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import pytest
from fastapi.testclient import TestClient
import numpy as np
from PIL import Image
from io import BytesIO
import pydicom
from pydicom.dataset import Dataset, FileDataset
import tempfile
import os
import sys
from pathlib import Path
# Add the src directory to the Python path
sys.path.insert(0, str(Path(__file__).resolve().parent.parent ))
from src.app.main import app
from src.pipeline import InferencePipeline
# Initialize test client
client = TestClient(app)
@pytest.fixture
def pipeline_config():
return {
"model": {
"weights": "weights/model.pth",
"scale": 4,
"device": "cpu"
},
"preprocessing": {
"unsharping_mask": {
"kernel_size": 7,
"strength": 0.5
}
},
"postprocessing": {
"clahe": {
"clipLimit": 2,
"tileGridSize": [16, 16]
}
}
}
@pytest.fixture
def pipeline(pipeline_config):
return InferencePipeline(pipeline_config)
def create_dummy_dicom():
"""Create a dummy DICOM file for testing."""
meta = Dataset()
meta.MediaStorageSOPClassUID = "1.2.840.10008.5.1.4.1.1.2"
meta.MediaStorageSOPInstanceUID = "1.2.3"
meta.TransferSyntaxUID = pydicom.uid.ExplicitVRLittleEndian
ds = FileDataset("", {}, file_meta=meta, preamble=b"\x00" * 128)
# Required Patient and Image Information
ds.PatientName = "Test"
ds.PatientID = "12345"
ds.Modality = "CT"
ds.StudyInstanceUID = "1.2.3.4.5.6.7.8.9.10"
ds.SeriesInstanceUID = "1.2.3.4.5.6.7.8.9.11"
ds.SOPInstanceUID = "1.2.3.4.5.6.7.8.9.12"
ds.StudyDate = "20240101"
ds.StudyTime = "120000"
ds.Manufacturer = "TestManufacturer"
# Required Image Data Information
ds.PhotometricInterpretation = "MONOCHROME2"
ds.Rows = 128
ds.Columns = 128
ds.BitsAllocated = 16
ds.BitsStored = 16 # Add missing Bits Stored
ds.HighBit = 15 # Highest bit set
ds.PixelRepresentation = 0 # Unsigned integer
ds.SamplesPerPixel = 1 # Single-channel (grayscale)
ds.PixelData = (np.random.rand(128, 128) * 65535).astype(np.uint16).tobytes()
# Save to a temporary file
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".dcm")
ds.save_as(temp_file.name)
return temp_file.name
def test_is_dicom(pipeline):
dicom_path = create_dummy_dicom()
# Test with file path
assert pipeline.is_dicom(dicom_path) is True
# Test with BytesIO
with open(dicom_path, "rb") as f:
dicom_bytes = BytesIO(f.read())
assert pipeline.is_dicom(dicom_bytes) is True
# Test with invalid BytesIO (non-DICOM content)
non_dicom_bytes = BytesIO()
non_dicom_bytes.write(b"\x89PNG\r\n\x1a\n" + b"\x00" * 128) # Write invalid header
non_dicom_bytes.seek(0)
assert pipeline.is_dicom(non_dicom_bytes) is False
os.remove(dicom_path)
def test_is_dicom(pipeline):
dicom_path = create_dummy_dicom()
# Test with file path
assert pipeline.is_dicom(dicom_path) is True, "DICOM file path should be recognized as DICOM"
# Test with BytesIO
with open(dicom_path, "rb") as f:
dicom_bytes = BytesIO(f.read())
assert pipeline.is_dicom(dicom_bytes) is True, "BytesIO DICOM content should be recognized as DICOM"
# Test with invalid BytesIO (non-DICOM content)
non_dicom_bytes = BytesIO()
non_dicom_bytes.write(b"\x89PNG\r\n\x1a\n" + b"\x00" * 128) # Write invalid header
non_dicom_bytes.seek(0)
assert pipeline.is_dicom(non_dicom_bytes) is False, "Non-DICOM BytesIO should not be recognized as DICOM"
# Test with invalid raw bytes
invalid_raw_bytes = b"\x89PNG\r\n\x1a\n" + b"\x00" * 128
assert pipeline.is_dicom(invalid_raw_bytes) is False, "Invalid raw bytes should not be recognized as DICOM"
os.remove(dicom_path)
def test_preprocess_normal_image(pipeline):
# Create a dummy image
image = Image.new("RGB", (128, 128), color="red")
# Test with BytesIO
image_bytes = BytesIO()
image.save(image_bytes, format="JPEG")
image_bytes.seek(0)
processed_image_bytes = pipeline.preprocess(image_bytes, is_dicom=False)
assert isinstance(processed_image_bytes, Image.Image)
# Test with file path
temp_image_path = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg").name
image.save(temp_image_path)
processed_image_path = pipeline.preprocess(temp_image_path, is_dicom=False)
assert isinstance(processed_image_path, Image.Image)
os.remove(temp_image_path)
def test_infer(pipeline):
# Create a dummy image
image = Image.new("RGB", (128, 128), color="red")
# Perform inference
result = pipeline.infer(image)
assert isinstance(result, Image.Image)
def test_postprocess(pipeline):
image = Image.new("RGB", (128, 128), color="red")
result = pipeline.postprocess(image)
assert isinstance(result, Image.Image)
def test_api_predict_normal_image():
# Create a dummy image
image = Image.new("RGB", (128, 128), color="red")
image_bytes = BytesIO()
image.save(image_bytes, format="JPEG")
image_bytes.seek(0)
response = client.post(
"/inference/predict", # Adjusted to include the prefix
files={"file": ("test.jpg", image_bytes, "image/jpeg")},
data={"apply_clahe_postprocess": "false"} # Ensure proper boolean conversion
)
assert response.status_code == 200, response.text
assert response.headers["content-type"] == "image/png"
def test_api_predict_dicom():
dicom_path = create_dummy_dicom()
# Use BytesIO for testing
with open(dicom_path, "rb") as f:
dicom_bytes = BytesIO(f.read())
response = client.post(
"/inference/predict", # Adjusted to include the prefix
files={"file": ("test.dcm", dicom_bytes, "application/dicom")},
data={"apply_clahe_postprocess": "false"} # Ensure proper boolean conversion
)
assert response.status_code == 200, response.text
assert response.headers["content-type"] == "image/png"
os.remove(dicom_path)
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