From 8e9ec5135c73193d8c34b2e31e8201082c747e7b Mon Sep 17 00:00:00 2001
From: Edmond Yoo <hj3yoo@uwaterloo.ca>
Date: Mon, 20 Aug 2018 22:40:33 +0000
Subject: [PATCH] Succcesful detection algo for a single card
---
card_detector.py | 146 +++++++++++++++++++++++++++++++++++-------------
1 files changed, 106 insertions(+), 40 deletions(-)
diff --git a/card_detector.py b/card_detector.py
index 81e3b6b..c405d8a 100644
--- a/card_detector.py
+++ b/card_detector.py
@@ -1,34 +1,44 @@
import cv2
import numpy as np
import pandas as pd
+import math
+from screeninfo import get_monitors
-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/li38_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, 50, 255, cv2.THRESH_BINARY)
- # cv2.imshow('original', img_test)
- cv2.imshow('threshold', img_thresh)
+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
- kernel = np.ones((7, 7), 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)
- img_edge = cv2.Canny(img_dilate, 100, 200)
- cv2.imshow('edge', img_edge)
+ # 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) / 100)
+ 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
- lines = cv2.HoughLines(img_edge, 1, np.pi / 180, 200)
- if lines is not None:
+ 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.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)
+
+ # 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)
+
+ # Apply Hough transformation to detect the edges
+ '''
+ detected_lines = cv2.HoughLines(img_canny, 1, np.pi / 180, 200)
+ if detected_lines is not None:
img_hough = cv2.cvtColor(img_dilate.copy(), cv2.COLOR_GRAY2BGR)
- for line in lines:
+ for line in detected_lines:
rho, theta = line[0]
a = np.cos(theta)
b = np.sin(theta)
@@ -39,25 +49,81 @@
x2 = int(x0 - 1000 * (-b))
y2 = int(y0 - 1000 * (a))
cv2.line(img_hough, (x1, y1), (x2, y2), (0, 0, 255), 2)
- cv2.imshow('hough', img_hough)
else:
print('Hough couldn\'t find any lines')
+ return False
'''
- lines = cv2.HoughLinesP(img_edge, 1, np.pi / 180, 200, 50, 100)
- if lines is not None:
- img_hough = cv2.cvtColor(img_dilate.copy(), cv2.COLOR_GRAY2BGR)
- for line in lines:
- x1, y1, x2, y2 = line[0]
- cv2.line(img_hough, (x1, y1), (x2, y2), (0, 0, 255), 3)
- cv2.imshow('hough', img_hough)
- else:
+ detected_lines = cv2.HoughLinesP(img_canny, 1, np.pi / 180, threshold=60,
+ minLineLength=min_line_length,
+ maxLineGap=max_line_gap)
+ card_found = detected_lines is not None
+
+ 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), 3)
+ elif not debug:
print('Hough couldn\'t find any lines')
- img_contour, contours, hierchy = cv2.findContours(img_dilate, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
- img_contour = cv2.cvtColor(img_contour, cv2.COLOR_GRAY2BGR)
- if len(contours) > 0:
- cv2.drawContours(img_contour, contours, -1, (0, 0, 255), 1)
+ # Debug: display intermediate results from various steps
+ if debug:
+ '''
+ cv2.imshow('Original', img)
+ cv2.imshow('Thresholded', img_thresh)
+ cv2.imshow('Dilated', img_dilate)
+ cv2.imshow('Canny Edge', img_canny)
+ if card_found:
+ cv2.imshow('Detected Lines', img_hough)
+ '''
+ 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
- cv2.imshow('contours', img_contour)
- '''
- cv2.waitKey(0
\ No newline at end of file
+ # Append all images together
+ img_row_1 = np.concatenate((img, img_thresh), axis=1)
+ img_row_2 = np.concatenate((img_canny, 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/tilted_card_2.jpg')
+ card_found = detect_a_card(img_test,
+ dilate_radius=2,
+ min_hyst=30,
+ max_hyst=80,
+ min_line_length=10,
+ max_line_gap=50,
+ debug=True)
+ if card_found:
+ 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__':
+ main()
--
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