def waitUntilMoveCompletes():
while True:
frame = video_capture_thread.get_frame()
frame = perspective_transform(frame, pts1)
fgmask = move_fgbg.apply(normalize_illumination(frame))
motion_fgbg.apply(frame)
mean = fgmask.mean()
if mean < MOVE_END_THRESHOLD:
break
while fgmask.mean() > MOVE_START_THRESHOLD:
frame = video_capture_thread.get_frame()
frame = perspective_transform(frame, pts1)
new_fgmask = move_fgbg.apply(normalize_illumination(frame))
motion_fgbg.apply(frame)
if abs(new_fgmask.mean() -
fgmask.mean()) <= MOVE_MEAN_DIFFERENCE_THRESHOLD:
fgmask = new_fgmask
else:
break
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
fgmask = cv2.morphologyEx(fgmask, cv2.MORPH_CLOSE, kernel)
return frame, fgmask
def waitUntilMotionCompletes():
while True:
frame = video_capture_thread.get_frame()
frame = perspective_transform(frame, pts1)
fgmask = motion_fgbg.apply(frame)
mean = fgmask.mean()
if mean < MOTION_END_THRESHOLD:
break
def initialize_background_subtractors():
while True:
frame = video_capture_thread.get_frame()
frame = perspective_transform(frame, pts1)
move_fgbg.apply(normalize_illumination(frame))
fgmask = motion_fgbg.apply(frame)
mean = fgmask.mean()
if mean < MOTION_END_THRESHOLD:
break
x = corner1[0][0] + (corner1[0][0] - corner2[0][0])
y = corner1[0][1] + (corner1[0][1] - corner2[0][1])
row.append((x, y))
augmented_corners.append(row)
print(augmented_corners)
while augmented_corners[0][0][0] > augmented_corners[8][8][0] or augmented_corners[0][0][1] > \
augmented_corners[8][8][1]:
rotateMatrix(augmented_corners)
print(augmented_corners)
pts1 = np.float32([list(augmented_corners[0][0]), list(augmented_corners[8][0]), list(augmented_corners[0][8]),
list(augmented_corners[8][8])])
empty_board = perspective_transform(gray, pts1)
#cv2.imwrite("empty_board.jpg", empty_board)
for i in range(len(augmented_corners)):
for j in range(len(augmented_corners[i])):
frame = cv2.putText(frame, str(i) + "," + str(j), augmented_corners[i][j], cv2.FONT_HERSHEY_SIMPLEX,
0.5, (255, 0, 0), 1, cv2.LINE_AA)
cv2.imshow('frame', frame)
cv2.waitKey(0)
break
cv2.imshow('frame', frame)
if cv2.waitKey(3) & 0xFF == ord('q'):
break
cap.release()
def lanefinding_pipeline(image, left_lane_line, right_lane_line):
#def find_lane_lines(image):
undist = camera_cal(image, mtx, dist)
thresholded = threshold(undist)
warped, M, M_inv = perspective_transform(thresholded, mtx, dist, src, dest)
#if not (left_lane_line.detected or right_lane_line.detected):
left_lane_line, right_lane_line, car_center, lane_center, y, out_img = sliding_window(
warped, left_lane_line, right_lane_line)
# print(left_fit)
# print(right_fit)
# update Line objects
# left_lane_line.detected = True
# right_lane_line.detected = True
# if abs(offset(car_center, lane_center)) > 0.3:
# left_lane_line, right_lane_line, car_center, lane_center = prior_frame_search(warped, 100, left_lane_line.current_fit, right_lane_line.current_fit)
# left_fit, right_fit, left_fitx, right_fitx, y, out_img = fit_poly(warped.shape, leftx, lefty, rightx, righty, out_img)
# sanity checks
# left_curve, right_curve = curvature(left_lane_line.fit_x, right_lane_line.fit_x, y)
# curverad = curvature(left_lane_line.fit_x, left_lane_line.fit_y)
#left_curve, right_curve = curvature(left_lane_line.fit_x, right_lane_line.fit_x, y)
left_curve = curvature(left_lane_line.fit_x, left_lane_line.fit_y)
right_curve = curvature(right_lane_line.fit_x, right_lane_line.fit_y)
# print(left_curve, right_curve)
# if left_curve < 1000 or right_curve < 1000:
# #leftx, lefty, rightx, righty, car_center, lane_center = prior_frame_search(warped, 100, left_lane_line.current_fit, right_lane_line.current_fit)
# left_fit, right_fit, left_fitx, right_fitx, y, out_img = fit_poly(warped.shape, leftx, lefty, rightx, righty, out_img)
# left_curve, right_curve = curvature(left_fit, right_fit, y)
# result = draw_lane_lines(image, undist, warped, left_lane_line.recent_xfitted, right_lane_line.recent_xfitted, y, M_inv, prev_car_center, prev_lane_center,
# left_lane_line.radius_of_curvature, right_lane_line.radius_of_curvature)
# else:
# # update Line objects
# left_lane_line.current_fit = left_fit
# right_lane_line.current_fit = right_fit
# left_lane_line.recent_xfitted = left_fitx
# right_lane_line.recent_xfitted = right_fitx
# left_lane_line.radius_of_curvature = left_curve
# right_lane_line.radius_of_curvature = right_curve
# prev_car_center = car_center
# prev_lane_center = lane_center
# leftx, lefty, rightx, righty, car_center, lane_center = base_lane_lines(warped)
# left_fit, right_fit, left_fitx, right_fitx, y = fit_poly(warped.shape, leftx, lefty, rightx, righty)
# left_curve, right_curve = curvature(left_fit, right_fit, y)
result = draw_lane_lines(image, undist, warped, left_lane_line.fit_x,
right_lane_line.fit_x, y, M_inv, car_center,
lane_center, left_curve, right_curve)
# draw_copy = np.copy(result)
# plt.imshow(draw_copy)
# plt.show()
#plt.imshow(warped, cmap = "gray")
# plt.imshow(result)
# plt.show()
return result
frame = video_capture_thread.get_frame()
frame = perspective_transform(frame, pts1)
move_fgbg.apply(normalize_illumination(frame))
fgmask = motion_fgbg.apply(frame)
mean = fgmask.mean()
if mean < MOTION_END_THRESHOLD:
break
initialize_background_subtractors()
speech_thread.put_text("Game started")
while not game.board.is_game_over():
frame = video_capture_thread.get_frame()
frame = perspective_transform(frame, pts1)
fgmask = motion_fgbg.apply(frame)
mean = fgmask.mean()
if mean > MOTION_START_THRESHOLD:
# cv2.imwrite("prev_frame.jpg", frame)
waitUntilMotionCompletes()
frame, fgmask = waitUntilMoveCompletes()
move_fgbg.apply(normalize_illumination(frame), learningRate=1.0)
waitUntilMoveCompletes()
if game.register_move(fgmask):
pass
# cv2.imwrite(game.executed_moves[-1] + " frame.jpg", frame)
# cv2.imwrite(game.executed_moves[-1] + " mask.jpg", fgmask)
else:
pass
# cv2.imwrite("frame_fail.jpg", frame)