From 6943e6eea0eee1ccf3ee9034699b6a94f334b003 Mon Sep 17 00:00:00 2001
From: Constantin Wenger <constantin.wenger@googlemail.com>
Date: Tue, 01 Feb 2022 11:38:21 +0000
Subject: [PATCH] added option to load from streams added option to set crop x and crop y added option to set percentage a card must take up added ability to scroll detected cards list with o and p added ability to remove topmost shown detected card with u
---
card_detector.py | 93 ++++++++++++++++++++++++----------------------
1 files changed, 48 insertions(+), 45 deletions(-)
diff --git a/card_detector.py b/card_detector.py
index c405d8a..5b8bd81 100644
--- a/card_detector.py
+++ b/card_detector.py
@@ -1,9 +1,18 @@
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)
@@ -13,80 +22,74 @@
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)
+ 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.threshold(img_blur, thresh_val, 255, cv2.THRESH_TRUNC)
+ 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.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.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 detected_lines:
- rho, theta = line[0]
- a = np.cos(theta)
- b = np.sin(theta)
- x0 = a * rho
- y0 = b * rho
- x1 = int(x0 + 1000 * (-b))
- y1 = int(y0 + 1000 * (a))
- x2 = int(x0 - 1000 * (-b))
- y2 = int(y0 - 1000 * (a))
- cv2.line(img_hough, (x1, y1), (x2, y2), (0, 0, 255), 2)
- else:
- print('Hough couldn\'t find any lines')
- return False
- '''
- detected_lines = cv2.HoughLinesP(img_canny, 1, np.pi / 180, threshold=60,
+ 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), 3)
+ 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:
- '''
- 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)
+ 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_canny, img_hough), 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
@@ -100,19 +103,19 @@
# TODO: output meaningful data
return card_found
-
def main():
- img_test = cv2.imread('data/tilted_card_2.jpg')
+ img_test = cv2.imread('data/li38_handOfCards.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,
+ #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):
--
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