| | |
|---|
| | | 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 |
|---|
| | | import fetch_data |
|---|
| | | import transform_data |
|---|
| | | |
|---|
| | | card_width = 315 |
|---|
| | | card_height = 440 |
|---|
| | | |
|---|
| | | |
|---|
| | | 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: |
|---|
| | | 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 |
|---|
| | | |
|---|
| | | ''' |
|---|
| | | 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: |
|---|
| | | # https://www.learnopencv.com/deep-learning-based-object-detection-using-yolov3-with-opencv-python-c/ |
|---|
| | | |
|---|
| | |
|---|
| | | 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 = [] |
|---|
| | |
|---|
| | | |
|---|
| | | 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() |
|---|
| | |
|---|
| | | 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_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) |
|---|
| | | 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)) |
|---|
| | |
|---|
| | | |
|---|
| | | 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" |
|---|
