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from PIL import Image | |
import torch | |
import numpy as np | |
import tempfile | |
import os | |
import uuid | |
# function to plot and save an AgentImage | |
def plot_and_save_agent_image(agent_image, save_path=None): | |
# Convert AgentImage to a raw PIL Image | |
pil_image = agent_image.to_raw() | |
# Plot the image using PIL's show method | |
pil_image.show() | |
# If save_path is provided, save the image | |
if save_path: | |
pil_image.save(save_path) | |
print(f"Image saved to {save_path}") | |
else: | |
print("No save path provided. Image not saved.") | |
def generate_prompts_for_object(object_name): | |
prompts = { | |
"past": f"Show an old version of a {object_name} from its early days.", | |
"present": f"Show a {object_name} with from present with current features/design/technology.", | |
"future": f"Show a futuristic version of a {object_name}, by predicting advanced features and futuristic design." | |
} | |
return prompts | |
# Function to generate the car industry history | |
def generate_object_history(object_name): | |
images = [] | |
# Get prompts for the object | |
prompts = generate_prompts_for_object(object_name) | |
# Generate sequential images and display them | |
for time_period, frame in prompts.items(): | |
print(f"Generating {time_period} frame: {frame}") | |
result = agent.run(frame) # The tool generates the image | |
# Append the image to the list for GIF creation | |
images.append(result.to_raw()) # Ensure we're using raw image for GIF | |
# Save each image with the appropriate name (past, present, future) | |
image_filename = f"{object_name}_{time_period}.png" | |
plot_and_save_agent_image(result, save_path=image_filename) | |
# Create GIF from images | |
gif_path = f"{object_name}_evolution.gif" | |
images[0].save( | |
gif_path, | |
save_all=True, | |
append_images=images[1:], | |
duration=1000, # Duration in milliseconds for each frame | |
loop=0 # Infinite loop | |
) | |
# Return images and GIF path | |
return images, gif_path | |