Fri 29 Nov 2024 04:30:50 PM CET

src/SimNDT/run_setup/SimulationVideo.py

@@ -0,0 +1,52 @@
+import numpy as np
+import os
+import cv2
+from datetime import datetime
+
+def create_vector_field_video(folder_path, video_path):
+    """
+    Reads all .npy files from the specified folder, separates Vx and Vy arrays,
+    calculates the net vector field V, and creates a video using the jet colormap.
+
+    Parameters:
+    folder_path (str): Path to the folder containing .npy files.
+    video_path (str): Path to save the output video.
+    """
+    # Get list of all .npy files in the folder
+    file_list = [f for f in os.listdir(folder_path) if f.endswith('.npy')]
+    file_list.sort()  # Ensure files are processed in order
+
+    # Initialize lists to hold Vx and Vy arrays
+    Vx_list = []
+    Vy_list = []
+
+    # Load each file and separate Vx and Vy
+    for file_name in file_list:
+        file_path = os.path.join(folder_path, file_name)
+        data = np.load(file_path)
+        Vx_list.append(data[0])
+        Vy_list.append(data[1])
+
+    # Convert lists to numpy arrays
+    Vx_array = np.array(Vx_list)
+    Vy_array = np.array(Vy_list)
+
+    # Calculate the net vector V
+    V = np.sqrt(Vx_array**2 + Vy_array**2)
+
+    # Create a video from the net vector V
+    height, width = V.shape[1], V.shape[2]
+    current_time = datetime.now().strftime("%Y%m%d_%H%M%S")
+    video_filename = 'output_video_{current_time}.avi'.format(current_time=current_time)
+    video_path = os.path.join(video_path, video_filename)
+    fourcc = cv2.VideoWriter_fourcc(*'XVID')
+    video = cv2.VideoWriter(video_path, fourcc, 1, (width, height), isColor=True)
+
+    for i in range(V.shape[0]):
+        normalized_frame = (V[i] * 255 / np.max(V[i])).astype(np.uint8)  # Normalize and convert to uint8
+        colored_frame = cv2.applyColorMap(normalized_frame, cv2.COLORMAP_JET)  # Apply jet colormap
+        video.write(colored_frame)
+
+    video.release()
+    print("Video saved at:", video_path)
+