Cluster estimation integration test (#251)

* Nearly completed test (with an unusual error)

* wrote cluster estimation worker integration test

* Nearly completed test (with an unusual error)

* wrote cluster estimation worker integration test

* Added review changes: type checking, created constants, and import style conventions

* review changes: corrected expected type of output_results

* review changes: made type checking more dynamic and refactored to remove duplication

* Final review change

Author: RohanKatreddy

tests/integration/test_cluster_estimation_worker.py

@@ -0,0 +1,155 @@
+"""
+Test cluster_estimation_worker process.
+"""
+
+import time
+import multiprocessing as mp
+from typing import List
+
+import numpy as np
+
+from utilities.workers import queue_proxy_wrapper, worker_controller
+from modules.cluster_estimation.cluster_estimation_worker import cluster_estimation_worker
+from modules.detection_in_world import DetectionInWorld
+from modules.object_in_world import ObjectInWorld
+
+MIN_ACTIVATION_THRESHOLD = 3
+MIN_NEW_POINTS_TO_RUN = 0
+MAX_NUM_COMPONENTS = 3
+RANDOM_STATE = 0
+
+
+def check_output_results(output_queue: queue_proxy_wrapper.QueueProxyWrapper) -> None:
+    """
+    Checking if the output from the worker is of the correct type
+    """
+
+    while not output_queue.queue.empty():
+        output_results: List[DetectionInWorld] = output_queue.queue.get()
+        assert isinstance(output_results, list)
+        assert all(isinstance(obj, ObjectInWorld) for obj in output_results)
+
+
+def test_cluster_estimation_worker() -> int:
+    """
+    Integration test for cluster estimation worker.
+    """
+
+    # Worker and controller setup.
+    controller = worker_controller.WorkerController()
+
+    mp_manager = mp.Manager()
+    input_queue = queue_proxy_wrapper.QueueProxyWrapper(mp_manager)
+    output_queue = queue_proxy_wrapper.QueueProxyWrapper(mp_manager)
+
+    worker_process = mp.Process(
+        target=cluster_estimation_worker,
+        args=(
+            MIN_ACTIVATION_THRESHOLD,
+            MIN_NEW_POINTS_TO_RUN,
+            MAX_NUM_COMPONENTS,
+            RANDOM_STATE,
+            input_queue,
+            output_queue,
+            controller,
+        ),
+    )
+
+    # Second test set: 1 clusters
+    test_data_1 = [
+        # Landing pad 1
+        DetectionInWorld.create(
+            np.array([[1, 1], [1, 2], [2, 2], [2, 1]]), np.array([1.5, 1.5]), 1, 0.9
+        )[1],
+        DetectionInWorld.create(
+            np.array([[1, 1], [1, 2], [2, 2], [2, 1]]), np.array([1.5, 1.5]), 1, 0.9
+        )[1],
+        DetectionInWorld.create(
+            np.array([[1, 1], [1, 2], [2, 2], [2, 1]]), np.array([1.5, 1.5]), 1, 0.9
+        )[1],
+        DetectionInWorld.create(
+            np.array([[1, 1], [1, 2], [2, 2], [2, 1]]), np.array([1.5, 1.5]), 1, 0.9
+        )[1],
+        DetectionInWorld.create(
+            np.array([[1, 1], [1, 2], [2, 2], [2, 1]]), np.array([1.5, 1.5]), 1, 0.9
+        )[1],
+    ]
+
+    # First test set: 2 clusters
+    test_data_2 = [
+        # Landing pad 1
+        DetectionInWorld.create(
+            np.array([[1, 1], [1, 2], [2, 2], [2, 1]]), np.array([1.5, 1.5]), 1, 0.9
+        )[1],
+        DetectionInWorld.create(
+            np.array([[1, 1], [1, 2], [2, 2], [2, 1]]), np.array([1.5, 1.5]), 1, 0.9
+        )[1],
+        DetectionInWorld.create(
+            np.array([[1, 1], [1, 2], [2, 2], [2, 1]]), np.array([1.5, 1.5]), 1, 0.9
+        )[1],
+        DetectionInWorld.create(
+            np.array([[1, 1], [1, 2], [2, 2], [2, 1]]), np.array([1.5, 1.5]), 1, 0.9
+        )[1],
+        DetectionInWorld.create(
+            np.array([[1, 1], [1, 2], [2, 2], [2, 1]]), np.array([1.5, 1.5]), 1, 0.9
+        )[1],
+        # Landing pad 2
+        DetectionInWorld.create(
+            np.array([[10, 10], [10, 11], [11, 11], [11, 10]]), np.array([10.5, 10.5]), 1, 0.9
+        )[1],
+        DetectionInWorld.create(
+            np.array([[10.1, 10.1], [10.1, 11.1], [11.1, 11.1], [11.1, 10.1]]),
+            np.array([10.6, 10.6]),
+            1,
+            0.92,
+        )[1],
+        DetectionInWorld.create(
+            np.array([[9.9, 9.9], [9.9, 10.9], [10.9, 10.9], [10.9, 9.9]]),
+            np.array([10.4, 10.4]),
+            1,
+            0.88,
+        )[1],
+        DetectionInWorld.create(
+            np.array([[10.2, 10.2], [10.2, 11.2], [11.2, 11.2], [11.2, 10.2]]),
+            np.array([10.7, 10.7]),
+            1,
+            0.95,
+        )[1],
+        DetectionInWorld.create(
+            np.array([[10.3, 10.3], [10.3, 11.3], [11.3, 11.3], [11.3, 10.3]]),
+            np.array([10.8, 10.8]),
+            1,
+            0.93,
+        )[1],
+    ]
+
+    # Testing with test_data_1 (1 cluster)
+
+    input_queue.queue.put(test_data_1)
+    worker_process.start()
+    time.sleep(1)
+
+    check_output_results(output_queue)
+
+    time.sleep(1)
+
+    # Testing with test_data_2 (2 clusters)
+
+    input_queue.queue.put(test_data_2)
+    time.sleep(1)
+
+    check_output_results(output_queue)
+
+    controller.request_exit()
+    input_queue.queue.put(None)
+    worker_process.join()
+
+    return 0
+
+
+if __name__ == "__main__":
+    result_main = test_cluster_estimation_worker()
+    if result_main < 0:
+        print(f"ERROR: Status code: {result_main}")
+
+    print("Done!")