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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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