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entireImage.py
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entireImage.py
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import torch, cv2, torchvision
from util.utils import plotPred, getDimensions, plotGroundTruth, loadModel, writeCoordinates
from tqdm import tqdm
import os
from itertools import repeat
from multiprocessing.dummy import Pool as ThreadPool
import multiprocessing as mp
# function that takes prediction of ML model and its plots results and saves in a txt file
def findPores(
model,
index,
probability,
imageFolder,
labelFolder,
fingerprintPredFolder,
porePredFolder,
coordinatePredFolder,
nsmThreshold,
nmsWindow,
device,
NUMBERLAYERS,
NUMBERFEATURES,
MAXPOOLING,
residual,
gabriel,
su,
boundingBoxSize,
preDefinedPrediction = None
):
if preDefinedPrediction == None:
# load the model on CPU
model = loadModel(
modelPath = model,
device = device,
NUMBERLAYERS = NUMBERLAYERS,
NUMBERFEATURES = NUMBERFEATURES,
MAXPOOLING = MAXPOOLING,
WINDOWSIZE=nmsWindow,
residual=residual,
gabriel=gabriel,
su=su
)
# turn on the testing mode
model.eval()
model.to(device)
# go through each file
torch.backends.cudnn.benchmark = True
files = [os.path.isfile(imageFolder + "%s.bmp" % fileIndex) for fileIndex in index]
if False in files:
return
ans1, ans2 = 0, 0
processes = []
for fileIndex in (index):
if preDefinedPrediction == None:
if not os.path.isfile(imageFolder + "%s.bmp" % fileIndex):
break
# read image and transoform it into proper tensor
image = cv2.imread(imageFolder + "%s.bmp" % fileIndex, cv2.IMREAD_GRAYSCALE)
transforms = torchvision.transforms.Compose([
torchvision.transforms.ToTensor(), ])
image = transforms(image).to(device)
y, x = getDimensions(image)
image = image.reshape(1, 1, y, x)
# get the prediction
with torch.no_grad():
pred = model(image.clone().detach().to(device)).cpu()
else:
pred = preDefinedPrediction[fileIndex-index[0]]
p = mp.Process(target=apply_nms, args=(
pred,
probability,
boundingBoxSize,
nsmThreshold,
porePredFolder,
fileIndex,
coordinatePredFolder,
nmsWindow,
))
p.start()
processes.append(p)
for p in (processes):
p.join()
def apply_nms(
pred,
probability,
boundingBoxSize,
nsmThreshold,
porePredFolder,
fileIndex,
coordinatePredFolder,
nmsWindow,
drawPredictions = False,
device="cpu"
):
#apply nms
workpred = pred.squeeze()
boxes = torch.tensor([], device=device)
scores = torch.tensor([], device=device)
mask = torch.ones(workpred.shape, dtype=torch.int16)
sim_vec = torch.nonzero((workpred >= probability)*mask)
sim_vec2 = sim_vec + boundingBoxSize
cat = torch.cat((sim_vec, sim_vec2), dim=1)
coordinates = cat[:, :2]
scorescat = torch.zeros(coordinates.shape[0])
for i, coordinate in enumerate(coordinates):
scorescat[i] = workpred[coordinate[0], coordinate[1]]
boxes, score = cat, scorescat
indices = torchvision.ops.boxes.nms(boxes.type(torch.float), score.type(torch.float), nsmThreshold)
pred[0][0] = torch.zeros(pred[0][0].shape)
for i in indices:
x1, y1, x2, y2 = boxes[i]
center = (x1, y1)
pred[0][0][int(center[0])][int(center[1])] = 1
# pred = (pred>0.95).float()
pred = pred.squeeze()
pred = pred.detach().numpy()
cv2.imwrite(porePredFolder + "%d.png" % fileIndex, pred*255)
writeCoordinates(coordinatePredFolder + "%d.txt" % fileIndex, pred * 255,
WINDOW_SIZE = nmsWindow,
)