A sprite import problem is often described as an image problem even when the immediate defect is in a small metadata record. The image may be the expected size, but the declared frame width may not divide it evenly. A list of direction labels may have the wrong length, or two entries may accidentally use the same name. Catching these inconsistencies before an importer uses the record can make its failure mode easier to understand.
This article presents a deliberately limited validator for declared grid metadata. It does not open an image, infer frame boundaries, determine visual quality, or prove that an animation works. Its output is a list of contract violations in one Python dictionary. That narrow scope is important: a record that passes this function is internally consistent under the stated rules, not necessarily true. Another stage must compare the declarations with an actual decoded asset if that is required.
Choose a small contract
The record describes image width and height, frame width and height, a declared number of occupied frames, and an ordered list of unique labels. All dimensions and the frame count must be positive integers. The image dimensions must be divisible by the corresponding frame dimensions. The occupied frame count may be smaller than the grid capacity, because a sheet may contain unused cells. It may not exceed the available capacity.
The order list must contain exactly one nonempty string for each declared occupied frame. Labels must be unique as exact strings. This implementation does not trim labels before comparing uniqueness, normalize Unicode, or decide that differently capitalized labels mean the same thing. A production importer can adopt a stronger naming policy, but that policy should be explicit and tested. Silent normalization can create surprising collisions that are difficult to diagnose after assets have already been named.
Reject types before doing arithmetic
Python makes a subtle distinction relevant to this contract: booleans behave like integers in many operations. A simple instance check can therefore accept True as a dimension. The implementation uses an exact integer type check to reject booleans, floats, strings and missing values. This is a policy choice for records decoded from ordinary JSON, where dimensions should be whole numeric values rather than flags or text that happens to contain digits.
The function returns early when dimensions are invalid. This prevents division or remainder operations with zero and avoids follow-on error messages that obscure the original issue. It then checks divisibility before computing capacity. That sequence is not just defensive programming; it makes the error list easier to interpret. A user should first fix an invalid dimension, then see any remaining label or capacity problems in a record whose basic geometry can be meaningfully evaluated.
Implementation
Save the first code block as sprite_manifest.py. The function has no network access and does not modify the supplied dictionary. Extra keys are ignored, allowing a caller to attach its own provenance or project identifiers. Ignoring extra keys is not the same as validating them: private fields, filesystem paths or engine settings would need their own policy. This example intentionally limits itself to the geometry and label declarations described above.
The code uses only ordinary dictionary access, integer arithmetic and list checks. It does not include a command-line parser or a file loader, so the calling application remains responsible for bounding input size and handling JSON decoding errors. A large list supplied by a trusted local script is a different operational setting from an untrusted upload endpoint. Do not expose this function directly as a service and assume the small example supplies a complete resource or security boundary.
"""Validate declared sprite-grid metadata, not image bytes or visual quality."""
def validate_grid(record):
errors = []
if not isinstance(record, dict):
return ["record must be an object"]
fields = ("image_width", "image_height", "frame_width", "frame_height", "frame_count")
for key in fields:
if type(record.get(key)) is not int or record[key] <= 0:
errors.append(f"{key} must be a positive integer")
if errors:
return errors
width, height = record["image_width"], record["image_height"]
fw, fh = record["frame_width"], record["frame_height"]
if width % fw:
errors.append("image_width must be divisible by frame_width")
if height % fh:
errors.append("image_height must be divisible by frame_height")
if errors:
return errors
capacity = (width // fw) * (height // fh)
if record["frame_count"] > capacity:
errors.append("frame_count exceeds grid capacity")
order = record.get("order")
if not isinstance(order, list) or len(order) != record["frame_count"]:
errors.append("order must contain one label per declared frame")
elif any(not isinstance(label, str) or not label.strip() for label in order):
errors.append("order labels must be nonempty strings")
elif len(set(order)) != len(order):
errors.append("order labels must be unique")
return errors
Test the declared behavior
Save the second code block as test_sprite_manifest.py in the same directory. Run it with python3 -m unittest -v test_sprite_manifest.py. The ten test methods below were executed locally and passed. They exercise only the metadata contract. No real sprite was decoded by these tests, no image quality was scored, and no game engine was launched. The successful result should be read with those limitations rather than as a broader product or asset certification.
The partial-grid case is particularly useful because it documents an intentional allowance. A two-frame record in a four-cell grid is valid here when its order list contains two labels. If your importer requires every cell to be occupied, change that contract and replace the test accordingly. Tests should make a design decision visible, not hide it behind a generic valid-file label. The desired behavior depends on how the downstream system interprets empty cells.
import unittest
from sprite_manifest import validate_grid
class GridTests(unittest.TestCase):
def record(self):
return dict(image_width=128, image_height=128, frame_width=64,
frame_height=64, frame_count=4,
order=["front", "right", "back", "left"])
def test_valid_grid(self):
self.assertEqual(validate_grid(self.record()), [])
def test_partial_grid_allowed(self):
r = self.record()
r.update(frame_count=2, order=["front", "back"])
self.assertEqual(validate_grid(r), [])
def test_boolean_is_not_dimension(self):
r = self.record()
r["frame_width"] = True
self.assertIn("frame_width must be a positive integer", validate_grid(r))
def test_zero_rejected(self):
r = self.record()
r["frame_height"] = 0
self.assertIn("frame_height must be a positive integer", validate_grid(r))
def test_remainder_rejected(self):
r = self.record()
r["image_width"] = 127
self.assertIn("image_width must be divisible by frame_width", validate_grid(r))
def test_capacity_rejected(self):
r = self.record()
r.update(frame_count=5, order=["a", "b", "c", "d", "e"])
self.assertIn("frame_count exceeds grid capacity", validate_grid(r))
def test_duplicate_label(self):
r = self.record()
r["order"] = ["front"] * 4
self.assertIn("order labels must be unique", validate_grid(r))
def test_wrong_order_length(self):
r = self.record()
r["order"] = []
self.assertIn("order must contain one label per declared frame", validate_grid(r))
def test_blank_label(self):
r = self.record()
r["order"][0] = " "
self.assertIn("order labels must be nonempty strings", validate_grid(r))
def test_missing_and_nonobject(self):
self.assertTrue(validate_grid({}))
self.assertEqual(validate_grid(None), ["record must be an object"])
if __name__ == "__main__":
unittest.main()
Keep declared metadata separate from decoded facts
An upstream process may produce a record saying the image is 128 by 128 pixels, but this validator never checks those bytes. A later decoding stage should obtain the actual dimensions and compare them against the declaration. That stage must use an appropriate image library and its own limits. Checking a PNG signature or a header alone would still not establish that the complete file can be decoded safely or that its alpha channel behaves as intended.
Likewise, a label such as front is only a label. The function cannot establish that the corresponding frame actually faces forward, that costumes remain consistent between views, or that a character reads clearly at its intended display size. Those are visual review questions. Separating them from metadata validation prevents a useful deterministic check from acquiring an unsupported meaning. A green contract check should not become pressure to accept a visually unsuitable candidate.
Apply the record to an AI-assisted workflow
For context, Sprite Maker offers a https://spritemaker.app/ workflow with character generation, direction views, NPC sets, pixel refinement and transparent PNG export. I am associated with promoting the project, and this article was prepared with AI assistance. The example validator is independent demonstration code, not a claim that the product exports this exact schema or integrates with this function. Users should check current features, credit allowances and terms on the actual site.
A practical workflow could first generate or draw a candidate, review the actual image, and then create metadata for the receiving project. The order of those actions matters less than keeping their evidence separate. A metadata record might be corrected without changing any pixels, while a visual revision might require a new export and another dimension check. Give each candidate a clear local identifier so that the record does not accidentally describe an earlier version with different dimensions.
Decide how failures reach the caller
The function returns messages rather than raising an exception for an ordinary invalid record. This makes it convenient for a small editor to display several actionable issues at once. A command-line wrapper could print the messages and choose a nonzero exit code. A web application might map them to field-level errors. Those integration choices are not implemented here, and the examples should not be described as a complete importer or a ready-made upload validation pipeline.
Consider whether a caller needs stable machine-readable error codes before exposing the messages as an API. Human-readable sentences are convenient during prototyping but can become awkward when translated or matched by another program. A later version could return records containing a code, field and message. That is a reasonable extension only when there is a consumer for it; adding an elaborate abstraction to a tiny local utility can make the simple contract harder to review.
Preserve the boundary in documentation
When recording a successful check, state that the declared grid metadata passed this contract. Keep image decoding, permissions, transparency review, animation testing and engine import results as separate entries. A complete asset pipeline may need all of them, but the presence of one does not imply the others. This separation also makes debugging easier: a label mismatch can be handled without blaming image generation, and a visual problem can be investigated without changing valid grid arithmetic.
The main lesson is to make a small validator honest about what it knows. Exact types, ordered checks and representative tests produce useful evidence about one bounded input structure. They do not turn declarations into facts about unseen files. By preserving that distinction in function names, tests and handoff notes, a team can automate repetitive consistency checks while leaving visual judgment and asset approval where they belong.
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