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inference_ill.py
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inference_ill.py
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import cv2
import numpy as np
import torch
from skimage.filters.rank import mean_bilateral
from skimage import morphology
from PIL import Image
from PIL import ImageEnhance
def padCropImg(img):
H = img.shape[0]
W = img.shape[1]
patchRes = 128
pH = patchRes
pW = patchRes
ovlp = int(patchRes * 0.125) # 32
padH = (int((H - patchRes) / (patchRes - ovlp) + 1) * (patchRes - ovlp) + patchRes) - H
padW = (int((W - patchRes) / (patchRes - ovlp) + 1) * (patchRes - ovlp) + patchRes) - W
padImg = cv2.copyMakeBorder(img, 0, padH, 0, padW, cv2.BORDER_REPLICATE)
ynum = int((padImg.shape[0] - pH) / (pH - ovlp)) + 1
xnum = int((padImg.shape[1] - pW) / (pW - ovlp)) + 1
totalPatch = np.zeros((ynum, xnum, patchRes, patchRes, 3), dtype=np.uint8)
for j in range(0, ynum):
for i in range(0, xnum):
x = int(i * (pW - ovlp))
y = int(j * (pH - ovlp))
if j == (ynum-1) and i == (xnum-1):
totalPatch[j, i] = img[-patchRes:, -patchRes:]
elif j == (ynum-1):
totalPatch[j, i] = img[-patchRes:, x:int(x + patchRes)]
elif i == (xnum-1):
totalPatch[j, i] = img[y:int(y + patchRes), -patchRes:]
else:
totalPatch[j, i] = padImg[y:int(y + patchRes), x:int(x + patchRes)]
return totalPatch, padH, padW
def illCorrection(model, totalPatch):
totalPatch = totalPatch.astype(np.float32) / 255.0
ynum = totalPatch.shape[0]
xnum = totalPatch.shape[1]
totalResults = np.zeros((ynum, xnum, 128, 128, 3), dtype=np.float32)
for j in range(0, ynum):
for i in range(0, xnum):
patchImg = torch.from_numpy(totalPatch[j, i]).permute(2,0,1)
patchImg = patchImg.cuda().view(1, 3, 128, 128)
output = model(patchImg)
output = output.permute(0, 2, 3, 1).data.cpu().numpy()[0]
output = output * 255.0
output = output.astype(np.uint8)
totalResults[j, i] = output
return totalResults
def composePatch(totalResults, padH, padW, img):
ynum = totalResults.shape[0]
xnum = totalResults.shape[1]
patchRes = totalResults.shape[2]
ovlp = int(patchRes * 0.125)
step = patchRes - ovlp
resImg = np.zeros((patchRes + (ynum - 1) * step, patchRes + (xnum - 1) * step, 3), np.uint8)
resImg = np.zeros_like(img).astype('uint8')
for j in range(0, ynum):
for i in range(0, xnum):
sy = int(j * step)
sx = int(i * step)
if j == 0 and i != (xnum-1):
resImg[sy:(sy + patchRes), sx:(sx + patchRes)] = totalResults[j, i]
elif i == 0 and j != (ynum-1):
resImg[sy+10:(sy + patchRes), sx:(sx + patchRes)] = totalResults[j, i,10:]
elif j == (ynum-1) and i == (xnum-1):
resImg[-patchRes+10:, -patchRes+10:] = totalResults[j, i,10:,10:]
elif j == (ynum-1) and i == 0:
resImg[-patchRes+10:, sx:(sx + patchRes)] = totalResults[j, i,10:]
elif j == (ynum-1) and i != 0:
resImg[-patchRes+10:, sx+10:(sx + patchRes)] = totalResults[j, i,10:,10:]
elif i == (xnum-1) and j == 0:
resImg[sy:(sy + patchRes), -patchRes+10:] = totalResults[j, i,:,10:]
elif i == (xnum-1) and j != 0:
resImg[sy+10:(sy + patchRes), -patchRes+10:] = totalResults[j, i,10:,10:]
else:
resImg[sy+10:(sy + patchRes), sx+10:(sx + patchRes)] = totalResults[j, i,10:,10:]
resImg[0,:,:] = 255
return resImg
def preProcess(img):
img[:,:,0] = mean_bilateral(img[:,:,0], morphology.disk(20), s0=10, s1=10)
img[:,:,1] = mean_bilateral(img[:,:,1], morphology.disk(20), s0=10, s1=10)
img[:,:,2] = mean_bilateral(img[:,:,2], morphology.disk(20), s0=10, s1=10)
return img
def postProcess(img):
img = Image.fromarray(img)
enhancer = ImageEnhance.Contrast(img)
factor = 2.0
img = enhancer.enhance(factor)
return img
def rec_ill(net, img, saveRecPath):
totalPatch, padH, padW = padCropImg(img)
totalResults = illCorrection(net, totalPatch)
resImg = composePatch(totalResults, padH, padW, img)
#resImg = postProcess(resImg)
resImg = Image.fromarray(resImg)
resImg.save(saveRecPath)