From 2292d6ff94a17c4e158c168de23cff0f62603dc1 Mon Sep 17 00:00:00 2001
From: SpeedProg <speedprog@googlemail.com>
Date: Thu, 02 Jan 2020 15:56:20 +0000
Subject: [PATCH] update stuff
---
card_detector.py | 150 ++++++++++++++++++++++++++++++++++++++++++-------
1 files changed, 128 insertions(+), 22 deletions(-)
diff --git a/card_detector.py b/card_detector.py
index 88af74d..5b8bd81 100644
--- a/card_detector.py
+++ b/card_detector.py
@@ -1,26 +1,132 @@
import cv2
import numpy as np
-import pandas as pd
+import math
+from screeninfo import get_monitors
+
+"""
+This is the first attempt of identifying MTG cards using only classical computer vision technique.
+Most of the processes are similar to the process used in opencv_dnn.py, but it instead tries to use
+Hough transformation to identify straight edges of the card.
+However, there were difficulties trying to associate multiple edges into a rectangle, as some of them
+either didn't show up or was too short to intersect.
+There were also no method to dynamically adjust various threshold, even finding all the edges were
+very conditional.
+"""
+
+def detect_a_card(img, thresh_val=80, blur_radius=None, dilate_radius=None, min_hyst=80, max_hyst=200,
+ min_line_length=None, max_line_gap=None, debug=False):
+ dim_img = (len(img[0]), len(img)) # (width, height)
+ # Intermediate variables
+
+ # Default values
+ if blur_radius is None:
+ blur_radius = math.floor(min(dim_img) / 100 + 0.5) // 2 * 2 + 1 # Rounded to the nearest odd
+ if dilate_radius is None:
+ dilate_radius = math.floor(min(dim_img) / 67 + 0.5)
+ if min_line_length is None:
+ min_line_length = min(dim_img) / 10
+ if max_line_gap is None:
+ max_line_gap = min(dim_img) / 10
+
+ thresh_radius = math.floor(min(dim_img) / 20 + 0.5) // 2 * 2 + 1 # Rounded to the nearest odd
+
+ img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ # Median blur better removes background textures than Gaussian blur
+ img_blur = cv2.medianBlur(img_gray, blur_radius)
+ # Truncate the bright area while detecting the border
+ img_thresh = cv2.adaptiveThreshold(img_blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
+ cv2.THRESH_BINARY_INV, thresh_radius, 20)
+ #_, img_thresh = cv2.threshold(img_blur, thresh_val, 255, cv2.THRESH_TRUNC)
+
+ # Dilate the image to emphasize thick borders around the card
+ kernel_dilate = np.ones((dilate_radius, dilate_radius), np.uint8)
+ #img_dilate = cv2.dilate(img_thresh, kernel_dilate, iterations=1)
+ img_dilate = cv2.erode(img_thresh, kernel_dilate, iterations=1)
+
+ img_contour = img_dilate.copy()
+ _, contours, _ = cv2.findContours(img_contour, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+ img_contour = cv2.cvtColor(img_contour, cv2.COLOR_GRAY2BGR)
+ img_contour = cv2.drawContours(img_contour, contours, -1, (128, 128, 128), 1)
+ card_found = contours is not None
+ print(len(contours))
+ print([len(contour) for contour in contours])
+
+ # find the biggest area
+ c = max(contours, key=cv2.contourArea)
+
+ x, y, w, h = cv2.boundingRect(c)
+ # draw the book contour (in green)
+ img_contour = cv2.drawContours(img_contour, [c], -1, (0, 255, 0), 1)
+
+ # Canny edge - low minimum hysteresis to detect glowed area,
+ # and high maximum hysteresis to compensate for high false positives.
+ img_canny = cv2.Canny(img_dilate, min_hyst, max_hyst)
+ #img_canny = img_dilate
+ # Apply Hough transformation to detect the edges
+ detected_lines = cv2.HoughLinesP(img_dilate, 1, np.pi / 180, threshold=60,
+ minLineLength=min_line_length,
+ maxLineGap=max_line_gap)
+ card_found = detected_lines is not None
+ print(len(detected_lines))
+
+ if card_found:
+ if debug:
+ img_hough = cv2.cvtColor(img_dilate.copy(), cv2.COLOR_GRAY2BGR)
+ for line in detected_lines:
+ x1, y1, x2, y2 = line[0]
+ cv2.line(img_hough, (x1, y1), (x2, y2), (0, 0, 255), 1)
+ elif not debug:
+ print('Hough couldn\'t find any lines')
+
+ # Debug: display intermediate results from various steps
+ if debug:
+ img_blank = np.zeros((len(img), len(img[0]), 3), np.uint8)
+ img_thresh = cv2.cvtColor(img_thresh, cv2.COLOR_GRAY2BGR)
+ img_dilate = cv2.cvtColor(img_dilate, cv2.COLOR_GRAY2BGR)
+ #img_canny = cv2.cvtColor(img_canny, cv2.COLOR_GRAY2BGR)
+ if not card_found:
+ img_hough = img_blank
+
+ # Append all images together
+ img_row_1 = np.concatenate((img, img_thresh), axis=1)
+ img_row_2 = np.concatenate((img_contour, img_hough), axis=1)
+ img_result = np.concatenate((img_row_1, img_row_2), axis=0)
+
+ # Resize the final image to fit into the main monitor's resolution
+ screen_size = get_monitors()[0]
+ resize_ratio = max(len(img_result[0]) / screen_size.width, len(img_result) / screen_size.height, 1)
+ img_result = cv2.resize(img_result, (int(len(img_result[0]) // resize_ratio),
+ int(len(img_result) // resize_ratio)))
+ cv2.imshow('Result', img_result)
+ cv2.waitKey(0)
+
+ # TODO: output meaningful data
+ return card_found
+
+def main():
+ img_test = cv2.imread('data/li38_handOfCards.jpg')
+ card_found = detect_a_card(img_test,
+ #dilate_radius=5,
+ #thresh_val=100,
+ #min_hyst=40,
+ #max_hyst=160,
+ #min_line_length=50,
+ #max_line_gap=100,
+ debug=True)
+ if card_found:
+ return
+ return
+ for dilate_radius in range(1, 6):
+ for min_hyst in range(50, 91, 10):
+ for max_hyst in range(180, 119, -20):
+ print('dilate_radius=%d, min_hyst=%d, max_hyst=%d: ' % (dilate_radius, min_hyst, max_hyst),
+ end='', flush=True)
+ card_found = detect_a_card(img_test, dilate_radius=dilate_radius,
+ min_hyst=min_hyst, max_hyst=max_hyst, debug=True)
+ if card_found:
+ print('Card found')
+ else:
+ print('Not found')
if __name__ == '__main__':
- #img_test = cv2.imread('data/rtr-174-jarad-golgari-lich-lord.jpg')
- #img_test = cv2.imread('data/cn2-78-queen-marchesa.png')
- #img_test = cv2.imread('data/c16-143-burgeoning.png')
- #img_test = cv2.imread('data/handOfCards.jpg')
- img_test = cv2.imread('data/pro_tour_side.png')
- img_gray = cv2.cvtColor(img_test, cv2.COLOR_BGR2GRAY)
- _, img_thresh = cv2.threshold(img_gray, 80, 255, cv2.THRESH_BINARY)
- #cv2.imshow('original', img_test)
- cv2.imshow('threshold', img_thresh)
-
- kernel = np.ones((4, 4), np.uint8)
- img_dilate = cv2.dilate(img_thresh, kernel, iterations=1)
- #img_erode = cv2.erode(img_thresh, kernel, iterations=1)
- #img_open = cv2.morphologyEx(img_thresh, cv2.MORPH_OPEN, kernel)
- img_close = cv2.morphologyEx(img_thresh, cv2.MORPH_CLOSE, kernel)
- cv2.imshow('dilated', img_dilate)
- #cv2.imshow('eroded', img_erode)
- #cv2.imshow('opened', img_open)
- #cv2.imshow('closed', img_close)
-
- cv2.waitKey(0)
+ main()
--
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