def brightness_compensation(PAN): #---- brightness compensation
#--- reference (no boundary but blurred) ---
ref = pano_tools.paste_and_blend(PAN, src= PAN.imgb, bw=70);
gref= CVP.rgb2gray(ref);
H = PAN.H.copy();
xa = np.argmin(H[:,6]); x0 = int(np.ceil(PAN.imgb[xa].shape[1]/2));
ya = np.argmin(H[:,5]); y0 = int(np.ceil(PAN.imgb[ya].shape[0]/2));
box_ratio = 0.99;
#--- ---
for k in range(0,PAN.count):
#--- capture source sampling window ---
imgb= CVP.rgb2gray(PAN.imgb[k]);
rowh= int(imgb.shape[0]/2); # half image height
colh= int(imgb.shape[1]/2); # half image width
roww= int(rowh* box_ratio); # capture window radius
colw= int(colh* box_ratio);
imgs= imgb[rowh-roww:rowh+roww, colh-colw:colh+colw]
cdfs= pano_tools.brightness_cdf(imgs); # brightness curve for source
#--- capture reference sampling window ---
yr = int(y0+ H[k,5]);
xr = int(x0+ H[k,6]);
imgr= gref[yr-roww:yr+roww, xr-colw:xr+colw];
cdfr= pano_tools.brightness_cdf(imgr); # brightness curve for reference
#--- brightness equalization ---
out = np.zeros_like(PAN.imgb[k]); # image with brightness compensation
for kb in range(0,256):
under= np.sum(cdfr< cdfs[kb]); #
under= np.minimum(under, 3*(kb+0.5)-0.5); # prevent from low gain boost
gain = (under+0.5)/(kb+0.5);
#--- find pixel---
th0 = kb/256; th1 = (kb+1)/256; # bin lower and upper bound
mask = ((imgb<th1)+0) - ((imgb<=th0)+0) # data inside the region
mask = mask* gain;
out[:,:,0]+= PAN.imgt[k][:,:,0]*mask;
out[:,:,1]+= PAN.imgt[k][:,:,1]*mask;
out[:,:,2]+= PAN.imgt[k][:,:,2]*mask;
PAN.imgb[k] = out;
def pre_processing(self, src_img):
# 執行以下動作: (1)轉灰階圖 (2)將資料改成[0~1] (3)影像放大2倍 (4)Gauss smooth
gray = CVP.rgb2gray(src_img)
# 灰階圖
if np.max(gray) > 2:
gray = gray / 256
# 影像強度正規化到 [0 ~ 1]
#doubled = CVP.resize(gray,ratio=2); # 雙倍影像圖
if self.doubled == True:
doubled = CVP.resize(gray, ratio=2)
# 雙倍影像圖
else:
doubled = gray
sig_diff = np.sqrt(self.sigma**2 - 4 * self.init_sigma**2)
# s=1.25
init_img = CVP.Gauss_blur2D(doubled, sigma=sig_diff)
return init_img