83 lines
3.3 KiB
Python
83 lines
3.3 KiB
Python
import json
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from pathlib import Path
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from PIL import Image, ImageChops, ImageFilter, ImageStat
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reference_path = Path("reference.png")
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desktop_path = Path("actual-desktop.png")
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mobile_path = Path("actual-mobile.png")
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metadata_path = Path("render-metadata.json")
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for path in (reference_path, desktop_path, mobile_path, metadata_path):
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if not path.is_file():
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raise SystemExit(f"missing real visual artifact: {path}")
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metadata = json.loads(metadata_path.read_text(encoding="utf-8"))
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if metadata.get("engine") != "playwright":
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raise SystemExit("visual render was not captured through Playwright")
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reference = Image.open(reference_path).convert("RGB")
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desktop = Image.open(desktop_path).convert("RGB")
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mobile = Image.open(mobile_path).convert("RGB")
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if desktop.size != (1440, 900) or mobile.size != (390, 844):
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raise SystemExit("browser screenshots do not match the required viewports")
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resampling = getattr(Image, "Resampling", Image).LANCZOS
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normalized_reference = reference.resize(desktop.size, resampling)
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difference = ImageChops.difference(normalized_reference, desktop)
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mean_error = sum(ImageStat.Stat(difference).mean) / (3 * 255)
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perceptual_similarity = max(0.0, 1.0 - mean_error)
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histogram = difference.convert("L").histogram()
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matched_pixels = sum(histogram[:32])
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pixel_similarity = matched_pixels / (desktop.width * desktop.height)
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reference_edges = normalized_reference.convert("L").filter(ImageFilter.FIND_EDGES)
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desktop_edges = desktop.convert("L").filter(ImageFilter.FIND_EDGES)
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edge_error = ImageStat.Stat(ImageChops.difference(reference_edges, desktop_edges)).mean[0] / 255
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edge_similarity = max(0.0, 1.0 - edge_error)
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viewport_facts = metadata["viewports"]
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responsive_checks = [
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not viewport_facts["desktop"]["horizontalOverflow"],
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not viewport_facts["mobile"]["horizontalOverflow"],
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viewport_facts["mobile"]["bodyWidth"] <= 391,
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]
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responsive_score = sum(responsive_checks) / len(responsive_checks)
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desktop_facts = viewport_facts["desktop"]
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accessibility_checks = [
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desktop_facts["language"] == "en",
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desktop_facts["mainCount"] == 1,
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desktop_facts["navCount"] >= 1,
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desktop_facts["headingCount"] >= 2,
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desktop_facts["labeledCount"] >= 1,
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desktop_facts["focusableCount"] >= 1,
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desktop_facts["unlabeledImages"] == 0,
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]
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accessibility_score = sum(accessibility_checks) / len(accessibility_checks)
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metrics = {
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"perceptual_similarity": perceptual_similarity,
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"pixel_similarity": pixel_similarity,
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"edge_similarity": edge_similarity,
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"responsive_score": responsive_score,
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"accessibility_score": accessibility_score,
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}
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default_thresholds = {
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"perceptual_similarity": 0.72,
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"pixel_similarity": 0.35,
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"edge_similarity": 0.72,
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"responsive_score": 1.0,
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"accessibility_score": 1.0,
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}
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holdout = Path("visual-holdout.json")
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thresholds = (
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json.loads(holdout.read_text(encoding="utf-8")) if holdout.is_file() else default_thresholds
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)
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if set(thresholds) != set(default_thresholds):
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raise SystemExit("visual holdout threshold schema is invalid")
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failures = [name for name, threshold in thresholds.items() if metrics[name] < threshold]
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if failures:
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raise SystemExit(
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"visual gates failed: " + ", ".join(f"{name}={metrics[name]:.3f}" for name in failures)
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)
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print(json.dumps({"metrics": metrics}, sort_keys=True))
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