feat: adding integration test (#23)

.github/workflows/ci.yaml

@@ -32,4 +32,7 @@ jobs:
           poetry install
 
       - name: Run Pre commit hooks
-        run: make format-code
+        run: make format-code
+
+      - name: Run Tests
+        run: poetry run pytest tests

README.md

@@ -42,3 +42,10 @@ ByteTrack is licensed under the MIT License. See the [LICENSE](LICENSE) file and
   year={2022}
 }
 ```
+
+```bibtex
+@video{intel-iot-devkit,
+  author={Intel IoT},
+  title={Intel IoT Devkit Sample Videos(https://github.com/intel-iot-devkit/sample-videos)},
+  license={Creative Commons Attribution 4.0 International License(https://creativecommons.org/licenses/by/4.0/)}
+```

tests/expected_output/objects_detected_and_tracked_video2.txt

@@ -0,0 +1,19 @@
+117 0.9745912458451594 0.5095593279999999 0.99959984 0.74230442 143 49 0.83191514
+118 0.9723770479661737 0.5005956551190778 0.9995706489616996 0.7455164254441724 143 49 0.8575639
+119 0.9692140420648577 0.49633454280262446 0.9995032670131737 0.7480595000990486 143 49 0.886084
+120 0.9597182832171389 0.49863391199158746 0.995071975596858 0.7532388859200183 143 49 0.9059332
+121 0.9137050982061102 0.4994814482436807 0.9549359765857107 0.7572477552931745 143 49 0.92144907
+122 0.8638723820710912 0.49995341002499705 0.9114848787732324 0.760094310118092 143 49 0.9317426
+123 0.8202442793180998 0.5008927419997994 0.8744436576830644 0.7617456794854497 143 49 0.9288402
+124 0.7771121850468138 0.5003854964349163 0.8379277554269561 0.7611541282108044 143 49 0.92904073
+125 0.7247772638550477 0.5017298388730855 0.7923321297682652 0.7630300613301431 143 49 0.9264275
+126 0.667130285243915 0.5027124324340133 0.7416950322200128 0.7661639605174826 143 49 0.93153256
+127 0.6166635597480964 0.5032080794919398 0.6977953131744735 0.7674052397214208 143 49 0.93632597
+128 0.5704205015010184 0.5034447579718662 0.6576470415989151 0.7674597007527504 143 49 0.9300204
+129 0.5200086616300771 0.5042046553066718 0.6129952681758294 0.7679312585911858 143 49 0.927421
+130 0.4664305059695041 0.5047469069888177 0.5650968833550777 0.7687824626031954 143 49 0.92839193
+131 0.41737613242284965 0.5047965654861531 0.5216674413663417 0.769888234103233 143 49 0.93056595
+132 0.3695027147879381 0.5046674259906719 0.4789313778430711 0.7704542275124632 143 49 0.9226942
+133 0.31862250657733115 0.5049336005081386 0.4326811327070279 0.7708076297478794 143 49 0.9281332
+134 0.26800115344630543 0.5048401760421602 0.38642684109755293 0.7709889020420381 143 49 0.92945176
+135 0.22018769086927653 0.5043750140688151 0.34284325134982213 0.7708960147555938 143 49 0.92946494

tests/test_input/objects_detected_video2.txt

@@ -0,0 +1,21 @@
+40 0.21998224 0.5836777 0.3745204 0.7639922 0.16180652 49
+116 0.9795788 0.53099704 0.9996632 0.7194981 0.51681966 49
+117 0.95138013 0.5041999 0.999584 0.748006 0.83191514 49
+118 0.9269222 0.49708003 0.9995592 0.7467762 0.8575639 49
+119 0.88959634 0.49609375 0.9994829 0.7485177 0.886084 49
+120 0.8366054 0.50106037 0.99332654 0.75461787 0.9059332 49
+121 0.7823308 0.5008052 0.93827456 0.7578524 0.92144907 49
+122 0.73994255 0.50089943 0.89590263 0.76007366 0.9317426 49
+123 0.70718193 0.5019245 0.86416876 0.76115876 0.9288402 49
+124 0.6728785 0.5005684 0.8293528 0.75955844 0.92904073 49
+125 0.624425 0.50280184 0.78042 0.76286423 0.9264275 49
+126 0.5726967 0.5034708 0.72873324 0.7666482 0.93153256 49
+127 0.53285456 0.50363165 0.6897488 0.7668706 0.93632597 49
+128 0.4952455 0.5036848 0.6524906 0.7663839 0.9300204 49
+129 0.44878444 0.50468314 0.60615635 0.7671972 0.927421 49
+130 0.39896855 0.5050585 0.5573927 0.76832783 0.92839193 49
+131 0.35776815 0.5048203 0.51712275 0.76961535 0.93056595 49
+132 0.31567994 0.5045963 0.4752435 0.7699328 0.9226942 49
+133 0.26709083 0.5050746 0.4275921 0.7702349 0.9281332 49
+134 0.22100373 0.5047798 0.3819117 0.77039224 0.92945176 49
+135 0.17729278 0.5041295 0.3402054 0.77022386 0.92946494 49

tests/test_model_regression.py

@@ -0,0 +1,102 @@
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+# bytetracker
+from bytetracker import BYTETracker
+
+# static paths
+OUTPUT_FOLDER = Path("tests/output")
+TEST_INPUT_FOLDER = Path("tests/test_input")
+EXPECTED_OUTPUT_FOLDER = Path("tests/expected_output")
+OUTPUT_FOLDER = Path("tests/output")
+
+OUTPUT_FOLDER.mkdir(parents=True, exist_ok=True)
+
+
+def reading_detections_file(video_number):
+    """Read the detections object file from a specific format
+
+    Args:
+        video_number (string): string representing the video name in folder
+
+    Returns:
+        df_detection (ndarray): A list of array of the tuples.
+    """
+    df_detection = np.loadtxt(
+        TEST_INPUT_FOLDER / f"objects_detected_{video_number}.txt", delimiter=" "
+    )
+
+    return df_detection
+
+
+def reading_expected_results_from_txt(video_number):
+    """Read the detections and tracked objects frames from Yolo and Bytetrack models
+
+    Args:
+        video_number (str): Video title to import
+
+    Returns:
+        expected_results_df (ndarray): array of frames detection and tracking imported from expected results.
+    """
+    expected_results_df = np.loadtxt(
+        EXPECTED_OUTPUT_FOLDER / f"objects_detected_and_tracked_{video_number}.txt"
+    )
+
+    return expected_results_df
+
+
+@pytest.mark.parametrize(
+    "expected_results, test_input, video_number",
+    [
+        (
+            reading_expected_results_from_txt("video1"),
+            reading_detections_file("video1"),
+            "video1",
+        ),
+        (
+            reading_expected_results_from_txt("video2"),
+            reading_detections_file("video2"),
+            "video2",
+        ),
+    ],
+)
+def test_video_prediction_tracking(expected_results, test_input, video_number):
+    """Execute the integration test: performing the tracking of test_input,
+    whose output is to compare with expected_results
+
+    Args:
+        expected_result (DataFrame): the dataframe of the results expected to match the test result
+        test_input (array): dataframe that is the import of prediction frames in test_input folder
+        video_number (string): string component specific to a video
+
+    Returns:
+        Test assertion results
+    """
+    tracker = BYTETracker(track_thresh=0.15, track_buffer=3, match_thresh=0.85, frame_rate=12)
+    # first column of test input is the frame id
+    frame_idx = sorted(np.unique(test_input[:, 0]))
+    test_results = []
+
+    for frame_id in frame_idx:
+        detections = test_input[test_input[:, 0] == frame_id][:, 1:]
+        tracked_objects = tracker.update(detections, frame_id)
+        if len(tracked_objects) > 0:
+            tracked_objects = np.insert(
+                tracked_objects, 0, np.full(len(tracked_objects), frame_id), axis=1
+            )
+            test_results.append(tracked_objects)
+
+    if test_results:  # Ensure test_results is not empty to avoid errors
+        combined_array = np.concatenate(test_results)
+    else:
+        combined_array = np.array([])  # Or handle the empty case as needed
+
+    output_file_path = OUTPUT_FOLDER / f"{video_number}_test_results.txt"
+    np.savetxt(output_file_path, combined_array, delimiter=" ", fmt="%s")
+
+    assert np.allclose(expected_results, combined_array)
+
+    # Remove the file if the test is successful
+    output_file_path.unlink()