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| | | import cv2 |
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| | | import numpy as np |
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| | | import pandas as pd |
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| | | import imagehash as ih |
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| | | import os |
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| | | import sys |
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| | | import math |
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| | | import random |
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| | | from operator import itemgetter |
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| | | from PIL import Image |
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| | | from .. import fetch_data |
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| | | from .. import transform_data |
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| | | |
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| | | card_width = 315 |
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| | | card_height = 440 |
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| | | |
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| | | df = fetch_data.load_all_cards_text('%s/csv/rsv.csv' % transform_data.data_dir) |
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| | | df['art_hash'] = np.NaN |
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| | | for _, card_info in card_pool.iterrows(): |
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| | | img_name = '%s/card_img/png/%s/%s_%s.png' % (data_dir, card_info['set'], card_info['collector_number'], |
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| | | fetch_data.get_valid_filename(card_info['name'])) |
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| | | card_img = cv2.imread(img_name) |
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| | | if card_img is None: |
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| | | fetch_data.fetch_card_image(card_info, out_dir='%s/card_img/png/%s' % (data_dir, card_info['set'])) |
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| | | card_img = cv2.imread(img_name) |
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| | | if card_img is None: |
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| | | print('WARNING: card %s is not found!' % img_name) |
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| | | img_art = Image.fromarray(card_img[121:580, 63:685]) |
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| | | card_info['art_hash'] = ih.phash(img_card, hash_size=32, highfreq_factor=4) |
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| | | |
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| | | print(df['art_hash']) |
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| | | |
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| | | # Disclaimer: majority of the basic framework in this file is modified from the following tutorial: |
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| | | # https://www.learnopencv.com/deep-learning-based-object-detection-using-yolov3-with-opencv-python-c/ |
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| | | return corrected |
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| | | |
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| | | |
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| | | def find_card(img, thresh_c=5, kernel_size=(3, 3), size_ratio=0.3): |
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| | | def find_card(img, thresh_c=5, kernel_size=(3, 3), size_ratio=0.15): |
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| | | # Typical pre-processing - grayscale, blurring, thresholding |
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| | | img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
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| | | img_blur = cv2.medianBlur(img_gray, 5) |
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| | | print('no contours') |
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| | | return [] |
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| | | #img_contour = cv2.cvtColor(img_contour, cv2.COLOR_GRAY2BGR) |
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| | | #img_contour = cv2.drawContours(img_contour, cnts, -1, (0, 255, 0), 1) |
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| | | #cv2.imshow('test', img_contour) |
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| | | |
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| | | # For each contours detected, check if they are large enough and are rectangle |
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| | | cnts_rect = [] |
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| | |
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| | | pts = np.float32([p[0] for p in cnt]) |
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| | | img_warp = four_point_transform(img_snip, pts) |
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| | | img_warp = cv2.resize(img_warp, (card_width, card_height)) |
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| | | img_card = img_warp[47:249, 22:294] |
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| | | img_card = Image.fromarray(img_card.astype('uint8'), 'RGB') |
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| | | card_hash = ih.phash(img_card, hash_size=32, highfreq_factor=4) |
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| | | print(card_hash - rift_hash) |
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| | | #img_thresh, img_dilate, img_contour = find_card(img_snip) |
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| | | #img_concat = np.concatenate((img_snip, img_contour), axis=1) |
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| | | cv2.rectangle(img_warp, (22, 47), (294, 249), (0, 255, 0), 2) |
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| | | |
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| | | cv2.imshow('card#%d' % i, img_warp) |
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| | | else: |
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| | | cv2.imshow('card#%d' % i, np.zeros((1, 1), dtype=np.uint8)) |
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