| | |
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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 math |
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| | | from screeninfo import get_monitors |
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| | | |
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| | | """ |
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| | | This is the first attempt of identifying MTG cards using only classical computer vision technique. |
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| | | Most of the processes are similar to the process used in opencv_dnn.py, but it instead tries to use |
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| | | Hough transformation to identify straight edges of the card. |
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| | | However, there were difficulties trying to associate multiple edges into a rectangle, as some of them |
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| | | either didn't show up or was too short to intersect. |
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| | | There were also no method to dynamically adjust various threshold, even finding all the edges were |
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| | | very conditional. |
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| | | """ |
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| | | |
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| | | def detect_a_card(img, thresh_val=80, blur_radius=None, dilate_radius=None, min_hyst=80, max_hyst=200, |
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| | | min_line_length=None, max_line_gap=None, debug=False): |
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| | | dim_img = (len(img[0]), len(img)) # (width, height) |
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