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excel_processing.py
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| """ | |||
| Abhängigkeiten: | |||
| - pyramids (für den Aufbau der Bildpyramiden) | |||
| - heartrate (zur Berechnung der Herzfrequenz) | |||
| - preprocessing (für die Video-Vorverarbeitung) | |||
| - eulerian (für die Euler'sche Video-Magnifikation) | |||
| - tkinter und constants (für die GUI und Konstantenverwaltung) | |||
| Autor: Roberto Gelsinger | |||
| Datum: 07.12.2023 | |||
| Version: Modulversion | |||
| """ | |||
| import pyramids | |||
| import heartrate | |||
| import facedetection | |||
| import eulerian | |||
| from constants import freq_max, freq_min | |||
| import pandas as pd | |||
| from excel_update import color_cells_based_on_deviation | |||
| from excel_evaluation import evaluation | |||
| def process_video_for_excel(selected_video_name): | |||
| """ | |||
| Verarbeitet ein ausgewähltes Video, um die Herzfrequenz der abgebildeten Person zu ermitteln. | |||
| Dieser Prozess umfasst die Vorverarbeitung des Videos, den Aufbau einer Laplace-Pyramide, | |||
| die Anwendung von FFT-Filterung und Euler'scher Magnifikation, und schließlich die Berechnung | |||
| der Herzfrequenz aus den Video-Daten. | |||
| Args: | |||
| selected_video_name (str): Der Name des zu verarbeitenden Videos. | |||
| Returns: | |||
| None: Die Funktion gibt direkt die berechnete Herzfrequenz auf der Konsole aus. | |||
| """ | |||
| print("Reading + preprocessing video...") | |||
| video_frames, frame_ct, fps = facedetection.read_video("code/videos/"+selected_video_name) | |||
| print("Building Laplacian video pyramid...") | |||
| lap_video = pyramids.build_video_pyramid(video_frames) | |||
| print(len(lap_video)) | |||
| for i, video in enumerate(lap_video): | |||
| print("test") | |||
| if i == 0 or i == len(lap_video)-1: | |||
| continue | |||
| print("Running FFT and Eulerian magnification...") | |||
| result, fft, frequencies = eulerian.fft_filter(video, freq_min, freq_max, fps) | |||
| lap_video[i] += result | |||
| print("Calculating heart rate...") | |||
| heart_rate = heartrate.find_heart_rate(fft, frequencies, freq_min, freq_max) | |||
| print("Heart rate: ", heart_rate*0.7, "bpm") | |||
| return heart_rate *0.7 | |||
| def process_all_videos_and_save_results(testcase_excel_file_path, testruns_excel_file_path, code_version, kommentar): | |||
| try: | |||
| df_testruns = pd.read_excel(testruns_excel_file_path) | |||
| except FileNotFoundError: | |||
| df_testruns = pd.DataFrame() | |||
| df_testcases = pd.read_excel(testcase_excel_file_path) | |||
| existing_testcases = [col for col in df_testruns.columns if col.startswith('Testcase_')] | |||
| new_testcases = [f'Testcase_{tc}' for tc in df_testcases['Testcase'] if f'Testcase_{tc}' not in existing_testcases] | |||
| if df_testruns.empty: | |||
| df_testruns = pd.DataFrame(columns=['Testnummer', 'Codeversion', 'Kommentar', 'Abweichung']) | |||
| for col in new_testcases: | |||
| df_testruns[col] = None | |||
| df_testruns.to_excel(testruns_excel_file_path, index=False) | |||
| if new_testcases: | |||
| print(f"Folgende neue Testcases wurden hinzugefügt: {new_testcases}") | |||
| else: | |||
| print("Keine neuen Testcases zum Hinzufügen gefunden.") | |||
| next_testcase_index = len(df_testruns) + 1 | |||
| new_run = { | |||
| 'Testnummer': next_testcase_index, | |||
| 'Codeversion': code_version, | |||
| 'Kommentar': kommentar, | |||
| 'Abweichung': 'Wert_für_Abweichung' | |||
| } | |||
| for index, row in df_testcases.iterrows(): | |||
| video_name = row['VideoName'] | |||
| heart_rate = process_video_for_excel(video_name) | |||
| testcase_column_name = f'Testcase_{row["Testcase"]}' | |||
| new_run[testcase_column_name] = heart_rate | |||
| try: | |||
| df_testruns = df_testruns._append(new_run, ignore_index=True) | |||
| except TypeError: | |||
| pass | |||
| df_testruns.to_excel(testruns_excel_file_path, index=False) | |||
| print("Testrun wurde verarbeitet und das Ergebnis in der Testruns-Excel-Datei gespeichert.") | |||
| color_cells_based_on_deviation(testruns_excel_file_path, testcase_excel_file_path) | |||
| print("Zellen gefärbt") | |||
| evaluation(testcase_excel_file_path, testruns_excel_file_path) | |||
| print("Testcases sortiert") | |||
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