Cleaning & commenting #3 - refactoring constants to Config class

Edmond Yoo

2018-10-13 23d94e4846bf4ec13069703a28b1d776f4bbe44f

 transform_data.py

@@ -1,20 +1,18 @@
import os
import random
import math
import cv2
import numpy as np
import imutils
import pandas as pd
import fetch_data
import generate_data
from shapely import geometry
import imgaug as ia
from imgaug import augmenters as iaa
from imgaug import parameters as iap
import imutils
import math
import numpy as np
import os
import pandas as pd
import random
from shapely import geometry

card_mask = cv2.imread('data/mask.png')
data_dir = os.path.abspath('/media/win10/data')
darknet_dir = os.path.abspath('.')
import fetch_data
import generate_data
from config import Config


def key_pts_to_yolo(key_pts, w_img, h_img):
@@ -104,6 +102,7 @@
        """
        self.check_visibility(visibility=visibility)
        img_result = np.zeros((self.height, self.width, 3), dtype=np.uint8)
        card_mask = cv2.imread(Config.card_mask_path)

        for card in self.cards:
            card_x = int(card.x + 0.5)
@@ -494,15 +493,15 @@
    random.seed()
    ia.seed(random.randrange(10000))

    bg_images = generate_data.load_dtd(dtd_dir='%s/dtd/images' % data_dir, dump_it=False)
    bg_images = generate_data.load_dtd(dtd_dir='%s/dtd/images' % Config.data_dir, dump_it=False)
    background = generate_data.Backgrounds(images=bg_images)

    card_pool = pd.DataFrame()
    for set_name in fetch_data.all_set_list:
        df = fetch_data.load_all_cards_text('%s/csv/%s.csv' % (data_dir, set_name))
    for set_name in Config.all_set_list:
        df = fetch_data.load_all_cards_text('%s/csv/%s.csv' % (Config.data_dir, set_name))
        card_pool = card_pool.append(df)
    class_ids = {}
    with open('%s/obj.names' % data_dir) as names_file:
    with open('%s/obj.names' % Config.data_dir) as names_file:
        class_name_list = names_file.read().splitlines()
        for i in range(len(class_name_list)):
            class_ids[class_name_list[i]] = i
@@ -522,12 +521,14 @@

        # Use 2 to 5 cards per generator
        for _, card_info in card_pool.sample(random.randint(2, 5)).iterrows():
            img_name = '%s/card_img/png/%s/%s_%s.png' % (data_dir, card_info['set'], card_info['collector_number'],
            img_name = '%s/card_img/png/%s/%s_%s.png' % (Config.data_dir, card_info['set'],
                                                         card_info['collector_number'],
                                                         fetch_data.get_valid_filename(card_info['name']))
            out_name += '%s%s_' % (card_info['set'], card_info['collector_number'])
            card_img = cv2.imread(img_name)
            if card_img is None:
                fetch_data.fetch_card_image(card_info, out_dir='%s/card_img/png/%s' % (data_dir, card_info['set']))
                fetch_data.fetch_card_image(card_info, out_dir='%s/card_img/png/%s' % (Config.data_dir,
                                                                                       card_info['set']))
                card_img = cv2.imread(img_name)
            if card_img is None:
                print('WARNING: card %s is not found!' % img_name)
@@ -547,15 +548,15 @@
            if i % 3 == 0:
                generator.generate_non_obstructive()
                generator.export_training_data(visibility=0.0, out_name='%s/train/non_obstructive_update/%s%d'
                                                                        % (data_dir, out_name, j), aug=seq)
                                                                        % (Config.data_dir, out_name, j), aug=seq)
            elif i % 3 == 1:
                generator.generate_horizontal_span(theta=random.uniform(-math.pi, math.pi))
                generator.export_training_data(visibility=0.0, out_name='%s/train/horizontal_span_update/%s%d'
                                                                        % (data_dir, out_name, j), aug=seq)
                                                                        % (Config.data_dir, out_name, j), aug=seq)
            else:
                generator.generate_vertical_span(theta=random.uniform(-math.pi, math.pi))
                generator.export_training_data(visibility=0.0, out_name='%s/train/vertical_span_update/%s%d'
                                                                        % (data_dir, out_name, j), aug=seq)
                                                                        % (Config.data_dir, out_name, j), aug=seq)

            #generator.generate_horizontal_span(theta=random.uniform(-math.pi, math.pi))
            #generator.render(display=True, aug=seq, debug=True)