From 176260d82a4d82ce4ce1f09cd6139a50e1a2aa84 Mon Sep 17 00:00:00 2001
From: Edmond Yoo <hj3yoo@uwaterloo.ca>
Date: Sun, 16 Sep 2018 06:29:06 +0000
Subject: [PATCH] Card matching algo
---
opencv_dnn.py | 120 ++++++++++++++++++++++++++++++++++-------------------------
1 files changed, 69 insertions(+), 51 deletions(-)
diff --git a/opencv_dnn.py b/opencv_dnn.py
index 8595505..1e71983 100644
--- a/opencv_dnn.py
+++ b/opencv_dnn.py
@@ -7,28 +7,57 @@
import math
import random
from PIL import Image
-from .. import fetch_data
-from .. import transform_data
+import fetch_data
+import transform_data
card_width = 315
card_height = 440
-df = fetch_data.load_all_cards_text('%s/csv/rsv.csv' % transform_data.data_dir)
-df['art_hash'] = np.NaN
-for _, card_info in card_pool.iterrows():
- img_name = '%s/card_img/png/%s/%s_%s.png' % (data_dir, card_info['set'], card_info['collector_number'],
- fetch_data.get_valid_filename(card_info['name']))
- 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']))
+
+def calc_image_hashes(card_pool, save_to=None):
+ card_pool['art_hash'] = np.NaN
+ for ind, card_info in card_pool.iterrows():
+ if ind % 100 == 0:
+ print(ind)
+ img_name = '%s/card_img/png/%s/%s_%s.png' % (transform_data.data_dir, card_info['set'],
+ card_info['collector_number'],
+ fetch_data.get_valid_filename(card_info['name']))
card_img = cv2.imread(img_name)
- if card_img is None:
- print('WARNING: card %s is not found!' % img_name)
- img_art = Image.fromarray(card_img[121:580, 63:685])
- card_info['art_hash'] = ih.phash(img_card, hash_size=32, highfreq_factor=4)
+ 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']))
+ card_img = cv2.imread(img_name)
+ if card_img is None:
+ print('WARNING: card %s is not found!' % img_name)
+ img_art = Image.fromarray(card_img[121:580, 63:685])
+ art_hash = ih.phash(img_art, hash_size=32, highfreq_factor=4)
+ card_pool.at[ind, 'art_hash'] = art_hash
+ img_card = Image.fromarray(card_img)
+ card_hash = ih.phash(img_card, hash_size=32, highfreq_factor=4)
+ card_pool.at[ind, 'card_hash'] = card_hash
+ card_pool = card_pool[['artist', 'border_color', 'collector_number', 'color_identity', 'colors', 'flavor_text',
+ 'image_uris', 'mana_cost', 'legalities', 'name', 'oracle_text', 'rarity', 'type_line',
+ 'set', 'set_name', 'power', 'toughness', 'art_hash', 'card_hash']]
+ if save_to is not None:
+ card_pool.to_pickle(save_to)
+ return card_pool
-print(df['art_hash'])
-
+'''
+df_list = []
+for set_name in fetch_data.all_set_list:
+ csv_name = '%s/csv/%s.csv' % (transform_data.data_dir, set_name)
+ df = fetch_data.load_all_cards_text(csv_name)
+ df_list.append(df)
+ #print(df)
+card_pool = pd.concat(df_list)
+card_pool.reset_index(drop=True, inplace=True)
+card_pool.drop('Unnamed: 0', axis=1, inplace=True, errors='ignore')
+card_pool = calc_image_hashes(card_pool, save_to='card_pool.pck')
+'''
+#csv_name = '%s/csv/%s.csv' % (transform_data.data_dir, 'rtr')
+#card_pool = fetch_data.load_all_cards_text(csv_name)
+#card_pool = calc_image_hashes(card_pool)
+card_pool = pd.read_pickle('card_pool.pck')
# Disclaimer: majority of the basic framework in this file is modified from the following tutorial:
@@ -192,7 +221,7 @@
return corrected
-def find_card(img, thresh_c=5, kernel_size=(3, 3), size_ratio=0.15):
+def find_card(img, thresh_c=5, kernel_size=(3, 3), size_ratio=0.3):
# Typical pre-processing - grayscale, blurring, thresholding
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_blur = cv2.medianBlur(img_gray, 5)
@@ -225,35 +254,6 @@
return cnts_rect
- '''
- #card_dim = [630, 880]
- #for cnt in cnts_rect:
- # pts = np.float32([p[0] for p in cnt])
- # img_warp = four_point_transform(img, pts)
-
- # Check which side is longer
- len_1 = math.sqrt((cnt[0][0][0] - cnt[1][0][0]) ** 2 + (cnt[0][0][1] - cnt[1][0][1]) ** 2)
- len_2 = math.sqrt((cnt[0][0][0] - cnt[-1][0][0]) ** 2 + (cnt[0][0][1] - cnt[-1][0][1]) ** 2)
- #print(len_1, len_2)
-
- orig_corner = np.array([p[0] for p in cnt], dtype=np.float32)
- if len_1 > len_2:
- new_corner = np.array([[0, 0], [0, card_dim[1]], [card_dim[0], card_dim[1]], [card_dim[0], 0]], dtype=np.float32)
- else:
- new_corner = np.array([[0, 0], [card_dim[0], 0], [card_dim[0], card_dim[1]], [0, card_dim[1]]],
- dtype=np.float32)
-
- M = cv2.getPerspectiveTransform(orig_corner, new_corner)
- img_warp = cv2.warpPerspective(img, M, (card_dim[0], card_dim[1]))
-
- #cv2.imshow('warp', img_warp)
- #cv2.waitKey(0)
- #img_contour = cv2.drawContours(img_contour, cnts_rect, -1, (0, 255, 0), 3)
- #img_thresh = cv2.cvtColor(img_thresh, cv2.COLOR_GRAY2BGR)
- #img_erode = cv2.cvtColor(img_erode, cv2.COLOR_GRAY2BGR)
- #img_dilate = cv2.cvtColor(img_dilate, cv2.COLOR_GRAY2BGR)
- #return img_thresh, img_erode, img_contour
- '''
def detect_frame(net, classes, img, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), display=True, out_path=None):
img_copy = img.copy()
@@ -347,14 +347,32 @@
pts = np.float32([p[0] for p in cnt])
img_warp = four_point_transform(img_snip, pts)
img_warp = cv2.resize(img_warp, (card_width, card_height))
- img_card = img_warp[47:249, 22:294]
- img_card = Image.fromarray(img_card.astype('uint8'), 'RGB')
+ '''
+ img_art = img_warp[47:249, 22:294]
+ img_art = Image.fromarray(img_art.astype('uint8'), 'RGB')
+ art_hash = ih.phash(img_art, hash_size=32, highfreq_factor=4)
+ card_pool['hash_diff'] = card_pool['art_hash'] - art_hash
+ min_cards = card_pool[card_pool['hash_diff'] == min(card_pool['hash_diff'])]
+ guttersnipe = card_pool[card_pool['name'] == 'Cyclonic Rift']
+ diff = guttersnipe['art_hash'] - art_hash
+ print(diff)
+ card_name = min_cards.iloc[0]['name']
+ #print(min_cards.iloc[0]['name'], min_cards.iloc[0]['hash_diff'])
+ '''
+ img_card = Image.fromarray(img_warp.astype('uint8'), 'RGB')
card_hash = ih.phash(img_card, hash_size=32, highfreq_factor=4)
- print(card_hash - rift_hash)
+ card_pool['hash_diff'] = card_pool['card_hash'] - card_hash
+ min_cards = card_pool[card_pool['hash_diff'] == min(card_pool['hash_diff'])]
+ card_name = min_cards.iloc[0]['name']
+ hash_diff = min_cards.iloc[0]['hash_diff']
+ #guttersnipe = card_pool[card_pool['name'] == 'Cyclonic Rift']
+ #diff = guttersnipe['card_hash'] - card_hash
+ #print(diff)
#img_thresh, img_dilate, img_contour = find_card(img_snip)
#img_concat = np.concatenate((img_snip, img_contour), axis=1)
cv2.rectangle(img_warp, (22, 47), (294, 249), (0, 255, 0), 2)
-
+ cv2.putText(img_warp, card_name + ', ' + str(hash_diff), (0, 50),
+ cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
cv2.imshow('card#%d' % i, img_warp)
else:
cv2.imshow('card#%d' % i, np.zeros((1, 1), dtype=np.uint8))
@@ -376,7 +394,7 @@
def main():
# Specify paths for all necessary files
- test_path = os.path.abspath('../data/test4.mp4')
+ test_path = os.path.abspath('test_file/test4.mp4')
#weight_path = 'backup/tiny_yolo_10_39500.weights'
#cfg_path = 'cfg/tiny_yolo_10.cfg'
#class_path = "data/obj_10.names"
--
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