【python】OpenCV—Extract Horizontal and Vertical Lines—Morphology
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文章目录
- 1、功能描述
- 2、代码实现
- 3、效果展示
- 4、完整代码
- 5、参考
- 6、其他的直线检测算法
更多有趣的代码示例，可参考【Programming】
1、功能描述
基于 opencv-python 库，利用形态学的腐蚀和膨胀，提取图片中的水平或者竖直线条
2、代码实现
导入基本的库函数
import numpy as np
import cv2 as cv
python
1.jpg
def main(save=False):
# Load the image
src = cv.imread("./1.jpg", cv.IMREAD_COLOR)
# Check if image is loaded fine
if src is None:
print('Error opening image')
return -1
python
可视化图片，并将其转化为灰度图
# Show source image
cv.imshow("src", src)
# [load_image]
# [gray]
# Transform source image to gray if it is not already
if len(src.shape) != 2:
gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
else:
gray = src
if save:
cv.imwrite("gray.jpg", gray)
# Show gray image
show_wait_destroy("gray", gray)
# [gray]
python
gray.jpg
show_wait_destroy 实现如下 ，关闭图片后才后续代码
def show_wait_destroy(winname, img):
cv.imshow(winname, img)
cv.moveWindow(winname, 500, 0)
cv.waitKey(0)
cv.destroyWindow(winname)
python
二进制求反灰度图， 并自适应阈值二值化
# [bin]
# Apply adaptiveThreshold at the bitwise_not of gray, notice the ~ symbol
gray = cv.bitwise_not(gray)
if save:
cv.imwrite("bitwise_not_gray.jpg", gray)
bw = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_MEAN_C, \
cv.THRESH_BINARY, 15, -2)
if save:
cv.imwrite("adaptiveThreshold.jpg", bw)
# Show binary image
show_wait_destroy("binary", bw)
# [bin]
python
bitwise_not_gray.jpg
adaptiveThreshold.jpg
复制图片 adaptiveThreshold.jpg ，准备提取水平线和竖直线
# [init]
# Create the images that will use to extract the horizontal and vertical lines
horizontal = np.copy(bw)
vertical = np.copy(bw)
# [init]
python
提取水平线
# [horiz]
# Specify size on horizontal axis
cols = horizontal.shape[1] # 1024 cols
horizontal_size = cols // 30 # 34
# Create structure element for extracting horizontal lines through morphology operations
horizontalStructure = cv.getStructuringElement(cv.MORPH_RECT, (horizontal_size, 1))
# Apply morphology operations
horizontal = cv.erode(horizontal, horizontalStructure)
if save:
cv.imwrite("erode-horizontal.jpg", horizontal)
horizontal = cv.dilate(horizontal, horizontalStructure)
if save:
cv.imwrite("dilate-horizontal.jpg", horizontal)
# Show extracted horizontal lines
show_wait_destroy("horizontal", horizontal)
# [horiz]
python
首先会构建结构元素 horizontalStructure（定义了形态学操作的邻域形状和大小）
图片列数 // 30 得到全为 1 的数组
array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=uint8)
python
接着腐蚀操作 erode-horizontal.jpg
最后膨胀操作 dilate-horizontal.jpg
至此我们就提取到了图片中的水平方向的线条
接下来我们提取竖直方向的线条
# [vert]
# Specify size on vertical axis
rows = vertical.shape[0] # 134
verticalsize = rows // 30 # 4
# Create structure element for extracting vertical lines through morphology operations
verticalStructure = cv.getStructuringElement(cv.MORPH_RECT, (1, verticalsize))
# Apply morphology operations
vertical = cv.erode(vertical, verticalStructure)
if save:
cv.imwrite("erode-vertical.jpg", vertical)
vertical = cv.dilate(vertical, verticalStructure)
if save:
cv.imwrite("dilate-vertical.jpg", vertical)
# Show extracted vertical lines
show_wait_destroy("vertical", vertical)
# [vert]
python
同理，也是先构建一个结构元素 verticalStructure
array([[1],
[1],
[1],
[1]], dtype=uint8)
python
腐蚀 erode-vertical.jpg
膨胀 dilate-vertical.jpg
至此我们提取出了竖直方向的线条
可以拓展一下，
As you can see we are almost there. However, at that point you will notice that the edges of the notes are a bit rough. For that reason we need to refine the edges in order to obtain a smoother result
'''
Extract edges and smooth image according to the logic
1. extract edges
2. dilate(edges)
3. src.copyTo(smooth)
4. blur smooth img
5. smooth.copyTo(src, edges)
'''
python
dilate-vertical.jpg 二进制求反，
# [smooth]
# Inverse vertical image
vertical = cv.bitwise_not(vertical)
if save:
cv.imwrite("bitwise_not_vertical.jpg", vertical)
show_wait_destroy("vertical_bit", vertical)
python
bitwise_not_vertical.jpg
cv2.adaptiveThreshold 适应性阈值二值化
# Step 1
edges = cv.adaptiveThreshold(vertical, 255, cv.ADAPTIVE_THRESH_MEAN_C, \
cv.THRESH_BINARY, 3, -2)
if save:
cv.imwrite("step1_edges.jpg", edges)
show_wait_destroy("edges", edges)
python
得到 step1_edges.jpg，实现了边缘检测
看看 cv2.adaptiveThreshold 的介绍仔细分析下实现过程
dst = cv2.adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C)
python
形参 C 从邻域像素的平均值或加权平均值中减去的常数，配置的为负数，附近颜色相近的变黑（eg 纯白区域，像素 255，阈值 255-（-2）=257，都变黑，再 eg，纯黑区域，像素 0，阈值 0-（-2）=2，也是黑），附近颜色变动的变白（黑白交替，白色的部分保留，黑色的部分变黑），可以实现边缘提取，妙
膨胀强化边缘
# Step 2
kernel = np.ones((2, 2), np.uint8)
edges = cv.dilate(edges, kernel)
if save:
cv.imwrite("step2_edges.jpg", edges)
show_wait_destroy("dilate", edges)
python
kernel 为
array([[1, 1],
[1, 1]], dtype=uint8)
python
step2_edges.jpg
复制 bitwise_not_vertical.jpg
# Step 3
smooth = np.copy(vertical)
python
模糊处理 step4_smooth.jpg
# Step 4
smooth = cv.blur(smooth, (2, 2))
if save:
cv.imwrite("step4_smooth.jpg", smooth)
python
记录下 step2_edges.jpg 中像素不为零的部分的坐标，也即边缘部分坐标
边缘部分用平滑后的像素替换原来的像素
# Step 5
(rows, cols) = np.where(edges != 0)
vertical[rows, cols] = smooth[rows, cols]
# Show final result
show_wait_destroy("smooth - final", vertical)
if save:
cv.imwrite("smooth_final.jpg", vertical)
# [smooth]
python
3、效果展示
输入
水平线条
竖直线条
平滑竖直线条后的结果
输入图片
水平线
竖直线
平滑竖直线条后的结果
4、完整代码
"""
@brief Use morphology transformations for extracting horizontal and vertical lines sample code
"""
import numpy as np
import cv2 as cv
def show_wait_destroy(winname, img):
cv.imshow(winname, img)
cv.moveWindow(winname, 500, 0)
cv.waitKey(0)
cv.destroyWindow(winname)
def main(save=False):
# Load the image
src = cv.imread("./1.jpg", cv.IMREAD_COLOR)
# Check if image is loaded fine
if src is None:
print('Error opening image')
return -1
# Show source image
cv.imshow("src", src)
# [load_image]
# [gray]
# Transform source image to gray if it is not already
if len(src.shape) != 2:
gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
else:
gray = src
if save:
cv.imwrite("gray.jpg", gray)
# Show gray image
show_wait_destroy("gray", gray)
# [gray]
# [bin]
# Apply adaptiveThreshold at the bitwise_not of gray, notice the ~ symbol
gray = cv.bitwise_not(gray) # (134, 1024)
if save:
cv.imwrite("bitwise_not_gray.jpg", gray)
bw = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_MEAN_C, \
cv.THRESH_BINARY, 15, -2)
if save:
cv.imwrite("adaptiveThreshold.jpg", bw)
# Show binary image
show_wait_destroy("binary", bw)
# [bin]
# [init]
# Create the images that will use to extract the horizontal and vertical lines
horizontal = np.copy(bw)
vertical = np.copy(bw)
# [init]
# [horiz]
# Specify size on horizontal axis
cols = horizontal.shape[1] # 1024 cols
horizontal_size = cols // 30 # 34
# Create structure element for extracting horizontal lines through morphology operations
horizontalStructure = cv.getStructuringElement(cv.MORPH_RECT, (horizontal_size, 1))
# Apply morphology operations
horizontal = cv.erode(horizontal, horizontalStructure)
if save:
cv.imwrite("erode-horizontal.jpg", horizontal)
horizontal = cv.dilate(horizontal, horizontalStructure)
if save:
cv.imwrite("dilate-horizontal.jpg", horizontal)
# Show extracted horizontal lines
show_wait_destroy("horizontal", horizontal)
# [horiz]
# [vert]
# Specify size on vertical axis
rows = vertical.shape[0] # 134
verticalsize = rows // 30 # 4
# Create structure element for extracting vertical lines through morphology operations
verticalStructure = cv.getStructuringElement(cv.MORPH_RECT, (1, verticalsize))
# Apply morphology operations
vertical = cv.erode(vertical, verticalStructure)
if save:
cv.imwrite("erode-vertical.jpg", vertical)
vertical = cv.dilate(vertical, verticalStructure)
if save:
cv.imwrite("dilate-vertical.jpg", vertical)
# Show extracted vertical lines
show_wait_destroy("vertical", vertical)
# [vert]
# [smooth]
# Inverse vertical image
vertical = cv.bitwise_not(vertical)
if save:
cv.imwrite("bitwise_not_vertical.jpg", vertical)
show_wait_destroy("vertical_bit", vertical)
'''
Extract edges and smooth image according to the logic
1. extract edges
2. dilate(edges)
3. src.copyTo(smooth)
4. blur smooth img
5. smooth.copyTo(src, edges)
'''
# Step 1
edges = cv.adaptiveThreshold(vertical, 255, cv.ADAPTIVE_THRESH_MEAN_C, \
cv.THRESH_BINARY, 3, -2)
if save:
cv.imwrite("step1_edges.jpg", edges)
show_wait_destroy("edges", edges)
# Step 2
kernel = np.ones((2, 2), np.uint8)
edges = cv.dilate(edges, kernel)
if save:
cv.imwrite("step2_edges.jpg", edges)
show_wait_destroy("dilate", edges)
# Step 3
smooth = np.copy(vertical)
# Step 4
smooth = cv.blur(smooth, (2, 2))
if save:
cv.imwrite("step4_smooth.jpg", smooth)
# Step 5
(rows, cols) = np.where(edges != 0)
vertical[rows, cols] = smooth[rows, cols]
# Show final result
show_wait_destroy("smooth - final", vertical)
if save:
cv.imwrite("smooth_final.jpg", vertical)
# [smooth]
return 0
if __name__ == "__main__":
main(save=True)
python
5、参考
6、其他的直线检测算法
来自 直线检测算法汇总
- LSD 直线检测算法，
cv2.createLineSegmentDetector - FLD 直线检测算法，
cv2.ximgproc.createFastLineDetector - EDlines 直线检测算法
- LSWMS 直线检测算法
- CannyLines 直线检测算法
- MCMLSD 直线检测算法
- LSM 直线检测算法
更多有趣的代码示例，可参考【Programming】
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