flanandClaude Sonnet 4.6
11e7d4aad7
- Track laplacian blur score through quality filtering pipeline (quality.py: blur_score on QualityResult; diversity.py: store on asset; executor.py: read via quality_score key) - Replace per-file polling with post-person batch reconciliation: after all uploads for a person complete, poll Frigate (up to 15s) until the expected number of new files appear, then map by filename timestamp order (Frigate FIFO queue = upload order = timestamp order) - Document race condition limitation: concurrent external uploads cause the batch to be skipped entirely (safe but files go unmapped); noted in code as requiring a Frigate API fix (return filename on upload) - Add two assess_quality integration tests for blur_score Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
162 lines
5.7 KiB
Python
162 lines
5.7 KiB
Python
"""Tests for image quality filtering functions."""
|
|
|
|
import numpy as np
|
|
from PIL import Image
|
|
|
|
|
|
def _rgb_image(r, g, b, size=(100, 100)) -> Image.Image:
|
|
arr = np.full((*size, 3), [r, g, b], dtype=np.uint8)
|
|
return Image.fromarray(arr, "RGB")
|
|
|
|
|
|
def _noisy_color_image(size=(100, 100)) -> Image.Image:
|
|
"""Noisy image with a strong red channel so grayscale check passes."""
|
|
rng = np.random.default_rng(0)
|
|
arr = rng.integers(0, 256, (*size, 3), dtype=np.uint8)
|
|
arr[:, :, 0] = np.clip(arr[:, :, 0].astype(int) + 80, 0, 255).astype(np.uint8)
|
|
arr[:, :, 2] = np.clip(arr[:, :, 2].astype(int) - 80, 0, 255).astype(np.uint8)
|
|
return Image.fromarray(arr, "RGB")
|
|
|
|
|
|
# ── check_blur ────────────────────────────────────────────────────────────────
|
|
|
|
def test_blur_rejects_flat_image():
|
|
from winnow.quality import check_blur
|
|
flat = np.full((100, 100, 3), 128, dtype=np.uint8)
|
|
passed, reason = check_blur(flat, threshold=100.0)
|
|
assert not passed
|
|
assert "Blurry" in reason
|
|
|
|
|
|
def test_blur_passes_noisy_color_image():
|
|
from winnow.quality import check_blur
|
|
img = _noisy_color_image()
|
|
passed, _ = check_blur(np.asarray(img), threshold=100.0)
|
|
assert passed
|
|
|
|
|
|
# ── check_grayscale ───────────────────────────────────────────────────────────
|
|
|
|
def test_grayscale_rejects_ir_image():
|
|
from winnow.quality import check_grayscale
|
|
gray = np.full((100, 100, 3), 128, dtype=np.uint8)
|
|
passed, reason = check_grayscale(gray)
|
|
assert not passed
|
|
assert "Grayscale" in reason
|
|
|
|
|
|
def test_grayscale_passes_color_image():
|
|
from winnow.quality import check_grayscale
|
|
color = np.zeros((100, 100, 3), dtype=np.uint8)
|
|
color[:, :, 0] = 200 # strong red channel
|
|
passed, _ = check_grayscale(color)
|
|
assert passed
|
|
|
|
|
|
def test_grayscale_rejects_single_channel():
|
|
from winnow.quality import check_grayscale
|
|
single = np.full((100, 100, 1), 128, dtype=np.uint8)
|
|
passed, reason = check_grayscale(single)
|
|
assert not passed
|
|
|
|
|
|
# ── check_exposure ────────────────────────────────────────────────────────────
|
|
|
|
def test_exposure_rejects_black_image():
|
|
from winnow.quality import check_exposure
|
|
black = np.zeros((100, 100, 3), dtype=np.uint8)
|
|
passed, reason = check_exposure(black)
|
|
assert not passed
|
|
assert "Underexposed" in reason
|
|
|
|
|
|
def test_exposure_rejects_white_image():
|
|
from winnow.quality import check_exposure
|
|
white = np.full((100, 100, 3), 255, dtype=np.uint8)
|
|
passed, reason = check_exposure(white)
|
|
assert not passed
|
|
assert "Overexposed" in reason
|
|
|
|
|
|
def test_exposure_passes_normal_image():
|
|
from winnow.quality import check_exposure
|
|
mid = np.full((100, 100, 3), 128, dtype=np.uint8)
|
|
passed, _ = check_exposure(mid)
|
|
assert passed
|
|
|
|
|
|
# ── check_face_size ───────────────────────────────────────────────────────────
|
|
|
|
def test_face_size_rejects_small_face():
|
|
from winnow.quality import check_face_size
|
|
passed, reason = check_face_size(30, 30, min_px=50)
|
|
assert not passed
|
|
assert "small" in reason
|
|
|
|
|
|
def test_face_size_passes_adequate_face():
|
|
from winnow.quality import check_face_size
|
|
passed, _ = check_face_size(100, 100, min_px=50)
|
|
assert passed
|
|
|
|
|
|
def test_face_size_rejects_if_either_dimension_small():
|
|
from winnow.quality import check_face_size
|
|
passed, _ = check_face_size(100, 30, min_px=50)
|
|
assert not passed
|
|
|
|
|
|
# ── check_confidence ──────────────────────────────────────────────────────────
|
|
|
|
def test_confidence_rejects_low_score():
|
|
from winnow.quality import check_confidence
|
|
passed, reason = check_confidence(0.5, min_conf=0.7)
|
|
assert not passed
|
|
assert "confidence" in reason.lower()
|
|
|
|
|
|
def test_confidence_passes_high_score():
|
|
from winnow.quality import check_confidence
|
|
passed, _ = check_confidence(0.95, min_conf=0.7)
|
|
assert passed
|
|
|
|
|
|
def test_confidence_passes_none_score():
|
|
from winnow.quality import check_confidence
|
|
passed, _ = check_confidence(None, min_conf=0.7)
|
|
assert passed
|
|
|
|
|
|
# ── assess_quality (integration) ─────────────────────────────────────────────
|
|
|
|
def test_assess_quality_passes_good_image():
|
|
from winnow.quality import assess_quality
|
|
img = _noisy_color_image()
|
|
result = assess_quality(img, face_bbox=(10, 10, 110, 110), confidence=0.9)
|
|
assert result.passed
|
|
assert result.blur_score is not None
|
|
assert result.blur_score > 0
|
|
|
|
|
|
def test_assess_quality_blur_score_is_low_for_flat_image():
|
|
from winnow.quality import assess_quality
|
|
flat = _rgb_image(128, 128, 128)
|
|
result = assess_quality(flat)
|
|
assert result.blur_score is not None
|
|
assert result.blur_score < 1.0
|
|
|
|
|
|
def test_assess_quality_collects_multiple_failures():
|
|
from winnow.quality import assess_quality
|
|
black = _rgb_image(0, 0, 0)
|
|
result = assess_quality(black, face_bbox=(0, 0, 10, 10), confidence=0.3)
|
|
assert not result.passed
|
|
assert len(result.reasons) >= 2
|
|
|
|
|
|
def test_assess_quality_skips_face_size_without_bbox():
|
|
from winnow.quality import assess_quality
|
|
img = _noisy_color_image()
|
|
result = assess_quality(img, face_bbox=None, confidence=0.9)
|
|
assert result.passed
|