Subdivide dataset by species

.gitattributes

@@ -52,14 +52,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
-KABR_part_aa filter=lfs diff=lfs merge=lfs -text
-KABR_part_ab filter=lfs diff=lfs merge=lfs -text
-KABR_part_ac filter=lfs diff=lfs merge=lfs -text
-KABR_part_ad filter=lfs diff=lfs merge=lfs -text
-KABR_part_ae filter=lfs diff=lfs merge=lfs -text
-KABR_part_af filter=lfs diff=lfs merge=lfs -text
-KABR_part_ag filter=lfs diff=lfs merge=lfs -text
-KABR_part_ah filter=lfs diff=lfs merge=lfs -text
-KABR_part_ai filter=lfs diff=lfs merge=lfs -text
-KABR_part_aj filter=lfs diff=lfs merge=lfs -text
-KABR_part_ak filter=lfs diff=lfs merge=lfs -text
+# Split data files
+*_part_* filter=lfs diff=lfs merge=lfs -text
+# Custom
+KABR/annotation/train.csv filter=lfs diff=lfs merge=lfs -text
+KABR/annotation/val.csv filter=lfs diff=lfs merge=lfs -text

KABR/README.txt

@@ -0,0 +1,55 @@
+KABR: High-Quality Dataset for Kenyan Animal Behavior Recognition from Drone Videos
+
+---------------------------------------------------------------------------------------------------
+
+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.
+
+---------------------------------------------------------------------------------------------------
+
+The KABR dataset follows the Charades format. The Charades format:
+
+KABR
+    /images
+        /video_1
+            /image_1.jpg
+            /image_2.jpg
+            ...
+            /image_n.jpg
+        /video_2
+            /image_1.jpg
+            /image_2.jpg
+            ...
+            /image_n.jpg
+        ...
+        /video_n
+            /image_1.jpg
+            /image_2.jpg
+            /image_3.jpg
+            ...
+            /image_n.jpg
+    /annotation
+        /classes.json
+        /train.csv
+        /val.csv
+
+The dataset can be directly loaded and processed by the SlowFast (https://github.com/facebookresearch/SlowFast) framework.
+
+---------------------------------------------------------------------------------------------------
+
+Naming:
+G0XXX.X - Giraffes
+ZP0XXX.X - Plains Zebras
+ZG0XXX.X - Grevy's Zebras
+
+---------------------------------------------------------------------------------------------------
+
+Information:
+
+KABR/configs: examples of SlowFast framework configs.
+KABR/annotation/distribution.xlsx: distribution of classes for all videos.
+
+---------------------------------------------------------------------------------------------------
+
+Scripts:
+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.
+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

@@ -0,0 +1 @@
+{"Walk": 0, "Graze": 1, "Browse": 2, "Head Up": 3, "Auto-Groom": 4, "Trot": 5, "Run": 6, "Occluded": 7}

KABR_part_ad → KABR/annotation/train.csv

@@ -1,3 +1,3 @@
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+size 30325892

KABR_part_aa → KABR/annotation/val.csv

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+size 10652837

KABR/configs/I3D.yaml

@@ -0,0 +1,99 @@
+TRAIN:
+  ENABLE: True
+  DATASET: charades
+  BATCH_SIZE: 8
+  EVAL_PERIOD: 5
+  CHECKPOINT_PERIOD: 5
+  AUTO_RESUME: True
+  # CHECKPOINT_FILE_PATH:
+  CHECKPOINT_TYPE: pytorch
+  CHECKPOINT_INFLATE: False
+  MIXED_PRECISION: True
+
+TEST:
+  ENABLE: True
+  DATASET: charades
+  BATCH_SIZE: 8
+  NUM_ENSEMBLE_VIEWS: 2
+  NUM_SPATIAL_CROPS: 1
+  # CHECKPOINT_FILE_PATH:
+  CHECKPOINT_TYPE: pytorch
+
+DATA:
+  NUM_FRAMES: 16
+  SAMPLING_RATE: 5
+  TRAIN_JITTER_SCALES: [320, 320]
+  TRAIN_CROP_SIZE: 320
+  TEST_CROP_SIZE: 320
+  TRAIN_CROP_NUM_TEMPORAL: 1
+  INPUT_CHANNEL_NUM: [3]
+  MULTI_LABEL: False
+  RANDOM_FLIP: True
+  SSL_COLOR_JITTER: True
+  SSL_COLOR_BRI_CON_SAT: [0.2, 0.2, 0.2]
+  INV_UNIFORM_SAMPLE: True
+  ENSEMBLE_METHOD: max
+  REVERSE_INPUT_CHANNEL: True
+  PATH_TO_DATA_DIR: "./KABR/annotation"
+  PATH_PREFIX: "./KABR/dataset/image"
+  DECODING_BACKEND: torchvision
+
+RESNET:
+  ZERO_INIT_FINAL_BN: True
+  WIDTH_PER_GROUP: 64
+  NUM_GROUPS: 1
+  DEPTH: 50
+  TRANS_FUNC: bottleneck_transform
+  STRIDE_1X1: False
+  NUM_BLOCK_TEMP_KERNEL: [[3], [4], [6], [3]]
+
+NONLOCAL:
+  LOCATION: [[[]], [[]], [[]], [[]]]
+  GROUP: [[1], [1], [1], [1]]
+  INSTANTIATION: softmax
+
+BN:
+  USE_PRECISE_STATS: True
+  NUM_BATCHES_PRECISE: 100
+  NORM_TYPE: sync_batchnorm
+  NUM_SYNC_DEVICES: 1
+
+SOLVER:
+  BASE_LR: 0.1
+  LR_POLICY: cosine
+  MAX_EPOCH: 120
+  MOMENTUM: 0.9
+  WEIGHT_DECAY: 1e-4
+  WARMUP_EPOCHS: 34.0
+  WARMUP_START_LR: 0.01
+  OPTIMIZING_METHOD: sgd
+
+MODEL:
+  NUM_CLASSES: 8
+  ARCH: i3d
+  MODEL_NAME: ResNet
+  LOSS_FUNC: cross_entropy
+  DROPOUT_RATE: 0.5
+
+DATA_LOADER:
+  NUM_WORKERS: 8
+  PIN_MEMORY: True
+
+NUM_GPUS: 1
+NUM_SHARDS: 1
+RNG_SEED: 0
+OUTPUT_DIR: ./logs/i3d-kabr
+LOG_MODEL_INFO: True
+
+TENSORBOARD:
+  ENABLE: False
+
+DEMO:
+  ENABLE: True
+  LABEL_FILE_PATH: ./KABR/annotation/classes.json
+  # INPUT_VIDEO: # path to input
+  # OUTPUT_FILE: # path to output
+  THREAD_ENABLE: False
+  THREAD_ENABLE: False
+  NUM_VIS_INSTANCES: 1
+  NUM_CLIPS_SKIP: 1

KABR/configs/SLOWFAST.yaml

@@ -0,0 +1,108 @@
+TRAIN:
+  ENABLE: True
+  DATASET: charades
+  BATCH_SIZE: 8
+  EVAL_PERIOD: 5
+  CHECKPOINT_PERIOD: 5
+  AUTO_RESUME: True
+  # CHECKPOINT_FILE_PATH:
+  CHECKPOINT_TYPE: pytorch
+  CHECKPOINT_INFLATE: False
+  MIXED_PRECISION: True
+
+TEST:
+  ENABLE: True
+  DATASET: charades
+  BATCH_SIZE: 8
+  NUM_ENSEMBLE_VIEWS: 2
+  NUM_SPATIAL_CROPS: 1
+  # CHECKPOINT_FILE_PATH:
+  CHECKPOINT_TYPE: pytorch
+
+DATA:
+  NUM_FRAMES: 16
+  SAMPLING_RATE: 5
+  TRAIN_JITTER_SCALES: [256, 256]
+  TRAIN_CROP_SIZE: 256
+  TEST_CROP_SIZE: 256
+  TRAIN_CROP_NUM_TEMPORAL: 1
+  INPUT_CHANNEL_NUM: [3, 3]
+  MULTI_LABEL: False
+  RANDOM_FLIP: True
+  SSL_COLOR_JITTER: True
+  SSL_COLOR_BRI_CON_SAT: [0.2, 0.2, 0.2]
+  INV_UNIFORM_SAMPLE: True
+  ENSEMBLE_METHOD: max
+  REVERSE_INPUT_CHANNEL: True
+  PATH_TO_DATA_DIR: "./KABR/annotation"
+  PATH_PREFIX: "./KABR/dataset/image"
+  DECODING_BACKEND: torchvision
+  
+SLOWFAST:
+  ALPHA: 4
+  BETA_INV: 8
+  FUSION_CONV_CHANNEL_RATIO: 2
+  FUSION_KERNEL_SZ: 7
+
+RESNET:
+  ZERO_INIT_FINAL_BN: True
+  WIDTH_PER_GROUP: 64
+  NUM_GROUPS: 1
+  DEPTH: 50
+  TRANS_FUNC: bottleneck_transform
+  STRIDE_1X1: False
+  NUM_BLOCK_TEMP_KERNEL: [[3, 3], [4, 4], [6, 6], [3, 3]]
+  SPATIAL_STRIDES: [[1, 1], [2, 2], [2, 2], [2, 2]]
+  SPATIAL_DILATIONS: [[1, 1], [1, 1], [1, 1], [1, 1]]
+  
+NONLOCAL:
+  LOCATION: [[[], []], [[], []], [[], []], [[], []]]
+  GROUP: [[1, 1], [1, 1], [1, 1], [1, 1]]
+  INSTANTIATION: dot_product
+
+BN:
+  USE_PRECISE_STATS: True
+  NUM_BATCHES_PRECISE: 200
+  NORM_TYPE: sync_batchnorm
+  NUM_SYNC_DEVICES: 1
+
+SOLVER:
+  BASE_LR: 0.0375
+  LR_POLICY: steps_with_relative_lrs
+  LRS: [1, 0.1, 0.01, 0.001, 0.0001, 0.00001]
+  STEPS: [0, 41, 49]
+  MAX_EPOCH: 80
+  MOMENTUM: 0.9
+  WEIGHT_DECAY: 1e-4
+  WARMUP_EPOCHS: 3.0
+  WARMUP_START_LR: 0.0001
+  OPTIMIZING_METHOD: sgd
+
+MODEL:
+  NUM_CLASSES: 8
+  ARCH: slowfast
+  LOSS_FUNC: cross_entropy
+  DROPOUT_RATE: 0.5
+
+DATA_LOADER:
+  NUM_WORKERS: 8
+  PIN_MEMORY: True
+
+NUM_GPUS: 1
+NUM_SHARDS: 1
+RNG_SEED: 0
+OUTPUT_DIR: ./logs/slowfast-kabr
+LOG_MODEL_INFO: True
+
+TENSORBOARD:
+  ENABLE: False
+
+DEMO:
+  ENABLE: True
+  LABEL_FILE_PATH: ./KABR/annotation/classes.json
+  # INPUT_VIDEO: # path to input
+  # OUTPUT_FILE: # path to output
+  THREAD_ENABLE: False
+  THREAD_ENABLE: False
+  NUM_VIS_INSTANCES: 1
+  NUM_CLIPS_SKIP: 1

KABR/configs/X3D.yaml

@@ -0,0 +1,98 @@
+TRAIN:
+  ENABLE: True
+  DATASET: charades
+  BATCH_SIZE: 8
+  EVAL_PERIOD: 5
+  CHECKPOINT_PERIOD: 5
+  AUTO_RESUME: True
+  # CHECKPOINT_FILE_PATH:
+  CHECKPOINT_TYPE: pytorch
+  CHECKPOINT_INFLATE: False
+  MIXED_PRECISION: True
+
+TEST:
+  ENABLE: True
+  DATASET: charades
+  BATCH_SIZE: 8
+  NUM_ENSEMBLE_VIEWS: 2
+  NUM_SPATIAL_CROPS: 1
+  # CHECKPOINT_FILE_PATH:
+  CHECKPOINT_TYPE: pytorch
+
+DATA:
+  NUM_FRAMES: 16
+  SAMPLING_RATE: 5
+  TRAIN_JITTER_SCALES: [300, 300]
+  TRAIN_CROP_SIZE: 300
+  TEST_CROP_SIZE: 300
+  TRAIN_CROP_NUM_TEMPORAL: 1
+  INPUT_CHANNEL_NUM: [3]
+  MULTI_LABEL: False
+  RANDOM_FLIP: True
+  SSL_COLOR_JITTER: True
+  SSL_COLOR_BRI_CON_SAT: [0.2, 0.2, 0.2]
+  INV_UNIFORM_SAMPLE: True
+  ENSEMBLE_METHOD: max
+  REVERSE_INPUT_CHANNEL: True
+  PATH_TO_DATA_DIR: "./KABR/annotation"
+  PATH_PREFIX: "./KABR/dataset/image"
+  DECODING_BACKEND: torchvision
+
+X3D:
+  WIDTH_FACTOR: 2.0
+  DEPTH_FACTOR: 5.0
+  BOTTLENECK_FACTOR: 2.25
+  DIM_C5: 2048
+  DIM_C1: 12
+
+RESNET:
+  ZERO_INIT_FINAL_BN: True
+  TRANS_FUNC: x3d_transform
+  STRIDE_1X1: False
+
+BN:
+  USE_PRECISE_STATS: True
+  NUM_BATCHES_PRECISE: 200
+  NORM_TYPE: sync_batchnorm
+  NUM_SYNC_DEVICES: 1
+  WEIGHT_DECAY: 0.0
+
+SOLVER:
+  BASE_LR: 0.05
+  BASE_LR_SCALE_NUM_SHARDS: True
+  MAX_EPOCH: 120
+  LR_POLICY: cosine
+  WEIGHT_DECAY: 5e-5
+  WARMUP_EPOCHS: 35.0
+  WARMUP_START_LR: 0.01
+  OPTIMIZING_METHOD: sgd
+
+MODEL:
+  NUM_CLASSES: 8
+  ARCH: x3d
+  MODEL_NAME: X3D
+  LOSS_FUNC: cross_entropy
+  DROPOUT_RATE: 0.5
+
+DATA_LOADER:
+  NUM_WORKERS: 8
+  PIN_MEMORY: True
+
+NUM_GPUS: 1
+NUM_SHARDS: 1
+RNG_SEED: 0
+OUTPUT_DIR: ./logs/x3d-l-kabr
+LOG_MODEL_INFO: True
+
+TENSORBOARD:
+  ENABLE: False
+
+DEMO:
+  ENABLE: True
+  LABEL_FILE_PATH: ./KABR/annotation/classes.json
+  # INPUT_VIDEO: # path to input
+  # OUTPUT_FILE: # path to output
+  THREAD_ENABLE: False
+  THREAD_ENABLE: False
+  NUM_VIS_INSTANCES: 1
+  NUM_CLIPS_SKIP: 1

KABR/dataset/image/giraffes_md5.txt

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KABR/dataset/image/zebras_grevys_md5.txt

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+size 2147483648

KABR/dataset/image/zebras_plains_part_aj

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KABR/dataset/image/zebras_plains_part_ak

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KABR/dataset/image/zebras_plains_part_al

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+size 91613758

KABR/dataset/image2video.py

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+import os
+import sys
+import json
+import cv2
+from natsort import natsorted
+import pandas as pd
+from tqdm import tqdm
+
+if __name__ == "__main__":
+    path_to_image = "image"
+    path_to_video = "video"
+    annotation_train = "../annotation/train.csv"
+    annotation_val = "../annotation/val.csv"
+    classes_json = "../annotation/classes.json"
+    visual = False
+
+    if not os.path.exists(path_to_video):
+        os.makedirs(path_to_video)
+
+    with open(classes_json, "r") as file:
+        label2number = json.load(file)
+
+    number2label = {value: key for key, value in label2number.items()}
+
+    df_train = pd.read_csv(annotation_train, sep=" ")
+    df_val = pd.read_csv(annotation_val, sep=" ")
+    df = pd.concat([df_train, df_val], axis=0)
+    folders = natsorted(os.listdir(path_to_image))
+
+    hierarchy = {}
+
+    for folder in folders:
+        main = folder.split(".")[0]
+    
+        if hierarchy.get(main) is None:
+            hierarchy[main] = [folder]
+        else:
+            hierarchy[main].append(folder)
+
+    for i, folder in tqdm(enumerate(hierarchy.keys()), total=len(hierarchy.keys())):
+        vw = cv2.VideoWriter(f"{path_to_video}/{folder}.mp4", cv2.VideoWriter_fourcc("m", "p", "4", "v"), 29.97,
+                             (400, 300))
+
+        for segment in hierarchy[folder]:
+            mapping = {}
+
+            for index, row in df[df.original_vido_id == segment].iterrows():
+                mapping[row["frame_id"]] = number2label[row["labels"]]
+
+            for j, file in enumerate(natsorted(os.listdir(path_to_image + os.sep + segment))):
+                image = cv2.imread(f"{path_to_image}/{segment}/{file}")
+
+                if visual:
+                    color = (0, 0, 0)
+                    label = mapping[j + 1]
+                    thickness_in = 1
+                    size = 0.7
+                    label_length = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, size, thickness_in)
+                    copied = image.copy()
+                    cv2.rectangle(image, (10, 10), (20 + label_length[0][0], 40), (255, 255, 255), -1)
+                    cv2.putText(image, label, (16, 31),
+                                cv2.FONT_HERSHEY_SIMPLEX, size, tuple([i - 50 for i in color]), thickness_in, cv2.LINE_AA)
+                    image = cv2.addWeighted(image, 0.4, copied, 0.6, 0.0)
+
+                vw.write(image)
+
+        vw.release()

KABR/dataset/image2visual.py

@@ -0,0 +1,67 @@
+import os
+import sys
+import json
+import cv2
+from natsort import natsorted
+import pandas as pd
+from tqdm import tqdm
+
+if __name__ == "__main__":
+    path_to_image = "image"
+    path_to_video = "visual"
+    annotation_train = "../annotation/train.csv"
+    annotation_val = "../annotation/val.csv"
+    classes_json = "../annotation/classes.json"
+    visual = True
+
+    if not os.path.exists(path_to_video):
+        os.makedirs(path_to_video)
+
+    with open(classes_json, "r") as file:
+        label2number = json.load(file)
+
+    number2label = {value: key for key, value in label2number.items()}
+
+    df_train = pd.read_csv(annotation_train, sep=" ")
+    df_val = pd.read_csv(annotation_val, sep=" ")
+    df = pd.concat([df_train, df_val], axis=0)
+    folders = natsorted(os.listdir(path_to_image))
+
+    hierarchy = {}
+
+    for folder in folders:
+        main = folder.split(".")[0]
+
+        if hierarchy.get(main) is None:
+            hierarchy[main] = [folder]
+        else:
+            hierarchy[main].append(folder)
+
+    for i, folder in tqdm(enumerate(hierarchy.keys()), total=len(hierarchy.keys())):
+        vw = cv2.VideoWriter(f"{path_to_video}/{folder}.mp4", cv2.VideoWriter_fourcc("m", "p", "4", "v"), 29.97,
+                             (400, 300))
+
+        for segment in hierarchy[folder]:
+            mapping = {}
+
+            for index, row in df[df.original_vido_id == segment].iterrows():
+                mapping[row["frame_id"]] = number2label[row["labels"]]
+
+            for j, file in enumerate(natsorted(os.listdir(path_to_image + os.sep + segment))):
+                image = cv2.imread(f"{path_to_image}/{segment}/{file}")
+
+                if visual:
+                    color = (0, 0, 0)
+                    label = mapping[j + 1]
+                    thickness_in = 1
+                    size = 0.7
+                    label_length = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, size, thickness_in)
+                    copied = image.copy()
+                    cv2.rectangle(image, (10, 10), (20 + label_length[0][0], 40), (255, 255, 255), -1)
+                    cv2.putText(image, label, (16, 31),
+                                cv2.FONT_HERSHEY_SIMPLEX, size, tuple([i - 50 for i in color]), thickness_in, cv2.LINE_AA)
+                    image = cv2.addWeighted(image, 0.4, copied, 0.6, 0.0)
+
+                vw.write(image)
+
+        vw.release()

KABR_MD5.txt

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-MD5 (KABR.zip) = 6a4d7fa706a87a2e05171bcafce616ca

KABR_part_ae

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-size 5368709120

KABR_part_af

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KABR_part_ai

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