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Archive data by species and split

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  1. KABR/README.txt +55 -0
  2. KABR/annotation/classes.json +1 -0
  3. KABR/annotation/distribution.xlsx +0 -0
  4. KABR_part_ad → KABR/annotation/train.csv +2 -2
  5. KABR_part_aa → KABR/annotation/val.csv +2 -2
  6. KABR/configs/I3D.yaml +99 -0
  7. KABR/configs/SLOWFAST.yaml +108 -0
  8. KABR/configs/X3D.yaml +98 -0
  9. KABR/dataset/image/giraffes_md5.txt +1 -0
  10. KABR_part_ab → KABR/dataset/image/giraffes_part_aa +2 -2
  11. KABR_part_ac → KABR/dataset/image/giraffes_part_ab +2 -2
  12. KABR/dataset/image/giraffes_part_ac +3 -0
  13. KABR/dataset/image/giraffes_part_ad +3 -0
  14. KABR/dataset/image/zebras_grevys_md5.txt +1 -0
  15. KABR/dataset/image/zebras_grevys_part_aa +3 -0
  16. KABR/dataset/image/zebras_grevys_part_ab +3 -0
  17. KABR/dataset/image/zebras_grevys_part_ac +3 -0
  18. KABR/dataset/image/zebras_grevys_part_ad +3 -0
  19. KABR/dataset/image/zebras_grevys_part_ae +3 -0
  20. KABR/dataset/image/zebras_grevys_part_af +3 -0
  21. KABR/dataset/image/zebras_grevys_part_ag +3 -0
  22. KABR/dataset/image/zebras_grevys_part_ah +3 -0
  23. KABR/dataset/image/zebras_grevys_part_ai +3 -0
  24. KABR/dataset/image/zebras_grevys_part_aj +3 -0
  25. KABR/dataset/image/zebras_grevys_part_ak +3 -0
  26. KABR/dataset/image/zebras_grevys_part_al +3 -0
  27. KABR/dataset/image/zebras_grevys_part_am +3 -0
  28. KABR/dataset/image/zebras_plains_md5.txt +1 -0
  29. KABR/dataset/image/zebras_plains_part_aa +3 -0
  30. KABR/dataset/image/zebras_plains_part_ab +3 -0
  31. KABR/dataset/image/zebras_plains_part_ac +3 -0
  32. KABR/dataset/image/zebras_plains_part_ad +3 -0
  33. KABR/dataset/image/zebras_plains_part_ae +3 -0
  34. KABR/dataset/image/zebras_plains_part_af +3 -0
  35. KABR/dataset/image/zebras_plains_part_ag +3 -0
  36. KABR/dataset/image/zebras_plains_part_ah +3 -0
  37. KABR/dataset/image/zebras_plains_part_ai +3 -0
  38. KABR/dataset/image/zebras_plains_part_aj +3 -0
  39. KABR/dataset/image/zebras_plains_part_ak +3 -0
  40. KABR/dataset/image/zebras_plains_part_al +3 -0
  41. KABR/dataset/image2video.py +67 -0
  42. KABR/dataset/image2visual.py +67 -0
  43. KABR_MD5.txt +0 -1
  44. KABR_part_ae +0 -3
  45. KABR_part_af +0 -3
  46. KABR_part_ag +0 -3
  47. KABR_part_ah +0 -3
  48. KABR_part_ai +0 -3
  49. KABR_part_aj +0 -3
  50. KABR_part_ak +0 -3
KABR/README.txt ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ KABR: High-Quality Dataset for Kenyan Animal Behavior Recognition from Drone Videos
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+
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+ ---------------------------------------------------------------------------------------------------
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+
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+ We present a novel high-quality dataset for animal behavior recognition from drone videos. The dataset is focused on Kenyan wildlife and contains behaviors of giraffes, plains zebras, and Grevy's zebras. The dataset consists of more than 10 hours of annotated videos, and it includes eight different classes, encompassing seven types of animal behavior and an additional category for occluded instances. In the annotation process for this dataset, a team of 10 people was involved, with an expert zoologist overseeing the process. Each behavior was labeled based on its distinctive features, using a standardized set of criteria to ensure consistency and accuracy across the annotations. The dataset was collected using drones that flew over the animals in the Mpala Research Centre in Kenya, providing high-quality video footage of the animal's natural behaviors. We believe that this dataset will be a valuable resource for researchers working on animal behavior recognition, as it provides a diverse and high-quality set of annotated videos that can be used for evaluating deep learning models. Additionally, the dataset can be used to study the behavior patterns of Kenyan animals and can help to inform conservation efforts and wildlife management strategies. We provide a detailed description of the dataset and its annotation process, along with some initial experiments on the dataset using conventional deep learning models. The results demonstrate the effectiveness of the dataset for animal behavior recognition and highlight the potential for further research in this area.
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+
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+ ---------------------------------------------------------------------------------------------------
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+
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+ The KABR dataset follows the Charades format. The Charades format:
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+
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+ KABR
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+ /images
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+ /video_1
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+ /image_1.jpg
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+ /image_2.jpg
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+ ...
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+ /image_n.jpg
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+ /video_2
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+ /image_1.jpg
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+ /image_2.jpg
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+ ...
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+ /image_n.jpg
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+ ...
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+ /video_n
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+ /image_1.jpg
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+ /image_2.jpg
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+ /image_3.jpg
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+ ...
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+ /image_n.jpg
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+ /annotation
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+ /classes.json
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+ /train.csv
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+ /val.csv
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+
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+ The dataset can be directly loaded and processed by the SlowFast (https://github.com/facebookresearch/SlowFast) framework.
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+
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+ ---------------------------------------------------------------------------------------------------
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+
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+ Naming:
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+ G0XXX.X - Giraffes
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+ ZP0XXX.X - Plains Zebras
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+ ZG0XXX.X - Grevy's Zebras
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+
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+ ---------------------------------------------------------------------------------------------------
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+
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+ Information:
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+
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+ KABR/configs: examples of SlowFast framework configs.
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+ KABR/annotation/distribution.xlsx: distribution of classes for all videos.
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+
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+ ---------------------------------------------------------------------------------------------------
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+
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+ Scripts:
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+ image2video.py: Encode image sequences into the original video. For example, [image/G0067.1, image/G0067.2, ..., image/G0067.24] will be encoded into video/G0067.mp4.
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+ image2visual.py: Encode image sequences into the original video with corresponding annotations. For example, [image/G0067.1, image/G0067.2, ..., image/G0067.24] will be encoded into visual/G0067.mp4.
KABR/annotation/classes.json ADDED
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+ {"Walk": 0, "Graze": 1, "Browse": 2, "Head Up": 3, "Auto-Groom": 4, "Trot": 5, "Run": 6, "Occluded": 7}
KABR/annotation/distribution.xlsx ADDED
Binary file (5.62 kB). View file
 
KABR_part_ad → KABR/annotation/train.csv RENAMED
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KABR_part_aa → KABR/annotation/val.csv RENAMED
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KABR/configs/I3D.yaml ADDED
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+ TRAIN:
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+ ENABLE: True
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+ DATASET: charades
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+ BATCH_SIZE: 8
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+ EVAL_PERIOD: 5
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+ CHECKPOINT_PERIOD: 5
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+ AUTO_RESUME: True
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+ # CHECKPOINT_FILE_PATH:
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+ CHECKPOINT_TYPE: pytorch
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+ CHECKPOINT_INFLATE: False
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+ MIXED_PRECISION: True
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+
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+ TEST:
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+ ENABLE: True
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+ DATASET: charades
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+ BATCH_SIZE: 8
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+ NUM_ENSEMBLE_VIEWS: 2
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+ NUM_SPATIAL_CROPS: 1
19
+ # CHECKPOINT_FILE_PATH:
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+ CHECKPOINT_TYPE: pytorch
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+
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+ DATA:
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+ NUM_FRAMES: 16
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+ SAMPLING_RATE: 5
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+ TRAIN_JITTER_SCALES: [320, 320]
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+ TRAIN_CROP_SIZE: 320
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+ TEST_CROP_SIZE: 320
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+ TRAIN_CROP_NUM_TEMPORAL: 1
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+ INPUT_CHANNEL_NUM: [3]
30
+ MULTI_LABEL: False
31
+ RANDOM_FLIP: True
32
+ SSL_COLOR_JITTER: True
33
+ SSL_COLOR_BRI_CON_SAT: [0.2, 0.2, 0.2]
34
+ INV_UNIFORM_SAMPLE: True
35
+ ENSEMBLE_METHOD: max
36
+ REVERSE_INPUT_CHANNEL: True
37
+ PATH_TO_DATA_DIR: "./KABR/annotation"
38
+ PATH_PREFIX: "./KABR/dataset/image"
39
+ DECODING_BACKEND: torchvision
40
+
41
+ RESNET:
42
+ ZERO_INIT_FINAL_BN: True
43
+ WIDTH_PER_GROUP: 64
44
+ NUM_GROUPS: 1
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+ DEPTH: 50
46
+ TRANS_FUNC: bottleneck_transform
47
+ STRIDE_1X1: False
48
+ NUM_BLOCK_TEMP_KERNEL: [[3], [4], [6], [3]]
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+
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+ NONLOCAL:
51
+ LOCATION: [[[]], [[]], [[]], [[]]]
52
+ GROUP: [[1], [1], [1], [1]]
53
+ INSTANTIATION: softmax
54
+
55
+ BN:
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+ USE_PRECISE_STATS: True
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+ NUM_BATCHES_PRECISE: 100
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+ NORM_TYPE: sync_batchnorm
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+ NUM_SYNC_DEVICES: 1
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+
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+ SOLVER:
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+ BASE_LR: 0.1
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+ LR_POLICY: cosine
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+ MAX_EPOCH: 120
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+ MOMENTUM: 0.9
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+ WEIGHT_DECAY: 1e-4
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+ WARMUP_EPOCHS: 34.0
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+ WARMUP_START_LR: 0.01
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+ OPTIMIZING_METHOD: sgd
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+
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+ MODEL:
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+ NUM_CLASSES: 8
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+ ARCH: i3d
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+ MODEL_NAME: ResNet
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+ LOSS_FUNC: cross_entropy
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+ DROPOUT_RATE: 0.5
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+
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+ DATA_LOADER:
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+ NUM_WORKERS: 8
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+ PIN_MEMORY: True
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+
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+ NUM_GPUS: 1
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+ NUM_SHARDS: 1
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+ RNG_SEED: 0
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+ OUTPUT_DIR: ./logs/i3d-kabr
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+ LOG_MODEL_INFO: True
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+
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+ TENSORBOARD:
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+ ENABLE: False
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+
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+ DEMO:
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+ ENABLE: True
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+ LABEL_FILE_PATH: ./KABR/annotation/classes.json
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+ # INPUT_VIDEO: # path to input
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+ # OUTPUT_FILE: # path to output
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+ THREAD_ENABLE: False
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+ THREAD_ENABLE: False
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+ NUM_VIS_INSTANCES: 1
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+ NUM_CLIPS_SKIP: 1
KABR/configs/SLOWFAST.yaml ADDED
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+ TRAIN:
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+ ENABLE: True
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+ DATASET: charades
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+ BATCH_SIZE: 8
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+ EVAL_PERIOD: 5
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+ CHECKPOINT_PERIOD: 5
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+ AUTO_RESUME: True
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+ # CHECKPOINT_FILE_PATH:
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+ CHECKPOINT_TYPE: pytorch
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+ CHECKPOINT_INFLATE: False
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+ MIXED_PRECISION: True
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+
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+ TEST:
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+ ENABLE: True
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+ DATASET: charades
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+ BATCH_SIZE: 8
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+ NUM_ENSEMBLE_VIEWS: 2
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+ NUM_SPATIAL_CROPS: 1
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+ # CHECKPOINT_FILE_PATH:
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+ CHECKPOINT_TYPE: pytorch
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+
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+ DATA:
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+ NUM_FRAMES: 16
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+ SAMPLING_RATE: 5
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+ TRAIN_JITTER_SCALES: [256, 256]
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+ TRAIN_CROP_SIZE: 256
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+ TEST_CROP_SIZE: 256
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+ TRAIN_CROP_NUM_TEMPORAL: 1
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+ INPUT_CHANNEL_NUM: [3, 3]
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+ MULTI_LABEL: False
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+ RANDOM_FLIP: True
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+ SSL_COLOR_JITTER: True
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+ SSL_COLOR_BRI_CON_SAT: [0.2, 0.2, 0.2]
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+ INV_UNIFORM_SAMPLE: True
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+ ENSEMBLE_METHOD: max
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+ REVERSE_INPUT_CHANNEL: True
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+ PATH_TO_DATA_DIR: "./KABR/annotation"
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+ PATH_PREFIX: "./KABR/dataset/image"
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+ DECODING_BACKEND: torchvision
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+
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+ SLOWFAST:
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+ ALPHA: 4
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+ BETA_INV: 8
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+ FUSION_CONV_CHANNEL_RATIO: 2
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+ FUSION_KERNEL_SZ: 7
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+
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+ RESNET:
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+ ZERO_INIT_FINAL_BN: True
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+ WIDTH_PER_GROUP: 64
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+ NUM_GROUPS: 1
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+ DEPTH: 50
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+ TRANS_FUNC: bottleneck_transform
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+ STRIDE_1X1: False
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+ NUM_BLOCK_TEMP_KERNEL: [[3, 3], [4, 4], [6, 6], [3, 3]]
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+ SPATIAL_STRIDES: [[1, 1], [2, 2], [2, 2], [2, 2]]
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+ SPATIAL_DILATIONS: [[1, 1], [1, 1], [1, 1], [1, 1]]
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+
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+ NONLOCAL:
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+ LOCATION: [[[], []], [[], []], [[], []], [[], []]]
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+ GROUP: [[1, 1], [1, 1], [1, 1], [1, 1]]
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+ INSTANTIATION: dot_product
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+
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+ BN:
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+ USE_PRECISE_STATS: True
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+ NUM_BATCHES_PRECISE: 200
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+ NORM_TYPE: sync_batchnorm
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+ NUM_SYNC_DEVICES: 1
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+
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+ SOLVER:
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+ BASE_LR: 0.0375
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+ LR_POLICY: steps_with_relative_lrs
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+ LRS: [1, 0.1, 0.01, 0.001, 0.0001, 0.00001]
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+ STEPS: [0, 41, 49]
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+ MAX_EPOCH: 80
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+ MOMENTUM: 0.9
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+ WEIGHT_DECAY: 1e-4
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+ WARMUP_EPOCHS: 3.0
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+ WARMUP_START_LR: 0.0001
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+ OPTIMIZING_METHOD: sgd
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+
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+ MODEL:
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+ NUM_CLASSES: 8
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+ ARCH: slowfast
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+ LOSS_FUNC: cross_entropy
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+ DROPOUT_RATE: 0.5
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+
87
+ DATA_LOADER:
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+ NUM_WORKERS: 8
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+ PIN_MEMORY: True
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+
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+ NUM_GPUS: 1
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+ NUM_SHARDS: 1
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+ RNG_SEED: 0
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+ OUTPUT_DIR: ./logs/slowfast-kabr
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+ LOG_MODEL_INFO: True
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+
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+ TENSORBOARD:
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+ ENABLE: False
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+
100
+ DEMO:
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+ ENABLE: True
102
+ LABEL_FILE_PATH: ./KABR/annotation/classes.json
103
+ # INPUT_VIDEO: # path to input
104
+ # OUTPUT_FILE: # path to output
105
+ THREAD_ENABLE: False
106
+ THREAD_ENABLE: False
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+ NUM_VIS_INSTANCES: 1
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+ NUM_CLIPS_SKIP: 1
KABR/configs/X3D.yaml ADDED
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+ TRAIN:
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+ ENABLE: True
3
+ DATASET: charades
4
+ BATCH_SIZE: 8
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+ EVAL_PERIOD: 5
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+ CHECKPOINT_PERIOD: 5
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+ AUTO_RESUME: True
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+ # CHECKPOINT_FILE_PATH:
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+ CHECKPOINT_TYPE: pytorch
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+ CHECKPOINT_INFLATE: False
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+ MIXED_PRECISION: True
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+
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+ TEST:
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+ ENABLE: True
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+ DATASET: charades
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+ BATCH_SIZE: 8
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+ NUM_ENSEMBLE_VIEWS: 2
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+ NUM_SPATIAL_CROPS: 1
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+ # CHECKPOINT_FILE_PATH:
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+ CHECKPOINT_TYPE: pytorch
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+
22
+ DATA:
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+ NUM_FRAMES: 16
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+ SAMPLING_RATE: 5
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+ TRAIN_JITTER_SCALES: [300, 300]
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+ TRAIN_CROP_SIZE: 300
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+ TEST_CROP_SIZE: 300
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+ TRAIN_CROP_NUM_TEMPORAL: 1
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+ INPUT_CHANNEL_NUM: [3]
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+ MULTI_LABEL: False
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+ RANDOM_FLIP: True
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+ SSL_COLOR_JITTER: True
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+ SSL_COLOR_BRI_CON_SAT: [0.2, 0.2, 0.2]
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+ INV_UNIFORM_SAMPLE: True
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+ ENSEMBLE_METHOD: max
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+ REVERSE_INPUT_CHANNEL: True
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+ PATH_TO_DATA_DIR: "./KABR/annotation"
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+ PATH_PREFIX: "./KABR/dataset/image"
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+ DECODING_BACKEND: torchvision
40
+
41
+ X3D:
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+ WIDTH_FACTOR: 2.0
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+ DEPTH_FACTOR: 5.0
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+ BOTTLENECK_FACTOR: 2.25
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+ DIM_C5: 2048
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+ DIM_C1: 12
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+ RESNET:
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+ ZERO_INIT_FINAL_BN: True
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+ TRANS_FUNC: x3d_transform
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+ STRIDE_1X1: False
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+ BN:
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+ USE_PRECISE_STATS: True
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+ NUM_BATCHES_PRECISE: 200
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+ NORM_TYPE: sync_batchnorm
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+ NUM_SYNC_DEVICES: 1
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+ WEIGHT_DECAY: 0.0
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+ SOLVER:
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+ BASE_LR: 0.05
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+ BASE_LR_SCALE_NUM_SHARDS: True
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+ MAX_EPOCH: 120
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+ LR_POLICY: cosine
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+ WEIGHT_DECAY: 5e-5
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+ WARMUP_EPOCHS: 35.0
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+ WARMUP_START_LR: 0.01
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+ OPTIMIZING_METHOD: sgd
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+ MODEL:
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+ NUM_CLASSES: 8
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+ ARCH: x3d
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+ MODEL_NAME: X3D
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+ LOSS_FUNC: cross_entropy
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+ DROPOUT_RATE: 0.5
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+ DATA_LOADER:
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+ PIN_MEMORY: True
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+ NUM_GPUS: 1
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+ RNG_SEED: 0
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+ LOG_MODEL_INFO: True
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+ TENSORBOARD:
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+ ENABLE: False
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+ DEMO:
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+ ENABLE: True
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+ LABEL_FILE_PATH: ./KABR/annotation/classes.json
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+ # INPUT_VIDEO: # path to input
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+ # OUTPUT_FILE: # path to output
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+ THREAD_ENABLE: False
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+ THREAD_ENABLE: False
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+ NUM_VIS_INSTANCES: 1
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+ NUM_CLIPS_SKIP: 1
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KABR/dataset/image2video.py ADDED
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1
+ import os
2
+ import sys
3
+ import json
4
+ import cv2
5
+ from natsort import natsorted
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+ import pandas as pd
7
+ from tqdm import tqdm
8
+
9
+ if __name__ == "__main__":
10
+ path_to_image = "image"
11
+ path_to_video = "video"
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+ annotation_train = "../annotation/train.csv"
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+ annotation_val = "../annotation/val.csv"
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+ classes_json = "../annotation/classes.json"
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+ visual = False
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+
17
+ if not os.path.exists(path_to_video):
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+ os.makedirs(path_to_video)
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+
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+ with open(classes_json, "r") as file:
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+ label2number = json.load(file)
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+
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+ number2label = {value: key for key, value in label2number.items()}
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+
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+ df_train = pd.read_csv(annotation_train, sep=" ")
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+ df_val = pd.read_csv(annotation_val, sep=" ")
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+ df = pd.concat([df_train, df_val], axis=0)
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+ folders = natsorted(os.listdir(path_to_image))
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+
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+ hierarchy = {}
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+
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+ for folder in folders:
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+ main = folder.split(".")[0]
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+
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+ if hierarchy.get(main) is None:
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+ hierarchy[main] = [folder]
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+ else:
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+ hierarchy[main].append(folder)
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+
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+ for i, folder in tqdm(enumerate(hierarchy.keys()), total=len(hierarchy.keys())):
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+ vw = cv2.VideoWriter(f"{path_to_video}/{folder}.mp4", cv2.VideoWriter_fourcc("m", "p", "4", "v"), 29.97,
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+ (400, 300))
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+
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+ for segment in hierarchy[folder]:
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+ mapping = {}
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+
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+ for index, row in df[df.original_vido_id == segment].iterrows():
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+ mapping[row["frame_id"]] = number2label[row["labels"]]
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+
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+ for j, file in enumerate(natsorted(os.listdir(path_to_image + os.sep + segment))):
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+ image = cv2.imread(f"{path_to_image}/{segment}/{file}")
52
+
53
+ if visual:
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+ color = (0, 0, 0)
55
+ label = mapping[j + 1]
56
+ thickness_in = 1
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+ size = 0.7
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+ label_length = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, size, thickness_in)
59
+ copied = image.copy()
60
+ cv2.rectangle(image, (10, 10), (20 + label_length[0][0], 40), (255, 255, 255), -1)
61
+ cv2.putText(image, label, (16, 31),
62
+ cv2.FONT_HERSHEY_SIMPLEX, size, tuple([i - 50 for i in color]), thickness_in, cv2.LINE_AA)
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+ image = cv2.addWeighted(image, 0.4, copied, 0.6, 0.0)
64
+
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+ vw.write(image)
66
+
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+ vw.release()
KABR/dataset/image2visual.py ADDED
@@ -0,0 +1,67 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import sys
3
+ import json
4
+ import cv2
5
+ from natsort import natsorted
6
+ import pandas as pd
7
+ from tqdm import tqdm
8
+
9
+ if __name__ == "__main__":
10
+ path_to_image = "image"
11
+ path_to_video = "visual"
12
+ annotation_train = "../annotation/train.csv"
13
+ annotation_val = "../annotation/val.csv"
14
+ classes_json = "../annotation/classes.json"
15
+ visual = True
16
+
17
+ if not os.path.exists(path_to_video):
18
+ os.makedirs(path_to_video)
19
+
20
+ with open(classes_json, "r") as file:
21
+ label2number = json.load(file)
22
+
23
+ number2label = {value: key for key, value in label2number.items()}
24
+
25
+ df_train = pd.read_csv(annotation_train, sep=" ")
26
+ df_val = pd.read_csv(annotation_val, sep=" ")
27
+ df = pd.concat([df_train, df_val], axis=0)
28
+ folders = natsorted(os.listdir(path_to_image))
29
+
30
+ hierarchy = {}
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+
32
+ for folder in folders:
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+ main = folder.split(".")[0]
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+
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+ if hierarchy.get(main) is None:
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+ hierarchy[main] = [folder]
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+ else:
38
+ hierarchy[main].append(folder)
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+
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+ for i, folder in tqdm(enumerate(hierarchy.keys()), total=len(hierarchy.keys())):
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+ vw = cv2.VideoWriter(f"{path_to_video}/{folder}.mp4", cv2.VideoWriter_fourcc("m", "p", "4", "v"), 29.97,
42
+ (400, 300))
43
+
44
+ for segment in hierarchy[folder]:
45
+ mapping = {}
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+
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+ for index, row in df[df.original_vido_id == segment].iterrows():
48
+ mapping[row["frame_id"]] = number2label[row["labels"]]
49
+
50
+ for j, file in enumerate(natsorted(os.listdir(path_to_image + os.sep + segment))):
51
+ image = cv2.imread(f"{path_to_image}/{segment}/{file}")
52
+
53
+ if visual:
54
+ color = (0, 0, 0)
55
+ label = mapping[j + 1]
56
+ thickness_in = 1
57
+ size = 0.7
58
+ label_length = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, size, thickness_in)
59
+ copied = image.copy()
60
+ cv2.rectangle(image, (10, 10), (20 + label_length[0][0], 40), (255, 255, 255), -1)
61
+ cv2.putText(image, label, (16, 31),
62
+ cv2.FONT_HERSHEY_SIMPLEX, size, tuple([i - 50 for i in color]), thickness_in, cv2.LINE_AA)
63
+ image = cv2.addWeighted(image, 0.4, copied, 0.6, 0.0)
64
+
65
+ vw.write(image)
66
+
67
+ vw.release()
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