def Depth_TM(img, AtomsphericLight):
DepthMap = depthMap(img)
t0, t1 = 0.05, 0.95
DepthMap = DepthMap.clip(t0, t1)
d_0 = minDepth(img, AtomsphericLight)
d_f = 8 * (DepthMap + d_0)
TM_R_modified = 0.85**d_f
return TM_R_modified
files = os.listdir(path)
files = natsort.natsorted(files)
for i in range(len(files)):
file = files[i]
filepath = path + "/" + file
prefix = file.split('.')[0]
if os.path.isfile(filepath):
print('******** file ********', file)
img = cv2.imread(folder + '/InputImages/' + file)
blockSize = 9
gimfiltR = 50 # 引导滤波时半径的大小
eps = 10**-3 # 引导滤波时epsilon的值
DepthMap = depthMap(img)
DepthMap = global_stretching(DepthMap)
guided_filter = GuidedFilter(img, gimfiltR, eps)
refineDR = guided_filter.filter(DepthMap)
refineDR = np.clip(refineDR, 0, 1)
cv2.imwrite('ULAPDepthMap.jpg', np.uint8(refineDR * 255))
plt.subplot(311), plt.imshow(np.uint8(refineDR * 255))
plt.show()
AtomsphericLight = BLEstimation(img, DepthMap) * 255
d_0 = minDepth(img, AtomsphericLight)
d_f = 8 * (DepthMap + d_0)
transmissionB, transmissionG, transmissionR = getRGBTransmissionESt(
d_f)