60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
import cv2
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import numpy as np
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import glob
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import os
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from tqdm import tqdm
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import random
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import sys
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sys.path.append('./data/MBD')
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from MBD import mask_base_dewarper
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def shadowExtract(cap_im, alb_im):
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im = cap_im
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alb = alb_im
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## Avoid some bad cases
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skip = False
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im_min = np.min(im,axis=-1)
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kernel = np.ones((3,3))
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_, mask = cv2.threshold(cv2.cvtColor(alb,cv2.COLOR_BGR2GRAY), 1, 255, cv2.THRESH_BINARY)
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mask_erode = cv2.dilate(mask,kernel=kernel)
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mask_erode = cv2.erode(mask_erode,kernel=kernel)
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mask_erode = cv2.erode(mask_erode,iterations=4,kernel=kernel)
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metric = np.min(im_min[mask_erode==255])
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metric_num = 0
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if metric==0 or metric==1:
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metric_num = np.sum(im_min[mask_erode==255]==metric)
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if metric_num>=20:
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skip = True
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pass
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# return None
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# it is recommended to skip this sample as it will introduce some artifacts.
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alb_temp = alb.astype(np.float64)
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alb_temp[alb_temp==0] = alb_temp[alb_temp==0]+1e-5
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shadow = np.clip(im.astype(np.float64)/alb_temp,0,1)
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shadow = (shadow*255).astype(np.uint8)
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return shadow,skip
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cap_im = cv2.imread('./data/images/2.png')
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alb_im = cv2.imread('./data/images/3.png')
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## get mask by binarizing alb_im
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_, mask = cv2.threshold(cv2.cvtColor(alb,cv2.COLOR_BGR2GRAY), 1, 255, cv2.THRESH_BINARY)
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kernel = np.ones((3,3))
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mask = cv2.dilate(mask,iterations=2,kernel=kernel)
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mask = cv2.erode(mask,iterations=2,kernel=kernel)
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## dewarp cap and alb based on the mask by using MBD method
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cap_im, _ = mask_base_dewarper(cap_im, mask_im)
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alb_im, _ = mask_base_dewarper(alb_im, mask_im)
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shadow_im,skip = shadowExtract(cap_im,alb_im) # It is recommended to skip this sample if skip is True. Based on our observations, images that meet this condition often introduce noise.
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cv2.imshow('shadow_im',shadow_im)
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cv2.imshow('cap_im',cap_im)
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cv2.imshow('alb_im',alb_im)
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cv2.imshow('mask',mask)
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cv2.waitKey(0) |