Upload folder using huggingface_hub
#1
by nalemadi - opened
- README.md +7 -8
- data_utils/CDSI_other_test.txt +0 -0
- data_utils/CDSI_other_train.txt +0 -0
- data_utils/CDSI_other_val.txt +2672 -0
- data_utils/CDSI_test.txt +5290 -0
- data_utils/CDSI_train.txt +0 -0
- data_utils/CDSI_val.txt +2089 -0
- data_utils/original_test.txt +0 -0
- data_utils/original_train.txt +0 -0
- data_utils/original_val.txt +5896 -0
- dior/dior_to_coco.py +195 -0
- dota2/dota2_to_coco.py +185 -0
- dota2/filter_dota_coco.py +102 -0
- dota2/readme.md +14 -0
- fair1m/fair1m_to_coco.py +242 -0
- requirements.txt +7 -0
- utils/coco_filter_cat_area.py +90 -0
- vedai/vedai_to_coco.py +199 -0
- xview/category_id_mapping.json +62 -0
- xview/xview_class_labels.txt +60 -0
- xview/xview_to_coco.py +199 -0
README.md
CHANGED
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@@ -1,7 +1,3 @@
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-
---
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license: cc-by-nc-nd-4.0
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-
---
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-
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## Environment Setup for data preparation and preprocessing
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```bash
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```
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## CDSI Dataset preparation
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For using the VME dataset, both OBB (YOLO) and HBB (MS-COCO) formats are available with the desired splits.
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In order to build the CDSI dataset, you need to follow the steps below:
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a. After installing some datasets, it's recommended to put all images under one directory and all annotations under one directory, to ease the MS-COCO conversion
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b. To generate SPLIT_FILE.txt for each setup (original
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```bash
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grep 'xview' CDSI_train.txt > xview_car_train.txt
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### Combining train-val-test splits
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To combine the train-val-test splits from each dataset to construct CDSI splits, please use the following command/package. The command takes 3 arguments:File1.json File2.json OutputFile.json.
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```bash
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pyodi coco merge SPLIT_A.json SPLIT_B.json SPLIT_A_B.json
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```
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## Environment Setup for data preparation and preprocessing
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```bash
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```
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## CDSI Dataset preparation
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+
For using the VME dataset, both OBB (YOLO) and HBB (MS-COCO) formats are available with the desired splits. VME images and annotations are available at XXX.
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In order to build the CDSI dataset, you need to follow the steps below:
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a. After installing some datasets, it's recommended to put all images under one directory and all annotations under one directory, to ease the MS-COCO conversion
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+
b. To generate SPLIT_FILE.txt for each setup (original|CDSI_other|CDSI), run this command under each dataset directory (example for car setup):
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```bash
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grep 'xview' CDSI_train.txt > xview_car_train.txt
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### Combining train-val-test splits
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+
To combine the train-val-test splits from each dataset to construct CDSI splits, please use the following command/package. The command takes 3 arguments:File1.json File2.json OutputFile.json. T$
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```bash
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pyodi coco merge SPLIT_A.json SPLIT_B.json SPLIT_A_B.json
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```
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>[!NOTE]
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+
__The benchmark scripts will be released soon! Stay Tuned!__
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data_utils/CDSI_other_test.txt
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The diff for this file is too large to render.
See raw diff
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data_utils/CDSI_other_train.txt
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The diff for this file is too large to render.
See raw diff
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data_utils/CDSI_other_val.txt
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VME/VME1087.png
|
| 2573 |
+
VME/VME2954.png
|
| 2574 |
+
VME/VME2379.png
|
| 2575 |
+
VME/VME1869.png
|
| 2576 |
+
VME/VME2377.png
|
| 2577 |
+
VME/VME0070.png
|
| 2578 |
+
VME/VME1976.png
|
| 2579 |
+
VME/VME0057.png
|
| 2580 |
+
VME/VME0390.png
|
| 2581 |
+
VME/VME0200.png
|
| 2582 |
+
VME/VME2257.png
|
| 2583 |
+
VME/VME1413.png
|
| 2584 |
+
VME/VME1268.png
|
| 2585 |
+
VME/VME4058.png
|
| 2586 |
+
VME/VME1063.png
|
| 2587 |
+
VME/VME2804.png
|
| 2588 |
+
VME/VME2368.png
|
| 2589 |
+
VME/VME2904.png
|
| 2590 |
+
VME/VME1426.png
|
| 2591 |
+
VME/VME0836.png
|
| 2592 |
+
VME/VME3838.png
|
| 2593 |
+
VME/VME0571.png
|
| 2594 |
+
VME/VME4077.png
|
| 2595 |
+
VME/VME2935.png
|
| 2596 |
+
VME/VME3684.png
|
| 2597 |
+
VME/VME3300.png
|
| 2598 |
+
VME/VME1739.png
|
| 2599 |
+
VME/VME4255.png
|
| 2600 |
+
VME/VME3349.png
|
| 2601 |
+
VME/VME1982.png
|
| 2602 |
+
VME/VME2144.png
|
| 2603 |
+
VME/VME3412.png
|
| 2604 |
+
VME/VME4209.png
|
| 2605 |
+
VME/VME2786.png
|
| 2606 |
+
VME/VME1538.png
|
| 2607 |
+
VME/VME2192.png
|
| 2608 |
+
VME/VME2767.png
|
| 2609 |
+
VME/VME0010.png
|
| 2610 |
+
VME/VME1181.png
|
| 2611 |
+
VME/VME0489.png
|
| 2612 |
+
VME/VME3177.png
|
| 2613 |
+
VME/VME3828.png
|
| 2614 |
+
VME/VME2908.png
|
| 2615 |
+
VME/VME1322.png
|
| 2616 |
+
VME/VME3691.png
|
| 2617 |
+
VME/VME1803.png
|
| 2618 |
+
VME/VME3620.png
|
| 2619 |
+
VME/VME3902.png
|
| 2620 |
+
VME/VME2714.png
|
| 2621 |
+
VME/VME1160.png
|
| 2622 |
+
VME/VME0481.png
|
| 2623 |
+
VME/VME2894.png
|
| 2624 |
+
VME/VME0082.png
|
| 2625 |
+
VME/VME3846.png
|
| 2626 |
+
VME/VME3054.png
|
| 2627 |
+
VME/VME0459.png
|
| 2628 |
+
VME/VME3019.png
|
| 2629 |
+
VME/VME2353.png
|
| 2630 |
+
VME/VME3854.png
|
| 2631 |
+
VME/VME0309.png
|
| 2632 |
+
VME/VME0103.png
|
| 2633 |
+
VME/VME4212.png
|
| 2634 |
+
VME/VME2162.png
|
| 2635 |
+
VME/VME1061.png
|
| 2636 |
+
VME/VME0779.png
|
| 2637 |
+
VME/VME1337.png
|
| 2638 |
+
VME/VME2182.png
|
| 2639 |
+
VME/VME2470.png
|
| 2640 |
+
VME/VME1731.png
|
| 2641 |
+
VME/VME3125.png
|
| 2642 |
+
VME/VME1238.png
|
| 2643 |
+
VME/VME1759.png
|
| 2644 |
+
VME/VME0334.png
|
| 2645 |
+
VME/VME0480.png
|
| 2646 |
+
VME/VME4220.png
|
| 2647 |
+
VME/VME0332.png
|
| 2648 |
+
VME/VME1142.png
|
| 2649 |
+
VME/VME1420.png
|
| 2650 |
+
VME/VME3013.png
|
| 2651 |
+
VME/VME3531.png
|
| 2652 |
+
VME/VME2946.png
|
| 2653 |
+
VME/VME3742.png
|
| 2654 |
+
VME/VME2645.png
|
| 2655 |
+
VME/VME0650.png
|
| 2656 |
+
VME/VME1251.png
|
| 2657 |
+
VME/VME0772.png
|
| 2658 |
+
VME/VME0863.png
|
| 2659 |
+
VME/VME3806.png
|
| 2660 |
+
VME/VME2395.png
|
| 2661 |
+
VME/VME4203.png
|
| 2662 |
+
VME/VME1654.png
|
| 2663 |
+
VME/VME0975.png
|
| 2664 |
+
VME/VME0323.png
|
| 2665 |
+
VME/VME3418.png
|
| 2666 |
+
VME/VME0752.png
|
| 2667 |
+
VME/VME1566.png
|
| 2668 |
+
VME/VME1583.png
|
| 2669 |
+
VME/VME2312.png
|
| 2670 |
+
VME/VME2795.png
|
| 2671 |
+
VME/VME2017.png
|
| 2672 |
+
VME/VME3745.png
|
data_utils/CDSI_test.txt
ADDED
|
@@ -0,0 +1,5290 @@
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VME/VME2762.png
|
| 5174 |
+
VME/VME2650.png
|
| 5175 |
+
VME/VME2670.png
|
| 5176 |
+
VME/VME3091.png
|
| 5177 |
+
VME/VME3820.png
|
| 5178 |
+
VME/VME3041.png
|
| 5179 |
+
VME/VME2763.png
|
| 5180 |
+
VME/VME2585.png
|
| 5181 |
+
VME/VME3711.png
|
| 5182 |
+
VME/VME2033.png
|
| 5183 |
+
VME/VME4147.png
|
| 5184 |
+
VME/VME2139.png
|
| 5185 |
+
VME/VME4138.png
|
| 5186 |
+
VME/VME1408.png
|
| 5187 |
+
VME/VME3538.png
|
| 5188 |
+
VME/VME3050.png
|
| 5189 |
+
VME/VME2958.png
|
| 5190 |
+
VME/VME0627.png
|
| 5191 |
+
VME/VME3974.png
|
| 5192 |
+
VME/VME0166.png
|
| 5193 |
+
VME/VME0016.png
|
| 5194 |
+
VME/VME2384.png
|
| 5195 |
+
VME/VME3748.png
|
| 5196 |
+
VME/VME1213.png
|
| 5197 |
+
VME/VME2210.png
|
| 5198 |
+
VME/VME0887.png
|
| 5199 |
+
VME/VME1143.png
|
| 5200 |
+
VME/VME0096.png
|
| 5201 |
+
VME/VME1164.png
|
| 5202 |
+
VME/VME1386.png
|
| 5203 |
+
VME/VME1738.png
|
| 5204 |
+
VME/VME0215.png
|
| 5205 |
+
VME/VME2326.png
|
| 5206 |
+
VME/VME1344.png
|
| 5207 |
+
VME/VME1271.png
|
| 5208 |
+
VME/VME4251.png
|
| 5209 |
+
VME/VME0243.png
|
| 5210 |
+
VME/VME3651.png
|
| 5211 |
+
VME/VME1692.png
|
| 5212 |
+
VME/VME3133.png
|
| 5213 |
+
VME/VME3727.png
|
| 5214 |
+
VME/VME1570.png
|
| 5215 |
+
VME/VME2640.png
|
| 5216 |
+
VME/VME3220.png
|
| 5217 |
+
VME/VME3227.png
|
| 5218 |
+
VME/VME1200.png
|
| 5219 |
+
VME/VME3182.png
|
| 5220 |
+
VME/VME4107.png
|
| 5221 |
+
VME/VME0442.png
|
| 5222 |
+
VME/VME1623.png
|
| 5223 |
+
VME/VME2744.png
|
| 5224 |
+
VME/VME1771.png
|
| 5225 |
+
VME/VME2485.png
|
| 5226 |
+
VME/VME0216.png
|
| 5227 |
+
VME/VME2453.png
|
| 5228 |
+
VME/VME2912.png
|
| 5229 |
+
VME/VME1516.png
|
| 5230 |
+
VME/VME3679.png
|
| 5231 |
+
VME/VME0375.png
|
| 5232 |
+
VME/VME1725.png
|
| 5233 |
+
VME/VME0060.png
|
| 5234 |
+
VME/VME0998.png
|
| 5235 |
+
VME/VME1718.png
|
| 5236 |
+
VME/VME0720.png
|
| 5237 |
+
VME/VME3382.png
|
| 5238 |
+
VME/VME3017.png
|
| 5239 |
+
VME/VME1476.png
|
| 5240 |
+
VME/VME0989.png
|
| 5241 |
+
VME/VME3484.png
|
| 5242 |
+
VME/VME4050.png
|
| 5243 |
+
VME/VME3336.png
|
| 5244 |
+
VME/VME1496.png
|
| 5245 |
+
VME/VME3760.png
|
| 5246 |
+
VME/VME1866.png
|
| 5247 |
+
VME/VME1225.png
|
| 5248 |
+
VME/VME4257.png
|
| 5249 |
+
VME/VME3020.png
|
| 5250 |
+
VME/VME3621.png
|
| 5251 |
+
VME/VME4127.png
|
| 5252 |
+
VME/VME1789.png
|
| 5253 |
+
VME/VME0957.png
|
| 5254 |
+
VME/VME2681.png
|
| 5255 |
+
VME/VME1021.png
|
| 5256 |
+
VME/VME0702.png
|
| 5257 |
+
VME/VME0277.png
|
| 5258 |
+
VME/VME3767.png
|
| 5259 |
+
VME/VME2092.png
|
| 5260 |
+
VME/VME3347.png
|
| 5261 |
+
VME/VME0007.png
|
| 5262 |
+
VME/VME0004.png
|
| 5263 |
+
VME/VME2226.png
|
| 5264 |
+
VME/VME1697.png
|
| 5265 |
+
VME/VME3231.png
|
| 5266 |
+
VME/VME2224.png
|
| 5267 |
+
VME/VME0387.png
|
| 5268 |
+
VME/VME0429.png
|
| 5269 |
+
VME/VME1292.png
|
| 5270 |
+
VME/VME1153.png
|
| 5271 |
+
VME/VME2028.png
|
| 5272 |
+
VME/VME0353.png
|
| 5273 |
+
VME/VME2770.png
|
| 5274 |
+
VME/VME1161.png
|
| 5275 |
+
VME/VME2152.png
|
| 5276 |
+
VME/VME1316.png
|
| 5277 |
+
VME/VME1174.png
|
| 5278 |
+
VME/VME3065.png
|
| 5279 |
+
VME/VME1105.png
|
| 5280 |
+
VME/VME0631.png
|
| 5281 |
+
VME/VME0005.png
|
| 5282 |
+
VME/VME0336.png
|
| 5283 |
+
VME/VME4250.png
|
| 5284 |
+
VME/VME1279.png
|
| 5285 |
+
VME/VME3803.png
|
| 5286 |
+
VME/VME0330.png
|
| 5287 |
+
VME/VME0704.png
|
| 5288 |
+
VME/VME1152.png
|
| 5289 |
+
VME/VME3160.png
|
| 5290 |
+
VME/VME2581.png
|
data_utils/CDSI_train.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
data_utils/CDSI_val.txt
ADDED
|
@@ -0,0 +1,2089 @@
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| 1 |
+
DOTA-v2.0/P0436.png
|
| 2 |
+
DOTA-v2.0/P2244.png
|
| 3 |
+
DOTA-v2.0/P0437.png
|
| 4 |
+
DOTA-v2.0/P5720.png
|
| 5 |
+
DOTA-v2.0/P2164.png
|
| 6 |
+
DOTA-v2.0/P2131.png
|
| 7 |
+
DOTA-v2.0/P2148.png
|
| 8 |
+
DOTA-v2.0/P0707.png
|
| 9 |
+
DOTA-v2.0/P2349.png
|
| 10 |
+
DOTA-v2.0/P0335.png
|
| 11 |
+
DOTA-v2.0/P4744.png
|
| 12 |
+
DOTA-v2.0/P0098.png
|
| 13 |
+
DOTA-v2.0/P0933.png
|
| 14 |
+
DOTA-v2.0/P1907.png
|
| 15 |
+
DOTA-v2.0/P2458.png
|
| 16 |
+
DOTA-v2.0/P2590.png
|
| 17 |
+
DOTA-v2.0/P1991.png
|
| 18 |
+
DOTA-v2.0/P2228.png
|
| 19 |
+
DOTA-v2.0/P2038.png
|
| 20 |
+
DOTA-v2.0/P2222.png
|
| 21 |
+
DOTA-v2.0/P0860.png
|
| 22 |
+
DOTA-v2.0/P1780.png
|
| 23 |
+
DOTA-v2.0/P2253.png
|
| 24 |
+
DOTA-v2.0/P1998.png
|
| 25 |
+
DOTA-v2.0/P1010.png
|
| 26 |
+
DOTA-v2.0/P0835.png
|
| 27 |
+
DOTA-v2.0/P2184.png
|
| 28 |
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DOTA-v2.0/P2226.png
|
| 29 |
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DOTA-v2.0/P2266.png
|
| 30 |
+
DOTA-v2.0/P0087.png
|
| 31 |
+
DOTA-v2.0/P2245.png
|
| 32 |
+
DOTA-v2.0/P2020.png
|
| 33 |
+
DOTA-v2.0/P1887.png
|
| 34 |
+
DOTA-v2.0/P4076.png
|
| 35 |
+
DOTA-v2.0/P1871.png
|
| 36 |
+
DOTA-v2.0/P2232.png
|
| 37 |
+
DOTA-v2.0/P1908.png
|
| 38 |
+
DOTA-v2.0/P2149.png
|
| 39 |
+
DOTA-v2.0/P2601.png
|
| 40 |
+
DOTA-v2.0/P2156.png
|
| 41 |
+
DOTA-v2.0/P1954.png
|
| 42 |
+
DOTA-v2.0/P1486.png
|
| 43 |
+
DOTA-v2.0/P0299.png
|
| 44 |
+
DOTA-v2.0/P2348.png
|
| 45 |
+
DOTA-v2.0/P2638.png
|
| 46 |
+
DOTA-v2.0/P2013.png
|
| 47 |
+
DOTA-v2.0/P0018.png
|
| 48 |
+
DOTA-v2.0/P2143.png
|
| 49 |
+
DOTA-v2.0/P0586.png
|
| 50 |
+
DOTA-v2.0/P6100.png
|
| 51 |
+
DOTA-v2.0/P2427.png
|
| 52 |
+
DOTA-v2.0/P0190.png
|
| 53 |
+
DOTA-v2.0/P2182.png
|
| 54 |
+
DOTA-v2.0/P0946.png
|
| 55 |
+
DOTA-v2.0/P0223.png
|
| 56 |
+
DOTA-v2.0/P1986.png
|
| 57 |
+
DOTA-v2.0/P11032.png
|
| 58 |
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DOTA-v2.0/P2062.png
|
| 59 |
+
DOTA-v2.0/P0067.png
|
| 60 |
+
DOTA-v2.0/P0119.png
|
| 61 |
+
DOTA-v2.0/P9072.png
|
| 62 |
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DOTA-v2.0/P5427.png
|
| 63 |
+
DOTA-v2.0/P2211.png
|
| 64 |
+
DOTA-v2.0/P0248.png
|
| 65 |
+
DOTA-v2.0/P0879.png
|
| 66 |
+
DOTA-v2.0/P0611.png
|
| 67 |
+
DOTA-v2.0/P1067.png
|
| 68 |
+
DOTA-v2.0/P0254.png
|
| 69 |
+
DOTA-v2.0/P2151.png
|
| 70 |
+
DOTA-v2.0/P0695.png
|
| 71 |
+
DOTA-v2.0/P1187.png
|
| 72 |
+
DOTA-v2.0/P3397.png
|
| 73 |
+
DOTA-v2.0/P1193.png
|
| 74 |
+
DOTA-v2.0/P1909.png
|
| 75 |
+
DOTA-v2.0/P2154.png
|
| 76 |
+
DOTA-v2.0/P2001.png
|
| 77 |
+
DOTA-v2.0/P2039.png
|
| 78 |
+
DOTA-v2.0/P1900.png
|
| 79 |
+
DOTA-v2.0/P2790.png
|
| 80 |
+
DOTA-v2.0/P1382.png
|
| 81 |
+
DOTA-v2.0/P1161.png
|
| 82 |
+
DOTA-v2.0/P2437.png
|
| 83 |
+
DOTA-v2.0/P1109.png
|
| 84 |
+
DOTA-v2.0/P2787.png
|
| 85 |
+
DOTA-v2.0/P0141.png
|
| 86 |
+
DOTA-v2.0/P0940.png
|
| 87 |
+
DOTA-v2.0/P1403.png
|
| 88 |
+
DOTA-v2.0/P1392.png
|
| 89 |
+
DOTA-v2.0/P2022.png
|
| 90 |
+
DOTA-v2.0/P0227.png
|
| 91 |
+
DOTA-v2.0/P2672.png
|
| 92 |
+
DOTA-v2.0/P1977.png
|
| 93 |
+
DOTA-v2.0/P2158.png
|
| 94 |
+
DOTA-v2.0/P2632.png
|
| 95 |
+
DOTA-v2.0/P1501.png
|
| 96 |
+
DOTA-v2.0/P2132.png
|
| 97 |
+
DOTA-v2.0/P0404.png
|
| 98 |
+
DOTA-v2.0/P2797.png
|
| 99 |
+
DOTA-v2.0/P10398.png
|
| 100 |
+
DOTA-v2.0/P1962.png
|
| 101 |
+
DOTA-v2.0/P0861.png
|
| 102 |
+
DOTA-v2.0/P2801.png
|
| 103 |
+
DOTA-v2.0/P0757.png
|
| 104 |
+
DOTA-v2.0/P2453.png
|
| 105 |
+
DOTA-v2.0/P7158.png
|
| 106 |
+
DOTA-v2.0/P0440.png
|
| 107 |
+
DOTA-v2.0/P0896.png
|
| 108 |
+
DOTA-v2.0/P0898.png
|
| 109 |
+
DOTA-v2.0/P0068.png
|
| 110 |
+
DOTA-v2.0/P0978.png
|
| 111 |
+
DOTA-v2.0/P1867.png
|
| 112 |
+
DOTA-v2.0/P0914.png
|
| 113 |
+
DOTA-v2.0/P0587.png
|
| 114 |
+
xview/2053.tif
|
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VME/VME2117.png
|
| 1663 |
+
VME/VME3853.png
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| 1664 |
+
VME/VME1569.png
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| 1665 |
+
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| 1670 |
+
VME/VME0064.png
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+
VME/VME1002.png
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+
VME/VME0928.png
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+
VME/VME4232.png
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VME/VME1807.png
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VME/VME1719.png
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VME/VME4011.png
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+
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+
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+
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VME/VME4266.png
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+
VME/VME0903.png
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+
VME/VME3779.png
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| 1686 |
+
VME/VME0999.png
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| 1687 |
+
VME/VME0099.png
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| 1688 |
+
VME/VME0689.png
|
| 1689 |
+
VME/VME2187.png
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| 1690 |
+
VME/VME1107.png
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| 1691 |
+
VME/VME0202.png
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| 1692 |
+
VME/VME3357.png
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| 1693 |
+
VME/VME2596.png
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| 1694 |
+
VME/VME0415.png
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| 1695 |
+
VME/VME0211.png
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| 1696 |
+
VME/VME3612.png
|
| 1697 |
+
VME/VME0020.png
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| 1698 |
+
VME/VME1603.png
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| 1699 |
+
VME/VME2544.png
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| 1700 |
+
VME/VME1826.png
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| 1701 |
+
VME/VME0992.png
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| 1702 |
+
VME/VME2146.png
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| 1703 |
+
VME/VME0880.png
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| 1704 |
+
VME/VME2390.png
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| 1705 |
+
VME/VME2526.png
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| 1706 |
+
VME/VME2432.png
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| 1707 |
+
VME/VME3730.png
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| 1708 |
+
VME/VME0586.png
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| 1709 |
+
VME/VME0669.png
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| 1710 |
+
VME/VME2849.png
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| 1711 |
+
VME/VME4044.png
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| 1712 |
+
VME/VME2154.png
|
| 1713 |
+
VME/VME0476.png
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| 1714 |
+
VME/VME2660.png
|
| 1715 |
+
VME/VME1610.png
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| 1716 |
+
VME/VME0539.png
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| 1717 |
+
VME/VME3925.png
|
| 1718 |
+
VME/VME2354.png
|
| 1719 |
+
VME/VME3121.png
|
| 1720 |
+
VME/VME0729.png
|
| 1721 |
+
VME/VME1577.png
|
| 1722 |
+
VME/VME2068.png
|
| 1723 |
+
VME/VME2687.png
|
| 1724 |
+
VME/VME4054.png
|
| 1725 |
+
VME/VME2014.png
|
| 1726 |
+
VME/VME2062.png
|
| 1727 |
+
VME/VME0236.png
|
| 1728 |
+
VME/VME2525.png
|
| 1729 |
+
VME/VME4145.png
|
| 1730 |
+
VME/VME3786.png
|
| 1731 |
+
VME/VME0302.png
|
| 1732 |
+
VME/VME0025.png
|
| 1733 |
+
VME/VME2901.png
|
| 1734 |
+
VME/VME3299.png
|
| 1735 |
+
VME/VME0278.png
|
| 1736 |
+
VME/VME1963.png
|
| 1737 |
+
VME/VME1336.png
|
| 1738 |
+
VME/VME0210.png
|
| 1739 |
+
VME/VME3116.png
|
| 1740 |
+
VME/VME0909.png
|
| 1741 |
+
VME/VME3387.png
|
| 1742 |
+
VME/VME2147.png
|
| 1743 |
+
VME/VME3141.png
|
| 1744 |
+
VME/VME2460.png
|
| 1745 |
+
VME/VME3547.png
|
| 1746 |
+
VME/VME0280.png
|
| 1747 |
+
VME/VME3033.png
|
| 1748 |
+
VME/VME1634.png
|
| 1749 |
+
VME/VME2167.png
|
| 1750 |
+
VME/VME1841.png
|
| 1751 |
+
VME/VME1390.png
|
| 1752 |
+
VME/VME3994.png
|
| 1753 |
+
VME/VME3809.png
|
| 1754 |
+
VME/VME4153.png
|
| 1755 |
+
VME/VME0124.png
|
| 1756 |
+
VME/VME0058.png
|
| 1757 |
+
VME/VME4279.png
|
| 1758 |
+
VME/VME2096.png
|
| 1759 |
+
VME/VME2355.png
|
| 1760 |
+
VME/VME1885.png
|
| 1761 |
+
VME/VME3780.png
|
| 1762 |
+
VME/VME2576.png
|
| 1763 |
+
VME/VME3611.png
|
| 1764 |
+
VME/VME0526.png
|
| 1765 |
+
VME/VME1612.png
|
| 1766 |
+
VME/VME0940.png
|
| 1767 |
+
VME/VME0884.png
|
| 1768 |
+
VME/VME1790.png
|
| 1769 |
+
VME/VME2077.png
|
| 1770 |
+
VME/VME0084.png
|
| 1771 |
+
VME/VME2584.png
|
| 1772 |
+
VME/VME2733.png
|
| 1773 |
+
VME/VME3248.png
|
| 1774 |
+
VME/VME2447.png
|
| 1775 |
+
VME/VME3173.png
|
| 1776 |
+
VME/VME0471.png
|
| 1777 |
+
VME/VME3298.png
|
| 1778 |
+
VME/VME2449.png
|
| 1779 |
+
VME/VME0941.png
|
| 1780 |
+
VME/VME2337.png
|
| 1781 |
+
VME/VME2950.png
|
| 1782 |
+
VME/VME1351.png
|
| 1783 |
+
VME/VME3302.png
|
| 1784 |
+
VME/VME2454.png
|
| 1785 |
+
VME/VME0821.png
|
| 1786 |
+
VME/VME2180.png
|
| 1787 |
+
VME/VME1785.png
|
| 1788 |
+
VME/VME3872.png
|
| 1789 |
+
VME/VME0418.png
|
| 1790 |
+
VME/VME2910.png
|
| 1791 |
+
VME/VME3255.png
|
| 1792 |
+
VME/VME2821.png
|
| 1793 |
+
VME/VME0108.png
|
| 1794 |
+
VME/VME1346.png
|
| 1795 |
+
VME/VME2718.png
|
| 1796 |
+
VME/VME2987.png
|
| 1797 |
+
VME/VME3059.png
|
| 1798 |
+
VME/VME2196.png
|
| 1799 |
+
VME/VME2577.png
|
| 1800 |
+
VME/VME0568.png
|
| 1801 |
+
VME/VME0912.png
|
| 1802 |
+
VME/VME0374.png
|
| 1803 |
+
VME/VME2836.png
|
| 1804 |
+
VME/VME4118.png
|
| 1805 |
+
VME/VME4162.png
|
| 1806 |
+
VME/VME0014.png
|
| 1807 |
+
VME/VME2472.png
|
| 1808 |
+
VME/VME0177.png
|
| 1809 |
+
VME/VME0389.png
|
| 1810 |
+
VME/VME0074.png
|
| 1811 |
+
VME/VME3315.png
|
| 1812 |
+
VME/VME1451.png
|
| 1813 |
+
VME/VME0053.png
|
| 1814 |
+
VME/VME2105.png
|
| 1815 |
+
VME/VME1749.png
|
| 1816 |
+
VME/VME1331.png
|
| 1817 |
+
VME/VME2822.png
|
| 1818 |
+
VME/VME1020.png
|
| 1819 |
+
VME/VME2461.png
|
| 1820 |
+
VME/VME0878.png
|
| 1821 |
+
VME/VME3434.png
|
| 1822 |
+
VME/VME1651.png
|
| 1823 |
+
VME/VME2717.png
|
| 1824 |
+
VME/VME1116.png
|
| 1825 |
+
VME/VME0338.png
|
| 1826 |
+
VME/VME3000.png
|
| 1827 |
+
VME/VME2947.png
|
| 1828 |
+
VME/VME2186.png
|
| 1829 |
+
VME/VME1779.png
|
| 1830 |
+
VME/VME1415.png
|
| 1831 |
+
VME/VME3425.png
|
| 1832 |
+
VME/VME1364.png
|
| 1833 |
+
VME/VME0050.png
|
| 1834 |
+
VME/VME1199.png
|
| 1835 |
+
VME/VME3886.png
|
| 1836 |
+
VME/VME0247.png
|
| 1837 |
+
VME/VME0829.png
|
| 1838 |
+
VME/VME3094.png
|
| 1839 |
+
VME/VME2664.png
|
| 1840 |
+
VME/VME2945.png
|
| 1841 |
+
VME/VME2708.png
|
| 1842 |
+
VME/VME3762.png
|
| 1843 |
+
VME/VME1611.png
|
| 1844 |
+
VME/VME2632.png
|
| 1845 |
+
VME/VME2403.png
|
| 1846 |
+
VME/VME0502.png
|
| 1847 |
+
VME/VME2249.png
|
| 1848 |
+
VME/VME2099.png
|
| 1849 |
+
VME/VME2583.png
|
| 1850 |
+
VME/VME2746.png
|
| 1851 |
+
VME/VME0719.png
|
| 1852 |
+
VME/VME2177.png
|
| 1853 |
+
VME/VME1042.png
|
| 1854 |
+
VME/VME3884.png
|
| 1855 |
+
VME/VME1294.png
|
| 1856 |
+
VME/VME3736.png
|
| 1857 |
+
VME/VME0149.png
|
| 1858 |
+
VME/VME3027.png
|
| 1859 |
+
VME/VME1556.png
|
| 1860 |
+
VME/VME0974.png
|
| 1861 |
+
VME/VME0680.png
|
| 1862 |
+
VME/VME2532.png
|
| 1863 |
+
VME/VME3949.png
|
| 1864 |
+
VME/VME1031.png
|
| 1865 |
+
VME/VME2976.png
|
| 1866 |
+
VME/VME3251.png
|
| 1867 |
+
VME/VME3528.png
|
| 1868 |
+
VME/VME2875.png
|
| 1869 |
+
VME/VME4274.png
|
| 1870 |
+
VME/VME0797.png
|
| 1871 |
+
VME/VME1576.png
|
| 1872 |
+
VME/VME1106.png
|
| 1873 |
+
VME/VME0511.png
|
| 1874 |
+
VME/VME3401.png
|
| 1875 |
+
VME/VME1667.png
|
| 1876 |
+
VME/VME2676.png
|
| 1877 |
+
VME/VME2172.png
|
| 1878 |
+
VME/VME1757.png
|
| 1879 |
+
VME/VME1147.png
|
| 1880 |
+
VME/VME1686.png
|
| 1881 |
+
VME/VME1754.png
|
| 1882 |
+
VME/VME2737.png
|
| 1883 |
+
VME/VME1446.png
|
| 1884 |
+
VME/VME2297.png
|
| 1885 |
+
VME/VME4106.png
|
| 1886 |
+
VME/VME3875.png
|
| 1887 |
+
VME/VME0042.png
|
| 1888 |
+
VME/VME0694.png
|
| 1889 |
+
VME/VME3554.png
|
| 1890 |
+
VME/VME2625.png
|
| 1891 |
+
VME/VME4271.png
|
| 1892 |
+
VME/VME0218.png
|
| 1893 |
+
VME/VME3504.png
|
| 1894 |
+
VME/VME0051.png
|
| 1895 |
+
VME/VME1291.png
|
| 1896 |
+
VME/VME0896.png
|
| 1897 |
+
VME/VME4002.png
|
| 1898 |
+
VME/VME3793.png
|
| 1899 |
+
VME/VME1377.png
|
| 1900 |
+
VME/VME1227.png
|
| 1901 |
+
VME/VME4151.png
|
| 1902 |
+
VME/VME1325.png
|
| 1903 |
+
VME/VME1348.png
|
| 1904 |
+
VME/VME3678.png
|
| 1905 |
+
VME/VME0081.png
|
| 1906 |
+
VME/VME2175.png
|
| 1907 |
+
VME/VME4246.png
|
| 1908 |
+
VME/VME0209.png
|
| 1909 |
+
VME/VME4273.png
|
| 1910 |
+
VME/VME2823.png
|
| 1911 |
+
VME/VME2051.png
|
| 1912 |
+
VME/VME1439.png
|
| 1913 |
+
VME/VME0273.png
|
| 1914 |
+
VME/VME1934.png
|
| 1915 |
+
VME/VME1394.png
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| 1916 |
+
VME/VME0728.png
|
| 1917 |
+
VME/VME2731.png
|
| 1918 |
+
VME/VME0623.png
|
| 1919 |
+
VME/VME0260.png
|
| 1920 |
+
VME/VME0787.png
|
| 1921 |
+
VME/VME4001.png
|
| 1922 |
+
VME/VME0796.png
|
| 1923 |
+
VME/VME0769.png
|
| 1924 |
+
VME/VME2382.png
|
| 1925 |
+
VME/VME1821.png
|
| 1926 |
+
VME/VME2785.png
|
| 1927 |
+
VME/VME1128.png
|
| 1928 |
+
VME/VME0519.png
|
| 1929 |
+
VME/VME0930.png
|
| 1930 |
+
VME/VME0494.png
|
| 1931 |
+
VME/VME0300.png
|
| 1932 |
+
VME/VME2271.png
|
| 1933 |
+
VME/VME0809.png
|
| 1934 |
+
VME/VME0978.png
|
| 1935 |
+
VME/VME1954.png
|
| 1936 |
+
VME/VME2471.png
|
| 1937 |
+
VME/VME1687.png
|
| 1938 |
+
VME/VME0021.png
|
| 1939 |
+
VME/VME1075.png
|
| 1940 |
+
VME/VME0990.png
|
| 1941 |
+
VME/VME2982.png
|
| 1942 |
+
VME/VME3328.png
|
| 1943 |
+
VME/VME2369.png
|
| 1944 |
+
VME/VME1352.png
|
| 1945 |
+
VME/VME1486.png
|
| 1946 |
+
VME/VME2486.png
|
| 1947 |
+
VME/VME3129.png
|
| 1948 |
+
VME/VME0094.png
|
| 1949 |
+
VME/VME2984.png
|
| 1950 |
+
VME/VME0924.png
|
| 1951 |
+
VME/VME0401.png
|
| 1952 |
+
VME/VME1521.png
|
| 1953 |
+
VME/VME3060.png
|
| 1954 |
+
VME/VME1138.png
|
| 1955 |
+
VME/VME1318.png
|
| 1956 |
+
VME/VME1930.png
|
| 1957 |
+
VME/VME3690.png
|
| 1958 |
+
VME/VME3196.png
|
| 1959 |
+
VME/VME1506.png
|
| 1960 |
+
VME/VME2018.png
|
| 1961 |
+
VME/VME0299.png
|
| 1962 |
+
VME/VME2240.png
|
| 1963 |
+
VME/VME3938.png
|
| 1964 |
+
VME/VME1243.png
|
| 1965 |
+
VME/VME1448.png
|
| 1966 |
+
VME/VME1438.png
|
| 1967 |
+
VME/VME1945.png
|
| 1968 |
+
VME/VME1986.png
|
| 1969 |
+
VME/VME1845.png
|
| 1970 |
+
VME/VME4059.png
|
| 1971 |
+
VME/VME2269.png
|
| 1972 |
+
VME/VME3030.png
|
| 1973 |
+
VME/VME2001.png
|
| 1974 |
+
VME/VME0983.png
|
| 1975 |
+
VME/VME0376.png
|
| 1976 |
+
VME/VME1518.png
|
| 1977 |
+
VME/VME1735.png
|
| 1978 |
+
VME/VME0871.png
|
| 1979 |
+
VME/VME3169.png
|
| 1980 |
+
VME/VME2323.png
|
| 1981 |
+
VME/VME0117.png
|
| 1982 |
+
VME/VME1094.png
|
| 1983 |
+
VME/VME3440.png
|
| 1984 |
+
VME/VME1076.png
|
| 1985 |
+
VME/VME1033.png
|
| 1986 |
+
VME/VME2083.png
|
| 1987 |
+
VME/VME3801.png
|
| 1988 |
+
VME/VME1419.png
|
| 1989 |
+
VME/VME3245.png
|
| 1990 |
+
VME/VME3573.png
|
| 1991 |
+
VME/VME0926.png
|
| 1992 |
+
VME/VME4090.png
|
| 1993 |
+
VME/VME1087.png
|
| 1994 |
+
VME/VME2954.png
|
| 1995 |
+
VME/VME2379.png
|
| 1996 |
+
VME/VME1869.png
|
| 1997 |
+
VME/VME2377.png
|
| 1998 |
+
VME/VME0070.png
|
| 1999 |
+
VME/VME1976.png
|
| 2000 |
+
VME/VME0057.png
|
| 2001 |
+
VME/VME0390.png
|
| 2002 |
+
VME/VME0200.png
|
| 2003 |
+
VME/VME2257.png
|
| 2004 |
+
VME/VME1413.png
|
| 2005 |
+
VME/VME1268.png
|
| 2006 |
+
VME/VME4058.png
|
| 2007 |
+
VME/VME1063.png
|
| 2008 |
+
VME/VME2804.png
|
| 2009 |
+
VME/VME2368.png
|
| 2010 |
+
VME/VME2904.png
|
| 2011 |
+
VME/VME0836.png
|
| 2012 |
+
VME/VME3838.png
|
| 2013 |
+
VME/VME0571.png
|
| 2014 |
+
VME/VME4077.png
|
| 2015 |
+
VME/VME2935.png
|
| 2016 |
+
VME/VME3684.png
|
| 2017 |
+
VME/VME3300.png
|
| 2018 |
+
VME/VME1739.png
|
| 2019 |
+
VME/VME4255.png
|
| 2020 |
+
VME/VME3349.png
|
| 2021 |
+
VME/VME1982.png
|
| 2022 |
+
VME/VME2144.png
|
| 2023 |
+
VME/VME3412.png
|
| 2024 |
+
VME/VME4209.png
|
| 2025 |
+
VME/VME2786.png
|
| 2026 |
+
VME/VME1538.png
|
| 2027 |
+
VME/VME2192.png
|
| 2028 |
+
VME/VME2767.png
|
| 2029 |
+
VME/VME0010.png
|
| 2030 |
+
VME/VME1181.png
|
| 2031 |
+
VME/VME0489.png
|
| 2032 |
+
VME/VME3177.png
|
| 2033 |
+
VME/VME3828.png
|
| 2034 |
+
VME/VME2908.png
|
| 2035 |
+
VME/VME1322.png
|
| 2036 |
+
VME/VME3691.png
|
| 2037 |
+
VME/VME1803.png
|
| 2038 |
+
VME/VME3620.png
|
| 2039 |
+
VME/VME2714.png
|
| 2040 |
+
VME/VME1160.png
|
| 2041 |
+
VME/VME0481.png
|
| 2042 |
+
VME/VME2894.png
|
| 2043 |
+
VME/VME0082.png
|
| 2044 |
+
VME/VME3846.png
|
| 2045 |
+
VME/VME3054.png
|
| 2046 |
+
VME/VME0459.png
|
| 2047 |
+
VME/VME3019.png
|
| 2048 |
+
VME/VME2353.png
|
| 2049 |
+
VME/VME3854.png
|
| 2050 |
+
VME/VME0309.png
|
| 2051 |
+
VME/VME0103.png
|
| 2052 |
+
VME/VME4212.png
|
| 2053 |
+
VME/VME2162.png
|
| 2054 |
+
VME/VME1061.png
|
| 2055 |
+
VME/VME0779.png
|
| 2056 |
+
VME/VME1337.png
|
| 2057 |
+
VME/VME2182.png
|
| 2058 |
+
VME/VME2470.png
|
| 2059 |
+
VME/VME1731.png
|
| 2060 |
+
VME/VME3125.png
|
| 2061 |
+
VME/VME1238.png
|
| 2062 |
+
VME/VME1759.png
|
| 2063 |
+
VME/VME0334.png
|
| 2064 |
+
VME/VME0480.png
|
| 2065 |
+
VME/VME4220.png
|
| 2066 |
+
VME/VME0332.png
|
| 2067 |
+
VME/VME1142.png
|
| 2068 |
+
VME/VME1420.png
|
| 2069 |
+
VME/VME3013.png
|
| 2070 |
+
VME/VME3531.png
|
| 2071 |
+
VME/VME2946.png
|
| 2072 |
+
VME/VME3742.png
|
| 2073 |
+
VME/VME2645.png
|
| 2074 |
+
VME/VME0650.png
|
| 2075 |
+
VME/VME1251.png
|
| 2076 |
+
VME/VME0772.png
|
| 2077 |
+
VME/VME0863.png
|
| 2078 |
+
VME/VME3806.png
|
| 2079 |
+
VME/VME2395.png
|
| 2080 |
+
VME/VME4203.png
|
| 2081 |
+
VME/VME1654.png
|
| 2082 |
+
VME/VME0323.png
|
| 2083 |
+
VME/VME3418.png
|
| 2084 |
+
VME/VME0752.png
|
| 2085 |
+
VME/VME1583.png
|
| 2086 |
+
VME/VME2312.png
|
| 2087 |
+
VME/VME2795.png
|
| 2088 |
+
VME/VME2017.png
|
| 2089 |
+
VME/VME3745.png
|
data_utils/original_test.txt
ADDED
|
The diff for this file is too large to render.
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|
|
|
data_utils/original_train.txt
ADDED
|
The diff for this file is too large to render.
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|
|
|
data_utils/original_val.txt
ADDED
|
@@ -0,0 +1,5896 @@
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VME/VME0571.png
|
| 5818 |
+
VME/VME4077.png
|
| 5819 |
+
VME/VME2935.png
|
| 5820 |
+
VME/VME3684.png
|
| 5821 |
+
VME/VME3300.png
|
| 5822 |
+
VME/VME1739.png
|
| 5823 |
+
VME/VME4255.png
|
| 5824 |
+
VME/VME3349.png
|
| 5825 |
+
VME/VME1982.png
|
| 5826 |
+
VME/VME2144.png
|
| 5827 |
+
VME/VME3412.png
|
| 5828 |
+
VME/VME4209.png
|
| 5829 |
+
VME/VME2786.png
|
| 5830 |
+
VME/VME1538.png
|
| 5831 |
+
VME/VME2192.png
|
| 5832 |
+
VME/VME2767.png
|
| 5833 |
+
VME/VME0010.png
|
| 5834 |
+
VME/VME1181.png
|
| 5835 |
+
VME/VME0489.png
|
| 5836 |
+
VME/VME3177.png
|
| 5837 |
+
VME/VME3828.png
|
| 5838 |
+
VME/VME2908.png
|
| 5839 |
+
VME/VME1322.png
|
| 5840 |
+
VME/VME3691.png
|
| 5841 |
+
VME/VME1803.png
|
| 5842 |
+
VME/VME3620.png
|
| 5843 |
+
VME/VME3902.png
|
| 5844 |
+
VME/VME2714.png
|
| 5845 |
+
VME/VME1160.png
|
| 5846 |
+
VME/VME0481.png
|
| 5847 |
+
VME/VME2894.png
|
| 5848 |
+
VME/VME0082.png
|
| 5849 |
+
VME/VME3846.png
|
| 5850 |
+
VME/VME3054.png
|
| 5851 |
+
VME/VME0459.png
|
| 5852 |
+
VME/VME3019.png
|
| 5853 |
+
VME/VME2353.png
|
| 5854 |
+
VME/VME3854.png
|
| 5855 |
+
VME/VME0309.png
|
| 5856 |
+
VME/VME0103.png
|
| 5857 |
+
VME/VME4212.png
|
| 5858 |
+
VME/VME2162.png
|
| 5859 |
+
VME/VME1061.png
|
| 5860 |
+
VME/VME0779.png
|
| 5861 |
+
VME/VME1337.png
|
| 5862 |
+
VME/VME2182.png
|
| 5863 |
+
VME/VME2470.png
|
| 5864 |
+
VME/VME1731.png
|
| 5865 |
+
VME/VME3125.png
|
| 5866 |
+
VME/VME1238.png
|
| 5867 |
+
VME/VME1759.png
|
| 5868 |
+
VME/VME0334.png
|
| 5869 |
+
VME/VME0480.png
|
| 5870 |
+
VME/VME4220.png
|
| 5871 |
+
VME/VME0332.png
|
| 5872 |
+
VME/VME1142.png
|
| 5873 |
+
VME/VME1420.png
|
| 5874 |
+
VME/VME3013.png
|
| 5875 |
+
VME/VME3531.png
|
| 5876 |
+
VME/VME2946.png
|
| 5877 |
+
VME/VME3742.png
|
| 5878 |
+
VME/VME2645.png
|
| 5879 |
+
VME/VME0650.png
|
| 5880 |
+
VME/VME1251.png
|
| 5881 |
+
VME/VME0772.png
|
| 5882 |
+
VME/VME0863.png
|
| 5883 |
+
VME/VME3806.png
|
| 5884 |
+
VME/VME2395.png
|
| 5885 |
+
VME/VME4203.png
|
| 5886 |
+
VME/VME1654.png
|
| 5887 |
+
VME/VME0975.png
|
| 5888 |
+
VME/VME0323.png
|
| 5889 |
+
VME/VME3418.png
|
| 5890 |
+
VME/VME0752.png
|
| 5891 |
+
VME/VME1566.png
|
| 5892 |
+
VME/VME1583.png
|
| 5893 |
+
VME/VME2312.png
|
| 5894 |
+
VME/VME2795.png
|
| 5895 |
+
VME/VME2017.png
|
| 5896 |
+
VME/VME3745.png
|
dior/dior_to_coco.py
ADDED
|
@@ -0,0 +1,195 @@
|
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|
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|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
from pathlib import Path
|
| 3 |
+
import fire
|
| 4 |
+
from PIL import Image
|
| 5 |
+
from sahi.utils.coco import Coco, CocoAnnotation, CocoCategory, CocoImage
|
| 6 |
+
from sahi.utils.file import save_json
|
| 7 |
+
from tqdm import tqdm
|
| 8 |
+
import xml.etree.ElementTree as ET
|
| 9 |
+
import sys;
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
NAME_TO_COCO_CATEGORY = {
|
| 13 |
+
"stadium": {"name": "stadium", "supercategory": "stadium"},
|
| 14 |
+
"Expressway-toll-station": {"name": "Expressway-toll-station", "supercategory": "Expressway-toll-station"},
|
| 15 |
+
"bridge": {"name": "bridge", "supercategory": "bridge"},
|
| 16 |
+
"groundtrackfield": {"name": "groundtrackfield", "supercategory": "groundtrackfield"},
|
| 17 |
+
"Expressway-Service-area": {"name": "groundtrackfield", "supercategory": "groundtrackfield"},
|
| 18 |
+
"dam": {"name": "dam", "supercategory": "dam"},
|
| 19 |
+
"ship": {"name": "ship", "supercategory": "ship"},
|
| 20 |
+
"basketballcourt": {"name": "basketballcourt", "supercategory": "basketballcourt"},
|
| 21 |
+
"vehicle": {"name": "vehicle", "supercategory": "vehicle"},
|
| 22 |
+
"golffield": {"name": "golffield", "supercategory": "golffield"},
|
| 23 |
+
"airplane": {"name": "airplane", "supercategory": "airplane"},
|
| 24 |
+
"baseballfield": {"name": "baseballfield", "supercategory": "baseballfield"},
|
| 25 |
+
"airport": {"name": "airport", "supercategory": "airport"},
|
| 26 |
+
"harbor": {"name": "harbor", "supercategory": "harbor"},
|
| 27 |
+
"chimney": {"name": "chimney", "supercategory": "chimney"},
|
| 28 |
+
"tenniscourt": {"name": "tenniscourt", "supercategory": "tenniscourt"},
|
| 29 |
+
"trainstation": {"name": "trainstation", "supercategory": "trainstation"},
|
| 30 |
+
"overpass": {"name": "overpass", "supercategory": "overpass"},
|
| 31 |
+
"storagetank": {"name": "storagetank", "supercategory": "storagetank"},
|
| 32 |
+
"windmill": {"name": "windmill", "supercategory": "windmill"}
|
| 33 |
+
}
|
| 34 |
+
|
| 35 |
+
CATEGORY_ID_REMAPPING = {
|
| 36 |
+
"stadium": "0",
|
| 37 |
+
"Expressway-toll-station": "1",
|
| 38 |
+
"bridge": "2",
|
| 39 |
+
"groundtrackfield": "3",
|
| 40 |
+
"Expressway-Service-area": "4",
|
| 41 |
+
"dam": "5",
|
| 42 |
+
"ship": "6",
|
| 43 |
+
"basketballcourt": "7",
|
| 44 |
+
"vehicle": "8",
|
| 45 |
+
"golffield": "9",
|
| 46 |
+
"airplane": "10",
|
| 47 |
+
"baseballfield": "11",
|
| 48 |
+
"airport":"12",
|
| 49 |
+
"harbor":"13",
|
| 50 |
+
"chimney":"14",
|
| 51 |
+
"tenniscourt":"15",
|
| 52 |
+
"trainstation":"16",
|
| 53 |
+
"overpass":"17",
|
| 54 |
+
"storagetank":"18",
|
| 55 |
+
"windmill":"19"
|
| 56 |
+
}
|
| 57 |
+
|
| 58 |
+
|
| 59 |
+
def dior_to_coco(
|
| 60 |
+
data_folder_dir,
|
| 61 |
+
split_images_path,
|
| 62 |
+
output_file_path,
|
| 63 |
+
mode,
|
| 64 |
+
category_id_remapping=None,
|
| 65 |
+
):
|
| 66 |
+
"""
|
| 67 |
+
Converts DIOR annotations into coco annotation.
|
| 68 |
+
|
| 69 |
+
Args:
|
| 70 |
+
data_folder_dir: str
|
| 71 |
+
'dior' folder directory
|
| 72 |
+
split_images_path: str
|
| 73 |
+
split_images_path txt file
|
| 74 |
+
output_file_path: str
|
| 75 |
+
Output file path
|
| 76 |
+
mode: str
|
| 77 |
+
mode original|car_other|car
|
| 78 |
+
category_id_remapping: dict
|
| 79 |
+
Used for selecting desired category ids and mapping them.
|
| 80 |
+
If not provided, vedai mapping will be used.
|
| 81 |
+
format: str(id) to str(id)
|
| 82 |
+
"""
|
| 83 |
+
|
| 84 |
+
# init paths/folders
|
| 85 |
+
input_image_folder = str(Path(data_folder_dir) / "JPEGImages-all")
|
| 86 |
+
input_ann_folder = str(Path(data_folder_dir) / "Annotations/Horizontal Bounding Boxes")
|
| 87 |
+
|
| 88 |
+
image_filepath_list = os.listdir(input_image_folder)
|
| 89 |
+
|
| 90 |
+
if category_id_remapping is None:
|
| 91 |
+
category_id_remapping = CATEGORY_ID_REMAPPING
|
| 92 |
+
|
| 93 |
+
# init coco object
|
| 94 |
+
coco = Coco()
|
| 95 |
+
|
| 96 |
+
if mode == 'original':
|
| 97 |
+
# append categories
|
| 98 |
+
categories = ["stadium", "Expressway-toll-station", "bridge", "groundtrackfield", "Expressway-Service-area", "dam",
|
| 99 |
+
"ship", "basketballcourt", "vehicle", "golffield", "airplane", "baseballfield", "airport",
|
| 100 |
+
"harbor", "chimney", "tenniscourt", "trainstation", "overpass", "storagetank", "windmill"]
|
| 101 |
+
for i, cat in enumerate(categories):
|
| 102 |
+
coco.add_category(CocoCategory(id=i, name=cat))
|
| 103 |
+
|
| 104 |
+
elif mode == 'car_other':
|
| 105 |
+
print('"car_other" mode categories')
|
| 106 |
+
coco.add_category(CocoCategory(id=0, name='Other'))
|
| 107 |
+
coco.add_category(CocoCategory(id=1, name='Car'))
|
| 108 |
+
for k,v in category_id_remapping.items():
|
| 109 |
+
if k in 'vehicle':
|
| 110 |
+
category_id_remapping[k] =1
|
| 111 |
+
else:
|
| 112 |
+
category_id_remapping[k]=0
|
| 113 |
+
elif mode == 'car':
|
| 114 |
+
# car related categories: car, pickup, van
|
| 115 |
+
print('"car" mode categories')
|
| 116 |
+
coco.add_category(CocoCategory(id=0, name='Car'))
|
| 117 |
+
category_id_remapping = {'vehicle': '0'}
|
| 118 |
+
else:
|
| 119 |
+
print('pick a defined mode: [original/car_other/car]')
|
| 120 |
+
sys.exit()
|
| 121 |
+
|
| 122 |
+
|
| 123 |
+
split_images_lst = []
|
| 124 |
+
with open(split_images_path, 'r') as f:
|
| 125 |
+
for line in f:
|
| 126 |
+
split_images_lst.append(line.strip().split('/')[1])
|
| 127 |
+
|
| 128 |
+
# convert dior annotations to coco
|
| 129 |
+
for image_filename in tqdm(image_filepath_list):
|
| 130 |
+
if image_filename in split_images_lst:
|
| 131 |
+
# get image properties
|
| 132 |
+
image_filepath = str(Path(input_image_folder) / image_filename)
|
| 133 |
+
annotation_filename = image_filename.split(".jpg")[0] + ".xml"
|
| 134 |
+
annotation_filepath = str(Path(input_ann_folder) / annotation_filename)
|
| 135 |
+
image = Image.open(image_filepath)
|
| 136 |
+
cocoimage_filename = str(Path(image_filepath)).split(str(Path(data_folder_dir)))[1]
|
| 137 |
+
if cocoimage_filename[0] == os.sep:
|
| 138 |
+
cocoimage_filename = cocoimage_filename[1:]
|
| 139 |
+
# create coco image object
|
| 140 |
+
coco_image = CocoImage(file_name=cocoimage_filename, height=image.size[1], width=image.size[0])
|
| 141 |
+
# read xml annotation file
|
| 142 |
+
tree = ET.parse(annotation_filepath)
|
| 143 |
+
xml_root = tree.getroot()
|
| 144 |
+
|
| 145 |
+
# get all object tags in the xml file
|
| 146 |
+
objects = xml_root.findall('object')
|
| 147 |
+
|
| 148 |
+
for obj in objects:
|
| 149 |
+
cat_name = obj[0].text
|
| 150 |
+
bbox_tag = obj[2]
|
| 151 |
+
xmin = int(obj[2][0].text)
|
| 152 |
+
ymin = int(obj[2][1].text)
|
| 153 |
+
xmax = int(obj[2][2].text)
|
| 154 |
+
ymax = int(obj[2][3].text)
|
| 155 |
+
#
|
| 156 |
+
# width = maxx-minx, height = maxy-miny
|
| 157 |
+
width = xmax - xmin
|
| 158 |
+
height = ymax - ymin
|
| 159 |
+
|
| 160 |
+
bbox = [xmin,
|
| 161 |
+
ymin,
|
| 162 |
+
width,
|
| 163 |
+
height]
|
| 164 |
+
|
| 165 |
+
# get category id and name
|
| 166 |
+
category_name = cat_name
|
| 167 |
+
if category_name in category_id_remapping.keys():
|
| 168 |
+
remapped_category_id = category_id_remapping[category_name]
|
| 169 |
+
else:
|
| 170 |
+
continue
|
| 171 |
+
|
| 172 |
+
# create coco annotation and append it to coco image
|
| 173 |
+
coco_annotation = CocoAnnotation.from_coco_bbox(
|
| 174 |
+
bbox=bbox,
|
| 175 |
+
category_id=int(remapped_category_id),
|
| 176 |
+
category_name=category_name,
|
| 177 |
+
)
|
| 178 |
+
if mode == 'original':
|
| 179 |
+
if coco_annotation.area > 0:
|
| 180 |
+
coco_image.add_annotation(coco_annotation)
|
| 181 |
+
elif mode == 'car_other':
|
| 182 |
+
if 0 < coco_annotation.area < 400:
|
| 183 |
+
coco_image.add_annotation(coco_annotation)
|
| 184 |
+
elif mode == 'car':
|
| 185 |
+
if 0 < coco_annotation.area < 400:
|
| 186 |
+
coco_image.add_annotation(coco_annotation)
|
| 187 |
+
coco.add_image(coco_image)
|
| 188 |
+
|
| 189 |
+
save_path = output_file_path
|
| 190 |
+
all_json_path = Path(save_path) / "{}.json".format(split_images_path.split('.')[0])
|
| 191 |
+
save_json(data=coco.json, save_path=all_json_path)
|
| 192 |
+
|
| 193 |
+
|
| 194 |
+
if __name__ == "__main__":
|
| 195 |
+
fire.Fire(dior_to_coco)
|
dota2/dota2_to_coco.py
ADDED
|
@@ -0,0 +1,185 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
| 1 |
+
import os
|
| 2 |
+
from pathlib import Path
|
| 3 |
+
import fire
|
| 4 |
+
from PIL import Image
|
| 5 |
+
from sahi.utils.coco import Coco, CocoAnnotation, CocoCategory, CocoImage
|
| 6 |
+
from sahi.utils.file import save_json
|
| 7 |
+
from tqdm import tqdm
|
| 8 |
+
import sys
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
CATEGORY_ID_REMAPPING = {
|
| 12 |
+
"plane": "0",
|
| 13 |
+
"baseball-diamond": "1",
|
| 14 |
+
"bridge": "2",
|
| 15 |
+
"ground-track-field": "3",
|
| 16 |
+
"small-vehicle": "4",
|
| 17 |
+
"large-vehicle": "5",
|
| 18 |
+
"ship": "6",
|
| 19 |
+
"tennis-court": "7",
|
| 20 |
+
"basketball-court": "8",
|
| 21 |
+
"storage-tank": "9",
|
| 22 |
+
"soccer-ball-field": "10",
|
| 23 |
+
"roundabout": "11",
|
| 24 |
+
"harbor": "12",
|
| 25 |
+
"swimming-pool": "13",
|
| 26 |
+
"helicopter": "14",
|
| 27 |
+
"container-crane": "15",
|
| 28 |
+
"airport": "16",
|
| 29 |
+
"helipad": "17"
|
| 30 |
+
}
|
| 31 |
+
|
| 32 |
+
def dota2_to_coco(
|
| 33 |
+
data_folder_dir,
|
| 34 |
+
split_images_path,
|
| 35 |
+
output_file_path,
|
| 36 |
+
mode,
|
| 37 |
+
category_id_remapping=None,
|
| 38 |
+
):
|
| 39 |
+
"""
|
| 40 |
+
Converts dota2 annotations into coco annotation.
|
| 41 |
+
|
| 42 |
+
Args:
|
| 43 |
+
data_folder_dir: str
|
| 44 |
+
'fair1m' folder directory
|
| 45 |
+
output_file_path: str
|
| 46 |
+
Output file path
|
| 47 |
+
category_id_remapping: dict
|
| 48 |
+
Used for selecting desired category ids and mapping them.
|
| 49 |
+
If not provided, vedai mapping will be used.
|
| 50 |
+
format: str(id) to str(id)
|
| 51 |
+
"""
|
| 52 |
+
|
| 53 |
+
# init paths/folders
|
| 54 |
+
input_image_folder = str(Path(data_folder_dir) / "images")
|
| 55 |
+
input_ann_folder = str(Path(data_folder_dir) / "labelTxt")
|
| 56 |
+
|
| 57 |
+
image_filepath_list = os.listdir(input_image_folder)
|
| 58 |
+
|
| 59 |
+
if category_id_remapping is None:
|
| 60 |
+
category_id_remapping = CATEGORY_ID_REMAPPING
|
| 61 |
+
|
| 62 |
+
# init coco object
|
| 63 |
+
coco = Coco()
|
| 64 |
+
|
| 65 |
+
if mode == 'original':
|
| 66 |
+
# append categories
|
| 67 |
+
categories = ["plane", "baseball-diamond", "bridge", "ground-track-field", "small-vehicle", "large-vehicle", "ship", "tennis-court", "basketball-court", "storage-tank", "soccer-ball-field", "roundabout", "harbor", "swimming-pool", "helicopter", "container-crane", "airport", "helipad"]
|
| 68 |
+
for i, cat in enumerate(categories):
|
| 69 |
+
coco.add_category(CocoCategory(id=i, name=cat))
|
| 70 |
+
elif mode == 'car_other':
|
| 71 |
+
print('"car_other" mode categories')
|
| 72 |
+
coco.add_category(CocoCategory(id=0, name='Other'))
|
| 73 |
+
coco.add_category(CocoCategory(id=1, name='Car'))
|
| 74 |
+
for k,v in category_id_remapping.items():
|
| 75 |
+
if k in 'small-vehicle':
|
| 76 |
+
category_id_remapping[k] =1
|
| 77 |
+
else:
|
| 78 |
+
category_id_remapping[k]=0
|
| 79 |
+
elif mode == 'car':
|
| 80 |
+
# car related categories: car, pickup, van
|
| 81 |
+
print('"car" mode categories')
|
| 82 |
+
coco.add_category(CocoCategory(id=0, name='Car'))
|
| 83 |
+
category_id_remapping = {'small-vehicle': '0'}
|
| 84 |
+
else:
|
| 85 |
+
print('pick a defined mode: [original/car_other/car]')
|
| 86 |
+
sys.exit()
|
| 87 |
+
|
| 88 |
+
split_images_lst = []
|
| 89 |
+
with open(split_images_path, 'r') as f:
|
| 90 |
+
for line in f:
|
| 91 |
+
broken = line.strip().split('/')
|
| 92 |
+
if len(broken) > 1:
|
| 93 |
+
split_images_lst.append(broken[1])
|
| 94 |
+
else:
|
| 95 |
+
split_images_lst.append(broken[0])
|
| 96 |
+
|
| 97 |
+
cnt=0
|
| 98 |
+
broken_set = set()
|
| 99 |
+
# convert fair1m annotations to coco
|
| 100 |
+
for image_filename in tqdm(image_filepath_list):
|
| 101 |
+
if image_filename in split_images_lst:
|
| 102 |
+
cnt+=1
|
| 103 |
+
# get image properties
|
| 104 |
+
image_filepath = str(Path(input_image_folder) / image_filename)
|
| 105 |
+
annotation_filename = image_filename.split(".png")[0] + ".txt"
|
| 106 |
+
annotation_filepath = str(Path(input_ann_folder) / annotation_filename)
|
| 107 |
+
image = Image.open(image_filepath)
|
| 108 |
+
cocoimage_filename = str(Path(image_filepath)).split(str(Path(data_folder_dir)))[1]
|
| 109 |
+
if cocoimage_filename[0] == os.sep:
|
| 110 |
+
cocoimage_filename = cocoimage_filename[1:]
|
| 111 |
+
# create coco image object
|
| 112 |
+
coco_image = CocoImage(file_name=cocoimage_filename, height=image.size[1], width=image.size[0])
|
| 113 |
+
# parse annotation file
|
| 114 |
+
###
|
| 115 |
+
file = open(annotation_filepath, "r")
|
| 116 |
+
lines = file.readlines()
|
| 117 |
+
for line in lines:
|
| 118 |
+
# x1 y1 x2 y2 x3 y3 x4 y4 category difficult
|
| 119 |
+
broken = line.strip().split(' ')
|
| 120 |
+
broken_set.add(len(broken))
|
| 121 |
+
if len(broken) > 1:
|
| 122 |
+
x_corners = []
|
| 123 |
+
y_corners = []
|
| 124 |
+
|
| 125 |
+
x_corners.append(int(float(broken[0])))
|
| 126 |
+
x_corners.append(int(float(broken[2])))
|
| 127 |
+
x_corners.append(int(float(broken[4])))
|
| 128 |
+
x_corners.append(int(float(broken[6])))
|
| 129 |
+
#
|
| 130 |
+
y_corners.append(int(float(broken[1])))
|
| 131 |
+
y_corners.append(int(float(broken[3])))
|
| 132 |
+
y_corners.append(int(float(broken[5])))
|
| 133 |
+
y_corners.append(int(float(broken[7])))
|
| 134 |
+
|
| 135 |
+
minx = min(x_corners)
|
| 136 |
+
miny = min(y_corners)
|
| 137 |
+
maxx = max(x_corners)
|
| 138 |
+
maxy = max(y_corners)
|
| 139 |
+
|
| 140 |
+
tbox = [minx, miny, maxx, maxy]
|
| 141 |
+
# width = maxx-minx, height = maxy-miny
|
| 142 |
+
width = tbox[2] - tbox[0]
|
| 143 |
+
height = tbox[3] - tbox[1]
|
| 144 |
+
|
| 145 |
+
bbox = [minx,
|
| 146 |
+
miny,
|
| 147 |
+
width,
|
| 148 |
+
height]
|
| 149 |
+
obj_area = width * height
|
| 150 |
+
|
| 151 |
+
category_name = broken[8]
|
| 152 |
+
if category_name in category_id_remapping.keys():
|
| 153 |
+
remapped_category_id = category_id_remapping[category_name]
|
| 154 |
+
else:
|
| 155 |
+
continue
|
| 156 |
+
|
| 157 |
+
# create coco annotation and append it to coco image
|
| 158 |
+
coco_annotation = CocoAnnotation.from_coco_bbox(
|
| 159 |
+
bbox=bbox,
|
| 160 |
+
category_id=int(remapped_category_id),
|
| 161 |
+
category_name=category_name,
|
| 162 |
+
)
|
| 163 |
+
if mode == 'original':
|
| 164 |
+
if coco_annotation.area > 0:
|
| 165 |
+
coco_image.add_annotation(coco_annotation)
|
| 166 |
+
elif mode == 'car_other':
|
| 167 |
+
if 0 < coco_annotation.area < 400:
|
| 168 |
+
coco_image.add_annotation(coco_annotation)
|
| 169 |
+
elif mode == 'car':
|
| 170 |
+
if 0 < coco_annotation.area < 400:
|
| 171 |
+
# if 0 < obj_area < 400:
|
| 172 |
+
coco_image.add_annotation(coco_annotation)
|
| 173 |
+
else:
|
| 174 |
+
continue
|
| 175 |
+
|
| 176 |
+
coco.add_image(coco_image)
|
| 177 |
+
|
| 178 |
+
print('cnt', cnt, 'broken_set', broken_set)
|
| 179 |
+
save_path = output_file_path
|
| 180 |
+
all_json_path = Path(save_path) / "{}.json".format(split_images_path.split('.')[0])
|
| 181 |
+
save_json(data=coco.json, save_path=all_json_path)
|
| 182 |
+
|
| 183 |
+
|
| 184 |
+
if __name__ == "__main__":
|
| 185 |
+
fire.Fire(dota2_to_coco)
|
dota2/filter_dota_coco.py
ADDED
|
@@ -0,0 +1,102 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""
|
| 2 |
+
this script is written to filter existing dota-coco files generated using dotadevkit for all categories
|
| 3 |
+
"""
|
| 4 |
+
import sys
|
| 5 |
+
import argparse
|
| 6 |
+
import json
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
def read_json_data(filename):
|
| 10 |
+
"""
|
| 11 |
+
Get json object from file
|
| 12 |
+
:param filename: path with filename
|
| 13 |
+
:return: json data
|
| 14 |
+
"""
|
| 15 |
+
with open(filename) as json_file:
|
| 16 |
+
data = json.load(json_file)
|
| 17 |
+
return data
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
def write_to_json(data, filename):
|
| 21 |
+
"""
|
| 22 |
+
Write dictionary to json file
|
| 23 |
+
:param data: dictionary data
|
| 24 |
+
:param filename: filename to write
|
| 25 |
+
:return: None
|
| 26 |
+
"""
|
| 27 |
+
print('writing the result in json file')
|
| 28 |
+
with open(filename, 'w') as outfile:
|
| 29 |
+
json.dump(data, outfile)
|
| 30 |
+
|
| 31 |
+
|
| 32 |
+
parser = argparse.ArgumentParser(description='DOTA-v2.0 coco files filter')
|
| 33 |
+
parser.add_argument('-f', '--input-file', dest='input_file', metavar='PATH', help='coco json file')
|
| 34 |
+
parser.add_argument('-o', '--out-file', dest='out_file', metavar='PATH', help='out coco json file')
|
| 35 |
+
parser.add_argument('-x', '--max', dest='max', type=int, metavar='N', help='max area')
|
| 36 |
+
parser.add_argument('--car-only', action='store_true', help='car-only mode')
|
| 37 |
+
args = parser.parse_args()
|
| 38 |
+
|
| 39 |
+
coco_json_file = args.input_file
|
| 40 |
+
out_coco_json_file = args.out_file
|
| 41 |
+
car_only_mode = args.car_only
|
| 42 |
+
max_area = args.max
|
| 43 |
+
|
| 44 |
+
data = read_json_data(coco_json_file)
|
| 45 |
+
|
| 46 |
+
print('original_images_len', len(data['images']))
|
| 47 |
+
print('original_annotations_len', len(data['annotations']))
|
| 48 |
+
print('original_categories_len', len(data['categories']))
|
| 49 |
+
print('original_categories', data['categories'])
|
| 50 |
+
|
| 51 |
+
new_lst = []
|
| 52 |
+
img_ids = set()
|
| 53 |
+
|
| 54 |
+
if car_only_mode:
|
| 55 |
+
for ann in data['annotations']:
|
| 56 |
+
if ann['area'] < max_area and ann['category_id'] == 5:
|
| 57 |
+
ann['category_id'] = 0
|
| 58 |
+
new_lst.append(ann)
|
| 59 |
+
img_ids.add(ann['image_id'])
|
| 60 |
+
|
| 61 |
+
data['annotations'] = new_lst
|
| 62 |
+
|
| 63 |
+
imgs_uniq = []
|
| 64 |
+
for i in data['images']:
|
| 65 |
+
if i['id'] in img_ids:
|
| 66 |
+
imgs_uniq.append(i)
|
| 67 |
+
|
| 68 |
+
|
| 69 |
+
data['images'] = imgs_uniq
|
| 70 |
+
data['categories'] = [{'id': 0, 'name': 'Car', 'supercategory': 'Car'}]
|
| 71 |
+
|
| 72 |
+
else:
|
| 73 |
+
print('=== car-other mode ===')
|
| 74 |
+
|
| 75 |
+
for ann in data['annotations']:
|
| 76 |
+
if ann['area'] < max_area:
|
| 77 |
+
if ann['category_id'] == 5: # car category
|
| 78 |
+
ann['category_id'] = 1
|
| 79 |
+
else:
|
| 80 |
+
ann['category_id'] = 0
|
| 81 |
+
new_lst.append(ann)
|
| 82 |
+
img_ids.add(ann['image_id'])
|
| 83 |
+
|
| 84 |
+
data['annotations'] = new_lst
|
| 85 |
+
|
| 86 |
+
imgs_uniq = []
|
| 87 |
+
for i in data['images']:
|
| 88 |
+
if i['id'] in img_ids:
|
| 89 |
+
imgs_uniq.append(i)
|
| 90 |
+
|
| 91 |
+
|
| 92 |
+
data['images'] = imgs_uniq
|
| 93 |
+
data['categories'] = [{'id': 0, 'name': 'Other', 'supercategory': 'Other'}, {'id': 1, 'name': 'Car', 'supercategory': 'Car'}]
|
| 94 |
+
|
| 95 |
+
print('=== END car-other mode ===')
|
| 96 |
+
|
| 97 |
+
print('====================================')
|
| 98 |
+
print('modified_images_len', len(data['images']))
|
| 99 |
+
print('modified_annotations_len', len(data['annotations']))
|
| 100 |
+
print('modified_categories_len', len(data['categories']))
|
| 101 |
+
|
| 102 |
+
write_to_json(data, out_coco_json_file)
|
dota2/readme.md
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# For DOTA-v2.0
|
| 2 |
+
|
| 3 |
+
1- Install the images and annotation files and follow the instructions given by the authors to construct DOTA-v2.0.
|
| 4 |
+
|
| 5 |
+
2- Create a folder with each split train-val-test, each has ``images`` and ``labelTxt`` subdirectories.
|
| 6 |
+
|
| 7 |
+
3- Under each one, create a symbolic link to the desired files in X_train.txt, X_val.txt, X_test.txt (X --> extract DOTA-v2.0 from Original_SPLIT.txt | CDSI_other_SPLIT.txt | CDSI_SPLIT.txt).
|
| 8 |
+
|
| 9 |
+
|
| 10 |
+
run the following for each split, specifiying its directory:
|
| 11 |
+
```bash
|
| 12 |
+
dotadevkit convert --version VERSION_NO DIRECTORY
|
| 13 |
+
```
|
| 14 |
+
|
fair1m/fair1m_to_coco.py
ADDED
|
@@ -0,0 +1,242 @@
|
|
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|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
from pathlib import Path
|
| 3 |
+
import fire
|
| 4 |
+
from PIL import Image
|
| 5 |
+
from sahi.utils.coco import Coco, CocoAnnotation, CocoCategory, CocoImage
|
| 6 |
+
from sahi.utils.file import save_json
|
| 7 |
+
from tqdm import tqdm
|
| 8 |
+
import sys
|
| 9 |
+
import xml.etree.ElementTree as ET
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
NAME_TO_COCO_CATEGORY = {
|
| 13 |
+
"A220": {"name": "A220", "supercategory": "A220"},
|
| 14 |
+
"A321": {"name": "A321", "supercategory": "A321"},
|
| 15 |
+
"A330": {"name": "A330", "supercategory": "A330"},
|
| 16 |
+
"A350": {"name": "A350", "supercategory": "A350"},
|
| 17 |
+
"ARJ21": {"name": "ARJ21", "supercategory": "ARJ21"},
|
| 18 |
+
"Baseball Field": {"name": "Baseball Field", "supercategory": "Baseball Field"},
|
| 19 |
+
"Basketball Court": {"name": "Basketball Court", "supercategory": "Basketball Court"},
|
| 20 |
+
"Boeing737": {"name": "Boeing737", "supercategory": "Boeing737"},
|
| 21 |
+
"Boeing747": {"name": "Boeing747", "supercategory": "Boeing747"},
|
| 22 |
+
"Boeing777": {"name": "Boeing777", "supercategory": "Boeing777"},
|
| 23 |
+
"Boeing787": {"name": "Boeing787", "supercategory": "Boeing787"},
|
| 24 |
+
"Bridge": {"name": "Bridge", "supercategory": "Bridge"},
|
| 25 |
+
"Bus": {"name": "Bus", "supercategory": "Bus"},
|
| 26 |
+
"C919": {"name": "C919", "supercategory": "C919"},
|
| 27 |
+
"Cargo Truck": {"name": "Cargo Truck", "supercategory": "Cargo Truck"},
|
| 28 |
+
"Dry Cargo Ship": {"name": "Dry Cargo Ship", "supercategory": "Dry Cargo Ship"},
|
| 29 |
+
"Dump Truck": {"name": "Dump Truck", "supercategory": "Dump Truck"},
|
| 30 |
+
"Engineering Ship": {"name": "Engineering Ship", "supercategory": "Engineering Ship"},
|
| 31 |
+
"Excavator": {"name": "Excavator", "supercategory": "Excavator"},
|
| 32 |
+
"Fishing Boat": {"name": "Fishing Boat", "supercategory": "Fishing Boat"},
|
| 33 |
+
"Football Field": {"name": "Football Field", "supercategory": "Football Field"},
|
| 34 |
+
"Intersection": {"name": "Intersection", "supercategory": "Intersection"},
|
| 35 |
+
"Liquid Cargo Ship": {"name": "Liquid Cargo Ship", "supercategory": "Liquid Cargo"},
|
| 36 |
+
"Motorboat": {"name": "Motorboat", "supercategory": "Motorboat"},
|
| 37 |
+
"other-airplane": {"name": "other-airplane", "supercategory": "other-airplane"},
|
| 38 |
+
"other-ship": {"name": "other-ship", "supercategory": "other-ship"},
|
| 39 |
+
"other-vehicle": {"name": "other-vehicle", "supercategory": "other-vehicle"},
|
| 40 |
+
"Passenger Ship": {"name": "Passenger Ship", "supercategory": "Passenger Ship"},
|
| 41 |
+
"Roundabout": {"name": "Roundabout", "supercategory": "Roundabout"},
|
| 42 |
+
"Small Car": {"name": "Small Car", "supercategory": "Small Car"},
|
| 43 |
+
"Tennis Court": {"name": "Tennis Court", "supercategory": "Tennis Court"},
|
| 44 |
+
"Tractor": {"name": "Tractor", "supercategory": "Tractor"},
|
| 45 |
+
"Trailer": {"name": "Trailer", "supercategory": "Trailer"},
|
| 46 |
+
"Truck Tractor": {"name": "Truck", "supercategory": "Truck"},
|
| 47 |
+
"Tugboat": {"name": "Tugboat", "supercategory": "Tugboat"},
|
| 48 |
+
"Van": {"name": "Van", "supercategory": "Van"},
|
| 49 |
+
"Warship": {"name": "Warship", "supercategory": "Warship"}
|
| 50 |
+
}
|
| 51 |
+
|
| 52 |
+
CATEGORY_ID_REMAPPING = {
|
| 53 |
+
"A220": "0",
|
| 54 |
+
"A321": "1",
|
| 55 |
+
"A330": "2",
|
| 56 |
+
"A350": "3",
|
| 57 |
+
"ARJ21": "4",
|
| 58 |
+
"Baseball Field": "5",
|
| 59 |
+
"Basketball Court": "6",
|
| 60 |
+
"Boeing737": "7",
|
| 61 |
+
"Boeing747": "8",
|
| 62 |
+
"Boeing777": "9",
|
| 63 |
+
"Boeing787": "10",
|
| 64 |
+
"Bridge": "11",
|
| 65 |
+
"Bus": "12",
|
| 66 |
+
"C919": "13",
|
| 67 |
+
"Cargo Truck": "14",
|
| 68 |
+
"Dry Cargo Ship": "15",
|
| 69 |
+
"Dump Truck": "16",
|
| 70 |
+
"Engineering Ship": "17",
|
| 71 |
+
"Excavator": "18",
|
| 72 |
+
"Fishing Boat": "19",
|
| 73 |
+
"Football Field": "20",
|
| 74 |
+
"Intersection": "21",
|
| 75 |
+
"Liquid Cargo Ship": "22",
|
| 76 |
+
"Motorboat": "23",
|
| 77 |
+
"other-airplane": "24",
|
| 78 |
+
"other-ship": "25",
|
| 79 |
+
"other-vehicle": "26",
|
| 80 |
+
"Passenger Ship": "27",
|
| 81 |
+
"Roundabout": "28",
|
| 82 |
+
"Small Car": "29",
|
| 83 |
+
"Tennis Court": "30",
|
| 84 |
+
"Tractor": "31",
|
| 85 |
+
"Trailer": "32",
|
| 86 |
+
"Truck Tractor": "33",
|
| 87 |
+
"Tugboat": "34",
|
| 88 |
+
"Van": "35",
|
| 89 |
+
"Warship": "36"
|
| 90 |
+
}
|
| 91 |
+
|
| 92 |
+
def fair1m_to_coco(
|
| 93 |
+
data_folder_dir,
|
| 94 |
+
split_images_path,
|
| 95 |
+
output_file_path,
|
| 96 |
+
mode,
|
| 97 |
+
category_id_remapping=None,
|
| 98 |
+
):
|
| 99 |
+
"""
|
| 100 |
+
Converts fair1m annotations into coco annotation.
|
| 101 |
+
|
| 102 |
+
Args:
|
| 103 |
+
data_folder_dir: str
|
| 104 |
+
'fair1m' folder directory
|
| 105 |
+
split_images_path: str
|
| 106 |
+
split_images_path txt file
|
| 107 |
+
output_file_path: str
|
| 108 |
+
Output file path
|
| 109 |
+
mode: str
|
| 110 |
+
mode original|car_other|car
|
| 111 |
+
category_id_remapping: dict
|
| 112 |
+
Used for selecting desired category ids and mapping them.
|
| 113 |
+
If not provided, vedai mapping will be used.
|
| 114 |
+
format: str(id) to str(id)
|
| 115 |
+
"""
|
| 116 |
+
|
| 117 |
+
# init paths/folders
|
| 118 |
+
input_image_folder = str(Path(data_folder_dir) / "images")
|
| 119 |
+
input_ann_folder = str(Path(data_folder_dir) / "labelXml")
|
| 120 |
+
|
| 121 |
+
image_filepath_list = os.listdir(input_image_folder)
|
| 122 |
+
|
| 123 |
+
if category_id_remapping is None:
|
| 124 |
+
category_id_remapping = CATEGORY_ID_REMAPPING
|
| 125 |
+
|
| 126 |
+
# init coco object
|
| 127 |
+
coco = Coco()
|
| 128 |
+
|
| 129 |
+
if mode == 'original':
|
| 130 |
+
# append categories
|
| 131 |
+
for category_id, category_name in CATEGORY_ID_TO_NAME.items():
|
| 132 |
+
if category_id in category_id_remapping.keys():
|
| 133 |
+
remapped_category_id = category_id_remapping[category_id]
|
| 134 |
+
coco_category = NAME_TO_COCO_CATEGORY[category_name]
|
| 135 |
+
coco.add_category(
|
| 136 |
+
CocoCategory(
|
| 137 |
+
id=int(remapped_category_id),
|
| 138 |
+
name=coco_category["name"],
|
| 139 |
+
supercategory=coco_category["supercategory"],
|
| 140 |
+
)
|
| 141 |
+
)
|
| 142 |
+
elif mode == 'car_other':
|
| 143 |
+
print('"car_other" mode categories')
|
| 144 |
+
coco.add_category(CocoCategory(id=0, name='Other'))
|
| 145 |
+
coco.add_category(CocoCategory(id=1, name='Car'))
|
| 146 |
+
for k,v in category_id_remapping.items():
|
| 147 |
+
if k in ['Small Car', 'Van']:
|
| 148 |
+
category_id_remapping[k] =1
|
| 149 |
+
else:
|
| 150 |
+
category_id_remapping[k]=0
|
| 151 |
+
elif mode == 'car':
|
| 152 |
+
# car related categories: car, pickup, van
|
| 153 |
+
print('"car" mode categories')
|
| 154 |
+
coco.add_category(CocoCategory(id=0, name='Car'))
|
| 155 |
+
category_id_remapping = {'Small Car': '0', 'Van': '0'}
|
| 156 |
+
else:
|
| 157 |
+
print('pick a defined mode: [original/car_other/car]')
|
| 158 |
+
sys.exit()
|
| 159 |
+
|
| 160 |
+
split_images_lst = []
|
| 161 |
+
with open(split_images_path, 'r') as f:
|
| 162 |
+
for line in f:
|
| 163 |
+
split_images_lst.append(line.strip().split('/')[1])
|
| 164 |
+
|
| 165 |
+
# convert fair1m annotations to coco
|
| 166 |
+
for image_filename in tqdm(image_filepath_list):
|
| 167 |
+
if image_filename in split_images_lst:
|
| 168 |
+
# get image properties
|
| 169 |
+
image_filepath = str(Path(input_image_folder) / image_filename)
|
| 170 |
+
annotation_filename = image_filename.split(".tif")[0] + ".xml"
|
| 171 |
+
annotation_filepath = str(Path(input_ann_folder) / annotation_filename)
|
| 172 |
+
image = Image.open(image_filepath)
|
| 173 |
+
cocoimage_filename = str(Path(image_filepath)).split(str(Path(data_folder_dir)))[1]
|
| 174 |
+
if cocoimage_filename[0] == os.sep:
|
| 175 |
+
cocoimage_filename = cocoimage_filename[1:]
|
| 176 |
+
# create coco image object
|
| 177 |
+
coco_image = CocoImage(file_name=cocoimage_filename, height=image.size[1], width=image.size[0])
|
| 178 |
+
# parse annotation file
|
| 179 |
+
###
|
| 180 |
+
tree = ET.parse(annotation_filepath)
|
| 181 |
+
xml_root = tree.getroot()
|
| 182 |
+
objects = xml_root[3]
|
| 183 |
+
for obj in objects:
|
| 184 |
+
cat = obj[3][0].text
|
| 185 |
+
points = obj[4]
|
| 186 |
+
p1 = points[0].text
|
| 187 |
+
p2 = points[1].text
|
| 188 |
+
p3 = points[2].text
|
| 189 |
+
p4 = points[3].text
|
| 190 |
+
x_corners = []
|
| 191 |
+
y_corners = []
|
| 192 |
+
for p in points:
|
| 193 |
+
broken = p.text.split(',')
|
| 194 |
+
x_corners.append(float(broken[0]))
|
| 195 |
+
y_corners.append(float(broken[1]))
|
| 196 |
+
|
| 197 |
+
minx = min(x_corners)
|
| 198 |
+
miny = min(y_corners)
|
| 199 |
+
maxx = max(x_corners)
|
| 200 |
+
maxy = max(y_corners)
|
| 201 |
+
|
| 202 |
+
tbox = [minx, miny, maxx, maxy]
|
| 203 |
+
# width = maxx-minx, height = maxy-miny
|
| 204 |
+
width = tbox[2] - tbox[0]
|
| 205 |
+
height = tbox[3] - tbox[1]
|
| 206 |
+
|
| 207 |
+
bbox = [minx,
|
| 208 |
+
miny,
|
| 209 |
+
width,
|
| 210 |
+
height]
|
| 211 |
+
obj_area = width * height
|
| 212 |
+
|
| 213 |
+
category_name = cat
|
| 214 |
+
if category_name in category_id_remapping.keys():
|
| 215 |
+
remapped_category_id = category_id_remapping[category_name]
|
| 216 |
+
else:
|
| 217 |
+
continue
|
| 218 |
+
|
| 219 |
+
# create coco annotation and append it to coco image
|
| 220 |
+
coco_annotation = CocoAnnotation.from_coco_bbox(
|
| 221 |
+
bbox=bbox,
|
| 222 |
+
category_id=int(remapped_category_id),
|
| 223 |
+
category_name=category_name,
|
| 224 |
+
)
|
| 225 |
+
if mode == 'original':
|
| 226 |
+
if coco_annotation.area > 0:
|
| 227 |
+
coco_image.add_annotation(coco_annotation)
|
| 228 |
+
elif mode == 'car_other':
|
| 229 |
+
if 0 < coco_annotation.area < 400:
|
| 230 |
+
coco_image.add_annotation(coco_annotation)
|
| 231 |
+
elif mode == 'car':
|
| 232 |
+
if 0 < coco_annotation.area < 400:
|
| 233 |
+
coco_image.add_annotation(coco_annotation)
|
| 234 |
+
coco.add_image(coco_image)
|
| 235 |
+
|
| 236 |
+
save_path = output_file_path
|
| 237 |
+
all_json_path = Path(save_path) / "{}.json".format(split_images_path.split('.')[0])
|
| 238 |
+
save_json(data=coco.json, save_path=all_json_path)
|
| 239 |
+
|
| 240 |
+
|
| 241 |
+
if __name__ == "__main__":
|
| 242 |
+
fire.Fire(fair1m_to_coco)
|
requirements.txt
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
sahi==0.11.14
|
| 2 |
+
fire==0.5.0
|
| 3 |
+
scipy==1.10.1
|
| 4 |
+
tqdm==4.65.0
|
| 5 |
+
pillow
|
| 6 |
+
dotadevkit==1.3.0
|
| 7 |
+
pyodi==0.0.7
|
utils/coco_filter_cat_area.py
ADDED
|
@@ -0,0 +1,90 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import json
|
| 2 |
+
import argparse
|
| 3 |
+
from collections import Counter
|
| 4 |
+
import pandas as pd
|
| 5 |
+
import numpy as np # to create dummy data
|
| 6 |
+
import sys
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
def read_json_data(filename):
|
| 10 |
+
"""
|
| 11 |
+
Get json object from file
|
| 12 |
+
:param filename: path with filename
|
| 13 |
+
:return: json data
|
| 14 |
+
"""
|
| 15 |
+
with open(filename) as json_file:
|
| 16 |
+
data = json.load(json_file)
|
| 17 |
+
return data
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
def write_to_json(data, filename):
|
| 21 |
+
"""
|
| 22 |
+
Write dictionary to json file
|
| 23 |
+
:param data: dictionary data
|
| 24 |
+
:param filename: filename to write
|
| 25 |
+
:return: None
|
| 26 |
+
"""
|
| 27 |
+
print('writing the result in json file', filename)
|
| 28 |
+
with open(filename, 'w') as outfile:
|
| 29 |
+
json.dump(data, outfile)
|
| 30 |
+
|
| 31 |
+
|
| 32 |
+
parser = argparse.ArgumentParser(description='coco files checker')
|
| 33 |
+
parser.add_argument('-o', '--out-file', dest='out_file', metavar='PATH', help='coco json file')
|
| 34 |
+
parser.add_argument('-i', '--input-file', dest='input_file', required=True, help='json file')
|
| 35 |
+
parser.add_argument('-m', '--min', dest='min', type=int, metavar='N', help='min area')
|
| 36 |
+
parser.add_argument('-x', '--max', dest='max', type=int, metavar='N', help='max area')
|
| 37 |
+
parser.add_argument('-c', '--cat', dest='cat', help='category')
|
| 38 |
+
|
| 39 |
+
args = parser.parse_args()
|
| 40 |
+
coco_json_file = args.input_file
|
| 41 |
+
out_json_file = args.out_file
|
| 42 |
+
min = args.min
|
| 43 |
+
max = args.max
|
| 44 |
+
cat = args.cat
|
| 45 |
+
|
| 46 |
+
data = read_json_data(coco_json_file)
|
| 47 |
+
|
| 48 |
+
print('orig annotation len', len(data['annotations']))
|
| 49 |
+
print('orig images len', len(data['images']))
|
| 50 |
+
print('orig categories', data['categories'])
|
| 51 |
+
|
| 52 |
+
cat_id = -1
|
| 53 |
+
for i in data['categories']:
|
| 54 |
+
if i['name'] == cat:
|
| 55 |
+
cat_id = i['id']
|
| 56 |
+
|
| 57 |
+
print('processing category:', cat, ', with id:', cat_id)
|
| 58 |
+
|
| 59 |
+
imgs = set()
|
| 60 |
+
obj_imgs = set()
|
| 61 |
+
new_ann = []
|
| 62 |
+
obj_range_area = []
|
| 63 |
+
cnt = 0
|
| 64 |
+
for i in data['annotations']:
|
| 65 |
+
|
| 66 |
+
if i['category_id'] == cat_id:
|
| 67 |
+
# get cars or others categories that are between min & max
|
| 68 |
+
if i['area'] >= min and i['area'] < max:
|
| 69 |
+
obj_range_area.append(i)
|
| 70 |
+
obj_imgs.add(i['image_id'])
|
| 71 |
+
new_ann.append(i)
|
| 72 |
+
imgs.add(i['image_id'])
|
| 73 |
+
|
| 74 |
+
|
| 75 |
+
# print('obj-area > 1024:', cnt)
|
| 76 |
+
print('len of small objects between min & max', len(new_ann))
|
| 77 |
+
print('len images-small between min & max', len(imgs))
|
| 78 |
+
########################
|
| 79 |
+
new_images = []
|
| 80 |
+
for img in data['images']:
|
| 81 |
+
if img['id'] in obj_imgs:
|
| 82 |
+
new_images.append(img)
|
| 83 |
+
|
| 84 |
+
data['images'] = new_images
|
| 85 |
+
data['annotations'] = obj_range_area
|
| 86 |
+
|
| 87 |
+
out_file = '{}/{}_{}_{}_{}.json'.format('/'.join(out_json_file.split('/')[:-1]), out_json_file.split('/')[-1].split('.')[0], cat, min, max)
|
| 88 |
+
if len(obj_range_area) > 0:
|
| 89 |
+
write_to_json(data, out_file)
|
| 90 |
+
|
vedai/vedai_to_coco.py
ADDED
|
@@ -0,0 +1,199 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
from pathlib import Path
|
| 3 |
+
import fire
|
| 4 |
+
from PIL import Image
|
| 5 |
+
from sahi.utils.coco import Coco, CocoAnnotation, CocoCategory, CocoImage
|
| 6 |
+
from sahi.utils.file import save_json
|
| 7 |
+
from tqdm import tqdm
|
| 8 |
+
import sys
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
CATEGORY_ID_TO_NAME = {
|
| 12 |
+
"1": "car",
|
| 13 |
+
"2":"truck",
|
| 14 |
+
"3": "pickup",
|
| 15 |
+
"4": "tractor",
|
| 16 |
+
"5": "camping",
|
| 17 |
+
"6": "boat",
|
| 18 |
+
"7": "motorcycle",
|
| 19 |
+
"9": "bus",
|
| 20 |
+
"10": "van",
|
| 21 |
+
"11": "other",
|
| 22 |
+
"12": "small car",
|
| 23 |
+
"13": "large car",
|
| 24 |
+
"31": "plane",
|
| 25 |
+
"23": "board"
|
| 26 |
+
}
|
| 27 |
+
|
| 28 |
+
CATEGORY_ID_REMAPPING = {
|
| 29 |
+
"1": "0",
|
| 30 |
+
"2": "1",
|
| 31 |
+
"4": "2",
|
| 32 |
+
"5": "3",
|
| 33 |
+
"7": "4",
|
| 34 |
+
"9": "5",
|
| 35 |
+
"10": "6",
|
| 36 |
+
"11": "7",
|
| 37 |
+
"31": "8",
|
| 38 |
+
"23": "9"
|
| 39 |
+
}
|
| 40 |
+
|
| 41 |
+
NAME_TO_COCO_CATEGORY = {
|
| 42 |
+
"car": {"name": "car", "supercategory": "car"},
|
| 43 |
+
"truck": {"name": "truck", "supercategory": "truck"},
|
| 44 |
+
"tractor": {"name": "tractor", "supercategory": "tractor"},
|
| 45 |
+
"camping": {"name": "camping", "supercategory": "camping"},
|
| 46 |
+
"motorcycle": {"name": "motorcycle", "supercategory": "motorcycle"},
|
| 47 |
+
"bus": {"name": "bus", "supercategory": "bus"},
|
| 48 |
+
"van": {"name": "van", "supercategory": "van"},
|
| 49 |
+
"other": {"name": "other", "supercategory": "other"},
|
| 50 |
+
"plane": {"name": "plane", "supercategory": "plane"},
|
| 51 |
+
"board": {"name": "board", "supercategory": "board"}
|
| 52 |
+
}
|
| 53 |
+
|
| 54 |
+
|
| 55 |
+
def vedai_to_coco(
|
| 56 |
+
data_folder_dir,
|
| 57 |
+
split_images_path,
|
| 58 |
+
output_dir,
|
| 59 |
+
mode,
|
| 60 |
+
category_id_remapping=None,
|
| 61 |
+
):
|
| 62 |
+
"""
|
| 63 |
+
Converts vedai annotations into coco annotation.
|
| 64 |
+
|
| 65 |
+
Args:
|
| 66 |
+
data_folder_dir: str
|
| 67 |
+
'vedai' folder directory
|
| 68 |
+
split_images_path: str
|
| 69 |
+
split_images_path txt file
|
| 70 |
+
output_file_path: str
|
| 71 |
+
Output file path
|
| 72 |
+
mode: str
|
| 73 |
+
mode original|car_other|car
|
| 74 |
+
category_id_remapping: dict
|
| 75 |
+
Used for selecting desired category ids and mapping them.
|
| 76 |
+
If not provided, vedai mapping will be used.
|
| 77 |
+
format: str(id) to str(id)
|
| 78 |
+
"""
|
| 79 |
+
|
| 80 |
+
# init paths/folders
|
| 81 |
+
input_image_folder = str(Path(data_folder_dir) / "Vehicules512")
|
| 82 |
+
input_ann_folder = str(Path(data_folder_dir) / "Annotations512")
|
| 83 |
+
|
| 84 |
+
image_filepath_list = os.listdir(input_image_folder)
|
| 85 |
+
|
| 86 |
+
# Path(output_dir).parents[0].mkdir(parents=True, exist_ok=True)
|
| 87 |
+
|
| 88 |
+
if category_id_remapping is None:
|
| 89 |
+
category_id_remapping = CATEGORY_ID_REMAPPING
|
| 90 |
+
|
| 91 |
+
# init coco object
|
| 92 |
+
coco = Coco()
|
| 93 |
+
|
| 94 |
+
# append categories
|
| 95 |
+
if mode == 'original':
|
| 96 |
+
for category_id, category_name in CATEGORY_ID_TO_NAME.items():
|
| 97 |
+
if category_id in category_id_remapping.keys():
|
| 98 |
+
remapped_category_id = category_id_remapping[category_id]
|
| 99 |
+
coco_category = NAME_TO_COCO_CATEGORY[category_name]
|
| 100 |
+
coco.add_category(
|
| 101 |
+
CocoCategory(
|
| 102 |
+
id=int(remapped_category_id),
|
| 103 |
+
name=coco_category["name"],
|
| 104 |
+
supercategory=coco_category["supercategory"],
|
| 105 |
+
)
|
| 106 |
+
)
|
| 107 |
+
elif mode == 'car_other':
|
| 108 |
+
print('"car_other" mode categories')
|
| 109 |
+
coco.add_category(CocoCategory(id=0, name='Other'))
|
| 110 |
+
coco.add_category(CocoCategory(id=1, name='Car'))
|
| 111 |
+
for k,v in category_id_remapping.items():
|
| 112 |
+
if k in ['1','10']:
|
| 113 |
+
category_id_remapping[k] =1
|
| 114 |
+
else:
|
| 115 |
+
category_id_remapping[k]=0
|
| 116 |
+
elif mode == 'car':
|
| 117 |
+
# car related categories: car, pickup, van
|
| 118 |
+
print('"car" mode categories')
|
| 119 |
+
coco.add_category(CocoCategory(id=0, name='Car'))
|
| 120 |
+
category_id_remapping = {'1': '0','10': '0'}
|
| 121 |
+
else:
|
| 122 |
+
print('pick a defined mode: [original/car_other/car]')
|
| 123 |
+
sys.exit()
|
| 124 |
+
|
| 125 |
+
split_images_lst = []
|
| 126 |
+
with open(split_images_path, 'r') as f:
|
| 127 |
+
for line in f:
|
| 128 |
+
split_images_lst.append(line.strip().split('/')[1])
|
| 129 |
+
|
| 130 |
+
# convert vedai annotations to coco
|
| 131 |
+
for image_name in tqdm(image_filepath_list):
|
| 132 |
+
# get image properties
|
| 133 |
+
if image_name in split_images_lst:
|
| 134 |
+
image_filepath = str(Path(input_image_folder) / image_name)
|
| 135 |
+
annotation_filename = image_name.split("_co.png")[0] + ".txt"
|
| 136 |
+
annotation_filepath = str(Path(input_ann_folder) / annotation_filename)
|
| 137 |
+
image = Image.open(image_filepath)
|
| 138 |
+
cocoimage_filename = str(Path(image_filepath)).split(str(Path(data_folder_dir)))[1]
|
| 139 |
+
if cocoimage_filename[0] == os.sep:
|
| 140 |
+
cocoimage_filename = cocoimage_filename[1:]
|
| 141 |
+
# create coco image object
|
| 142 |
+
coco_image = CocoImage(file_name=cocoimage_filename, height=image.size[1], width=image.size[0])
|
| 143 |
+
# parse annotation file
|
| 144 |
+
file = open(annotation_filepath, "r")
|
| 145 |
+
lines = file.readlines()
|
| 146 |
+
for line in lines:
|
| 147 |
+
# parse annotation bboxes
|
| 148 |
+
new_line = line.strip("\n").split(" ")
|
| 149 |
+
y_corners = [int(x) for x in new_line[-4:]]
|
| 150 |
+
x_corners = [int(x) for x in new_line[-8:-4]]
|
| 151 |
+
minx_miny = (min(x_corners), min(y_corners))
|
| 152 |
+
maxx_maxy = (max(x_corners), max(y_corners))
|
| 153 |
+
#
|
| 154 |
+
i_bbox = [minx_miny[0], minx_miny[1], maxx_maxy[0], maxx_maxy[1]]
|
| 155 |
+
# width = maxx-minx, height = maxy-miny
|
| 156 |
+
width = i_bbox[2] - i_bbox[0]
|
| 157 |
+
height = i_bbox[3] - i_bbox[1]
|
| 158 |
+
|
| 159 |
+
#top left point
|
| 160 |
+
minx = min(x_corners)
|
| 161 |
+
miny = min(y_corners)
|
| 162 |
+
|
| 163 |
+
bbox = [minx,
|
| 164 |
+
miny,
|
| 165 |
+
width,
|
| 166 |
+
height]
|
| 167 |
+
# parse category id and name
|
| 168 |
+
category_id = new_line[3]
|
| 169 |
+
if category_id in category_id_remapping.keys():
|
| 170 |
+
category_name = CATEGORY_ID_TO_NAME[category_id]
|
| 171 |
+
remapped_category_id = category_id_remapping[category_id]
|
| 172 |
+
else:
|
| 173 |
+
continue
|
| 174 |
+
|
| 175 |
+
# create coco annotation and append it to coco image
|
| 176 |
+
coco_annotation = CocoAnnotation.from_coco_bbox(
|
| 177 |
+
bbox=bbox,
|
| 178 |
+
category_id=int(remapped_category_id),
|
| 179 |
+
category_name=category_name,
|
| 180 |
+
)
|
| 181 |
+
if mode == 'original':
|
| 182 |
+
if coco_annotation.area > 0:
|
| 183 |
+
coco_image.add_annotation(coco_annotation)
|
| 184 |
+
elif mode == 'car_other':
|
| 185 |
+
if coco_annotation.area < 400:
|
| 186 |
+
coco_image.add_annotation(coco_annotation)
|
| 187 |
+
elif mode == 'car':
|
| 188 |
+
if coco_annotation.area < 400:
|
| 189 |
+
coco_image.add_annotation(coco_annotation)
|
| 190 |
+
coco.add_image(coco_image)
|
| 191 |
+
|
| 192 |
+
save_path = output_dir
|
| 193 |
+
all_json_path = Path(save_path) / "{}.json".format(split_images_path.split('.')[0])
|
| 194 |
+
save_json(data=coco.json, save_path=all_json_path)
|
| 195 |
+
|
| 196 |
+
|
| 197 |
+
|
| 198 |
+
if __name__ == "__main__":
|
| 199 |
+
fire.Fire(vedai_to_coco)
|
xview/category_id_mapping.json
ADDED
|
@@ -0,0 +1,62 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"11": "0",
|
| 3 |
+
"12": "1",
|
| 4 |
+
"13": "2",
|
| 5 |
+
"15": "3",
|
| 6 |
+
"17": "4",
|
| 7 |
+
"18": "5",
|
| 8 |
+
"19": "6",
|
| 9 |
+
"20": "7",
|
| 10 |
+
"21": "8",
|
| 11 |
+
"23": "9",
|
| 12 |
+
"24": "10",
|
| 13 |
+
"25": "11",
|
| 14 |
+
"26": "12",
|
| 15 |
+
"27": "13",
|
| 16 |
+
"28": "14",
|
| 17 |
+
"29": "15",
|
| 18 |
+
"32": "16",
|
| 19 |
+
"33": "17",
|
| 20 |
+
"34": "18",
|
| 21 |
+
"35": "19",
|
| 22 |
+
"36": "20",
|
| 23 |
+
"37": "21",
|
| 24 |
+
"38": "22",
|
| 25 |
+
"40": "23",
|
| 26 |
+
"41": "24",
|
| 27 |
+
"42": "25",
|
| 28 |
+
"44": "26",
|
| 29 |
+
"45": "27",
|
| 30 |
+
"47": "28",
|
| 31 |
+
"49": "29",
|
| 32 |
+
"50": "30",
|
| 33 |
+
"51": "31",
|
| 34 |
+
"52": "32",
|
| 35 |
+
"53": "33",
|
| 36 |
+
"54": "34",
|
| 37 |
+
"55": "35",
|
| 38 |
+
"56": "36",
|
| 39 |
+
"57": "37",
|
| 40 |
+
"59": "38",
|
| 41 |
+
"60": "39",
|
| 42 |
+
"61": "40",
|
| 43 |
+
"62": "41",
|
| 44 |
+
"63": "42",
|
| 45 |
+
"64": "43",
|
| 46 |
+
"65": "44",
|
| 47 |
+
"66": "45",
|
| 48 |
+
"71": "46",
|
| 49 |
+
"72": "47",
|
| 50 |
+
"73": "48",
|
| 51 |
+
"74": "49",
|
| 52 |
+
"76": "50",
|
| 53 |
+
"77": "51",
|
| 54 |
+
"79": "52",
|
| 55 |
+
"83": "53",
|
| 56 |
+
"84": "54",
|
| 57 |
+
"86": "55",
|
| 58 |
+
"89": "56",
|
| 59 |
+
"91": "57",
|
| 60 |
+
"93": "58",
|
| 61 |
+
"94": "59"
|
| 62 |
+
}
|
xview/xview_class_labels.txt
ADDED
|
@@ -0,0 +1,60 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
11:Fixed-wing Aircraft
|
| 2 |
+
12:Small Aircraft
|
| 3 |
+
13:Cargo Plane
|
| 4 |
+
15:Helicopter
|
| 5 |
+
17:Passenger Vehicle
|
| 6 |
+
18:Small Car
|
| 7 |
+
19:Bus
|
| 8 |
+
20:Pickup Truck
|
| 9 |
+
21:Utility Truck
|
| 10 |
+
23:Truck
|
| 11 |
+
24:Cargo Truck
|
| 12 |
+
25:Truck w/Box
|
| 13 |
+
26:Truck Tractor
|
| 14 |
+
27:Trailer
|
| 15 |
+
28:Truck w/Flatbed
|
| 16 |
+
29:Truck w/Liquid
|
| 17 |
+
32:Crane Truck
|
| 18 |
+
33:Railway Vehicle
|
| 19 |
+
34:Passenger Car
|
| 20 |
+
35:Cargo Car
|
| 21 |
+
36:Flat Car
|
| 22 |
+
37:Tank car
|
| 23 |
+
38:Locomotive
|
| 24 |
+
40:Maritime Vessel
|
| 25 |
+
41:Motorboat
|
| 26 |
+
42:Sailboat
|
| 27 |
+
44:Tugboat
|
| 28 |
+
45:Barge
|
| 29 |
+
47:Fishing Vessel
|
| 30 |
+
49:Ferry
|
| 31 |
+
50:Yacht
|
| 32 |
+
51:Container Ship
|
| 33 |
+
52:Oil Tanker
|
| 34 |
+
53:Engineering Vehicle
|
| 35 |
+
54:Tower crane
|
| 36 |
+
55:Container Crane
|
| 37 |
+
56:Reach Stacker
|
| 38 |
+
57:Straddle Carrier
|
| 39 |
+
59:Mobile Crane
|
| 40 |
+
60:Dump Truck
|
| 41 |
+
61:Haul Truck
|
| 42 |
+
62:Scraper/Tractor
|
| 43 |
+
63:Front loader/Bulldozer
|
| 44 |
+
64:Excavator
|
| 45 |
+
65:Cement Mixer
|
| 46 |
+
66:Ground Grader
|
| 47 |
+
71:Hut/Tent
|
| 48 |
+
72:Shed
|
| 49 |
+
73:Building
|
| 50 |
+
74:Aircraft Hangar
|
| 51 |
+
76:Damaged Building
|
| 52 |
+
77:Facility
|
| 53 |
+
79:Construction Site
|
| 54 |
+
83:Vehicle Lot
|
| 55 |
+
84:Helipad
|
| 56 |
+
86:Storage Tank
|
| 57 |
+
89:Shipping container lot
|
| 58 |
+
91:Shipping Container
|
| 59 |
+
93:Pylon
|
| 60 |
+
94:Tower
|
xview/xview_to_coco.py
ADDED
|
@@ -0,0 +1,199 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import random
|
| 2 |
+
from collections import defaultdict
|
| 3 |
+
from pathlib import Path
|
| 4 |
+
from typing import Dict, List
|
| 5 |
+
|
| 6 |
+
import fire
|
| 7 |
+
import numpy as np
|
| 8 |
+
from PIL import Image
|
| 9 |
+
from sahi.utils.coco import Coco, CocoAnnotation, CocoCategory, CocoImage
|
| 10 |
+
from sahi.utils.file import load_json, save_json
|
| 11 |
+
from tqdm import tqdm
|
| 12 |
+
import sys
|
| 13 |
+
|
| 14 |
+
# fix the seed
|
| 15 |
+
random.seed(13)
|
| 16 |
+
|
| 17 |
+
|
| 18 |
+
def xview_to_coco(
|
| 19 |
+
images_dir,
|
| 20 |
+
split_images_path,
|
| 21 |
+
train_geojson_path,
|
| 22 |
+
output_dir,
|
| 23 |
+
mode,
|
| 24 |
+
category_id_remapping=None
|
| 25 |
+
):
|
| 26 |
+
"""
|
| 27 |
+
Converts xView annotations into coco annotation.
|
| 28 |
+
|
| 29 |
+
Args:
|
| 30 |
+
images_dir: str
|
| 31 |
+
'train_images' folder directory
|
| 32 |
+
split_images_path: str
|
| 33 |
+
split_images_path txt file
|
| 34 |
+
train_geojson_path: str
|
| 35 |
+
'xView_train.geojson' file path
|
| 36 |
+
output_dir: str
|
| 37 |
+
Output folder directory
|
| 38 |
+
mode: str
|
| 39 |
+
mode original|car_other|car
|
| 40 |
+
category_id_remapping: dict
|
| 41 |
+
Used for selecting desired category ids and mapping them.
|
| 42 |
+
If not provided, vedai mapping will be used.
|
| 43 |
+
format: str(id) to str(id)
|
| 44 |
+
"""
|
| 45 |
+
|
| 46 |
+
|
| 47 |
+
# init vars
|
| 48 |
+
category_id_to_name = {}
|
| 49 |
+
with open("xview_class_labels.txt", encoding="utf8") as f:
|
| 50 |
+
lines = f.readlines()
|
| 51 |
+
for line in lines:
|
| 52 |
+
category_id = line.split(":")[0]
|
| 53 |
+
category_name = line.split(":")[1].replace("\n", "")
|
| 54 |
+
category_id_to_name[category_id] = category_name
|
| 55 |
+
|
| 56 |
+
if category_id_remapping is None:
|
| 57 |
+
category_id_remapping = load_json("category_id_mapping.json")
|
| 58 |
+
category_id_remapping
|
| 59 |
+
|
| 60 |
+
# init coco object
|
| 61 |
+
coco = Coco()
|
| 62 |
+
# append categories
|
| 63 |
+
if mode == 'original':
|
| 64 |
+
for category_id, category_name in category_id_to_name.items():
|
| 65 |
+
if category_id in category_id_remapping.keys():
|
| 66 |
+
remapped_category_id = category_id_remapping[category_id]
|
| 67 |
+
coco.add_category(
|
| 68 |
+
CocoCategory(id=int(remapped_category_id), name=category_name)
|
| 69 |
+
)
|
| 70 |
+
elif mode == 'car_other':
|
| 71 |
+
print('"car_other" mode categories')
|
| 72 |
+
coco.add_category(CocoCategory(id=0, name='Other'))
|
| 73 |
+
coco.add_category(CocoCategory(id=1, name='Car'))
|
| 74 |
+
for k,v in category_id_remapping.items():
|
| 75 |
+
if k == '18':
|
| 76 |
+
category_id_remapping[k] =1
|
| 77 |
+
else:
|
| 78 |
+
category_id_remapping[k]=0
|
| 79 |
+
|
| 80 |
+
elif mode == 'car':
|
| 81 |
+
print('"car" mode categories')
|
| 82 |
+
# xview_category_id for 'Small Car' = '18'
|
| 83 |
+
coco.add_category(CocoCategory(id=0, name='Car'))
|
| 84 |
+
category_id_remapping = {'18': 0}
|
| 85 |
+
else:
|
| 86 |
+
print('pick a defined mode: [original/car_other/car]')
|
| 87 |
+
sys.exit()
|
| 88 |
+
|
| 89 |
+
# parse xview data
|
| 90 |
+
coords, chips, classes, image_name_to_annotation_ind = get_labels(
|
| 91 |
+
train_geojson_path
|
| 92 |
+
)
|
| 93 |
+
image_name_list = get_ordered_image_name_list(image_name_to_annotation_ind)
|
| 94 |
+
|
| 95 |
+
split_images_lst = []
|
| 96 |
+
with open(split_images_path, 'r') as f:
|
| 97 |
+
for line in f:
|
| 98 |
+
split_images_lst.append(line.strip().split('/')[1])
|
| 99 |
+
|
| 100 |
+
# convert xView data to COCO format
|
| 101 |
+
for image_name in tqdm(image_name_list, "Converting xView data into COCO format"):
|
| 102 |
+
# create coco image object
|
| 103 |
+
if image_name in split_images_lst:
|
| 104 |
+
width, height = Image.open(Path(images_dir) / image_name).size
|
| 105 |
+
coco_image = CocoImage(file_name=image_name, height=height, width=width)
|
| 106 |
+
|
| 107 |
+
annotation_ind_list = image_name_to_annotation_ind[image_name]
|
| 108 |
+
|
| 109 |
+
# iterate over image annotations
|
| 110 |
+
for annotation_ind in annotation_ind_list:
|
| 111 |
+
bbox = coords[annotation_ind].tolist()
|
| 112 |
+
category_id = str(int(classes[annotation_ind].item()))
|
| 113 |
+
coco_bbox = [bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]]
|
| 114 |
+
if category_id in category_id_remapping.keys():
|
| 115 |
+
category_name = category_id_to_name[category_id]
|
| 116 |
+
remapped_category_id = category_id_remapping[category_id]
|
| 117 |
+
else:
|
| 118 |
+
continue
|
| 119 |
+
# create coco annotation and append it to coco image
|
| 120 |
+
coco_annotation = CocoAnnotation(
|
| 121 |
+
bbox=coco_bbox,
|
| 122 |
+
category_id=int(remapped_category_id),
|
| 123 |
+
category_name=category_name,
|
| 124 |
+
)
|
| 125 |
+
if mode == 'original':
|
| 126 |
+
if coco_annotation.area > 0:
|
| 127 |
+
coco_image.add_annotation(coco_annotation)
|
| 128 |
+
elif mode == 'car_other':
|
| 129 |
+
if coco_annotation.area < 400:
|
| 130 |
+
coco_image.add_annotation(coco_annotation)
|
| 131 |
+
elif mode == 'car':
|
| 132 |
+
if coco_annotation.area < 400:
|
| 133 |
+
coco_image.add_annotation(coco_annotation)
|
| 134 |
+
coco.add_image(coco_image)
|
| 135 |
+
###
|
| 136 |
+
save_path = output_dir
|
| 137 |
+
all_json_path = Path(save_path) / "{}.json".format(split_images_path.split('.')[0])
|
| 138 |
+
save_json(data=coco.json, save_path=all_json_path)
|
| 139 |
+
|
| 140 |
+
|
| 141 |
+
def get_ordered_image_name_list(image_name_to_annotation_ind: Dict):
|
| 142 |
+
image_name_list: List[str] = list(image_name_to_annotation_ind.keys())
|
| 143 |
+
|
| 144 |
+
def get_image_ind(image_name: str):
|
| 145 |
+
return int(image_name.split(".")[0])
|
| 146 |
+
|
| 147 |
+
image_name_list.sort(key=get_image_ind)
|
| 148 |
+
|
| 149 |
+
return image_name_list
|
| 150 |
+
|
| 151 |
+
|
| 152 |
+
def get_labels(fname):
|
| 153 |
+
"""
|
| 154 |
+
Gets label data from a geojson label file
|
| 155 |
+
Args:
|
| 156 |
+
fname: file path to an xView geojson label file
|
| 157 |
+
Output:
|
| 158 |
+
Returns three arrays: coords, chips, and classes corresponding to the
|
| 159 |
+
coordinates, file-names, and classes for each ground truth.
|
| 160 |
+
Modified from https://github.com/DIUx-xView.
|
| 161 |
+
"""
|
| 162 |
+
data = load_json(fname)
|
| 163 |
+
|
| 164 |
+
coords = np.zeros((len(data["features"]), 4))
|
| 165 |
+
chips = np.zeros((len(data["features"])), dtype="object")
|
| 166 |
+
classes = np.zeros((len(data["features"])))
|
| 167 |
+
image_name_to_annotation_ind = defaultdict(list)
|
| 168 |
+
|
| 169 |
+
for i in tqdm(range(len(data["features"])), "Parsing xView data"):
|
| 170 |
+
if data["features"][i]["properties"]["bounds_imcoords"] != []:
|
| 171 |
+
b_id = data["features"][i]["properties"]["image_id"]
|
| 172 |
+
# https://github.com/DIUx-xView/xView1_baseline/issues/3
|
| 173 |
+
if b_id == "1395.tif":
|
| 174 |
+
continue
|
| 175 |
+
val = np.array(
|
| 176 |
+
[
|
| 177 |
+
int(num)
|
| 178 |
+
for num in data["features"][i]["properties"][
|
| 179 |
+
"bounds_imcoords"
|
| 180 |
+
].split(",")
|
| 181 |
+
]
|
| 182 |
+
)
|
| 183 |
+
chips[i] = b_id
|
| 184 |
+
classes[i] = data["features"][i]["properties"]["type_id"]
|
| 185 |
+
|
| 186 |
+
image_name_to_annotation_ind[b_id].append(i)
|
| 187 |
+
|
| 188 |
+
if val.shape[0] != 4:
|
| 189 |
+
print("Issues at %d!" % i)
|
| 190 |
+
else:
|
| 191 |
+
coords[i] = val
|
| 192 |
+
else:
|
| 193 |
+
chips[i] = "None"
|
| 194 |
+
|
| 195 |
+
return coords, chips, classes, image_name_to_annotation_ind
|
| 196 |
+
|
| 197 |
+
|
| 198 |
+
if __name__ == "__main__":
|
| 199 |
+
fire.Fire(xview_to_coco)
|