def test_generate_thumbnail(
data_dir,
input_file,
params,
expected_thumb,
expected_original_size,
expected_thumb_size,
num_pages,
status
):
# Resolve the input file path
input_file = data_dir / input_file
# Mock the request
url = f"https://example.com/{input_file}"
responses.add(
responses.GET,
url=url,
body=input_file.read_bytes(),
status=200
)
# Create the lambda request event
event = _make_event({"url": url, **params})
# Get the response
response = t4_lambda_thumbnail.lambda_handler(event, None)
# Assert the request was handled with no errors
assert response["statusCode"] == 200, f"response: {response}"
# only check the body and expected image if it's a successful call
# Parse the body / the returned thumbnail
body = json.loads(read_body(response))
# Assert basic metadata was filled properly
assert body["info"]["thumbnail_size"] == expected_thumb_size
if expected_original_size: # PDFs don't have an expected size
assert body["info"]["original_size"] == expected_original_size
if "countPages" in params:
assert body["info"]["page_count"] == num_pages
# Assert the produced image is the same as the expected
if params.get('input') in ('pdf', "pptx"):
actual = Image.open(BytesIO(base64.b64decode(body['thumbnail'])))
expected = Image.open(data_dir / expected_thumb)
actual_array = np.array(actual)
expected_array = np.array(expected)
assert actual_array.shape == expected_array.shape
assert np.allclose(expected_array, actual_array, atol=15, rtol=0.1)
else:
actual = AICSImage(base64.b64decode(body['thumbnail']))
expected = AICSImage(data_dir / expected_thumb)
assert actual.size() == expected.size()
assert np.array_equal(actual.reader.data, expected.reader.data)
def test_generate_thumbnail(data_dir, input_file, params, expected_thumb,
expected_original_size, expected_thumb_size,
num_pages, status):
# Resolve the input file path
input_file = data_dir / input_file
# Mock the request
url = f"https://example.com/{input_file}"
responses.add(responses.GET,
url=url,
body=input_file.read_bytes(),
status=200)
# Create the lambda request event
event = _make_event({"url": url, **params})
# Get the response
if expected_thumb == "I16-mode-128-fallback.png":
# Note that if this set of params fails, it may be that better resamplers
# have been added for this mode, and either the image or test will need
# to be updated.
with _mock(t4_lambda_thumbnail, '_convert_I16_to_L', Image.fromarray):
response = t4_lambda_thumbnail.lambda_handler(event, None)
else:
response = t4_lambda_thumbnail.lambda_handler(event, None)
# Assert the request was handled with no errors
assert response["statusCode"] == 200, f"response: {response}"
# only check the body and expected image if it's a successful call
# Parse the body / the returned thumbnail
body = read_body(response)
# Assert basic metadata was filled properly
info = json.loads(response["headers"][QUILT_INFO_HEADER])
assert info["thumbnail_size"] == expected_thumb_size
if expected_original_size: # PDFs don't have an expected size
assert info["original_size"] == expected_original_size
if "countPages" in params:
assert info["page_count"] == num_pages
# Assert the produced image is the same as the expected
if params.get('input') in ('pdf', "pptx"):
actual = Image.open(BytesIO(body))
expected = Image.open(data_dir / expected_thumb)
actual_array = np.array(actual)
expected_array = np.array(expected)
assert actual_array.shape == expected_array.shape
assert np.allclose(expected_array, actual_array, atol=15, rtol=0.1)
else:
actual = AICSImage(body)
expected = AICSImage(data_dir / expected_thumb)
assert actual.size() == expected.size()
assert np.array_equal(actual.reader.data, expected.reader.data)
def test_known_dims(data, dims, expected_shape):
img = AICSImage(data, known_dims=dims)
assert img.data.shape == expected_shape
assert img.size_x == expected_shape[5]
assert img.size_y == expected_shape[4]
assert img.size_z == expected_shape[3]
assert img.size_c == expected_shape[2]
assert img.size_t == expected_shape[1]
assert img.size_s == expected_shape[0]
assert img.size(dims) == data.shape
def test_force_dims(data_shape, dims, expected):
img = AICSImage(data=da.zeros(data_shape))
img._reader._dims = dims
assert img.data.shape == expected
assert data_shape == img.get_image_data(out_orientation=dims).shape
assert img.size_x == expected[5]
assert img.size_y == expected[4]
assert img.size_z == expected[3]
assert img.size_c == expected[2]
assert img.size_t == expected[1]
assert img.size_s == expected[0]
assert img.size(dims) == data_shape
def test_known_dims(data, dims, expected_shape):
# Check basics
with Profiler() as prof:
img = AICSImage(data, known_dims=dims)
assert img.data.shape == expected_shape
assert img.size_x == expected_shape[5]
assert img.size_y == expected_shape[4]
assert img.size_z == expected_shape[3]
assert img.size_c == expected_shape[2]
assert img.size_t == expected_shape[1]
assert img.size_s == expected_shape[0]
assert img.size(dims) == data.shape
# Due to reshape and transpose there will be 2 tasks in the graph
assert len(prof.results) == 2
def test_force_dims(data_shape, dims, expected):
# Check basics
with Profiler() as prof:
img = AICSImage(data=da.zeros(data_shape))
img._reader._dims = dims
assert img.data.shape == expected
assert data_shape == img.get_image_data(out_orientation=dims).shape
assert img.size_x == expected[5]
assert img.size_y == expected[4]
assert img.size_z == expected[3]
assert img.size_c == expected[2]
assert img.size_t == expected[1]
assert img.size_s == expected[0]
assert img.size(dims) == data_shape
# Two operations are happening
# First, img.data is called and so two tasks of reshape and transpose are ran
# Then get_image_data is ran and two more reshape and transpose are ran
assert len(prof.results) == 4