From 6522fd9f77e18fd581520af680e5c71952591773 Mon Sep 17 00:00:00 2001
From: Edmond Yoo <hj3yoo@uwaterloo.ca>
Date: Sun, 16 Sep 2018 03:27:28 +0000
Subject: [PATCH] moving few files
---
opencv_dnn.py | 31 +++++++++++++++++++++++++++++--
1 files changed, 29 insertions(+), 2 deletions(-)
diff --git a/opencv_dnn.py b/opencv_dnn.py
index bebbc3c..8595505 100644
--- a/opencv_dnn.py
+++ b/opencv_dnn.py
@@ -1,15 +1,35 @@
import cv2
import numpy as np
+import pandas as pd
import imagehash as ih
import os
import sys
import math
import random
-from operator import itemgetter
+from PIL import Image
+from .. import fetch_data
+from .. 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']))
+ 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)
+
+print(df['art_hash'])
+
+
# Disclaimer: majority of the basic framework in this file is modified from the following tutorial:
# https://www.learnopencv.com/deep-learning-based-object-detection-using-yolov3-with-opencv-python-c/
@@ -172,7 +192,7 @@
return corrected
-def find_card(img, thresh_c=5, kernel_size=(3, 3), size_ratio=0.3):
+def find_card(img, thresh_c=5, kernel_size=(3, 3), size_ratio=0.15):
# Typical pre-processing - grayscale, blurring, thresholding
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_blur = cv2.medianBlur(img_gray, 5)
@@ -190,6 +210,8 @@
print('no contours')
return []
#img_contour = cv2.cvtColor(img_contour, cv2.COLOR_GRAY2BGR)
+ #img_contour = cv2.drawContours(img_contour, cnts, -1, (0, 255, 0), 1)
+ #cv2.imshow('test', img_contour)
# For each contours detected, check if they are large enough and are rectangle
cnts_rect = []
@@ -325,9 +347,14 @@
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')
+ card_hash = ih.phash(img_card, hash_size=32, highfreq_factor=4)
+ print(card_hash - rift_hash)
#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.imshow('card#%d' % i, img_warp)
else:
cv2.imshow('card#%d' % i, np.zeros((1, 1), dtype=np.uint8))
--
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