9r70I4eWG0z92bj/bTl/u+xe/l7PflUVIUC0bbKhmA1OcxLLHcG4x08vGMBqft73P3Xi729n/dlk8+Jh4jetu3dXHJroHLFmVe9jk0Jw+sWqo/F+OFictLznc0J2U/LpwcBXofH7y0Fx0+1/X3wYV1XZPbJRGhL68egNQrCj0u9L93JdWFLQYgZb5CzKepPMlKKSVJf1tH0wF/+zS2CQAAMEvTigu/RtJnSfoK9UdAAgAAOJeax4UppU+U9I2S/mrO+d0ppc9qvc1ObaVhVFFYUmlYUl3o/a3ScDvoEkR+YZ8oOgxee2e5G3nz+rLts8/bZqJBRz3q2u8NVtlFSFHMEsVGHmmtFgyUGvEo8KAXcU7ez6jKr6TSMIoOSyLCaABS37e4orOuutAHlV0Nqwu76zaK/KL3ftEu6KKI0JZ7VaPvmy+Pql/jStil8PtosNfa2DuqNPQYsSQ6vP5C177zIRsZ90P2BkoiwrEGKe0pqSgsmcewRbUgMJ+a3mSllJYlfZek/yDpz1a87ilJTx199+h9+wIAAMyj1k+yvl7SJ0v69Jzz7lmd78k5Py3paUlK6WPyGd0BAADmTrObrJTSp+jo6dU355zf++Brypr8GDmK/2rnN4yiwKhaJao0jGLBnWC5VR0eBJWGJTHijaBP9NaDPnu6etJ+5qD7wcG1qKJw8hx2O2El2uQIyWOjKC70CrKygVK7/TwIBkqNoqHa6ND7bwcxYu36exVvhxZn7Vn0eTA5IowsL1tFns1feWHJqzi96nPywKQ3gz7bvRjRz28Xl0Xnfb13DUyucPTrwePO2utBimPj+Jo4OzqMKg2j9vWgivDOdYsIoyrCkurCIXMXhnMUTrOicEikOFbFIjBckw++W0z4i5L+fIttAAAAzLNW1YVbkj5e0idK2rMBSLOkv3Dc528eL/uWRvsAAAAwM63iwn1J3xn87Lfr6HNaPy7pFyQ9YJQ4pHLQlUSEtZWG/li9JF4sqDRs0Xa2a3cOusjiWYulbj9mEeHS5Agsqij0uDCqLIsqCkuiov5bmRwRRoN51lYa9t/j5CrCknhxd98iQosCb+91+3PXlmsvuM5LLlu31l3Dj3h0uGbRoS1fX50c50WVhuvB+fXosCQu9Jh4tXcNTK6ULLkepDg2jioN4wFqo0rDyfMb3jicPBdhb6DRqIpwSEVhbYzY06KiUAXLSzAAKeZfk5us4w+5T5w2J6X0Nh3dZP2dnPN3tNg+AADArDFBNAAAQANTm7twNmZVaVhSdejtlcntIdFhiYJCoLt7XZTxvEdaj1kl1ubk+Kw/EOXkuDAaALNkANKl4LyUDNoZRUMeI5bEhXEfG2j0lsWp293yu9YumssyOl8letdJd43dXevaezav5d5aV1V2c8vOl7Vvbk6uNPT2jYLKwWgA0toBaUuuh6Pvz74mdnsR+NnVs9EAtdu3rG0Roa7b73lt5eCQuQuLIkL/OxhVFJbEiCXVgtF2x6ooBGZr6k+ycs5vyzknokIAAHCeERcCAAA0cE7iwnmuNCypOnQ2GOHBgOiwRPQWoyf+Vt22vf1RJ+2dx7oIZXvLYpNNryybHBWVDEAaVZaViCoK4wqzyXFhNJ+gVwh6FHhn2wab3bbzGA02WxsRlkSHtRGzD3K71sWXd7e6GHHbIsXtre76X9my82uRYlSZ6PFfSVwYzbdYez1IZVWmJQOT9gaitViwFwl75WAU55XEgiXLayPC3nXjf8tetPZu0C6pIiyJ/2pz79b9gXHxJAsAAKABbrIAAAAaOCdxoYuiw5JKw5JHyyWVhtFc2LV9fHJBqz4Lq4IqhbFg0A5ih7vbFiFd8Tipi1DWehVqNuDk0uRYsGS+wpK56mrnMexVHe5bZORRoA8Qum0ZWxT/lcSCJXGhL1fQx4WDkQZ91irbW921eseqFF/YumTtbqdXbIBTjxQvrD74ILS118Pp76N5DKP5I3csBt7z6lC/DoYMFtqiHV1/4byEUTuqmo76zGqOwlpUJqIdnmQBAAA0wE0WAABAAwsQF2ZNzkNqBxeNokMFfUrWWbI8esQeCeJCebWaRYclaivUSmKsqGLJY4qtLkLZ67UtprD58jxOWlq2eGjZIqGg7Q5tvkVvH/SWd8e2F/9Zn7AqsOSYDIkLh1QURgZVGgbtrZLl3Q/uWPuFNauitSpF2TltcT2c/r7omrC5JHu/eyUVfFFsVxLt1UaBJdWF1RFhSSw4VvwXoaIQi4snWQAAAA1wkwUAANDAAsSFkdp5CUsMeaQd7UPJvnmfnbBXx6uaCrrXRoRXguXe9khoO1gexkkWuXictOzRkvUfcmhr33ttlWVtteB5igurqxGjPhbLLnvFol8bwX7WXgP3+1nJdVASCY8VI9ZGgUXzEnrkd9Pa/ncnmruw9WCkVBTi/OFJFgAAQAPcZAEAADSwwHGhK4kOZzVIaSSa07BWUHU4pKKwJDIrqSwL46GgHcVVtXFh9ZyMBcvHGkS0tn/JeylRcgyHDFI6VtRYsj9jXQ+nfzbk/EVRXW2kOCQWLBpoNIoIa2PBqE9t1WGEikKcDzzJAgAAaICbLAAAgAbOSVzoxooOFfSJlFSrlMxdOIRFh3sDokNvV1cOBstro6Ih1WRuyHsfK0asXedYFYWR2urCsSLFIbHgWNeD1GZw3iEViEOqFHvXxFgRYe28hEMGHS2JEUsGeq7dFjAdPMkCAABogJssAACABs5hXOjGGrB0rMfM/qjeBhQdLTr092vR4YHFlF69VBKJrAXLoxjRl7eIilSwvLa6sEV02DoibFFd2Do6bB0RDq0ubBEb185nWbK8p2Tg0LEiwp1g+TwPOgrMFk+yAAAAGuAmCwAAoIFzHheWqB2ktPXcWbXRYfQY3tuXrG3vMYomfJVRXOjLSyLCknjI+yjoM6S6MFo+VmRU+9oh7SGGxHO1kV+LuFAFy++n5JjWntex2mHloPPf7RetXTKgaIsqwiERYetBR4kdMVs8yQIAAGiAmywAAIAGFiAuzOoe+ZYMChqZh/kNa0XRYVBFWGR9cvvABi/1CsSx5qqrjZM0YHltRd5Y0WGLuFAFy2sNqTQcEvm1jgiHVhdGy8eKDkv690SDiw5pR9FhFO2VzFE41ryErT+KAUwfT7IAAAAa4CYLAACggQWIC130GLg2Rmw9v+GQXMe3Gw1YWrL+qNLQX+vrDGJEnwPRY43WFWRjVZPVRoQlfcaqFqyWz+5SwqPh3vKC17auRizZVtSn1KwGqw2Pb0ksGA0KGvUpiQtbDDSqAX0iLT6KAUwHT7IAAAAaaHqTlVL6ppTSv0wp/XpKaTel9HxK6adTSn8hpXSt5bYBAABmKeU8UgQxaeUp3Zb0U5J+XtKzkjYlfaqkV0r6oKRPzTn/+v3X8ZIsPVWx1SEViCXZQ7T+5QF9apevB8uXC/r4cu+/UdDHlweRkxtr0MuS9bvaAqex4qMi/vs2JGYZS+11W3Deo5cOiYZbXw+n+zWJgaNYMIr5oghvVtWC8xwRUlGIsT2tnD9Y+QfvI7X+TNalnPNHFCinlP43SX9W0v8s6X9ovA8AAABT1zQunHSDdewfHH/9uJbbBwAAmJVZVRf+nuOvPzv+qqOqwBIlVYdD9qFF9FM7UKrv23rQx2OKqMKxIIL0Krbat966urC2TyiK/6YZrQzROP5ucQ3U9jmtyWCv0XUwVmVfbRQY9WkdEQ5BRIjzZyo3WSmlt0raknRZR5/H+nQd3WB9Y9D/KZ18EOvyNHYRAABgVNN6kvVWSU/Y9z8k6Q/knP/TpM4556clPS3d++A7AADAYpnKTVbO+UlJSik9IenVOnqC9dMppS/IOf9Uuy23iA5r5zecZnToouhguWB5FCNG8dBuQZ+S5b7ZAQNmFonu3WujvSiyKIlrarfbQkkUGPUvOI9F10Cw3d4haX09SGXXxJC4raRPSeTXYrtjRYRj9QfOh6kORppzfibn/H2S/ltJ1yT93WluHwAAYFpmMuJ7zvnXdDR21m9NKT02i30AAABoaZZzF77k+OvhdDY3q+gwUtu/JPKrjTJrlw8ZQHVILBUZOvrkPbWRX/TaRYlW/LhFFaNj9Y/61EaNJYZeD0PO01iR3DRjQTXoE/V3Y83tCsy/Zk+yUkqfkFJ6csLyR44HI31c0r/KOX+41T4AAADMSssnWZ8r6S+nlN4t6f+T9JyOKgxfK+llkj4k6asabh8AAGBmWt5k/QsdDcPwaZJeLumKpFuSflHSd0n61pzz8w23fx/TjA6j17Y2zRgxipBqB7SMDJmPMtI6wpu3iDAyzejQtYiPH8SQefTGqjgdKxZssW9RnwgRIeCa3WTlnH9O0h9ttX4AAIB5NpPqQgAAgPNultWFc6J1dOhqBy+tVTv3Ym2MWBILRrFAiyrCWkPijpL1tBhodJpxoWsdHe4GfaZ5PUhtIrBpDlzbeo7MWV27RIQ4H3iSBQAA0AA3WQAAAA0QF/a0iA5L1j/NeQ9L1llbKTmr2Ggs04wIa7c7pH/Ej+1Ysc9YVYclr22l9XXQekDbWVXAlry2BBEhzh+eZAEAADTATRYAAEADxIWheYgOx1IbU6qgz5DBV0suuxaxUW0cMVbkNw9VhLXbqq2WHRKblkSH0XaHGuuaKIkCa9czDwPdEhECQ/AkCwAAoAFusgAAABogLiwyzejQtR68NDJWtWPJ+yoxJDoccnxmFS8OWX+J2oFqXcl5r70+p3k9SG2uidaDmg7pP9ZrS9ZTgogQDw+eZAEAADTATRYAAEADxIXVWkeHJdsaK6YpMaQycawKyhbvq3XcMc9xypAYd8i1V1uFGq3fDT1Ws7oOZvXa2vXUrnOsbQHnA0+yAAAAGuAmCwAAoAHiwkHGig7dWBWI0TpbPLZvPTDpNKOGWUUi0xyMNFIbZ7sh14BrFQsOUXtuphnnzVssOGS7wPnDkywAAIAGuMkCAABogLhwNGMNvLnoMeKQCko35vx0NdsdYtFjltZzaJaY1fVwv22XaBEp1q6fWBCYNzzJAgAAaICbLAAAgAaIC5sbUoHoWsRwtdVe81DlV3vJTjP2an18Ws9dOM+xz1jXw/3WNcQ8R41DttViH4CHB0+yAAAAGuAmCwAAoAHiwqmaVQXikH2Yh3kGh5jVPszD4KKRFvs2b+f9fhYl1iUWBBYdT7IAAAAa4CYLAACgAeLCudCiAtG1mB9wrHgxMs0KuHkbeHPelFyTrSPIyJjHsPV1sCjX7qJcl8D840kWAABAA01uslJK11JKX5lS+r6U0i+nlHZTSi+klH48pfSHUkrc3AEAgHOtVVz4Zkl/XdJvSHqXpP8g6QlJXyzpOyR9XkrpzTnn3Gj7C6zF3G21g46WGCtejMxzdd7DFqeM9X7P0zUgLU6VYuRhu46B6Wt1k/WLkr5Q0j/NOd+9tzCl9Gcl/aSkL9HRDdc/arR9AACAmWoS2+WcfyTn/H/7Ddbx8g9J+vbjb1/XYtsAAADzYBbVhfeeUc97FjBnWsSIrkWk6Igm7m/efh1a/GngGuicpzk1AUSmepOVUlqW9PuPv/2h+/R7StJTR99dbr5fAAAAY5t2ld83SvokSe/MOf9w1Cnn/HTO+ZU551dKG9PbOwAAgJFM7UlWSulrJP1JSf+vpC+f1nbPv9YxohsScZyncW/nLdprYVaDiy6SebsOiAWBeTOVJ1kppT8q6a9K+nlJn5lzfn4a2wUAAJiV5jdZKaU/LumvSfo5Hd1gfaj1NgEAAGat6fP7lNKf1tHnsH5G0ufknK+33B5cSXTQIlKMzFu0gunjGnhwRIHAImr2JCul9Od1dIP1/0j6XdxgAQCAh0mTJ1kppbdI+ouSDiW9R9LXpJROd/vVnPPbW2wfAABg1lrFhS89/rok6Y8HfX5M0tsbbR9F5i1SxP3Nag5BtEUUCJxXrabVeVvOOZ3x73Uttg0AADAPpj0YKQAAwEPhvI0OiNHVRhkPcxS1KLFP6/18mK8BaXGuAwCt8SQLAACgAW6yAAAAGiAuxMiISsA1AAAST7IAAACa4CYLAACgAW6yAAAAGuAmCwAAoAFusgAAABrgJgsAAKABbrIAAAAa4CYLAACgAW6yAAAAGuAmCwAAoAFusgAAABrgJgsAAKABbrIAAAAa4CYLAACgAW6yAAAAGuAmCwAAoAFusgAAABrgJgsAAKABbrIAAAAa4CYLAACgAW6yAAAAGuAmCwAAoAFusgAAABrgJgsAAKABbrIAAAAaaHaTlVJ6U0rp21JK70kpvZhSyimld7TaHgAAwDxZbrjur5P0cknbkj4g6RMabgsAAGCutIwL/4Skj5d0SdJXN9wOAADA3Gn2JCvn/K577ZRSq80AAADMJT74DgAA0EDLz2Q9sJTSU5KeOvru8kz3BQAA4EHM5ZOsnPPTOedX5pxfKW3MencAAACqzeVNFgAAwKLjJgsAAKABbrIAAAAa4CYLAACgAW6yAAAAGmg2hENK6Q2S3nD87ZPHX1+VUnr7cft6zvmtrbYPAAAwSy3HyXqFpLecWvay43+S9GuSuMkCAADnUrO4MOf8tpxzus+/39Rq2wAAALPGZ7IAAAAa4CYLAACgAW6yAAAAGuAmCwAAoAFusgAAABrgJgsAAKABbrIAAAAa4CYLAACgAW6yAAAAGuAmCwAAoAFusgAAABrgJgsAAKABbrIAAAAa4CYLAACgAW6yAAAAGuAmCwAAoAFusgAAABrgJgsAAKABbrIAAAAa4CYLAACgAW6yAAAAGuAmCwAAoAFusgAAABrgJgsAAKABbrIAAAAa4CYLAACgAW6yAAAAGuAmCwAAoAFusgAAABrgJgsAAKCBpjdZKaX/PKX0t1JKH0wp7af/v71zj56qquL452sqkqGmZGQPH+QrbaVGRGgkmIRaaCblKjEtJcsVmlqxeqhYrqWVifYyykSz0rJFWmFiipGVmaVZvuNRPlBDUlFAUnd/7DMyTnd+vxn93XvuyP6sNev85pyZud+5vzvn7HvO3vtIiyXNkPTSMo8bBEEQBEGQm3XL+mBJw4HfA5sDlwK3AyOBY4AJknY3s4fKOn4QBEEQBEFOypzJ+iZuYE01swPMbJqZjQPOBLYHTi3x2EEQBEEQBFmRmQ38h0rbAAuAxcBwM3u6qW0IsAQQsLmZPd73Z21hMGXANQZBEARBEBQzE7P79Hw/payZrHGpnNtsYAGY2XLgd8CLgVElHT8IgiAIgiArZRlZ26fyzjbtd6Vyu6JGSVMk3SDpBlgx4OKCIAiCIAjKpiwja+NUPtKmvVG/SVGjmc00sxFmNsInvIIgCIIgCHqL0qIL+6GxztmBQ9iSpTD9cWBpmYJKYCihuQpCczWE5uroRd2huRpCczUMBTYciA8qy8hqzFRt3KZ9o5bXtcXMXibpBp/V6h1CczWE5moIzdXRi7pDczWE5mpImrcaiM8qa7nwjlQW+lwB26aync9WEARBEARBT1OWkTUvleMlPesYKYXD7sBK4LqSjh8EQRAEQZCVUowsM1sAzAW2Ao5uaZ6Or3Ve0F+OrCZmDpy6ygjN1RCaqyE0V0cv6g7N1RCaq2HANJeSjBQKt9W5DXgzMBZfJhwd2+oEQRAEQfBCpTQjC0DSq4FTgAnAZnim958B081sWWkHDoIgCIIgyEypRlYQBEEQBMHaSpkbRAdBEARBEKy11NbIkvQqSd+TdJ+kJyQtljRD0ktzaytC0kGSvibpt5IelWSSLsytqx2SNpN0hKTZkv4haaWkRyRdK+nDrVGhdULS6ZKuknR30r1M0o2STpK0WW59nSBpcrpGTNIRufUUkX5z1uZxf259fSHprZJ+KmlJ6j+WSJorad/c2pqRdFgf57jxeCq3ziIk7ZfO6T3pd7hQ0k8kvSW3tiLkfEjSdZKWS1qR+o2pkl6UUVfXY4ek0ZLmpL5vhaSbJR1b1ffoRrOk9SQdI+k8STdJWp2j3+tS87aSPi3p6jTOrJb0gKRLJY3t5ri5Mr73SYHT/O3ASOAYYIKk3WvoNP854A3AY8A9wA555fTLJOBbuJ/cPOBfwMuBA4HvAvtImmT1XE/+BPAX4ErgQTxadRRwMjBF0igzuzufvL5Jvopfw6+Vl2TDANNNAAAJe0lEQVSW0x+PADMK6h+rWkinSPoc8AU8y/Qv8Gt8KLArsCcwJ5u4/+cmPOK6iLcC44DLq5PTGZJOBz4FPIT72S4FXgvsD7xH0qFmVrebzPOByXifcTHwOPB24CxgTMb+rquxQ9L+wE+BVfj3WAa8CzgTT480qUyxiW40b8iaPuQB4H7g1aWqK6YbzV8A3gfcivcXy/A9mScCEyUdY2Znd3RUM6vdA7gC33Ln4y31X0315+TWWKB5LJ5kVXhHbsCFuXX1oXcc/sNcp6V+GG5wGfCe3DrbaN+gTf2pSfc3c2vsQ7uAXwMLgC8nvUfk1tVG62JgcW4dXWqelM7plcCQgvb1cmvs4rv8IX2Xibm1tOgaBjyFD5abt7SNTZoX5tbZouuAhi5gaPP1AMxObYdl0tbx2IHvlvIg8AQwoql+A3xiwoCDa6Z5fWAf4BXp+ck5+r0uNR8G7FpQ/zZgdTr/r+jkuLVbEpK0DTAe7+C/0dJ8En73MVnSgOwrNFCY2Twzu8vSf6LumNnVZvZzM3u6pf5+4Jz0dM/KhXWAma1q0/TjVG7bpr0OTMUN3MPxazkYINIS9+nACuD9Zra89TVm9t/KhT0HJO2Mz87eC/wys5xWtsRdTf5oZg82N5jZPGA58LIcwvrgwFSeYWbP7KOXrofPp6cfr1wVXY8dB+Hn9iIzu6HpM1bhMzUAHy1B5rPoRrOZrTazy81sSdm6+tHRjeZZZnZjQf1vgGtww3F0J8et43LhuFTOLTAAlkv6HW6EjQKuqlrcWkJjIHoyq4rueVcqb86qog2SdgROA84ys/mSxvX3nhowSNIhwGtwo/BmYL6Z1dFPaDSwNXAJ8B9J+wE748sq15vZH3KK65KPpPLcGp7ru/C7+ZGShjYbLZLGAEPwJcQ6MSyVCwvaGnW7SdrEzB6uSNNzodFn/KqgbT5+gzFa0iAze6I6WWsVXY2PdTSytk9lu30N78KNrO0II2vAkbQucGh6WvRDrg2STsB9mjYGRgB74EbAaTl1FZHO6/fxpdjPZJbTDcNw3c0sknR4uqurE29K5QO4z97rmxslzQcOMrN/Vy2sGyQNBg4Bnsb9I2uFmS2T9GncfeNWST/DfbOG4z4rV7LGSKwLDUNw64K2bZr+3oF6b/fWdnw0syclLQJ2wr/TbVUKWxuQtCWwF27Mzu/kPbVbLsQHTHCH2yIa9ZtUoGVt5DT87n+OmV2RW0w/nIAvIR+LG1i/AsbXdBA9EXe8PszMVuYW0yHn4R3KMNx59fXAt/Htsi6X9IZ80grZPJVHAYNxp+Yh+PV8BTAG+EkeaV3xXrx/u9xqGsBhZjPwJbh1gSOBabg/3N3ArNZlxBrwi1QeJ2nTRmW6+WkOPKhl9HoTMT5mQtIg4AfAIOBkM/tPJ++ro5HVH0plT/g+9RKSpgLH49GckzPL6RczG2Zmwo2AA/G7txsl7ZZX2bORNBKfvTqjl5aszGx68t17wMxWmNnfzewofAZjMO7AWica4evCZ6yuMrPHzOwW4N14RNHb6ppioIkpqfx2VhV9IOlT+LLsLHwGa0PgjfjS2w8kfSmfukIuwqM0h+OzbzMlzcCjO/fFV0jAHfp7mRgfSyClxvg+Hr15MfCVTt9bRyOrYYlv3KZ9o5bXBQOApKPxUOZbgbHWQ9seJSNgNr6MvBlwQWZJz9C0THgnaxxse51GYMSYrCr+n8ad5UIz+2tzQ5o9bMzMjqxUVRdIeh3uW3YP9Uo18QyS9sQDDC4zs+PMbGEywv+CG7P3AsenIKZakPx7J+Kz3/fjN5Efws/zHvhyJ3jkXp2J8bFikoF1IT5T+2PgkG4C3OpoZN2Ryu3atDcix9r5bAVdIulY4OvA33EDq9aJJtthZv/EjcSdJA3NrSfxEvxa3hFY1ZxkEl/qBPhOqivKR1VHGgNRrSJ8WdN3tHNcbhhhgyvQ8lyps8N7g3emcl5rg5mtAK7Hx5ZdqxTVH2b2pJmdYWa7mNlgM9vIzCbgfcYuwErglrwq+6Xt+Jhu6LbGHbKLHPyDLknn9EfAwcAP8ajlrgLC6uj43vjhjpe0TnOEoaQh+HTdSurtnNgzJAfW0/Bp872bI4V6lC1SWZcB6gng3DZtu+ED0bV459krS4mN5ba6deTz8QFmW0nrm9nqlvadU7m4UlUdImkDfIbladpfM3VgUCrbpWlo1Lee/7oyGc8zdX4PpPi4GvgAMAEf/JsZA7wYj/6NyMLniaT18Zmr/fHVkcNbMx50Qu1mssxsATAXd649uqV5On73fIGZRY6h54mkz+MG1p+BvXrBwJK0g6RhBfXrSDoVd37+fadOiWVjZivN7IiiB3BZetn5qe7inFqbkbRTs4NwU/2W+Kwn+BR6bUjX78X4UsqJzW2S9gbegS+j1DVqdhLueD2nrg7vid+mcoqkVzY3SNoHvxFehSfHrA2SNiqoexPeBz4GnFK5qO65BI+UPFjSiEZlMtC/mJ5+K4ewFxLJyX02bmCdy3M0sKCeM1kAH8N/oGdL2gsPRX0znrH1TuCzGbUVIukAPKswrMnJ8hZJs9LfS83shMqFtUHSB/FO5Sm805wqqfVli81sVsXS+mMC8OUUjr8A96V4OZ6Jdxvc3+LIfPJeMEwCpkmaByzCE0wOB/bD7/rn0IXzZ4Uch/cVn005m67Hk2e+G7/Wj6xxHqSGw/vMrCr65xJ814K3A7dJmo3/7nbElxIFTLP6bX12paSVuFvEcjzVwb74bPOBZpZlZrabscPMHpV0JP4/uEbSRfiWLxPx9A6X4DcatdGcXj+NNdvY7JLKwyXtkf6+1sxKTVfSpeZz8GtjKe5jeGLB+HiNmV3T74EtwzYCnTzwvY3Ow/cdWw38E3fM3jS3tjZ6T8YjOto9FufW2KVeSxdRdq0tunfGdwK4Kf0AnsRnJ/6UvlMtr49+/ge121YHN1p/hEeaPown4Ps3ngPpUEC5NfahfVM8AnJR6jsewvdAHZVbWx+ad0zXwt3Ai3Lr6UDvenjqlOuAR9Pv8EE8VcL43PraaP4kPmv/MG5YLcIH060y6+p67MBnC+fgfoYrgb/he7pWcu10qxnPkt7X62fVSXMHeg1P49DvcZU+MAiCIAiCIBhAaueTFQRBEARB8EIgjKwgCIIgCIISCCMrCIIgCIKgBMLICoIgCIIgKIEwsoIgCIIgCEogjKwgCIIgCIISCCMrCIIgCIKgBMLICoIgCIIgKIEwsoIgCIIgCErgfzjphhx+gdsMAAAAAElFTkSuQmCC\n",
250 "text/plain": [
251 "<Figure size 720x720 with 1 Axes>"
252 ]
253 },
254 "metadata": {
255 "needs_background": "light"
256 },
257 "output_type": "display_data"
258 }
259 ],
260 "source": [
261 "plot_gaussian_blur_with_center_x(m1+m2+m3, m1_blur + m2_blur + m3_blur, \"p123\")"
262 ]
263 },
264 {
265 "cell_type": "code",
266 "execution_count": null,
267 "metadata": {},
268 "outputs": [],
269 "source": []
270 },
271 {
272 "cell_type": "markdown",
273 "metadata": {},
274 "source": [
275 "# Thử xem có thể crop cái filter theo 3 sigma được không ?"
276 ]
277 },
278 {
279 "cell_type": "code",
280 "execution_count": 19,
281 "metadata": {},
282 "outputs": [],
283 "source": [
284 "def create_point_with_blur_t(x, y, truncate_by_sd):\n",
285 " m = np.zeros((181, 181))\n",
286 " m[x*15, (12-y)*15] = 1\n",
287 " m_blur = scipy.ndimage.filters.gaussian_filter(m, 15, truncate=truncate_by_sd)\n",
288 " return m, m_blur"
289 ]
290 },
291 {
292 "cell_type": "code",
293 "execution_count": 20,
294 "metadata": {},
295 "outputs": [],
296 "source": [
297 "mt1, mt1_blur = create_point_with_blur_t(6, 6, 1)"
298 ]
299 },
300 {
301 "cell_type": "code",
302 "execution_count": 21,
303 "metadata": {},
304 "outputs": [
305 {
306 "data": {
307 "image/png": 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\n",
308 "text/plain": [
309 "<Figure size 720x720 with 1 Axes>"
310 ]
311 },
312 "metadata": {
313 "needs_background": "light"
314 },
315 "output_type": "display_data"
316 }
317 ],
318 "source": [
319 "plot_gaussian_blur_with_center_x(mt1, mt1_blur, None)"
320 ]
321 },
322 {
323 "cell_type": "code",
324 "execution_count": 22,
325 "metadata": {},
326 "outputs": [],
327 "source": [
328 "mt2, mt2_blur = create_point_with_blur_t(6, 6, 2)"
329 ]
330 },
331 {
332 "cell_type": "code",
333 "execution_count": 23,
334 "metadata": {},
335 "outputs": [
336 {
337 "data": {
338 "image/png": 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\n",
339 "text/plain": [
340 "<Figure size 720x720 with 1 Axes>"
341 ]
342 },
343 "metadata": {
344 "needs_background": "light"
345 },
346 "output_type": "display_data"
347 }
348 ],
349 "source": [
350 "plot_gaussian_blur_with_center_x(mt2, mt2_blur, None)"
351 ]
352 },
353 {
354 "cell_type": "code",
355 "execution_count": 24,
356 "metadata": {},
357 "outputs": [],
358 "source": [
359 "mt3, mt3_blur = create_point_with_blur_t(6, 6, 3)"
360 ]
361 },
362 {
363 "cell_type": "code",
364 "execution_count": 25,
365 "metadata": {},
366 "outputs": [
367 {
368 "data": {
369 "image/png": 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\n",
370 "text/plain": [
371 "<Figure size 720x720 with 1 Axes>"
372 ]
373 },
374 "metadata": {
375 "needs_background": "light"
376 },
377 "output_type": "display_data"
378 }
379 ],
380 "source": [
381 "plot_gaussian_blur_with_center_x(mt3, mt3_blur, None)"
382 ]
383 },
384 {
385 "cell_type": "code",
386 "execution_count": null,
387 "metadata": {},
388 "outputs": [],
389 "source": []
390 }
391 ],
392 "metadata": {
393 "kernelspec": {
394 "display_name": "Python 3",
395 "language": "python",
396 "name": "python3"
397 },
398 "language_info": {
399 "codemirror_mode": {
400 "name": "ipython",
401 "version": 3
402 },
403 "file_extension": ".py",
404 "mimetype": "text/x-python",
405 "name": "python",
406 "nbconvert_exporter": "python",
407 "pygments_lexer": "ipython3",
408 "version": "3.8.1"
409 }
410 },
411 "nbformat": 4,
412 "nbformat_minor": 4
413 }
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