From ada8e8118643e57f2180351ab359369d06f8f70e Mon Sep 17 00:00:00 2001
From: Edmond Yoo <hj3yoo@uwaterloo.ca>
Date: Thu, 30 Aug 2018 21:45:23 +0000
Subject: [PATCH] Coordinate conversion, etc
---
generate_data.py | 22 +++++++++++-----------
1 files changed, 11 insertions(+), 11 deletions(-)
diff --git a/generate_data.py b/generate_data.py
index b7525dd..54f4d4d 100644
--- a/generate_data.py
+++ b/generate_data.py
@@ -11,6 +11,7 @@
import sys
import numpy as np
import pandas as pd
+from transform_data import ExtractedObject
# Referenced from geaxgx's playing-card-detection: https://github.com/geaxgx/playing-card-detection
class Backgrounds:
@@ -63,8 +64,9 @@
def apply_bounding_box(img, card_info, display=False):
- # List of (object class, bounding box pts) pair of each objects
- object_info_list = []
+ # List of detected objects to be fed into the neural net
+ # The first object is the entire card
+ detected_object_list = [ExtractedObject('card', [(0, 0), (len(img[0]), 0), (len(img[0]), len(img)), (0, len(img))])]
# 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
@@ -95,7 +97,7 @@
# Append them to the list of bounding box with the appropriate label
symbol_name = 'mana_symbol:' + mana_cost[i]
key_pts = [(x1, y1), (x2, y1), (x2, y2), (x1, y2)]
- object_info_list.append((symbol_name, key_pts))
+ detected_object_list.append(ExtractedObject(symbol_name, key_pts))
if display:
img_symbol = img[y1:y2, x1:x2]
@@ -160,7 +162,7 @@
# Append them to the list of bounding box with the appropriate label
symbol_name = 'set_symbol:' + card_info['set']
key_pts = [(x1, y1), (x2, y1), (x2, y2), (x1, y2)]
- object_info_list.append((symbol_name, key_pts))
+ detected_object_list.append(ExtractedObject(symbol_name, key_pts))
if display:
img_symbol = img[y1:y2, x1:x2]
@@ -175,19 +177,17 @@
# Image box - the large image on the top half of the card
# TODO
- return object_info_list
+ return detected_object_list
def main():
+ random.seed()
#bg_images = load_dtd()
#bg = Backgrounds()
#bg.get_random(display=True)
card_pool = pd.DataFrame()
- for set_name in ['8ed', 'mrd', 'dst', '5dn', 'chk', 'bok', 'sok', '9ed', 'rav', 'gpt', 'dis', 'csp', 'tsp', 'plc',
- 'fut', '10e', 'lrw', 'mor', 'shm', 'eve', 'ala', 'con', 'arb', 'm10', 'zen', 'wwk', 'roe', 'm11',
- 'som', 'mbs', 'nph', 'm12', 'isd', 'dka', 'avr', 'm13', 'rtr', 'gtc', 'dgm', 'm14', 'ths', 'bng',
- 'jou']:
+ 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)]
@@ -204,8 +204,8 @@
if card_img is None:
fetch_data.fetch_card_image(card_info, out_dir='../usb/data/png/%s' % card_info['set'])
card_img = cv2.imread(img_name)
- object_list_info = apply_bounding_box(card_img, card_info, display=True)
- print(object_list_info)
+ detected_object_list = apply_bounding_box(card_img, card_info, display=True)
+ print(detected_object_list)
return
--
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