import cv2 import numpy as np import os import glob import pandas as pd import re from PIL import Image from datetime import datetime # --- PFAD ANPASSEN --- PFAD = r"" def natural_sort_key(s): return [int(text) if text.isdigit() else text.lower() for text in re.split('([0-9]+)', s)] def bild_laden(pfad): try: stream = np.fromfile(pfad, dtype=np.uint8) img = cv2.imdecode(stream, cv2.IMREAD_COLOR) return img except Exception as e: print(f"Fehler beim Laden: {e}") return None # --- NEU: ZEITSTEMPEL AUSLESEN --- def get_timestamp(path): # Versucht, das Aufnahmedatum zu finden try: img = Image.open(path) dt_obj = None # 1. Versuche Standard EXIF (JPG/TIFF) # Tags: 36867 (DateTimeOriginal), 306 (DateTime) exif = getattr(img, '_getexif', lambda: None)() if exif: dt_str = exif.get(36867) or exif.get(306) if dt_str: return datetime.strptime(str(dt_str), "%Y:%m:%d %H:%M:%S") # 2. Versuche TIFF-Tags (falls EXIF leer) if hasattr(img, 'tag_v2'): dt_str = img.tag_v2.get(306) if dt_str: return datetime.strptime(str(dt_str), "%Y:%m:%d %H:%M:%S") except Exception: pass # 3. Notlösung: Dateisystem-Zeit (Änderungsdatum) try: return datetime.fromtimestamp(os.path.getmtime(path)) except: return None def smart_ellipse_final_timed(): input_folder = PFAD if not os.path.exists(input_folder): print("Pfad nicht gefunden!") return # 1. Ordner suchen & Sortieren entries = [f.path for f in os.scandir(input_folder) if f.is_dir()] subfolders = [] for f in entries: if os.path.basename(f).isdigit(): subfolders.append(f) subfolders.sort(key=lambda f: natural_sort_key(os.path.basename(f))) if not subfolders: print("Keine Ordner 1..10 gefunden!") return print(f"Ordner-Reihenfolge: {[os.path.basename(f) for f in subfolders]}") # 2. Bilder sammeln all_files = [] for sf in subfolders: unique_files = set() patterns = ["*.jpg", "*.JPG", "*.tif", "*.tiff", "*.TIF"] for p in patterns: unique_files.update(glob.glob(os.path.join(sf, p))) imgs = sorted(list(unique_files)) imgs.sort(key=lambda f: natural_sort_key(os.path.basename(f))) for img_path in imgs: all_files.append((img_path, os.path.basename(sf))) if not all_files: print("Keine Bilder gefunden.") return print(f"{len(all_files)} Bilder geladen. Ermittle Startzeit...") # --- STARTZEIT FESTLEGEN (t0) --- start_time = get_timestamp(all_files[0][0]) if start_time is None: print("WARNUNG: Kein Zeitstempel im ersten Bild gefunden! Zeit wird 0 sein.") start_time = datetime.now() # Fallback, damit Code nicht crasht print(f"Startzeit (t0) ist: {start_time}") # --- FENSTER --- cv2.namedWindow("Analyse", cv2.WINDOW_NORMAL) cv2.resizeWindow("Analyse", 1000, 800) def nichts(x): pass cv2.createTrackbar("Threshold", "Analyse", 120, 255, nichts) cv2.createTrackbar("Invertieren", "Analyse", 1, 1, nichts) cv2.createTrackbar("Stab-Winkel", "Analyse", 60, 160, nichts) # --- ROI AUSWAHL --- print("Lade erstes Bild für Auswahl...") first_img = bild_laden(all_files[0][0]) h_orig, w_orig = first_img.shape[:2] screen_h = 800 scale = screen_h / h_orig if h_orig > screen_h else 1.0 img_select = cv2.resize(first_img, None, fx=scale, fy=scale) roi_small = cv2.selectROI("Analyse", img_select, showCrosshair=True) x_fixed = int(roi_small[0] / scale) y_fixed = int(roi_small[1] / scale) w_fixed = int(roi_small[2] / scale) h_fixed = int(roi_small[3] / scale) if w_fixed == 0 or h_fixed == 0: return ergebnisse = [] # --- HAUPTSCHLEIFE --- for i, (file_path, folder_name) in enumerate(all_files): filename = os.path.basename(file_path) img_raw = bild_laden(file_path) if img_raw is None: continue # ZEIT BERECHNEN current_time = get_timestamp(file_path) time_diff = 0 if current_time and start_time: delta = current_time - start_time time_diff = delta.total_seconds() # ROI Crop y = int(max(0, y_fixed)) x = int(max(0, x_fixed)) h = int(min(h_fixed, h_orig - y)) w = int(min(w_fixed, w_orig - x)) crop = img_raw[y:y+h, x:x+w].copy() saved = False while True: th_val = cv2.getTrackbarPos("Threshold", "Analyse") inv_val = cv2.getTrackbarPos("Invertieren", "Analyse") angle_cut = cv2.getTrackbarPos("Stab-Winkel", "Analyse") gray = cv2.cvtColor(crop, cv2.COLOR_BGR2GRAY) blurred = cv2.GaussianBlur(gray, (5, 5), 0) thresh_mode = cv2.THRESH_BINARY_INV if inv_val == 1 else cv2.THRESH_BINARY _, mask = cv2.threshold(blurred, th_val, 255, thresh_mode) mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, np.ones((3,3),np.uint8), iterations=2) contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) vis_img = crop.copy() d_ellipse_min = 0 # Das ist der Durchmesser! if contours: c = max(contours, key=cv2.contourArea) M = cv2.moments(c) if M["m00"] > 0: cX = int(M["m10"] / M["m00"]) cY = int(M["m01"] / M["m00"]) pts = c.squeeze() if pts.ndim == 1: pts = pts.reshape(-1, 2) # Winkel Filter angles_rad = np.arctan2(pts[:, 1] - cY, pts[:, 0] - cX) angles_deg = np.degrees(angles_rad) diff = np.abs(angles_deg - (-90)) diff = np.minimum(diff, 360 - diff) valid_mask = diff > (angle_cut / 2.0) valid_pts = pts[valid_mask] invalid_pts = pts[~valid_mask] # Zeichnen for p in valid_pts[::3]: vis_img[p[1], p[0]] = (255, 0, 0) for p in invalid_pts[::3]: vis_img[p[1], p[0]] = (0, 0, 255) if len(valid_pts) > 10: try: ellipse = cv2.fitEllipse(valid_pts) cv2.ellipse(vis_img, ellipse, (0, 255, 0), 1) (ex, ey), (MA, ma), angle = ellipse d_ellipse_min = min(MA, ma) # Breite = Durchmesser cv2.drawMarker(vis_img, (int(ex), int(ey)), (0, 255, 0), cv2.MARKER_CROSS, 10, 1) except: pass info_txt = f"{folder_name}/{filename} | t={time_diff:.0f}s" cv2.putText(vis_img, info_txt, (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1) cv2.putText(vis_img, f"D: {d_ellipse_min:.2f} px", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2) cv2.imshow("Analyse", vis_img) k = cv2.waitKey(20) & 0xFF if k == 27: cv2.destroyAllWindows() if ergebnisse: pd.DataFrame(ergebnisse).to_excel(os.path.join(input_folder, "Abbruch_Daten.xlsx")) return elif k == 32: ergebnisse.append({ "Ordner": folder_name, "Dateiname": filename, "Zeit_seit_Start_sek": int(time_diff), # Neue Spalte "Durchmesser_Kugel_px": round(d_ellipse_min, 2), # Neue Spalte (klarer Name) "Stab_Winkel": angle_cut }) print(f"[{folder_name}] {filename} (t={int(time_diff)}s) -> {d_ellipse_min:.2f}") saved = True break if not saved: break cv2.destroyAllWindows() df = pd.DataFrame(ergebnisse) out = os.path.join(input_folder, "Ergebnisse_Final_Zeit.xlsx") df.to_excel(out, index=False) print(f"FERTIG! Daten in: {out}") if __name__ == "__main__": smart_ellipse_final_timed()