Super zoom images for video creation

SuperZoom.py

import os
import numpy as np
import scipy.ndimage as nd
import PIL.Image
from scipy.signal import convolve2d

# ------------------------
# Setup
# ------------------------
os.makedirs("framez", exist_ok=True)

# Load original image as float32
img = np.float32(
    PIL.Image.open("/home/jack/Desktop/Images/shakki-aug14/AI_Image_Generation-Shakker.AI_files/00039.jpg").convert("RGB")
)

h, w = img.shape[:2]
num_frames = 1500
zoom_per_frame = 0.001  # small zoom increment

# Light sharpening kernel (ultra gentle)
kernel_sharpening = np.array([[-0.05, -0.05, -0.05],
                              [-0.05,  1.3, -0.05],
                              [-0.05, -0.05, -0.05]])

# ------------------------
# Ultra-light sharpening function
# ------------------------
def sharpen_frame(frame, kernel=kernel_sharpening):
    sharpened = np.zeros_like(frame)
    for c in range(3):
        sharpened[..., c] = convolve2d(frame[..., c], kernel, mode='same', boundary='symm')
    return np.clip(sharpened, 0, 255)

# ------------------------
# Frame generation loop
# ------------------------
for i in range(num_frames):
    # Compute zoom factor relative to original
    zoom_factor = (1 - zoom_per_frame) ** i

    # Affine transform from original image (prevents cumulative blur)
    frame = nd.affine_transform(
        img,
        [zoom_factor, zoom_factor, 1],
        [h * (1 - zoom_factor) / 2, w * (1 - zoom_factor) / 2, 0],
        order=3  # cubic interpolation keeps edges sharp
    )

    # Optional: sharpen every 10 frames
    if i % 10 == 0:
        frame = sharpen_frame(frame)

    # Save frame
    PIL.Image.fromarray(np.uint8(frame)).save(f"framez/{i:05d}.jpg")

to create a video from the images:
ffmpeg -hide_banner -framerate 3 -i %05d.jpg -c:v libx265 -r 30 -pix_fmt yuv420p -y slow-3per-sec-jpgs.mp4