490 lines
16 KiB
Python
490 lines
16 KiB
Python
import json
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import os
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import sys
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import time
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import cv2
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import matplotlib
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import numpy as np
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import tqdm
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import utils_2d_pose
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import utils_pipeline
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from skelda import evals
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sys.path.append("/RapidPoseTriangulation/swig/")
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import rpt
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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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# dataset_use = "ntu"
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# dataset_use = "koarob"
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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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# ==================================================================================================
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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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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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# 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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# Load 2D pose model
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if utils_pipeline.use_whole_body(whole_body):
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kpt_model = utils_2d_pose.load_wb_model(min_bbox_score, min_bbox_area, batch_poses)
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else:
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kpt_model = utils_2d_pose.load_model(min_bbox_score, min_bbox_area, batch_poses)
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# Manually set matplotlib backend
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try:
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matplotlib.use("TkAgg")
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except ImportError:
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print("WARNING: Using headless mode, no visualizations will be shown.")
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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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print("\nPrefetching images ...")
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for label in tqdm.tqdm(labels):
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# If the images are stored on a HDD, it sometimes takes a while to load them
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# Prefetching them results in more stable timings of the following steps
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# To prevent memory overflow, the code only loads the images, but does not store them
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try:
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for i in range(len(label["imgpaths"])):
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imgpath = label["imgpaths"][i]
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img = utils_pipeline.load_image(imgpath)
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except cv2.error:
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print("One of the paths not found:", label["imgpaths"])
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continue
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time.sleep(3)
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print("\nCalculating 2D predictions ...")
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all_poses_2d = []
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times = []
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for label in tqdm.tqdm(labels):
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images_2d = []
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start = time.time()
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try:
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for i in range(len(label["imgpaths"])):
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imgpath = label["imgpaths"][i]
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img = utils_pipeline.load_image(imgpath)
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images_2d.append(img)
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except cv2.error:
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print("One of the paths not found:", label["imgpaths"])
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continue
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if dataset_use == "human36m":
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for i in range(len(images_2d)):
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# Since the images don't have the same shape, rescale some of them
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img = images_2d[i]
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ishape = img.shape
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if ishape != (1000, 1000, 3):
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cam = label["cameras"][i]
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cam["K"][1][1] = cam["K"][1][1] * (1000 / ishape[0])
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cam["K"][1][2] = cam["K"][1][2] * (1000 / ishape[0])
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cam["K"][0][0] = cam["K"][0][0] * (1000 / ishape[1])
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cam["K"][0][2] = cam["K"][0][2] * (1000 / ishape[1])
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images_2d[i] = cv2.resize(img, (1000, 1000))
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# Convert image format to Bayer encoding to simulate real camera input
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# This also resulted in notably better MPJPE results in most cases, presumbly since the
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# demosaicing algorithm from OpenCV is better than the default one from the cameras
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for i in range(len(images_2d)):
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images_2d[i] = utils_pipeline.rgb2bayer(images_2d[i])
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time_imgs = time.time() - start
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start = time.time()
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for i in range(len(images_2d)):
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images_2d[i] = utils_pipeline.bayer2rgb(images_2d[i])
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poses_2d = utils_2d_pose.get_2d_pose(kpt_model, images_2d)
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poses_2d = utils_pipeline.update_keypoints(poses_2d, joint_names_2d, whole_body)
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time_2d = time.time() - start
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all_poses_2d.append(poses_2d)
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times.append([time_imgs, time_2d, 0])
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print("\nCalculating 3D predictions ...")
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all_poses_3d = []
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all_ids = []
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triangulator = rpt.Triangulator(
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min_match_score=min_match_score, min_group_size=min_group_size
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)
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old_scene = ""
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old_index = -1
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for i in tqdm.tqdm(range(len(labels))):
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label = labels[i]
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poses_2d = all_poses_2d[i]
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if old_scene != label.get("scene", "") or (
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old_index + datasets[dataset_use]["take_interval"] < label["index"]
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):
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# Reset last poses if scene changes
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old_scene = label.get("scene", "")
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triangulator.reset()
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start = time.time()
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if sum(np.sum(p) for p in poses_2d) == 0:
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poses3D = np.zeros([1, len(joint_names_3d), 4]).tolist()
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else:
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rpt_cameras = rpt.convert_cameras(label["cameras"])
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roomparams = [label["room_size"], label["room_center"]]
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poses3D = triangulator.triangulate_poses(
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poses_2d, rpt_cameras, roomparams, joint_names_2d
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)
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time_3d = time.time() - start
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old_index = label["index"]
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all_poses_3d.append(np.array(poses3D).tolist())
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all_ids.append(label["id"])
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times[i][2] = time_3d
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# Print per-step triangulation timings
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print("")
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triangulator.print_stats()
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warmup_iters = 10
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if len(times) > warmup_iters:
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times = times[warmup_iters:]
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avg_time_im = np.mean([t[0] for t in times])
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avg_time_2d = np.mean([t[1] for t in times])
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avg_time_3d = np.mean([t[2] for t in times])
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tstats = {
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"img_loading": avg_time_im,
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"avg_time_2d": avg_time_2d,
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"avg_time_3d": avg_time_3d,
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"avg_fps": 1.0 / (avg_time_2d + avg_time_3d),
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}
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print("\nMetrics:")
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print(json.dumps(tstats, indent=2))
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_ = evals.mpjpe.run_eval(
|
|
labels,
|
|
all_poses_3d,
|
|
all_ids,
|
|
joint_names_net=joint_names_3d,
|
|
joint_names_use=eval_joints,
|
|
save_error_imgs=output_dir,
|
|
)
|
|
_ = evals.pcp.run_eval(
|
|
labels,
|
|
all_poses_3d,
|
|
all_ids,
|
|
joint_names_net=joint_names_3d,
|
|
joint_names_use=eval_joints,
|
|
replace_head_with_nose=True,
|
|
)
|
|
|
|
|
|
# ==================================================================================================
|
|
|
|
if __name__ == "__main__":
|
|
main()
|