217 lines
8.1 KiB
Python
217 lines
8.1 KiB
Python
import numpy as np
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import time
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import math
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import os
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from PIL import Image, ImageDraw
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import random
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# --- Hilfsfunktionen ---
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def normalize(v):
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""" Normalisiert einen Vektor (macht seine Länge zu 1). """
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norm = np.linalg.norm(v)
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if norm < 1e-9:
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return np.zeros_like(v)
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return v / norm
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def look_at_matrix(eye, target, up):
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"""
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Erzeugt eine View-Matrix, die Weltkoordinaten in Kamerakoordinaten transformiert.
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:param eye: Position der Kamera (np.array).
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:param target: Punkt, auf den die Kamera schaut (np.array).
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:param up: Up-Vektor der Kamera (zeigt, wo "oben" für die Kamera ist, np.array).
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:return: 4x4 View-Matrix (NumPy-Array).
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"""
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if np.linalg.norm(eye - target) < 1e-6:
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target = eye - np.array([0.0, 0.0, 1.0]) # Standard-Blickrichtung bei Kollision
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forward = normalize(eye - target)
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up_normalized = normalize(up)
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if abs(np.dot(up_normalized, forward)) > 0.999:
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if abs(forward[1]) < 0.99:
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temp_up = np.array([0.0, 1.0, 0.0])
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else:
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temp_up = np.array([0.0, 0.0, -1.0 if forward[1] > 0 else 1.0])
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right = normalize(np.cross(temp_up, forward))
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else:
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right = normalize(np.cross(up_normalized, forward))
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camera_up = normalize(np.cross(forward, right))
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rotation = np.identity(4)
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rotation[0, 0:3] = right
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rotation[1, 0:3] = camera_up
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rotation[2, 0:3] = forward
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translation = np.identity(4)
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translation[0:3, 3] = -eye
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view_mat = np.dot(rotation, translation)
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return view_mat
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def perspective_projection(point_cam_space, fov_deg, aspect_ratio, near, far):
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"""
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Projiziert einen Punkt aus dem Kamera-Koordinatenraum auf eine 2D-Ebene.
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Gibt normalisierte Gerätekoordinaten (NDC) zurück (-1 bis +1).
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"""
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if point_cam_space[2] > -near or point_cam_space[2] < -far:
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return None
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if abs(point_cam_space[2]) < 1e-9:
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return None
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f = 1.0 / math.tan(math.radians(fov_deg) / 2.0)
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x_ndc = (f / aspect_ratio) * point_cam_space[0] / -point_cam_space[2]
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y_ndc = f * point_cam_space[1] / -point_cam_space[2]
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if not (-1 <= x_ndc <= 1 and -1 <= y_ndc <= 1):
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return None
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return np.array([x_ndc, y_ndc])
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def ndc_to_pixel(ndc_coords, width, height):
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""" Wandelt NDC-Koordinaten (-1 bis 1) in Pixelkoordinaten (0 bis width/height) um. """
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pixel_x = (ndc_coords[0] + 1.0) / 2.0 * width
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pixel_y = (1.0 - ndc_coords[1]) / 2.0 * height # Y invertieren
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px = int(round(pixel_x))
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py = int(round(pixel_y))
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px = max(0, min(width - 1, px))
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py = max(0, min(height - 1, py))
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return px, py
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# --- Simulationsparameter ---
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object_start_pos = np.array([0.0, 0.0, 10.0])
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# --- NEU: Fixer Zielpunkt für die Kameras ---
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# Die Kameras schauen immer auf die ursprüngliche Startposition des Objekts.
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# Alternativ könnte hier jeder andere feste Punkt im Raum definiert werden.
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fixed_camera_target = object_start_pos.copy()
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# Parameter für zufällige Bewegung
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random_step_scale = 0.8
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world_bounds_min = np.array([-10.0, -5.0, 5.0])
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world_bounds_max = np.array([ 10.0, 5.0, 15.0])
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# Kameraeinstellungen
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cameras = [
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{
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"name": "Kamera_1_Frontal",
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"pos": np.array([0.0, 0.0, 0.0]),
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"target": fixed_camera_target, # Verwendet den fixen Zielpunkt
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"up": np.array([0.0, 1.0, 0.0]),
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"fov_deg": 60,
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},
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{
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"name": "Kamera_2_Seitlich_Links",
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"pos": np.array([-15.0, 0.0, 5.0]),
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"target": fixed_camera_target, # Verwendet den fixen Zielpunkt
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"up": np.array([0.0, 1.0, 0.0]),
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"fov_deg": 45,
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},
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{
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"name": "Kamera_3_Von_Oben",
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"pos": np.array([0.0, 15.0, 10.0]),
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"target": fixed_camera_target, # Verwendet den fixen Zielpunkt
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"up": np.array([0.0, 0.0, -1.0]),
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"fov_deg": 70,
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}
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]
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# Clipping-Ebenen
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near_plane = 0.1
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far_plane = 100.0
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simulation_duration = 10
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time_step = 1
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# Bildparameter
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IMG_WIDTH = 320
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IMG_HEIGHT = 180
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OUTPUT_DIR = "./bilder"
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# Ausgabeverzeichnis erstellen
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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print(f"Bilder werden in '{OUTPUT_DIR}' gespeichert.")
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# --- Simulationsschleife ---
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current_object_pos = object_start_pos.copy()
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current_time = 0.0
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print("Starte Simulation...")
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while current_time <= simulation_duration:
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print(f"\n--- Zeitpunkt: {current_time:.1f}s ---")
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# Objektposition zufällig aktualisieren
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delta_x = (random.random() - 0.5) * 2 * random_step_scale
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delta_y = (random.random() - 0.5) * 2 * random_step_scale
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delta_z = (random.random() - 0.5) * 2 * random_step_scale
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random_displacement = np.array([delta_x, delta_y, delta_z])
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current_object_pos += random_displacement
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current_object_pos = np.clip(current_object_pos, world_bounds_min, world_bounds_max)
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print(f"Objekt Welt-Position: ({current_object_pos[0]:.2f}, {current_object_pos[1]:.2f}, {current_object_pos[2]:.2f})")
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# Objektposition in homogene Koordinaten umwandeln
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object_pos_h = np.append(current_object_pos, 1.0)
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# Für jede Kamera die Ansicht berechnen und Bild erzeugen
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for i, cam in enumerate(cameras):
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print(f"\n Kamera {i+1}: {cam['name']}")
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print(f" Position: ({cam['pos'][0]:.2f}, {cam['pos'][1]:.2f}, {cam['pos'][2]:.2f})")
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# --- WICHTIGE ÄNDERUNG: Verwende den *fixen* Zielpunkt ---
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# Die Kamera schaut *nicht* mehr auf current_object_pos, sondern auf cam['target'],
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# welches im Kamera-Dictionary auf fixed_camera_target gesetzt wurde.
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current_target_for_view = cam['target'] # Dies ist jetzt immer fixed_camera_target
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effective_aspect_ratio = IMG_WIDTH / IMG_HEIGHT
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# Ausgabe zeigt den fixen Zielpunkt an
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print(f" Schaut auf (Fixer Target): ({current_target_for_view[0]:.2f}, {current_target_for_view[1]:.2f}, {current_target_for_view[2]:.2f})")
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print(f" Up-Vektor: ({cam['up'][0]:.2f}, {cam['up'][1]:.2f}, {cam['up'][2]:.2f})")
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print(f" FOV: {cam['fov_deg']} Grad, Aspekt (für Projektion): {effective_aspect_ratio:.2f}")
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# View-Matrix mit dem fixen Zielpunkt berechnen
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view_mat = look_at_matrix(cam['pos'], current_target_for_view, cam['up'])
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# Objektposition in Kamera-Koordinaten transformieren
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# Dieser Schritt ist weiterhin nötig, um das *bewegliche* Objekt zu projizieren
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point_in_cam_space_h = np.dot(view_mat, object_pos_h)
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if point_in_cam_space_h[3] <= 1e-9:
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print(" Objekt hinter oder auf der Kameraebene (w <= 0).")
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projected_point_ndc = None
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point_in_cam_space = np.array([np.nan, np.nan, np.nan])
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else:
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point_in_cam_space = point_in_cam_space_h[:3] / point_in_cam_space_h[3]
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print(f" Bewegliches Objekt in Kamera-Koordinaten: ({point_in_cam_space[0]:.2f}, {point_in_cam_space[1]:.2f}, {point_in_cam_space[2]:.2f})") # Klärung der Ausgabe
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# Projiziere das *bewegliche* Objekt in 2D (NDC)
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projected_point_ndc = perspective_projection(
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point_in_cam_space,
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cam['fov_deg'],
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effective_aspect_ratio,
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near_plane,
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far_plane
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)
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# Bild erstellen und speichern
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img = Image.new('RGB', (IMG_WIDTH, IMG_HEIGHT), color='black')
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draw = ImageDraw.Draw(img)
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if projected_point_ndc is not None:
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print(f" Projizierte 2D-Koordinaten des Objekts (NDC): ({projected_point_ndc[0]:.3f}, {projected_point_ndc[1]:.3f})") # Klärung der Ausgabe
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px, py = ndc_to_pixel(projected_point_ndc, IMG_WIDTH, IMG_HEIGHT)
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print(f" Pixelkoordinaten des Objekts (im Bild): ({px}, {py})")
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draw.rectangle([(px-1, py-1), (px+1, py+1)], fill='white', outline='white')
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else:
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print(" Bewegliches Objekt befindet sich ausserhalb des Sichtbereichs dieser Kamera.") # Klärung der Ausgabe
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timestamp_str = f"{current_time:.1f}".replace('.', '_')
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filename = f"{cam['name']}_t_{timestamp_str}.png"
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filepath = os.path.join(OUTPUT_DIR, filename)
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img.save(filepath)
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print(f" Bild gespeichert: {filepath}")
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current_time += time_step
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print("\nSimulation beendet.") |