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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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| | | 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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| | | # Intermediate variables |
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| | | |
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| | | # Default values |
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| | | if blur_radius is None: |
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| | | blur_radius = math.floor(min(dim_img) / 100 + 0.5) // 2 * 2 + 1 # Rounded to the nearest odd |
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| | | if dilate_radius is None: |
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| | | dilate_radius = math.floor(min(dim_img) / 67 + 0.5) |
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| | | if min_line_length is None: |
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| | | min_line_length = min(dim_img) / 10 |
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| | | if max_line_gap is None: |
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| | | max_line_gap = min(dim_img) / 10 |
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| | | |
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| | | thresh_radius = math.floor(min(dim_img) / 20 + 0.5) // 2 * 2 + 1 # Rounded to the nearest odd |
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| | | |
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| | | img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
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| | | # Median blur better removes background textures than Gaussian blur |
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| | | img_blur = cv2.medianBlur(img_gray, blur_radius) |
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| | | # Truncate the bright area while detecting the border |
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| | | img_thresh = cv2.adaptiveThreshold(img_blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, |
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| | | cv2.THRESH_BINARY_INV, thresh_radius, 20) |
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| | | #_, img_thresh = cv2.threshold(img_blur, thresh_val, 255, cv2.THRESH_TRUNC) |
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| | | |
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| | | # Dilate the image to emphasize thick borders around the card |
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| | | kernel_dilate = np.ones((dilate_radius, dilate_radius), np.uint8) |
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| | | #img_dilate = cv2.dilate(img_thresh, kernel_dilate, iterations=1) |
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| | | img_dilate = cv2.erode(img_thresh, kernel_dilate, iterations=1) |
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| | | |
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| | | img_contour = img_dilate.copy() |
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| | | _, contours, _ = cv2.findContours(img_contour, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) |
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| | | img_contour = cv2.cvtColor(img_contour, cv2.COLOR_GRAY2BGR) |
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| | | img_contour = cv2.drawContours(img_contour, contours, -1, (128, 128, 128), 1) |
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| | | card_found = contours is not None |
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| | | print(len(contours)) |
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| | | print([len(contour) for contour in contours]) |
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| | | |
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| | | # find the biggest area |
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| | | c = max(contours, key=cv2.contourArea) |
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| | | |
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| | | x, y, w, h = cv2.boundingRect(c) |
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| | | # draw the book contour (in green) |
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| | | img_contour = cv2.drawContours(img_contour, [c], -1, (0, 255, 0), 1) |
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| | | |
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| | | # Canny edge - low minimum hysteresis to detect glowed area, |
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| | | # and high maximum hysteresis to compensate for high false positives. |
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| | | img_canny = cv2.Canny(img_dilate, min_hyst, max_hyst) |
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| | | #img_canny = img_dilate |
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| | | # Apply Hough transformation to detect the edges |
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| | | detected_lines = cv2.HoughLinesP(img_dilate, 1, np.pi / 180, threshold=60, |
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| | | minLineLength=min_line_length, |
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| | | maxLineGap=max_line_gap) |
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| | | card_found = detected_lines is not None |
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| | | print(len(detected_lines)) |
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| | | |
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| | | if card_found: |
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| | | if debug: |
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| | | img_hough = cv2.cvtColor(img_dilate.copy(), cv2.COLOR_GRAY2BGR) |
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| | | for line in detected_lines: |
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| | | x1, y1, x2, y2 = line[0] |
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| | | cv2.line(img_hough, (x1, y1), (x2, y2), (0, 0, 255), 1) |
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| | | elif not debug: |
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| | | print('Hough couldn\'t find any lines') |
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| | | |
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| | | # Debug: display intermediate results from various steps |
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| | | if debug: |
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| | | img_blank = np.zeros((len(img), len(img[0]), 3), np.uint8) |
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| | | img_thresh = cv2.cvtColor(img_thresh, cv2.COLOR_GRAY2BGR) |
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| | | img_dilate = cv2.cvtColor(img_dilate, cv2.COLOR_GRAY2BGR) |
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| | | #img_canny = cv2.cvtColor(img_canny, cv2.COLOR_GRAY2BGR) |
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| | | if not card_found: |
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| | | img_hough = img_blank |
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| | | |
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| | | # Append all images together |
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| | | img_row_1 = np.concatenate((img, img_thresh), axis=1) |
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| | | img_row_2 = np.concatenate((img_contour, img_hough), axis=1) |
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| | | img_result = np.concatenate((img_row_1, img_row_2), axis=0) |
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| | | |
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| | | # Resize the final image to fit into the main monitor's resolution |
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| | | screen_size = get_monitors()[0] |
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| | | resize_ratio = max(len(img_result[0]) / screen_size.width, len(img_result) / screen_size.height, 1) |
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| | | img_result = cv2.resize(img_result, (int(len(img_result[0]) // resize_ratio), |
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| | | int(len(img_result) // resize_ratio))) |
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| | | cv2.imshow('Result', img_result) |
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| | | cv2.waitKey(0) |
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| | | |
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| | | # TODO: output meaningful data |
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| | | return card_found |
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| | | |
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| | | def main(): |
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| | | img_test = cv2.imread('data/li38_handOfCards.jpg') |
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| | | card_found = detect_a_card(img_test, |
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| | | #dilate_radius=5, |
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| | | #thresh_val=100, |
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| | | #min_hyst=40, |
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| | | #max_hyst=160, |
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| | | #min_line_length=50, |
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| | | #max_line_gap=100, |
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| | | debug=True) |
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| | | if card_found: |
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| | | return |
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| | | return |
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| | | for dilate_radius in range(1, 6): |
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| | | for min_hyst in range(50, 91, 10): |
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| | | for max_hyst in range(180, 119, -20): |
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| | | print('dilate_radius=%d, min_hyst=%d, max_hyst=%d: ' % (dilate_radius, min_hyst, max_hyst), |
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| | | end='', flush=True) |
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| | | card_found = detect_a_card(img_test, dilate_radius=dilate_radius, |
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| | | min_hyst=min_hyst, max_hyst=max_hyst, debug=True) |
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| | | if card_found: |
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| | | print('Card found') |
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| | | else: |
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| | | print('Not found') |
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| | | |
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| | | if __name__ == '__main__': |
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| | | #img_test = cv2.imread('data/rtr-174-jarad-golgari-lich-lord.jpg') |
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| | | #img_test = cv2.imread('data/cn2-78-queen-marchesa.png') |
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| | | #img_test = cv2.imread('data/c16-143-burgeoning.png') |
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| | | #img_test = cv2.imread('data/handOfCards.jpg') |
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| | | img_test = cv2.imread('data/pro_tour_side.png') |
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| | | img_gray = cv2.cvtColor(img_test, cv2.COLOR_BGR2GRAY) |
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| | | _, img_thresh = cv2.threshold(img_gray, 80, 255, cv2.THRESH_BINARY) |
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| | | #cv2.imshow('original', img_test) |
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| | | cv2.imshow('threshold', img_thresh) |
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| | | |
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| | | kernel = np.ones((4, 4), np.uint8) |
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| | | img_dilate = cv2.dilate(img_thresh, kernel, iterations=1) |
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| | | #img_erode = cv2.erode(img_thresh, kernel, iterations=1) |
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| | | #img_open = cv2.morphologyEx(img_thresh, cv2.MORPH_OPEN, kernel) |
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| | | img_close = cv2.morphologyEx(img_thresh, cv2.MORPH_CLOSE, kernel) |
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| | | cv2.imshow('dilated', img_dilate) |
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| | | #cv2.imshow('eroded', img_erode) |
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| | | #cv2.imshow('opened', img_open) |
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| | | #cv2.imshow('closed', img_close) |
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| | | |
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| | | cv2.waitKey(0) |
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| | | main() |
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