dev/utils2.py

import numpy as np
import cv2
import numexpr
import re
from PIL import Image, ImageOps

# --- Math & Schedule Parsing ---

def get_inbetweens(key_frames, max_frames, integer=False):
    """Interpolates values between keyframes (simple linear for now, but robust)."""
    key_frames = dict(sorted(key_frames.items()))
    keys = list(key_frames.keys())
    vals = list(key_frames.values())
    
    # Fill array
    series = np.linspace(vals[0], vals[0], max_frames)
    for i in range(len(keys)-1):
        idx_start, idx_end = keys[i], keys[i+1]
        val_start, val_end = vals[i], vals[i+1]
        if idx_end > max_frames: idx_end = max_frames
        
        # Linear interpolation
        range_len = idx_end - idx_start
        if range_len > 0:
            segment = np.linspace(val_start, val_end, range_len, endpoint=False)
            series[idx_start:idx_end] = segment
            
    # Fill tail
    if keys[-1] < max_frames:
        series[keys[-1]:] = vals[-1]
        
    return series.astype(int) if integer else series

def parse_weight_string(string, max_frames):
    """
    Parses '0:(0.5), 10:(1.0)' including Math like '0:(sin(t/10))'.
    Returns a numpy array of float values for every frame.
    """
    # Clean string
    string = re.sub(r'\s+', '', string)
    keyframes = {}
    
    # Split by comma, respecting parentheses might be needed in complex regex, 
    # but simple split usually works for Deforum format
    parts = string.split(',')
    
    for part in parts:
        try:
            frame_str, val_str = part.split(':')
            frame = int(frame_str)
            # Remove parentheses
            val_str = val_str.strip('()')
            keyframes[frame] = val_str
        except:
            continue
            
    if 0 not in keyframes:
        keyframes[0] = "0"
        
    sorted_frames = sorted(keyframes.keys())
    series = np.zeros(max_frames)
    
    # Evaluate math for every frame
    for i in range(len(sorted_frames)):
        f_start = sorted_frames[i]
        f_end = sorted_frames[i+1] if i < len(sorted_frames)-1 else max_frames
        
        formula = keyframes[f_start]
        
        for f in range(f_start, f_end):
            t = f # Deforum standard variable
            try:
                # Safe evaluation environment
                val = numexpr.evaluate(formula, local_dict={'t': t, 's': f_start, 'pi': np.pi, 'sin': np.sin, 'cos': np.cos, 'tan': np.tan})
                series[f] = float(val)
            except Exception as e:
                # If static value or fail
                try:
                    series[f] = float(formula)
                except:
                    series[f] = series[f-1] if f > 0 else 0.0
                    
    return series

# --- Image Processing ---

def maintain_colors(prev_img, color_match_sample):
    """
    Matches the coloring of the previous frame (or frame 0) to prevent color drift.
    Uses LAB color space transfer.
    """
    prev_img_np = np.array(prev_img).astype(np.uint8)
    sample_np = np.array(color_match_sample).astype(np.uint8)
    
    prev_lab = cv2.cvtColor(prev_img_np, cv2.COLOR_RGB2LAB)
    sample_lab = cv2.cvtColor(sample_np, cv2.COLOR_RGB2LAB)
    
    l_avg_p, a_avg_p, b_avg_p = np.mean(prev_lab[:,:,0]), np.mean(prev_lab[:,:,1]), np.mean(prev_lab[:,:,2])
    l_avg_s, a_avg_s, b_avg_s = np.mean(sample_lab[:,:,0]), np.mean(sample_lab[:,:,1]), np.mean(sample_lab[:,:,2])
    
    l, a, b = cv2.split(prev_lab)
    
    # Shift current image logic towards sample mean
    # Note: Deforum usually matches the NEW generation to the OLD image.
    # Here we are adjusting the image we just warped (prev) to match the original anchor? 
    # Actually, standard Deforum 'Match Frame 0' means we force the init image to look like Frame 0 colors.
    
    l = l - l_avg_p + l_avg_s
    a = a - a_avg_p + a_avg_s
    b = b - b_avg_p + b_avg_s
    
    l = np.clip(l, 0, 255)
    a = np.clip(a, 0, 255)
    b = np.clip(b, 0, 255)
    
    matched_lab = cv2.merge([l.astype(np.uint8), a.astype(np.uint8), b.astype(np.uint8)])
    return Image.fromarray(cv2.cvtColor(matched_lab, cv2.COLOR_LAB2RGB))

def add_noise(img, noise_amt):
    """Adds uniform noise to the image to give the diffusion model texture to latch onto."""
    if noise_amt <= 0: return img
    
    img_np = np.array(img).astype(np.float32)
    noise = np.random.normal(0, noise_amt * 255, img_np.shape).astype(np.float32)
    noisy_img = np.clip(img_np + noise, 0, 255).astype(np.uint8)
    return Image.fromarray(noisy_img)

def anim_frame_warp_2d(prev_img_pil, args_dict):
    """
    Standard Deforum 2D Warping.
    args_dict must contain: angle, zoom, translation_x, translation_y
    """
    cv2_img = np.array(prev_img_pil)
    height, width = cv2_img.shape[:2]
    
    center = (width // 2, height // 2)
    
    # Rotation & Zoom
    angle = args_dict.get('angle', 0)
    zoom = args_dict.get('zoom', 1.0)
    
    trans_mat = cv2.getRotationMatrix2D(center, angle, zoom)
    
    # Translation
    tx = args_dict.get('translation_x', 0)
    ty = args_dict.get('translation_y', 0)
    trans_mat[0, 2] += tx
    trans_mat[1, 2] += ty
    
    # Warp with Reflection to handle edges naturally
    warped = cv2.warpAffine(
        cv2_img, 
        trans_mat, 
        (width, height), 
        borderMode=cv2.BORDER_REFLECT_101
    )
    
    return Image.fromarray(warped)