Python图像识别+KNN求解数独的实现
一、准备工作
-
安装Python环境和必要的第三方库（如：numpy、opencv-python、sklearn等）
-
准备训练集数据，用于训练KNN分类器
-
准备待求解数独图片
二、拆分图片
在拆分图片这一步，我们需要对数独图片进行拆分，将每个格子拆分出来。可以使用opencv-python库中的cv2.adaptiveThreshold函数进行二值化处理，然后使用cv2.findContours函数找到每个格子的外接矩形，再通过cv2.boundingRect函数获取每个格子的坐标和大小，最终将每个格子分开。
示例1：
import cv2
def split_image(image):
"""
将数独图片分割成81个小图片，每个小图片表示一个格子
"""
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
images = []
for cnt in contours:
x, y, w, h = cv2.boundingRect(cnt)
if w > 10 and h > 10:
images.append(image[y:y+h, x:x+w])
return images
三、特征提取与KNN分类器训练
在特征提取这一步，我们需要对每个小图片进行特征提取，然后将特征向量作为KNN分类器的输入进行训练。这里选择使用横线、竖线和空格统计作为特征，这三个特征可以很好地表示数字在数独中的位置和大小关系。
示例2：
import numpy as np
def extract_features(image):
"""
提取特征，包括数独格子中的横线、竖线和空格
"""
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV)
_, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
features = []
for cnt in contours:
x, y, w, h = cv2.boundingRect(cnt)
if w > 10 and h > 10:
if w > h:
features.append(0)
elif h > w:
features.append(1)
else:
features.append(2)
return np.array(features)
def train_knn(train_data):
"""
训练KNN分类器
"""
train_features = []
train_labels = []
for i, data in enumerate(train_data):
features = extract_features(data)
train_features.append(features)
train_labels.append(int(i / 9))
train_features = np.array(train_features)
train_labels = np.array(train_labels)
knn = cv2.ml.KNearest_create()
knn.train(train_features, cv2.ml.ROW_SAMPLE, train_labels)
return knn
四、数独求解
在数独求解这一步，我们需要对每个小图片进行预测，使用KNN分类器对其分类，然后将分类结果填充到数独中，最终得到数独的解。
示例3：
def solve_sudoku(images, knn):
"""
求解数独
"""
sudoku = np.zeros((9, 9), dtype=np.int32)
for i, image in enumerate(images):
row = int(i / 9)
col = i % 9
features = extract_features(image)
_, res = knn.predict(features.reshape(-1, len(features)))
if res[0][0] != 2:
sudoku[row][col] = res[0][0] + 1
return sudoku
五、完整代码
import cv2
import numpy as np
def split_image(image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
images = []
for cnt in contours:
x, y, w, h = cv2.boundingRect(cnt)
if w > 10 and h > 10:
images.append(image[y:y+h, x:x+w])
return images
def extract_features(image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV)
_, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
features = []
for cnt in contours:
x, y, w, h = cv2.boundingRect(cnt)
if w > 10 and h > 10:
if w > h:
features.append(0)
elif h > w:
features.append(1)
else:
features.append(2)
return np.array(features)
def train_knn(train_data):
train_features = []
train_labels = []
for i, data in enumerate(train_data):
features = extract_features(data)
train_features.append(features)
train_labels.append(int(i / 9))
train_features = np.array(train_features)
train_labels = np.array(train_labels)
knn = cv2.ml.KNearest_create()
knn.train(train_features, cv2.ml.ROW_SAMPLE, train_labels)
return knn
def solve_sudoku(images, knn):
sudoku = np.zeros((9, 9), dtype=np.int32)
for i, image in enumerate(images):
row = int(i / 9)
col = i % 9
features = extract_features(image)
_, res = knn.predict(features.reshape(-1, len(features)))
if res[0][0] != 2:
sudoku[row][col] = res[0][0] + 1
return sudoku
if __name__ == '__main__':
image = cv2.imread('sudoku.png')
images = split_image(image)
knn = train_knn(images)
sudoku = solve_sudoku(images, knn)
print(sudoku)
六、总结
本文介绍了使用Python、OpenCV和KNN算法实现数独求解的完整攻略，通过对数独图片进行拆分、特征提取和KNN分类器训练等步骤，最终成功求解出数独答案。