SYMBOL INDEX (103 symbols across 15 files)

FILE: src/data_gen/data_generator.py
  class DataGenerator (line 15) | class DataGenerator(object):
    method __init__ (line 17) | def __init__(self, category, annfile):
    method get_dim_order (line 22) | def get_dim_order(self):
    method get_dataset_size (line 26) | def get_dataset_size(self):
    method generator_with_mask_ohem (line 29) | def generator_with_mask_ohem(self, graph, kerasModel, batchSize=16, in...
    method _initialize (line 83) | def _initialize(self):
    method _load_anno (line 86) | def _load_anno(self):
    method _prcoess_img (line 98) | def _prcoess_img(self, dfrow, inputSize, rotateFlag, flipFlag, cropFla...
    method __generate_hmap (line 148) | def __generate_hmap(self, cvmat, kpAnnolst):
    method flip_image (line 171) | def flip_image(self, orgimg, orgKpAnolst):
    method flip_annlst (line 177) | def flip_annlst(self, kpannlst, imgshape):

FILE: src/data_gen/data_process.py
  function normalize_image (line 7) | def normalize_image(cvmat):
  function resize_image (line 13) | def resize_image(cvmat, targetWidth, targetHeight):
  function pad_image (line 40) | def pad_image(cvmat, kpAnno, targetWidth, targetHeight):
  function pad_image_inference (line 74) | def pad_image_inference(cvmat, targetWidth, targetHeight):
  function rotate_image (line 104) | def rotate_image(cvmat, kpAnnLst, rotateAngle):
  function rotate_image_with_invrmat (line 133) | def rotate_image_with_invrmat(cvmat, rotateAngle):
  function rotate_mask (line 158) | def rotate_mask(mask, rotateAngle):
  function rotate_image_float (line 164) | def rotate_image_float(cvmat, rotateAngle, borderValue=(0.0, 0.0, 0.0)):
  function crop_image (line 187) | def crop_image(cvmat, kpAnnLst, lowLimitRatio, upLimitRatio):

FILE: src/data_gen/dataset.py
  function getKpNum (line 3) | def getKpNum(category):
  function getFlipKeys (line 49) | def getFlipKeys(category):
  function getFlipMapID (line 70) | def getFlipMapID(category, partid):
  function getKpKeys (line 76) | def getKpKeys(category):
  function fill_dataframe (line 116) | def fill_dataframe(kplst, category, dfrow):
  function get_kp_index_from_allkeys (line 129) | def get_kp_index_from_allkeys(kpname):
  function generate_input_mask (line 138) | def generate_input_mask(image_category, shape, nobgFlag=True):

FILE: src/data_gen/kpAnno.py
  class KpAnno (line 4) | class KpAnno(object):
    method __init__ (line 8) | def __init__(self, x, y, visibility):
    method readFromStr (line 14) | def readFromStr(cls, xstr):
    method applyScale (line 22) | def applyScale(cls, kpAnno, scale):
    method applyRotate (line 29) | def applyRotate(cls, kpAnno, rotateMatrix):
    method applyOffset (line 35) | def applyOffset(cls, kpAnno, offset):
    method calcDistance (line 42) | def calcDistance(kpA, kpB):

FILE: src/data_gen/ohem.py
  function generate_topk_mask_ohem (line 10) | def generate_topk_mask_ohem(input_data, gthmap, keras_model, graph, topK...
  function adjsut_mask (line 58) | def adjsut_mask(loss, input_mask,  topk):

FILE: src/data_gen/utils.py
  function make_gaussian (line 6) | def make_gaussian(width, height, sigma=3, center=None):
  function split_csv_train_val (line 25) | def split_csv_train_val(allcsv, traincsv, valcsv, ratio=0.8):
  function np_euclidean_l2 (line 41) | def np_euclidean_l2(x, y):
  function load_annotation_from_df (line 48) | def load_annotation_from_df(df, category):

FILE: src/eval/eval_callback.py
  class NormalizedErrorCallBack (line 7) | class NormalizedErrorCallBack(keras.callbacks.Callback):
    method __init__ (line 9) | def __init__(self, foldpath, category, multiOut=False, resumeFolder=No...
    method get_folder_path (line 23) | def get_folder_path(self):
    method on_epoch_end (line 26) | def on_epoch_end(self, epoch, logs=None):

FILE: src/eval/evaluation.py
  class Evaluation (line 21) | class Evaluation(object):
    method __init__ (line 22) | def __init__(self, category, modelFile):
    method init_from_model (line 28) | def init_from_model(self, model):
    method eval (line 32) | def eval(self, multiOut=False, details=False, flip=True):
    method _initialize (line 55) | def _initialize(self, modelFile):
    method _initialize_network (line 59) | def _initialize_network(self, modelFile):
    method _load_anno (line 62) | def _load_anno(self):
    method _get_groundtruth_kpAnno (line 74) | def _get_groundtruth_kpAnno(self, dfrow):
    method _net_inference_with_mask (line 81) | def _net_inference_with_mask(self, imgFile, imgCategory):
    method _heatmap_sum (line 101) | def _heatmap_sum(self, heatmaplst):
    method predict_kp (line 107) | def predict_kp(self, imgFile, imgCategory, multiOutput=False):
    method predict_kp_with_flip (line 129) | def predict_kp_with_flip(self, imgFile, imgCategory):
    method _net_inference_flip (line 144) | def _net_inference_flip(self, imgFile, imgCategory):
    method predict_kp_with_rotate (line 190) | def predict_kp_with_rotate(self, imgFile, imgCategory):
    method _net_inference_rotate (line 210) | def _net_inference_rotate(self, imgFile, imgCategory):
    method _flip_out_heatmap (line 248) | def _flip_out_heatmap(self, flipout):
    method get_normized_distance (line 258) | def get_normized_distance(category, gtKp):
    method calc_ne_score (line 290) | def calc_ne_score(category, dtKp, gtKp):

FILE: src/eval/post_process.py
  function post_process_heatmap (line 7) | def post_process_heatmap(heatMap, kpConfidenceTh=0.2):
  function non_max_supression (line 23) | def non_max_supression(plain, windowSize=3, threshold=1e-6):

FILE: src/top/demo.py
  function visualize_keypoint (line 13) | def visualize_keypoint(imageName, category, dtkp):
  function demo (line 22) | def demo(modelfile):

FILE: src/top/test.py
  function get_best_single_model (line 16) | def get_best_single_model(valfile):
  function fill_dataframe (line 40) | def fill_dataframe(kplst, keys, dfrow, image_category):
  function get_kp_from_dict (line 51) | def get_kp_from_dict(mdict, image_category, image_id):
  function submission (line 58) | def submission(pklpath):
  function load_image_names (line 91) | def load_image_names(annfile, category):
  function main_test (line 97) | def main_test(savepath, modelpath, augmentFlag):

FILE: src/unet/fashion_net.py
  class FashionNet (line 19) | class FashionNet(object):
    method __init__ (line 21) | def __init__(self, inputHeight, inputWidth, nClasses):
    method build_model (line 26) | def build_model(self, modelName='v2', show=False):
    method train (line 37) | def train(self, category, batchSize=8, epochs=20, lrschedule=False):
    method load_model (line 53) | def load_model(self, netWeightFile):
    method resume_train (line 56) | def resume_train(self, category, pretrainModel, modelName, initEpoch, ...
    method predict_image (line 80) | def predict_image(self, imgfile):
    method predict (line 91) | def predict(self, input):

FILE: src/unet/refinenet.py
  function Res101RefineNetDilated (line 9) | def Res101RefineNetDilated(n_classes, inputHeight, inputWidth):
  function Res101RefineNetStacked (line 13) | def Res101RefineNetStacked(n_classes, inputHeight, inputWidth, nStackNum):
  function euclidean_loss (line 17) | def euclidean_loss(x, y):
  function create_global_net (line 21) | def create_global_net(lowlevelFeatures, n_classes):
  function create_refine_net (line 62) | def create_refine_net(inputFeatures, n_classes):
  function create_refine_net_bottleneck (line 93) | def create_refine_net_bottleneck(inputFeatures, n_classes):
  function create_stack_refinenet (line 125) | def create_stack_refinenet(inputFeatures, n_classes, layerName):
  function create_global_net_dilated (line 157) | def create_global_net_dilated(lowlevelFeatures, n_classes):
  function build_network_resnet101 (line 199) | def build_network_resnet101(inputHeight, inputWidth, n_classes, frozenla...
  function build_network_resnet101_stack (line 219) | def build_network_resnet101_stack(inputHeight, inputWidth, n_classes, nS...
  function load_backbone_res101net (line 240) | def load_backbone_res101net(inputHeight, inputWidth):

FILE: src/unet/refinenet_mask_v3.py
  function Res101RefineNetMaskV3 (line 9) | def Res101RefineNetMaskV3(n_classes, inputHeight, inputWidth, nStackNum):
  function euclidean_loss (line 13) | def euclidean_loss(x, y):
  function apply_mask_to_output (line 16) | def apply_mask_to_output(output, mask):
  function build_resnet101_stack_mask_v3 (line 20) | def build_resnet101_stack_mask_v3(inputHeight, inputWidth, n_classes, nS...

FILE: src/unet/resnet101.py
  class Scale (line 38) | class Scale(Layer):
    method __init__ (line 73) | def __init__(self,
    method build (line 87) | def build(self, input_shape):
    method call (line 103) | def call(self, x, mask=None):
    method get_config (line 113) | def get_config(self):
  function identity_block (line 119) | def identity_block(input_tensor, kernel_size, filters, stage, block):
  function conv_block (line 165) | def conv_block(input_tensor,
  function ResNet101 (line 224) | def ResNet101(include_top=True,