I'm wanting the image to be separated in tiny pieces and then elegantly morph into the message text on file. The gif should begin with the image.png for a few seconds and then beautifully the image is separated in tiny pieces and them tiny pieces move around the screen to form the message text and after it's completed, it holds message text on screen for 4 to 5 seconds and the starts at the beginning of the gif. This is for advertising for businesses. What can you do to help with this script? import os
import numpy as np
from PIL import Image, ImageDraw, ImageFont
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import logging
import time
import argparse

# Configure logging
logging.basicConfig(
   filename='process.log',
   level=logging.DEBUG,
   format='%(asctime)s - %(levelname)s - %(message)s'
)

def log_and_print(message, level='info'):
   """Log and print messages."""
   if level == 'info':
       logging.info(message)
   elif level == 'debug':
       logging.debug(message)
   elif level == 'error':
       logging.error(message)
   print(message)

def resize_image(image_path, new_size=(256, 256)):
   """Resize the image to a specified size."""
   try:
       img = Image.open(image_path)
       # Handle PIL versions for resampling
       try:
           resample_method = Image.Resampling.LANCZOS
       except AttributeError:
           resample_method = Image.LANCZOS  # For older PIL versions
       img = img.resize(new_size, resample=resample_method)
       resized_dir = 'resized_images'
       os.makedirs(resized_dir, exist_ok=True)
       base_name = os.path.splitext(os.path.basename(image_path))[0]
       resized_path = os.path.join(resized_dir, f"{base_name}_{new_size[0]}x{new_size[1]}.png")
       img.save(resized_path)
       log_and_print(f"Image resized and saved to {resized_path}")
       return resized_path
   except Exception as e:
       log_and_print(f"Error resizing image: {e}", 'error')
       return None

def normalize_data(data):
   """Standardize the data to have mean 0 and variance 1."""
   from sklearn.preprocessing import StandardScaler
   scaler = StandardScaler()
   return scaler.fit_transform(data)

def reduce_dimensionality(data, n_components=50):
   """Reduce dimensionality using PCA."""
   pca = PCA(n_components=n_components, random_state=0)
   return pca.fit_transform(data)

def process_image(image_path):
   """Process the image and return t-SNE transformed data."""
   resized_image_path = resize_image(image_path)
   if not resized_image_path:
       return None, None

   try:
       log_and_print(f"Processing image: {resized_image_path}")
      
       # Read image
       img = Image.open(resized_image_path).convert('RGB')
       img_array = np.array(img)
       height, width = img_array.shape[0], img_array.shape[1]
      
       # Flatten image
       img_flattened = img_array.reshape(-1, 3)
      
       # Normalize the data
       img_flattened_normalized = normalize_data(img_flattened)
       log_and_print(f"Normalized Data Statistics - Min: {img_flattened_normalized.min()}, "
                    f"Max: {img_flattened_normalized.max()}, "
                    f"Mean: {img_flattened_normalized.mean():.2f}, "
                    f"Std: {img_flattened_normalized.std():.2f}")
      
       # Apply PCA
       img_pca = reduce_dimensionality(img_flattened_normalized, n_components=50)
       log_and_print("PCA reduced dimensionality before t-SNE.")
      
       # Apply t-SNE
       log_and_print("Applying t-SNE...")
       start_time = time.time()
       tsne = TSNE(n_components=2, random_state=0, verbose=1, perplexity=20)
       img_tsne = tsne.fit_transform(img_pca)
       end_time = time.time()
       log_and_print(f"t-SNE computation time: {end_time - start_time:.2f} seconds")
      
       # Normalize t-SNE output for visualization
       tsne_min = np.min(img_tsne, axis=0)
       tsne_max = np.max(img_tsne, axis=0)
       img_tsne_normalized = (img_tsne - tsne_min) / (tsne_max - tsne_min)
       img_tsne_normalized *= 255
       img_tsne_normalized = img_tsne_normalized.astype(np.uint8)
      
       # Map to RGB by adding a third channel
       third_channel = np.full((img_tsne_normalized.shape[0], 1), 128, dtype=np.uint8)
       img_tsne_rgb = np.hstack((img_tsne_normalized, third_channel))
      
       # Reshape to image dimensions
       img_tsne_reshaped = img_tsne_rgb.reshape(height, width, 3)
      
       log_and_print("Image processing and t-SNE transformation completed.")
      
       return img_array, img_tsne_reshaped
   except Exception as e:
       log_and_print(f"Error processing image: {e}", 'error')
       return None, None

def read_text_from_file(file_path, image_size):
   """Read text message from a file and render it as an image."""
   try:
       with open(file_path, 'r') as file:
           text_message = file.read().strip()
       log_and_print(f"Text message read from {file_path}: {text_message}")
      
       # Create a blank image with white background
       img_text = Image.new('RGB', image_size, color=(255, 255, 255))
       draw = ImageDraw.Draw(img_text)
      
       # Choose a font size based on image size
       font_size = min(image_size) // 5
       try:
           # Attempt to use a truetype font
           font = ImageFont.truetype("arial.ttf", font_size)
       except IOError:
           # Fallback to default font
           font = ImageFont.load_default()
           log_and_print("Default font loaded. Truetype font not found.", 'error')
      
       # Get text size
       text_width, text_height = draw.textsize(text_message, font=font)
      
       # Calculate position to center the text
       position = ((image_size[0]-text_width)//2, (image_size[1]-text_height)//2)
      
       # Draw the text
       draw.text(position, text_message, fill=(0, 0, 0), font=font)
      
       log_and_print("Text image created successfully.")
       return np.array(img_text)
   except Exception as e:
       log_and_print(f"Error reading or rendering text: {e}", 'error')
       return None

def create_animation(original_img, transformed_img, text_duration=5, morph_duration=3, fps=30):
   """
   Create an animation that morphs the original image into the transformed image (text).
  
   Parameters:
   - original_img: numpy array of the original image.
   - transformed_img: numpy array of the transformed image (text).
   - text_duration: seconds to hold the transformed text.
   - morph_duration: seconds to perform the morph.
   - fps: frames per second for the animation.
  
   Returns:
   - anim: Matplotlib animation object.
   """
   try:
       fig, ax = plt.subplots()
       plt.axis('off')
      
       # Display the original image
       img_display = ax.imshow(original_img, animated=True)
      
       # Number of frames for morphing and holding
       morph_frames = morph_duration * fps
       hold_frames = text_duration * fps
       total_frames = morph_frames + hold_frames
      
       # Precompute the frame data
       frames = []
       for i in range(total_frames):
           if i < morph_frames:
               alpha = i / morph_frames
               # Linear interpolation between original and transformed image
               frame = (1 - alpha) * original_img + alpha * transformed_img
               frame = frame.astype(np.uint8)
               frames.append(frame)
           else:
               frames.append(transformed_img.astype(np.uint8))
      
       def update(frame):
           img_display.set_array(frame)
           return [img_display]
      
       anim = animation.FuncAnimation(
           fig,
           update,
           frames=frames,
           interval=1000/fps,
           blit=True,
           repeat=True
       )
      
       log_and_print("Animation created successfully.")
       return anim
   except Exception as e:
       log_and_print(f"Error creating animation: {e}", 'error')
       return None

def main():
   # Argument parsing for flexibility
   parser = argparse.ArgumentParser(description='Image to Text Morphing Animation Script.')
   parser.add_argument('image_path', type=str, help='Path to the input image.')
   parser.add_argument('--text_file', type=str, default='text_message.txt',
                       help='Path to the text file containing the desired message.')
   parser.add_argument('--hold_duration', type=int, default=5,
                       help='Seconds to hold the transformed text.')
   parser.add_argument('--morph_duration', type=int, default=3,
                       help='Seconds to perform the morphing animation.')
   parser.add_argument('--fps', type=int, default=30,
                       help='Frames per second for the animation.')
   args = parser.parse_args()

   image_path = args.image_path
   text_file_path = args.text_file
   hold_duration = args.hold_duration
   morph_duration = args.morph_duration
   fps = args.fps

   text_message = read_text_from_file(text_file_path, image_size=(256, 256))
   if text_message is None:
       log_and_print("Failed to read or render text. Exiting.", 'error')
       return

   if not os.path.isfile(image_path):
       log_and_print(f"Image file does not exist: {image_path}", 'error')
       return

   log_and_print(f"Starting process with image: {image_path}")
   log_and_print(f"Text message: {text_message}")
   log_and_print(f"Hold Duration: {hold_duration} seconds")
   log_and_print(f"Morph Duration: {morph_duration} seconds")
   log_and_print(f"Frames Per Second: {fps}")

   original_img, transformed_img = process_image(image_path)
   if original_img is None or transformed_img is None:
       log_and_print("Image processing failed. Exiting.", 'error')
       return

   # Render the text into an image
   text_img_array = read_text_from_file(text_file_path, image_size=original_img.shape[:2][::-1])
   if text_img_array is None:
       log_and_print("Failed to create text image. Exiting.", 'error')
       return

   # Ensure both images have the same dimensions
   if original_img.shape != text_img_array.shape:
       log_and_print("Original and text images have different sizes. Resizing text image.", 'error')
       text_img = Image.fromarray(text_img_array).resize(original_img.shape[1::-1], Image.ANTIALIAS)
       text_img_array = np.array(text_img)

   # Create the animation
   anim = create_animation(original_img, text_img_array, text_duration=hold_duration,
                           morph_duration=morph_duration, fps=fps)
   if anim is None:
       log_and_print("Failed to create animation. Exiting.", 'error')
       return

   # Save the animation as a GIF
   transformed_dir = 'animations'
   os.makedirs(transformed_dir, exist_ok=True)
   gif_path = os.path.join(transformed_dir, 'image_to_text_morph.gif')
   try:
       anim.save(gif_path, writer='pillow', fps=fps)
       log_and_print(f"Animation saved as GIF to {gif_path}")
   except Exception as e:
       log_and_print(f"Error saving animation: {e}", 'error')
       return

   # Optionally, display the animation
   plt.show()

if name == "main":
   main()