temp

Edmond Yoo

2018-09-16 ee25ad42c5e9ecdc5a3aa7125e657ce26cc9535c

temp

 __init__.py

 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))