def preprocess(camera, f, imgs):
img = cam.image(camera, f)
###img object contains four data fields: rgb, red, rbr, and cm
img.undistort(rgb=True)
###undistortion
if img.red is None:
return
imgs += [img.red[500:1500, 500:1500]]
def preprocess(camera, f):
img = cam.image(camera, f)
###img object contains four data fields: rgb, red, rbr, and cm
img.undistort(camera, rgb=True)
###undistortion
if img.red is None:
return
ims = Image.fromarray(img.rgb)
ims.save(outpath + camID + '/' + os.path.basename(f)[:-4] + '.png', "PNG")
def motion(args):
camera, day = args
ymd = day[:8]
flist = sorted(
glob.glob(inpath + camera.camID + '/' + ymd + '/' + camera.camID +
'_' + day + '*jpg'))
if len(flist) <= 0:
return None
q = deque()
fh = open(outpath + camera.camID + '/' + camera.camID + '.' + ymd + '.txt',
'w')
for f in flist:
# print("Start preprocessing ", f[-23:])
img = cam.image(camera, f)
###img object contains four data fields: rgb, red, rbr, and cm
img.undistort(camera, rgb=True)
###undistortion
# print("Undistortion completed ", f[-23:])
if img.rgb is None:
continue
q.append(img)
if len(q) <= 1:
continue
####len(q) is always 2 beyond this point
if (q[-1].time - q[-2].time).seconds >= MAX_INTERVAL:
q.popleft()
q.popleft()
continue
r1 = q[-2].rgb[..., 0].astype(np.float32)
r1[r1 <= 0] = np.nan
r2 = q[-1].rgb[..., 0].astype(np.float32)
r2[r2 <= 0] = np.nan
err0 = r2 - r1
err0 -= np.nanmean(err0)
cam.cloud_mask(camera, q[-1], q[-2])
###one-layer cloud masking
fh.write(f[-10:-4], ', ', np.sum(q[-1].cm))
# q[-1].dump_img(tmpfs+f[-18:-10]+'/'+f[-23:-4]+'.msk');
q.popleft()
def preprocess(camera,f):
img=cam.image(camera,f); ###img object contains four data fields: rgb, red, rbr, and cm
img.undistort(rgb=True); ###undistortion
if img.rgb is None:
return
# plt.imshow(img.rbr,vmin=-0.7,vmax=0.2); plt.show();
mask=(img.rgb[:,:,2]>0) & ((img.rgb[:,:,2]<76)) ####all other cameras
# mask=(img.rgb[:,:,2]>0) & ((img.rgb[:,:,2]<80) | ((img.rgb[:,:,1]<img.rgb[:,:,0]-5) & (img.rgb[:,:,1]<img.rgb[:,:,2]-5))) ####HD5A
mask=morphology.binary_closing(mask,np.ones((9,9)))
mask=remove_small_objects(mask, min_size=15, connectivity=4)
mask=morphology.binary_dilation(mask,np.ones((21,21)))
mask=morphology.binary_closing(mask,np.ones((17,17)))
mask=remove_small_objects(mask, min_size=1000, connectivity=4)
fig,ax=plt.subplots(2,2,sharex=True,sharey=True);
ax[0,0].imshow(img.rgb); ax[0,1].imshow(img.rbr,vmin=-0.2,vmax=0.1);
ax[1,0].imshow(mask); ax[1,1].imshow(img.rgb[:,:,2])
# plt.figure(); plt.hist(img.rbr[img.rbr>-1],bins=100);
plt.show()
np.save(camID+'_mask',mask);
TimeStamp = os.path.basename(file)[-18:-4]
TxtFile = open(TxtFileName, 'a')
TxtFile.write("#############################\n")
TxtFile.write("Working at time step %s\n" % TimeStamp)
TxtFile.write("#############################\n")
TxtFile.close()
print("Working at time step %s\n" % TimeStamp)
ResultTable = ResultTable.append({'TimeStamp': TimeStamp},
ignore_index=True)
ResultTable = ResultTable.append({'Qlen': len(q)},
ignore_index=True)
img = cam.image(
camera, file
) ###img object contains four data fields: rgb, red, rbr, and cm
img.undistort(rgb=True)
if img.rbr is None:
TxtFile = open(TxtFileName, 'a')
TxtFile.write(
"Image rbr is None. Getting into the next loop\n")
TxtFile.close()
q.clear()
err.clear()
fft.clear()
Tq.clear()
continue
def height_computing(args):
imager, neighbors, Date, TimeStamp, DocumFilename = args
f = inpath + imager.camID + '/' + Date + '/' + imager.camID + '_' + Date + TimeStamp + '.jpg'
if not os.path.exists(f):
return -1
img = cam.image(imager, f)
img.undistort(imager, rgb=True)
if img.red is not None:
img.cloud_mask(imager)
else:
# if img.red is None or img.layers <= 0:
if WRITE:
Document = open(DocumFilename, 'a')
Document.write("img.red is None or img.layers <=0")
Document.close()
return -1
h = [[]] * img.layers
for neighbor in neighbors:
f_nb = f.replace(imager.camID, neighbor.camID)
img_nb = cam.image(neighbor, f_nb)
img_nb.undistort(neighbor, rgb=True)
if img_nb.red is not None:
img_nb.cloud_mask(neighbor)
else:
if PRINT:
print("img_nb.red is None")
continue
distance = 6367e3 * geo.distance_sphere(img.lat, img.lon, img_nb.lat,
img_nb.lon)
for ih in range(img.layers):
if (len(h[ih])) >= 1:
continue
res = cam.cloud_height(img, img_nb, layer=ih, distance=distance)
if len(res) >= 1 and res[0] < 30 * distance and res[
0] > 0.5 * distance:
h[ih] = res[0]
if PRINT:
print("with camera " + imager.camID + "neighbor " +
neighbor.camID)
print("the cloud height is")
print(res[0])
return res[0]
# if len(h) >= img.layers:
# if PRINT:
# print("with camera " + imager.camID + " neighbor " + neighbor.camID)
# print("len(h) = " + str(len(h)))
# print("img.layers =" + str(img.layers))
# break
if (len(h[0]) == 0):
if PRINT:
print("len(h[0]) == 0")
return -1
def preprocess(camera,f,q,err,fft,convolver,flag):
img=cam.image(camera,f); ###img object contains four data fields: rgb, red, rbr, and cm
img.undistort(rgb=True); ###undistortion
if img.red is None:
return
# ims = Image.fromarray(img.rgb); ims.save(outpath+camID+'/'+os.path.basename(f), "PNG"); continue
img.cloud_mask(); ###one-layer cloud masking
q.append(img)
if len(q)<=1:
return
####len(q) is always 2 beyond this point
if (q[-1].time-q[-2].time).seconds>=MAX_INTERVAL:
q.popleft();
return;
#####cloud motion for the dominant layer
# im1=q[-2].red.copy().astype(np.float32); im2=q[-1].red.copy().astype(np.float32);
# vy,vx,max_corr = cam.cloud_motion(im1,im2,mask1=im1>5,mask2=np.abs(im1-im2)>15, ratio=0.7, threads=4)
# print(camera.camID+', ', f[-18:-4]+', first layer2:',max_corr,vy,vx)
if convolver is None:
shape=(camera.nx,camera.ny)
convolver = mncc.Convolver(shape, shape, threads=4, dtype=np.float32) #
for ii in range(len(fft)-2,0):
im=q[ii].red.astype(np.float32);
mask = im>0; #im[~mask]=0
fft.append(convolver.FFT(im,mask,reverse=flag[0]>0));
flag[0] *= -1
vy,vx,max_corr = cam.cloud_motion_fft(convolver,fft[-2],fft[-1],ratio=0.8);
if vx is None or vy is None: #####invalid cloud motion
q.popleft(); fft.popleft();
return
vy*=flag[0]; vx*=flag[0];
fft.popleft();
print(camera.camID+', ', f[-18:-4]+', first layer:',max_corr,vy,vx)
# plt.figure(); plt.imshow(q[-2].red); plt.colorbar(); plt.show();
return;
q[-2].v+=[[vy,vx]]
red1=st.shift_2d(q[-1].rgb[:,:,0].astype(np.float32),-vx,-vy); red1[red1<=0]=np.nan
red2=q[-2].rgb[:,:,0].astype(np.float32); red2[red2<=0]=np.nan #red2-=np.nanmean(red2-q[-1].rgb[:,:,0])
er=red2-red1; ###difference image after cloud motion adjustment
er[(red1==0)|(red2==0)]=np.nan;
a=er.copy(); a[a>0]=0; er-=st.rolling_mean2(a,500);
if len(err) <= 0:
err += [-st.shift_2d(er,vx,vy)]
return
err_2=err[0].copy(); err[0] = (-st.shift_2d(er,vx,vy))
# if vy**2+vx**2>=50**2: ######The motion of the dominant layer is fast, likely low clouds. Do NOT trigger the second layer algorithm
# return
#####process the secondary layer
ert=er+err_2;
# cm2=(er>15) | (er_p>15); cm2=remove_small_objects(cm2, min_size=500, connectivity=4);
scale=red2/np.nanmean(red2); nopen=max(5,int(np.sqrt(vx**2+vy**2)/3))
cm2=(ert>15*scale) & (q[-2].cm);
cm2=morphology.binary_opening(cm2,np.ones((nopen,nopen)))
cm2=remove_small_objects(cm2, min_size=500, connectivity=4);
# sec_layer=np.sum(cm2)/len(cm2.ravel()) ###the amount of clouds in secondary layer
sec_layer=np.sum(cm2)/np.sum(q[-2].cm) ###the amount of clouds in secondary layer
if sec_layer<5e-2: ###too few pixels in second layer, ignore the second cloud layer
print('Second layer is small:', sec_layer*100, '%')
return
#####cloud motion for the secondary layer
mask2=np.abs(err_2)>5;
mask2=remove_small_objects(mask2, min_size=500, connectivity=4)
mask2=filters.maximum_filter(mask2,20)
vy,vx,max_corr = cam.cloud_motion(err[0],err_2,mask1=None,mask2=mask2, ratio=None, threads=4)
if vx is None or vy is None:
return
q[-2].v+=[[vy,vx]]
print(camera.camID+', ', f[-18:-4]+', second layer:',max_corr,vy,vx)
if np.abs(vy-q[-2].v[0][0])+np.abs(vx-q[-2].v[0][1])>10:
# if np.abs(vy)+np.abs(vx)>0:
#####obtain the mask for secondar cloud layer using a watershed-like algorithm
mred=q[-2].rgb[:,:,0].astype(np.float32)-st.fill_by_mean2(q[-2].rgb[:,:,0],200,mask=~cm2)
mrbr=q[-2].rbr-st.fill_by_mean2(q[-2].rbr,200,mask=~cm2)
merr=st.rolling_mean2(ert,200,ignore=np.nan); var_err=(st.rolling_mean2(ert**2,200,ignore=np.nan)-merr**2)
# mk=(np.abs(q[-2].rgb[:,:,0].astype(np.float32)-mred)<3) & ((total_err)>-2) & (np.abs(q[-2].rbr-mrbr)<0.05)
mk=(np.abs(mred)<3) & (ert>-15) & (np.abs(mrbr)<0.05) & (var_err>20*20)
cm2=morphology.binary_opening(mk|cm2,np.ones((nopen,nopen))) ####remove line objects produced by cloud deformation
cm2=remove_small_objects(cm2, min_size=500, connectivity=4)
q[-2].layers=2; q[-2].cm[cm2]=2; #####update the cloud mask with secondary cloud layer
# fig,ax=plt.subplots(2,2, sharex=True,sharey=True); ####visualize the cloud masking results
# ax[0,0].imshow(q[-2].rgb); ax[0,1].imshow(q[-2].cm)
# ax[1,0].imshow(st.shift_2d(q[-1].rgb,-vx,-vy))
# ax[1,1].imshow(er,vmin=-25,vmax=25); plt.show();
return;
def preprocess(args):
camera, day = args
ymd = day[:8]
flist = sorted(
glob.glob(inpath + camera.camID + '/' + ymd + '/' + camera.camID +
'_' + day + '*jpg'))
if len(flist) <= 0:
return None
q = deque()
fft = deque()
###queues
flag = [-1]
shape = (camera.nx, camera.ny)
convolver = mncc.Convolver(shape, shape, threads=4, dtype=np.float32) #
for f in flist:
# print("Start preprocessing ", f[-23:])
img = cam.image(camera, f)
###img object contains four data fields: rgb, red, rbr, and cm
img.undistort(camera, rgb=True)
###undistortion
# print("Undistortion completed ", f[-23:])
if img.rgb is None:
continue
img.cloud_mask(camera)
###one-layer cloud masking
# print("Cloud masking completed ", f[-23:])
q.append(img)
if len(q) <= 1:
img.dump_img(tmpfs + f[-23:-4] + '.pkl')
continue
####len(q) is always 2 beyond this point
if (q[-1].time - q[-2].time).seconds >= MAX_INTERVAL:
img.dump_img(tmpfs + f[-23:-4] + '.pkl')
q.popleft()
continue
#####cloud motion for the dominant layer
for ii in range(len(fft) - 2, 0):
im = q[ii].red.astype(np.float32)
mask = im > 0
#im[~mask]=0
fft.append(convolver.FFT(im, mask, reverse=flag[0] > 0))
flag[0] *= -1
vy, vx, max_corr = cam.cloud_motion_fft(convolver,
fft[-2],
fft[-1],
ratio=0.8)
if vx is None or vy is None: #####invalid cloud motion
img.dump_img(tmpfs + f[-23:-4] + '.pkl')
q.popleft()
fft.popleft()
continue
vy *= flag[0]
vx *= flag[0]
img.v += [[vy, vx]]
img.dump_img(tmpfs + f[-23:-4] + '.pkl')
print(camera.camID + ', ', f[-18:-4] + ', first layer:', len(fft),
max_corr, vy, vx)
if SAVE_FIG:
fig, ax = plt.subplots(2,
2,
figsize=(12, 6),
sharex=True,
sharey=True)
ax[0, 0].imshow(q[-2].rgb)
ax[0, 1].imshow(q[-1].rgb)
ax[1, 0].imshow(q[-2].cm)
ax[1, 1].imshow(q[-1].cm)
plt.tight_layout()
plt.show()
fft.popleft()
q.popleft()
def mask_motion(args):
camera, day = args
ymd = day[:8]
flist = sorted(
glob.glob(inpath + camera.camID + '/' + ymd + '/' + camera.camID +
'_' + day + '*jpg'))
if len(flist) <= 0:
return
q = deque()
for f in flist:
print('Processing', f)
if (~REPROCESS) and os.path.isfile(tmpfs + f[-18:-10] + '/' +
f[-23:-4] + '.pkl'):
continue
img = cam.image(camera, f)
###img object contains four data fields: rgb, red, rbr, and cm
img.undistort(camera, rgb=True)
###undistortion
if img.rgb is None:
continue
q.append(img)
if len(q) <= 1:
continue
####len(q) is always 2 beyond this point
if (q[-1].time - q[-2].time).seconds >= MAX_INTERVAL:
q.popleft()
q.popleft()
continue
r1 = q[-2].red.astype(np.float32)
r1[r1 <= 0] = np.nan
r2 = q[-1].red.astype(np.float32)
r2[r2 <= 0] = np.nan
err0 = r2 - r1
dif = np.abs(err0)
dif = st.rolling_mean2(dif, 20)
semi_static = (abs(dif) < 10) & (r1 - 127 > 100)
semi_static = morphology.binary_closing(semi_static, np.ones((10, 10)))
semi_static = remove_small_objects(semi_static,
min_size=200,
in_place=True)
q[-1].rgb[semi_static] = 0
r2[semi_static] = np.nan
cam.cloud_mask(camera, q[-1], q[-2])
###one-layer cloud masking
if (q[-1].cm is None):
q.popleft()
continue
if (np.sum(
(q[-1].cm > 0)) < 2e-2 * img.nx * img.ny): ######cloud free case
q[-1].layers = 0
else:
dilated_cm = morphology.binary_dilation(q[-1].cm, np.ones(
(15, 15)))
dilated_cm &= (r2 > 0)
vy, vx, max_corr = cam.cloud_motion(r1,
r2,
mask1=r1 > 0,
mask2=dilated_cm,
ratio=0.7,
threads=4)
if np.isnan(vy):
q[-1].layers = 0
else:
q[-1].v += [[vy, vx]]
q[-1].layers = 1
q[-1].dump_img(tmpfs + f[-18:-10] + '/' + f[-23:-4] + '.pkl')
q.popleft()
q = deque()
err = deque()
fft = deque()
for i, f in enumerate(flist):
####visualize the cloud mask
if len(q) >= 2:
fig, ax = plt.subplots(1, 2, sharex=True, sharey=True)
ax[0].imshow(q[-2].rgb)
ax[0].axis('off') #####original
ax[1].imshow(q[-2].cm)
ax[1].axis('off') ####cloud mask
fig.savefig(dest + os.path.basename(f))
plt.close(fig)
q.popleft()
img = cam.image(camera, f)
###img object contains four data fields: rgb, red, rbr, and cm
img.undistort(rgb=True)
###undistortion
if img.rbr is None:
q.clear()
err.clear()
fft.clear()
continue
# ims = Image.fromarray(img.rgb); ims.save(outpath+camID+'/'+os.path.basename(f), "PNG"); continue
img.cloud_mask()
###cloud masking
q.append(img)
if len(q) <= 1: continue
#####cloud motion for the dominant layer
Document.write(Date + '\n')
Document.close()
flist = sorted(
glob.glob(inpath + camCenterID + '/' + Date + '/' + camCenterID + '_' +
Date + str(hour) + '*jpg'))
if len(flist) == 0:
if PRINT:
print("Warning: File list is empty")
continue
try:
for f in flist:
TimeStamp = os.path.basename(f)[-10:-4]
img = cam.image(cameraCenterObject, f)
img.undistort(cameraCenterObject, rgb=True)
if img.rgb is None:
continue
img.cloud_mask(cameraCenterObject)
cloudMask = img.cm
cloudFraction = (cloudMask > 0).sum() / (cloudMask.shape[0] *
cloudMask.shape[1])
q.append(img)
if len(q) <= 1:
continue
if (q[-1].time - q[-2].time).seconds >= MAX_INTERVAL:
def motion(args):
camera,day=args
ymd=day[:8]
flist = sorted(glob.glob(inpath+camera.camID+'/'+ymd+'/'+camera.camID+'_'+day+'*jpg'))
if len(flist)<=0:
return None
q=deque();
for f in flist:
# print("Start preprocessing ", f[-23:])
img=cam.image(camera,f); ###img object contains four data fields: rgb, red, rbr, and cm
img.undistort(camera,rgb=True); ###undistortion
# print("Undistortion completed ", f[-23:])
if img.rgb is None:
continue
q.append(img)
# gray_img = cv2.cvtColor(img.rgb, cv2.COLOR_BGR2GRAY)
# cv2.imwrite(tmpfs+f[-23:],gray_img);
if len(q)<=1:
continue
####len(q) is always 2 beyond this point
if (q[-1].time-q[-2].time).seconds>=MAX_INTERVAL:
q.popleft(); q.popleft();
continue;
cam.cloud_mask(camera,q[-1],q[-2]); ###one-layer cloud masking
r1=q[-2].red.astype(np.float32); r1[r1<=0]=np.nan
r2=q[-1].red.astype(np.float32); r2[r2<=0]=np.nan
err0 = r2-r1;
dilated_cm=morphology.binary_dilation(q[-1].cm,np.ones((15,15))); dilated_cm &= (r2>0)
vy,vx,max_corr = cam.cloud_motion(r1,r2,mask1=r1>0,mask2=dilated_cm, ratio=0.7, threads=4);
if vy is None:
q.popleft();
continue
q[-1].v += [[vy,vx]]; q[-1].layers+=1;
err = r2-st.shift2(r1,-vx,-vy); err[(r2==0) | (st.shift2(r1,-vx,-vy)==0)]=np.nan;
print(f[-23:],', first layer:',vy,vx,max_corr)
mask2=st.rolling_mean2(np.abs(err)-np.abs(err0),40)<0
# mask2 = (np.abs(err)-np.abs(err0)<0) #| (np.abs(err)<1);
mask2=remove_small_objects(mask2,min_size=900, in_place=True)
mask2=morphology.binary_dilation(mask2,np.ones((15,15)))
mask2 = (~mask2) & (r2>0) & (np.abs(r2-127)<30) & (np.abs(err)>=1) #& (q[-1].cm>0)
# print(np.sum(mask2 & (q[-1].cm>0))/(img.nx*img.ny))
if np.sum(mask2 & (q[-1].cm>0))>2e-2*img.nx*img.ny:
vy,vx,max_corr = cam.cloud_motion(r1,r2,mask1=r1>0,mask2=mask2, ratio=0.7, threads=4);
if vy is None:
q.popleft();
continue
vdist = np.sqrt((vy-q[-1].v[-1][0])**2+(vx-q[-1].v[-1][1])**2)
if vdist>=5 and np.abs(vy)+np.abs(vx)>2.5 and vdist>0.3*np.sqrt(q[-1].v[-1][0]**2+q[-1].v[-1][1]**2):
score1=np.nanmean(np.abs(err[mask2]));
err2=r2-st.shift2(r1,-vx,-vy); err2[(r2==0) | (st.shift2(r1,-vx,-vy)==0)]=np.nan;
score2=np.nanmean(np.abs(err2[mask2]));
print("Score 1 and score 2: ", score1,score2);
if score2<score1:
q[-1].v += [[vy,vx]]; q[-1].layers=2;
dif=st.rolling_mean2(np.abs(err)-np.abs(err2),40)>0
dif=remove_small_objects(dif,min_size=300, in_place=True)
q[-1].cm[dif & (q[-1].cm>0)]=q[-1].layers;
print(f[-23:],', second layer:',vy,vx,max_corr)
q[-1].dump_img(tmpfs+f[-18:-10]+'/'+f[-23:-4]+'.pkl');
if SAVE_FIG:
fig,ax=plt.subplots(2,2,figsize=(9,9),sharex=True,sharey=True);
ax[0,0].imshow(mask2); ax[0,1].imshow(q[-1].red);
ax[1,0].imshow(q[-1].cm); ax[1,1].imshow(q[-1].rgb);
plt.tight_layout();
plt.show();
# fig.savefig(outpath+ymdhms);
q.popleft();