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Commit a3406f8d authored by zalonzo2's avatar zalonzo2
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cleaned up comments, now finding midpoints of intersections

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with 49 additions and 73 deletions
board_detector.py
+ 49
− 73
View file @ a3406f8d
......@@ -10,32 +10,6 @@ def init_show_cv(val):
def find_longest_lines(img):
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# hsv_mask = cv2.inRange(hsv_img[:,:,1], 50, 255)
# hsv_after = cv2.bitwise_and(img, img, mask=hsv_mask)
# if (show_cv):
# cv2.imshow('HSV', hsv_after)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# gray_hsv = cv2.cvtColor(hsv_after, cv2.COLOR_HSV2BGR)
# gray_hsv = cv2.cvtColor(hsv_after, cv2.COLOR_BGR2GRAY)
# gray_img = gray_img - gray_hsv
# # sobel gradients
# sobel_x = cv2.Sobel(gray_img, cv2.CV_64F, 1, 0, ksize=3)
# sobel_y = cv2.Sobel(gray_img, cv2.CV_64F, 0, 1, ksize=3)
# abs_sobel_x = np.absolute(sobel_x)
# abs_sobel_y = np.absolute(sobel_y)
# # threshold on abs values of sobel gradients and combine them
# _, threshold_x = cv2.threshold(abs_sobel_x, 25, 255, cv2.THRESH_BINARY)
# _, threshold_y = cv2.threshold(abs_sobel_y, 25, 255, cv2.THRESH_BINARY)
# combined_threshold = cv2.bitwise_or(threshold_x, threshold_y)
# combined_threshold = np.uint8(combined_threshold) # median blur needs this
# combined_threshold = cv2.medianBlur(combined_threshold, 5) # this gets rid of outliers so weird diagonal lines don't get made
# edges = combined_threshold
edges = cv2.Canny(gray_img, 50, 100, apertureSize=3)
if (show_cv):
......@@ -43,41 +17,24 @@ def find_longest_lines(img):
cv2.waitKey(0)
cv2.destroyAllWindows()
# lines = cv2.HoughLinesP(edges, 1, np.pi/180, 200, minLineLength=400, maxLineGap=15)
# print(lines)
# lines = cv2.HoughLines(edges, 1, np.pi / 180, 200)
# lines = cv2.HoughLines(edges, rho=1, theta=np.pi/180, threshold=80, min_theta=0, max_theta=np.pi)
theta_thresh = 50
horizontal_lines = cv2.HoughLines(edges, rho=1, theta=np.pi/180, threshold=80, min_theta=(theta_thresh/2-1)*np.pi/theta_thresh, max_theta=(theta_thresh/2+1)*np.pi/theta_thresh)
vertical_lines = cv2.HoughLines(edges, rho=1, theta=np.pi/180, threshold=80, min_theta=-np.pi/theta_thresh, max_theta=np.pi/theta_thresh)
vertical_line_points = convert_lines(vertical_lines)
horizontal_line_points = convert_lines(horizontal_lines)
# vertical_lines = []
# horizontal_lines = []
# # separate horizontal and vertical lines
# if line_points is not None:
# for line in line_points:
# x1, y1, x2, y2 = line[0]
# if abs(x2 - x1) < abs(y2 - y1): # vertical line
# vertical_lines.append(line)
# else:
# horizontal_lines.append(line)
# # filter lines too close to each other
theta_thresh = 60
horizontal_lines = cv2.HoughLines(edges, rho=1, theta=np.pi/180, threshold=60, min_theta=(theta_thresh/2-1)*np.pi/theta_thresh, max_theta=(theta_thresh/2+1)*np.pi/theta_thresh)
vertical_lines = cv2.HoughLines(edges, rho=1, theta=np.pi/180, threshold=60, min_theta=-np.pi/theta_thresh, max_theta=np.pi/theta_thresh)
vertical_line_points = convert_to_cartesian(vertical_lines)
horizontal_line_points = convert_to_cartesian(horizontal_lines)
# filter lines too close to each other
filtered_vertical = filter_lines(vertical_line_points, 50)
filtered_horizontal = filter_lines(horizontal_line_points, 50)
# get the 9 largest lines
sorted_vertical = sorted(filtered_vertical, key=lambda line: min(line[0][1], line[0][3]))[:9]
sorted_horizontal = sorted(filtered_horizontal, key=lambda line: min(line[0][0], line[0][2]))[:9]
return sorted_vertical, sorted_horizontal
# return vertical_line_points, horizontal_line_points
def convert_lines(lines):
def convert_to_cartesian(lines):
line_points = []
if lines is not None:
for line in lines:
......@@ -151,12 +108,30 @@ def detect_board(img):
board_lines = cv2.bitwise_or(vertical_mask, horizontal_mask)
contours, hierarchy = cv2.findContours(board_lines, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
intersection_points, hierarchy = cv2.findContours(intersection, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
intersections, hierarchy = cv2.findContours(intersection, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# find midpoints of intersection contours (this gives us exact coordinates of the corners of the grid)
intersection_points = []
for contour in intersections:
M = cv2.moments(contour)
if (M["m00"] != 0):
midpoint_x = int(M["m10"] / M["m00"])
midpoint_y = int(M["m01"] / M["m00"])
intersection_points.append((midpoint_x, midpoint_y))
# sort the coordinates from left to right then top to bottom
sorted_intersection_points = sort_square_grid_coords(intersection_points, unpacked=False)
if (show_cv):
board_lines_img = img.copy()
cv2.drawContours(board_lines_img, contours, -1, (255, 255, 0), 2)
cv2.drawContours(board_lines_img, intersection_points, -1, (0, 0, 255), 2)
# cv2.drawContours(board_lines_img, intersections, -1, (0, 0, 255), 2)
i = 0
for points in sorted_intersection_points:
for point in points:
cv2.circle(board_lines_img, point, 5, (255 - (3 * i), (i % 9) * 28, 3 * i), -1)
i += 1
print(i)
cv2.imshow('Lines of Board', board_lines_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
......@@ -201,23 +176,22 @@ def detect_board(img):
corners = max_rect.reshape(-1, 2) # reshapes it so each row has 2 elements
corners = [tuple(corner) for corner in corners] # convert to tuples
print(corners)
# corners.sort(key=lambda coord: (coord[0], coord[1])) # sort coords. goes from bottom left clockwise to bottom right
corners_sorted = sort_square_grid_coords(corners)
# corners.sort(key=lambda coord: (coord[0], coord[1])) # sort coords. goes from bottom left clockwise to bottom right - DIDN'T WORK
corners_sorted = sort_square_grid_coords(corners, unpacked=True)
print(corners_sorted)
corners = corners_sorted
corners_img = img.copy()
for i, corner in enumerate(corners):
cv2.circle(corners_img, corner, 5, (60 * i, 60 * i, 60 * i), -1)
if (show_cv):
cv2.imshow('Canny Filter', corners_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
# corners_img = img.copy()
# for i, corner in enumerate(corners_sorted):
# cv2.circle(corners_img, corner, 5, (60 * i, 60 * i, 60 * i), -1)
# if (show_cv):
# cv2.imshow('Canny Filter', corners_img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
tl = corners[0]
tr = corners[1]
bl = corners[2]
br = corners[3]
tl = corners_sorted[0]
tr = corners_sorted[1]
bl = corners_sorted[2]
br = corners_sorted[3]
src = np.float32([list(tl), list(tr), list(bl), list(br)])
dest = np.float32([[0,0], [width, 0], [0, height], [width, height]])
M = cv2.getPerspectiveTransform(src, dest)
......@@ -228,7 +202,7 @@ def detect_board(img):
M = cv2.getPerspectiveTransform(src, dest)
Minv = cv2.getPerspectiveTransform(dest, src)
warped_ip = img.copy()
warped_ip = cv2.drawContours(warped_ip, intersection_points, -1, (0, 0, 255), 2)
warped_ip = cv2.drawContours(warped_ip, intersections, -1, (0, 0, 255), 2)
warped_ip = cv2.warpPerspective(np.uint8(warped_ip), M, (width, height))
if (show_cv):
......@@ -254,7 +228,7 @@ def detect_board(img):
# cv2.waitKey(0)
# cv2.destroyAllWindows()
def sort_square_grid_coords(coordinates):
def sort_square_grid_coords(coordinates, unpacked):
sqrt_len = int(math.sqrt(len(coordinates)))
sorted_coords = sorted(coordinates, key=lambda coord: coord[1]) # first sort by y values
# then group rows of the square (for example, 9x9 grid would be 81 coordinates so split into 9 arrays of 9)
......@@ -262,6 +236,8 @@ def sort_square_grid_coords(coordinates):
for group in groups:
group.sort(key=lambda coord: coord[0]) # now sort each row by x
collapsed_groups = [coord for sublist in groups for coord in sublist]
if (unpacked == False):
return groups
collapsed_groups = [coord for sublist in groups for coord in sublist] # unpack/collapse groups to just be an array of tuples
return collapsed_groups
\ No newline at end of file
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