def test_both_rois(self):
pipeline_config = PipelineConfig("test_pipeline")
simulated_camera = CameraSimulation(CameraConfig("simulation"))
image = simulated_camera.get_image()
x_axis, y_axis = simulated_camera.get_x_y_axis()
parameters = pipeline_config.get_configuration()
camera_name = simulated_camera.get_name()
parameters["roi_signal"] = [0, 200, 0, 200]
parameters["roi_background"] = [0, 200, 0, 200]
for i in range(10):
result = process_image(image=image,
pulse_id=i,
timestamp=time.time(),
x_axis=x_axis,
y_axis=y_axis,
parameters=parameters)
required_fields_in_result = [
camera_name + ".processing_parameters",
camera_name + '.roi_signal_x_profile',
# camera_name + '.edge_position',
# camera_name + '.cross_correlation_amplitude',
camera_name + '.roi_background_x_profile'
]
self.assertSetEqual(set(required_fields_in_result), set(result.keys()),
"Not all required keys are present in the result")
def test_wrong_background_size(self):
pipeline_parameters = {
"camera_name": "simulation",
"image_background": "white_background"
}
simulated_camera = CameraSimulation(CameraConfig("simulation"))
image = simulated_camera.get_image()
x_axis, y_axis = simulated_camera.get_x_y_axis()
background_provider = MockBackgroundManager()
# Invalid background size.
background_provider.save_background("white_background", numpy.zeros(shape=(100, 100)),
append_timestamp=False)
parameters = PipelineConfig("test_pipeline", pipeline_parameters).get_configuration()
image_background_array = background_provider.get_background("white_background")
with self.assertRaisesRegex(RuntimeError, "Invalid background_image size "):
process_image(image=image,
timestamp=time.time(),
x_axis=x_axis,
y_axis=y_axis,
parameters=parameters,
image_background_array=image_background_array)
def test_single_function(self):
# Profile only if LineProfiler present.
# To install: conda install line_profiler
try:
from line_profiler import LineProfiler
except ImportError:
return
function_to_perf = functions.subtract_background
n_iterations = 200
n_tests = 5
simulated_camera = CameraSimulation(CameraConfig("simulation"), size_x=2048, size_y=2048)
for _ in range(n_tests):
profile = LineProfiler()
wrapped_function = profile(function_to_perf)
images = []
backgrounds = []
for _ in range(n_iterations):
images.append(simulated_camera.get_image())
backgrounds.append(simulated_camera.get_image())
for index in range(n_iterations):
wrapped_function(images[index], backgrounds[index])
profile.print_stats()
def test_get_image(self):
simulated_camera = CameraSimulation(CameraConfig("simulation"))
image = simulated_camera.get_image()
self.assertIsNotNone(image)
raw_image = simulated_camera.get_image(raw=True)
self.assertIsNotNone(raw_image)
def test_background_roi(self):
pipeline_config = PipelineConfig("test_pipeline")
simulated_camera = CameraSimulation(CameraConfig("simulation"))
image = simulated_camera.get_image()
x_axis, y_axis = simulated_camera.get_x_y_axis()
parameters = pipeline_config.get_configuration()
camera_name = simulated_camera.get_name()
parameters["roi_background"] = [0, 200, 0, 200]
result = process_image(image=image,
pulse_id=0,
timestamp=time.time(),
x_axis=x_axis,
y_axis=y_axis,
parameters=parameters)
required_fields_in_result = [
camera_name + ".processing_parameters",
camera_name + '.roi_background_x_profile'
]
self.assertSetEqual(set(required_fields_in_result), set(result.keys()),
"Not all required keys are present in the result")
def test_noop_pipeline(self):
pipeline_config = PipelineConfig("test_pipeline")
simulated_camera = CameraSimulation(CameraConfig("simulation"))
image = simulated_camera.get_image()
x_axis, y_axis = simulated_camera.get_x_y_axis()
parameters = pipeline_config.get_configuration()
result = process_image(image=image,
timestamp=time.time(),
x_axis=x_axis,
y_axis=y_axis,
parameters=parameters)
required_fields_in_result = ['x_center_of_mass', 'x_axis', 'y_axis', 'x_profile', 'y_fit_standard_deviation',
'y_rms', 'timestamp', 'y_profile', 'image', 'max_value', 'x_fit_offset',
'x_fit_gauss_function', 'y_center_of_mass', 'min_value', 'y_fit_mean',
'x_fit_mean', 'x_rms', 'y_fit_amplitude', 'x_fit_amplitude',
'y_fit_gauss_function', 'x_fit_standard_deviation', 'y_fit_offset',
"processing_parameters", "intensity"]
self.assertSetEqual(set(required_fields_in_result), set(result.keys()),
"Not all required keys are present in the result")
self.assertTrue(numpy.array_equal(result["image"], image),
"The input and output image are not the same, but the pipeline should not change it.")
self.assertDictEqual(parameters, json.loads(result["processing_parameters"]),
"The passed and the received processing parameters are not the same.")
def test_process_image_performance(self):
# Profile only if LineProfiler present.
# To install: conda install line_profiler
try:
from line_profiler import LineProfiler
except ImportError:
return
simulated_camera = CameraSimulation(CameraConfig("simulation"), size_x=2048, size_y=2048)
x_axis, y_axis = simulated_camera.get_x_y_axis()
x_size, y_size = simulated_camera.get_geometry()
image_background_array = numpy.zeros(shape=(y_size, x_size), dtype="uint16") + 3
parameters = {
"image_threshold": 1,
"image_region_of_interest": [0, 2048, 0, 2048],
"image_good_region": {
"threshold": 0.3,
"gfscale": 1.8
},
"image_slices": {
"number_of_slices": 5,
"scale": 1.0,
"orientation": "horizontal"
}
}
profile = LineProfiler(process_image)
process_image_wrapper = profile(process_image)
n_iterations = 300
print("Generating images.")
images = []
for _ in range(n_iterations):
images.append(simulated_camera.get_image())
print("Processing images.")
start_time = time.time()
for image in images:
process_image_wrapper(image=image,
timestamp=time.time(),
x_axis=x_axis,
y_axis=y_axis,
parameters=parameters,
image_background_array=image_background_array)
end_time = time.time()
time_difference = end_time - start_time
rate = n_iterations / time_difference
print("Processing rate: ", rate)
profile.print_stats()
def test_image_background(self):
pipeline_parameters = {
"camera_name": "simulation",
"image_background": "white_background"
}
simulated_camera = CameraSimulation(CameraConfig("simulation"))
image = simulated_camera.get_image()
x_axis, y_axis = simulated_camera.get_x_y_axis()
background_provider = MockBackgroundManager()
x_size, y_size = simulated_camera.get_geometry()
background_provider.save_background("white_background", numpy.zeros(shape=(y_size, x_size)),
append_timestamp=False)
pipeline_config = PipelineConfig("test_pipeline", pipeline_parameters)
parameters = pipeline_config.get_configuration()
image_background_array = background_provider.get_background(parameters.get("image_background"))
result = process_image(image=image,
timestamp=time.time(),
x_axis=x_axis,
y_axis=y_axis,
parameters=parameters,
image_background_array=image_background_array)
self.assertTrue(numpy.array_equal(result["image"], image),
"A zero background should not change the image.")
max_value_in_image = result["max_value"]
pipeline_parameters = {
"camera_name": "simulation",
"image_background": "max_background",
"image_threshold": 0
}
max_background = numpy.zeros(shape=(y_size, x_size), dtype="uint16")
max_background.fill(max_value_in_image)
background_provider.save_background("max_background", max_background, append_timestamp=False)
pipeline_config = PipelineConfig("test_pipeline", pipeline_parameters)
parameters = pipeline_config.get_configuration()
image_background_array = background_provider.get_background(parameters.get("image_background"))
expected_image = numpy.zeros(shape=(y_size, x_size))
result = process_image(image=image,
timestamp=time.time(),
x_axis=x_axis,
y_axis=y_axis,
parameters=parameters,
image_background_array=image_background_array)
self.assertTrue(numpy.array_equal(result["image"], expected_image),
"The image should be all zeros - negative numbers are not allowed.")
def run_the_pipeline(configuration, simulated_image=None):
parameters = PipelineConfig("test_pipeline", configuration).get_configuration()
simulated_camera = CameraSimulation(CameraConfig("simulation"))
if simulated_image is None:
simulated_image = simulated_camera.get_image()
x_axis, y_axis = simulated_camera.get_x_y_axis()
return process_image(image=simulated_image, timestamp=time.time(), x_axis=x_axis, y_axis=y_axis,
parameters=parameters)
def test_camera_calibration(self):
camera = CameraSimulation(CameraConfig("simulation"))
size_x, size_y = camera.get_geometry()
image = camera.get_image()
self.assertEqual(image.shape[0], size_y)
self.assertEqual(image.shape[1], size_x)
x_axis, y_axis = camera.get_x_y_axis()
self.assertEqual(x_axis.shape[0], size_x)
self.assertEqual(y_axis.shape[0], size_y)
def test_sum_images(self):
simulated_camera = CameraSimulation(CameraConfig("simulation"))
image = simulated_camera.get_image()
accumulated_image = None
n_images = 1000
for _ in range(n_images):
accumulated_image = sum_images(image=image, accumulator_image=accumulated_image)
processed_image = accumulated_image / n_images
processed_image = processed_image.astype(dtype="uint16")
numpy.testing.assert_array_equal(image, processed_image)
def test_camera_frame_rate(self):
camera = CameraSimulation(CameraConfig("simulation"))
self.assertEqual(camera.frame_rate, 10)
new_frame_rate = 1
camera_config = CameraConfig("simulation")
configuration = camera_config.get_configuration()
configuration["frame_rate"] = new_frame_rate
camera_config.set_configuration(configuration)
camera = CameraSimulation(camera_config)
self.assertEqual(camera.frame_rate, new_frame_rate)
def test_camera_simulation_interval(self):
camera = CameraSimulation(CameraConfig("simulation"))
self.assertEqual(camera.simulation_interval,
config.DEFAULT_CAMERA_SIMULATION_INTERVAL)
new_simulation_interval = 1
camera_config = CameraConfig("simulation")
configuration = camera_config.get_configuration()
configuration["simulation_interval"] = new_simulation_interval
camera_config.set_configuration(configuration)
camera = CameraSimulation(camera_config)
self.assertEqual(camera.simulation_interval, new_simulation_interval)
def test_intensity(self):
simulated_camera = CameraSimulation(CameraConfig("simulation"))
image = simulated_camera.get_image()
x_axis, y_axis = simulated_camera.get_x_y_axis()
parameters = PipelineConfig("test_pipeline", {
"camera_name": "simulation"
}).get_configuration()
result = process_image(image=image,
timestamp=time.time(),
x_axis=x_axis,
y_axis=y_axis,
parameters=parameters)
x_sum = result["x_profile"].sum()
y_sum = result["y_profile"].sum()
# The sums of X and Y profile should always give us the same result as the intensity.
self.assertAlmostEqual(x_sum, result["intensity"], delta=10000)
self.assertAlmostEqual(y_sum, result["intensity"], delta=10000)
def test_image_threshold(self):
simulated_camera = CameraSimulation(CameraConfig("simulation"))
image = simulated_camera.get_image()
x_axis, y_axis = simulated_camera.get_x_y_axis()
x_size, y_size = simulated_camera.get_geometry()
pipeline_parameters = {
"camera_name": "simulation",
"image_threshold": 9999999
}
pipeline_config = PipelineConfig("test_pipeline", pipeline_parameters)
parameters = pipeline_config.get_configuration()
result = process_image(image=image,
timestamp=time.time(),
x_axis=x_axis,
y_axis=y_axis,
parameters=parameters)
expected_image = numpy.zeros(shape=(y_size, x_size))
self.assertTrue(numpy.array_equal(result["image"], expected_image),
"An image of zeros should have been produced.")
pipeline_parameters = {
"camera_name": "simulation",
"image_threshold": 0
}
pipeline_config = PipelineConfig("test_pipeline", pipeline_parameters)
parameters = pipeline_config.get_configuration()
result = process_image(image=image,
timestamp=time.time(),
x_axis=x_axis,
y_axis=y_axis,
parameters=parameters)
self.assertTrue(numpy.array_equal(result["image"], image),
"The image should be the same as the original image.")
def test_camera_simulation(self):
camera = CameraSimulation(CameraConfig("simulation"))
n_images_to_receive = 5
def callback_method(image, timestamp):
self.assertIsNotNone(image, "Image should not be None")
self.assertIsNotNone(timestamp, "Timestamp should not be None")
nonlocal n_images_to_receive
if n_images_to_receive <= 0:
camera.clear_callbacks()
camera.simulation_stop_event.set()
n_images_to_receive -= 1
camera.connect()
camera.add_callback(callback_method)
camera.simulation_stop_event.wait()
def get_simulated_camera(path="camera_config/", name="simulation"):
return CameraSimulation(CameraConfig(name, get_config(path, name)))
from cam_server.pipeline.data_processing.processor import process_image
# Size of simulated image.
image_size_x = 1280
image_size_y = 960
# Select compression options. Only this 2 are available in Python - cam_server uses "bitshuffle_lz4".
compression = "bitshuffle_lz4"
# compression = "none"
# Stream configuration. cam_server uses PUB for the output stream.
output_stream_port = 9999
output_stream_mode = PUB
# output_stream_mode = PUSH
simulated_camera = CameraSimulation(camera_config=CameraConfig("simulation"),
size_x=image_size_x, size_y=image_size_y)
x_axis, y_axis = simulated_camera.get_x_y_axis()
x_size, y_size = simulated_camera.get_geometry()
# Documentation: https://github.com/datastreaming/cam_server#pipeline_configuration
pipeline_parameters = {
"camera_name": "simulation"
}
pipeline_config = PipelineConfig("test_pipeline", pipeline_parameters)
parameters = pipeline_config.get_configuration()
image_number = 0
with sender(port=output_stream_port, mode=output_stream_mode) as output_stream:
# Get simulated image.
def test_client(self):
server_info = self.client.get_server_info()
self.assertIsNot(server_info["active_instances"],
"There should be no running instances.")
expected_cameras = set([
"camera_example_1", "camera_example_2", "camera_example_3",
"camera_example_4", "simulation"
])
self.assertSetEqual(set(self.client.get_cameras()), expected_cameras,
"Not getting all expected cameras")
camera_stream_address = self.client.get_camera_stream("simulation")
self.assertTrue(bool(camera_stream_address),
"Camera stream address cannot be empty.")
self.assertTrue(
"simulation" in self.client.get_server_info()["active_instances"],
"Simulation camera not present in server info.")
# Check if we can connect to the stream and receive data (in less than 2 seconds).
host, port = get_host_port_from_stream_address(camera_stream_address)
with source(host=host, port=port, receive_timeout=2000,
mode=SUB) as stream:
data = stream.receive()
self.assertIsNotNone(data, "Received data was none.")
required_fields = set(
["image", "timestamp", "width", "height", "x_axis", "y_axis"])
self.assertSetEqual(required_fields, set(data.data.data.keys()),
"Required fields missing.")
image = data.data.data["image"].value
x_size, y_size = CameraSimulation(
CameraConfig("simulation")).get_geometry()
self.assertListEqual(
list(image.shape), [y_size, x_size],
"Original and received image are not the same.")
self.assertEqual(data.data.data["width"].value, x_size,
"Width not correct.")
self.assertEqual(data.data.data["height"].value, y_size,
"Height not correct.")
# Stop the simulation instance.
self.client.stop_camera("simulation")
self.assertTrue(
"simulation"
not in self.client.get_server_info()["active_instances"],
"Camera simulation did not stop.")
self.client.get_camera_stream("simulation")
self.assertTrue(
"simulation" in self.client.get_server_info()["active_instances"],
"Camera simulation did not start.")
self.client.stop_all_cameras()
self.assertTrue(
"simulation"
not in self.client.get_server_info()["active_instances"],
"Camera simulation did not stop.")
example_1_config = self.client.get_camera_config("camera_example_1")
self.assertTrue(bool(example_1_config), "Cannot retrieve config.")
# Change the name to reflect tha camera.
example_1_config["name"] = "testing_camera"
self.client.set_camera_config("testing_camera", example_1_config)
testing_camera_config = self.client.get_camera_config("testing_camera")
self.assertDictEqual(example_1_config, testing_camera_config,
"Saved and loaded configs are not the same.")
geometry = self.client.get_camera_geometry("simulation")
simulated_camera = CameraSimulation(CameraConfig("simulation"))
size_x, size_y = simulated_camera.get_geometry()
self.assertListEqual(
geometry, [size_x, size_y],
'The geometry of the simulated camera is not correct.')
self.assertTrue("testing_camera" in self.client.get_cameras(),
"Testing camera should be present.")
self.client.delete_camera_config("testing_camera")
self.assertTrue("testing_camera" not in self.client.get_cameras(),
"Testing camera should not be present.")
# Test if it fails quickly enough.
with self.assertRaisesRegex(
ValueError,
"Camera with prefix EPICS_example_1 not online - Status None"):
self.client.get_camera_stream("camera_example_1")
self.assertTrue(self.client.is_camera_online("simulation"),
"Simulation should be always online")
self.assertFalse(self.client.is_camera_online("camera_example_1"),
"Epics not working in this tests.")
self.client.set_camera_config(
"simulation_temp", self.client.get_camera_config("simulation"))
stream_address = self.client.get_camera_stream("simulation_temp")
camera_host, camera_port = get_host_port_from_stream_address(
stream_address)
sim_x, sim_y = CameraSimulation(
CameraConfig("simulation")).get_geometry()
instance_info = self.client.get_server_info(
)["active_instances"]["simulation_temp"]
self.assertTrue("last_start_time" in instance_info)
self.assertTrue("statistics" in instance_info)
# Collect from the pipeline.
with source(host=camera_host, port=camera_port, mode=SUB) as stream:
data = stream.receive()
x_size = data.data.data["width"].value
y_size = data.data.data["height"].value
self.assertEqual(x_size, sim_x)
self.assertEqual(y_size, sim_y)
x_axis_1 = data.data.data["x_axis"].value
y_axis_1 = data.data.data["y_axis"].value
self.assertEqual(x_axis_1.shape[0], sim_x)
self.assertEqual(y_axis_1.shape[0], sim_y)
camera_config = self.client.get_camera_config("simulation_temp")
camera_config["rotate"] = 1
self.client.set_camera_config("simulation_temp", camera_config)
sleep(0.5)
# Collect from the pipeline.
with source(host=camera_host, port=camera_port, mode=SUB) as stream:
data = stream.receive()
x_size = data.data.data["width"].value
y_size = data.data.data["height"].value
# We rotate the image for 90 degrees - X and Y size should be inverted.
self.assertEqual(x_size, sim_y)
self.assertEqual(y_size, sim_x)
x_axis_2 = data.data.data["x_axis"].value
y_axis_2 = data.data.data["y_axis"].value
# We rotate the image for 90 degrees - X and Y size should be inverted.
self.assertEqual(x_axis_2.shape[0], sim_y)
self.assertEqual(y_axis_2.shape[0], sim_x)
self.client.delete_camera_config("simulation_temp")
image = self.client.get_camera_image("simulation")
self.assertGreater(len(image.content), 0)
image = self.client.get_camera_image_bytes("simulation")
dtype = image["dtype"]
shape = image["shape"]
bytes = base64.b64decode(image["bytes"].encode())
x_size, y_size = CameraSimulation(
CameraConfig("simulation")).get_geometry()
self.assertEqual(shape, [y_size, x_size])
image_array = numpy.frombuffer(bytes, dtype=dtype).reshape(shape)
self.assertIsNotNone(image_array)
self.client.stop_all_cameras()
def test_camera_settings_change(self):
stream_address = self.instance_manager.get_instance_stream(
"simulation")
simulated_camera = CameraSimulation(CameraConfig("simulation"))
sim_x, sim_y = simulated_camera.get_geometry()
camera_host, camera_port = get_host_port_from_stream_address(
stream_address)
# Collect from the pipeline.
with source(host=camera_host, port=camera_port, mode=SUB) as stream:
data = stream.receive()
x_size = data.data.data["width"].value
y_size = data.data.data["height"].value
self.assertEqual(x_size, sim_x)
self.assertEqual(y_size, sim_y)
x_axis_1 = data.data.data["x_axis"].value
y_axis_1 = data.data.data["y_axis"].value
self.assertEqual(x_axis_1.shape[0], sim_x)
self.assertEqual(y_axis_1.shape[0], sim_y)
new_config = update_camera_config(
self.instance_manager.get_instance(
"simulation").get_configuration(), {"rotate": 1})
new_config = CameraConfig("simulation", new_config).get_configuration()
self.instance_manager.set_camera_instance_config(
"simulation", new_config)
sleep(0.5)
# Collect from the pipeline.
with source(host=camera_host, port=camera_port, mode=SUB) as stream:
data = stream.receive()
x_size = data.data.data["width"].value
y_size = data.data.data["height"].value
# We rotate the image for 90 degrees - X and Y size should be inverted.
self.assertEqual(x_size, sim_y)
self.assertEqual(y_size, sim_x)
x_axis_2 = data.data.data["x_axis"].value
y_axis_2 = data.data.data["y_axis"].value
# We rotate the image for 90 degrees - X and Y size should be inverted.
self.assertEqual(x_axis_2.shape[0], sim_y)
self.assertEqual(y_axis_2.shape[0], sim_x)
# The axis should just be switched.
self.assertTrue(numpy.array_equal(x_axis_1, y_axis_2))
self.assertTrue(numpy.array_equal(y_axis_1, x_axis_2))
new_config = update_camera_config(
self.instance_manager.get_instance(
"simulation").get_configuration(), {"camera_calibration": {}})
new_config = CameraConfig("simulation", new_config).get_configuration()
self.instance_manager.set_camera_instance_config(
"simulation", new_config)
with source(host=camera_host, port=camera_port, mode=SUB) as stream:
data = stream.receive()
x_size = data.data.data["width"].value
y_size = data.data.data["height"].value
# We rotate the image for 90 degrees - X and Y size should be inverted.
self.assertEqual(x_size, sim_y)
self.assertEqual(y_size, sim_x)
x_axis_3 = data.data.data["x_axis"].value
y_axis_3 = data.data.data["y_axis"].value
# We rotate the image for 90 degrees - X and Y size should be inverted.
self.assertEqual(x_axis_3.shape[0], sim_y)
self.assertEqual(y_axis_3.shape[0], sim_x)
self.instance_manager.stop_all_instances()
def test_slices(self):
def run_the_pipeline(configuration, simulated_image=None):
parameters = PipelineConfig("test_pipeline", configuration).get_configuration()
simulated_camera = CameraSimulation(CameraConfig("simulation"))
if simulated_image is None:
simulated_image = simulated_camera.get_image()
x_axis, y_axis = simulated_camera.get_x_y_axis()
return process_image(image=simulated_image, timestamp=time.time(), x_axis=x_axis, y_axis=y_axis,
parameters=parameters)
pipeline_configuration = {
"camera_name": "simulation",
"image_good_region": {
"threshold": 1
},
"image_slices": {
"number_of_slices": 9
}
}
result = run_the_pipeline(pipeline_configuration)
self.assertEqual(result["slice_amount"], 9)
self.assertEqual(result["slice_orientation"], "vertical", "Default slice orientation should be vertical.")
self.assertTrue("slice_length" in result)
pipeline_configuration = {
"camera_name": "simulation",
"image_good_region": {
"threshold": 1
},
"image_slices": {
"orientation": "horizontal"
}
}
result = run_the_pipeline(pipeline_configuration)
self.assertEqual(result["slice_orientation"], "horizontal")
self.assertTrue("slice_length" in result)
with self.assertRaisesRegex(ValueError, "Invalid slice orientation 'invalid'."):
pipeline_configuration = {
"camera_name": "simulation",
"image_good_region": {
"threshold": 1
},
"image_slices": {
"orientation": "invalid"
}
}
run_the_pipeline(pipeline_configuration)
image = CameraSimulation(CameraConfig("simulation")).get_image()
pipeline_configuration = {
"camera_name": "simulation",
"image_good_region": {
"threshold": 0.1
},
"image_slices": {
"orientation": "vertical",
"number_of_slices": 3
}
}
result_1 = run_the_pipeline(pipeline_configuration, image)
result_2 = run_the_pipeline(pipeline_configuration, image)
# 2 calculations with the same data should give the same result.
self.assertEqual(result_1["slice_0_center_x"], result_2["slice_0_center_x"])
self.assertEqual(result_1["slice_0_center_y"], result_2["slice_0_center_y"])
pipeline_configuration = {
"camera_name": "simulation",
"image_good_region": {
"threshold": 0.1
},
"image_slices": {
"orientation": "horizontal",
"number_of_slices": 3
}
}
result_3 = run_the_pipeline(pipeline_configuration, image)
# If we orientate the slices horizontally, the slice center has to change.
self.assertNotEqual(result_1["slice_0_center_x"], result_3["slice_0_center_x"])
self.assertNotEqual(result_1["slice_0_center_y"], result_3["slice_0_center_y"])
def test_region_of_interest_default_values(self):
simulated_camera = CameraSimulation(CameraConfig("simulation"))
image = simulated_camera.get_image()
x_axis, y_axis = simulated_camera.get_x_y_axis()
parameters = PipelineConfig("test_pipeline", {
"camera_name": "simulation"
}).get_configuration()
good_region_keys = set(["good_region", "gr_x_axis", "gr_y_axis", "gr_x_fit_gauss_function", "gr_x_fit_offset",
"gr_x_fit_amplitude", "gr_x_fit_standard_deviation", "gr_x_fit_mean",
"gr_y_fit_gauss_function", "gr_y_fit_offset", "gr_y_fit_amplitude",
"gr_y_fit_standard_deviation", "gr_y_fit_mean", "gr_intensity",
"gr_x_profile", "gr_y_profile"])
slices_key_formats = set(["slice_%s_center_x", "slice_%s_center_y", "slice_%s_standard_deviation",
"slice_%s_intensity"])
result = process_image(image=image,
timestamp=time.time(),
x_axis=x_axis,
y_axis=y_axis,
parameters=parameters)
self.assertFalse(any((x in result for x in good_region_keys)), 'There should not be good region keys.')
parameters = PipelineConfig("test_pipeline", {
"camera_name": "simulation",
"image_good_region": {
"threshold": 99999
}
}).get_configuration()
result = process_image(image=image,
timestamp=time.time(),
x_axis=x_axis,
y_axis=y_axis,
parameters=parameters)
self.assertTrue(all((x in result for x in good_region_keys)), 'There should be good region keys.')
self.assertTrue(all((result[x] is None for x in good_region_keys)), 'All values should be None.')
number_of_slices = 7
parameters = PipelineConfig("test_pipeline", {
"camera_name": "simulation",
"image_good_region": {
"threshold": 99999
},
"image_slices": {
"number_of_slices": number_of_slices
}
}).get_configuration()
result = process_image(image=image,
timestamp=time.time(),
x_axis=x_axis,
y_axis=y_axis,
parameters=parameters)
self.assertTrue(all((x in result for x in good_region_keys)), 'There should be good region keys.')
self.assertTrue(all((x in result for x in (x % counter
for x in slices_key_formats
for counter in range(number_of_slices)))))