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import os
import cv2
import torch
import argparse
from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
from diffusers.utils import load_image
import numpy as np
from PIL import Image
def split_video_into_frames(video_path, frames_dir):
if not os.path.exists(frames_dir):
os.makedirs(frames_dir)
print("splitting video")
vidcap = cv2.VideoCapture(video_path)
success, image = vidcap.read()
count = 0
while success:
frame_path = os.path.join(frames_dir, f"frame{count:04d}.png")
cv2.imwrite(frame_path, image)
success, image = vidcap.read()
count += 1
def frame_number(frame_filename):
# Extract the frame number from the filename and convert it to an integer
return int(frame_filename[5:-4])
def count_frame_images(frames_dir):
# Count the number of frame images in the directory
frame_files = [f for f in os.listdir(frames_dir) if f.startswith('frame') and f.endswith('.png')]
return len(frame_files)
# Argument parser
parser = argparse.ArgumentParser(description='Generate images based on video frames.')
parser.add_argument('--prompt', default='a woman', help='the stable diffusion prompt')
parser.add_argument('--video_path', default='./None.mp4', help='Path to the input video file.')
parser.add_argument('--frames_dir', default='./frames', help='Directory to save the extracted video frames.')
parser.add_argument('--output_frames_dir', default='./output_frames', help='Directory to save the generated images.')
parser.add_argument('--init_image_path', default=None, help='Path to the initial conditioning image.')
args = parser.parse_args()
video_path = args.video_path
frames_dir = args.frames_dir
output_frames_dir = args.output_frames_dir
init_image_path = args.init_image_path
prompt = args.prompt
# If frame images do not already exist, split video into frames
if count_frame_images(frames_dir) == 0:
split_video_into_frames(video_path, frames_dir)
# Create output frames directory if it doesn't exist
if not os.path.exists(output_frames_dir):
os.makedirs(output_frames_dir)
# Load the initial conditioning image, if provided
if init_image_path:
print(f"using image {init_image_path}")
last_generated_image = load_image(init_image_path)
else:
initial_frame_path = os.path.join(frames_dir, "frame0000.png")
last_generated_image = load_image(initial_frame_path)
# ... (rest of the script remains unchanged)
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