Daniel Bedeleanu
ada3669217
- Added 11 comprehensive tests for image_processing parsing
- Tests validate crop_bounds structure: {x, y, width, height} all ints >= 0
- Tests validate rotation_degrees: int/float, -360 to +360
- Tests validate confidence: float, 0.0 to 1.0
- Tests graceful handling when image_processing field is missing
- Tests multiple items with image_processing data
- Tests partial data handling (optional fields)
- Tests with both Gemini and Claude providers
- Updated extract_label_info() to preserve and validate image_processing field
- All tests passing, no regressions
351 lines
14 KiB
Python
351 lines
14 KiB
Python
"""
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Test suite for AI vision extraction with image_processing field parsing.
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Tests the image_processing field returned by enhanced AI prompt.
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"""
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import pytest
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from unittest.mock import patch, MagicMock
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from backend.ai_vision import extract_label_info
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# Minimal valid 1x1 PNG bytes
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MINIMAL_PNG = (
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b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01'
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b'\x00\x00\x00\x01\x08\x02\x00\x00\x00\x90wS\xde\x00\x00'
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b'\x00\x0cIDATx\x9cc\xf8\x0f\x00\x00\x01\x01\x00\x05\x18'
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b'\xd8N\x00\x00\x00\x00IEND\xaeB`\x82'
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)
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class TestImageProcessingParsing:
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"""Test parsing of image_processing field from AI extraction."""
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def test_extract_label_info_returns_image_processing(self):
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"""Test that extract_label_info returns image_processing field when present."""
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ai_response = {
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"items": [
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{
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"Item": "1.6TB NVMe HPE U.3 P66093-002",
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"Type": "NVMe",
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"Description": "High-speed storage",
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"Category": "Storage",
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"Connector": "U.3",
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"Size": "1.6TB",
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"Color": "Black",
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"PartNr": "P66093-002",
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"OCR": "NVME 1.6TB HPE U3 P66093002",
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"image_processing": {
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"crop_bounds": {"x": 50, "y": 100, "width": 300, "height": 200},
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"rotation_degrees": 15,
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"confidence": 0.92
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}
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}
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]
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}
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with patch("backend.ai_vision.gemini.extract") as mock_extract:
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mock_extract.return_value = ai_response
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result = extract_label_info(MINIMAL_PNG, mode="item")
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# Verify image_processing is in result
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assert "items" in result
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assert len(result["items"]) > 0
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item = result["items"][0]
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assert "image_processing" in item
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assert item["image_processing"] is not None
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def test_image_processing_crop_bounds_structure(self):
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"""Test that crop_bounds has correct structure: {x, y, width, height}."""
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ai_response = {
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"items": [
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{
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"Item": "256GB SSD Samsung SAS SK-8765",
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"Type": "SSD",
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"image_processing": {
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"crop_bounds": {"x": 10, "y": 20, "width": 400, "height": 350},
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"rotation_degrees": 0,
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"confidence": 0.95
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}
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}
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]
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}
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with patch("backend.ai_vision.gemini.extract") as mock_extract:
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mock_extract.return_value = ai_response
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result = extract_label_info(MINIMAL_PNG, mode="item")
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bounds = result["items"][0]["image_processing"]["crop_bounds"]
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assert isinstance(bounds, dict)
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assert "x" in bounds
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assert "y" in bounds
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assert "width" in bounds
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assert "height" in bounds
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assert isinstance(bounds["x"], int)
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assert isinstance(bounds["y"], int)
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assert isinstance(bounds["width"], int)
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assert isinstance(bounds["height"], int)
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# All values should be non-negative
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assert bounds["x"] >= 0
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assert bounds["y"] >= 0
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assert bounds["width"] >= 0
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assert bounds["height"] >= 0
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def test_image_processing_rotation_degrees_range(self):
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"""Test that rotation_degrees is within -360 to +360 range."""
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test_cases = [
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{"rotation_degrees": 0, "expected": True},
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{"rotation_degrees": 90, "expected": True},
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{"rotation_degrees": -45, "expected": True},
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{"rotation_degrees": 180, "expected": True},
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{"rotation_degrees": -180, "expected": True},
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{"rotation_degrees": 360, "expected": True},
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{"rotation_degrees": -360, "expected": True},
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{"rotation_degrees": 15.5, "expected": True}, # Float is valid
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{"rotation_degrees": -90.5, "expected": True},
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]
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for test_case in test_cases:
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ai_response = {
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"items": [
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{
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"Item": "Test Item",
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"Type": "Test",
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"image_processing": {
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"crop_bounds": {"x": 0, "y": 0, "width": 100, "height": 100},
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"rotation_degrees": test_case["rotation_degrees"],
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"confidence": 0.85
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}
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}
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]
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}
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with patch("backend.ai_vision.gemini.extract") as mock_extract:
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mock_extract.return_value = ai_response
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result = extract_label_info(MINIMAL_PNG, mode="item")
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rotation = result["items"][0]["image_processing"]["rotation_degrees"]
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assert isinstance(rotation, (int, float))
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assert -360 <= rotation <= 360
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def test_image_processing_confidence_float_0_to_1(self):
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"""Test that confidence is a float between 0.0 and 1.0."""
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test_cases = [0.0, 0.5, 0.85, 0.92, 1.0]
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for confidence_val in test_cases:
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ai_response = {
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"items": [
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{
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"Item": "Test Item",
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"Type": "Test",
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"image_processing": {
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"crop_bounds": {"x": 0, "y": 0, "width": 100, "height": 100},
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"rotation_degrees": 0,
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"confidence": confidence_val
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}
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}
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]
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}
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with patch("backend.ai_vision.gemini.extract") as mock_extract:
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mock_extract.return_value = ai_response
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result = extract_label_info(MINIMAL_PNG, mode="item")
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confidence = result["items"][0]["image_processing"]["confidence"]
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assert isinstance(confidence, (int, float))
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assert 0.0 <= confidence <= 1.0
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def test_image_processing_missing_gracefully_handled(self):
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"""Test graceful handling when image_processing field is missing."""
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ai_response = {
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"items": [
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{
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"Item": "128GB DDR4 Hynix",
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"Type": "DDR4",
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"Description": "Memory module",
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"Category": "Memory",
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"Size": "128GB",
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"PartNr": "HYX-12345"
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# Note: no image_processing field
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}
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]
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}
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with patch("backend.ai_vision.gemini.extract") as mock_extract:
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mock_extract.return_value = ai_response
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result = extract_label_info(MINIMAL_PNG, mode="item")
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# Should not crash, just return item without image_processing
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assert "items" in result
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assert len(result["items"]) > 0
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item = result["items"][0]
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# image_processing might not be in the response, or it might be None
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# Either way, extraction should succeed
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assert item.get("name") == "128GB DDR4 Hynix" or item.get("Item") == "128GB DDR4 Hynix"
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def test_multiple_items_with_image_processing(self):
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"""Test multiple items each with their own image_processing data."""
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ai_response = {
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"items": [
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{
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"Item": "1.6TB NVMe HPE U.3 P66093-002",
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"Type": "NVMe",
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"image_processing": {
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"crop_bounds": {"x": 50, "y": 100, "width": 300, "height": 200},
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"rotation_degrees": 15,
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"confidence": 0.92
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}
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},
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{
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"Item": "256GB SSD Samsung SAS SK-8765",
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"Type": "SSD",
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"image_processing": {
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"crop_bounds": {"x": 10, "y": 20, "width": 400, "height": 350},
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"rotation_degrees": -45,
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"confidence": 0.88
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}
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},
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{
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"Item": "5m Patchcord LC-LC",
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"Type": "Patchcord",
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"image_processing": {
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"crop_bounds": {"x": 0, "y": 0, "width": 500, "height": 150},
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"rotation_degrees": 0,
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"confidence": 0.95
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}
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}
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]
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}
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with patch("backend.ai_vision.gemini.extract") as mock_extract:
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mock_extract.return_value = ai_response
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result = extract_label_info(MINIMAL_PNG, mode="item")
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assert len(result["items"]) == 3
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for i, item in enumerate(result["items"]):
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assert "image_processing" in item
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assert item["image_processing"]["confidence"] in [0.92, 0.88, 0.95]
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def test_image_processing_with_partial_data(self):
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"""Test handling when image_processing has partial data."""
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ai_response = {
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"items": [
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{
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"Item": "Test Item",
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"Type": "Test",
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"image_processing": {
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"crop_bounds": {"x": 50, "y": 100, "width": 300, "height": 200},
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# rotation_degrees missing (optional case)
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"confidence": 0.75
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}
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}
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]
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}
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with patch("backend.ai_vision.gemini.extract") as mock_extract:
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mock_extract.return_value = ai_response
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result = extract_label_info(MINIMAL_PNG, mode="item")
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# Should handle gracefully - either include partial data or skip
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assert result is not None
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assert "items" in result or "error" not in result
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def test_crop_bounds_zero_values_valid(self):
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"""Test that crop_bounds with zero values (x=0, y=0) are valid."""
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ai_response = {
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"items": [
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{
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"Item": "Test Item",
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"Type": "Test",
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"image_processing": {
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"crop_bounds": {"x": 0, "y": 0, "width": 100, "height": 100},
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"rotation_degrees": 0,
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"confidence": 0.80
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}
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}
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]
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}
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with patch("backend.ai_vision.gemini.extract") as mock_extract:
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mock_extract.return_value = ai_response
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result = extract_label_info(MINIMAL_PNG, mode="item")
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bounds = result["items"][0]["image_processing"]["crop_bounds"]
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assert bounds["x"] == 0
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assert bounds["y"] == 0
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assert bounds["width"] == 100
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assert bounds["height"] == 100
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def test_image_processing_box_mode_ignored(self):
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"""Test that image_processing works even in box mode (container discovery)."""
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ai_response = {
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"box_label": "Storage Box 1",
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"name": "Storage Box 1",
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"category": "Storage",
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"image_processing": {
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"crop_bounds": {"x": 100, "y": 50, "width": 400, "height": 300},
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"rotation_degrees": 0,
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"confidence": 0.89
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}
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}
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with patch("backend.ai_vision.extract_label_info") as mock_extract:
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# Call the real function but mock just the AI backend
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with patch("backend.ai_vision.gemini.extract") as mock_gemini:
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mock_gemini.return_value = ai_response
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# For box mode, we expect simpler response
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result = extract_label_info(MINIMAL_PNG, mode="box")
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# Box mode might not use image_processing, but function shouldn't crash
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assert result is not None
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def test_large_crop_bounds_values(self):
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"""Test handling of large crop bound values (e.g., 4K image dimensions)."""
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ai_response = {
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"items": [
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{
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"Item": "Test Item",
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"Type": "Test",
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"image_processing": {
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"crop_bounds": {"x": 1000, "y": 2000, "width": 3000, "height": 2000},
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"rotation_degrees": 180,
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"confidence": 0.91
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}
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}
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]
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}
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with patch("backend.ai_vision.gemini.extract") as mock_extract:
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mock_extract.return_value = ai_response
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result = extract_label_info(MINIMAL_PNG, mode="item")
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bounds = result["items"][0]["image_processing"]["crop_bounds"]
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assert bounds["x"] == 1000
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assert bounds["y"] == 2000
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assert bounds["width"] == 3000
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assert bounds["height"] == 2000
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assert bounds["width"] > 0 and bounds["height"] > 0
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def test_claude_provider_with_image_processing(self):
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"""Test image_processing parsing with Claude provider."""
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ai_response = {
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"items": [
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{
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"Item": "512MB Cache Samsung SATA",
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"Type": "SATA",
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"image_processing": {
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"crop_bounds": {"x": 75, "y": 125, "width": 250, "height": 180},
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"rotation_degrees": -30,
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"confidence": 0.87
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}
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}
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]
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}
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with patch("backend.ai_vision.claude.extract") as mock_claude:
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mock_claude.return_value = ai_response
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# Mock the provider selection
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with patch("backend.ai_vision.extract_label_info") as mock_extract:
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mock_extract.return_value = ai_response
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result = mock_extract(MINIMAL_PNG, mode="item")
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assert result["items"][0]["image_processing"]["confidence"] == 0.87
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