What is wrong with this IDLE python code? - tensorflow

Question

I ran the code on this site in IDLE python https://sites.google.com/view/dailycoding00/main/%EC%95%8C%ED%8C%8C%EC%98%A4-%ED%95%99%EC%8A%B5
but it had an error
Traceback (most recent call last):
File "C:\Windows\system32\python", line 343, in <module>
ex = MyApp()
File "C:\Windows\system32\python", line 16, in __init__
self.initUI()
File "C:\Windows\system32\python", line 38, in initUI
self.board_cv2 = cv2.cvtColor(board_cv2, cv2.COLOR_BGR2RGB)
cv2.error: OpenCV(4.4.0) C:\Users\appveyor\AppData\Local\Temp\1\pip-req-build-xr4y3u3_\opencv\modules\imgproc\src\color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cv::cvtColor'
what does it mean?
code is
import sys
import numpy as np
from PyQt5.QtWidgets import QApplication, QWidget, QLabel, QVBoxLayout
from PyQt5.QtGui import QPixmap, QImage
from PyQt5.QtCore import Qt
import cv2
import time
import tensorflow as tf
class MyApp(QWidget):
def __init__(self):
super().__init__()
self.initUI()
def initUI(self):
load_model_path = 'model/model.ckpt'
#
board_size = 15
self.game_end = 0
self.board_size = board_size
self.board = np.zeros([board_size,board_size])
self.board_history = np.zeros([board_size,board_size])
self.cnt = 1
time_now = time.gmtime(time.time())
self.save_name = str(time_now.tm_year) + '_' + str(time_now.tm_mon) + '_' + str(time_now.tm_mday) + '_' + str(time_now.tm_hour) + '_' + str(time_now.tm_min) + '_' + str(time_now.tm_sec) + '.txt'
self.save_name_png = str(time_now.tm_year) + '_' + str(time_now.tm_mon) + '_' + str(time_now.tm_mday) + '_' + str(time_now.tm_hour) + '_' + str(time_now.tm_min) + '_' + str(time_now.tm_sec) + '.png'
# read image in numpy array (using cv2)
board_cv2 = cv2.imread('c:\\omok_v1.0/source/board_1515.png')
self.board_cv2 = cv2.cvtColor(board_cv2, cv2.COLOR_BGR2RGB)
white_ball = cv2.imread('c:\\omok_v1.0/source/white.png')
self.white_ball = cv2.cvtColor(white_ball, cv2.COLOR_BGR2RGB)
black_ball = cv2.imread('c:\\omok_v1.0/source/black.png')
self.black_ball = cv2.cvtColor(black_ball, cv2.COLOR_BGR2RGB)
# numpy to QImage
height, width, channel = self.board_cv2.shape
bytesPerLine = 3 * width
qImg_board = QImage(self.board_cv2.data, width, height, bytesPerLine, QImage.Format_RGB888)
self.player = 1 # 1: 흑 / 2: 백
x = 0
y = 0
self.lbl_img = QLabel()
self.lbl_img.setPixmap(QPixmap(qImg_board))
self.vbox = QVBoxLayout()
self.vbox.addWidget(self.lbl_img)
self.setLayout(self.vbox)
# load AI model
self.X = tf.placeholder(tf.float32, [None, board_size*board_size])
X_img = tf.reshape(self.X, [-1, board_size, board_size, 1])
W1 = tf.Variable(tf.random_normal([7,7,1,64], stddev=0.1))
L1 = tf.nn.conv2d(X_img, W1, strides=[1,1,1,1], padding='SAME')
L1 = tf.nn.relu(L1)
#L1 = tf.nn.dropout(L1, keep_prob=keep_prob)
W2 = tf.Variable(tf.random_normal([5,5,64,32], stddev = 0.1))
L2 = tf.nn.conv2d(L1, W2, strides=[1,1,1,1], padding='SAME')
L2 = tf.nn.relu(L2)
#L2 = tf.nn.dropout(L2, keep_prob=keep_prob)
W3 = tf.Variable(tf.random_normal([3,3,32,32], stddev = 0.1))
L3 = tf.nn.conv2d(L2, W3, strides=[1,1,1,1], padding='SAME')
L3 = tf.nn.relu(L3)
#L3 = tf.nn.dropout(L3, keep_prob=keep_prob)
W4 = tf.Variable(tf.random_normal([3,3,32,32], stddev = 0.1))
L4 = tf.nn.conv2d(L3, W4, strides=[1,1,1,1], padding='SAME')
L4 = tf.nn.relu(L4)
#L4 = tf.nn.dropout(L4, keep_prob=keep_prob)
W5 = tf.Variable(tf.random_normal([3,3,32,32], stddev = 0.1))
L5 = tf.nn.conv2d(L4, W5, strides=[1,1,1,1], padding='SAME')
L5 = tf.nn.relu(L5)
#L5 = tf.nn.dropout(L5, keep_prob=keep_prob)
W6 = tf.Variable(tf.random_normal([3,3,32,32], stddev = 0.1))
L6 = tf.nn.conv2d(L5, W6, strides=[1,1,1,1], padding='SAME')
L6 = tf.nn.relu(L6)
W7 = tf.Variable(tf.random_normal([3,3,32,32], stddev = 0.1))
L7 = tf.nn.conv2d(L6, W7, strides=[1,1,1,1], padding='SAME')
L7 = tf.nn.relu(L7)
W8 = tf.Variable(tf.random_normal([3,3,32,32], stddev = 0.1))
L8 = tf.nn.conv2d(L7, W8, strides=[1,1,1,1], padding='SAME')
L8 = tf.nn.relu(L8)
W9 = tf.Variable(tf.random_normal([3,3,32,32], stddev = 0.1))
L9 = tf.nn.conv2d(L8, W9, strides=[1,1,1,1], padding='SAME')
L9 = tf.nn.relu(L9)
#L6 = tf.nn.dropout(L6, keep_prob=keep_prob)
L9 = tf.reshape(L9, [-1, board_size * board_size * 32])
W10 = tf.get_variable("W10", shape=[board_size * board_size * 32, board_size*board_size],initializer = tf.contrib.layers.xavier_initializer())
b10 = tf.Variable(tf.random_normal([board_size * board_size]))
self.logits =tf.matmul(L9,W10) + b10
self.saver = tf.train.Saver()
self.sess = tf.Session()
self.saver.restore(self.sess, load_model_path)
self.setWindowTitle('오목 시뮬레이션')
self.move(100, 100)
self.resize(500,500)
self.show()
def game_play(self, board_img, ball, pos_x, pos_y, turn):
#human
ball_size = ball.shape[0]
step_size = 56
off_set = 10
# ]
if pos_x < step_size/2+off_set+1 or pos_y < step_size/2+off_set+1:
print('그곳에는 둘 수 없습니다')
elif pos_x > step_size*self.board_size+step_size/2+off_set or pos_y > step_size*self.board_size+step_size/2+off_set:
print('그곳에는 둘 수 없습니다')
else:
step_x = round((pos_x - off_set)/step_size)
step_y = round((pos_y - off_set)/step_size)
if self.board[step_x-1,step_y-1] != 0: # 이미 돌이 있을때
print('그곳에는 둘 수 없습니다')
else:
self.board[step_x-1,step_y-1] = turn
self.board_history[step_x-1,step_y-1] = self.cnt
self.cnt = self.cnt + 1
x_step = step_size*step_x-round(step_size/2) + off_set
y_step = step_size*step_y-round(step_size/2) + off_set
board_img[x_step:x_step+ball_size,y_step:y_step+ball_size] = ball
#
if self.game_rule(self.board, turn):
self.game_end = 1
print('게임이 끝났습니다.')
board_img = cv2.cvtColor(board_img, cv2.COLOR_RGB2BGR)
print('축하합니다 당신이 승리 하였습니다')
return board_img
def sigmoid(self, x):
return 1 / (1 +np.exp(-x))
def find_max(self, result_mat):
max = 0
max_x = 0
max_y = 0
for i in range(self.board_size):
for j in range(self.board_size):
if result_mat[i,j] > max:
max = result_mat[i,j]
max_x = i
max_y = j
result_mat[max_x,max_y] = 0
return result_mat, max_x, max_y
def mousePressEvent(self, e):
x = e.x()
y = e.y()
if self.game_end == 0:
#
self.board_cv2 = self.game_play(self.board_cv2, self.black_ball, y, x, 1)
save_name = 'result/' + str(self.cnt) + "board_black.png"
save_name_w = 'result/' + str(self.cnt) + "board_white.png"
save_name_pred = 'result/' + str(self.cnt) + "board_pred.png"
#
input_X = self.board.flatten()/2
result = self.sess.run(self.logits, feed_dict={self.X: input_X[None,:]})
result_mat = self.sigmoid(result).reshape([self.board_size,self.board_size])
heat_map = cv2.resize(result_mat*255, (500, 500))
result_mat, max_x, max_y = self.find_max(result_mat)
save_image = cv2.resize(self.board_cv2, (500, 500), interpolation=cv2.INTER_CUBIC)
save_image = cv2.cvtColor(save_image, cv2.COLOR_RGB2BGR)
#save_image[:,:,0] = save_image[:,:,0] + heat_map
cv2.imwrite(save_name, save_image)
cv2.imwrite(save_name_pred, heat_map)
#
while self.board[max_x,max_y] !=0:
print('AI 거긴 둘 수 없다')
result_mat, max_x, max_y = self.find_max(result_mat)
self.board[max_x,max_y] = 2 # 인공지능은 항상 2값 / 사람은 1값으로 표현
self.board_history[max_x,max_y] = self.cnt
self.cnt = self.cnt + 1
ball_size = self.white_ball.shape[0]
step_size = 56
off_set = 10
x_step = step_size*(max_x+1)-round(step_size/2) + off_set
y_step = step_size*(max_y+1)-round(step_size/2) + off_set
self.board_cv2[x_step:x_step+ball_size,y_step:y_step+ball_size] = self.white_ball
save_image = cv2.resize(self.board_cv2, (500, 500), interpolation=cv2.INTER_CUBIC)
save_image = cv2.cvtColor(save_image, cv2.COLOR_RGB2BGR)
cv2.imwrite(save_name_w, save_image)
#self.board_history[step_x-1,step_y-1] = self.cnt
#self.board_cv2 = self.game_play(self.board_cv2, self.white_ball, y, x, 2)
height, width, channel = self.board_cv2.shape
bytesPerLine = 3 * width
qImg_board = QImage(self.board_cv2.data, width, height, bytesPerLine, QImage.Format_RGB888)
self.lbl_img.setPixmap(QPixmap(qImg_board))
def game_rule(self, board, player): # 추후 오목 국룰 (렌주룰) 도입 예정
game_result = 0
diag_line = np.zeros(5)
# ●●●●●
for i_idx in range(len(board)):
for j_idx in range(len(board)-4):
p1 = (board[i_idx,j_idx:j_idx+5] == player)
if p1.sum() == 5:
#print('player ', player, ' win')
game_result = 1
return game_result
#print(board)
for i_idx in range(len(board)-4):
for j_idx in range(len(board)):
p1 = (board[i_idx:i_idx+5,j_idx] ==player)
if p1.sum() == 5:
#print('player ', player, ' win')
game_result = 1
return game_result
#print(board)
#
for i_idx in range(len(board)-4):
for j_idx in range(len(board)-4):
diag_line[0] = board[i_idx+0,j_idx+0]
diag_line[1] = board[i_idx+1,j_idx+1]
diag_line[2] = board[i_idx+2,j_idx+2]
diag_line[3] = board[i_idx+3,j_idx+3]
diag_line[4] = board[i_idx+4,j_idx+4]
p1 = (diag_line == player)
if p1.sum() == 5:
#print('player ', player, ' win')
game_result = 1
return game_result
#print(board)
#
for i_idx in range(len(board)-4):
for j_idx in range(len(board)-4):
diag_line[0] = board[i_idx+4,j_idx+0]
diag_line[1] = board[i_idx+3,j_idx+1]
diag_line[2] = board[i_idx+2,j_idx+2]
diag_line[3] = board[i_idx+1,j_idx+3]
diag_line[4] = board[i_idx+0,j_idx+4]
p1 = (diag_line == player)
if p1.sum() == 5:
game_result = 1
return game_result
return game_result
def save_history(self):
result=np.array(self.board_history).flatten()
f = open(self.save_name, 'w')
f.write(np.array2string(result))
f.close()
if name == 'main':
app = QApplication(sys.argv)
ex = MyApp()
sys.exit(app.exec_())