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395
scripts/test_skelda_dataset.py
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395
scripts/test_skelda_dataset.py
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import json
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import os
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import utils_pipeline
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from skelda import evals
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from skelda.writers import json_writer
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# ==================================================================================================
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whole_body = {
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"foots": False,
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"face": False,
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"hands": False,
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}
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dataset_use = "human36m"
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# dataset_use = "panoptic"
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# dataset_use = "mvor"
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# dataset_use = "shelf"
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# dataset_use = "campus"
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# dataset_use = "ikeaasm"
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# dataset_use = "chi3d"
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# dataset_use = "tsinghua"
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# dataset_use = "human36m_wb"
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# dataset_use = "egohumans_tagging"
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# dataset_use = "egohumans_legoassemble"
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# dataset_use = "egohumans_fencing"
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# dataset_use = "egohumans_basketball"
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# dataset_use = "egohumans_volleyball"
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# dataset_use = "egohumans_badminton"
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# dataset_use = "egohumans_tennis"
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# Describes the minimum area as fraction of the image size for a 2D bounding box to be considered
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# If the persons are small in the image, use a lower value
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default_min_bbox_area = 0.1 * 0.1
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# Describes how confident a 2D bounding box needs to be to be considered
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# If the persons are small in the image, or poorly recognizable, use a lower value
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default_min_bbox_score = 0.3
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# Describes how good two 2D poses need to match each other to create a valid triangulation
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# If the quality of the 2D detections is poor, use a lower value
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default_min_match_score = 0.94
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# Describes the minimum number of camera pairs that need to detect the same person
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# If the number of cameras is high, and the views are not occluded, use a higher value
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default_min_group_size = 1
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# Batch poses per image for faster processing
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# If most of the time only one person is in a image, disable it, because it is slightly slower then
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default_batch_poses = True
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datasets = {
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"human36m": {
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"path": "/datasets/human36m/skelda/pose_test.json",
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"take_interval": 5,
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"min_match_score": 0.95,
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"min_group_size": 1,
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"min_bbox_score": 0.4,
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"min_bbox_area": 0.1 * 0.1,
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"batch_poses": False,
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},
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"panoptic": {
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"path": "/datasets/panoptic/skelda/test.json",
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"cams": ["00_03", "00_06", "00_12", "00_13", "00_23"],
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# "cams": ["00_03", "00_06", "00_12"],
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# "cams": ["00_03", "00_06", "00_12", "00_13", "00_23", "00_15", "00_10", "00_21", "00_09", "00_01"],
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"take_interval": 3,
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"min_match_score": 0.95,
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"use_scenes": ["160906_pizza1", "160422_haggling1", "160906_ian5"],
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"min_group_size": 1,
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# "min_group_size": 4,
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"min_bbox_area": 0.05 * 0.05,
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},
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"mvor": {
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"path": "/datasets/mvor/skelda/all.json",
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"take_interval": 1,
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"with_depth": False,
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"min_match_score": 0.85,
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"min_bbox_score": 0.25,
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},
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"campus": {
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"path": "/datasets/campus/skelda/test.json",
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"take_interval": 1,
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"min_match_score": 0.90,
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"min_bbox_score": 0.5,
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},
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"shelf": {
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"path": "/datasets/shelf/skelda/test.json",
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"take_interval": 1,
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"min_match_score": 0.96,
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"min_group_size": 2,
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},
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"ikeaasm": {
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"path": "/datasets/ikeaasm/skelda/test.json",
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"take_interval": 2,
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"min_match_score": 0.92,
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"min_bbox_score": 0.20,
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},
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"chi3d": {
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"path": "/datasets/chi3d/skelda/all.json",
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"take_interval": 5,
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},
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"tsinghua": {
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"path": "/datasets/tsinghua/skelda/test.json",
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"take_interval": 3,
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"min_match_score": 0.95,
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"min_group_size": 2,
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},
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"human36m_wb": {
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"path": "/datasets/human36m/skelda/wb/test.json",
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"take_interval": 100,
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"min_bbox_score": 0.4,
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"batch_poses": False,
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},
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"egohumans_tagging": {
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"path": "/datasets/egohumans/skelda/all.json",
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"take_interval": 2,
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"subset": "tagging",
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"min_group_size": 2,
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"min_bbox_score": 0.2,
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"min_bbox_area": 0.05 * 0.05,
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},
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"egohumans_legoassemble": {
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"path": "/datasets/egohumans/skelda/all.json",
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"take_interval": 2,
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"subset": "legoassemble",
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"min_group_size": 2,
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},
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"egohumans_fencing": {
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"path": "/datasets/egohumans/skelda/all.json",
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"take_interval": 2,
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"subset": "fencing",
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"min_group_size": 7,
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"min_bbox_score": 0.5,
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"min_bbox_area": 0.05 * 0.05,
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},
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"egohumans_basketball": {
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"path": "/datasets/egohumans/skelda/all.json",
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"take_interval": 2,
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"subset": "basketball",
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"min_group_size": 7,
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"min_bbox_score": 0.25,
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"min_bbox_area": 0.025 * 0.025,
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},
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"egohumans_volleyball": {
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"path": "/datasets/egohumans/skelda/all.json",
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"take_interval": 2,
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"subset": "volleyball",
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"min_group_size": 11,
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"min_bbox_score": 0.25,
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"min_bbox_area": 0.05 * 0.05,
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},
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"egohumans_badminton": {
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"path": "/datasets/egohumans/skelda/all.json",
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"take_interval": 2,
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"subset": "badminton",
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"min_group_size": 7,
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"min_bbox_score": 0.25,
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"min_bbox_area": 0.05 * 0.05,
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},
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"egohumans_tennis": {
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"path": "/datasets/egohumans/skelda/all.json",
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"take_interval": 2,
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"subset": "tennis",
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"min_group_size": 11,
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"min_bbox_area": 0.025 * 0.025,
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},
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}
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joint_names_2d = utils_pipeline.get_joint_names(whole_body)
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joint_names_3d = list(joint_names_2d)
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eval_joints = [
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"head",
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"shoulder_left",
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"shoulder_right",
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"elbow_left",
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"elbow_right",
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"wrist_left",
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"wrist_right",
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"hip_left",
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"hip_right",
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"knee_left",
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"knee_right",
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"ankle_left",
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"ankle_right",
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]
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if dataset_use == "human36m":
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eval_joints[eval_joints.index("head")] = "nose"
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if dataset_use == "panoptic":
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eval_joints[eval_joints.index("head")] = "nose"
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if dataset_use == "human36m_wb":
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if utils_pipeline.use_whole_body(whole_body):
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eval_joints = list(joint_names_2d)
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else:
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eval_joints[eval_joints.index("head")] = "nose"
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# output_dir = "/RapidPoseTriangulation/data/testoutput/"
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output_dir = ""
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# ==================================================================================================
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def load_json(path: str):
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with open(path, "r", encoding="utf-8") as file:
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data = json.load(file)
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return data
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def save_json(data: dict, path: str):
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with open(path, "w+", encoding="utf-8") as file:
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json.dump(data, file, indent=0)
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# ==================================================================================================
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def load_labels(dataset: dict):
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"""Load labels by dataset description"""
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if "panoptic" in dataset:
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labels = load_json(dataset["panoptic"]["path"])
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labels = [lb for i, lb in enumerate(labels) if i % 1500 < 90]
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# Filter by maximum number of persons
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labels = [l for l in labels if len(l["bodies3D"]) <= 10]
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# Filter scenes
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if "use_scenes" in dataset["panoptic"]:
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labels = [
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l for l in labels if l["scene"] in dataset["panoptic"]["use_scenes"]
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]
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# Filter cameras
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if not "cameras_depth" in labels[0]:
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for label in labels:
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for i, cam in reversed(list(enumerate(label["cameras"]))):
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if cam["name"] not in dataset["panoptic"]["cams"]:
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label["cameras"].pop(i)
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label["imgpaths"].pop(i)
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elif "human36m" in dataset:
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labels = load_json(dataset["human36m"]["path"])
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labels = [lb for lb in labels if lb["subject"] == "S9"]
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labels = [lb for i, lb in enumerate(labels) if i % 4000 < 150]
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for label in labels:
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label.pop("action")
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label.pop("frame")
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elif "mvor" in dataset:
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labels = load_json(dataset["mvor"]["path"])
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# Rename keys
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for label in labels:
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label["cameras_color"] = label["cameras"]
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label["imgpaths_color"] = label["imgpaths"]
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elif "ikeaasm" in dataset:
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labels = load_json(dataset["ikeaasm"]["path"])
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cams0 = str(labels[0]["cameras"])
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labels = [lb for lb in labels if str(lb["cameras"]) == cams0]
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elif "shelf" in dataset:
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labels = load_json(dataset["shelf"]["path"])
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labels = [lb for lb in labels if "test" in lb["splits"]]
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elif "campus" in dataset:
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labels = load_json(dataset["campus"]["path"])
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labels = [lb for lb in labels if "test" in lb["splits"]]
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elif "tsinghua" in dataset:
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labels = load_json(dataset["tsinghua"]["path"])
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labels = [lb for lb in labels if "test" in lb["splits"]]
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labels = [lb for lb in labels if lb["seq"] == "seq_1"]
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labels = [lb for i, lb in enumerate(labels) if i % 300 < 90]
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for label in labels:
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label["bodyids"] = list(range(len(label["bodies3D"])))
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elif "chi3d" in dataset:
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labels = load_json(dataset["chi3d"]["path"])
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labels = [lb for lb in labels if lb["setup"] == "s03"]
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labels = [lb for i, lb in enumerate(labels) if i % 2000 < 150]
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elif "human36m_wb" in dataset:
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labels = load_json(dataset["human36m_wb"]["path"])
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elif any(("egohumans" in key for key in dataset)):
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labels = load_json(dataset[dataset_use]["path"])
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labels = [lb for lb in labels if "test" in lb["splits"]]
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labels = [lb for lb in labels if dataset[dataset_use]["subset"] in lb["seq"]]
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if dataset[dataset_use]["subset"] in ["volleyball", "tennis"]:
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labels = [lb for i, lb in enumerate(labels) if i % 150 < 60]
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else:
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raise ValueError("Dataset not available")
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# Optionally drop samples to speed up train/eval
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if "take_interval" in dataset:
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take_interval = dataset["take_interval"]
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if take_interval > 1:
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labels = [l for i, l in enumerate(labels) if i % take_interval == 0]
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# Add default values
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for label in labels:
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if "scene" not in label:
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label["scene"] = "default"
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for cam in label["cameras"]:
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if not "type" in cam:
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cam["type"] = "pinhole"
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return labels
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# ==================================================================================================
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def main():
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global joint_names_3d, eval_joints
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print("Loading dataset ...")
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labels = load_labels(
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{
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dataset_use: datasets[dataset_use],
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"take_interval": datasets[dataset_use]["take_interval"],
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}
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)
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# Print a dataset sample for debugging
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print(labels[0])
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# Save dataset
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tmp_export_dir = "/tmp/rpt/"
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for label in labels:
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if "splits" in label:
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label.pop("splits")
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json_writer.save_dataset(labels, tmp_export_dir)
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# Load dataset specific parameters
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min_match_score = datasets[dataset_use].get(
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"min_match_score", default_min_match_score
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)
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min_group_size = datasets[dataset_use].get("min_group_size", default_min_group_size)
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min_bbox_score = datasets[dataset_use].get("min_bbox_score", default_min_bbox_score)
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min_bbox_area = datasets[dataset_use].get("min_bbox_area", default_min_bbox_area)
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batch_poses = datasets[dataset_use].get("batch_poses", default_batch_poses)
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# Save config
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config_path = tmp_export_dir + "config.json"
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config = {
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"min_match_score": min_match_score,
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"min_group_size": min_group_size,
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"min_bbox_score": min_bbox_score,
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"min_bbox_area": min_bbox_area,
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"batch_poses": batch_poses,
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"whole_body": whole_body,
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"take_interval": datasets[dataset_use]["take_interval"],
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}
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save_json(config, config_path)
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# Call the CPP binary
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os.system("/RapidPoseTriangulation/scripts/test_skelda_dataset.bin")
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# Load the results
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print("Loading exports ...")
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res_path = tmp_export_dir + "results.json"
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results = load_json(res_path)
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all_poses_3d = results["all_poses_3d"]
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all_ids = results["all_ids"]
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joint_names_3d = results["joint_names_3d"]
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# Run evaluation
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_ = evals.mpjpe.run_eval(
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labels,
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all_poses_3d,
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all_ids,
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joint_names_net=joint_names_3d,
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joint_names_use=eval_joints,
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save_error_imgs=output_dir,
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)
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_ = evals.pcp.run_eval(
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labels,
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all_poses_3d,
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all_ids,
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joint_names_net=joint_names_3d,
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joint_names_use=eval_joints,
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replace_head_with_nose=True,
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)
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# ==================================================================================================
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if __name__ == "__main__":
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main()
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