Spaces:
Running
Running
File size: 4,391 Bytes
26f23ad |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 |
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)
|