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import numpy as np
import cv2
import numexpr
import re
import torch
from PIL import Image
def parse_weight_schedule(string, max_frames):
string = re.sub(r'\s+', '', str(string))
keyframes = {}
parts = string.split(',')
for part in parts:
try:
if ':' not in part: continue
f_str, v_str = part.split(':', 1)
keyframes[int(f_str)] = v_str.strip('()')
except: continue
if 0 not in keyframes: keyframes[0] = "0"
series = np.zeros(int(max_frames))
sorted_keys = sorted(keyframes.keys())
for i in range(len(sorted_keys)):
f_start = sorted_keys[i]
f_end = sorted_keys[i+1] if i < len(sorted_keys)-1 else int(max_frames)
formula = keyframes[f_start]
for f in range(f_start, f_end):
t = f
try:
val = numexpr.evaluate(formula, local_dict={'t':t, 'pi':np.pi, 'sin':np.sin, 'cos':np.cos})
series[f] = float(val)
except:
try: series[f] = float(formula)
except: series[f] = series[f-1] if f > 0 else 0.0
return series
def get_border_mode(mode_str):
return {'Reflect': cv2.BORDER_REFLECT_101, 'Replicate': cv2.BORDER_REPLICATE, 'Wrap': cv2.BORDER_WRAP, 'Black': cv2.BORDER_CONSTANT}.get(mode_str, cv2.BORDER_REFLECT_101)
def maintain_colors(image, anchor, mode='LAB'):
if mode == 'None' or anchor is None: return image
img_np = np.array(image).astype(np.uint8)
anc_np = np.array(anchor).astype(np.uint8)
if mode == 'LAB':
img_cvt = cv2.cvtColor(img_np, cv2.COLOR_RGB2LAB)
anc_cvt = cv2.cvtColor(anc_np, cv2.COLOR_RGB2LAB)
for i in range(3):
img_cvt[:,:,i] = np.clip(img_cvt[:,:,i] - img_cvt[:,:,i].mean() + anc_cvt[:,:,i].mean(), 0, 255)
return Image.fromarray(cv2.cvtColor(img_cvt, cv2.COLOR_LAB2RGB))
elif mode == 'HSV':
img_cvt = cv2.cvtColor(img_np, cv2.COLOR_RGB2HSV)
anc_cvt = cv2.cvtColor(anc_np, cv2.COLOR_RGB2HSV)
for i in [1, 2]:
img_cvt[:,:,i] = np.clip(img_cvt[:,:,i] - img_cvt[:,:,i].mean() + anc_cvt[:,:,i].mean(), 0, 255)
return Image.fromarray(cv2.cvtColor(img_cvt, cv2.COLOR_HSV2RGB))
return image
def anim_frame_warp_2d(prev_img, args, border_mode):
if prev_img is None: return None
cv_img = np.array(prev_img)
h, w = cv_img.shape[:2]
center = (w // 2, h // 2)
mat = cv2.getRotationMatrix2D(center, args.get('angle',0), args.get('zoom',1))
mat[0, 2] += args.get('tx',0); mat[1, 2] += args.get('ty',0)
return Image.fromarray(cv2.warpAffine(cv_img, mat, (w, h), borderMode=get_border_mode(border_mode)))
def add_noise(img, noise_amt):
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)
return Image.fromarray(np.clip(img_np + noise, 0, 255).astype(np.uint8)) |