Cleaning & commenting #3 - refactoring constants to Config class

Edmond Yoo

2018-10-13 23d94e4846bf4ec13069703a28b1d776f4bbe44f

 opencv_dnn.py

@@ -9,8 +9,8 @@
from PIL import Image
import time

from config import Config
import fetch_data
import transform_data


"""
@@ -54,7 +54,7 @@
        for card_name in card_names:
            # Fetch the image - name can be found based on the card's information
            card_info['name'] = card_name
            img_name = '%s/card_img/png/%s/%s_%s.png' % (transform_data.data_dir, card_info['set'],
            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']))
            card_img = cv2.imread(img_name)
@@ -62,7 +62,7 @@
            # If the image doesn't exist, download it from the URL
            if card_img is None:
                fetch_data.fetch_card_image(card_info,
                                            out_dir='%s/card_img/png/%s' % (transform_data.data_dir, card_info['set']))
                                            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)
@@ -284,7 +284,7 @@
    # Find the contour
    _, cnts, hier = cv2.findContours(img_erode, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    if len(cnts) == 0:
        print('no contours')
        #print('no contours')
        return []

    # The hierarchy from cv2.findContours() is similar to a tree: each node has an access to the parent, the first child
@@ -343,7 +343,7 @@
        card_set = key[key.find('(') + 1:key.find(')')]
        confidence = sum(val) / f_len
        card_info = card_pool[(card_pool['name'] == card_name) & (card_pool['set'] == card_set)].iloc[0]
        img_name = '%s/card_img/tiny/%s/%s_%s.png' % (transform_data.data_dir, card_info['set'],
        img_name = '%s/card_img/tiny/%s/%s_%s.png' % (Config.data_dir, card_info['set'],
                                                      card_info['collector_number'],
                                                      fetch_data.get_valid_filename(card_info['name']))
        # If the card image is not found, just leave it blank
@@ -542,8 +542,8 @@
    else:
        # Merge database for all cards, then calculate pHash values of each, store them
        df_list = []
        for set_name in fetch_data.all_set_list:
            csv_name = '%s/csv/%s.csv' % (transform_data.data_dir, set_name)
        for set_name in Config.all_set_list:
            csv_name = '%s/csv/%s.csv' % (Config.data_dir, set_name)
            df = fetch_data.load_all_cards_text(csv_name)
            df_list.append(df)
        card_pool = pd.concat(df_list, sort=True)