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helper.py
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helper.py
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import cv2
import numpy as np
import scipy.io as sc
import os
import h5py
import pickle
import detection
import matplotlib.pyplot as plt
def extract_trainRGB():
train = os.path.join("train")
dirname = 'finalProjectData/train/'
out = sc.loadmat(os.path.join(dirname,'trainDigits.mat'))
data = out["digitStruct"]
bbox = data['bbox'].squeeze()
name = data['name'].squeeze()
_,dm = data.shape
negimg = []
tr_label = []
img = []
tr_labels5 = []
imgBW = []
negimgBW = []
sz = (48,48)
neglabel = np.asarray([0, 10, 10, 10, 10, 10],dtype = 'uint8')
filler = [0, 10, 10, 10, 10, 10]
# sz2 = (224,224)
hf = h5py.File('datasets/train.h5', 'w')
for i in range(0,dm,1): #dm
im = cv2.imread(os.path.join(dirname,name[i][0].squeeze()))
b = bbox[i].flatten()
ht,wd,_ = im.shape
ha = np.max([0, np.int16(np.min(b["top"].squeeze()).squeeze())])
wa = np.max([0, np.int16(np.min(b["left"].squeeze()).squeeze())])
hb = np.int16(np.max(b["height"].squeeze()).squeeze()) + ha
wb = np.int16(np.sum(b["width"].squeeze()).squeeze()) + wa
h3 = (hb - ha) * .3
w3 = (wb - wa) * .3
ha = np.max([0, np.int16(ha - h3)])
hb = np.min([ht, np.int16(hb + h3)])
wa = np.max([0, np.int16(wa - w3)])
wb = np.min([wd, np.int16(wb + w3)])
if (wb-wa == 0) | (hb-ha == 0):
continue
numdig = b["label"].__len__()
if numdig>=5:
continue # skip >5 digits
bboxIm = im[ha:hb,wa:wb,:]
totIm = cv2.resize(bboxIm,sz)
img.append(totIm)
imgBW.append(cv2.cvtColor(totIm,cv2.COLOR_BGR2GRAY))
lab = np.copy(filler)
lab[0] = b["label"].__len__()
for n in range(0,numdig,1):
currlab = np.int16(b["label"][n].squeeze())
if currlab == 10:
currlab = 0
lab[n+1] = currlab
currLabel = np.asarray(lab, dtype='uint8')
tr_labels5.append(currLabel)
if (wa > 10) & (ha >10):
cropIm = cv2.resize(im[0: ha-1, 0:wa-1 ,:],sz)
negimg.append(cropIm)
tr_label.append(neglabel)
negimgBW.append(cv2.cvtColor(cropIm, cv2.COLOR_BGR2GRAY))
cropIm = cv2.resize(im[ha:hb, 0:wa-1,:], sz)
negimg.append(cropIm)
negimgBW.append(cv2.cvtColor(cropIm, cv2.COLOR_BGR2GRAY))
tr_label.append(neglabel)
cropIm = cv2.resize(im[0:ha-1, wa:wb,:], sz)
negimg.append(cropIm)
negimgBW.append(cv2.cvtColor(cropIm, cv2.COLOR_BGR2GRAY))
tr_label.append(neglabel)
if (ht-hb > 10) & ((wd -wb)>10):
cropIm = cv2.resize(im[hb+1: ht, wb+1:wd,:],sz)
negimg.append(cropIm)
negimgBW.append(cv2.cvtColor(cropIm, cv2.COLOR_BGR2GRAY))
tr_label.append(neglabel)
cropIm = cv2.resize(im[ha: hb, wb+1:wd,:],sz)
negimg.append(cropIm)
negimgBW.append(cv2.cvtColor(cropIm, cv2.COLOR_BGR2GRAY))
tr_label.append(neglabel)
cropIm = cv2.resize(im[hb+1: ht, wa:wb,:],sz)
negimg.append(cropIm)
negimgBW.append(cv2.cvtColor(cropIm, cv2.COLOR_BGR2GRAY))
tr_label.append(neglabel)
print(i)
hf.create_dataset('negdigits', data=negimg)
hf.create_dataset('digits', data=img)
hf.create_dataset('negdigitsBW', data=negimgBW)
hf.create_dataset('digitsBW', data=imgBW)
hf.create_dataset('neglab', data=tr_label)
hf.create_dataset('labs5', data=tr_labels5)
hf.close()
def extract_testRGB():
test = os.path.join("test")
# dirname = 'finalProjectData/test/'
# out = sc.loadmat('finalProjectData/test/testDigits.mat')
dirname = 'test/'
out = sc.loadmat('test/testDigits.mat')
data = out["digitStruct"]
bbox = data['bbox'].squeeze()
name = data['name'].squeeze()
_,dm = data.shape
img = []
tr_labels5 = []
imgBW = []
sz = (48,48)
filler = [0, 10, 10, 10, 10, 10]
hf = h5py.File('datasets/test.h5', 'w')
for i in range(0,dm,1): #dm
im = cv2.imread(os.path.join(dirname,name[i][0].squeeze()))
b = bbox[i].flatten()
ht,wd,_ = im.shape
ha = np.max([0, np.int16(np.min(b["top"].squeeze()).squeeze())])
wa = np.max([0, np.int16(np.min(b["left"].squeeze()).squeeze())])
hb = np.int16(np.max(b["height"].squeeze()).squeeze()) + ha
wb = np.int16(np.sum(b["width"].squeeze()).squeeze()) + wa
h3 = (hb - ha) * .3
w3 = (wb - wa) * .3
ha = np.max([0, np.int16(ha - h3)])
hb = np.min([ht, np.int16(hb + h3)])
wa = np.max([0, np.int16(wa - w3)])
wb = np.min([wd, np.int16(wb + w3)])
if (wb-wa == 0) | (hb-ha == 0):
continue
numdig = b["label"].__len__()
if numdig>=5:
continue # skip >5 digits
bboxIm = im[ha:hb,wa:wb,:]
totIm = cv2.resize(bboxIm,sz)
img.append(totIm)
imgBW.append(cv2.cvtColor(totIm,cv2.COLOR_BGR2GRAY))
lab = np.copy(filler)
lab[0] = b["label"].__len__()
for n in range(0,numdig,1):
currlab = np.int16(b["label"][n].squeeze())
if currlab == 10:
currlab = 0
lab[n+1] = currlab
currLabel = np.asarray(lab,dtype = 'uint8')
tr_labels5.append(currLabel)
print(i)
hf.create_dataset('digits', data=img)
hf.create_dataset('digitsBW', data=imgBW)
hf.create_dataset('labs5', data=tr_labels5)
hf.close()
def extract_extraTrainRGB():
# out = sc.loadmat('E:/extra/digitStruct.mat')
# data = out["digitStruct"]
out = sc.loadmat('digitStruct.mat')
data = out["digitStruct"]
extrain = "E:/extra"
bbox = data['bbox'].squeeze()
name = data['name'].squeeze()
_, dm = data.shape
img = []
tr_labels5 = []
imgBW = []
sz = (48,48)
filler = [0, 10, 10, 10, 10, 10]
# open the file for writing
hf = h5py.File('datasets/extraTrain.h5', 'w')
for i in range(0,dm,1): #dm
im = cv2.imread(os.path.join(extrain,name[i][0].squeeze()))
b = bbox[i].flatten()
ht,wd,_ = im.shape
ha = np.max([0, np.int16(np.min(b["top"].squeeze()).squeeze())])
wa = np.max([0, np.int16(np.min(b["left"].squeeze()).squeeze())])
hb = np.int16(np.max(b["height"].squeeze()).squeeze()) + ha
wb = np.int16(np.sum(b["width"].squeeze()).squeeze()) + wa
h3 = (hb - ha) * .3
w3 = (wb - wa) * .3
ha = np.max([0, np.int16(ha - h3)])
hb = np.min([ht, np.int16(hb + h3)])
wa = np.max([0, np.int16(wa - w3)])
wb = np.min([wd, np.int16(wb + w3)])
if (wb-wa == 0) | (hb-ha == 0):
continue
numdig = b["label"].__len__()
if numdig>=5:
continue # skip >5 digits
bboxIm = im[ha:hb,wa:wb,:]
totIm = cv2.resize(bboxIm,sz)
img.append(totIm)
imgBW.append(cv2.cvtColor(totIm,cv2.COLOR_BGR2GRAY))
lab = np.copy(filler)
lab[0] = b["label"].__len__()
for n in range(0,numdig,1):
currlab = np.int16(b["label"][n].squeeze())
if currlab == 10:
currlab = 0
lab[n+1] = currlab
currLabel = np.asarray(lab,dtype = 'uint8')
tr_labels5.append(currLabel)
print(i)
hf.create_dataset('digits', data=img)
hf.create_dataset('digitsBW', data=imgBW)
hf.create_dataset('labs5', data=tr_labels5)
hf.close()
def preprocessDigDetector():
htr = h5py.File('datasets/training.h5', 'r')
hts = h5py.File('datasets/testing.h5', 'r')
htn = h5py.File('datasets/trainNegatives.h5', 'r')
numNeg = range(0,35000,1)
negLabs = htn["labs"][numNeg]
x = np.vstack((htr["train48"][:],hts["digit48"][:],htn["digit48"][numNeg])).astype('float32')
y = np.vstack((np.reshape(htr["labs5"][:,0],(len(htr["labs5"][:,0]),1)),
np.reshape(hts["labs5"][:,0],(len(hts["labs5"][:,0]),1)),
np.reshape(negLabs[:,0] ,(len(negLabs),1)))).astype('uint8')
y[y>0] = 1
# x = x/255.
for i in range(x.shape[0]):
x[i]-= np.mean(x[i].flatten(),axis = 0)
x = x - np.mean(x,axis=0)
x = x/np.std(x,axis = 0)
return x,y
def prepDataforCNN(numChannel = 1, feat_norm = False):
htr = h5py.File('datasets/train.h5', 'r')
hts = h5py.File('datasets/test.h5' , 'r')
hte = h5py.File('datasets/extraTrain.h5', 'r')
if numChannel == 1:
digits = htr["digitsBW"]
testdigits = hts["digitsBW"]
negdigits = htr["negdigitsBW"]
extdigits = hte["digitsBW"]
else:
digits = htr["digits"]
testdigits = hts["digits"]
negdigits = htr["negdigits"]
extdigits = hte["digits"]
trainlabs = htr["labs5"]
testlabs = hts["labs5"]
neglabs = htr["neglab"]
extlabs = hte["labs5"]
digits = digits[:]
testdigits = testdigits[:]
negdigits = negdigits[:]
extdigits = extdigits[:]
trainlabs = trainlabs[:]
testlabs = testlabs[:]
neglabs = neglabs[:]
negdigits = negdigits[:]
seed = 25
np.random.seed(seed)
countNeg = 30000
countX = 90000
negIdx = np.random.randint(0,negdigits.shape[0],countNeg)
numNegTs = np.arange(negdigits.shape[0] - 500,negdigits.shape[0],1)
numtran = np.arange(0,countX,1) # Ran on 0. Tried on 80000(with 2 512). % Tried on 30000(50%) % Tried on 12K
# numtest = np.arange(extdigits.shape[0] - 1,extdigits.shape[0],1)
# negIdx = np.random.randint(0,negdigits.shape[0],3000)# Ran on 0. Tried on 80000(with 2 512). % Tried on 30000(50%) Tried on 12k
# numtran = range(0,50,1) # Ran on 0. Tried on 80000(with 2 512). % Tried on 30000(50%) % Tried on 12K
xtrdigits = extdigits[numtran,:]
xtrlab = extlabs[numtran,:]
ntrdigits = negdigits[negIdx]
ntrlab = neglabs[negIdx,:]
ntest = negdigits[numNegTs,:]
ntslab = neglabs[numNegTs,:]
preTrain = np.vstack((digits,xtrdigits,ntrdigits)).astype('float32') #
preTest = np.vstack((testdigits,ntest)).astype('float32') #xtest
# Lets remove digits > 4. Only 9 cases of n = 5
trainlabs = np.vstack((trainlabs,xtrlab,ntrlab)).astype('uint8') #xtrlab
testlabs = np.vstack((testlabs,ntslab)).astype('uint8') #xtslab
ind = np.argwhere(trainlabs[:,0]<5)
ind = ind[:,0]
preTrain = preTrain[ind,:]
trainlabs = trainlabs[ind,:]
nb = np.reshape(np.asarray(trainlabs[:,0] > 0, dtype = 'uint8'),(trainlabs.shape[0],1))
trl = np.hstack((trainlabs, nb))
ind = np.argwhere(testlabs[:,0]<5)
ind = ind[:,0]
preTest = preTest[ind,:]
testlabs = testlabs[ind,:]
nb = np.reshape(np.asarray(testlabs[:,0] > 0, dtype = 'uint8'),(testlabs.shape[0],1))
tsl = np.hstack((testlabs, nb))
# train = np.float64(preTrain/255.)
# test = np.float64(preTest/255.)
train = np.float64(preTrain)
test = np.float64(preTest)
for i in range(preTrain.shape[0]):
if numChannel >1:
for channel in range(0,numChannel,1):
train[i][:,:,channel] -= np.mean(preTrain[i][:,:,channel].flatten(),axis = 0)
else:
train[i] -= np.mean(preTrain[i].flatten(),axis = 0)
for i in range(preTest.shape[0]):
if numChannel > 1:
for channel in range(0,numChannel,1):
test[i][:,:,channel] -= np.mean(preTest[i][:,:,channel] .flatten(),axis = 0)
else:
test[i] -= np.mean(preTest[i].flatten(),axis = 0)
if feat_norm:
M = np.mean(train, axis = 0)
train = train - M
sd = np.std(train, axis = 0)
train = train/sd
test = test - M
test = test/sd
featNorm = {'mean': M, 'std': sd}
if numChannel > 1:
with open('datasets/BGRnorm.pickle', 'wb') as handle:
pickle.dump(featNorm, handle, protocol = pickle.HIGHEST_PROTOCOL)
else:
with open('datasets/BWnorm.pickle', 'wb') as handle:
pickle.dump(featNorm, handle, protocol = pickle.HIGHEST_PROTOCOL)
# np.save('datasets/BWnorm.npz', featNorm)
numtrain = train.shape[0]
numtest = test.shape[0]
row = train.shape[1]
col = train.shape[2]
train = np.reshape(train,(numtrain,row,col,numChannel))
test = np.reshape(test, (numtest, row,col,numChannel))
p = 0.9
seed = 25
np.random.seed(seed)
split = np.int32(np.round((p * numtrain))) #.85
idx = np.random.permutation(numtrain)
trIdx = idx[0:split]
vlIdx = idx[split:numtrain]
trlab = [ np.reshape(trl[:,0],(numtrain,1)).astype('uint8'),
np.reshape(trl[:,1],(numtrain,1)).astype('uint8'),
np.reshape(trl[:,2],(numtrain,1)).astype('uint8'),
np.reshape(trl[:,3],(numtrain,1)).astype('uint8'),
np.reshape(trl[:,4],(numtrain,1)).astype('uint8'),
np.reshape(trl[:,6],(numtrain,1)).astype('uint8')]
tslab = [ np.reshape(tsl[:,0],(numtest,1)).astype('uint8'),
np.reshape(tsl[:,1],(numtest,1)).astype('uint8'),
np.reshape(tsl[:,2],(numtest,1)).astype('uint8'),
np.reshape(tsl[:,3],(numtest,1)).astype('uint8'),
np.reshape(tsl[:,4],(numtest,1)).astype('uint8'),
np.reshape(tsl[:,6],(numtest,1)).astype('uint8')]
ctrlab = [trlab[0][trIdx], trlab[1][trIdx], trlab[2][trIdx], trlab[3][trIdx], trlab[4][trIdx], trlab[5][trIdx]]
cvlab = [trlab[0][vlIdx], trlab[1][vlIdx], trlab[2][vlIdx], trlab[3][vlIdx], trlab[4][vlIdx], trlab[5][vlIdx]]
ctslab = [tslab[0], tslab[1], tslab[2], tslab[3], tslab[4], tslab[5]]
data = {'trainX': train[trIdx], 'trainY': ctrlab,
'testX': test, 'testY': ctslab,
'valdX': train[vlIdx], 'valdY': cvlab}
return data