Cleaning & commenting #2 - updating comments & docstrings

Edmond Yoo

2018-10-13 c59db54775606349f6ba5c6cab7fcb34498bb31d

 generate_data.py

@@ -5,15 +5,18 @@
import math
import random
import os
import re
import cv2
import fetch_data
import sys
import numpy as np
import pandas as pd
import transform_data

# Referenced from geaxgx's playing-card-detection: https://github.com/geaxgx/playing-card-detection

class Backgrounds:
    """
    Container class for all background images for generator
    Referenced from geaxgx's playing-card-detection: https://github.com/geaxgx/playing-card-detection
    """
    def __init__(self, images=None, dumps_dir='data/dtd/images'):
        if images is not None:
            self._images = images
@@ -39,8 +42,15 @@


def load_dtd(dtd_dir='data/dtd/images', dump_it=True, dump_batch_size=1000):
    """
    Load Describable Texture Dataset (DTD) from local
    :param dtd_dir: path of the DTD images folder
    :param dump_it: flag for pickling it
    :param dump_batch_size: # of images stored per pickle file
    :return: list of all DTD images
    """
    if not os.path.exists(dtd_dir):
        print('Warning: directory for DTD 5s doesn\'t exist.' % dtd_dir)
        print('Warning: directory for DTD %s doesn\'t exist.' % dtd_dir)
        print('You can download the dataset using this command:'
              '!wget https://www.robots.ox.ac.uk/~vgg/data/dtd/download/dtd-r1.0.1.tar.gz')
        return []
@@ -63,54 +73,62 @@


def apply_bounding_box(img, card_info, display=False):
    # Mana symbol - They are located on the top right side of the card, next to the name.
    # Their position is stationary, and is right-aligned.
    has_mana_cost = isinstance(card_info['mana_cost'], str)  # Cards with no mana cost will have nan
    is_planeswalker = 'Planeswalker' in card_info['type_line']
    if has_mana_cost:
        mana_cost = re.findall('\{(.*?)\}', card_info['mana_cost'])
        x2 = 683
        y1 = 67

        # Cards with specific type or from old sets have their symbol at a different position
        if is_planeswalker:
            y1 -= 17
        if card_info['set'] in ['8ed', 'mrd', 'dst', '5dn']:
            y1 -= 2

        for i in reversed(range(len(mana_cost))):
            # Hybrid mana symbol are larger than a normal symbol
            is_hybrid = '/' in mana_cost[i]
            if is_hybrid:
                box = [(x2 - 47, y1 - 8), (x2 + 2, y1 + 43)]  # (x1, y1), (x2, y2)
                x2 -= 45
            else:
                box = [(x2 - 39, y1), (x2, y1 + 41)]  # (x1, y1), (x2, y2)
                x2 -= 37
            img_symbol = img[box[0][1]:box[1][1], box[0][0]:box[1][0]]
            if display:
                cv2.imshow('symbol', img_symbol)
                cv2.waitKey(0)
    """
    Given a card image, extract specific features that can be used to train a model.
    Note: Mana & set symbols are deprecated from the feature list. Refer to previous commits for their implementation:
    https://github.com/hj3yoo/mtg_card_detector/tree/bb34d4e13da0f4753fbdefee837f54b16149d3ef
    :param img: image of the card
    :param card_info: characteristics of this card
    :param display: flag for displaying the extracted features
    :return:
    """
    # List of detected objects to be fed into the neural net
    # The first object is the entire card
    detected_object_list = [transform_data.ExtractedObject('card', [(0, 0), (len(img[0]), 0), (len(img[0]), len(img)),
                                                                    (0, len(img))])]
    return detected_object_list


def main():
    random.seed()
    #bg_images = load_dtd()
    #bg = Backgrounds()
    #bg.get_random(display=True)
    df = fetch_data.load_all_cards_text('data/csv/dgm.csv')
    #repeat = 'y'
    while True:
        card_info = df.iloc[random.randint(0, df.shape[0] - 1)]
        print(card_info['name'])
        card_img = cv2.imread('data/png/%s/%s_%s.png' % (card_info['set'], card_info['collector_number'],
                                                         fetch_data.get_valid_filename(card_info['name'])))

    card_pool = pd.DataFrame()
    for set_name in fetch_data.all_set_list:
        df = fetch_data.load_all_cards_text('data/csv/%s.csv' % set_name)
        #for _ in range(3):
        #    card_info = df.iloc[random.randint(0, df.shape[0] - 1)]
        #    # Currently ignoring planeswalker cards due to their different card layout
        #    is_planeswalker = 'Planeswalker' in card_info['type_line']
        #    if not is_planeswalker:
        #        card_pool = card_pool.append(card_info)
        card_pool = card_pool.append(df)
    '''
    print(card_pool)
    mana_symbol_set = set()
    for _, card_info in card_pool.iterrows():
        has_mana_cost = isinstance(card_info['mana_cost'], str)
        if has_mana_cost:
            mana_cost = re.findall('\{(.*?)\}', card_info['mana_cost'])
            for symbol in mana_cost:
                mana_symbol_set.add(symbol)

    print(mana_symbol_set)
    '''

    for _, card_info in card_pool.iterrows():
        img_name = '../usb/data/png/%s/%s_%s.png' % (card_info['set'], card_info['collector_number'],
                                                     fetch_data.get_valid_filename(card_info['name']))
        print(img_name)
        card_img = cv2.imread(img_name)
        if card_img is None:
            fetch_data.fetch_card_image(card_info)
            card_img = cv2.imread('data/png/%s/%s_%s.png' % (card_info['set'], card_info['collector_number'],
                                                             fetch_data.get_valid_filename(card_info['name'])))
        sys.stdout.flush()
        apply_bounding_box(card_img, card_info, display=True)
        #repeat = input('y to repeat, n to finish')
            fetch_data.fetch_card_image(card_info, out_dir='../usb/data/png/%s' % card_info['set'])
            card_img = cv2.imread(img_name)
        detected_object_list = apply_bounding_box(card_img, card_info, display=True)
        print(detected_object_list)

    return