CedarandClaude Sonnet 4.6
80d982fa1d
Adds integration tests exercising the full ReorientFaceMesh -> RenderTexturedFaceMesh data flow with synthetic inputs, verifying output shape contracts and value ranges. Also adds pytest.ini and conftest.py for standalone test execution, guards __init__.py relative imports with try/except for pytest 8.x Package compatibility, and removes tests/ from .gitignore. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
100 lines
3.5 KiB
Python
100 lines
3.5 KiB
Python
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import sys
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import os
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import unittest
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import torch
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import numpy as np
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# Add custom nodes directory to path so we can import nodes
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current_dir = os.path.dirname(os.path.abspath(__file__))
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custom_nodes_dir = os.path.dirname(current_dir)
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sys.path.insert(0, custom_nodes_dir)
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from nodes.extract import ExtractSkeletonFromPose
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class TestExtractSplitData(unittest.TestCase):
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def test_merge_split_data(self):
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extractor = ExtractSkeletonFromPose()
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# Mock DWPose output (Body-18 + Face + Hands split)
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# 18 Body points
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body_kps = [0.1 * i for i in range(18 * 3)]
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# 68 Face points
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face_kps = [0.2 for _ in range(68 * 3)]
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# 21 Left Hand points
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lhand_kps = [0.3 for _ in range(21 * 3)]
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# 21 Right Hand points
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rhand_kps = [0.4 for _ in range(21 * 3)]
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mock_person = {
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"pose_keypoints_2d": body_kps,
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"face_keypoints_2d": face_kps,
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"hand_left_keypoints_2d": lhand_kps,
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"hand_right_keypoints_2d": rhand_kps
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}
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# Test resilience: person with None keys
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mock_person_none = {
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"pose_keypoints_2d": body_kps,
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"face_keypoints_2d": None,
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"hand_left_keypoints_2d": None,
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"hand_right_keypoints_2d": None
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}
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# Mock pose object (list of frames, each frame is list of people or dicts)
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# The node expects `pose` to be a list of frames.
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# Inside the node:
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# if isinstance(frame_content, list): people_list = frame_content
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# else: people_list = [frame_content] (if it's a dict per frame?)
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# Let's check the code: "if isinstance(frame_content, dict) and 'people' in frame_content: people_list = frame_content['people']"
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# The standard OpenPose JSON structure is a dict with "people" key.
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mock_pose_data = [
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{"people": [mock_person]},
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{"people": [mock_person_none]} # Second frame with None data
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]
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# Call extract
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# Format "COCO-133", person_selection="largest"
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result = extractor.extract(mock_pose_data, "COCO-133", "largest", min_confidence=0.0)
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# Result is (first_skeleton, skeleton_sequence, mask)
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# We want the sequence
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skeleton_seq = result[1]
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# Expected shape: [Frames, Keypoints, 3] -> [2, 133, 3] (2 frames now)
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# (Node selects 1 person per frame)
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print(f"Output shape: {skeleton_seq.shape}")
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self.assertEqual(skeleton_seq.shape, (2, 133, 3))
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# Verify Body Mapping
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# Nose is index 0 in both OP18 and C133
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# data[0] = body_kps[0] = 0.0
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self.assertAlmostEqual(skeleton_seq[0, 0, 0].item(), 0.0)
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# Verify Face Mapping
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# Face starts at index 23 in COCO-133
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# Should be 0.2
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print(f"Face start value: {skeleton_seq[0, 23, 0].item()}")
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self.assertAlmostEqual(skeleton_seq[0, 23, 0].item(), 0.2)
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# Verify Hand Mapping
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# Left Hand starts at 91
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# Should be 0.3
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self.assertAlmostEqual(skeleton_seq[0, 91, 0].item(), 0.3)
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# Right Hand starts at 112
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# Should be 0.4
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self.assertAlmostEqual(skeleton_seq[0, 112, 0].item(), 0.4)
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if __name__ == '__main__':
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try:
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t = TestExtractSplitData()
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t.test_merge_split_data()
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print("TEST PASSED")
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except Exception as e:
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import traceback
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traceback.print_exc(file=sys.stdout)
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print("TEST FAILED")
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sys.exit(1)
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