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import os
import numpy as np
import cv2
import math
from PIL import Image, ImageOps
from skimage.transform import resize
from scipy.misc import imresize
def clamp(n,a,b):
return max(a, min(n, b))
def lerp(n,a,b):
return (b-a)*n+a
def process_image(opt, data_opt, im):
img = im[:, :, ::-1].copy()
processed = False
if data_opt.process_frac == 1.0:
return img
if data_opt.transform != False:
(h, w) = img.shape[:2]
center = (w / 2, h / 2)
M = cv2.getRotationMatrix2D(center, data_opt.rotate, data_opt.scale)
img = cv2.warpAffine(img, M, (w, h))
if data_opt.hsl is True:
processed = True
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv)
cv2.add(h, data_opt.hue, h)
cv2.add(s, data_opt.saturation, s)
cv2.add(v, data_opt.luminosity, v)
hslimg = cv2.merge((h,s,v))
img = cv2.cvtColor(hslimg, cv2.COLOR_HSV2BGR)
if data_opt.clahe is True:
processed = True
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
l, a, b = cv2.split(lab)
clahe = cv2.createCLAHE(clipLimit=data_opt.clip_limit, tileGridSize=(8,8))
l = clahe.apply(l)
limg = cv2.merge((l,a,b))
img = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)
if data_opt.posterize is True:
processed = True
img = cv2.pyrMeanShiftFiltering(img, data_opt.spatial_window, data_opt.color_window)
if data_opt.grayscale is True:
processed = True
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if data_opt.blur is True:
processed = True
img = cv2.GaussianBlur(img, (data_opt.blur_radius, data_opt.blur_radius), data_opt.blur_sigma)
if data_opt.canny is True:
processed = True
img = cv2.Canny(img, data_opt.canny_lo, data_opt.canny_hi)
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
if processed is False:
return img
src_img = im[:, :, ::-1].copy()
frac_a = data_opt.process_frac
frac_b = 1.0 - frac_a
array_a = np.multiply(src_img.astype('float64'), frac_a)
array_b = np.multiply(img.astype('float64'), frac_b)
img = np.add(array_a, array_b).astype('uint8')
return img
last_im = None
def mix_next_image(opt, data_opt, rpc_client, im, i, sequence, sequence_i):
global last_im
if (i % 100) == 0:
print('%04d: process image...' % (i))
last_path = opt.render_dir + "frame_{:05d}.png".format(i)
tmp_path = opt.render_dir + "frame_{:05d}_tmp.png".format(i+1)
next_path = opt.render_dir + "frame_{:05d}.png".format(i+1)
current_path = opt.render_dir + "ren_{:05d}.png".format(i+1)
meta = { 'i': i, 'sequence_i': math.floor(sequence_i), 'sequence_len': len(sequence) }
if data_opt.sequence and len(sequence):
sequence_path = sequence[math.floor(sequence_i)]
if not data_opt.sequence_paused and data_opt.sequence_step != 0:
sequence_i += data_opt.sequence_step
if sequence_i >= len(sequence):
print('(((( sequence looped ))))')
sequence_i = sequence_i % len(sequence)
if sequence_i < 0:
print('(((( sequence looped ))))')
sequence_i += len(sequence)
if data_opt.store_a is not True:
os.remove(last_path)
if data_opt.store_b is True:
image_pil = Image.fromarray(im, mode='RGB')
image_pil.save(tmp_path)
os.rename(tmp_path, current_path)
if data_opt.recursive and last_im is not None:
if data_opt.sequence and len(sequence):
A_img = Image.open(sequence_path).convert('RGB')
A_im = np.asarray(A_img)
frac_a = data_opt.recursive_frac
frac_b = data_opt.sequence_frac
frac_sum = frac_a + frac_b
if frac_sum > 1.0:
frac_a = frac_a / frac_sum
frac_b = frac_b / frac_sum
if data_opt.transition:
t = lerp(math.sin(i / data_opt.transition_period * math.pi * 2.0 ) / 2.0 + 0.5, data_opt.transition_min, data_opt.transition_max)
frac_a *= 1.0 - t
frac_b *= 1.0 - t
frac_c = 1.0 - frac_a - frac_b
array_a = np.multiply(last_im.astype('float64'), frac_a)
array_b = np.multiply(A_im.astype('float64'), frac_b)
array_c = np.multiply(im.astype('float64'), frac_c)
array_ab = np.add(array_a, array_b)
array_abc = np.add(array_ab, array_c)
next_im = array_abc.astype('uint8')
else:
frac_a = data_opt.recursive_frac
frac_b = 1.0 - frac_a
array_a = np.multiply(last_im.astype('float64'), frac_a)
array_b = np.multiply(im.astype('float64'), frac_b)
next_im = np.add(array_a, array_b).astype('uint8')
if data_opt.recurse_roll != 0:
last_im = np.roll(im, data_opt.recurse_roll, axis=data_opt.recurse_roll_axis)
else:
last_im = next_im.copy().astype('uint8')
elif data_opt.sequence and len(sequence):
A_img = Image.open(sequence_path).convert('RGB')
A_im = np.asarray(A_img)
frac_b = data_opt.sequence_frac
if data_opt.transition:
t = lerp(math.sin(i / data_opt.transition_period * math.pi * 2.0 ) / 2.0 + 0.5, data_opt.transition_min, data_opt.transition_max)
frac_b *= 1.0 - t
frac_c = 1.0 - frac_b
array_b = np.multiply(A_im.astype('float64'), frac_b)
array_c = np.multiply(im.astype('float64'), frac_c)
array_bc = np.add(array_b, array_c)
next_im = array_bc.astype('uint8')
else:
last_im = im.copy().astype('uint8')
next_im = im
next_img = process_image(opt, data_opt, next_im)
img_to_send = None
if data_opt.send_image == 'a':
rgb_im = cv2.cvtColor(next_img, cv2.COLOR_BGR2RGB)
img_to_send = Image.fromarray(rgb_im)
if data_opt.send_image == 'b':
img_to_send = Image.fromarray(im, mode='RGB')
if data_opt.send_image == 'sequence':
img_to_send = A_img
if data_opt.send_image == 'recursive':
img_to_send = Image.fromarray(next_im)
if img_to_send is not None:
if data_opt.resize_before_sending:
img_to_send.resize((256, 256), Image.BICUBIC)
rpc_client.send_pil_image("frame_{:05d}.png".format(i+1), meta, img_to_send, data_opt.output_format)
cv2.imwrite(tmp_path, next_img)
os.rename(tmp_path, next_path)
if (i % 20) == 0:
print("created {}".format(next_path))
return sequence_i
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