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skyboxengine.py
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skyboxengine.py
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from skybox_utils import *
from cv2.ximgproc import guidedFilter
import synrain
import os
class SkyBox():
def __init__(self, args):
self.args = args
self.load_skybox()
self.rainmodel = synrain.Rain(
rain_intensity=0.8, haze_intensity=0.0, gamma=1.0, light_correction=1.0)
# motion parameters
self.M = np.array([[1, 0, 0], [0, 1, 0]], dtype=np.float32)
self.frame_id = 0
def tile_skybox_img(self, imgtile):
screen_y1 = int(imgtile.shape[0] / 2 - self.args.out_size_h / 2)
screen_x1 = int(imgtile.shape[1] / 2 - self.args.out_size_w / 2)
imgtile = np.concatenate(
[imgtile[screen_y1:,:,:], imgtile[0:screen_y1,:,:]], axis=0)
imgtile = np.concatenate(
[imgtile[:,screen_x1:,:], imgtile[:,0:screen_x1,:]], axis=1)
return imgtile
def load_skybox(self):
print('initialize skybox...')
if '.jpg' in self.args.skybox:
# static backgroud
skybox_img = cv2.imread(os.path.join(r'./skybox', self.args.skybox), cv2.IMREAD_COLOR)
skybox_img = cv2.cvtColor(skybox_img, cv2.COLOR_BGR2RGB)
self.skybox_img = cv2.resize(
skybox_img, (self.args.out_size_w, self.args.out_size_h))
cc = 1. / self.args.skybox_cernter_crop
imgtile = cv2.resize(
skybox_img, (int(cc * self.args.out_size_w),
int(cc*self.args.out_size_h)))
self.skybox_imgx2 = self.tile_skybox_img(imgtile)
self.skybox_imgx2 = np.expand_dims(self.skybox_imgx2, axis=0)
else:
# video backgroud
cap = cv2.VideoCapture(os.path.join(r'./skybox', self.args.skybox))
m_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
cc = 1. / self.args.skybox_cernter_crop
self.skybox_imgx2 = np.zeros(
[m_frames, int(cc*self.args.out_size_h),
int(cc*self.args.out_size_w), 3], np.uint8)
for i in range(m_frames):
_, skybox_img = cap.read()
skybox_img = cv2.cvtColor(skybox_img, cv2.COLOR_BGR2RGB)
imgtile = cv2.resize(
skybox_img, (int(cc * self.args.out_size_w),
int(cc * self.args.out_size_h)))
skybox_imgx2 = self.tile_skybox_img(imgtile)
self.skybox_imgx2[i,:] = skybox_imgx2
def skymask_refinement(self, G_pred, img):
r, eps = 20, 0.01
refined_skymask = guidedFilter(img[:,:,2], G_pred[:,:,0], r, eps)
refined_skymask = np.stack(
[refined_skymask, refined_skymask, refined_skymask], axis=-1)
return np.clip(refined_skymask, a_min=0, a_max=1)
def get_skybg_from_box(self, m):
self.M = update_transformation_matrix(self.M, m)
nbgs, bgh, bgw, c = self.skybox_imgx2.shape
fetch_id = self.frame_id % nbgs
skybg_warp = cv2.warpAffine(
self.skybox_imgx2[fetch_id, :,:,:], self.M,
(bgw, bgh), borderMode=cv2.BORDER_WRAP)
skybg = skybg_warp[0:self.args.out_size_h, 0:self.args.out_size_w, :]
self.frame_id += 1
return np.array(skybg, np.float32)/255.
def skybox_tracking(self, frame, frame_prev, skymask):
if np.mean(skymask) < 0.05:
print('sky area is too small')
return np.array([[1, 0, 0], [0, 1, 0]], dtype=np.float32)
prev_gray = cv2.cvtColor(frame_prev, cv2.COLOR_RGB2GRAY)
prev_gray = np.array(255*prev_gray, dtype=np.uint8)
curr_gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
curr_gray = np.array(255*curr_gray, dtype=np.uint8)
mask = np.array(skymask[:,:,0] > 0.99, dtype=np.uint8)
mask = cv2.erode(mask, np.ones([20, 20]))
# ShiTomasi corner detection
prev_pts = cv2.goodFeaturesToTrack(
prev_gray, mask=mask, maxCorners=200,
qualityLevel=0.01, minDistance=30, blockSize=3)
if prev_pts is None:
print('no feature point detected')
return np.array([[1, 0, 0], [0, 1, 0]], dtype=np.float32)
# Calculate optical flow (i.e. track feature points)
curr_pts, status, err = cv2.calcOpticalFlowPyrLK(
prev_gray, curr_gray, prev_pts, None)
# Filter only valid points
idx = np.where(status == 1)[0]
if idx.size == 0:
print('no good point matched')
return np.array([[1, 0, 0], [0, 1, 0]], dtype=np.float32)
prev_pts, curr_pts = removeOutliers(prev_pts, curr_pts)
if curr_pts.shape[0] < 3:
print('no good point matched')
return np.array([[1, 0, 0], [0, 1, 0]], dtype=np.float32)
m = cv2.estimateAffinePartial2D(
np.array(prev_pts), np.array(curr_pts))[0]
return m
def relighting(self, img, skybg, skymask):
# color matching, reference: skybox_img
step = int(img.shape[0]/20)
skybg_thumb = skybg[::step, ::step, :]
img_thumb = img[::step, ::step, :]
skymask_thumb = skymask[::step, ::step, :]
skybg_mean = np.mean(skybg_thumb, axis=(0, 1), keepdims=True)
img_mean = np.sum(img_thumb * (1-skymask_thumb), axis=(0, 1), keepdims=True) \
/ ((1-skymask_thumb).sum(axis=(0,1), keepdims=True) + 1e-9)
diff = skybg_mean - img_mean
img_colortune = img + self.args.recoloring_factor*diff
if self.args.auto_light_matching:
img = img_colortune
else:
#keep foreground ambient_light and maunally adjust lighting
img = self.args.relighting_factor*(img_colortune + (img.mean() - img_colortune.mean()))
return img
def halo(self, syneth, skybg, skymask):
# reflection
halo = 0.5*cv2.blur(
skybg*skymask, (int(self.args.out_size_w/5),
int(self.args.out_size_w/5)))
# screen blend 1 - (1-a)(1-b)
syneth_with_halo = 1 - (1-syneth) * (1-halo)
return syneth_with_halo
def skyblend(self, img, img_prev, skymask):
m = self.skybox_tracking(img, img_prev, skymask)
skybg = self.get_skybg_from_box(m)
img = self.relighting(img, skybg, skymask)
syneth = img * (1 - skymask) + skybg * skymask
if self.args.halo_effect:
# halo effect brings better visual realism but will slow down the speed
syneth = self.halo(syneth, skybg, skymask)
if 'rainy' in self.args.skybox:
syneth = self.rainmodel.forward(syneth)
return np.clip(syneth, a_min=0, a_max=1)