Data structures#
In this notebook, we will explore some of the major data structures used in SLEAP and how they can be manipulated when generating predictions from trained models.
A quick overview of the data structures before we start:
Point/PredictedPoint→ Contains thexandycoordinates (andscorefor predictions) of a landmark.Instance/PredictedInstance→ Contains a set ofPoint/PredictedPoints. This represent a single individual within a frame and may also contain an associatedTrack.Skeleton→ Defines the nodes and edges that define the set of unique landmark types that each point represents, e.g., “head”, “tail”, etc. This does not contain positions – those are stored in individualPoints.LabeledFrame→ Contains a set ofInstance/PredictedInstances for a single frame.Labels→ Contains a set ofLabeledFrames and the associated metadata for the videos and other information related to the project or predictions.
1. Setup SLEAP and data#
We’ll start by installing SLEAP and downloading some data and models to play around with.
If you get a dependency error in subsequent cells, just click Runtime → Restart runtime to reload the packages.
# This should take care of all the dependencies on colab:
!pip uninstall -qqq -y opencv-python opencv-contrib-python
!pip install -qqq "sleap[pypi]>=1.3.3"
# But to do it locally, we'd recommend the conda package (available on Windows + Linux):
# conda create -n sleap -c sleap -c conda-forge -c nvidia sleap
# Test video:
!wget https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.mp4
# Test video labels (from predictions/not necessary for inference benchmarking):
!wget https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.slp
# Bottom-up model:
# !wget https://storage.googleapis.com/sleap-data/reference/flies13/bu.210506_230852.multi_instance.n%3D1800.zip
# Top-down model (two-stage):
!wget https://storage.googleapis.com/sleap-data/reference/flies13/centroid.fast.210504_182918.centroid.n%3D1800.zip
!wget https://storage.googleapis.com/sleap-data/reference/flies13/td_fast.210505_012601.centered_instance.n%3D1800.zip
--2023-08-31 12:03:50-- https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.mp4
Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.176.16, 142.250.72.144, 172.217.12.144, ...
Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.176.16|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 85343812 (81M) [video/mp4]
Saving to: ‘190719_090330_wt_18159206_rig1.2@15000-17560.mp4.1’
190719_090330_wt_18 100%[===================>] 81.39M 27.7MB/s in 2.9s
2023-08-31 12:03:53 (27.7 MB/s) - ‘190719_090330_wt_18159206_rig1.2@15000-17560.mp4.1’ saved [85343812/85343812]
--2023-08-31 12:03:53-- https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.slp
Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.188.240, 142.250.217.144, 142.250.68.16, ...
Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.188.240|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 1581400 (1.5M) [application/octet-stream]
Saving to: ‘190719_090330_wt_18159206_rig1.2@15000-17560.slp.1’
190719_090330_wt_18 100%[===================>] 1.51M 3.99MB/s in 0.4s
2023-08-31 12:03:54 (3.99 MB/s) - ‘190719_090330_wt_18159206_rig1.2@15000-17560.slp.1’ saved [1581400/1581400]
--2023-08-31 12:03:54-- https://storage.googleapis.com/sleap-data/reference/flies13/centroid.fast.210504_182918.centroid.n%3D1800.zip
Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.72.240, 142.250.188.240, 142.250.189.16, ...
Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.72.240|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 6372537 (6.1M) [application/zip]
Saving to: ‘centroid.fast.210504_182918.centroid.n=1800.zip.1’
centroid.fast.21050 100%[===================>] 6.08M --.-KB/s in 0.1s
2023-08-31 12:03:54 (56.6 MB/s) - ‘centroid.fast.210504_182918.centroid.n=1800.zip.1’ saved [6372537/6372537]
--2023-08-31 12:03:54-- https://storage.googleapis.com/sleap-data/reference/flies13/td_fast.210505_012601.centered_instance.n%3D1800.zip
Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.14.112, 142.250.176.16, 142.250.72.176, ...
Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.14.112|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 30775963 (29M) [application/zip]
Saving to: ‘td_fast.210505_012601.centered_instance.n=1800.zip.1’
td_fast.210505_0126 100%[===================>] 29.35M 21.3MB/s in 1.4s
2023-08-31 12:03:56 (21.3 MB/s) - ‘td_fast.210505_012601.centered_instance.n=1800.zip.1’ saved [30775963/30775963]
!ls -lah
total 239M
drwxrwxr-x 3 talmolab talmolab 4.0K Aug 31 12:03 .
drwxrwxr-x 7 talmolab talmolab 4.0K Aug 31 11:39 ..
-rw-rw-r-- 1 talmolab talmolab 82M May 20 2021 190719_090330_wt_18159206_rig1.2@15000-17560.mp4
-rw-rw-r-- 1 talmolab talmolab 82M May 20 2021 190719_090330_wt_18159206_rig1.2@15000-17560.mp4.1
-rw-rw-r-- 1 talmolab talmolab 1.6M May 20 2021 190719_090330_wt_18159206_rig1.2@15000-17560.slp
-rw-rw-r-- 1 talmolab talmolab 1.6M May 20 2021 190719_090330_wt_18159206_rig1.2@15000-17560.slp.1
drwxrwxr-x 2 talmolab talmolab 4.0K Jun 20 10:00 analysis_example
-rw-rw-r-- 1 talmolab talmolab 713K Jun 20 10:00 Analysis_examples.ipynb
-rw-rw-r-- 1 talmolab talmolab 6.1M May 20 2021 'centroid.fast.210504_182918.centroid.n=1800.zip'
-rw-rw-r-- 1 talmolab talmolab 6.1M May 20 2021 'centroid.fast.210504_182918.centroid.n=1800.zip.1'
-rw-rw-r-- 1 talmolab talmolab 486K Aug 31 11:39 Data_structures.ipynb
-rw-rw-r-- 1 talmolab talmolab 4.1K Jun 20 10:00 index.rst
-rw-rw-r-- 1 talmolab talmolab 197K Aug 31 11:39 Interactive_and_realtime_inference.ipynb
-rw-rw-r-- 1 talmolab talmolab 398K Aug 31 11:39 Interactive_and_resumable_training.ipynb
-rw-rw-r-- 1 talmolab talmolab 149K Aug 31 11:39 Model_evaluation.ipynb
-rw-rw-r-- 1 talmolab talmolab 150K Aug 31 11:39 Post_inference_tracking.ipynb
-rw-rw-r-- 1 talmolab talmolab 30M May 20 2021 'td_fast.210505_012601.centered_instance.n=1800.zip'
-rw-rw-r-- 1 talmolab talmolab 30M May 20 2021 'td_fast.210505_012601.centered_instance.n=1800.zip.1'
-rw-rw-r-- 1 talmolab talmolab 9.5K Aug 31 11:39 Training_and_inference_on_an_example_dataset.ipynb
-rw-rw-r-- 1 talmolab talmolab 12K Aug 31 11:39 Training_and_inference_using_Google_Drive.ipynb
import sleap
# This prevents TensorFlow from allocating all the GPU memory, which leads to issues on
# some GPUs/platforms:
sleap.disable_preallocation()
# This would hide GPUs from the TensorFlow altogether:
# sleap.use_cpu_only()
# Print some info:
sleap.versions()
sleap.system_summary()
2023-08-31 12:03:56.989133: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-31 12:03:57.058048: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2023-08-31 12:03:57.060007: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:
2023-08-31 12:03:57.060013: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2023-08-31 12:03:57.445179: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:
2023-08-31 12:03:57.445232: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:
2023-08-31 12:03:57.445236: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
SLEAP: 1.3.2
TensorFlow: 2.11.0
Numpy: 1.21.6
Python: 3.7.12
OS: Linux-5.15.0-78-generic-x86_64-with-debian-bookworm-sid
GPUs: None detected.
2023-08-31 12:03:58.223182: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-31 12:03:58.223923: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:
2023-08-31 12:03:58.223968: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:
2023-08-31 12:03:58.223999: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:
2023-08-31 12:03:58.224028: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:
2023-08-31 12:03:58.224057: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcurand.so.10'; dlerror: libcurand.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:
2023-08-31 12:03:58.224084: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusolver.so.11'; dlerror: libcusolver.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:
2023-08-31 12:03:58.224111: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:
2023-08-31 12:03:58.224140: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/talmolab/micromamba/envs/sleap_jupyter/lib/python3.7/site-packages/cv2/../../lib64:
2023-08-31 12:03:58.224144: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1934] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
2. Data structures and inference#
SLEAP can read videos in a variety of different formats through the sleap.load_video high level API. Once loaded, the sleap.Video object allows you to access individual frames as if the it were a standard numpy array.
Note: The actual frames are not loaded until you access them so we don’t blow up our memory when using long videos.
# Videos can be represented agnostic to the backend format
video = sleap.load_video("190719_090330_wt_18159206_rig1.2@15000-17560.mp4")
# sleap.Video objects have a numpy-like interface:
print(video.shape)
# And we can load images in the video using array indexing:
imgs = video[:4]
print(imgs.shape, imgs.dtype)
(2560, 1024, 1024, 1)
(4, 1024, 1024, 1) uint8
The high level interface for loading models (sleap.load_model()) takes model folders or zipped folders as input. These are outputs from our training procedure and need to contain a "best_model.h5" and "training_config.json".
best_model.h5 is an HDF5-serialized tf.keras.Model that was checkpointed during
training. It includes the architecture as well as the weights, so they’re standalone
and don’t need SLEAP – BUT they do not contain the inference methods.
training_config.json is a serialized sleap.TrainingJobConfig that contains metadata
like what channels of the model correspond to what landmarks and etc.
Top-down models have two stages: centroid and centered instance confidence maps, which we train and save out separately, so loading them together links them up into a single inference model.
# Top-down
predictor = sleap.load_model([
"centroid.fast.210504_182918.centroid.n=1800.zip",
"td_fast.210505_012601.centered_instance.n=1800.zip"
])
# Bottom-up
# predictor = sleap.load_model("bu.210506_230852.multi_instance.n=1800.zip")
2023-08-31 12:03:58.498908: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
The high level predictor creates all the SLEAP data structures after doing inference. For example:
labels = predictor.predict(video)
labels
2023-08-31 12:04:01.923466: W tensorflow/core/grappler/costs/op_level_cost_estimator.cc:690] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -45 } dim { size: -46 } dim { size: -47 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -15 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -15 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: "CPU" vendor: "GenuineIntel" model: "103" frequency: 3600 num_cores: 16 environment { key: "cpu_instruction_set" value: "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 49152 l2_cache_size: 524288 l3_cache_size: 16777216 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -15 } dim { size: -48 } dim { size: -49 } dim { size: 1 } } }
2023-08-31 12:04:01.923717: W tensorflow/core/grappler/costs/op_level_cost_estimator.cc:690] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_UINT8 } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_UINT8 shape { dim { size: 4 } dim { size: 1024 } dim { size: 1024 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -15 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -15 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: "CPU" vendor: "GenuineIntel" model: "103" frequency: 3600 num_cores: 16 environment { key: "cpu_instruction_set" value: "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 49152 l2_cache_size: 524288 l3_cache_size: 16777216 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -15 } dim { size: -56 } dim { size: -57 } dim { size: 1 } } }
2023-08-31 12:04:01.926044: W tensorflow/core/grappler/costs/op_level_cost_estimator.cc:690] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -90 } dim { size: -91 } dim { size: -92 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -20 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -20 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: "CPU" vendor: "GenuineIntel" model: "103" frequency: 3600 num_cores: 16 environment { key: "cpu_instruction_set" value: "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 49152 l2_cache_size: 524288 l3_cache_size: 16777216 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -20 } dim { size: -94 } dim { size: -95 } dim { size: 1 } } }
Labels(labeled_frames=2560, videos=1, skeletons=1, tracks=0)
Labels contain not just the predicted data, but all the other associated data structures and metadata:
labels.videos
[Video(backend=MediaVideo(filename='190719_090330_wt_18159206_rig1.2@15000-17560.mp4', grayscale=True, bgr=True, dataset='', input_format='channels_last'))]
labels.skeletons
[Skeleton(name='Skeleton-0', description='None', nodes=['head', 'thorax', 'abdomen', 'wingL', 'wingR', 'forelegL4', 'forelegR4', 'midlegL4', 'midlegR4', 'hindlegL4', 'hindlegR4', 'eyeL', 'eyeR'], edges=[('thorax', 'head'), ('thorax', 'abdomen'), ('thorax', 'wingL'), ('thorax', 'wingR'), ('thorax', 'forelegL4'), ('thorax', 'forelegR4'), ('thorax', 'midlegL4'), ('thorax', 'midlegR4'), ('thorax', 'hindlegL4'), ('thorax', 'hindlegR4'), ('head', 'eyeL'), ('head', 'eyeR')], symmetries=[('forelegL4', 'forelegR4'), ('wingL', 'wingR'), ('eyeL', 'eyeR'), ('midlegL4', 'midlegR4'), ('hindlegL4', 'hindlegR4')])]
Individual labeled frames are accessible through a list-like interface:
labeled_frame = labels[0] # shortcut for labels.labeled_frames[0]
labeled_frame
LabeledFrame(video=MediaVideo('190719_090330_wt_18159206_rig1.2@15000-17560.mp4'), frame_idx=0, instances=2)
Convenient methods allow for easy inspection:
labels[0].plot(scale=0.5)
The labeled frame is itself a container for instances:
labeled_frame.instances
[PredictedInstance(video=Video(filename=190719_090330_wt_18159206_rig1.2@15000-17560.mp4, shape=(2560, 1024, 1024, 1), backend=MediaVideo), frame_idx=0, points=[head: (234.2, 430.5, 0.98), thorax: (271.6, 436.1, 0.94), abdomen: (308.0, 438.6, 0.59), wingL: (321.8, 440.1, 0.39), wingR: (322.0, 436.8, 0.49), forelegL4: (246.1, 450.6, 0.92), forelegR4: (242.3, 413.9, 0.78), midlegL4: (285.8, 459.9, 0.47), midlegR4: (272.3, 406.7, 0.77), hindlegR4: (317.6, 430.6, 0.30), eyeL: (242.1, 441.9, 0.89), eyeR: (245.3, 420.9, 0.92)], score=0.95, track=None, tracking_score=0.00),
PredictedInstance(video=Video(filename=190719_090330_wt_18159206_rig1.2@15000-17560.mp4, shape=(2560, 1024, 1024, 1), backend=MediaVideo), frame_idx=0, points=[head: (319.4, 435.9, 0.83), thorax: (354.4, 435.2, 0.80), abdomen: (368.3, 433.8, 0.71), wingL: (393.9, 480.3, 0.83), wingR: (398.4, 430.0, 0.81), forelegL4: (307.8, 445.7, 0.26), forelegR4: (305.6, 421.4, 0.69), midlegL4: (325.7, 475.0, 0.94), midlegR4: (331.8, 385.1, 0.88), hindlegL4: (363.7, 474.1, 0.88), hindlegR4: (376.0, 398.4, 0.52), eyeL: (329.3, 445.6, 0.90), eyeR: (327.9, 425.1, 0.84)], score=0.84, track=None, tracking_score=0.00)]
instance = labeled_frame[0] # shortcut for labeled_frame.instances[0]
instance
PredictedInstance(video=Video(filename=190719_090330_wt_18159206_rig1.2@15000-17560.mp4, shape=(2560, 1024, 1024, 1), backend=MediaVideo), frame_idx=0, points=[head: (234.2, 430.5, 0.98), thorax: (271.6, 436.1, 0.94), abdomen: (308.0, 438.6, 0.59), wingL: (321.8, 440.1, 0.39), wingR: (322.0, 436.8, 0.49), forelegL4: (246.1, 450.6, 0.92), forelegR4: (242.3, 413.9, 0.78), midlegL4: (285.8, 459.9, 0.47), midlegR4: (272.3, 406.7, 0.77), hindlegR4: (317.6, 430.6, 0.30), eyeL: (242.1, 441.9, 0.89), eyeR: (245.3, 420.9, 0.92)], score=0.95, track=None, tracking_score=0.00)
Finally, instances are containers for points:
instance.points
(PredictedPoint(x=234.24440002441406, y=430.52008056640625, visible=True, complete=False, score=0.9790770411491394),
PredictedPoint(x=271.58941650390625, y=436.1461486816406, visible=True, complete=False, score=0.9358043670654297),
PredictedPoint(x=308.02960205078125, y=438.57135009765625, visible=True, complete=False, score=0.5861632227897644),
PredictedPoint(x=321.81768798828125, y=440.08721923828125, visible=True, complete=False, score=0.39127233624458313),
PredictedPoint(x=322.0193176269531, y=436.7702941894531, visible=True, complete=False, score=0.48629727959632874),
PredictedPoint(x=246.14295959472656, y=450.5621643066406, visible=True, complete=False, score=0.9176925420761108),
PredictedPoint(x=242.2632598876953, y=413.9497375488281, visible=True, complete=False, score=0.780803382396698),
PredictedPoint(x=285.78155517578125, y=459.91552734375, visible=True, complete=False, score=0.47393468022346497),
PredictedPoint(x=272.280029296875, y=406.71759033203125, visible=True, complete=False, score=0.7721256017684937),
PredictedPoint(x=317.598876953125, y=430.6053466796875, visible=True, complete=False, score=0.296230286359787),
PredictedPoint(x=242.10415649414062, y=441.9450378417969, visible=True, complete=False, score=0.8855596780776978),
PredictedPoint(x=245.32009887695312, y=420.9360656738281, visible=True, complete=False, score=0.9200019240379333))
These can be converted into concrete arrays:
pts = instance.numpy()
print(pts)
[[234.24440002 430.52008057]
[271.5894165 436.14614868]
[308.02960205 438.5713501 ]
[321.81768799 440.08721924]
[322.01931763 436.77029419]
[246.14295959 450.56216431]
[242.26325989 413.94973755]
[285.78155518 459.91552734]
[272.2800293 406.71759033]
[ nan nan]
[317.59887695 430.60534668]
[242.10415649 441.94503784]
[245.32009888 420.93606567]]
Images can be embedded together with the predictions in the same format:
labels = sleap.Labels(labels.labeled_frames[:4]) # crop to the first few labels for this example
labels.save("labels_with_images.pkg.slp", with_images=True, embed_all_labeled=True)
Let’s delete the source data:
!rm "190719_090330_wt_18159206_rig1.2@15000-17560.mp4"
And check out what happens when we load in some labels with embedded images:
labels = sleap.load_file("labels_with_images.pkg.slp")
labels
Labels(labeled_frames=4, videos=1, skeletons=1, tracks=0)
labels[0].plot(scale=0.5)