def submission(pklpath):
xdf = pd.read_csv("../../data/train/Annotations/train.csv")
trainKeys = xdf.keys()
testdf = pd.read_csv("../../data/test/test.csv")
print len(testdf), " samples in test.csv"
mdict = dict()
for xfile in os.listdir(pklpath):
if xfile.endswith('.pkl'):
category = xfile.strip().split('.')[0]
pkl = open(os.path.join(pklpath, xfile))
mdict[category] = pickle.load(pkl)
print testdf.keys()
print mdict.keys()
submissionDf = pd.DataFrame(columns=trainKeys,
index=np.arange(testdf.shape[0]))
submissionDf = submissionDf.fillna(value='-1_-1_-1')
submissionDf['image_id'] = testdf['image_id']
submissionDf['image_category'] = testdf['image_category']
for _index, _row in submissionDf.iterrows():
image_id = _row['image_id']
image_category = _row['image_category']
kplst = get_kp_from_dict(mdict, image_category, image_id)
fill_dataframe(kplst, getKpKeys('all')[1:], _row, image_category)
print len(submissionDf), "save to ", os.path.join(pklpath,
'submission.csv')
submissionDf.to_csv(os.path.join(pklpath, 'submission.csv'), index=False)
def visualize_keypoint(imageName, category, dtkp):
cvmat = cv2.imread(imageName)
for key in getKpKeys(category)[1:]:
index = get_kp_index_from_allkeys(key)
_kp = dtkp[index]
cv2.circle(cvmat,
center=(_kp.x, _kp.y),
radius=7,
color=(1.0, 0.0, 0.0),
thickness=2)
cv2.imshow('demo', cvmat)
cv2.waitKey()
def _prcoess_img(self, dfrow, inputSize, rotateFlag, flipFlag, cropFlag, nobgFlag):
mlist = dfrow[getKpKeys(self.category)]
imgName, kpStr = mlist[0], mlist[1:]
# read kp annotation from csv file
kpAnnlst = list()
for _kpstr in kpStr:
_kpAn = KpAnno.readFromStr(_kpstr)
kpAnnlst.append(_kpAn)
assert (len(kpAnnlst) == getKpNum(self.category)), str(len(kpAnnlst))+" is not the same as "+str(getKpNum(self.category))
xcvmat = cv2.imread(os.path.join(self.train_img_path, imgName))
if xcvmat is None:
return None, None
#flip as first operation.
# flip image
if random.choice([0, 1]) and flipFlag:
xcvmat, kpAnnlst = self.flip_image(xcvmat, kpAnnlst)
#if cropFlag:
# xcvmat, kpAnnlst = crop_image(xcvmat, kpAnnlst, 0.8, 0.95)
# pad image to 512x512
paddedImg, kpAnnlst = pad_image(xcvmat, kpAnnlst, inputSize[0], inputSize[1])
assert (len(kpAnnlst) == getKpNum(self.category)), str(len(kpAnnlst)) + " is not the same as " + str(
getKpNum(self.category))
# output ground truth heatmap is 256x256
trainGtHmap = self.__generate_hmap(paddedImg, kpAnnlst)
if random.choice([0,1]) and rotateFlag:
rAngle = np.random.randint(-1*40, 40)
rotatedImage, _ = rotate_image(paddedImg, list(), rAngle)
rotatedGtHmap = rotate_mask(trainGtHmap, rAngle)
else:
rotatedImage = paddedImg
rotatedGtHmap = trainGtHmap
# resize image
resizedImg = cv2.resize(rotatedImage, inputSize)
resizedGtHmap = cv2.resize(rotatedGtHmap, (inputSize[0]//2, inputSize[1]//2))
return normalize_image(resizedImg), resizedGtHmap
def _get_groundtruth_kpAnno(self, dfrow):
mlist = dfrow[getKpKeys(self.category)]
imgName, kpStr = mlist[0], mlist[1:]
# read kp annotation from csv file
kpAnnlst = [KpAnno.readFromStr(_kpstr) for _kpstr in kpStr]
return kpAnnlst